scientific discoveries essay

• The Magazine
• Stay Curious
• The Sciences
• Environment
• Planet Earth

10 Scientific Discoveries That Changed The World

Dna, gravity, and germ theory are a few of the key findings in history that forever shifted the course of human civilization. learn how these scientific discoveries changed the world..

The only constant is change. At least, that’s what the Greek philosopher Heraclitus is credited to have said. And while science and philosophy don’t always go hand in hand, there is some truth to Heraclitus’ notion. Change is inevitable and, in some cases, necessary for our species to evolve . While some change happens automatically, like the tides going in and out, some changes bloomed from scientific discoveries. 

Using fire to cook food and keep warm propelled our ancestors toward the foundations of early settlements and continued the growth of civilization. Using fire to shape metals for weapons and building materials led to more and more discoveries and more and more advancements. While many advances shaped humanity, we’ve focused on ten significant scientific discoveries that changed the world.

The discovery of DNA didn’t so much change the world as it did our understanding of it — more so, our understanding of life. DNA is a term we’ve only started using in the 20th century, though its initial discovery dates back decades into the 19th century.

What Is DNA?

DNA is the molecule that encodes genetic information for all living things. It plays a key role in passing traits from parents to offspring and is the primary component of chromosomes in the cell nuclei of complex organisms.

Who Discovered DNA?

Many people think scientists James Watson and Francis Crick discovered DNA in the 1950s. Nope, not so fast. DNA was actually first discovered in 1869 by Swiss physician Friedrich Miescher . He identified what he referred to as “nuclein” in blood cells. Several other researchers have worked on projects around identifying DNA up until Watson and Crick. 

What Does DNA Stand For?

The term nuclein eventually evolved into what we know as DNA, the shorthand for deoxyribonucleic acid. German biochemist Albrecht Kossel , who would later go on to win the Nobel Prize, is often credited with the name.

Levene’s Polynucleotide Model

Other scientists, such as Phoebus Levene , built on Miescher’s work over the years. Levene didn't know how DNA's nucleotide components were arranged. He proposed the polynucleotide model, correctly suggesting that nucleic acids are chains of nucleotides, each with a base, a sugar, and a phosphate group. 

Watson and Crick's Double-Stranded Helix

Watson and Crick and “their” groundbreaking discovery in the field of genetics accurately identified DNA’s double-stranded helix structure, connected by hydrogen bonds. For their discovery, Watson and Crick won a Nobel Prize in 1962 and worldwide acclaim. 

Though Watson and Crick won a Nobel Prize, years later, we’ve learned that the duo likely took research without permission from chemist Rosalind Franklin . Thanks to her research, the double helix structure was realized, though her Nobel Prize was not. 

In 2014, Watson auctioned off his Nobel Prize medal for over $4 million. The buyer was a Russian billionaire who returned it to Watson a year later. In 2019, Watson was stripped of his honorary titles because of racist comments.

Read More: DNA in Unlikely Places Helps Piece Together Ancient Humans' Family Trees

2. Earth in Motion

While it may be common knowledge that Earth spins on an axis and revolves around the sun, at one point, this idea was extremely outlandish. How could the planet move and we not feel it? Thanks to a few clever scientists, the Earth in Motion theory became more than a wild idea.

What Is Earth in Motion?

Earth in motion refers to the understanding that Earth is not stationary but moves in different ways. Earth rotates on an axis and revolves around a star. 

Earth’s Rotation

Earth rotates on its axis , which is an imaginary line running from the North Pole to the South Pole. This rotation is responsible for the day-night cycle, with one complete rotation taking about 24 hours.

Earth’s Revolution

Earth revolves around the Sun, completing one orbit approximately every 365 days. This revolution, combined with the tilt of the Earth's axis, leads to the changing seasons.

Who Discovered Earth's Motion?

The discovery and acceptance of Earth's motion was a gradual process involving several key figures in the history of science.

Aristarchus Hypothesis of Earth’s Motion

An ancient Greek astronomer, Aristarchus of Samos, was one of the first to suggest that Earth orbits the Sun . This view was not widely accepted in his time as it was believed Earth was the center of the Universe, and stars, planets, and the sun all revolved around our planet.

Copernicus Creates the First Model of Earth’s Motion

Mathematician and astronomer Nicolaus Copernicus is often credited with proposing the first heliocentric model of the universe. In 1543, he published his great work, On the Revolutions of the Heavenly Spheres , which explained his theories. 

Among them was that day and night was created by the Earth spinning on its axis. Copernican heliocentrism replaced the conventionally accepted Ptolemaic theory , which asserted that the Earth was stationary. Copernicus’ work was largely unknown during his lifetime but later gained support.

Galileo Galilei’s Telescopic Observations

Galileo Galilei agreed with Copernicus’ theory and proved it through his telescopic observations. In 1610, he observed phases of Venus and the moons of Jupiter, which were strong evidence against the Earth-centered model of the universe.

Galileo agreed with Copernicus’ theory and proved it by using a telescope to confirm that the different phases Venus went through resulted from orbiting around the sun.

Johannes Kepler’s Planetary Laws

German mathematician Johannes Kepler formulated a series of laws detailing the orbits of planets around the Sun. These laws, which remain relevant today, provided mathematical equations for accurately predicting planetary movements in line with the Copernican theory.

Why Don’t We Feel Earth Spinning? 

According to researchers at the California Institute of Technology (CalTech), Earth spins smoothly and at a consistent speed. If Earth were to change speeds at any time, we’d feel it. 

Read More: Earth's Rotation Has Slowed Down Over Billions of Years

3. Electricity

Did benjamin franklin discover electricity.

It’s a common misconception that Ben Franklin discovered electricity with his famous kite experiment. But his 1752 experiment, which used a key and kite, instead demonstrated that lightning is a form of electricity . Another myth is that Franklin was struck by lightning. He wasn’t, but the storm did charge the kite. 

Who First Observed Electricity?

Back in 600 B.C.E., it was the ancient Greek philosopher Thales of Miletus who first observed static electricity when fur was rubbed against fossilized tree resin, known as amber. 

Who Invented Electricity?

British scientist and doctor William Gilbert coined the word “electric,” derived from the Greek word for amber. Regarded as the “father of electricity,” Gilbert was also the first person to use the terms magnetic pole, electric force, and electric attraction. In 1600, his six-volume book set, De Magnete , was published. Among other ideas, it included the hypothesis that Earth itself is a magnet.

Read More: Ben Franklin: Founding Father, Citizen Scientist

4. Germ Theory of Disease

What is the germ theory of disease.

Germ theory is a scientific principle in medicine that attributes the cause of many diseases to microorganisms, such as bacteria and viruses, that invade and multiply within the human body. This theory was a significant shift from previous beliefs about disease causation.

Who Invented the Germ Theory?

Louis Pasteur discovered germ theory when he demonstrated that living microorganisms caused fermentation , which could make milk and wine turn sour. From there, his experiments revealed that these microbes could be destroyed by heating them — a process we now know as pasteurization. 

This advance was a game changer, saving people from getting sick from the bacteria in unpasteurized foods , such as eggs, milk, and cheeses. Before Pasteur, everyday people and scientists alike believed that disease came from inside the body. 

Pasteur’s work proved that germ theory was true and that disease was the result of microorganisms attacking the body. Because of Pasteur, attitudes changed, and became more accepting of germ theory.

How Did Koch’s Postulates Contribute to Germ Theory?

The German physician and microbiologist Robert Koch played a crucial role in establishing a systematic methodology for proving the causal relationship between microbes and diseases .

He formulated Koch's postulates and applied these principles to identify the bacteria responsible for tuberculosis and cholera, among other diseases.

Together, Pasteur and Koch laid the foundation for bacteriology as a science and dramatically shifted the medical community's understanding of infectious diseases. Their work led to improved hygiene, the development of vaccines, and the advancement of public health measures.

Read More: Why Do Some People Get Sick All the Time, While Others Stay in Freakishly Good Health?

Who Discovered Gravity?

Isaac Newton didn’t really get hit on the head with an apple, as far as we know. But seeing an apple fall from a tree did spark an idea that would lead the mathematician and physicist to discover gravity at the age of just 23. 

He pondered about how the force pulls objects straight to the ground, as opposed to following a curved path, like a fired cannonball. Gravity was the answer — a force that pulls objects toward each other. 

How Does Gravity Work?

The greater the mass an object has, the greater the force or gravitational pull. When objects are farther apart, the weaker the force. Newton’s work and his understanding of gravity are used to explain everything from the trajectory of a baseball to the Earth’s orbit around the sun. But Newton’s discoveries didn’t stop there. 

Newton’s Laws of Motion

In 1687, Newton published his book Principia , which expanded on his laws of universal gravitation and his three laws of motion. His work laid the foundation for modern physics. 

Building on the discovery, advancements in the field of electricity continued. 

In 1800, Italian physicist Alessandro Volta created the first voltaic pile , an early form of an electric battery.

Einstein’s Theory of General Relativity

In 1915, Einstein proposed the theory of general relativity . This theory redefined gravity not as a force but as a curvature of spacetime caused by the presence of mass and energy.

According to Einstein, massive objects cause a distortion in the fabric of space and time, similar to how a heavy ball placed on a trampoline causes it to warp. Other objects move along the curves in spacetime created by this distortion.

Both Newton and Einstein significantly advanced our understanding of gravity. Their theories marked critical milestones in the field of physics and have had far-reaching implications in science and technology.

Read More: 5 Eccentric Facts About Isaac Newton

6. Antibiotics

Much like Germ Theory revolutionized modern medicine, so too did the invention of antibiotics. This discovery would go on to save countless lives.  

When Were Antibiotics Invented?

According to the Microbiology Society , humans have been using some form of antibiotics for millennia. It was only in recent history that humans realized that bacteria caused certain infections and that we could now provide readily available treatment. 

In 1909, German physician Paul Ehrlich noticed that certain chemical dyes did not color certain bacteria cells as it did for others. Because of this, he believed that it would be possible to kill certain bacteria without killing the other cells around it. Ehrlich went on to discover the cure for syphilis, which many in the scientific community refer to as the first antibiotic. However, Ehrlich referred to his discovery as chemotherapy because it used chemicals to treat a disease. Ehrlich is referred to as the “Father of Immunology” for his discoveries. 

Ukrainian-American microbiologist Selman Waksman coined the term “antibiotic” about 30 years later, according to the Microbiology Society.

Who Discovered Penicillin? 

One of the most recognizable antibiotics known today is penicillin. Health professionals prescribe millions of patients with this antibiotic each year. However, one of the most well-known antibiotics was discovered by accident. 

In 1928, after some time away from the lab, Alexander Fleming — a Scottish microbiologist — discovered that a fungus Penicillium notatum had contaminated a culture plate with Staph bacteria. Fleming noticed that the fungus had created bacteria-free areas on the plate. After multiple trials, Fleming was able to successfully prove that P. notatum prevented the growth of Staph. Soon the antibiotic was ready for mass production and helped save many lives during World War Two. 

What Is Penicillin Used For? 

Penicillin is used to treat infections caused by bacteria. The medication works by stopping and preventing the growth of bacteria. 

Read More: Antibiotic-Resistant Bacteria: What They Are and How Scientists Are Combating Them

7. The Big Bang Theory

The Big Bang Theory is one of the most widely accepted theories on the beginning of the universe. The theory claims that about 13.7 billion years ago, all matter of the universe was condensed into one small point. After a massive explosion, the contents of the universe burst forth and expanded and continue to expand today. 

Who Came Up With the Big Bang Theory?

This first mention of the Big Bang came from Georges Lemaître, a Belgian cosmologist and Catholic priest. Initially, in 1927, Lemaître published a paper about General Relativity and solutions to the equations around it. Though it mostly went unnoticed. 

Though many scientists didn’t believe that the universe was expanding, a group of cosmologists was beginning to go against the grain. After Edwin Hubble noticed that galaxies further away from our own seemed to be pulling away faster than those closer to us, the idea of the universe expanding seemed to make more sense. Lemaître’s 1927 paper was recognized, and the term Big Bang appeared in Lemaître’s 1931 paper on the subject. 

What Is the Hubble Space Telescope?

Edwin Hubble’s discovery that galaxies are moving away from our own, dubbed Hubble’s Law, is on a long list of his many discoveries. Though this discovery helped add evidence to the Big Bang Theory, this discovery was hindered by the same thing that had been distributing telescopes since their inception: Earth’s atmosphere. According to NASA , Earth’s atmosphere distorts light, limiting how far a telescope can see, even on a clear night. 

Because of this, researchers, specifically Lyman Spitzer , suggested putting a telescope in space, just beyond Earth’s atmosphere and into its orbit. After a few attempts in the 1960s and 70s, NASA, along with contributions from the European Space Agency (ESA), launched a space telescope on April 24, 1990 . The Hubble Space Telescope, named for the pioneering cosmologist, became the strongest telescope known to humankind until the 2021 launch of the James Webb Space Telescope . 

What Is The Cosmic Microwave Background?

The Big Bang emitted large amounts of primeval light , according to the ESA. Over time, this light “cooled” and was no longer visible. However, researchers are able to detect what is known as Cosmic Microwave Background (CMB), which is, according to the ESA, the cooled remnant of the first light to travel through the universe. Some researchers even refer to CMB as an echo of the Big Bang. 

Read More: Did the Big Bang Happen More Than Once?

8. Vaccines

“An ounce of prevention is worth a pound of cure,” Benjamin Franklin once said. A statement that, at the time, applied to making towns safer against fires. However, the same statement can  be true for health and wellness. The advent of vaccines has helped prevent several serious diseases and keep people safe. Thanks to vaccines, people rarely get diseases like polio, and smallpox has been eradicated . 

What Is a Vaccine?

According to the Centers for Disease Control (CDC), a vaccine is a method of protection that introduces a small amount of disease to the human body so that the body can form an immune response should that disease try to enter the body again. 

Basically, through a vaccine, the human body is exposed to a small out of a disease so that the immune system can build a defense against it. 

When Was the First Vaccine Created?

According to the World Health Organization (WHO), Dr. Edward Jenner created the first vaccine in 1796 by using infected material from a cowpox sore — a disease similar to smallpox. He inoculated an 8-year-old boy named James Phipps with the matter and found that the boy, though he didn’t feel well at first, recovered from the illness. 

A few months later, Jenner tested Phipps with material from a smallpox sore and found that Phipps did not get ill at all. From there, the smallpox vaccine prevented countless deaths in the centuries to come. 

When Was the Polio Vaccine Invented?

From 1796 to 1945, doctors and scientists worked hard to create vaccines for other serious illnesses like the Spanish Flu, yellow fever, and influenza. One of these doctors was Jonas Salk. After Salk helped develop an influenza vaccine in 1945, he began working on the Polio vaccine. Between 1952 and 1955, Salk finished the vaccine, and clinical trials began. Salk’s vacation method required a needle and syringe, though, by 1960, Albert Sabin had created a different delivery method for the polio vaccine. Sabin’s version could be administered by drops or on a sugar cube.

Read More: The History of the Polio Vaccine

9. Evolution

What is evolution .

Evolution is a theory that suggests that organisms change and adapt to their environment on a genetic level from one generation to the next. This can take millions of years through methods such as natural selection. An animal’s color or beak may alter over time depending on the changes in their environment, helping them hide from predators or better capture prey. 

Who Is the Father of Evolution? 

After studying animals in the Galapagos , particularly the finches, a naturalist named Charles Darwin determined that the birds — who all resided on different Galapagos islands — were the same or similar species but had distinct characteristics. Darwin noted that the finches from each island had different beaks. These beaks helped the finches forage for their main food source on their specific island. Some had larger beaks for cracking open nuts and seeds, while others had smaller and more narrow beaks for finding insects. 

These observations earned Charles Darwin the title of the Father of Evolution. Though the theory of evolution has changed since Darwin published On the Origin of Species in 1859, he helped lay the framework for modern scientists. 

Is Evolution a Theory or Fact? 

The long-held belief for thousands of years was that the world and all of its organisms were created by one power. But, as science has advanced, there is clear evidence to argue against that. 

The answer to this question is complicated because evolution is both fact and theory. According to the National Center for Science Education , scientific understanding needs both theories and facts. There is proof that organisms have changed or evolved over time, and scientists now have the means to study and identify how those changes happen. 

Read More : 7 Things You May Not Know About Charles Darwin

What Does CRISPR Stand For? 

According to the National Human Genome Research Institute, CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats. Researchers use this technology to modify the DNA of a living organism. 

Who Discovered CRISPR? 

There are several people involved and decades of research into the discovery of CRISPR . These researchers include Yoshizumi Ishino, Francisco Mojica, and the duo who recently won the Nobel Prize in Chemistry for CRISPR, Jennifer Doudna and Emmanuelle Charpentier. 

What Is CRISPR?

CRISPR is a technology that can edit genes or even turn a gene “on” or “off.” Researchers have described CRISPR as a molecular scissors that clips apart DNA, then replaces, deletes, or modifies genes. According to a 2018 study, scientists can use this technology to help replace certain genes that may cause diseases such as cancer or heritable diseases like Duchenne muscular dystrophy — a degenerative disorder that can cause premature death.   

How Does CRISPR Work?

In short, scientists use CRISPR technology to find specific pieces of DNA inside of a cell. Scientists then alter that piece of DNA or replace it with a different DNA sequence. CRISPR technology also ensures that the changed gene passes on to the next offspring through gene drive. 

Read More: CRISPR Gene-Editing Technology Enters the Body — and Space

This article was originally published on Oct. 22, 2021 and has since been updated with new information from the Discover staff.

• earth science

Already a subscriber?

Register or Log In

Keep reading for as low as $1.99!

Sign up for our weekly science updates.

Save up to 40% off the cover price when you subscribe to Discover magazine.

Science Essay Examples

Best Science Essay Examples to Learn From

Published on: May 3, 2023

Last updated on: Jan 31, 2024

Share this article

Are you struggling to write a science essay that stands out? 

Are you tired of feeling overwhelmed by scientific jargon and complicated concepts? 

You're not alone. 

Science essays can be a challenge for even the most dedicated students. It's no wonder that so many students struggle to produce top-notch papers.

But fear not! 

In this blog post, we'll provide you with some science essay examples and tips. We will help you write a top-notch paper that impresses your professor and earns you a high grade. 

So buckle up and get ready to tackle science essays like a pro!

On This Page On This Page -->

Science Essay Examples for Students

Writing a science essay can be a daunting task for students. However, with the right guidance and examples, it can also be a rewarding and enlightening experience.

Here, we'll provide you with examples so you can elevate your own writing.

Science Essay Example SPM

Scientific Essay Example Pdf (Insert

Science Paper Example

Science Project Essay Example

Science Essay Examples for Different Subjects

Science is a vast field that encompasses many different subjects, from biology to physics to chemistry. As a student, you may find yourself tasked with writing a science essay on a subject that you're not particularly familiar with. 

We have provided you with science essay examples for different subjects to help you get started.

Social Science Essay Example

Political Science Essay Example

Environmental Science Essay Example

Health Science Essay Example

Computer Science Essay Example

University Science Essay Examples

Science essays are important part of university-level education. However, different universities may have different requirements and expectations when it comes to writing these essays. 

That's why we've compiled some science essay examples for different universities. You can see what works and what doesn't, and tailor your own writing accordingly.

Scientific Essay Example University

Mcmaster Health Science Essay Example

Cornell Arts And Science Essay Example

Tough Essay Due? Hire Tough Writers!

Structure of a Science Essay

Science essays are a crucial part of many subjects, and learning to structure them effectively is essential for achieving academic success. 

Let’s explore scientific essay structure.

Introduction

The introduction of a science essay should introduce the topic and provide some context for the reader. 

You should explain the purpose of the essay and provide a thesis statement that outlines the main argument you will make in the essay. A good introduction should also capture the reader's interest and motivate them to read on.

Check out these how to start a science essay examples for better understanding:

Body Paragraphs

The body paragraphs of a science essay should provide evidence to support the thesis statement. You should use scientific evidence, research, and data to support your argument. 

Each paragraph should focus on one key point, and the points should be organized logically to create a coherent argument. It is essential to provide citations for all sources you use in your essay.

Here is an example for you:

The conclusion of a science essay should summarize the main points of the essay and restate the thesis statement in a compelling manner. 

You should also provide some final thoughts or recommendations based on the evidence presented in the essay. 

The conclusion should be concise and leave a lasting impression on the reader.

Natural Science Essay Topics

There are countless interesting, thought-provoking and problem solving essay topics in science.

Explore some compelling natural science essay topics to inspire your writing.

Science Essay Topics for 5th Graders

• The importance of recycling for our environment
• The different types of clouds and how they form
• How animals hibernate during the winter months
• The different types of rocks and how they are formed
• The role of bees in pollination and food production
• How light travels and how we see objects
• The properties of magnets and how they work
• The different stages of stem cell research 
• The human digestive system and how it works
• The effects of pollution on our environment and health

Science Essay Topics for 6th Graders

• The impact of climate change on the planet
• The different types of energy and how they are produced
• The importance of water conservation and management
• The role of artificial intelligence in human life
• The structure and function of the human respiratory system
• The properties and uses of acids and bases
• The effect of light on plant growth and development
• The differences between renewable and non-renewable energy sources
• The process of photosynthesis and its importance for life on Earth
• The impact of technology on the environment and society

Science Essay Topics for 7th Graders

• The structure and function of the human circulatory system
• The different types of fossils and how they are formed
• The impact of natural disasters on the environment and human life
• The pros and cons of bacteria in our bodies and in the environment
• The physics of sound and how it travels
• The effects of air pollution in United States
• The properties and uses of different types of waves (sound, light, etc.)
• The process of cell division and its role in growth and repair
• The structure and function of the human nervous system
• The different types of ecosystems and their unique characteristics

Paper Due? Why Suffer? That's our Job!

Tips for Writing a Science Essay

Writing a science essay can be challenging, especially if you don't have much experience in writing academic papers. 

However, with the right approach and strategies, you can produce a high-quality science essays. 

Here are some tips to help you write a successful science essay:

Understand the assignment requirements: Before you start writing your essay, make sure you understand the assignment requirements. Read the prompt carefully and make note of any specific guidelines or formatting requirements.

Choose a topic that interests you: Writing about a topic that you find interesting and engaging can make the process enjoyable and rewarding. Consider topics that you have studied in class or that you have a personal interest in.

Conduct thorough research: To write a successful science essay, you need to have a deep understanding of the topic you are writing about. Conduct thorough research using reliable sources such as academic journals, textbooks, and reputable websites.

Develop a clear and concise thesis statement: Your thesis statement should clearly state your argument or position on the topic you are writing about. It should be concise and specific, and should be supported by evidence throughout your essay.

Use evidence to support your claims: When writing a science essay, it's important to use evidence to support your claims and arguments. This can include scientific data, research findings, and expert opinions.

Edit and proofread your essay: Before submitting your essay, make sure to edit and proofread it carefully. Check for spelling and grammatical errors. Ensure that your essay is formatted correctly according to the assignment requirements.

In conclusion, this blog has provided a comprehensive guide to writing a successful science essay. 

By following the tips, students can produce high-quality essays that showcase their understanding of science.

If you're struggling to write a science essay or need additional assistance, CollegeEssay.org is one of the best online essay services to help you out,

Our expert writers have extensive experience in writing science essays for students of all levels. 

So why wait? Contact our science essay writing service today!

Frequently Asked Questions

What are some common mistakes to avoid when writing a science essay.

Some common mistakes to avoid include:

• Plagiarizing content
• Using incorrect or unreliable sources
• Failing to clearly state your thesis
• Using overly complex language 

How can I make my science essay stand out?

To make your science essay stand out, consider choosing a unique or controversial topic. Using relevant and up-to-date sources, and present your information in a clear and concise manner. You can also consider using visuals such as graphs or charts to enhance your essay.

What should I do if I'm struggling to come up with a topic for my science essay?

If you're struggling to come up with a topic for your science essay, consider discussing potential topics with your instructor or classmates. You can also conduct research online or in academic journals to find inspiration.

How important is research when writing a science essay?

Research is an essential component of writing a science essay. Your essay should be grounded in accurate and reliable scientific information. That is why it's important to conduct thorough research using reputable sources.

Can I use personal anecdotes or experiences in my science essay?

While personal anecdotes or experiences can be engaging, they may not always be relevant to a science essay. It's important to focus on presenting factual information and scientific evidence to support your argument or position.

Caleb S. (Law, Literature)

Caleb S. has extensive experience in writing and holds a Masters from Oxford University. He takes great satisfaction in helping students exceed their academic goals. Caleb always puts the needs of his clients first and is dedicated to providing quality service.

Paper Due? Why Suffer? That’s our Job!

Legal & Policies

• Privacy Policy
• Cookies Policy
• Terms of Use
• Refunds & Cancellations
• Our Writers
• Success Stories
• Our Guarantees
• Affiliate Program
• Referral Program
• AI Essay Writer

Disclaimer: All client orders are completed by our team of highly qualified human writers. The essays and papers provided by us are not to be used for submission but rather as learning models only.

• Table of Contents
• Random Entry
• Chronological
• Editorial Information
• About the SEP
• Editorial Board
• How to Cite the SEP
• Special Characters
• Advanced Tools
• Support the SEP
• PDFs for SEP Friends
• Make a Donation
• SEPIA for Libraries
• Entry Contents

Bibliography

Academic tools.

• Friends PDF Preview
• Author and Citation Info
• Back to Top

Scientific Discovery

Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example). Most philosophical discussions of scientific discoveries focus on the generation of new hypotheses that fit or explain given data sets or allow for the derivation of testable consequences. Philosophical discussions of scientific discovery have been intricate and complex because the term “discovery” has been used in many different ways, both to refer to the outcome and to the procedure of inquiry. In the narrowest sense, the term “discovery” refers to the purported “eureka moment” of having a new insight. In the broadest sense, “discovery” is a synonym for “successful scientific endeavor” tout court. Some philosophical disputes about the nature of scientific discovery reflect these terminological variations.

Philosophical issues related to scientific discovery arise about the nature of human creativity, specifically about whether the “eureka moment” can be analyzed and about whether there are rules (algorithms, guidelines, or heuristics) according to which such a novel insight can be brought about. Philosophical issues also arise about the analysis and evaluation of heuristics, about the characteristics of hypotheses worthy of articulation and testing, and, on the meta-level, about the nature and scope of philosophical analysis itself. This essay describes the emergence and development of the philosophical problem of scientific discovery and surveys different philosophical approaches to understanding scientific discovery. In doing so, it also illuminates the meta-philosophical problems surrounding the debates, and, incidentally, the changing nature of philosophy of science.

1. Introduction

2. scientific inquiry as discovery, 3. elements of discovery, 4. pragmatic logics of discovery, 5. the distinction between the context of discovery and the context of justification, 6.1 discovery as abduction, 6.2 heuristic programming, 7. anomalies and the structure of discovery, 8.1 discoverability, 8.2 preliminary appraisal, 8.3 heuristic strategies, 9.1 kinds and features of creativity, 9.2 analogy, 9.3 mental models, 10. machine discovery, 11. social epistemology and discovery, 12. integrated approaches to knowledge generation, other internet resources, related entries.

Philosophical reflection on scientific discovery occurred in different phases. Prior to the 1930s, philosophers were mostly concerned with discoveries in the broad sense of the term, that is, with the analysis of successful scientific inquiry as a whole. Philosophical discussions focused on the question of whether there were any discernible patterns in the production of new knowledge. Because the concept of discovery did not have a specified meaning and was used in a very wide sense, almost all discussions of scientific method and practice could potentially be considered as early contributions to reflections on scientific discovery. In the course of the 18 th century, as philosophy of science and science gradually became two distinct endeavors with different audiences, the term “discovery” became a technical term in philosophical discussions. Different elements of scientific inquiry were specified. Most importantly, during the 19 th century, the generation of new knowledge came to be clearly and explicitly distinguished from its assessment, and thus the conditions for the narrower notion of discovery as the act or process of conceiving new ideas emerged. This distinction was encapsulated in the so-called “context distinction,” between the “context of discovery” and the “context of justification”.

Much of the discussion about scientific discovery in the 20 th century revolved around this distinction It was argued that conceiving a new idea is a non-rational process, a leap of insight that cannot be captured in specific instructions. Justification, by contrast, is a systematic process of applying evaluative criteria to knowledge claims. Advocates of the context distinction argued that philosophy of science is exclusively concerned with the context of justification. The assumption underlying this argument is that philosophy is a normative project; it determines norms for scientific practice. Given this assumption, only the justification of ideas, not their generation, can be the subject of philosophical (normative) analysis. Discovery, by contrast, can only be a topic for empirical study. By definition, the study of discovery is outside the scope of philosophy of science proper.

The introduction of the context distinction and the disciplinary distinction between empirical science studies and normative philosophy of science that was tied to it spawned meta-philosophical disputes. For a long time, philosophical debates about discovery were shaped by the notion that philosophical and empirical analyses are mutually exclusive. Some philosophers insisted, like their predecessors prior to the 1930s, that the philosopher’s tasks include the analysis of actual scientific practices and that scientific resources be used to address philosophical problems. They maintained that it is a legitimate task for philosophy of science to develop a theory of heuristics or problem solving. But this position was the minority view in philosophy of science until the last decades of the 20 th century. Philosophers of discovery were thus compelled to demonstrate that scientific discovery was in fact a legitimate part of philosophy of science. Philosophical reflections about the nature of scientific discovery had to be bolstered by meta-philosophical arguments about the nature and scope of philosophy of science.

Today, however, there is wide agreement that philosophy and empirical research are not mutually exclusive. Not only do empirical studies of actual scientific discoveries in past and present inform philosophical thought about the structure and cognitive mechanisms of discovery, but works in psychology, cognitive science, artificial intelligence and related fields have become integral parts of philosophical analyses of the processes and conditions of the generation of new knowledge. Social epistemology has opened up another perspective on scientific discovery, reconceptualizing knowledge generation as group process.

Prior to the 19 th century, the term “discovery” was used broadly to refer to a new finding, such as a new cure, an unknown territory, an improvement of an instrument, or a new method of measuring longitude. One strand of the discussion about discovery dating back to ancient times concerns the method of analysis as the method of discovery in mathematics and geometry, and, by extension, in philosophy and scientific inquiry. Following the analytic method, we seek to find or discover something – the “thing sought,” which could be a theorem, a solution to a geometrical problem, or a cause – by analyzing it. In the ancient Greek context, analytic methods in mathematics, geometry, and philosophy were not clearly separated; the notion of finding or discovering things by analysis was relevant in all these fields.

In the ensuing centuries, several natural and experimental philosophers, including Avicenna and Zabarella, Bacon and Boyle, the authors of the Port-Royal Logic and Newton, and many others, expounded rules of reasoning and methods for arriving at new knowledge. The ancient notion of analysis still informed these rules and methods. Newton’s famous thirty-first query in the second edition of the Opticks outlines the role of analysis in discovery as follows: “As in Mathematicks, so in Natural Philosophy, the Investigation of difficult Things by the Method of Analysis, ought ever to precede the Method of Composition. This Analysis consists in making Experiments and Observations, and in drawing general Conclusions from them by Induction, and admitting of no Objections against the Conclusions, but such as are taken from Experiments, or other certain Truths … By this way of Analysis we may proceed from Compounds to Ingredients, and from Motions to the Forces producing them; and in general, from Effects to their Causes, and from particular Causes to more general ones, till the Argument end in the most general. This is the Method of Analysis” (Newton 1718, 380, see Koertge 1980, section VI). Early modern accounts of discovery captured knowledge-seeking practices in the study of living and non-living nature, ranging from astronomy and physics to medicine, chemistry, and agriculture. These rich accounts of scientific inquiry were often expounded to bolster particular theories about the nature of matter and natural forces and were not explicitly labeled “methods of discovery ”, yet they are, in fact, accounts of knowledge generation and proper scientific reasoning, covering topics such as the role of the senses in knowledge generation, observation and experimentation, analysis and synthesis, induction and deduction, hypotheses, probability, and certainty.

Bacon’s work is a prominent example. His view of the method of science as it is presented in the Novum Organum showed how best to arrive at knowledge about “form natures” (the most general properties of matter) via a systematic investigation of phenomenal natures. Bacon described how first to collect and organize natural phenomena and experimentally produced facts in tables, how to evaluate these lists, and how to refine the initial results with the help of further trials. Through these steps, the investigator would arrive at conclusions about the “form nature” that produces particular phenomenal natures. Bacon expounded the procedures of constructing and evaluating tables of presences and absences to underpin his matter theory. In addition, in his other writings, such as his natural history Sylva Sylvarum or his comprehensive work on human learning De Augmentis Scientiarium , Bacon exemplified the “art of discovery” with practical examples and discussions of strategies of inquiry.

Like Bacon and Newton, several other early modern authors advanced ideas about how to generate and secure empirical knowledge, what difficulties may arise in scientific inquiry, and how they could be overcome. The close connection between theories about matter and force and scientific methodologies that we find in early modern works was gradually severed. 18 th - and early 19 th -century authors on scientific method and logic cited early modern approaches mostly to model proper scientific practice and reasoning, often creatively modifying them ( section 3 ). Moreover, they developed the earlier methodologies of experimentation, observation, and reasoning into practical guidelines for discovering new phenomena and devising probable hypotheses about cause-effect relations.

It was common in 20 th -century philosophy of science to draw a sharp contrast between those early theories of scientific method and modern approaches. 20 th -century philosophers of science interpreted 17 th - and 18 th -century approaches as generative theories of scientific method. They function simultaneously as guides for acquiring new knowledge and as assessments of the knowledge thus obtained, whereby knowledge that is obtained “in the right way” is considered secure (Laudan 1980; Schaffner 1993: chapter 2). On this view, scientific methods are taken to have probative force (Nickles 1985). According to modern, “consequentialist” theories, propositions must be established by comparing their consequences with observed and experimentally produced phenomena (Laudan 1980; Nickles 1985). It was further argued that, when consequentialist theories were on the rise, the two processes of generation and assessment of an idea or hypothesis became distinct, and the view that the merit of a new idea does not depend on the way in which it was arrived at became widely accepted.

More recent research in history of philosophy of science has shown, however, that there was no such sharp contrast. Consequentialist ideas were advanced throughout the 18 th century, and the early modern generative theories of scientific method and knowledge were more pragmatic than previously assumed. Early modern scholars did not assume that this procedure would lead to absolute certainty. One could only obtain moral certainty for the propositions thus secured.

During the 18 th and 19 th centuries, the different elements of discovery gradually became separated and discussed in more detail. Discussions concerned the nature of observations and experiments, the act of having an insight and the processes of articulating, developing, and testing the novel insight. Philosophical discussion focused on the question of whether and to what extent rules could be devised to guide each of these processes.

Numerous 19 th -century scholars contributed to these discussions, including Claude Bernard, Auguste Comte, George Gore, John Herschel, W. Stanley Jevons, Justus von Liebig, John Stuart Mill, and Charles Sanders Peirce, to name only a few. William Whewell’s work, especially the two volumes of Philosophy of the Inductive Sciences of 1840, is a noteworthy and, later, much discussed contribution to the philosophical debates about scientific discovery because he explicitly distinguished the creative moment or “happy thought” as he called it from other elements of scientific inquiry and because he offered a detailed analysis of the “discoverer’s induction”, i.e., the pursuit and evaluation of the new insight. Whewell’s approach is not unique, but for late 20 th -century philosophers of science, his comprehensive, historically informed philosophy of discovery became a point of orientation in the revival of interest in scientific discovery processes.

For Whewell, discovery comprised three elements: the happy thought, the articulation and development of that thought, and the testing or verification of it. His account was in part a description of the psychological makeup of the discoverer. For instance, he held that only geniuses could have those happy thoughts that are essential to discovery. In part, his account was an account of the methods by which happy thoughts are integrated into the system of knowledge. According to Whewell, the initial step in every discovery is what he called “some happy thought, of which we cannot trace the origin; some fortunate cast of intellect, rising above all rules. No maxims can be given which inevitably lead to discovery” (Whewell 1996 [1840]: 186). An “art of discovery” in the sense of a teachable and learnable skill does not exist according to Whewell. The happy thought builds on the known facts, but according to Whewell it is impossible to prescribe a method for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important sense, scientific discoveries are not accidental. The happy thought is not a wild guess. Only the person whose mind is prepared to see things will actually notice them. The “previous condition of the intellect, and not the single fact, is really the main and peculiar cause of the success. The fact is merely the occasion by which the engine of discovery is brought into play sooner or later. It is, as I have elsewhere said, only the spark which discharges a gun already loaded and pointed; and there is little propriety in speaking of such an accident as the cause why the bullet hits its mark.” (Whewell 1996 [1840]: 189).

Having a happy thought is not yet a discovery, however. The second element of a scientific discovery consists in binding together—“colligating”, as Whewell called it—a set of facts by bringing them under a general conception. Not only does the colligation produce something new, but it also shows the previously known facts in a new light. Colligation involves, on the one hand, the specification of facts through systematic observation, measurements and experiment, and on the other hand, the clarification of ideas through the exposition of the definitions and axioms that are tacitly implied in those ideas. This process is extended and iterative. The scientists go back and forth between binding together the facts, clarifying the idea, rendering the facts more exact, and so forth.

The final part of the discovery is the verification of the colligation involving the happy thought. This means, first and foremost, that the outcome of the colligation must be sufficient to explain the data at hand. Verification also involves judging the predictive power, simplicity, and “consilience” of the outcome of the colligation. “Consilience” refers to a higher range of generality (broader applicability) of the theory (the articulated and clarified happy thought) that the actual colligation produced. Whewell’s account of discovery is not a deductivist system. It is essential that the outcome of the colligation be inferable from the data prior to any testing (Snyder 1997).

Whewell’s theory of discovery clearly separates three elements: the non-analyzable happy thought or eureka moment; the process of colligation which includes the clarification and explication of facts and ideas; and the verification of the outcome of the colligation. His position that the philosophy of discovery cannot prescribe how to think happy thoughts has been a key element of 20 th -century philosophical reflection on discovery. In contrast to many 20 th -century approaches, Whewell’s philosophical conception of discovery also comprises the processes by which the happy thoughts are articulated. Similarly, the process of verification is an integral part of discovery. The procedures of articulation and test are both analyzable according to Whewell, and his conception of colligation and verification serve as guidelines for how the discoverer should proceed. To verify a hypothesis, the investigator needs to show that it accounts for the known facts, that it foretells new, previously unobserved phenomena, and that it can explain and predict phenomena which are explained and predicted by a hypothesis that was obtained through an independent happy thought-cum-colligation (Ducasse 1951).

Whewell’s conceptualization of scientific discovery offers a useful framework for mapping the philosophical debates about discovery and for identifying major issues of concern in 20 th -century philosophical debates. Until the late 20 th century, most philosophers operated with a notion of discovery that is narrower than Whewell’s. In more recent treatments of discovery, however, the scope of the term “discovery” is limited to either the first of these elements, the “happy thought”, or to the happy thought and its initial articulation. In the narrower conception, what Whewell called “verification” is not part of discovery proper. Secondly, until the late 20 th century, there was wide agreement that the eureka moment, narrowly construed, is an unanalyzable, even mysterious leap of insight. The main disagreements concerned the question of whether the process of developing a hypothesis (the “colligation” in Whewell’s terms) is, or is not, a part of discovery proper – and if it is, whether and how this process is guided by rules. Much of the controversies in the 20 th century about the possibility of a philosophy of discovery can be understood against the background of the disagreement about whether the process of discovery does or does not include the articulation and development of a novel thought. Philosophers also disagreed on the issue of whether it is a philosophical task to explicate these rules.

In early 20 th -century logical empiricism, the view that discovery is or at least crucially involves a non-analyzable creative act of a gifted genius was widespread. Alternative conceptions of discovery especially in the pragmatist tradition emphasize that discovery is an extended process, i.e., that the discovery process includes the reasoning processes through which a new insight is articulated and further developed.

In the pragmatist tradition, the term “logic” is used in the broad sense to refer to strategies of human reasoning and inquiry. While the reasoning involved does not proceed according to the principles of demonstrative logic, it is systematic enough to deserve the label “logical”. Proponents of this view argued that traditional (here: syllogistic) logic is an inadequate model of scientific discovery because it misrepresents the process of knowledge generation as grossly as the notion of an “aha moment”.

Early 20 th -century pragmatic logics of discovery can best be described as comprehensive theories of the mental and physical-practical operations involved in knowledge generation, as theories of “how we think” (Dewey 1910). Among the mental operations are classification, determination of what is relevant to an inquiry, and the conditions of communication of meaning; among the physical operations are observation and (laboratory) experiments. These features of scientific discovery are either not or only insufficiently represented by traditional syllogistic logic (Schiller 1917: 236–7).

Philosophers advocating this approach agree that the logic of discovery should be characterized as a set of heuristic principles rather than as a process of applying inductive or deductive logic to a set of propositions. These heuristic principles are not understood to show the path to secure knowledge. Heuristic principles are suggestive rather than demonstrative (Carmichael 1922, 1930). One recurrent feature in these accounts of the reasoning strategies leading to new ideas is analogical reasoning (Schiller 1917; Benjamin 1934, see also section 9.2 .). However, in academic philosophy of science, endeavors to develop more systematically the heuristics guiding discovery processes were soon eclipsed by the advance of the distinction between contexts of discovery and justification.

The distinction between “context of discovery” and “context of justification” dominated and shaped the discussions about discovery in 20 th -century philosophy of science. The context distinction marks the distinction between the generation of a new idea or hypothesis and the defense (test, verification) of it. As the previous sections have shown, the distinction among different elements of scientific inquiry has a long history but in the first half of the 20 th century, the distinction between the different features of scientific inquiry turned into a powerful demarcation criterion between “genuine” philosophy and other fields of science studies, which became potent in philosophy of science. The boundary between context of discovery (the de facto thinking processes) and context of justification (the de jure defense of the correctness of these thoughts) was now understood to determine the scope of philosophy of science, whereby philosophy of science is conceived as a normative endeavor. Advocates of the context distinction argue that the generation of a new idea is an intuitive, nonrational process; it cannot be subject to normative analysis. Therefore, the study of scientists’ actual thinking can only be the subject of psychology, sociology, and other empirical sciences. Philosophy of science, by contrast, is exclusively concerned with the context of justification.

The terms “context of discovery” and “context of justification” are often associated with Hans Reichenbach’s work. Reichenbach’s original conception of the context distinction is quite complex, however (Howard 2006; Richardson 2006). It does not map easily on to the disciplinary distinction mentioned above, because for Reichenbach, philosophy of science proper is partly descriptive. Reichenbach maintains that philosophy of science includes a description of knowledge as it really is. Descriptive philosophy of science reconstructs scientists’ thinking processes in such a way that logical analysis can be performed on them, and it thus prepares the ground for the evaluation of these thoughts (Reichenbach 1938: § 1). Discovery, by contrast, is the object of empirical—psychological, sociological—study. According to Reichenbach, the empirical study of discoveries shows that processes of discovery often correspond to the principle of induction, but this is simply a psychological fact (Reichenbach 1938: 403).

While the terms “context of discovery” and “context of justification” are widely used, there has been ample discussion about how the distinction should be drawn and what their philosophical significance is (c.f. Kordig 1978; Gutting 1980; Zahar 1983; Leplin 1987; Hoyningen-Huene 1987; Weber 2005: chapter 3; Schickore and Steinle 2006). Most commonly, the distinction is interpreted as a distinction between the process of conceiving a theory and the assessment of that theory, specifically the assessment of the theory’s epistemic support. This version of the distinction is not necessarily interpreted as a temporal distinction. In other words, it is not usually assumed that a theory is first fully developed and then assessed. Rather, generation and assessment are two different epistemic approaches to theory: the endeavor to articulate, flesh out, and develop its potential and the endeavor to assess its epistemic worth. Within the framework of the context distinction, there are two main ways of conceptualizing the process of conceiving a theory. The first option is to characterize the generation of new knowledge as an irrational act, a mysterious creative intuition, a “eureka moment”. The second option is to conceptualize the generation of new knowledge as an extended process that includes a creative act as well as some process of articulating and developing the creative idea.

Both of these accounts of knowledge generation served as starting points for arguments against the possibility of a philosophy of discovery. In line with the first option, philosophers have argued that neither is it possible to prescribe a logical method that produces new ideas nor is it possible to reconstruct logically the process of discovery. Only the process of testing is amenable to logical investigation. This objection to philosophies of discovery has been called the “discovery machine objection” (Curd 1980: 207). It is usually associated with Karl Popper’s Logic of Scientific Discovery .

The initial state, the act of conceiving or inventing a theory, seems to me neither to call for logical analysis not to be susceptible of it. The question how it happens that a new idea occurs to a man—whether it is a musical theme, a dramatic conflict, or a scientific theory—may be of great interest to empirical psychology; but it is irrelevant to the logical analysis of scientific knowledge. This latter is concerned not with questions of fact (Kant’s quid facti ?) , but only with questions of justification or validity (Kant’s quid juris ?) . Its questions are of the following kind. Can a statement be justified? And if so, how? Is it testable? Is it logically dependent on certain other statements? Or does it perhaps contradict them? […]Accordingly I shall distinguish sharply between the process of conceiving a new idea, and the methods and results of examining it logically. As to the task of the logic of knowledge—in contradistinction to the psychology of knowledge—I shall proceed on the assumption that it consists solely in investigating the methods employed in those systematic tests to which every new idea must be subjected if it is to be seriously entertained. (Popper 2002 [1934/1959]: 7–8)

With respect to the second way of conceptualizing knowledge generation, many philosophers argue in a similar fashion that because the process of discovery involves an irrational, intuitive process, which cannot be examined logically, a logic of discovery cannot be construed. Other philosophers turn against the philosophy of discovery even though they explicitly acknowledge that discovery is an extended, reasoned process. They present a meta-philosophical objection argument, arguing that a theory of articulating and developing ideas is not a philosophical but a psychological or sociological theory. In this perspective, “discovery” is understood as a retrospective label, which is attributed as a sign of accomplishment to some scientific endeavors. Sociological theories acknowledge that discovery is a collective achievement and the outcome of a process of negotiation through which “discovery stories” are constructed and certain knowledge claims are granted discovery status (Brannigan 1981; Schaffer 1986, 1994).

The impact of the context distinction on 20 th -century studies of scientific discovery and on philosophy of science more generally can hardly be overestimated. The view that the process of discovery (however construed) is outside the scope of philosophy of science proper was widely shared amongst philosophers of science for most of the 20 th century. The last section shows that there were some attempts to develop logics of discovery in the 1920s and 1930s, especially in the pragmatist tradition. But for several decades, the context distinction dictated what philosophy of science should be about and how it should proceed. The dominant view was that theories of mental operations or heuristics had no place in philosophy of science and that, therefore, discovery was not a legitimate topic for philosophy of science. Until the last decades of the 20 th century, there were few attempts to challenge the disciplinary distinction tied to the context distinction. Only during the 1970s did the interest in philosophical approaches to discovery begin to increase again. But the context distinction remained a challenge for philosophies of discovery.

There are several lines of response to the disciplinary distinction tied to the context distinction. Each of these lines of response opens a philosophical perspective on discovery. Each proceeds on the assumption that philosophy of science may legitimately include some form of analysis of actual reasoning patterns as well as information from empirical sciences such as cognitive science, psychology, and sociology. All of these responses reject the idea that discovery is nothing but a mystical event. Discovery is conceived as an analyzable reasoning process, not just as a creative leap by which novel ideas spring into being fully formed. All of these responses agree that the procedures and methods for arriving at new hypotheses and ideas are no guarantee that the hypothesis or idea that is thus formed is necessarily the best or the correct one. Nonetheless, it is the task of philosophy of science to provide rules for making this process better. All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient.

But the different approaches to scientific discovery employ different terminologies. In particular, the term “logic” of discovery is sometimes used in a narrow sense and sometimes broadly understood. In the narrow sense, “logic” of discovery is understood to refer to a set of formal, generally applicable rules by which novel ideas can be mechanically derived from existing data. In the broad sense, “logic” of discovery refers to the schematic representation of reasoning procedures. “Logical” is just another term for “rational”. Moreover, while each of these responses combines philosophical analyses of scientific discovery with empirical research on actual human cognition, different sets of resources are mobilized, ranging from AI research and cognitive science to historical studies of problem-solving procedures. Also, the responses parse the process of scientific inquiry differently. Often, scientific inquiry is regarded as having two aspects, viz. generation and assessments of new ideas. At times, however, scientific inquiry is regarded as having three aspects, namely generation, pursuit or articulation, and assessment of knowledge. In the latter framework, the label “discovery” is sometimes used to refer just to generation and sometimes to refer to both generation and pursuit.

One response to the challenge of the context distinction draws on a broad understanding of the term “logic” to argue that we cannot but admit a general, domain-neutral logic if we do not want to assume that the success of science is a miracle (Jantzen 2016) and that a logic of scientific discovery can be developed ( section 6 ). Another response, drawing on a narrow understanding of the term “logic”, is to concede that there is no logic of discovery, i.e., no algorithm for generating new knowledge, but that the process of discovery follows an identifiable, analyzable pattern ( section 7 ).

Others argue that discovery is governed by a methodology . The methodology of discovery is a legitimate topic for philosophical analysis ( section 8 ). Yet another response assumes that discovery is or at least involves a creative act. Drawing on resources from cognitive science, neuroscience, computational research, and environmental and social psychology, philosophers have sought to demystify the cognitive processes involved in the generation of new ideas. Philosophers who take this approach argue that scientific creativity is amenable to philosophical analysis ( section 9.1 ).

All these responses assume that there is more to discovery than a eureka moment. Discovery comprises processes of articulating, developing, and assessing the creative thought, as well as the scientific community’s adjudication of what does, and does not, count as “discovery” (Arabatzis 1996). These are the processes that can be examined with the tools of philosophical analysis, augmented by input from other fields of science studies such as sociology, history, or cognitive science.

6. Logics of discovery after the context distinction

One way of responding to the demarcation criterion described above is to argue that discovery is a topic for philosophy of science because it is a logical process after all. Advocates of this approach to the logic of discovery usually accept the overall distinction between the two processes of conceiving and testing a hypothesis. They also agree that it is impossible to put together a manual that provides a formal, mechanical procedure through which innovative concepts or hypotheses can be derived: There is no discovery machine. But they reject the view that the process of conceiving a theory is a creative act, a mysterious guess, a hunch, a more or less instantaneous and random process. Instead, they insist that both conceiving and testing hypotheses are processes of reasoning and systematic inference, that both of these processes can be represented schematically, and that it is possible to distinguish better and worse paths to new knowledge.

This line of argument has much in common with the logics of discovery described in section 4 above but it is now explicitly pitched against the disciplinary distinction tied to the context distinction. There are two main ways of developing this argument. The first is to conceive of discovery in terms of abductive reasoning ( section 6.1 ). The second is to conceive of discovery in terms of problem-solving algorithms, whereby heuristic rules aid the processing of available data and enhance the success in finding solutions to problems ( section 6.2 ). Both lines of argument rely on a broad conception of logic, whereby the “logic” of discovery amounts to a schematic account of the reasoning processes involved in knowledge generation.

One argument, elaborated prominently by Norwood R. Hanson, is that the act of discovery—here, the act of suggesting a new hypothesis—follows a distinctive logical pattern, which is different from both inductive logic and the logic of hypothetico-deductive reasoning. The special logic of discovery is the logic of abductive or “retroductive” inferences (Hanson 1958). The argument that it is through an act of abductive inferences that plausible, promising scientific hypotheses are devised goes back to C.S. Peirce. This version of the logic of discovery characterizes reasoning processes that take place before a new hypothesis is ultimately justified. The abductive mode of reasoning that leads to plausible hypotheses is conceptualized as an inference beginning with data or, more specifically, with surprising or anomalous phenomena.

In this view, discovery is primarily a process of explaining anomalies or surprising, astonishing phenomena. The scientists’ reasoning proceeds abductively from an anomaly to an explanatory hypothesis in light of which the phenomena would no longer be surprising or anomalous. The outcome of this reasoning process is not one single specific hypothesis but the delineation of a type of hypotheses that is worthy of further attention (Hanson 1965: 64). According to Hanson, the abductive argument has the following schematic form (Hanson 1960: 104):

• Some surprising, astonishing phenomena p 1 , p 2 , p 3 … are encountered.
• But p 1 , p 2 , p 3 … would not be surprising were an hypothesis of H ’s type to obtain. They would follow as a matter of course from something like H and would be explained by it.
• Therefore there is good reason for elaborating an hypothesis of type H—for proposing it as a possible hypothesis from whose assumption p 1 , p 2 , p 3 … might be explained.

Drawing on the historical record, Hanson argues that several important discoveries were made relying on abductive reasoning, such as Kepler’s discovery of the elliptic orbit of Mars (Hanson 1958). It is now widely agreed, however, that Hanson’s reconstruction of the episode is not a historically adequate account of Kepler’s discovery (Lugg 1985). More importantly, while there is general agreement that abductive inferences are frequent in both everyday and scientific reasoning, these inferences are no longer considered as logical inferences. Even if one accepts Hanson’s schematic representation of the process of identifying plausible hypotheses, this process is a “logical” process only in the widest sense whereby the term “logical” is understood as synonymous with “rational”. Notably, some philosophers have even questioned the rationality of abductive inferences (Koehler 1991; Brem and Rips 2000).

Another argument against the above schema is that it is too permissive. There will be several hypotheses that are explanations for phenomena p 1 , p 2 , p 3 …, so the fact that a particular hypothesis explains the phenomena is not a decisive criterion for developing that hypothesis (Harman 1965; see also Blackwell 1969). Additional criteria are required to evaluate the hypothesis yielded by abductive inferences.

Finally, it is worth noting that the schema of abductive reasoning does not explain the very act of conceiving a hypothesis or hypothesis-type. The processes by which a new idea is first articulated remain unanalyzed in the above schema. The schema focuses on the reasoning processes by which an exploratory hypothesis is assessed in terms of its merits and promise (Laudan 1980; Schaffner 1993).

In more recent work on abduction and discovery, two notions of abduction are sometimes distinguished: the common notion of abduction as inference to the best explanation (selective abduction) and creative abduction (Magnani 2000, 2009). Selective abduction—the inference to the best explanation—involves selecting a hypothesis from a set of known hypotheses. Medical diagnosis exemplifies this kind of abduction. Creative abduction, by contrast, involves generating a new, plausible hypothesis. This happens, for instance, in medical research, when the notion of a new disease is articulated. However, it is still an open question whether this distinction can be drawn, or whether there is a more gradual transition from selecting an explanatory hypothesis from a familiar domain (selective abduction) to selecting a hypothesis that is slightly modified from the familiar set and to identifying a more drastically modified or altered assumption.

Another recent suggestion is to broaden Peirce’s original account of abduction and to include not only verbal information but also non-verbal mental representations, such as visual, auditory, or motor representations. In Thagard’s approach, representations are characterized as patterns of activity in mental populations (see also section 9.3 below). The advantage of the neural account of human reasoning is that it covers features such as the surprise that accompanies the generation of new insights or the visual and auditory representations that contribute to it. Surprise, for instance, could be characterized as resulting from rapid changes in activation of the node in a neural network representing the “surprising” element (Thagard and Stewart 2011). If all mental representations can be characterized as patterns of firing in neural populations, abduction can be analyzed as the combination or “convolution” (Thagard) of patterns of neural activity from disjoint or overlapping patterns of activity (Thagard 2010).

The concern with the logic of discovery has also motivated research on artificial intelligence at the intersection of philosophy of science and cognitive science. In this approach, scientific discovery is treated as a form of problem-solving activity (Simon 1973; see also Newell and Simon 1971), whereby the systematic aspects of problem solving are studied within an information-processing framework. The aim is to clarify with the help of computational tools the nature of the methods used to discover scientific hypotheses. These hypotheses are regarded as solutions to problems. Philosophers working in this tradition build computer programs employing methods of heuristic selective search (e.g., Langley et al. 1987). In computational heuristics, search programs can be described as searches for solutions in a so-called “problem space” in a certain domain. The problem space comprises all possible configurations in that domain (e.g., for chess problems, all possible arrangements of pieces on a board of chess). Each configuration is a “state” of the problem space. There are two special states, namely the goal state, i.e., the state to be reached, and the initial state, i.e., the configuration at the starting point from which the search begins. There are operators, which determine the moves that generate new states from the current state. There are path constraints, which limit the permitted moves. Problem solving is the process of searching for a solution of the problem of how to generate the goal state from an initial state. In principle, all states can be generated by applying the operators to the initial state, then to the resulting state, until the goal state is reached (Langley et al. 1987: chapter 9). A problem solution is a sequence of operations leading from the initial to the goal state.

The basic idea behind computational heuristics is that rules can be identified that serve as guidelines for finding a solution to a given problem quickly and efficiently by avoiding undesired states of the problem space. These rules are best described as rules of thumb. The aim of constructing a logic of discovery thus becomes the aim of constructing a heuristics for the efficient search for solutions to problems. The term “heuristic search” indicates that in contrast to algorithms, problem-solving procedures lead to results that are merely provisional and plausible. A solution is not guaranteed, but heuristic searches are advantageous because they are more efficient than exhaustive random trial and error searches. Insofar as it is possible to evaluate whether one set of heuristics is better—more efficacious—than another, the logic of discovery turns into a normative theory of discovery.

Arguably, because it is possible to reconstruct important scientific discovery processes with sets of computational heuristics, the scientific discovery process can be considered as a special case of the general mechanism of information processing. In this context, the term “logic” is not used in the narrow sense of a set of formal, generally applicable rules to draw inferences but again in a broad sense as a label for a set of procedural rules.

The computer programs that embody the principles of heuristic searches in scientific inquiry simulate the paths that scientists followed when they searched for new theoretical hypotheses. Computer programs such as BACON (Simon et al. 1981) and KEKADA (Kulkarni and Simon 1988) utilize sets of problem-solving heuristics to detect regularities in given data sets. The program would note, for instance, that the values of a dependent term are constant or that a set of values for a term x and a set of values for a term y are linearly related. It would thus “infer” that the dependent term always has that value or that a linear relation exists between x and y . These programs can “make discoveries” in the sense that they can simulate successful discoveries such as Kepler’s third law (BACON) or the Krebs cycle (KEKADA).

Computational theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying specific differences between the present and the goal situation and searches for operators (processes that will change the situation) that are associated with the differences that were detected. Another important heuristic is to divide the problem into sub-problems and to begin solving the one with the smallest number of unknowns to be determined (Simon 1977). Computational approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.

As accounts of scientific discoveries, the early computational heuristics have some limitations. Compared to the problem spaces given in computational heuristics, the complex problem spaces for scientific problems are often ill defined, and the relevant search space and goal state must be delineated before heuristic assumptions could be formulated (Bechtel and Richardson 1993: chapter 1). Because a computer program requires the data from actual experiments, the simulations cover only certain aspects of scientific discoveries; in particular, it cannot determine by itself which data is relevant, which data to relate and what form of law it should look for (Gillies 1996). However, as a consequence of the rise of so-called “deep learning” methods in data-intensive science, there is renewed philosophical interest in the question of whether machines can make discoveries ( section 10 ).

Many philosophers maintain that discovery is a legitimate topic for philosophy of science while abandoning the notion that there is a logic of discovery. One very influential approach is Thomas Kuhn’s analysis of the emergence of novel facts and theories (Kuhn 1970 [1962]: chapter 6). Kuhn identifies a general pattern of discovery as part of his account of scientific change. A discovery is not a simple act, but an extended, complex process, which culminates in paradigm changes. Paradigms are the symbolic generalizations, metaphysical commitments, values, and exemplars that are shared by a community of scientists and that guide the research of that community. Paradigm-based, normal science does not aim at novelty but instead at the development, extension, and articulation of accepted paradigms. A discovery begins with an anomaly, that is, with the recognition that the expectations induced by an established paradigm are being violated. The process of discovery involves several aspects: observations of an anomalous phenomenon, attempts to conceptualize it, and changes in the paradigm so that the anomaly can be accommodated.

It is the mark of success of normal science that it does not make transformative discoveries, and yet such discoveries come about as a consequence of normal, paradigm-guided science. The more detailed and the better developed a paradigm, the more precise are its predictions. The more precisely the researchers know what to expect, the better they are able to recognize anomalous results and violations of expectations:

novelty ordinarily emerges only for the man who, knowing with precision what he should expect, is able to recognize that something has gone wrong. Anomaly appears only against the background provided by the paradigm. (Kuhn 1970 [1962]: 65)

Drawing on several historical examples, Kuhn argues that it is usually impossible to identify the very moment when something was discovered or even the individual who made the discovery. Kuhn illustrates these points with the discovery of oxygen (see Kuhn 1970 [1962]: 53–56). Oxygen had not been discovered before 1774 and had been discovered by 1777. Even before 1774, Lavoisier had noticed that something was wrong with phlogiston theory, but he was unable to move forward. Two other investigators, C. W. Scheele and Joseph Priestley, independently identified a gas obtained from heating solid substances. But Scheele’s work remained unpublished until after 1777, and Priestley did not identify his substance as a new sort of gas. In 1777, Lavoisier presented the oxygen theory of combustion, which gave rise to fundamental reconceptualization of chemistry. But according to this theory as Lavoisier first presented it, oxygen was not a chemical element. It was an atomic “principle of acidity” and oxygen gas was a combination of that principle with caloric. According to Kuhn, all of these developments are part of the discovery of oxygen, but none of them can be singled out as “the” act of discovery.

In pre-paradigmatic periods or in times of paradigm crisis, theory-induced discoveries may happen. In these periods, scientists speculate and develop tentative theories, which may lead to novel expectations and experiments and observations to test whether these expectations can be confirmed. Even though no precise predictions can be made, phenomena that are thus uncovered are often not quite what had been expected. In these situations, the simultaneous exploration of the new phenomena and articulation of the tentative hypotheses together bring about discovery.

In cases like the discovery of oxygen, by contrast, which took place while a paradigm was already in place, the unexpected becomes apparent only slowly, with difficulty, and against some resistance. Only gradually do the anomalies become visible as such. It takes time for the investigators to recognize “both that something is and what it is” (Kuhn 1970 [1962]: 55). Eventually, a new paradigm becomes established and the anomalous phenomena become the expected phenomena.

Recent studies in cognitive neuroscience of brain activity during periods of conceptual change support Kuhn’s view that conceptual change is hard to achieve. These studies examine the neural processes that are involved in the recognition of anomalies and compare them with the brain activity involved in the processing of information that is consistent with preferred theories. The studies suggest that the two types of data are processed differently (Dunbar et al. 2007).

8. Methodologies of discovery

Advocates of the view that there are methodologies of discovery use the term “logic” in the narrow sense of an algorithmic procedure to generate new ideas. But like the AI-based theories of scientific discovery described in section 6 , methodologies of scientific discovery interpret the concept “discovery” as a label for an extended process of generating and articulating new ideas and often describe the process in terms of problem solving. In these approaches, the distinction between the contexts of discovery and the context of justification is challenged because the methodology of discovery is understood to play a justificatory role. Advocates of a methodology of discovery usually rely on a distinction between different justification procedures, justification involved in the process of generating new knowledge and justification involved in testing it. Consequential or “strong” justifications are methods of testing. The justification involved in discovery, by contrast, is conceived as generative (as opposed to consequential) justification ( section 8.1 ) or as weak (as opposed to strong) justification ( section 8.2 ). Again, some terminological ambiguity exists because according to some philosophers, there are three contexts, not two: Only the initial conception of a new idea (the creative act is the context of discovery proper, and between it and justification there exists a separate context of pursuit (Laudan 1980). But many advocates of methodologies of discovery regard the context of pursuit as an integral part of the process of justification. They retain the notion of two contexts and re-draw the boundaries between the contexts of discovery and justification as they were drawn in the early 20 th century.

The methodology of discovery has sometimes been characterized as a form of justification that is complementary to the methodology of testing (Nickles 1984, 1985, 1989). According to the methodology of testing, empirical support for a theory results from successfully testing the predictive consequences derived from that theory (and appropriate auxiliary assumptions). In light of this methodology, justification for a theory is “consequential justification,” the notion that a hypothesis is established if successful novel predictions are derived from the theory or claim. Generative justification complements consequential justification. Advocates of generative justification hold that there exists an important form of justification in science that involves reasoning to a claim from data or previously established results more generally.

One classic example for a generative methodology is the set of Newton’s rules for the study of natural philosophy. According to these rules, general propositions are established by deducing them from the phenomena. The notion of generative justification seeks to preserve the intuition behind classic conceptions of justification by deduction. Generative justification amounts to the rational reconstruction of the discovery path in order to establish its discoverability had the researchers known what is known now, regardless of how it was first thought of (Nickles 1985, 1989). The reconstruction demonstrates in hindsight that the claim could have been discovered in this manner had the necessary information and techniques been available. In other words, generative justification—justification as “discoverability” or “potential discovery”—justifies a knowledge claim by deriving it from results that are already established. While generative justification does not retrace exactly those steps of the actual discovery path that were actually taken, it is a better representation of scientists’ actual practices than consequential justification because scientists tend to construe new claims from available knowledge. Generative justification is a weaker version of the traditional ideal of justification by deduction from the phenomena. Justification by deduction from the phenomena is complete if a theory or claim is completely determined from what we already know. The demonstration of discoverability results from the successful derivation of a claim or theory from the most basic and most solidly established empirical information.

Discoverability as described in the previous paragraphs is a mode of justification. Like the testing of novel predictions derived from a hypothesis, generative justification begins when the phase of finding and articulating a hypothesis worthy of assessing is drawing to a close. Other approaches to the methodology of discovery are directly concerned with the procedures involved in devising new hypotheses. The argument in favor of this kind of methodology is that the procedures of devising new hypotheses already include elements of appraisal. These preliminary assessments have been termed “weak” evaluation procedures (Schaffner 1993). Weak evaluations are relevant during the process of devising a new hypothesis. They provide reasons for accepting a hypothesis as promising and worthy of further attention. Strong evaluations, by contrast, provide reasons for accepting a hypothesis as (approximately) true or confirmed. Both “generative” and “consequential” testing as discussed in the previous section are strong evaluation procedures. Strong evaluation procedures are rigorous and systematically organized according to the principles of hypothesis derivation or H-D testing. A methodology of preliminary appraisal, by contrast, articulates criteria for the evaluation of a hypothesis prior to rigorous derivation or testing. It aids the decision about whether to take that hypothesis seriously enough to develop it further and test it. For advocates of this version of the methodology of discovery, it is the task of philosophy of science to characterize sets of constraints and methodological rules guiding the complex process of prior-to-test evaluation of hypotheses.

In contrast to the computational approaches discussed above, strategies of preliminary appraisal are not regarded as subject-neutral but as specific to particular fields of study. Philosophers of biology, for instance, have developed a fine-grained framework to account for the generation and preliminary evaluation of biological mechanisms (Darden 2002; Craver 2002; Bechtel and Richardson 1993; Craver and Darden 2013). Some philosophers have suggested that the phase of preliminary appraisal be further divided into two phases, the phase of appraising and the phase of revising. According to Lindley Darden, the phases of generation, appraisal and revision of descriptions of mechanisms can be characterized as reasoning processes governed by reasoning strategies. Different reasoning strategies govern the different phases (Darden 1991, 2002; Craver 2002; Darden 2009). The generation of hypotheses about mechanisms, for instance, is governed by the strategy of “schema instantiation” (see Darden 2002). The discovery of the mechanism of protein synthesis involved the instantiation of an abstract schema for chemical reactions: reactant 1 + reactant 2 = product. The actual mechanism of protein synthesis was found through specification and modification of this schema.

Neither of these strategies is deemed necessary for discovery, and they are not prescriptions for biological research. Rather, these strategies are deemed sufficient for the discovery of mechanisms. The methodology of the discovery of mechanisms is an extrapolation from past episodes of research on mechanisms and the result of a synthesis of rational reconstructions of several of these historical episodes. The methodology of discovery is weakly normative in the sense that the strategies for the discovery of mechanisms that were successful in the past may prove useful in future biological research (Darden 2002).

As philosophers of science have again become more attuned to actual scientific practices, interest in heuristic strategies has also been revived. Many analysts now agree that discovery processes can be regarded as problem solving activities, whereby a discovery is a solution to a problem. Heuristics-based methodologies of discovery are neither purely subjective and intuitive nor algorithmic or formalizable; the point is that reasons can be given for pursuing one or the other problem-solving strategy. These rules are open and do not guarantee a solution to a problem when applied (Ippoliti 2018). On this view, scientific researchers are no longer seen as Kuhnian “puzzle solvers” but as problem solvers and decision makers in complex, variable, and changing environments (Wimsatt 2007).

Philosophers of discovery working in this tradition draw on a growing body of literature in cognitive psychology, management science, operations research, and economy on human reasoning and decision making in contexts with limited information, under time constraints, and with sub-optimal means (Gigerenzer & Sturm 2012). Heuristic strategies characterized in these studies, such as Gigerenzer’s “tools to theory heuristic” are then applied to understand scientific knowledge generation (Gigerenzer 1992, Nickles 2018). Other analysts specify heuristic strategies in a range of scientific fields, including climate science, neurobiology, and clinical medicine (Gramelsberger 2011, Schaffner 2008, Gillies 2018). Finally, in analytic epistemology, formal methods are developed to identify and assess distinct heuristic strategies currently in use, such as Bayesian reverse engineering in cognitive science (Zednik and Jäkel 2016).

As the literature on heuristics continues to grow, it has become clear that the term “heuristics” is itself used in a variety of different ways. (For a valuable taxonomy of meanings of “heuristic,” see Chow 2015, see also Ippoliti 2018.) Moreover, as in the context of earlier debates about computational heuristics, debates continue about the limitations of heuristics. The use of heuristics may come at a cost if heuristics introduce systematic biases (Wimsatt 2007). Some philosophers thus call for general principles for the evaluation of heuristic strategies (Hey 2016).

9. Cognitive perspectives on discovery

The approaches to scientific discovery presented in the previous sections focus on the adoption, articulation, and preliminary evaluation of ideas or hypotheses prior to rigorous testing, not on how a novel hypothesis or idea is first thought up. For a long time, the predominant view among philosophers of discovery was that the initial step of discovery is a mysterious intuitive leap of the human mind that cannot be analyzed further. More recent accounts of discovery informed by evolutionary biology also do not explicate how new ideas are formed. The generation of new ideas is akin to random, blind variations of thought processes, which have to be inspected by the critical mind and assessed as neutral, productive, or useless (Campbell 1960; see also Hull 1988), but the key processes by which new ideas are generated are left unanalyzed.

With the recent rapprochement among philosophy of mind, cognitive science and psychology and the increased integration of empirical research into philosophy of science, these processes have been submitted to closer analysis, and philosophical studies of creativity have seen a surge of interest (e.g. Paul & Kaufman 2014a). The distinctive feature of these studies is that they integrate philosophical analyses with empirical work from cognitive science, psychology, evolutionary biology, and computational neuroscience (Thagard 2012). Analysts have distinguished different kinds and different features of creative thinking and have examined certain features in depth, and from new angles. Recent philosophical research on creativity comprises conceptual analyses and integrated approaches based on the assumption that creativity can be analyzed and that empirical research can contribute to the analysis (Paul & Kaufman 2014b). Two key elements of the cognitive processes involved in creative thinking that have been in the focus of philosophical analysis are analogies ( section 9.2 ) and mental models ( section 9.3 ).

General definitions of creativity highlight novelty or originality and significance or value as distinctive features of a creative act or product (Sternberg & Lubart 1999, Kieran 2014, Paul & Kaufman 2014b, although see Hills & Bird 2019). Different kinds of creativity can be distinguished depending on whether the act or product is novel for a particular individual or entirely novel. Psychologist Margaret Boden distinguishes between psychological creativity (P-creativity) and historical creativity (H-creativity). P-creativity is a development that is new, surprising and important to the particular person who comes up with it. H-creativity, by contrast, is radically novel, surprising, and important—it is generated for the first time (Boden 2004). Further distinctions have been proposed, such as anthropological creativity (construed as a human condition) and metaphysical creativity, a radically new thought or action in the sense that it is unaccounted for by antecedents and available knowledge, and thus constitutes a radical break with the past (Kronfeldner 2009, drawing on Hausman 1984).

Psychological studies analyze the personality traits and creative individuals’ behavioral dispositions that are conducive to creative thinking. They suggest that creative scientists share certain distinct personality traits, including confidence, openness, dominance, independence, introversion, as well as arrogance and hostility. (For overviews of recent studies on personality traits of creative scientists, see Feist 1999, 2006: chapter 5).

Recent work on creativity in philosophy of mind and cognitive science offers substantive analyses of the cognitive and neural mechanisms involved in creative thinking (Abrams 2018, Minai et al 2022) and critical scrutiny of the romantic idea of genius creativity as something deeply mysterious (Blackburn 2014). Some of this research aims to characterize features that are common to all creative processes, such as Thagard and Stewart’s account according to which creativity results from combinations of representations (Thagard & Stewart 2011, but see Pasquale and Poirier 2016). Other research aims to identify the features that are distinctive of scientific creativity as opposed to other forms of creativity, such as artistic creativity or creative technological invention (Simonton 2014).

Many philosophers of science highlight the role of analogy in the development of new knowledge, whereby analogy is understood as a process of bringing ideas that are well understood in one domain to bear on a new domain (Thagard 1984; Holyoak and Thagard 1996). An important source for philosophical thought about analogy is Mary Hesse’s conception of models and analogies in theory construction and development. In this approach, analogies are similarities between different domains. Hesse introduces the distinction between positive, negative, and neutral analogies (Hesse 1966: 8). If we consider the relation between gas molecules and a model for gas, namely a collection of billiard balls in random motion, we will find properties that are common to both domains (positive analogy) as well as properties that can only be ascribed to the model but not to the target domain (negative analogy). There is a positive analogy between gas molecules and a collection of billiard balls because both the balls and the molecules move randomly. There is a negative analogy between the domains because billiard balls are colored, hard, and shiny but gas molecules do not have these properties. The most interesting properties are those properties of the model about which we do not know whether they are positive or negative analogies. This set of properties is the neutral analogy. These properties are the significant properties because they might lead to new insights about the less familiar domain. From our knowledge about the familiar billiard balls, we may be able to derive new predictions about the behavior of gas molecules, which we could then test.

Hesse offers a more detailed analysis of the structure of analogical reasoning through the distinction between horizontal and vertical analogies between domains. Horizontal analogies between two domains concern the sameness or similarity between properties of both domains. If we consider sound and light waves, there are similarities between them: sound echoes, light reflects; sound is loud, light is bright, both sound and light are detectable by our senses. There are also relations among the properties within one domain, such as the causal relation between sound and the loud tone we hear and, analogously, between physical light and the bright light we see. These analogies are vertical analogies. For Hesse, vertical analogies hold the key for the construction of new theories.

Analogies play several roles in science. Not only do they contribute to discovery but they also play a role in the development and evaluation of scientific theories. Current discussions about analogy and discovery have expanded and refined Hesse’s approach in various ways. Some philosophers have developed criteria for evaluating analogy arguments (Bartha 2010). Other work has identified highly significant analogies that were particularly fruitful for the advancement of science (Holyoak and Thagard 1996: 186–188; Thagard 1999: chapter 9). The majority of analysts explore the features of the cognitive mechanisms through which aspects of a familiar domain or source are applied to an unknown target domain in order to understand what is unknown. According to the influential multi-constraint theory of analogical reasoning developed by Holyoak and Thagard, the transfer processes involved in analogical reasoning (scientific and otherwise) are guided or constrained in three main ways: 1) by the direct similarity between the elements involved; 2) by the structural parallels between source and target domain; as well as 3) by the purposes of the investigators, i.e., the reasons why the analogy is considered. Discovery, the formulation of a new hypothesis, is one such purpose.

“In vivo” investigations of scientists reasoning in their laboratories have not only shown that analogical reasoning is a key component of scientific practice, but also that the distance between source and target depends on the purpose for which analogies are sought. Scientists trying to fix experimental problems draw analogies between targets and sources from highly similar domains. In contrast, scientists attempting to formulate new models or concepts draw analogies between less similar domains. Analogies between radically different domains, however, are rare (Dunbar 1997, 2001).

In current cognitive science, human cognition is often explored in terms of model-based reasoning. The starting point of this approach is the notion that much of human reasoning, including probabilistic and causal reasoning as well as problem solving takes place through mental modeling rather than through the application of logic or methodological criteria to a set of propositions (Johnson-Laird 1983; Magnani et al. 1999; Magnani and Nersessian 2002). In model-based reasoning, the mind constructs a structural representation of a real-world or imaginary situation and manipulates this structure. In this perspective, conceptual structures are viewed as models and conceptual innovation as constructing new models through various modeling operations. Analogical reasoning—analogical modeling—is regarded as one of three main forms of model-based reasoning that appear to be relevant for conceptual innovation in science. Besides analogical modeling, visual modeling and simulative modeling or thought experiments also play key roles (Nersessian 1992, 1999, 2009). These modeling practices are constructive in that they aid the development of novel mental models. The key elements of model-based reasoning are the call on knowledge of generative principles and constraints for physical models in a source domain and the use of various forms of abstraction. Conceptual innovation results from the creation of new concepts through processes that abstract and integrate source and target domains into new models (Nersessian 2009).

Some critics have argued that despite the large amount of work on the topic, the notion of mental model is not sufficiently clear. Thagard seeks to clarify the concept by characterizing mental models in terms of neural processes (Thagard 2010). In his approach, mental models are produced through complex patterns of neural firing, whereby the neurons and the interconnections between them are dynamic and changing. A pattern of firing neurons is a representation when there is a stable causal correlation between the pattern or activation and the thing that is represented. In this research, questions about the nature of model-based reasoning are transformed into questions about the brain mechanisms that produce mental representations.

The above sections again show that the study of scientific discovery integrates different approaches, combining conceptual analysis of processes of knowledge generation with empirical work on creativity, drawing heavily and explicitly on current research in psychology and cognitive science, and on in vivo laboratory observations, as well as brain imaging techniques (Kounios & Beeman 2009, Thagard & Stewart 2011).

Earlier critics of AI-based theories of scientific discoveries argued that a computer cannot devise new concepts but is confined to the concepts included in the given computer language (Hempel 1985: 119–120). It cannot design new experiments, instruments, or methods. Subsequent computational research on scientific discovery was driven by the motivation to contribute computational tools to aid scientists in their research (Addis et al. 2016). It appears that computational methods can be used to generate new results leading to refereed scientific publications in astrophysics, cancer research, ecology, and other fields (Langley 2000). However, the philosophical discussion has continued about the question of whether these methods really generate new knowledge or whether they merely speed up data processing. It is also still an open question whether data-intensive science is fundamentally different from traditional research, for instance regarding the status of hypothesis or theory in data-intensive research (Pietsch 2015).

In the wake of recent developments in machine learning, some older discussions about automated discovery have been revived. The availability of vastly improved computational tools and software for data analysis has stimulated new discussions about computer-generated discovery (see Leonelli 2020). It is largely uncontroversial that machine learning tools can aid discovery, for instance in research on antibiotics (Stokes et al, 2020). The notion of “robot scientist” is mostly used metaphorically, and the vision that human scientists may one day be replaced by computers – by successors of the laboratory automation systems “Adam” and “Eve”, allegedly the first “robot scientists” – is evoked in writings for broader audiences (see King et al. 2009, Williams et al. 2015, for popularized descriptions of these systems), although some interesting ethical challenges do arise from “superhuman AI” (see Russell 2021). It also appears that, on the notion that products of creative acts are both novel and valuable, AI systems should be called “creative,” an implication which not all analysts will find plausible (Boden 2014)

Philosophical analyses focus on various questions arising from the processes involving human-machine complexes. One issue relevant to the problem of scientific discovery arises from the opacity of machine learning. If machine learning indeed escapes human understanding, how can we be warranted to say that knowledge or understanding is generated by deep learning tools? Might we have reason to say that humans and machines are “co-developers” of knowledge (Tamaddoni-Nezhad et al. 2021)?

New perspectives on scientific discovery have also opened up in the context of social epistemology (see Goldman & O’Connor 2021). Social epistemology investigates knowledge production as a group process, specifically the epistemic effects of group composition in terms of cognitive diversity and unity and social interactions within groups or institutions such as testimony and trust, peer disagreement and critique, and group justification, among others. On this view, discovery is a collective achievement, and the task is to explore how assorted social-epistemic activities or practices have an impact on the knowledge generated by groups in question. There are obvious implications for debates about scientific discovery of recent research in the different branches of social epistemology. Social epistemologists have examined individual cognitive agents in their roles as group members (as providers of information or as critics) and the interactions among these members (Longino 2001), groups as aggregates of diverse agents, or the entire group as epistemic agent (e.g., Koons 2021, Dragos 2019).

Standpoint theory, for instance, explores the role of outsiders in knowledge generation, considering how the sociocultural structures and practices in which individuals are embedded aid or obstruct the generation of creative ideas. According to standpoint theorists, people with standpoint are politically aware and politically engaged people outside the mainstream. Because people with standpoint have different experiences and access to different domains of expertise than most members of a culture, they can draw on rich conceptual resources for creative thinking (Solomon 2007).

Social epistemologists examining groups as aggregates of agents consider to what extent diversity among group members is conducive to knowledge production and whether and to what extent beliefs and attitudes must be shared among group members to make collective knowledge possible (Bird 2014). This is still an open question. Some formal approaches to model the influence of diversity on knowledge generation suggest that cognitive diversity is beneficial to collective knowledge generation (Weisberg and Muldoon 2009), but others have criticized the model (Alexander et al (2015), see also Thoma (2015) and Poyhönen (2017) for further discussion).

This essay has illustrated that philosophy of discovery has come full circle. Philosophy of discovery has once again become a thriving field of philosophical study, now intersecting with, and drawing on philosophical and empirical studies of creative thinking, problem solving under uncertainty, collective knowledge production, and machine learning. Recent approaches to discovery are typically explicitly interdisciplinary and integrative, cutting across previous distinctions among hypothesis generation and theory building, data collection, assessment, and selection; as well as descriptive-analytic, historical, and normative perspectives (Danks & Ippoliti 2018, Michel 2021). The goal no longer is to provide one overarching account of scientific discovery but to produce multifaceted analyses of past and present activities of knowledge generation in all their complexity and heterogeneity that are illuminating to the non-scientist and the scientific researcher alike.

• Abraham, A. 2019, The Neuroscience of Creativity, Cambridge: Cambridge University Press.
• Addis, M., Sozou, P.D., Gobet, F. and Lane, P. R., 2016, “Computational scientific discovery and cognitive science theories”, in Mueller, V. C. (ed.) Computing and Philosophy , Springer, 83–87.
• Alexander, J., Himmelreich, J., and Thompson, C. 2015, Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor, Philosophy of Science 82: 424–453.
• Arabatzis, T. 1996, “Rethinking the ‘Discovery’ of the Electron,” Studies in History and Philosophy of Science Part B Studies In History and Philosophy of Modern Physics , 27: 405–435.
• Bartha, P., 2010, By Parallel Reasoning: The Construction and Evaluation of Analogical Arguments , New York: Oxford University Press.
• Bechtel, W. and R. Richardson, 1993, Discovering Complexity , Princeton: Princeton University Press.
• Benjamin, A.C., 1934, “The Mystery of Scientific Discovery ” Philosophy of Science , 1: 224–36.
• Bird, A. 2014, “When is There a Group that Knows? Distributed Cognition, Scientific Knowledge, and the Social Epistemic Subject”, in J. Lackey (ed.), Essays in Collective Epistemology , Oxford: Oxford University Press, 42–63.
• Blackburn, S. 2014, “Creativity and Not-So-Dumb Luck”, in Paul, E. S. and Kaufman, S. B. (eds.), The Philosophy of Creativity: New Essays , New York: Oxford Academic online edn. https://doi.org/10.1093/acprof:oso/9780199836963.003.0008.
• Blackwell, R.J., 1969, Discovery in the Physical Sciences , Notre Dame: University of Notre Dame Press.
• Boden, M.A., 2004, The Creative Mind: Myths and Mechanisms , London: Routledge.
• –––, 2014, “Creativity and Artificial Intelligence: A Contradiction in Terms?”, in Paul, E. S. and Kaufman, S. B. (eds.), The Philosophy of Creativity: New Essays (New York: Oxford Academic online edn., https://doi.org/10.1093/acprof:oso/9780199836963.003.0012 .
• Brannigan, A., 1981, The Social Basis of Scientific Discoveries , Cambridge: Cambridge University Press.
• Brem, S. and L.J. Rips, 2000, “Explanation and Evidence in Informal Argument”, Cognitive Science , 24: 573–604.
• Campbell, D., 1960, “Blind Variation and Selective Retention in Creative Thought as in Other Knowledge Processes”, Psychological Review , 67: 380–400.
• Carmichael, R.D., 1922, “The Logic of Discovery”, The Monist , 32: 569–608.
• –––, 1930, The Logic of Discovery , Chicago: Open Court.
• Chow, S. 2015, “Many Meanings of ‘Heuristic’”, British Journal for the Philosophy of Science , 66: 977–1016
• Craver, C.F., 2002, “Interlevel Experiments, Multilevel Mechanisms in the Neuroscience of Memory”, Philosophy of Science Supplement , 69: 83–97.
• Craver, C.F. and L. Darden, 2013, In Search of Mechanisms: Discoveries across the Life Sciences , Chicago: University of Chicago Press.
• Curd, M., 1980, “The Logic of Discovery: An Analysis of Three Approaches”, in T. Nickles (ed.) Scientific Discovery, Logic, and Rationality , Dordrecht: D. Reidel, 201–19.
• Danks, D. & Ippoliti, E. (eds.) 2018, Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer.
• Darden, L., 1991, Theory Change in Science: Strategies from Mendelian Genetics , New York: Oxford University Press.
• –––, 2002, “Strategies for Discovering Mechanisms: Schema Instantiation, Modular Subassembly, Forward/Backward Chaining”, Philosophy of Science , 69: S354-S65.
• –––, 2009, “Discovering Mechanisms in Molecular Biology: Finding and Fixing Incompleteness and Incorrectness”, in J. Meheus and T. Nickles (eds.), Models of Discovery and Creativity , Dordrecht: Springer, 43–55.
• Dewey, J. 1910, How We Think . Boston: D.C. Heath
• Dragos, C., 2019, “Groups Can Know How” American Philosophical Quarterly 56: 265–276
• Ducasse, C.J., 1951, “Whewell’s Philosophy of Scientific Discovery II”, The Philosophical Review , 60(2): 213–34.
• Dunbar, K., 1997, “How scientists think: On-line creativity and conceptual change in science”, in T.B. Ward, S.M. Smith, and J. Vaid (eds.), Conceptual Structures and Processes: Emergence, Discovery, and Change , Washington, DC: American Psychological Association Press, 461–493.
• –––, 2001, “The Analogical Paradox: Why Analogy is so Easy in Naturalistic Settings Yet so Difficult in Psychological Laboratories”, in D. Gentner, K.J. Holyoak, and B.N. Kokinov (eds.), The Analogical Mind: Perspectives from Cognitive Science , Cambridge, MA: MIT Press.
• Dunbar, K, J. Fugelsang, and C Stein, 2007, “Do Naïve Theories Ever Go Away? Using Brain and Behavior to Understand Changes in Concepts”, in M. Lovett and P. Shah (eds.), Thinking with Data: 33rd Carnegie Symposium on Cognition , Mahwah: Erlbaum, 193–205.
• Feist, G.J., 1999, “The Influence of Personality on Artistic and Scientific Creativity”, in R.J. Sternberg (ed.), Handbook of Creativity , New York: Cambridge University Press, 273–96.
• –––, 2006, The psychology of science and the origins of the scientific mind , New Haven: Yale University Press.
• Gillies D., 1996, Artificial intelligence and scientific method . Oxford: Oxford University Press.
• –––, 2018 “Discovering Cures in Medicine” in Danks, D. & Ippoliti, E. (eds.), Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer, 83–100.
• Goldman, Alvin & O’Connor, C., 2021, “Social Epistemology”, The Stanford Encyclopedia of Philosophy (Winter 2021 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/win2021/entries/epistemology-social/>.
• Gramelsberger, G. 2011, “What Do Numerical (Climate) Models Really Represent?” Studies in History and Philosophy of Science 42: 296–302.
• Gutting, G., 1980, “Science as Discovery”, Revue internationale de philosophie , 131: 26–48.
• Hanson, N.R., 1958, Patterns of Discovery , Cambridge: Cambridge University Press.
• –––, 1960, “Is there a Logic of Scientific Discovery?”, Australasian Journal of Philosophy , 38: 91–106.
• –––, 1965, “Notes Toward a Logic of Discovery”, in R.J. Bernstein (ed.), Perspectives on Peirce. Critical Essays on Charles Sanders Peirce , New Haven and London: Yale University Press, 42–65.
• Harman, G.H., 1965, “The Inference to the Best Explanation”, Philosophical Review , 74.
• Hausman, C. R. 1984, A Discourse on Novelty and Creation , New York: SUNY Press.
• Hempel, C.G., 1985, “Thoughts in the Limitations of Discovery by Computer”, in K. Schaffner (ed.), Logic of Discovery and Diagnosis in Medicine , Berkeley: University of California Press, 115–22.
• Hesse, M., 1966, Models and Analogies in Science , Notre Dame: University of Notre Dame Press.
• Hey, S. 2016 “Heuristics and Meta-heuristics in Scientific Judgement”, British Journal for the Philosophy of Science , 67: 471–495
• Hills, A., Bird, A. 2019, “Against Creativity”, Philosophy and Phenomenological Research , 99: 694–713.
• Holyoak, K.J. and P. Thagard, 1996, Mental Leaps: Analogy in Creative Thought , Cambridge, MA: MIT Press.
• Howard, D., 2006, “Lost Wanderers in the Forest of Knowledge: Some Thoughts on the Discovery-Justification Distinction”, in J. Schickore and F. Steinle (eds.), Revisiting Discovery and Justification. Historical and Philosophical Perspectives on the Context Distinction , Dordrecht: Springer, 3–22.
• Hoyningen-Huene, P., 1987, “Context of Discovery and Context of Justification”, Studies in History and Philosophy of Science , 18: 501–15.
• Hull, D.L., 1988, Science as Practice: An Evolutionary Account of the Social and Conceptual Development of Science , Chicago: University of Chicago Press.
• Ippoliti, E. 2018, “Heuristic Logic. A Kernel” in Danks, D. & Ippoliti, E. (eds.) Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer, 191–212
• Jantzen, B.C., 2016, “Discovery without a ‘Logic’ would be a Miracle”, Synthese , 193: 3209–3238.
• Johnson-Laird, P., 1983, Mental Models , Cambridge: Cambridge University Press.
• Kieran, M., 2014, “Creativity as a Virtue of Character,” in E. Paul and S. B. Kaufman (eds.), The Philosophy of Creativity: New Essays . Oxford: Oxford University Press, 125–44
• King, R. D. et al. 2009, “The Automation of Science”, Science 324: 85–89.
• Koehler, D.J., 1991, “Explanation, Imagination, and Confidence in Judgment”, Psychological Bulletin , 110: 499–519.
• Koertge, N. 1980, “Analysis as a Method of Discovery during the Scientific Revolution” in Nickles, T. (ed.) Scientific Discovery, Logic, and Rationality vol. I, Dordrecht: Reidel, 139–157
• Koons, J.R. 2021, “Knowledge as a Collective Status”, Analytic Philosophy , https://doi.org/10.1111/phib.12224
• Kounios, J. and Beeman, M. 2009, “The Aha! Moment : The Cognitive Neuroscience of Insight”, Current Directions in Psychological Science , 18: 210–16.
• Kordig, C., 1978, “Discovery and Justification”, Philosophy of Science , 45: 110–17.
• Kronfeldner, M. 2009, “Creativity Naturalized”, The Philosophical Quarterly 59: 577–592.
• Kuhn, T.S., 1970 [1962], The Structure of Scientific Revolutions , 2 nd edition, Chicago: The University of Chicago Press; first edition, 1962.
• Kulkarni, D. and H.A. Simon, 1988, “The processes of scientific discovery: The strategy of experimentation”, Cognitive Science , 12: 139–76.
• Langley, P., 2000, “The Computational Support of Scientific Discovery”, International Journal of Human-Computer Studies , 53: 393–410.
• Langley, P., H.A. Simon, G.L. Bradshaw, and J.M. Zytkow, 1987, Scientific Discovery: Computational Explorations of the Creative Processes , Cambridge, MA: MIT Press.
• Laudan, L., 1980, “Why Was the Logic of Discovery Abandoned?” in T. Nickles (ed.), Scientific Discovery (Volume I), Dordrecht: D. Reidel, 173–83.
• Leonelli, S. 2020, “Scientific Research and Big Data”, The Stanford Encyclopedia of Philosophy (Summer 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2020/entries/science-big-data/>
• Leplin, J., 1987, “The Bearing of Discovery on Justification”, Canadian Journal of Philosophy , 17: 805–14.
• Longino, H. 2001, The Fate of Knowledge , Princeton: Princeton University Press
• Lugg, A., 1985, “The Process of Discovery”, Philosophy of Science , 52: 207–20.
• Magnani, L., 2000, Abduction, Reason, and Science: Processes of Discovery and Explanation , Dordrecht: Kluwer.
• –––, 2009, “Creative Abduction and Hypothesis Withdrawal”, in J. Meheus and T. Nickles (eds.), Models of Discovery and Creativity , Dordrecht: Springer.
• Magnani, L. and N.J. Nersessian, 2002, Model-Based Reasoning: Science, Technology, and Values , Dordrecht: Kluwer.
• Magnani, L., N.J. Nersessian, and P. Thagard, 1999, Model-Based Reasoning in Scientific Discovery , Dordrecht: Kluwer.
• Michel, J. (ed.) 2021, Making Scientific Discoveries. Interdisciplinary Reflections , Brill | mentis.
• Minai, A., Doboli, S., Iyer, L. 2022 “Models of Creativity and Ideation: An Overview” in Ali A. Minai, Jared B. Kenworthy, Paul B. Paulus, Simona Doboli (eds.), Creativity and Innovation. Cognitive, Social, and Computational Approaches , Springer, 21–46.
• Nersessian, N.J., 1992, “How do scientists think? Capturing the dynamics of conceptual change in science”, in R. Giere (ed.), Cognitive Models of Science , Minneapolis: University of Minnesota Press, 3–45.
• –––, 1999, “Model-based reasoning in conceptual change”, in L. Magnani, N.J. Nersessian and P. Thagard (eds.), Model-Based Reasoning in Scientific Discovery , New York: Kluwer, 5–22.
• –––, 2009, “Conceptual Change: Creativity, Cognition, and Culture ” in J. Meheus and T. Nickles (eds.), Models of Discovery and Creativity , Dordrecht: Springer, 127–66.
• Newell, A. and H. A Simon, 1971, “Human Problem Solving: The State of the Theory in 1970”, American Psychologist , 26: 145–59.
• Newton, I. 1718, Opticks; or, A Treatise of the Reflections, Inflections and Colours of Light , London: Printed for W. and J. Innys, Printers to the Royal Society.
• Nickles, T., 1984, “Positive Science and Discoverability”, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , 1984: 13–27.
• –––, 1985, “Beyond Divorce: Current Status of the Discovery Debate”, Philosophy of Science , 52: 177–206.
• –––, 1989, “Truth or Consequences? Generative versus Consequential Justification in Science”, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , 1988, 393–405.
• –––, 2018, “TTT: A Fast Heuristic to New Theories?” in Danks, D. & Ippoliti, E. (eds.) Building Theories: Heuristics and Hypotheses in Sciences , Cham: Springer, 213–244.
• Pasquale, J.-F. de and Poirier, P. 2016, “Convolution and Modal Representations in Thagard and Stewart’s Neural Theory of Creativity: A Critical Analysis ”, Synthese , 193: 1535–1560
• Paul, E. S. and Kaufman, S. B. (eds.), 2014a, The Philosophy of Creativity: New Essays , New York: Oxford Academic online edn., https://doi.org/10.1093/acprof:oso/9780199836963.001.0001.
• –––, 2014b, “Introducing: The Philosophy of Creativity”, in Paul, E. S. and Kaufman, S. B. (eds.), The Philosophy of Creativity: New Essays (New York: Oxford Academic online edn., https://doi.org/10.1093/acprof:oso/9780199836963.003.0001.
• Pietsch, W. 2015, “Aspects of Theory-Ladenness in Data-Intensive Science”, Philosophy of Science 82: 905–916.
• Popper, K., 2002 [1934/1959], The Logic of Scientific Discovery , London and New York: Routledge; original published in German in 1934; first English translation in 1959.
• Pöyhönen, S. 2017, “Value of Cognitive Diversity in Science”, Synthese , 194(11): 4519–4540. doi:10.1007/s11229–016-1147-4
• Pulte, H. 2019, “‘‘Tis Much Better to Do a Little with Certainty’: On the Reception of Newton’s Methodology”, in The Reception of Isaac Newton in Europe , Pulte, H, and Mandelbrote, S. (eds.), Continuum Publishing Corporation, 355–84.
• Reichenbach, H., 1938, Experience and Prediction. An Analysis of the Foundations and the Structure of Knowledge , Chicago: The University of Chicago Press.
• Richardson, A., 2006, “Freedom in a Scientific Society: Reading the Context of Reichenbach’s Contexts”, in J. Schickore and F. Steinle (eds.), Revisiting Discovery and Justification. Historical and Philosophical Perspectives on the Context Distinction , Dordrecht: Springer, 41–54.
• Russell, S. 2021, “Human-Compatible Artificial Intelligence”, in Human Like Machine Intelligence , Muggleton, S. and Charter, N. (eds.), Oxford: Oxford University Press, 4–23
• Schaffer, S., 1986, “Scientific Discoveries and the End of Natural Philosophy”, Social Studies of Science , 16: 387–420.
• –––, 1994, “Making Up Discovery”, in M.A. Boden (ed.), Dimensions of Creativity , Cambridge, MA: MIT Press, 13–51.
• Schaffner, K., 1993, Discovery and Explanation in Biology and Medicine , Chicago: University of Chicago Press.
• –––, 2008 “Theories, Models, and Equations in Biology: The Heuristic Search for Emergent Simplifications in Neurobiology”, Philosophy of Science , 75: 1008–21.
• Schickore, J. and F. Steinle, 2006, Revisiting Discovery and Justification. Historical and Philosophical Perspectives on the Context Distinction , Dordrecht: Springer.
• Schiller, F.C.S., 1917, “Scientific Discovery and Logical Proof”, in C.J. Singer (ed.), Studies in the History and Method of Science (Volume 1), Oxford: Clarendon, 235–89.
• Simon, H.A., 1973, “Does Scientific Discovery Have a Logic?”, Philosophy of Science , 40: 471–80.
• –––, 1977, Models of Discovery and Other Topics in the Methods of Science , Dordrecht: D. Reidel.
• Simon, H.A., P.W. Langley, and G.L. Bradshaw, 1981, “Scientific Discovery as Problem Solving”, Synthese , 47: 1–28.
• Smith, G.E., 2002, “The Methodology of the Principia ”, in G.E. Smith and I.B. Cohen (eds), The Cambridge Companion to Newton , Cambridge: Cambridge University Press, 138–73.
• Simonton, D. K., “Hierarchies of Creative Domains: Disciplinary Constraints on Blind Variation and Selective Retention”, in Paul, E. S. and Kaufman, S. B. (eds), The Philosophy of Creativity: New Essays , New York: Oxford Academic online edn. https://doi.org/10.1093/acprof:oso/9780199836963.003.0013
• Snyder, L.J., 1997, “Discoverers’ Induction”, Philosophy of Science , 64: 580–604.
• Solomon, M., 2009, “Standpoint and Creativity”, Hypatia : 226–37.
• Sternberg, R J. and T. I. Lubart, 1999, “The concept of creativity: Prospects and paradigms,” in R. J. Sternberg (ed.) Handbook of Creativity , Cambridge: Cambridge University Press, 3–15.
• Stokes, D., 2011, “Minimally Creative Thought”, Metaphilosophy , 42: 658–81.
• Tamaddoni-Nezhad, A., Bohan, D., Afroozi Milani, G., Raybould, A., Muggleton, S., 2021, “Human–Machine Scientific Discovery”, in Human Like Machine Intelligence , Muggleton, S. and Charter, N., (eds.), Oxford: Oxford University Press, 297–315
• Thagard, P., 1984, “Conceptual Combination and Scientific Discovery”, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association , 1984(1): 3–12.
• –––, 1999, How Scientists Explain Disease , Princeton: Princeton University Press.
• –––, 2010, “How Brains Make Mental Models”, in L. Magnani, N.J. Nersessian and P. Thagard (eds.), Model-Based Reasoning in Science & Technology , Berlin and Heidelberg: Springer, 447–61.
• –––, 2012, The Cognitive Science of Science , Cambridge, MA: MIT Press.
• Thagard, P. and Stewart, T. C., 2011, “The AHA! Experience: Creativity Through Emergent Binding in Neural Networks”, Cognitive Science , 35: 1–33.
• Thoma, Johanna, 2015, “The Epistemic Division of Labor Revisited”, Philosophy of Science , 82: 454–472. doi:10.1086/681768
• Weber, M., 2005, Philosophy of Experimental Biology , Cambridge: Cambridge University Press.
• Whewell, W., 1996 [1840], The Philosophy of the Inductive Sciences (Volume II), London: Routledge/Thoemmes.
• Weisberg, M. and Muldoon, R., 2009, “Epistemic Landscapes and the Division of Cognitive Labor”, Philosophy of Science , 76: 225–252. doi:10.1086/644786
• Williams, K. et al. 2015, “Cheaper Faster Drug Development Validated by the Repositioning of Drugs against Neglected Tropical Diseases”, Journal of the Royal Society Interface 12: 20141289. http://dx.doi.org/10.1098/rsif.2014.1289.
• Zahar, E., 1983, “Logic of Discovery or Psychology of Invention?”, British Journal for the Philosophy of Science , 34: 243–61.
• Zednik, C. and Jäkel, F. 2016 “Bayesian Reverse-Engineering Considered as a Research Strategy for Cognitive Science”, Synthese , 193, 3951–3985.

How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

[Please contact the author with suggestions.]

abduction | analogy and analogical reasoning | cognitive science | epistemology: social | knowledge: analysis of | Kuhn, Thomas | models in science | Newton, Isaac: Philosophiae Naturalis Principia Mathematica | Popper, Karl | rationality: historicist theories of | scientific method | scientific research and big data | Whewell, William

Copyright © 2022 by Jutta Schickore < jschicko @ indiana . edu >

• Accessibility

Support SEP

Mirror sites.

View this site from another server:

• Info about mirror sites

The Stanford Encyclopedia of Philosophy is copyright © 2023 by The Metaphysics Research Lab , Department of Philosophy, Stanford University

Library of Congress Catalog Data: ISSN 1095-5054

• BEST OF 2019

These are the top 20 scientific discoveries of the decade

The 2010s yielded many incredible finds and important milestones. Here are our favorites.

Two neutron stars crash into each other in an explosive event called a kilonova in an illustration. On October 16, 2017, astronomers announced the first confirmed detection of ripples in spacetime called gravitational waves created by this kind of violent—and visible—event.

As the 2010s come to an end, we can look back on an era rife with discovery. In the past 10 years, scientists around the world made remarkable progress toward understanding the human body, our planet, and the cosmos that surrounds us. What’s more, science in the 2010s became more global and collaborative than ever before. These days, major breakthroughs are likelier to come from groups of 3,000 scientists than groups of three.

So much has happened, thanks to so many, that National Geographic’s writers and editors decided not to whittle down the last decade into just a handful of discoveries. Instead, we’ve put our heads together to identify 20 trends and milestones that we found especially noteworthy, and that we think will set the stage for more amazing finds in the decade to come.

Detecting the first gravitational waves

In 1916, Albert Einstein proposed that when objects with enough mass accelerate, they can sometimes create waves that move through the fabric of space and time like ripples on a pond’s surface. Though Einstein later doubted their existence, these spacetime wrinkles—called gravitational waves—are a key prediction of relativity, and the search for them captivated researchers for decades. Though compelling hints of the waves first emerged in the 1970s, nobody directly detected them until 2015, when the U.S.-based observatory LIGO felt the aftershock of a distant collision between two black holes. The discovery, announced in 2016 , opened up a new way to “hear” the cosmos.

In 2017, LIGO and the European observatory Virgo felt another set of tremors, this time made when two ultra-dense objects called neutron stars collided. Telescopes around the world saw the related explosion, making the event the first ever observed in both light and gravitational waves . The landmark data have given scientists an unprecedented look at how gravity works and how elements such as gold and silver form.

Shaking up the human family tree

While primitive in some respects, the face, skull, and teeth (seen in this reconstruction) show enough modern features to justify H. naledi 's placement in the genus Homo . Artist John Gurche spent some 700 hours reconstructing the head from bone scans, using bear fur for hair.

The decade has seen numerous advances in understanding our complex origin story, including new dates on known fossils , spectacularly complete fossil skulls , and the addition of multiple new branches. In 2010, National Geographic explorer-at-large Lee Berger unveiled a distant ancestor named Australopithecus sediba . Five years later, he announced that South Africa’s Cradle of Humankind cave system contained fossils of a new species: Homo naledi , a hominin whose “mosaic” anatomy resembles that of both modern humans and far more ancient cousins. A follow-up study also showed that H. naledi is surprisingly young, living at least between 236,000 and 335,000 years ago .

Other remarkable discoveries piled up in Asia. In 2010, a team announced that DNA pulled from an ancient Siberian pinky bone was unlike any modern human’s , the first evidence of a shadowy lineage now called the Denisovans. In 2018, a site in China yielded 2.1-million-year-old stone tools , confirming that toolmakers spread into Asia hundreds of thousands of years earlier than once thought. In 2019, researchers in the Philippines announced fossils of Homo luzonensis , a new type of hominin similar to Homo floresiensis , the “hobbit” of Flores. And newfound stone tools on Sulawesi predate modern humans’ arrival , which suggests the presence of a third, unidentified island hominin in Southeast Asia.

Revolutionizing the study of ancient DNA

As DNA sequencing technologies have improved exponentially, the past decade has seen huge leaps in understanding how our genetic past shapes modern humans. In 2010, researchers published the first near-complete genome from an ancient Homo sapiens , kicking off a revolutionary decade in the study of our ancestors’ DNA. Since then, more than 3,000 ancient genomes have been sequenced, including the DNA of Naia , a girl who died in what is now Mexico 13,000 years ago. Her remains are among the oldest intact human skeletons ever found in the Americas. Also in 2010, researchers announced the first draft of a Neanderthal genome , providing the first solid genetic evidence that one to four percent of all modern non-Africans’ DNA comes from these close relatives.

In another striking discovery, scientists studying ancient DNA revealed in 2018 that a 90,000-year-old bone belonged to a teenage girl whose mother was Neanderthal and whose father was Denisovan, making her the first hybrid ancient human ever found . In another find, scientists compared Denisovan DNA to fossil proteins to confirm that Denisovans once lived in Tibet , expanding the mysterious group’s known range. As the field of ancient DNA has matured, so too has its handling of ethical concerns, such as the need for community engagement and the repatriation of indigenous human remains .

Revealing thousands of new exoplanets

Human knowledge of planets orbiting distant stars took a giant leap forward in the 2010s, in no small part thanks to NASA’s Kepler Space Telescope. From 2009 to 2018, Kepler alone found more than 2,700 confirmed exoplanets, more than half the current total. Among Kepler’s greatest hits: the first confirmed rocky exoplanet . Its successor TESS, launched in 2018 , is starting its survey of the night sky and has already bagged 34 confirmed exoplanets.

Ground-based surveys were also in on the action. In 2017, researchers announced the discovery of TRAPPIST-1 , a star system just 39 light-years away that hosts a whopping seven Earth-size planets, the most found around any star other than the sun. The year before, the Pale Red Dot project announced the discovery of Proxima b , an Earth-size planet that’s orbiting Proxima Centauri, the star closest to the sun at a mere 4.25 light-years away.

Entering the Crispr era

The 2010s marked huge advances in our ability to precisely edit DNA, in large part thanks to the identification of the Crispr-Cas9 system . Some bacteria naturally use Crispr-Cas9 as an immune system , since it lets them store snippets of viral DNA, recognize any future matching virus, and then cut the virus’s DNA to ribbons. In 2012, researchers proposed that Crispr-Cas9 could be used as a powerful genetic editing tool , since it precisely cuts DNA in ways that scientists can easily customize. Within months, other teams confirmed that the technique worked on human DNA . Ever since, labs all over the world have raced to identify similar systems, to modify Crispr-Cas9 to make it even more precise , and to experiment with its applications in agriculture and medicine.

While Crispr-Cas9’s possible benefits are huge, the ethical quandaries it poses are also staggering. To the horror of the global medical community, Chinese researcher He Jiankui announced in 2018 the birth of two girls whose genomes he had edited with Crispr , the first humans born with heritable edits to their DNA. The announcement sparked calls for a global moratorium on heritable “germline” edits in humans .

Seeing the cosmos as never before

The Event Horizon Telescope—a planet-scale array of ground-based radio telescopes—unveiled the first image of a supermassive black hole and its shadow in 2019. The image reveals the central black hole of Messier 87, a massive galaxy in the Virgo cluster.

The 2010s brought with them several major observations that are revolutionizing our study of the universe. In 2013, the European Space Agency launched Gaia, a spacecraft that is collecting distance measurements for more than a billion stars in the Milky Way, as well as velocity data for more than 150 million stars. The dataset helped scientists make a 3D movie of our home galaxy , yielding an unprecedented look at how galaxies form and change over time.

In 2018, scientists released the final version of the Planck satellite’s measurements of the early universe’s faint afterglow, which contains vital clues to cosmic ingredients, structure, and rate of expansion. Puzzlingly, the expansion rate Planck saw differs from today’s, a potential "crisis in cosmology" that may require new physics to explain . Also in 2018, the massive Dark Energy Survey released its first batch of data , which will help with searches for hidden patterns in our universe’s structure. And in April 2019, scientists with the Event Horizon Telescope revealed the first-ever image of a black hole’s silhouette , thanks to a massive global effort to peer into the heart of the galaxy M87.

Unveiling ancient art

A worker takes measurements of stone rings inside Bruniquel Cave in France that may have been constructed by Neanderthals.

Discoveries from around the world have reinforced that art—or at least doodling—was an older and more global phenomenon that once thought. In 2014, researchers showed that hand stencils and a “pig-deer” painting in Sulawesi’s Maros cave sites were at least 39,000 years old , making them as old as Europe’s most ancient cave paintings. Then, in 2018, researchers announced the discovery of cave art in Borneo that’s between 40,000 and 52,000 years old, further pushing back the origins of figurative painting . And another 2018 find in South Africa, a stone flake that was cross-hatched some 73,000 years ago, may well be the world’s oldest doodle .

Other controversial finds stoked debate over Neanderthals’ artistic skills. In 2018, researchers unveiled pigments and perforated marine shells found in Spain that were 115,000 years old , when only Neanderthals lived in Europe. That same year, another study claimed that some of Spain’s cave paintings are 65,000 years old . Many cave-art specialists have disputed the find , but if it holds, it could be the first evidence of Neanderthal cave paintings. And in 2016, researchers announced that a French cave contained bizarre circles of stalagmites set up about 176,000 years ago. If cave bears didn’t somehow make them, the circles’ age suggests yet more Neanderthal handiwork.

Making interstellar firsts

Future historians might look back on the 2010s as the interstellar decade: For the first time, our spacecraft punctured the veil between the sun and interstellar space, and we got our first visits from objects that formed around distant stars.

In August 2012, NASA’s Voyager 1 probe crossed the outer boundary of the heliosphere , the bubble of charged particles our sun gives off. Voyager 2 joined its twin in the interstellar medium in November 2018 and captured groundbreaking data along the way . But the interstellar road is a two-way street. In October 2017, astronomers found ‘Oumuamua, the first object ever detected that formed in another star system and passed through ours. In August 2019, amateur astronomer Gennady Borisov found the second such interstellar interloper, a highly active comet that now bears his name .

Opening doors to ancient civilizations

Archaeologists made many extraordinary discoveries in the 2010s. In 2013, British researchers finally found the body of King Richard III —beneath what’s now a parking lot. In 2014, researchers announced that Peru’s Castillo de Huarmey temple complex still had an untouched royal tomb . In 2016, archaeologists revealed the first Philistine cemetery, offering an unprecedented window into the lives of the Hebrew Bible’s most notorious, enigmatic people. The following year, researchers announced that Jerusalem's Church of the Holy Sepulchre dates back more than 1,700 years to Rome's first Christian emperor, appearing to confirm that it's built on the site identified by Rome as the burial place of Christ . And in 2018, teams working in Peru announced the largest mass child sacrifice site ever uncovered , while other scientists scouring Guatemala detected more than 60,000 newly identified ancient Maya buildings with airborne lasers.

Big archaeological discoveries also surfaced from deep underwater. In 2014, a Canadian team finally found the H.M.S. Erebus , an ill-fated Arctic research vessel that sank in 1846. Two years later, another expedition located its sister ship, the H.M.S. Terror . In 2017, an effort led by Microsoft co-founder Paul Allen found the long-lost U.S.S. Indianapolis , which sank in 1945 and became one of the deadliest disasters in U.S. naval history. The Black Sea Maritime Archaeology Project has found more than 60 historic shipwrecks at the bottom of the Black Sea—including a pristine 2,400-year-old vessel discovered in 2018 . And in 2019, Alabama officials announced the discovery of the long-lost Clotilda , the last ship that ferried enslaved Africans to the United States .

Breaking new ground in the solar system

In July 2015, NASA’s New Horizons probe made good on a decades-long quest to visit the icy world Pluto, sending back the first-ever images of the dwarf planet’s shockingly varied surface . And on New Year’s Day 2019, New Horizons pulled off the most distant flyby ever attempted when it snapped the first pictures of the icy body Arrokoth , a primordial leftover from the solar system’s infancy.

A little closer to home, NASA’s Dawn spacecraft arrived at Vesta , the second-biggest body in the asteroid belt, in 2011. After mapping that world, Dawn darted off to orbit the dwarf planet Ceres , the asteroid belt’s largest object—becoming the first mission ever to orbit a dwarf planet, and the first to orbit two different extraterrestrial bodies. Near the decade’s end, NASA’s OSIRIS-REx and JAXA’s Hayabusa2 visited the asteroids Bennu and Ryugu, respectively, with the goal of returning samples back to Earth.

You May Also Like

The 11 most astonishing scientific discoveries of 2023

The 22 most amazing discoveries of 2022

How do shells get their shapes? These are the forces behind their twists and coils

Changing the course of disease.

In response to the 2014-2016 Ebola outbreak in West Africa , public health officials and the pharmaceutical company Merck fast-tracked rVSV-ZEBOV, an experimental Ebola vaccine. After a highly successful field trial in 2015 , European officials approved the vaccine in 2019—a milestone in the fight against the deadly disease . Several landmark studies also opened new avenues to preventing the spread of HIV. A 2011 trial showed that preventatively taking antiretroviral drugs greatly reduced the spread of HIV among heterosexual couples , a finding confirmed in follow-up studies that included same-sex couples .

Pushing reproductive limits

Using gene editing, two mouse moms birthed this pup, as described in a 2018 study. Grown to adulthood, the mouse born to same-sex parents now has her own babies.

In 2016, clinicians announced the birth of a “three-parent baby” grown from the father’s sperm, the mother’s cell nucleus, and a third donor’s egg that had its nucleus removed. The therapy— which remains ethically controversial —aims to correct for disorders in the mother’s mitochondria. One 2018 study made precursors of human sperm or eggs out of reprogrammed skin and blood cells , while another showed that gene editing could let two same-sex mice conceive pups . And in 2018, Chinese scientists announced the birth of two cloned macaques , the first time that a primate had ever been cloned like Dolly the sheep. Though researchers avow that the technique won’t be used on humans, it’s possible that it could work with other primates, including us.

Tracking down the Higgs boson

A Higgs boson erupts from a collision of protons in an illustration.

How does matter get mass? In the 1960s and 1970s, physicists including Peter Higgs and François Englert proposed a solution in the form of a novel energy field that permeates the universe, now called the Higgs field. This theorized field also came with its associated fundamental particle, what’s now called the Higgs boson. In July 2012, a decades-long search ended when two teams at CERN’s Large Hadron Collider announced the detection of the Higgs boson . The discovery filled in the last missing piece of the Standard Model, the spectacularly successful—albeit incomplete—theory that describes three of the four fundamental forces in physics and all known elementary particles.

Rewriting paleontology textbooks

This decade has seen an explosion in our understanding of prehistoric life, as scientists have found stunning new fossils while expanding their analytical toolkits. In 2010, researchers supported by the National Geographic Society published the first full-body color reconstruction for a dinosaur , based on the discovery of fossilized pigments. In the years since, the palette has widened, as paleontologists have found dino-camouflage , feathers that ranged from black to blue to iridescent rainbow , and reddish skin on one of the best-ever fossils of an armored dinosaur . And in a remarkable feat of chemical sleuthing, researchers analyzed preserved fatty molecules and proved in 2018 that Dickinsonia , a primitive creature that lived more than 540 million years ago, was an animal.

In 2014, paleontologists also revealed new fossils of the predatory dinosaur Spinosaurus that suggested it was a semiaquatic predator —the first known among dino-kind. A year later, a team in China unveiled the stunning fossil of Yi qi , a truly weird feathered dinosaur with membraned wings like a bat’s . Also in the last decade, scientists’ interest in Myanmar’s 99-million-year-old amber has surged, revealing a feathered dinosaur tail , a primitive baby bird , and all sorts of invertebrates trapped in the fossilized tree resin.

Finding life’s building blocks on other worlds

In the last 10 years, space missions have given us a more sophisticated look at other worlds’ carbon-based organic molecules, which are necessary ingredients for life as we know it. The European Space Agency’s Rosetta mission orbited and landed on Comet 67P Churyumov–Gerasimenko . The data it collected between 2014 and 2016 gave us an astonishingly close look at the raw materials that ancient impacts might have brought to Earth. Before NASA’s Cassini probe died in 2017, it confirmed that the watery plumes of Saturn’s moon Enceladus contain large organic molecules , a clue that it has the right stuff for life. And in 2018, NASA announced that its Curiosity rover had found organic compounds on Mars , as well as a bizarre seasonal cycle in the red planet’s atmospheric methane levels.

Ringing climate alarms louder than ever

Alexandria Villasenor , 13, skips school on Fridays to strike in the name of climate change. Every week, rain or shine, she sits on a bench in front of the United Nations in New York City with her signs, bringing attention to the issue of climate change. Villasenor and other young activists from across the country organized a global school strike for climate on March 15.

Throughout this decade, atmospheric carbon dioxide were reaching levels that are unprecedented in modern times, with record temperatures to match. On May 9, 2013, global CO2 levels reached 400 parts per million for the first time in human history, and by 2016, CO2 levels were staying firmly above this threshold. As a result, the whole world felt an uptick in warming; 2015, 2016, 2017, 2018, and 2019 were the five hottest years on record since 1880. Starting in 2014, warming oceans kicked off a global coral bleaching event . Corals around the world suffered die-offs, including parts of the Great Barrier Reef . In 2019, Australia declared the island-dwelling Bramble Cay melomys extinct from sea level rise, the first known mammal lost to modern climate change .

In a series of major reports, the world’s scientists forcefully called attention to Earth’s altered climate, the risks it poses, and the need to respond. In 2014, the Intergovernmental Panel on Climate Change released its fifth assessment of climate change’s reality and consequences , and a year later, the world’s nations negotiated the Paris Agreement , the global climate accord that aims to keep warming below 2 degrees Celsius—which world leaders and scientists consider a dangerous threshold. In October 2018, the IPCC published another grim report that outlined the huge costs of warming even 1.5 degrees Celsius by 2100 —which is likely the minimum the planet will face. In the face of such huge challenges, record-breaking climate protests have swept the world, many led by youth activists .

Discovering—and rediscovering—species

Modern biologists are identifying new species at a blistering pace, naming 18,000 new species a year on average. In the past decade, scientists described several charismatic mammal species for the first time, such as the Myanmar snub-nosed monkey , the Vangunu giant rat , and the olinguito , the first newfound carnivore in the Western Hemisphere since the late 1970s. The ranks of other animals groups also swelled, as scientists described newfound fish with “hands,” tiny frogs smaller than a dime , a giant Florida salamander , and many others. In addition, some animals, such as Vietnam’s saola and China’s Ili pika , were spotted once again after having gone missing for years.

But along with these many finds, scientists have tallied the exponential rate of modern extinctions. In 2019, scientists warned that a quarter of plant and animal groups are threatened with extinction, suggesting that as many as a million species—both known and unknown to science— are now at risk of dying out, some within decades .

Kicking off a new spaceflight era

The 2010s were a pivotal transition period for spaceflight, as access to low-Earth orbit and beyond became a more global—and commercial—enterprise. In 2011, China launched its first space laboratory, Tiangong-1 , into orbit. In 2014, India’s Mars Orbiter Mission arrived at the red planet , making India the first country ever to successfully arrive at Mars on its first try. In 2019, Israeli nonprofit SpaceIL attempted the first privately funded lunar landing , and China’s Chang’e-4 mission performed the first soft landing on the lunar farside . The global astronaut corps also grew more diverse: Tim Peake became the first professional British astronaut, Aidyn Aimbetov became the first post-Soviet Kazakh cosmonaut, and the United Arab Emirates and Denmark sent their first astronauts to space. What’s more, NASA astronauts Jessica Meir and Christina Koch performed the first all-female spacewalk .

In the U.S., after the last space shuttle mission launched in 2011, private companies angled to fill the void. In 2012, SpaceX launched the first commercial resupply mission to the ISS, and in 2015, Blue Origin and SpaceX became the first companies to successfully launch reusable rockets to space and then vertically land them back on Earth , a milestone for cheaper launches to low-Earth orbit.

Seeing animals’ unexpected sides

The past decade has revealed unusual traits and behaviors across the animal kingdom. In 2015, National Geographic explorer David Gruber found that hawksbill sea turtles fluoresce green and red— the first biofluorescence ever recorded in a reptile . In 2016, researchers showed that the Greenland shark can live at least 272 years , making it the longest-lived vertebrate yet known. Our understanding of animal tool use also improved: One 2019 study showed for the first time that Visayan warty pigs use tools , and several studies showed that Brazil’s capuchins have been using tools for at least 3,000 years , the oldest such non-human record found outside Africa. In an extremely rare 2018 sighting, biologists in Kenya scientifically documented a black leopard in Africa for the first time since 1909 .

Redefining the units of science

To understand the natural world, scientists must measure it—but how do we define our units? Over the decades, scientists have gradually redefined classic units in terms of universal constants, such as using the speed of light to help define the length of a meter. But the scientific unit of mass, the kilogram, remained pegged to “Le Grand K,” a metallic cylinder stored at a facility in France. If that ingot’s mass varied for whatever reason, scientists would have to recalibrate their instruments. No more: In 2019, scientists agreed to adopt a new kilogram definition based on a fundamental factor in physics called Planck’s constant and the improved definitions for the units of electrical current, temperature, and the number of particles in a given substance. For the first time ever, all our scientific units now stem from universal constants—ensuring a more accurate era of measurement.

Related Topics

• CLIMATE CHANGE
• ARCHAEOLOGY
• PALEONTOLOGY

Noise pollution harms more than your hearing

He returned to the ‘cave of bones’ to solve the mysteries of human origins

Step inside the factory where the NFL’s footballs are made

Tonga's volcanic eruption triggered a staggering 2,600 lightning flashes a minute

Volcanoes blow smoke rings. Here's how they do it.

• Environment
• Paid Content

History & Culture

• History & Culture
• Terms of Use
• Privacy Policy
• Your US State Privacy Rights
• Children's Online Privacy Policy
• Interest-Based Ads
• About Nielsen Measurement
• Do Not Sell or Share My Personal Information
• Nat Geo Home
• Attend a Live Event
• Book a Trip
• Inspire Your Kids
• Shop Nat Geo
• Visit the D.C. Museum
• Learn About Our Impact
• Support Our Mission
• Advertise With Us
• Customer Service
• Renew Subscription
• Manage Your Subscription
• Work at Nat Geo
• Sign Up for Our Newsletters
• Contribute to Protect the Planet

Copyright © 1996-2015 National Geographic Society Copyright © 2015-2024 National Geographic Partners, LLC. All rights reserved

The Ten Most Significant Science Stories of 2021

Thrilling discoveries, hurdles in the fight against Covid and advancements in space exploration defined the past year

Associate Editor, Science

Covid-19 dominated science coverage again in 2021, and deservedly so. The disease garnered two entries on this list of our picks for the most important science stories of the year. But other key discoveries and achievements marked the year in science too, and they deserve more attention. NASA and private companies notched firsts in space. Scientists discovered more about the existence of early humans. And researchers documented how climate change has impacted everything from coral reefs to birds. Covid-19 will continue to garner even more attention next year as scientists work to deal with new variants and develop medical advances to battle the virus. But before you let stories about those topics dominate your reading in 2022, it’s worth it to take a look back at the biggest discoveries and accomplishments of this past year. To that end, here are our picks for the most important science stories of 2021.

The Covid Vaccine Rollout Encounters Hurdles

Last year the biggest science story of the year was that scientists developed two mRNA Covid vaccines in record time. This year the biggest Covid story is that the rollout of those vaccines by Pfizer and Moderna, and one other by Johnson and Johnson, haven’t made their way into a large proportion of the United States population and a significant portion of the world. As of this writing on December 21 , roughly 73 percent of the U.S. population has received one dose, and roughly 61 percent of the U.S. population has been fully vaccinated. An incomplete rollout allowed for a deadly summer surge, driven by the highly contagious Delta variant . Experts pointed out that vaccination rates lagged due to widespread disinformation and misinformation campaigns . It didn’t help that some popular public figures —like Packers’ quarterback Aaron Rodgers , musician Nick Minaj , podcast host Joe Rogan and rapper Ice Cube —chose not to get vaccinated. Luckily, by November, U.S. health officials had approved the Pfizer vaccine for children as young as five, providing another barrier against the deadly disease’s spread, and Covid rates declined. But while the wall against the disease in the U.S. is growing, it is not finished. As cases surge as the Omicron variant spreads around the country, building that wall and reinforcing it with booster shots is critically important. In much of the rest of the world, the wall is severely lacking where populations haven’t been given decent access to the vaccine. Only 8 percent of individuals in low-income countries have received at least one dose of the vaccine, and a WHO Africa report from this fall said that on that continent, less than 10 percent of countries would hit the goal of vaccinating at least 40 percent of their citizens by the end of the year. Globally, less than 60 percent of the population has been vaccinated. The holes in vaccination coverage will allow the virus to continue to kill a large number of individuals, and allow an environment where possibly other dangerous variants can emerge.

Perseverance Notches Firsts on Mars

NASA took a huge step forward in exploring the Red Planet after the rover Perseverance landed safely on Mars in February. Scientists outfitted the vehicle with an ultralight helicopter that successfully flew in the thin Martian atmosphere , a toaster-sized device called MOXIE that successfully converted carbon dioxide to oxygen , and sampling elements that successfully collected rocks from the planet’s floor. All of the achievements will lend themselves to a better understanding of Mars, and how to investigate it in the future. The flight success will give scientists clues on how to build larger helicopters, the oxygen creation will help scientists come up with grander plans for conversion devices, and the rocks will make their way back to Earth for analysis when they are picked up on a future mission. In addition to the rover’s triumphs, other countries notched major firsts too. The United Arab Emirates Hope space probe successfully entered orbit around the planet and is studying the Martian atmosphere and weather. China’s Zhurong rover landed on Mars in May and is exploring the planet’s geology and looking for signs of water. With these ongoing missions, scientists around the world are learning more and more about what the planet is like and how we might better explore it, maybe one day in person.

Is “Dragon Man” a New Species of Human?

The backstory of the skull that scientists used to suggest there was a new species of later Pleistocene human—to join Homo sapiens and Neanderthals—garnered a lot of ink. After the fossil was discovered at a construction site in China nearly 90 years ago, a family hid it until a farmer gave it to a university museum in 2018. Since then, scientists in China pored over the skull—analyzing its features, conducting uranium series dating, and using X-ray fluorescence to compare it to other fossils—before declaring it a new species of archaic human. They dubbed the discovery Homo longi , or “Dragon Man.” The skull had a large cranium capable of holding a big brain, a thick brow and almost square eye sockets—details scientists used to differentiate it from other Homo species. Some scientists questioned whether the find warranted designation as a new species. “It’s exciting because it is a really interesting cranium, and it does have some things to say about human evolution and what’s going on in Asia. But it’s also disappointing that it’s 90 years out from discovery, and it is just an isolated cranium, and you’re not quite sure exactly how old it is or where it fits,” Michael Petraglia of the Smithsonian Institution’s Human Origins Initiative told Smithsonian magazine back in June. Other scientists supported the new species designation, and so the debate continues, and likely will until more fossils are discovered that help to fill in the holes of human history.

Climate Change Wreaks Havoc on Coral Reefs

Increasing natural disasters—forest fires, droughts and heat waves—may be the most noticeable events spurred by climate change; a warming Earth has helped drive a five-fold uptick in such weather-related events over the last 50 years according the a 2021 report by the World Meteorological Organization . But one of the biggest impacts wrought by climate change over the past decade has occurred underwater. Warming temps cause coral reefs to discard the symbiotic algae that help them survive, and they bleach and die. This year a major report from the Global Coral Reef Monitoring Network announced that the oceans lost about 14 percent of their reefs in the decade after 2009, mostly because of climate change. In November, new research showed that less than 2 percent of the coral reefs on the Great Barrier Reef—the world’s largest such feature—escaped bleaching since 1998. That news came just two months after a different study stated that half of coral reefs have been lost since the 1950s , in part due to climate change. The reef declines impact fisheries, local economies based on tourism and coastal developments—which lose the offshore buffer zone from storms the living structures provide. Scientists say if temperatures continue to rise, coral reefs are in serious danger. But not all hope is lost—if humans reduce carbon emissions rapidly now, more reefs will have a better chance of surviving .

The Space Tourism Race Heats Up

This year the famous billionaires behind the space tourism race completed successful missions that boosted more than just their egos. They put a host of civilians in space. Early in July, billionaire Richard Branson and his employees flew just above the boundary of space—a suborbital flight—in Virgin Galactic’s first fully crewed trip. (But Virgin Galactic did delay commercial missions until at least late next year.) Just over a week after Branson’s mission, the world’s richest person, Jeff Bezos, completed Blue Origin’s first crewed suborbital flight with the youngest and oldest travelers to reach space. In October, his company Blue Origin repeated the feat when it took Star Trek actor William Shatner up. A month before that, a crew of four became the first all-civilian crew to circle the Earth from space in Elon Musk’s SpaceX Dragon capsule Resilience. More ambitious firsts for civilians are in the works. In 2022, SpaceX plans to send a retired astronaut and three paying passengers to the International Space Station. And beyond that, Bezos announced Blue Origin hopes to deploy a private space station fit for ten—called “Orbital Reef”—sometime between 2025 and 2030.

WHO Approves First Vaccine Against Malaria

In October, the World Health Organization approved the first vaccine against malaria. The approval was not only a first for that disease, but also for any parasitic disease. The moment was 30 years in the making, as Mosquirix—the brand name of the drug— cost more than $750 million since 1987 to develop and test. Malaria kills nearly a half million individuals a year, including 260,000 children under the age of five. Most of these victims live in sub-Saharan Africa. The new vaccine fights the deadliest of five malaria pathogens and the most prevalent in Africa, and is administered to children under five in a series of four injections. The vaccine is not a silver bullet; it prevents only about 30 percent of severe malaria cases. But one modeling study showed that still could prevent 5.4 million cases and 23,000 deaths in children under five each year. Experts say the vaccine is a valuable tool that should be used in conjunction with existing methods—such as drug combination treatments and insecticide-treated bed nets—to combat the deadly disease.

Discoveries Move Key Dates Back for Humans in the Americas

Two very different papers in two of the world’s most prestigious scientific journals documented key moments of human habitation in the Americas. In September, a study in Science dated footprints found at White Sands National Park to between 21,000 and 23,000 years ago. Researchers estimated the age of the dried tracks known as “ghost prints” using radiocarbon dating of dried ditchgrass seeds found above and below the impressions. Previously, many archaeologists placed the start of human life in the Americas at around 13,000 years ago, at the end of the last Ice Age, based on tools found in New Mexico. The new paper, whose results have been debated , suggests humans actually lived on the continent at the height of the Ice Age. A month after that surprising find, a study in Nature published evidence showing that Vikings lived on North America earlier than previously thought. Researchers examined cut wood left by the explorers at a site in Newfoundland and found evidence in the samples of a cosmic ray event that happened in 993 C.E. The scientists then counted the rings out from that mark and discovered the wood had been cut in 1021 C.E. The find means that the Norse explorers completed the first known crossing of the Atlantic from Europe to the Americas.

Humans Are Affecting the Evolution of Animals

New research published this year shows that humans have both directly and indirectly affected how animals evolve. In probably the starkest example of humans impacting animal evolution, a Science study found a sharp increase in tuskless African elephants after years of poaching. During the Mozambican Civil War from 1977 to 1992, poachers killed so many of the giant mammals with tusks that those females without the long ivory teeth were more likely to pass on their genes. Before the war, 20 percent were tuskless. Now, roughly half of the female elephants are tuskless. Males who have the genetic make-up for tusklessness die , likely before they are born. And killing animals isn’t the only way humans are impacting evolution. A large study in Trends in Ecology and Evolution found that animals are changing shape to deal with rising temps. For example, over various time periods bats grew bigger wings and rabbits sprouted longer ears—both likely to dissipate more heat into the surrounding air. More evidence along those lines was published later in the year in Science Advances . A 40-year-study of birds in a remote, intact patch of Amazon rainforest showed 77 species weighed less on average, and many had longer wings, than they used to. Scientists said the changes likely occurred due to rising temperatures and changes in rainfall.

Antiviral Pills That Fight Covid Show Promising Results

Almost a year after scientists released tests showing the success of mRNA vaccines in fighting Covid, Merck released promising interim test results from a Phase III trial of an antiviral pill. On October 1, the pharmaceutical giant presented data that suggested molnupiravir could cut hospitalizations in half. Ten days later, the company submitted results to the FDA in hopes of gaining emergency use. In mid-November, the U.K. jumped ahead of the U.S. and granted approval for the treatment. By late November, advisers to the FDA recommended emergency authorization of the pill, though it was shown by this time to reduce death or disease by 30—not 50—percent. The drug should be taken —four pills a day for five days—starting within five days of the appearance of symptoms. It works by disrupting SARS-CoV-2’s ability to replicate effectively inside a human cell.

Molnupiravir isn’t the only viral drug with positive results. In November, Pfizer announced its antiviral pill, Paxlovid, was effective against severe Covid. By December, the pharmaceutical giant shared final results that it reduced the risk of hospitalization and death by 88 percent in a key group. News about both pills was welcome , as they are expected to work against all versions of the virus, including Omicron. Though the drugs aren’t as big of a breakthrough as the vaccines, a doctor writing for the New Yorker called them “the most important pharmacologic advance of the pandemic.” Many wealthy countries have already agreed to contracts for molnupiravir, and the Gates Foundation pledged $120 million to help get the pill to poor countries. If approved and distributed fast enough, the oral antivirals can be prescribed in places, like Africa, where vaccines have been lacking. The pills represent another crucial tool, in addition to masks and vaccines, in the fight against Covid.

The James Webb Space Telescope May Finally Launch

Get the latest Science stories in your inbox.

Joe Spring | READ MORE

Joe Spring is the associate digital science editor for Smithsonian magazine.

Impact of Scientific Discoveries on the World Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

William and Colin (2009) define scientific discoveries as the incoming achievements that are majorly grasped through a thorough exploitation and research on nature and the societal needs.

Horton and Freire (2006) assert that, for decades, scientific discoveries have formed the backbone for the worldwide technological advancement and revolutions, which have created a turning point to the direction of the economic development and revolution in the social production. Notably, discoveries bear a special significance on the modern stages of technological progress.

As envisioned by Juan Enriquez, scientific, coupled with the discoveries of genomics have spearheaded the growth of the business world and increased economic influence. This paper, therefore, presents a discussion on the basis that Enriquez envisions for the future for the future of the global economy.

According to William and Colin (2009), future global economy may refer to what the world may look like by the year 2040. Predictions have foretold of tremendous developments in the scientific and the technological world. A plethora of nations are to pull out of repression, poverty, and starvation caused by inadequate food supply.

In addition, other predictive reports have shown the negative future trends in the future. These include overpopulation, rise on the level of terrorism and violence, climate change characterised by global warming, mass migration in search of food as the gap between the rich and the poor widening (Horton and Freire, 2006).

Moreover, Juan Enriquez envisions a future with a full potential in the supply of the energy resources (Teitel, 2002). The supply of energy resources is one of the most critical aspects of an economic model. Most economic activities in a country or across the globe such as industrialisation owe their success to the adequate and reliable source of energy.

The global economic progress depends on the development of the hydrocarbon sources of energy. The hydrocarbon sources of energy should be sustained for the longest duration possible (Santayan, 2008). The use of energy should also take proper care of their impacts on the environment as well as the cost of production.

Similarly, the global economic base is predicted to broaden in the near future (Teitel, 2002). The widening of the economic base across the globe is a program that aims at expanding the marketing resources to not only one superior and economically stable state such as the US markets but also encouraging the individual local market resources.

Achieving this will limit the vulnerability accompanied with the overreliance on the sales of energy resources. The program also aims at creating new markets in the future from where products are expected to rise from both the natural and human resources supply (Horton and Freire, 2006).

Integrating the discoveries of genomics and science with the world of business provides a firm foundation for the sustenance of a sound economic plan and growth across the globe.

In order to provide the sound economic platform for the growth of the global economy, wise and a reliable management system is required. Technological management systems including the use of computers and other automated systems in management of resources such as electricity and transportation systems are necessary to support global interaction (Teitel, 2002).

As claimed by Teitel (2002), the future of the global economy is also envisioned to be consisting of both local and foreign investment. Foreign investments are those often implemented on a large scale on the major natural resources such as the energy resources. Santayan (2008) elaborates in his book that the investments are mostly fueled by the scientific and discoveries of genomics. The management of the future investments requires a more strategic as well as a more disciplined approach in the management of the natural resources.

As a wrap up, therefore, Juan Enriquez envisions the future of the global economy which is characterised by tremendous developments in the scientific and the technological world (Horton & Freire, 2006). The envisioned state in the global economy could be achieved through full utilisation of the sources of energy to the potential as well as broadening the market resources.

Horton, M., & Freire, P. (2006). We Make the Road by Walking: Conversations on Education and Social Change. Philadelphia: Temple University Press.

Santayan, G. (2008). Character and Opinion in the United States. New Brunwick, NJ: Transaction Publishers.

Teitel,G. (2002). Transitional Justice . Madison Avenue,NY. Oxford University Press.

William, H., & Colin, M. (2009). Mexicans in Revolution, 1910-1946: An Introduction. Lincoln, NE: University of Nebraska Press.

• The Importance of Improving the Digital Divide between All Countries of Different Socio-Economic Climates
• The Technology's Influence on Lives
• Re-evaluating Freire and Seneca
• Arguments on Pedagogy of the Oppressed by Paulo Freire
• Paulo Freire's Life, Philosophy and Teachings
• The Influence of Computer on the Living Standards of People All Over the World
• Technology Impact on Society and Environment
• The Causes and Effect of the Computer Revolution
• “Capturing” in Culture and Beyond
• Effects of Computer Programming and Technology on Human Behavior
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2018, December 11). Impact of Scientific Discoveries on the World. https://ivypanda.com/essays/impact-of-scientific-discoveries-on-the-world/

"Impact of Scientific Discoveries on the World." IvyPanda , 11 Dec. 2018, ivypanda.com/essays/impact-of-scientific-discoveries-on-the-world/.

IvyPanda . (2018) 'Impact of Scientific Discoveries on the World'. 11 December.

IvyPanda . 2018. "Impact of Scientific Discoveries on the World." December 11, 2018. https://ivypanda.com/essays/impact-of-scientific-discoveries-on-the-world/.

1. IvyPanda . "Impact of Scientific Discoveries on the World." December 11, 2018. https://ivypanda.com/essays/impact-of-scientific-discoveries-on-the-world/.

Bibliography

IvyPanda . "Impact of Scientific Discoveries on the World." December 11, 2018. https://ivypanda.com/essays/impact-of-scientific-discoveries-on-the-world/.

45,000+ students realised their study abroad dream with us. Take the first step today

Meet top uk universities from the comfort of your home, here’s your new year gift, one app for all your, study abroad needs, start your journey, track your progress, grow with the community and so much more.

Verification Code

An OTP has been sent to your registered mobile no. Please verify

Thanks for your comment !

Our team will review it before it's shown to our readers.

• School Education /

Essay on Science: Sample for Students in 100,200 Words

• Updated on  
• Oct 28, 2023

Science, the relentless pursuit of knowledge and understanding, has ignited the flames of human progress for centuries. It’s a beacon guiding us through the uncharted realms of the universe, unlocking secrets that shape our world. In this blog, we embark on an exhilarating journey through the wonders of science. We’ll explore the essence of science and its profound impact on our lives. With this we will also provide you with sample essay on science in 100 and 200 words.

Must Read: Essay On Internet   

What Is Science?

Science is a systematic pursuit of knowledge about the natural world through observation, experimentation, and analysis. It aims to understand the underlying principles governing the universe, from the smallest particles to the vast cosmos. Science plays a crucial role in advancing technology, improving our understanding of life and the environment, and driving innovation for a better future.

Branches Of Science

The major branches of science can be categorized into the following:

• Physical Science: This includes physics and chemistry, which study the fundamental properties of matter and energy.
• Biological Science : Also known as life sciences, it encompasses biology, genetics, and ecology, focusing on living organisms and their interactions.
• Earth Science: Geology, meteorology, and oceanography fall under this category, investigating the Earth’s processes, climate, and natural resources.
• Astronomy : The study of celestial objects, space, and the universe, including astrophysics and cosmology.
• Environmental Science : Concentrating on environmental issues, it combines aspects of biology, chemistry, and Earth science to address concerns like climate change and conservation. 
• Social Sciences : This diverse field covers anthropology, psychology, sociology, and economics, examining human behavior, society, and culture.  
• Computer Science : Focused on algorithms, data structures, and computing technology, it drives advancements in information technology. 
• Mathematics : A foundational discipline, it underpins all sciences, providing the language and tools for scientific analysis and modeling.  

Wonders Of Science

Science has numerous applications that profoundly impact our lives and society: Major applications of science are stated below:

• Medicine: Scientific research leads to the development of vaccines, medicines, and medical technologies, improving healthcare and saving lives.
• Technology: Science drives technological innovations, from smartphones to space exploration.
• Energy: Advances in physics and chemistry enable the development of renewable energy sources, reducing reliance on fossil fuels.
• Agriculture: Biology and genetics improve crop yields, while chemistry produces fertilizers and pesticides.
• Environmental Conservation : Scientific understanding informs efforts to protect ecosystems and combat climate change.
• Transportation : Physics and engineering create efficient and sustainable transportation systems.
• Communication : Physics and computer science underpin global communication networks.
• Space Exploration : Astronomy and physics facilitate space missions, expanding our understanding of the cosmos.

Must Read: Essay On Scientific Discoveries  

Sample Essay On Science in 100 words

Science, the bedrock of human progress, unveils the mysteries of our universe through empirical investigation and reason. Its profound impact permeates every facet of modern life. In medicine, it saves countless lives with breakthroughs in treatments and vaccines. Technology, a child of science, empowers communication and innovation. Agriculture evolves with scientific methods, ensuring food security. Environmental science guides conservation efforts, preserving our planet. Space exploration fuels dreams of interstellar travel.

Yet, science requires responsibility, as unchecked advancement can harm nature and society. Ethical dilemmas arise, necessitating careful consideration. Science, a double-edged sword, holds the potential for both salvation and destruction, making it imperative to harness its power wisely for the betterment of humanity.

Sample Essay On Science in 250 words

Science, often regarded as humanity’s greatest intellectual endeavor, plays an indispensable role in shaping our world and advancing our civilization.

At its core, science is a methodical pursuit of knowledge about the natural world. Through systematic observation, experimentation, and analysis, it seeks to uncover the underlying principles that govern our universe. This process has yielded profound insights into the workings of the cosmos, from the subatomic realm to the vastness of space.

One of the most remarkable contributions of science is to the field of medicine. Through relentless research and experimentation, scientists have discovered vaccines, antibiotics, and groundbreaking treatments for diseases that once claimed countless lives. 

Furthermore, science has driven technological advancements that have reshaped society. The rapid progress in computing, for instance, has revolutionized communication, industry, and research. From the ubiquitous smartphones in our pockets to the complex algorithms that power our digital lives, science, and technology are inseparable partners in progress.

Environmental conservation is another critical arena where science is a guiding light. Climate change, a global challenge, is addressed through rigorous scientific study and the development of sustainable practices. Science empowers us to understand the impact of human activities on our planet and to make informed decisions to protect it.

In conclusion, science is not just a field of study; it is a driving force behind human progress. As we continue to explore the frontiers of knowledge, science will remain the beacon guiding us toward a brighter future.

Science is a boon due to innovations, medical advancements, and a deeper understanding of nature, improving human lives exponentially.

Galileo Galilei is known as the Father of Science.

Science can’t address questions about personal beliefs, emotions, ethics, or matters of subjective experience beyond empirical observation and measurement.

We hope this blog gave you an idea about how to write and present an essay on science that puts forth your opinions. The skill of writing an essay comes in handy when appearing for standardized language tests. Thinking of taking one soon? Leverage Edu provides the best online test prep for the same via Leverage Live . Register today to know more!

Amisha Khushara

Hey there! I'm a content writer who turns complex ideas into clear, engaging stories. Think of me as your translator, taking expert knowledge and making it interesting and relatable for everyone.

Leave a Reply Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Contact no. *

Connect With Us

45,000+ students realised their study abroad dream with us. take the first step today..

Resend OTP in

Need help with?

Study abroad.

UK, Canada, US & More

IELTS, GRE, GMAT & More

Scholarship, Loans & Forex

Country Preference

New Zealand

Which English test are you planning to take?

Which academic test are you planning to take.

Not Sure yet

When are you planning to take the exam?

Already booked my exam slot

Within 2 Months

Want to learn about the test

Which Degree do you wish to pursue?

When do you want to start studying abroad.

January 2024

September 2024

What is your budget to study abroad?

How would you describe this article ?

Please rate this article

We would like to hear more.

Have something on your mind?

Make your study abroad dream a reality in January 2022 with

India's Biggest Virtual University Fair

Essex Direct Admission Day

Why attend .

Don't Miss Out

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 02 August 2023

Scientific discovery in the age of artificial intelligence

• Hanchen Wang   ORCID: orcid.org/0000-0002-1691-024X 1 , 2   na1   nAff37   nAff38 ,
• Tianfan Fu 3   na1 ,
• Yuanqi Du 4   na1 ,
• Wenhao Gao 5 ,
• Kexin Huang 6 ,
• Ziming Liu 7 ,
• Payal Chandak   ORCID: orcid.org/0000-0003-1097-803X 8 ,
• Shengchao Liu   ORCID: orcid.org/0000-0003-2030-2367 9 , 10 ,
• Peter Van Katwyk   ORCID: orcid.org/0000-0002-3512-0665 11 , 12 ,
• Andreea Deac 9 , 10 ,
• Anima Anandkumar 2 , 13 ,
• Karianne Bergen 11 , 12 ,
• Carla P. Gomes   ORCID: orcid.org/0000-0002-4441-7225 4 ,
• Shirley Ho 14 , 15 , 16 , 17 ,
• Pushmeet Kohli   ORCID: orcid.org/0000-0002-7466-7997 18 ,
• Joan Lasenby 1 ,
• Jure Leskovec   ORCID: orcid.org/0000-0002-5411-923X 6 ,
• Tie-Yan Liu 19 ,
• Arjun Manrai 20 ,
• Debora Marks   ORCID: orcid.org/0000-0001-9388-2281 21 , 22 ,
• Bharath Ramsundar 23 ,
• Le Song 24 , 25 ,
• Jimeng Sun 26 ,
• Jian Tang 9 , 27 , 28 ,
• Petar Veličković 18 , 29 ,
• Max Welling 30 , 31 ,
• Linfeng Zhang 32 , 33 ,
• Connor W. Coley   ORCID: orcid.org/0000-0002-8271-8723 5 , 34 ,
• Yoshua Bengio   ORCID: orcid.org/0000-0002-9322-3515 9 , 10 &
• Marinka Zitnik   ORCID: orcid.org/0000-0001-8530-7228 20 , 22 , 35 , 36  

Nature volume  620 ,  pages 47–60 ( 2023 ) Cite this article

112k Accesses

287 Citations

601 Altmetric

Metrics details

• Computer science
• Machine learning
• Scientific community

A Publisher Correction to this article was published on 30 August 2023

This article has been updated

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Buy this article

• Purchase on SpringerLink
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Accelerating science with human-aware artificial intelligence

Accelerating material design with the generative toolkit for scientific discovery

A data science roadmap for open science organizations engaged in early-stage drug discovery

Change history, 30 august 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-023-06559-7

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015). This survey summarizes key elements of deep learning and its development in speech recognition, computer vision and and natural language processing .

Article   ADS   CAS   PubMed   Google Scholar  

de Regt, H. W. Understanding, values, and the aims of science. Phil. Sci. 87 , 921–932 (2020).

Article   MathSciNet   Google Scholar  

Pickstone, J. V. Ways of Knowing: A New History of Science, Technology, and Medicine (Univ. Chicago Press, 2001).

Han, J. et al. Deep potential: a general representation of a many-body potential energy surface. Commun. Comput. Phys. 23 , 629–639 (2018). This paper introduced a deep neural network architecture that learns the potential energy surface of many-body systems while respecting the underlying symmetries of the system by incorporating group theory.

Akiyama, K. et al. First M87 Event Horizon Telescope results. IV. Imaging the central supermassive black hole. Astrophys. J. Lett. 875 , L4 (2019).

Article   ADS   CAS   Google Scholar  

Wagner, A. Z. Constructions in combinatorics via neural networks. Preprint at https://arxiv.org/abs/2104.14516 (2021).

Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365 , eaax1566 (2019).

Article   CAS   PubMed   Google Scholar  

Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021).

Davies, A. et al. Advancing mathematics by guiding human intuition with AI. Nature 600 , 70–74 (2021). This paper explores how AI can aid the development of pure mathematics by guiding mathematical intuition.

Article   ADS   CAS   PubMed   PubMed Central   MATH   Google Scholar  

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 , 583–589 (2021). This study was the first to demonstrate the ability to predict protein folding structures using AI methods with a high degree of accuracy, achieving results that are at or near the experimental resolution. This accomplishment is particularly noteworthy, as predicting protein folding has been a grand challenge in the field of molecular biology for over 50 years.

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180 , 688–702 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bohacek, R. S., McMartin, C. & Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16 , 3–50 (1996).

Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40 , 932–937 (2022).

Bellemare, M. G. et al. Autonomous navigation of stratospheric balloons using reinforcement learning. Nature 588 , 77–82 (2020). This paper describes a reinforcement-learning algorithm for navigating a super-pressure balloon in the stratosphere, making real-time decisions in the changing environment.

Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 , 95–98 (2019).

Zhang, L. et al. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120 , 143001 (2018).

Deiana, A. M. et al. Applications and techniques for fast machine learning in science. Front. Big Data 5 , 787421 (2022).

Karagiorgi, G. et al. Machine learning in the search for new fundamental physics. Nat. Rev. Phys. 4 , 399–412 (2022).

Zhou, C. & Paffenroth, R. C. Anomaly detection with robust deep autoencoders. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 665–674 (2017).

Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313 , 504–507 (2006).

Article   ADS   MathSciNet   CAS   PubMed   MATH   Google Scholar  

Kasieczka, G. et al. The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics. Rep. Prog. Phys. 84 , 124201 (2021).

Govorkova, E. et al. Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Nat. Mach. Intell. 4 , 154–161 (2022).

Article   Google Scholar  

Chamberland, M. et al. Detecting microstructural deviations in individuals with deep diffusion MRI tractometry. Nat. Comput. Sci. 1 , 598–606 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Rafique, M. et al. Delegated regressor, a robust approach for automated anomaly detection in the soil radon time series data. Sci. Rep. 10 , 3004 (2020).

Pastore, V. P. et al. Annotation-free learning of plankton for classification and anomaly detection. Sci. Rep. 10 , 12142 (2020).

Naul, B. et al. A recurrent neural network for classification of unevenly sampled variable stars. Nat. Astron. 2 , 151–155 (2018).

Article   ADS   Google Scholar  

Lee, D.-H. et al. Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In ICML Workshop on Challenges in Representation Learning (2013).

Zhou, D. et al. Learning with local and global consistency. In Advances in Neural Information Processing Systems 16 , 321–328 (2003).

Radivojac, P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10 , 221–227 (2013).

Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16 , 695–698 (2019).

Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution. Nat. Catal. 1 , 696–703 (2018).

Article   CAS   Google Scholar  

Jablonka, K. M. et al. Bias free multiobjective active learning for materials design and discovery. Nat. Commun. 12 , 2312 (2021).

Roussel, R. et al. Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning. Nat. Commun. 12 , 5612 (2021).

Ratner, A. J. et al. Data programming: creating large training sets, quickly. In Advances in Neural Information Processing Systems 29 , 3567–3575 (2016).

Ratner, A. et al. Snorkel: rapid training data creation with weak supervision. In International Conference on Very Large Data Bases 11 , 269–282 (2017). This paper presents a weakly-supervised AI system designed to annotate massive amounts of data using labeling functions.

Butter, A. et al. GANplifying event samples. SciPost Phys. 10 , 139 (2021).

Brown, T. et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems 33 , 1877–1901 (2020).

Ramesh, A. et al. Zero-shot text-to-image generation. In International Conference on Machine Learning 139 , 8821–8831 (2021).

Littman, M. L. Reinforcement learning improves behaviour from evaluative feedback. Nature 521 , 445–451 (2015).

Cubuk, E. D. et al. Autoaugment: learning augmentation strategies from data. In IEEE Conference on Computer Vision and Pattern Recognition 113–123 (2019).

Reed, C. J. et al. Selfaugment: automatic augmentation policies for self-supervised learning. In IEEE Conference on Computer Vision and Pattern Recognition 2674–2683 (2021).

ATLAS Collaboration et al. Deep generative models for fast photon shower simulation in ATLAS. Preprint at https://arxiv.org/abs/2210.06204 (2022).

Mahmood, F. et al. Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans. Med. Imaging 39 , 3257–3267 (2019).

Teixeira, B. et al. Generating synthetic X-ray images of a person from the surface geometry. In IEEE Conference on Computer Vision and Pattern Recognition 9059–9067 (2018).

Lee, D., Moon, W.-J. & Ye, J. C. Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks. Nat. Mach. Intell. 2 , 34–42 (2020).

Kench, S. & Cooper, S. J. Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. Nat. Mach. Intell. 3 , 299–305 (2021).

Wan, C. & Jones, D. T. Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. Nat. Mach. Intell. 2 , 540–550 (2020).

Repecka, D. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3 , 324–333 (2021).

Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11 , 166 (2020).

Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 521 , 452–459 (2015). This survey provides an introduction to probabilistic machine learning, which involves the representation and manipulation of uncertainty in models and predictions, playing a central role in scientific data analysis.

Cogan, J. et al. Jet-images: computer vision inspired techniques for jet tagging. J. High Energy Phys. 2015 , 118 (2015).

Zhao, W. et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat. Biotechnol. 40 , 606–617 (2022).

Brbić, M. et al. MARS: discovering novel cell types across heterogeneous single-cell experiments. Nat. Methods 17 , 1200–1206 (2020).

Article   PubMed   Google Scholar  

Qiao, C. et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat. Methods 18 , 194–202 (2021).

Andreassen, A. et al. OmniFold: a method to simultaneously unfold all observables. Phys. Rev. Lett. 124 , 182001 (2020).

Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40 , 476–479 (2021).

Vincent, P. et al. Extracting and composing robust features with denoising autoencoders. In International Conference on Machine Learning 1096–1103 (2008).

Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. In International Conference on Learning Representations (2014).

Eraslan, G. et al. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10 , 390 (2019).

Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).

Olshausen, B. A. & Field, D. J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381 , 607–609 (1996).

Bengio, Y. Deep learning of representations for unsupervised and transfer learning. In ICML Workshop on Unsupervised and Transfer Learning (2012).

Detlefsen, N. S., Hauberg, S. & Boomsma, W. Learning meaningful representations of protein sequences. Nat. Commun. 13 , 1914 (2022).

Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37 , 38–44 (2019).

Bronstein, M. M. et al. Geometric deep learning: going beyond euclidean data. IEEE Signal Process Mag. 34 , 18–42 (2017).

Anderson, P. W. More is different: broken symmetry and the nature of the hierarchical structure of science. Science 177 , 393–396 (1972).

Qiao, Z. et al. Informing geometric deep learning with electronic interactions to accelerate quantum chemistry. Proc. Natl Acad. Sci. USA 119 , e2205221119 (2022).

Bogatskiy, A. et al. Symmetry group equivariant architectures for physics. Preprint at https://arxiv.org/abs/2203.06153 (2022).

Bronstein, M. M. et al. Geometric deep learning: grids, groups, graphs, geodesics, and gauges. Preprint at https://arxiv.org/abs/2104.13478 (2021).

Townshend, R. J. L. et al. Geometric deep learning of RNA structure. Science 373 , 1047–1051 (2021).

Wicky, B. I. M. et al. Hallucinating symmetric protein assemblies. Science 378 , 56–61 (2022).

Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (2017).

Veličković, P. et al. Graph attention networks. In International Conference on Learning Representations (2018).

Hamilton, W. L., Ying, Z. & Leskovec, J. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30 , 1024–1034 (2017).

Gilmer, J. et al. Neural message passing for quantum chemistry. In International Conference on Machine Learning 1263–1272 (2017).

Li, M. M., Huang, K. & Zitnik, M. Graph representation learning in biomedicine and healthcare. Nat. Biomed. Eng. 6 , 1353–1369 (2022).

Satorras, V. G., Hoogeboom, E. & Welling, M. E( n ) equivariant graph neural networks. In International Conference on Machine Learning 9323–9332 (2021). This study incorporates principles of physics into the design of neural models, advancing the field of equivariant machine learning .

Thomas, N. et al. Tensor field networks: rotation-and translation-equivariant neural networks for 3D point clouds. Preprint at https://arxiv.org/abs/1802.08219 (2018).

Finzi, M. et al. Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data. In International Conference on Machine Learning 3165–3176 (2020).

Fuchs, F. et al. SE(3)-transformers: 3D roto-translation equivariant attention networks. In Advances in Neural Information Processing Systems 33 , 1970-1981 (2020).

Zaheer, M. et al. Deep sets. In Advances in Neural Information Processing Systems 30 , 3391–3401 (2017). This paper is an early study that explores the use of deep neural architectures on set data, which consists of an unordered list of elements.

Cohen, T. S. et al. Spherical CNNs. In International Conference on Learning Representations (2018).

Gordon, J. et al. Permutation equivariant models for compositional generalization in language. In International Conference on Learning Representations (2019).

Finzi, M., Welling, M. & Wilson, A. G. A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups. In International Conference on Machine Learning 3318–3328 (2021).

Dijk, D. V. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174 , 716–729 (2018).

Gainza, P. et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17 , 184–192 (2020).

Hatfield, P. W. et al. The data-driven future of high-energy-density physics. Nature 593 , 351–361 (2021).

Bapst, V. et al. Unveiling the predictive power of static structure in glassy systems. Nat. Phys. 16 , 448–454 (2020).

Zhang, R., Zhou, T. & Ma, J. Multiscale and integrative single-cell Hi-C analysis with Higashi. Nat. Biotechnol. 40 , 254–261 (2022).

Sammut, S.-J. et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 601 , 623–629 (2022).

DeZoort, G. et al. Graph neural networks at the Large Hadron Collider. Nat. Rev. Phys . 5 , 281–303 (2023).

Liu, S. et al. Pre-training molecular graph representation with 3D geometry. In International Conference on Learning Representations (2022).

The LIGO Scientific Collaboration. et al. A gravitational-wave standard siren measurement of the Hubble constant. Nature 551 , 85–88 (2017).

Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566 , 195–204 (2019).

Goenka, S. D. et al. Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencing. Nat. Biotechnol. 40 , 1035–1041 (2022).

Bengio, Y. et al. Greedy layer-wise training of deep networks. In Advances in Neural Information Processing Systems 19 , 153–160 (2006).

Hinton, G. E., Osindero, S. & Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 18 , 1527–1554 (2006).

Article   MathSciNet   PubMed   MATH   Google Scholar  

Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349 , 255–260 (2015).

Devlin, J. et al. BERT: pre-training of deep bidirectional transformers for language understanding. In North American Chapter of the Association for Computational Linguistics 4171–4186 (2019).

Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118 , e2016239118 (2021).

Elnaggar, A. et al. ProtTrans: rowards cracking the language of lifes code through self-supervised deep learning and high performance computing. In IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).

Hie, B. et al. Learning the language of viral evolution and escape. Science 371 , 284–288 (2021). This paper modeled viral escape with machine learning algorithms originally developed for human natural language.

Biswas, S. et al. Low- N protein engineering with data-efficient deep learning. Nat. Methods 18 , 389–396 (2021).

Ferruz, N. & Höcker, B. Controllable protein design with language models. Nat. Mach. Intell. 4 , 521–532 (2022).

Hsu, C. et al. Learning inverse folding from millions of predicted structures. In International Conference on Machine Learning 8946–8970 (2022).

Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373 , 871–876 (2021). Inspired by AlphaFold2, this study reported RoseTTAFold, a novel three-track neural module capable of simultaneously processing protein’s sequence, distance and coordinates.

Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28 , 31–36 (1988).

Lin, T.-S. et al. BigSMILES: a structurally-based line notation for describing macromolecules. ACS Cent. Sci. 5 , 1523–1531 (2019).

Krenn, M. et al. SELFIES and the future of molecular string representations. Patterns 3 , 100588 (2022).

Flam-Shepherd, D., Zhu, K. & Aspuru-Guzik, A. Language models can learn complex molecular distributions. Nat. Commun. 13 , 3293 (2022).

Skinnider, M. A. et al. Chemical language models enable navigation in sparsely populated chemical space. Nat. Mach. Intell. 3 , 759–770 (2021).

Chithrananda, S., Grand, G. & Ramsundar, B. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. In Machine Learning for Molecules Workshop at NeurIPS (2020).

Schwaller, P. et al. Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chem. Sci. 11 , 3316–3325 (2020).

Tetko, I. V. et al. State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat. Commun. 11 , 5575 (2020).

Schwaller, P. et al. Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell. 3 , 144–152 (2021).

Kovács, D. P., McCorkindale, W. & Lee, A. A. Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias. Nat. Commun. 12 , 1695 (2021).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Pesciullesi, G. et al. Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates. Nat. Commun. 11 , 4874 (2020).

Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems 30 , 5998–6008 (2017). This paper introduced the transformer, a modern neural network architecture that can process sequential data in parallel, revolutionizing natural language processing and sequence modeling.

Mousavi, S. M. et al. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat. Commun. 11 , 3952 (2020).

Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18 , 1196–1203 (2021).

Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. In Advances in Neural Information Processing Systems 34 , 29287–29303 (2021).

Kamienny, P.-A. et al. End-to-end symbolic regression with transformers. In Advances in Neural Information Processing Systems 35 , 10269–10281 (2022).

Jaegle, A. et al. Perceiver: general perception with iterative attention. In International Conference on Machine Learning 4651–4664 (2021).

Chen, L. et al. Decision transformer: reinforcement learning via sequence modeling. In Advances in Neural Information Processing Systems 34 , 15084–15097 (2021).

Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. In International Conference on Learning Representations (2020).

Choromanski, K. et al. Rethinking attention with performers. In International Conference on Learning Representations (2021).

Li, Z. et al. Fourier neural operator for parametric partial differential equations. In International Conference on Learning Representations (2021).

Kovachki, N. et al. Neural operator: learning maps between function spaces. J. Mach. Learn. Res. 24 , 1–97 (2023).

Russell, J. L. Kepler’s laws of planetary motion: 1609–1666. Br. J. Hist. Sci. 2 , 1–24 (1964).

Huang, K. et al. Artificial intelligence foundation for therapeutic science. Nat. Chem. Biol. 18 , 1033–1036 (2022).

Guimerà, R. et al. A Bayesian machine scientist to aid in the solution of challenging scientific problems. Sci. Adv. 6 , eaav6971 (2020).

Liu, G. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat. Chem. Biol. https://doi.org/10.1038/s41589-023-01349-8 (2023).

Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15 , 1120–1127 (2016). This paper proposes using a black-box AI predictor to accelerate high-throughput screening of molecules in materials science.

Article   ADS   PubMed   Google Scholar  

Sadybekov, A. A. et al. Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601 , 452–459 (2022).

The NNPDF Collaboration Evidence for intrinsic charm quarks in the proton. Nature 606 , 483–487 (2022).

Graff, D. E., Shakhnovich, E. I. & Coley, C. W. Accelerating high-throughput virtual screening through molecular pool-based active learning. Chem. Sci. 12 , 7866–7881 (2021).

Janet, J. P. et al. Accurate multiobjective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization. ACS Cent. Sci. 6 , 513–524 (2020).

Bacon, F. Novum Organon Vol. 1620 (2000).

Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324 , 81–85 (2009).

Petersen, B. K. et al. Deep symbolic regression: recovering mathematical expressions from data via risk-seeking policy gradients. In International Conference on Learning Representations (2020).

Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37 , 1038–1040 (2019). This paper describes a reinforcement-learning algorithm for navigating molecular combinatorial spaces, and it validates generated molecules using wet-lab experiments.

Zhou, Z. et al. Optimization of molecules via deep reinforcement learning. Sci. Rep. 9 , 10752 (2019).

You, J. et al. Graph convolutional policy network for goal-directed molecular graph generation. In Advances in Neural Information Processing Systems 31 , 6412–6422 (2018).

Bengio, Y. et al. GFlowNet foundations. Preprint at https://arxiv.org/abs/2111.09266 (2021). This paper describes a generative flow network that generates objects by sampling them from a distribution optimized for drug design.

Jain, M. et al. Biological sequence design with GFlowNets. In International Conference on Machine Learning 9786–9801 (2022).

Malkin, N. et al. Trajectory balance: improved credit assignment in GFlowNets. In Advances in Neural Information Processing Systems 35 , 5955–5967 (2022).

Borkowski, O. et al. Large scale active-learning-guided exploration for in vitro protein production optimization. Nat. Commun. 11 , 1872 (2020). This study introduced a dynamic programming approach to determine the optimal locations and capacities of hydropower dams in the Amazon Basin, balancing between energy production and environmental impact .

Flecker, A. S. et al. Reducing adverse impacts of Amazon hydropower expansion. Science 375 , 753–760 (2022). This study introduced a dynamic programming approach to determine the optimal locations and capacities of hydropower dams in the Amazon basin, achieving a balance between the benefits of energy production and the potential environmental impacts.

Pion-Tonachini, L. et al. Learning from learning machines: a new generation of AI technology to meet the needs of science. Preprint at https://arxiv.org/abs/2111.13786 (2021).

Kusner, M. J., Paige, B. & Hernández-Lobato, J. M. Grammar variational autoencoder. In International Conference on Machine Learning 1945–1954 (2017). This paper describes a grammar variational autoencoder that generates novel symbolic laws and drug molecules.

Brunton, S. L., Proctor, J. L. & Kutz, J. N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. USA 113 , 3932–3937 (2016).

Article   ADS   MathSciNet   CAS   PubMed   PubMed Central   MATH   Google Scholar  

Liu, Z. & Tegmark, M. Machine learning hidden symmetries. Phys. Rev. Lett. 128 , 180201 (2022).

Article   ADS   MathSciNet   CAS   PubMed   Google Scholar  

Gabbard, H. et al. Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy. Nat. Phys. 18 , 112–117 (2022).

Chen, D. et al. Automating crystal-structure phase mapping by combining deep learning with constraint reasoning. Nat. Mach. Intell. 3 , 812–822 (2021).

Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4 , 268–276 (2018).

Anishchenko, I. et al. De novo protein design by deep network hallucination. Nature 600 , 547–552 (2021).

Fu, T. et al. Differentiable scaffolding tree for molecular optimization. In International Conference on Learning Representations (2021).

Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361 , 360–365 (2018).

Huang, K. et al. Therapeutics Data Commons: machine learning datasets and tasks for drug discovery and development. In NeurIPS Datasets and Benchmarks (2021). This study describes an initiative with open AI models, datasets and education programmes to facilitate advances in therapeutic science across all stages of drug discovery and development.

Dance, A. Lab hazard. Nature 458 , 664–665 (2009).

Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555 , 604–610 (2018). This paper describes an approach that combines deep neural networks with Monte Carlo tree search to plan chemical synthesis.

Gao, W., Raghavan, P. & Coley, C. W. Autonomous platforms for data-driven organic synthesis. Nat. Commun. 13 , 1075 (2022).

Kusne, A. G. et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat. Commun. 11 , 5966 (2020).

Gormley,A. J. & Webb, M. A. Machine learning in combinatorial polymer chemistry. Nat. Rev. Mater. 6 , 642–644 (2021).

Ament, S. et al. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. Sci. Adv. 7 , eabg4930 (2021).

Degrave, J. et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602 , 414–419 (2022). This paper describes an approach for controlling tokamak plasmas, using a reinforcement-learning agent to command-control coils and satisfy physical and operational constraints.

Melnikov, A. A. et al. Active learning machine learns to create new quantum experiments. Proc. Natl Acad. Sci. USA 115 , 1221–1226 (2018).

Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8 , 3192–3203 (2017).

Wang, D. et al. Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics. Nat. Comput. Sci. 2 , 20–29 (2022). This paper describes a neural network for reliable uncertainty estimations in molecular dynamics, enabling efficient sampling of high-dimensional free energy landscapes.

Wang, W. & Gómez-Bombarelli, R. Coarse-graining auto-encoders for molecular dynamics. npj Comput. Mater. 5 , 125 (2019).

Hermann, J., Schätzle, Z. & Noé, F. Deep-neural-network solution of the electronic Schrödinger equation. Nat. Chem. 12 , 891–897 (2020). This paper describes a method to learn the wavefunction of quantum systems using deep neural networks in conjunction with variational quantum Monte Carlo.

Carleo, G. & Troyer, M. Solving the quantum many-body problem with artificial neural networks. Science 355 , 602–606 (2017).

Em Karniadakis, G. et al. Physics-informed machine learning. Nat. Rev. Phys. 3 , 422–440 (2021).

Li, Z. et al. Physics-informed neural operator for learning partial differential equations. Preprint at https://arxiv.org/abs/2111.03794 (2021).

Kochkov, D. et al. Machine learning–accelerated computational fluid dynamics. Proc. Natl Acad. Sci. USA 118 , e2101784118 (2021). This paper describes an approach to accelerating computational fluid dynamics by training a neural network to interpolate from coarse to fine grids and generalize to varying forcing functions and Reynolds numbers.

Ji, W. et al. Stiff-PINN: physics-informed neural network for stiff chemical kinetics. J. Phys. Chem. A 125 , 8098–8106 (2021).

Smith, J. D., Azizzadenesheli, K. & Ross, Z. E. EikoNet: solving the Eikonal equation with deep neural networks. IEEE Trans. Geosci. Remote Sens. 59 , 10685–10696 (2020).

Waheed, U. B. et al. PINNeik: Eikonal solution using physics-informed neural networks. Comput. Geosci. 155 , 104833 (2021).

Chen, R. T. Q. et al. Neural ordinary differential equations. In Advances in Neural Information Processing Systems 31 , 6572–6583 (2018). This paper established a connection between neural networks and differential equations by introducing the adjoint method to learn continuous-time dynamical systems from data, replacing backpropagation.

Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378 , 686–707 (2019). This paper describes a deep-learning approach for solving forwards and inverse problems in nonlinear partial differential equations and can find solutions to differential equations from data.

Article   ADS   MathSciNet   MATH   Google Scholar  

Lu, L. et al. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3 , 218–229 (2021).

Brandstetter, J., Worrall, D. & Welling, M. Message passing neural PDE solvers. In International Conference on Learning Representations (2022).

Noé, F. et al. Boltzmann generators: sampling equilibrium states of many-body systems with deep learning. Science 365 , eaaw1147 (2019). This paper presents an efficient sampling algorithm using normalizing flows to simulate equilibrium states in many-body systems.

Rezende, D. & Mohamed, S. Variational inference with normalizing flows. In International Conference on Machine Learning 37 , 1530–1538, (2015).

Dinh, L., Sohl-Dickstein, J. & Bengio, S. Density estimation using real NVP. In International Conference on Learning Representations (2017).

Nicoli, K. A. et al. Estimation of thermodynamic observables in lattice field theories with deep generative models. Phys. Rev. Lett. 126 , 032001 (2021).

Kanwar, G. et al. Equivariant flow-based sampling for lattice gauge theory. Phys. Rev. Lett. 125 , 121601 (2020).

Gabrié, M., Rotskoff, G. M. & Vanden-Eijnden, E. Adaptive Monte Carlo augmented with normalizing flows. Proc. Natl Acad. Sci. USA 119 , e2109420119 (2022).

Article   MathSciNet   PubMed   PubMed Central   Google Scholar  

Jasra, A., Holmes, C. C. & Stephens, D. A. Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Stat. Sci. 20 , 50–67 (2005).

Bengio, Y. et al. Better mixing via deep representations. In International Conference on Machine Learning 552–560 (2013).

Pompe, E., Holmes, C. & Łatuszyński, K. A framework for adaptive MCMC targeting multimodal distributions. Ann. Stat. 48 , 2930–2952 (2020).

Article   MathSciNet   MATH   Google Scholar  

Townshend, R. J. L. et al. ATOM3D: tasks on molecules in three dimensions. In NeurIPS Datasets and Benchmarks (2021).

Kearnes, S. M. et al. The open reaction database. J. Am. Chem. Soc. 143 , 18820–18826 (2021).

Chanussot, L. et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catal. 11 , 6059–6072 (2021).

Brown, N. et al. GuacaMol: benchmarking models for de novo molecular design. J. Chem. Inf. Model. 59 , 1096–1108 (2019).

Notin, P. et al. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In International Conference on Machine Learning 16990–17017 (2022).

Mitchell, M. et al. Model cards for model reporting. In Conference on Fairness, Accountability, and Transparency 220–229 (2019).

Gebru, T. et al. Datasheets for datasets. Commun. ACM 64 , 86–92 (2021).

Bai, X. et al. Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nat. Mach. Intell. 3 , 1081–1089 (2021).

Warnat-Herresthal, S. et al. Swarm learning for decentralized and confidential clinical machine learning. Nature 594 , 265–270 (2021).

Hie, B., Cho, H. & Berger, B. Realizing private and practical pharmacological collaboration. Science 362 , 347–350 (2018).

Rohrbach, S. et al. Digitization and validation of a chemical synthesis literature database in the ChemPU. Science 377 , 172–180 (2022).

Gysi, D. M. et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc. Natl Acad. Sci. USA 118 , e2025581118 (2021).

King, R. D. et al. The automation of science. Science 324 , 85–89 (2009).

Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19 , 679–682 (2022).

Doerr, S. et al. TorchMD: a deep learning framework for molecular simulations. J. Chem. Theory Comput. 17 , 2355–2363 (2021).

Schoenholz, S. S. & Cubuk, E. D. JAX MD: a framework for differentiable physics. In Advances in Neural Information Processing Systems 33 , 11428–11441 (2020).

Peters, J., Janzing, D. & Schölkopf, B. Elements of Causal Inference: Foundations and Learning Algorithms (MIT Press, 2017).

Bengio, Y. et al. A meta-transfer objective for learning to disentangle causal mechanisms. In International Conference on Learning Representations (2020).

Schölkopf, B. et al. Toward causal representation learning. Proc. IEEE 109 , 612–634 (2021).

Goyal, A. & Bengio, Y. Inductive biases for deep learning of higher-level cognition. Proc. R. Soc. A 478 , 20210068 (2022).

Deleu, T. et al. Bayesian structure learning with generative flow networks. In Conference on Uncertainty in Artificial Intelligence 518–528 (2022).

Geirhos, R. et al. Shortcut learning in deep neural networks. Nat. Mach. Intell. 2 , 665–673 (2020).

Koh, P. W. et al. WILDS: a benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning 5637–5664 (2021).

Luo, Z. et al. Label efficient learning of transferable representations across domains and tasks. In Advances in Neural Information Processing Systems 30 , 165–177 (2017).

Mahmood, R. et al. How much more data do I need? estimating requirements for downstream tasks. In IEEE Conference on Computer Vision and Pattern Recognition 275–284 (2022).

Coley, C. W., Eyke, N. S. & Jensen, K. F. Autonomous discovery in the chemical sciences part II: outlook. Angew. Chem. Int. Ed. 59 , 23414–23436 (2020).

Gao, W. & Coley, C. W. The synthesizability of molecules proposed by generative models. J. Chem. Inf. Model. 60 , 5714–5723 (2020).

Kogler, R. et al. Jet substructure at the Large Hadron Collider. Rev. Mod. Phys. 91 , 045003 (2019).

Acosta, J. N. et al. Multimodal biomedical AI. Nat. Med. 28 , 1773–1784 (2022).

Alayrac, J.-B. et al. Flamingo: a visual language model for few-shot learning. In Advances in Neural Information Processing Systems 35 , 23716–23736 (2022).

Elmarakeby, H. A. et al. Biologically informed deep neural network for prostate cancer discovery. Nature 598 , 348–352 (2021).

Qin, Y. et al. A multi-scale map of cell structure fusing protein images and interactions. Nature 600 , 536–542 (2021).

Schaffer, L. V. & Ideker, T. Mapping the multiscale structure of biological systems. Cell Systems 12 , 622–635 (2021).

Stiglic, G. et al. Interpretability of machine learning-based prediction models in healthcare. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 10 , e1379 (2020).

Erion, G. et al. A cost-aware framework for the development of AI models for healthcare applications. Nat. Biomed. Eng. 6 , 1384–1398 (2022).

Lundberg, S. M. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2 , 749–760 (2018).

Sanders, L. M. et al. Beyond low Earth orbit: biological research, artificial intelligence, and self-driving labs. Preprint at https://arxiv.org/abs/2112.12582 (2021).

Gagne, D. J. II et al. Interpretable deep learning for spatial analysis of severe hailstorms. Mon. Weather Rev. 147 , 2827–2845 (2019).

Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1 , 206–215 (2019).

Koh, P. W. & Liang, P. Understanding black-box predictions via influence functions. In International Conference on Machine Learning 1885–1894 (2017).

Mirzasoleiman, B., Bilmes, J. & Leskovec, J. Coresets for data-efficient training of machine learning models. In International Conference on Machine Learning 6950–6960 (2020).

Kim, B. et al. Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In International Conference on Machine Learning 2668–2677 (2018).

Silver, D. et al. Mastering the game of go without human knowledge. Nature 550 , 354–359 (2017).

Baum, Z. J. et al. Artificial intelligence in chemistry: current trends and future directions. J. Chem. Inf. Model. 61 , 3197–3212 (2021).

Finlayson, S. G. et al. Adversarial attacks on medical machine learning. Science 363 , 1287–1289 (2019).

Urbina, F. et al. Dual use of artificial-intelligence-powered drug discovery. Nat. Mach. Intell. 4 , 189–191 (2022).

Norgeot, B. et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat. Med. 26 , 1320–1324 (2020).

Download references

Acknowledgements

M.Z. gratefully acknowledges the support of the National Institutes of Health under R01HD108794, U.S. Air Force under FA8702-15-D-0001, awards from Harvard Data Science Initiative, Amazon Faculty Research, Google Research Scholar Program, Bayer Early Excellence in Science, AstraZeneca Research, Roche Alliance with Distinguished Scientists, and Kempner Institute for the Study of Natural and Artificial Intelligence. C.P.G. and Y.D. acknowledge the support from the U.S. Air Force Office of Scientific Research under Multidisciplinary University Research Initiatives Program (MURI) FA9550-18-1-0136, Defense University Research Instrumentation Program (DURIP) FA9550-21-1-0316, and awards from Scientific Autonomous Reasoning Agent (SARA), and AI for Discovery Assistant (AIDA). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. We thank D. Hassabis, A. Davies, S. Mohamed, Z. Li, K. Ma, Z. Qiao, E. Weinstein, A. V. Weller, Y. Zhong and A. M. Brandt for discussions on the paper.

Author information

Hanchen Wang

Present address: Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA

Present address: Department of Computer Science, Stanford University, Stanford, CA, USA

These authors contributed equally: Hanchen Wang, Tianfan Fu, Yuanqi Du

Authors and Affiliations

Department of Engineering, University of Cambridge, Cambridge, UK

Hanchen Wang & Joan Lasenby

Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

Hanchen Wang & Anima Anandkumar

Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Department of Computer Science, Cornell University, Ithaca, NY, USA

Yuanqi Du & Carla P. Gomes

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Wenhao Gao & Connor W. Coley

Department of Computer Science, Stanford University, Stanford, CA, USA

Kexin Huang & Jure Leskovec

Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA

Payal Chandak

Mila – Quebec AI Institute, Montreal, Quebec, Canada

Shengchao Liu, Andreea Deac, Jian Tang & Yoshua Bengio

Université de Montréal, Montreal, Quebec, Canada

Shengchao Liu, Andreea Deac & Yoshua Bengio

Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA

Peter Van Katwyk & Karianne Bergen

Data Science Institute, Brown University, Providence, RI, USA

NVIDIA, Santa Clara, CA, USA

Anima Anandkumar

Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA

Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA

Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA

Department of Physics and Center for Data Science, New York University, New York, NY, USA

Google DeepMind, London, UK

Pushmeet Kohli & Petar Veličković

Microsoft Research, Beijing, China

Tie-Yan Liu

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Arjun Manrai & Marinka Zitnik

Department of Systems Biology, Harvard Medical School, Boston, MA, USA

Debora Marks

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Debora Marks & Marinka Zitnik

Deep Forest Sciences, Palo Alto, CA, USA

Bharath Ramsundar

BioMap, Beijing, China

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

University of Illinois at Urbana-Champaign, Champaign, IL, USA

HEC Montréal, Montreal, Quebec, Canada

CIFAR AI Chair, Toronto, Ontario, Canada

Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

Petar Veličković

University of Amsterdam, Amsterdam, Netherlands

Max Welling

Microsoft Research Amsterdam, Amsterdam, Netherlands

DP Technology, Beijing, China

Linfeng Zhang

AI for Science Institute, Beijing, China

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Connor W. Coley

Harvard Data Science Initiative, Cambridge, MA, USA

Marinka Zitnik

Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the design and writing of the paper, helped shape the research, provided critical feedback, and commented on the paper and its revisions. H.W., T.F., Y.D. and M.Z conceived the study and were responsible for overall direction and planning. W.G., K.H. and Z.L. contributed equally to this work (equal second authorship) and are listed alphabetically.

Corresponding author

Correspondence to Marinka Zitnik .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Brian Gallagher and Benjamin Nachman for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Wang, H., Fu, T., Du, Y. et al. Scientific discovery in the age of artificial intelligence. Nature 620 , 47–60 (2023). https://doi.org/10.1038/s41586-023-06221-2

Download citation

Received : 30 March 2022

Accepted : 16 May 2023

Published : 02 August 2023

Issue Date : 03 August 2023

DOI : https://doi.org/10.1038/s41586-023-06221-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies.

• Renan Gonçalves Leonel da Silva

Globalization and Health (2024)

Embracing data science in catalysis research

• Manu Suvarna
• Javier Pérez-Ramírez

Nature Catalysis (2024)

Artificial intelligence to predict soil temperatures by development of novel model

• Lakindu Mampitiya
• Kenjabek Rozumbetov
• Upaka Rathnayake

Scientific Reports (2024)

Machine learning for micro- and nanorobots

• Lidong Yang
• Jialin Jiang

Nature Machine Intelligence (2024)

Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules

• Joonbum Lee
• Junhee Seok

Scientific Data (2024)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

All About Science

10 Greatest Scientific Discoveries and Inventions of 21st Century

For the past centuries, there have been countless developments and advancements in the world. Scientists and researchers have continued to discover new things and expand our understanding and knowledge of the natural phenomena happening around us.

In the 21st century , there are thousands of scientific breakthroughs. These have helped in improving our way of living while some are the key to greater innovation in the future.

In this article, we ranked the greatest scientific discoveries and inventions of the 21st century.

Detection of Gravitational Waves

Scientists considered this the greatest discovery of the 21st century . Let us go back to the time when Albert Einstein first predicted in his theory of relativity that time travel will be possible. Now, it has been proven by the recent findings. The LIGO project based in the United States has detected gravitational waves that could allow scientists to develop a time machine and travel to the earliest and darkest parts of the universe. This was the first time that they witnessed the “ripples in the fabric of space-time.”

Evidence of Water on Mars

The National Aeronautics and Space Administration confirmed last September 2015 that there is evidence proving the existence of liquid water on Mars. Using the imaging spectrometer of NASA’s Mars Reconnaissance Orbiter (MRO), scientists detected hydrated salts in different locations on Mars. During the warm season, the hydrated salts darken and flow down steep. However, they fade in cooler seasons. The detection of hydrated salts means that water plays a vital role in their formation.

Robotic Body Parts

Through the help of  biomechanics  and engineering, scientists have devised robotic body parts. The University of Twente has developed robotic arms that can aid those individuals affected by Duchenne muscular dystrophy. This will allow patients to amplify residual function in the arm. They also applied Darpa’s Revolutionizing Prosthetics project of creating prosthetics to wounded US military personnel, in developing robotic limbs. Today, scientists are studying the viability of making these robotic body parts or exoskeletons controlled by the mind to help disabled individuals, survivors of stroke, and elderly people.

T. Rex Tissue

Paleontologists have discovered a partially fossilized and decomposing femur of a Tyrannosaurus rex which was believed to be 70 million years old already or a date closer to the biblical date of creation. Mary Higby Schweitzer of North Carolina State University and Montana State University found out flexible and transparent vessels. This soft tissue discovered is preserved because of the iron between the leg bones. The T.Rex tissue is very essential in determining the physiology of dinosaurs and studying their cellular and molecular structures. They have found out that dinosaurs are closely related to big birds, like the ostrich.

Advancement in HIV Cure

According to HIV.gov, there are over 36.7 million people worldwide living with HIV/AIDS, of which 1.8 million it is children. HIV/AIDS remains to be one of the deadliest diseases in the world. On the other hand, HIV treatment has been available in Germany for more than two decades already. Antiretroviral therapy allows HIV/AIDS patients to live longer. However, no definite cure is still discovered. In 2007, Dr. GeroHütter was the first one to successfully cure an HIV/AIDS patient named Timothy Ray Brown by transplanting bone marrow from an HIV-immune patient.

Existence of Dark Matter

In 2006, a team of researchers has found evidence that proves the existence of dark matter. They inferred the presence of dark matter by measuring the bullet clusters or the location of mass in the collision of galaxies. According to Maxim Markevitch of the Harvard-Smithsonian Center for Astrophysics in Cambridge, dark matter can be proven by the bulk of visible matter in the clusters that have been disconnected from the rest of the mass. According to NASA, it is still a complete mystery. What they can prove for now is that 68% of the universe is composed of dark energy.

Sequencing Genome of Cancer Patient

In 2003, scientists completed the sequencing of the human genome or genetic blueprint that points out the mutations leading to cancer. It took three years for them to finish drafting the three billion letters that compose the human DNA. The Human Genome Project helped scientists in treating a deadly type of skin cancer and understanding the genes involved in leukemia, eczema, and diabetes. Now, cancer genome sequencing is integrated into medical care facilities. It characterizes and identifies DNA or RNA sequences of cancer cells.

Creation of Human Organs

Stem Cell research has paved the way to greater access to organs, instead of waiting for donors or taking harsh medications. Scientists from Massachusetts General Hospital and Harvard Medical School have discovered how to regenerate the function of human heart tissue through adult skin cells. Through stem cells, humans can grow another organ. This is associated with the regenerative nature of living organisms. Recently, various research all around the world enables the growth of fallopian tubes, the heart, the brain, lung, and kidneys, among others through stem cells.

Water as Fuel

German Cleantech Company has developed a futuristic machine that converts water into fuel.  Through Power-to-Liquid Technology, they can convert water and carbon dioxide into liquid hydrocarbons which take the form of synthetic diesel, petrol, and kerosene. This technology was based on the Fischer-Tropsch process and solid oxide electrolyzer cells (SOECs) which convert electricity to steam. In 2017, Joint Center for Artificial Photosynthesis (JCAP) and Berkeley Lab’s Materials Project also devised a technology that turns sunlight, water, and carbon dioxide into fuel which can be a viable source of power, replacing coal, oil, and other fossil fuels.

Face Transplants

A face transplant is a medical procedure that replaces a person’s face using the tissues of a dead person. In 2005, Isabelle Dinoire of France was the first person to have a partial face transplant while the first full-face transplant happened in Spain in 2010. Face transplants have been popularly carried out in the United States, Spain, France, and Turkey. This is applicable for people with birth defects or disfigures caused by burns, disease, and trauma.

• latest-news

Recent Posts

• Why Technology Is Important In Our Daily Life?
• The Evolution and Importance of Science Education in the 21st Century
• Role of Science and Technology in the 21st Century
• The role of science in the 21st century
• The Greatest Scientists of the 21st Century
• February 2024
• December 2023
• November 2023
• October 2023
• September 2023
• January 2023
• August 2022
• October 2021
• September 2021
• August 2021
• November 2018

About Us !!!

Times are changing. In the earlier days, we used to go to the library, today we search and archive our papers online. We have collaborations per email, hold telephone seminars, organize virtual networks, write blogs, and make our seminars available on the internet.

Popular Posts

© 2024 Science 21st Century

Theme by Anders Noren — Up ↑

• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• Games & Quizzes
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center
• Introduction & Top Questions

What is the Scientific Revolution?

How is the scientific revolution connected to the enlightenment, what did the scientific revolution lead to.

• Why is Nicolaus Copernicus famous?
• What did Nicolaus Copernicus do for a living?

Scientific Revolution

Our editors will review what you’ve submitted and determine whether to revise the article.

• Stanford Encyclopedia of Philosophy - Scientific Revolutions
• World History Encyclopedia - Scientific Revolution
• Khan Academy - The Scientific Revolution
• Historic UK - The Scientific Revolution
• National Center for Biotechnology Information - PubMed Central - Quantifying the scientific revolution
• Academia - The Scientific Revolution
• Pressbooks Create - A Brief History of the World Since 1500 - Making Connections between Ideas: The Scientific Revolution
• Humanities LibreTexts - Scientific Revolution
• Table Of Contents

Scientific Revolution is the name given to a period of drastic change in scientific thought that took place during the 16th and 17th centuries. It replaced the Greek view of nature that had dominated science for almost 2,000 years. The Scientific Revolution was characterized by an emphasis on abstract reasoning, quantitative thought, an understanding of how nature works, the view of nature as a machine , and the development of an experimental scientific method .

The Enlightenment , like the Scientific Revolution, began in Europe . Taking place during the 17th and 18th centuries, this intellectual movement synthesized ideas concerning God, reason, nature, and humanity into a worldview that celebrated reason. This emphasis on reason grew out of discoveries made by prominent thinkers—including the astronomy of Nicolaus Copernicus and Galileo , the philosophy of René Descartes , and the physics and cosmology of Isaac Newton —many of whom preceded the Enlightenment.

The sudden emergence of new information during the Scientific Revolution called into question religious beliefs, moral principles, and the traditional scheme of nature. It also strained old institutions and practices, necessitating new ways of communicating and disseminating information. Prominent innovations included scientific societies (which were created to discuss and validate new discoveries) and scientific papers (which were developed as tools to communicate new information comprehensibly and test the discoveries and hypotheses made by their authors).

Scientific Revolution , drastic change in scientific thought that took place during the 16th and 17th centuries. A new view of nature emerged during the Scientific Revolution, replacing the Greek view that had dominated science for almost 2,000 years. Science became an autonomous discipline , distinct from both philosophy and technology , and it came to be regarded as having utilitarian goals. By the end of this period, it may not be too much to say that science had replaced Christianity as the focal point of European civilization. Out of the ferment of the Renaissance and Reformation there arose a new view of science, bringing about the following transformations: the reeducation of common sense in favour of abstract reasoning; the substitution of a quantitative for a qualitative view of nature; the view of nature as a machine rather than as an organism; the development of an experimental, scientific method that sought definite answers to certain limited questions couched in the framework of specific theories; and the acceptance of new criteria for explanation, stressing the “how” rather than the “why” that had characterized the Aristotelian search for final causes.

The growing flood of information that resulted from the Scientific Revolution put heavy strains upon old institutions and practices. It was no longer sufficient to publish scientific results in an expensive book that few could buy; information had to be spread widely and rapidly. Natural philosophers had to be sure of their data, and to that end they required independent and critical confirmation of their discoveries. New means were created to accomplish these ends. Scientific societies sprang up, beginning in Italy in the early years of the 17th century and culminating in the two great national scientific societies that mark the zenith of the Scientific Revolution: the Royal Society of London for Improving Natural Knowledge , created by royal charter in 1662, and the Académie des Sciences of Paris, formed in 1666. In these societies and others like them all over the world, natural philosophers could gather to examine, discuss, and criticize new discoveries and old theories. To provide a firm basis for these discussions, societies began to publish scientific papers. The old practice of hiding new discoveries in private jargon, obscure language, or even anagrams gradually gave way to the ideal of universal comprehensibility. New canons of reporting were devised so that experiments and discoveries could be reproduced by others. This required new precision in language and a willingness to share experimental or observational methods. The failure of others to reproduce results cast serious doubts upon the original reports. Thus were created the tools for a massive assault on nature’s secrets.

The Scientific Revolution began in astronomy. Although there had been earlier discussions of the possibility of Earth’s motion, the Polish astronomer Nicolaus Copernicus was the first to propound a comprehensive heliocentric theory equal in scope and predictive capability to Ptolemy’s geocentric system . Motivated by the desire to satisfy Plato’s dictum, Copernicus was led to overthrow traditional astronomy because of its alleged violation of the principle of uniform circular motion and its lack of unity and harmony as a system of the world. Relying on virtually the same data as Ptolemy had possessed, Copernicus turned the world inside out, putting the Sun at the centre and setting Earth into motion around it. Copernicus’s theory , published in 1543, possessed a qualitative simplicity that Ptolemaic astronomy appeared to lack. To achieve comparable levels of quantitative precision, however, the new system became just as complex as the old. Perhaps the most revolutionary aspect of Copernican astronomy lay in Copernicus’s attitude toward the reality of his theory. In contrast to Platonic instrumentalism , Copernicus asserted that to be satisfactory astronomy must describe the real, physical system of the world.

The reception of Copernican astronomy amounted to victory by infiltration. By the time large-scale opposition to the theory had developed in the church and elsewhere, most of the best professional astronomers had found some aspect or other of the new system indispensable. Copernicus’s book De revolutionibus orbium coelestium libri VI (“Six Books Concerning the Revolutions of the Heavenly Orbs”), published in 1543, became a standard reference for advanced problems in astronomical research, particularly for its mathematical techniques. Thus, it was widely read by mathematical astronomers, in spite of its central cosmological hypothesis , which was widely ignored. In 1551 the German astronomer Erasmus Reinhold published the Tabulae prutenicae (“Prutenic Tables”), computed by Copernican methods. The tables were more accurate and more up-to-date than their 13th-century predecessor and became indispensable to both astronomers and astrologers.

During the 16th century the Danish astronomer Tycho Brahe , rejecting both the Ptolemaic and Copernican systems, was responsible for major changes in observation, unwittingly providing the data that ultimately decided the argument in favour of the new astronomy. Using larger, stabler, and better calibrated instruments, he observed regularly over extended periods, thereby obtaining a continuity of observations that were accurate for planets to within about one minute of arc—several times better than any previous observation. Several of Tycho’s observations contradicted Aristotle’s system: a nova that appeared in 1572 exhibited no parallax (meaning that it lay at a very great distance) and was thus not of the sublunary sphere and therefore contrary to the Aristotelian assertion of the immutability of the heavens; similarly, a succession of comets appeared to be moving freely through a region that was supposed to be filled with solid, crystalline spheres. Tycho devised his own world system —a modification of Heracleides’ —to avoid various undesirable implications of the Ptolemaic and Copernican systems.

At the beginning of the 17th century, the German astronomer Johannes Kepler placed the Copernican hypothesis on firm astronomical footing. Converted to the new astronomy as a student and deeply motivated by a neo- Pythagorean desire for finding the mathematical principles of order and harmony according to which God had constructed the world, Kepler spent his life looking for simple mathematical relationships that described planetary motions. His painstaking search for the real order of the universe forced him finally to abandon the Platonic ideal of uniform circular motion in his search for a physical basis for the motions of the heavens.

In 1609 Kepler announced two new planetary laws derived from Tycho’s data: (1) the planets travel around the Sun in elliptical orbits , one focus of the ellipse being occupied by the Sun; and (2) a planet moves in its orbit in such a manner that a line drawn from the planet to the Sun always sweeps out equal areas in equal times. With these two laws, Kepler abandoned uniform circular motion of the planets on their spheres, thus raising the fundamental physical question of what holds the planets in their orbits. He attempted to provide a physical basis for the planetary motions by means of a force analogous to the magnetic force , the qualitative properties of which had been recently described in England by William Gilbert in his influential treatise , De Magnete, Magneticisque Corporibus et de Magno Magnete Tellure (1600; “On the Magnet, Magnetic Bodies, and the Great Magnet of the Earth”). The impending marriage of astronomy and physics had been announced. In 1618 Kepler stated his third law, which was one of many laws concerned with the harmonies of the planetary motions: (3) the square of the period in which a planet orbits the Sun is proportional to the cube of its mean distance from the Sun.

A powerful blow was dealt to traditional cosmology by Galileo Galilei , who early in the 17th century used the telescope , a recent invention of Dutch lens grinders, to look toward the heavens. In 1610 Galileo announced observations that contradicted many traditional cosmological assumptions. He observed that the Moon is not a smooth, polished surface, as Aristotle had claimed, but that it is jagged and mountainous. Earthshine on the Moon revealed that Earth, like the other planets, shines by reflected light. Like Earth, Jupiter was observed to have satellites; hence, Earth had been demoted from its unique position. The phases of Venus proved that that planet orbits the Sun, not Earth.

Winning Essays

[vc_row][vc_column width=”1/1″][vc_button2 title=”Go back” style=”square” color=”sky” size=”sm” link=”url:http%3A%2F%2Fwww.yalescientific.org%2Fsynapse%2Fcontest-winners%2F|title:Contest%20Winners|”][vc_empty_space height=”32px”][vc_column_text css_animation=”top-to-bottom” el_class=”winning-essays”]

Congratulations to the winner of the 2019 Yale Scientific Synapse High School Essay Contest!

This year’s essay prompt was:

There is a moment that defines success, that “ah-ha” moment when the barrier of your expectations of what is possible to achieve is shattered. Yet, for every Nobel Prize success story or every innovation that is deemed media frenzy worthy, there are hundreds of breakthroughs that go unnoticed by the general public. Choose an important but under-discussed breakthrough from the past 5 years, and describe why it is so significant.

Entangled in a Quantum Future

1st Place Winner, Yale Scientific Magazine National Essay Competition 2019 Kelvin Kim Bergen Catholic High School, Oradell, NJ

The rate of discovery in science has accelerated dramatically since the 20th century. This should not be surprising since our knowledge base doubles approximately every 13 months. Some scientists even predict that the “internet of things” will lead to even more dramatic accelerations. Many of these advancements have gained widespread recognition while others are relatively unknown to the general public.

For example, Chinese researchers at Shanghai’s University of Science and Technology made advances on data teleportation based on quantum entanglement but remained underrecognized. In 2017, this team, led by Ji-Gang Ren, shattered previous distance records for such teleportation experiments. The previous record, set in 2015, achieved successful transmissions using 104 kilometers of superconducting molybdenum silicide fiber. Firing a high-altitude laser from Tibet to the orbiting Micius satellite, the Chinese team achieved successful transmissions over distances up to 1400 kilometers. Later, they successfully transmitted quantum data from the satellite back to Earth at distances ranging from 1600 to 2400 kilometers. In doing so, they demonstrated the viability of someday being able to create a “quantum internet,” over which information could be exchanged far more securely than is possible today.

The phrase quantum teleportation is somewhat misleading. In the Chinese experiments, no particles were physically teleported from Earth to space like most people might imagine after watching sci-fi programs like Star Trek . “Quantum teleportation” involves information, not matter. To grasp this, we need to understand the basic nature of quantum entanglement.

Quantum entanglement is a way of describing two particles with matching quantum states. The states in question, of which there are four possibilities, have to do with vertical or horizontal polarization. The entangled particles are linked in such a way as to mutually influence one another. Moreover, when one particle is observed, information about the other can be known. These effects hold true even if the entangled particles are separated by great distances.

Dr. Chien-Shiung Wu first experimentally demonstrated quantum entanglement in a laboratory, showing an Einstein-type correlation between two photons that were well separated from one another. Back then, all she could do was show correlations between entangled photons separated by a small distance. The experiment conducted by Dr. Ren’s team in 2017 is fundamentally the same as the experiment that was conducted by Dr. Wu almost seventy years ago. However, the Chinese researchers’ achievement is significant because they strove to do what Dr. Wu did at a far greater scale. Instead of performing the experiment in a laboratory, the Chinese physicists demonstrated entanglement between a photon on Earth and a photon on an orbiting satellite. These particles were separated by distances of at least 500 kilometers—the greatest distances that quantum entanglement have ever been recorded. This accomplishment was all the more impressive as it was achieved using detectors on a satellite that was traveling around Earth at orbital speeds.

Quantum entanglement means that data can seemingly be “teleported” since the information about one of the particles in an entangled pair will always reflect information relevant to the other particle. This is the main concept behind the potential applications being investigated by scientists. While nothing may be physically teleported, the fact that information about an object can be accessed instantaneously from anywhere has significant implications for the future.

One potential application of this concept is the quantum internet. The researchers showed that working with entangled particles while they are separated and moving at fast speeds is possible. This could provide a means of ensuring data security. Since the mere act of observing a particle changes its quantum properties, recipients of information over a quantum network could instantly know, by comparing the state of the paired particle at the point of transmission to that of its partner at the point of reception, not only if a message had been decrypted, but even if it had been merely observed. To this end, the Chinese scientists—in collaboration with European partners at the University of Vienna and the Austrian Academy of Sciences—aim to establish a secure quantum-encrypted channel by next year, and a global network in the following decade.

It is not surprising that the first practical applications of quantum entanglement are expected to appear in the realm of cyber-security. The regular internet is vulnerable to hacking because data still flows through cables in the form of bits, into which the hacker can tap and decrypt. A bit can either represent a zero or a one, but not both at the same time. The quantum internet, on the other hand, doesn’t have this problem because it utilizes qubits, a quantum state a particle is in when it represents both zero and one simultaneously. If a hacker tried to access a stream of qubits, the qubits would seem to have values that are either zero or one, but not both. This means that by trying to access information in the stream of qubits, the hacker would just end up destroying the data he is trying to hack.

Beyond this, the term “quantum internet” doesn’t actually have a clear definition. “Quantum internet is still a vague term,” explains physicist Thomas Jennewein of the University of Waterloo.

In summary, the research being conducted by Dr. Ren, his colleagues, and their European partners on data teleportation via quantum entanglement is significant because it represents the scaling-up of this technology to the point where its practical application is imminent. Before 2017, no previous experiments in this field had been done over comparable distances with such reliable results. The fact that global partners are planning to establish secure quantum channels based on these experiments in the near future ensures not only that such networks will soon be a global reality, but also that scientists will be delving ever deeper into the mystery of quantum entanglement. This research places humanity on the threshold of a new world of quantum applications that we can scarcely imagine today.

Congratulations to the winners of the 2018 Yale Scientific Synapse High School Essay Contest!

A Plantastic Solution to an Aqueous Problem

By John Lin

Water covers about 71 percent of Earth’s surface, but throughout the world, this natural resource appears to be drying up.1​ ​Due to global warming, desertification is rapidly spreading across the world. The world is finding that critical freshwater reserves are disappearing in the face of increasing population growth.2​ ​Just as more water is needed, less water is available. However, cacti have dealt with this problem for millennia and have adapted to arid climates. We can learn from these prickly plants to solve one of the world’s most pressing problems.

Our current stopgap measures are failing. Most modern water storage methods use jerry cans, lidded buckets, and clay pots but require backbreaking labor that is predominantly done by females.3​ ​UNICEF estimates that across the world, women and girls spend 200 million hours collecting water each day, forcing them to abandon their education and employment and enter a cycle of poverty and dependence.4​ ​Additionally, this water is often dirty, resulting in major waterborne disease outbreaks that devastate developing nations, Finally, these buckets require a tradeoff between water supplies, temperature, and sanitation. For example, clay pots lose water to evaporation but are cooler.5​ ​On the other hand, buckets create a warm environment ripe for bacteria growth.

Instead of using costly chemical reactions to synthesize hydrogen and oxygen, scientists can find a cheap solution in biomimicry. Succulent plants are uniquely adapted to absorb and retain water from their arid surroundings. Learning from them will help us efficiently deal with desertification and minimize water conflicts. Cacti are among the most effective succulents, surviving in habitats from the Atacama Desert to the Patagonian steppe.6​ ​Semiarid and arid areas experience varying levels of rainfall, demanding different tissue thicknesses and structural designs. We should study cacti to produce location-specific containers that can absorb and store safe water at optimal temperatures.

Scientists should explore water retrieval methods including cacti’s water absorption. Cacti build shallow roots that can branch out, allowing them to react quickly to rainfall.7​ ​We can utilize capillary action, much like plant roots, to gather water at a cheap energy cost. Researchers at the Chinese Academy of the Sciences are studying artificial root systems that could store rainwater.8​ ​Some cacti also store fog water, thanks to spines that collect water molecules. Scientists from Beihang University are already developing similar structures by electrospinning polyimide and polystyrene.9​ ​Moreover, this could help improve filtration systems. Dr. Norma Alcantar from the University of South Florida found that prickly pear cactus gum effectively removes sediment and bacteria from water.1​ 0​ We could eliminate common diseases, free women to pursue studies, leisure, or careers, and save millions of lives.

Researchers can also improve water storage by focusing on cacti because of their high water retention. Because of their fleshy tissue, many cacti can hold large amounts of water. In fact, Charles Gritzner, Distinguished Professor Emeritus of Geography at South Dakota State University, notes that some can store up to 2 tons of water, or 1,800 liters.1​ 1​ We can learn from their thick structures to maximize the quantity of water stored. Cacti also have unique structural designs including protective hair to deflect sunlight, which defends against dangerous heat levels.1​ 2​ Cacti have additionally developed waxy skin to prevent water loss.1​ 3​ We can combine this with biodegradable material to promote environmental sustainability by avoiding plastic. These innovations fix the current temperature-water loss tradeoff and maximize utility.

This large, bulky bucket would be incredibly adaptable. In foggier areas like the Atacama Desert, artificial spines would help collect water, while mechanical roots would work better in drier places. The layer of gum-like lining on the inner walls of the pail would improve sanitation. The water would be protected from heat through intricate designs of folds and hair. The outer waxy coating would help preserve water while maintaining cooler temperatures. Humanitarian organizations could distribute this in developing nations, ensuring that each family has a stable, safe source of water.

The consequences of ignoring water shortages are dire because water is the most precious resource of life. Not only is approximately 60 percent of the adult human body made of water, each American uses around 80-100 gallons of water every day.1​ 4,15​ This has promoted hygiene and eliminated disease outbreaks, with handwashing alone reducing diarrheal disease-related deaths by almost 50%.1​ 6​ With antibiotic-resistant bacteria developing rapidly, hygiene is critical for public health. Water is also heavily used in food production, irrigating 62.4 million acres of American cropland in 2010.1​ 7​ Agriculture accounts for 70% of freshwater withdrawals each year.1​ 8​ As global warming intensifies regional climates, more water is needed. Otherwise, the world would be torn apart by hunger and thirst.

Losing water will also have major geopolitical implications. The World Economic Forum has ranked water crises among the five most impactful global issues for the past four years.1​ 9​ As countries compete for an ever-shrinking supply of water, wars are bound to break out. The Global Policy Forum predicts that more than 50 countries across five continents will likely be forced into water conflicts.2​ 0​ Already, nuclear armed states such as India and Pakistan engage in water fights.2​ 1​ The resulting wars could claim billions of innocent human lives.

Although more advanced technology is being developed, biomimicry provides a cheap, clean, and quick answer to the billions of people surviving on inadequate and unsafe water. Unless we take action, water wars, food shortages, and disease outbreaks will tear the world apart. For the sake of humanity’s survival, we must turn to cacti to guide our water foraging efforts in the developing world.

Congratulations to the winners of the 2017 Yale Scientific Synapse High School Essay Contest!

If Science were to make a huge breakthrough in the next year, what do you think would be the most beneficial one to society? Why?

Breaking Through Ocean Acidification

1st Place Winner, Yale Scientific Magazine National Essay Competition 2017 Clara Benadon Poolesville High School, MD

As a Marylander, one of my favorite things to do is make the trek up to the Chesapeake Bay. Its sparkling waters and abundant wildlife set it apart as a prime jewel of the East Coast. Nothing can compare to the experience of paddling down the Potomac River on a sunny day, the boughs of a sycamore arching overhead.

Apart from being a stunner, the Bay provides major cultural and economic benefits. Its unique way of life is perfectly encapsulated in the small towns of Smith Island, where watermen make a living from the estuary’s riches. On a recent visit, one local said to me, “We truly build our lives around the water.” From the local fisherman to larger commercial operations, the Chesapeake provides $3.39 billion annually in seafood sales alone, part of a total economic value topping $1 trillion. The stability of these waters is endangered by the growing problem of ocean acidification. This occurs when the carbon dioxide in the atmosphere is absorbed into bodies of water, causing surging acidity levels. Acidification leads to the protective carbonate coverings of shellfish to disintegrate, killing off large amounts of oysters, mussels, and scallops. Oyster reefs filter the Bay; without a thriving population, harmful pollutants run rampant. The low oxygen conditions caused by high acidity also make it hard for fish to breathe. Even with survivable oxygen levels, low pH can be fatal for fish.

The plummeting numbers of these Chesapeake staples make a dent on the economy. According to the Chesapeake Bay Foundation, Maryland and Virginia have suffered losses exceeding $4 billion over the last three decades stemming from the decline of oyster health and distribution. High acidity causes oysters’ growth to be stunted, so that shellfish fisheries cannot profit from the smaller, thinner shells.

The losses aren’t economic alone. An estimated 2,700 species call the Bay their home, a remarkable level of biodiversity that is threatened by ocean acidification. The loss of even one species causes a ripple effect through the entire food web, sending it into a state of unbalance.  According to a 2004 study in Science, the survival of threatened and nonthreatened species is closely intertwined: when an endangered species goes extinct, dependent ones suffer. Moreover, biodiversity keeps in check the amount of carbon dioxide in any body of water. Zoom out from the Chesapeake to the world ocean. Skyrocketing acidity is present in almost every aquatic biome on our planet. When pH is low, coral reefs cannot absorb the calcium carbonate that makes up their skeleton. Corals, along with snails, clams, and urchins, disintegrate en masse. A particularly disturbing image of ocean acidification is its effect on the neurology of fish. Their decision making skills are significantly delayed to the level where they sometimes swim directly into the jaws of predators.

Economically, the UN estimates that ocean acidification will take a $1 trillion bite out of the world economy by the year 2100. This massive cost has direct human implications, including health, job security, and cultural heritage. In addition, the economies of many countries are wholly dependent upon reef based tourism and other activities built around the water.

We need a solution to our world’s rapidly acidifying oceans. If science were to make a major breakthrough, solving this problem would be beneficial to our economy and ecology on an unprecedented scale. Methods that at first appeared brilliant have either been limited by their feasibility or come to be outweighed by their negative side effects, ultimately prolonging the search for a solution.

The unorthodox method of dumping enormous amounts of iron sulphate into the water is based on the principle that iron fertilizes phytoplankton, microscopic organisms found in every body of water. The energy phytoplankton gain from the iron allows them to bloom, absorbing CO 2 from the atmosphere and the ocean. When the phytoplankton die they sink to the bottom of the ocean, locking the CO 2 there for centuries. In 1988, the late oceanographer John Martin proclaimed, “Give me a half tanker of iron, and I will give you an ice age.” It is theorized that fertilizing 2% of the Southern Ocean could set back global warming by 10 years.

Why not implement this magic fix? First off, iron fertilization has come under fire for its negative side effects. A 2016 study in Nature determined that the planktonic blooms would deplete the waters of necessary nutrients. Additionally, when the large bloom dies, it would create large “dead zones,” areas devoid of oxygen and life. Side effects aside, this technique may be entirely ineffective. Carbon dioxide may simply move up the food chain when the phytoplankton are eaten and be respired back into the water. This was observed when the 2009 Lohafex expedition unloaded six tons of iron off the Southern Atlantic. The desired phytoplankton bloom it caused was promptly gobbled up by miniscule organisms known as copepods.

The alternative solution of planting kelp is less drastic. Revitalizing expansive forests of algae has proven to be effective in sucking up underwater CO 2 . Kelp grows as quickly as 18 inches a day, and once established offers the added benefits of providing a habitat for marine species and removing anthropogenic nutrient pollution. Researchers from the Puget Sound Restoration Fund, who have been monitoring the capability of this process, have found that kelp forests are effective at diminishing acidification on a local scale. While planting carbonsucking species across the ocean would not be a feasible global solution, kelp forests could help solve the acidification crises found in less expansive areas.

To date, there is not one straightforward fix to combat ocean acidification and its corrosive effects. If a scientific breakthrough were to occur, it would perhaps be comprised of a combination of methods. However, as science and technology continuously evolve, the key to deacidifying our oceans may well turn out to be something beyond our wildest dreams.

A Revolutionary Combatant to Global Warming

2nd Place Winner, Yale Scientific Magazine National Essay Competition 2017 Arjun Marwaha Fairmont Schools, Anaheim CA

Accelerated industrialization and incredible innovation by the human species has completely morphed our 4.54 billion year-old planetary home in just a few centuries. Through feats of agriculture and language, humans have profoundly suggested superiority over all domains that dwell on Earth. Just recently, the culmination of human capability appears evident; through scientific means such as CRISPR’s gene splicing technique and Elon Musk’s inconceivable vision to send people around the moon, humanity is on the verge of a new creation: a feasible “dominance” over our galaxy.

Nonetheless, several ramifications have scarred our Earth ever since humans have undertook these robust, industrial actions. As first priority, scientists should direct their focus onto preserving our planet from the cataclysmic effects of the greenhouse effect — the trapped carbon dioxide gas in Earth’s atmosphere which thereby generates additional heat into our planet. This can be achieved by developing a renewable energy-based device to chemically convert carbon dioxide into clean products, which in turn will inherently benefit our environment and most definitely the society with the future generation of useful, renewable products.

One prominent solar example of this was physically engineered at the University of Illinois in Chicago, by mechanical engineer Amin Salehi-Khojin, in July of 2016. In their prototyping phase, the research team was able to construct a device that can absorb carbon dioxide, utilize sunlight to break CO2 into “syngas” (gas similar to hydrogen and carbon monoxide), and then use this synthesized gas directly as diesel or be turned into other liquid fuels. Just from this experiment alone, it is discernible that the potential to create such a device to eliminate the excess carbon dioxide exists within the scientific community; thus one can expect multiple breakthroughs in this field in the coming year alone, from solar to maybe even wind based technology. Furthermore, this prototype exemplifies the truly infinite possibilities that renewable energy sources can harness by converting the harmful gas into beneficial compounds.

Indisputably, this methodology has positive consequences, with little to no risk, hence producing an overall positive for both the Earth’s maintenance, and all animals and humans in regards to air quality. However, one may argue that this “breakthrough” has existed for epochs: plants, as they convert the carbon dioxide from the air into valuable sugars through the cyclical, self-sufficient process known as photosynthesis. But due to recent industrialization leading to deforestation, plants in general are becoming more and more rare in an industrial-based city. So without having the plants absorb the toxins and carbon dioxide in the air, the breeding ground for extreme pollution in cities, like New Delhi, India, exists. This eventually triggers an urgent necessity for renewable methods to get rid of these pollutants and toxins; and if plants cease to exist in harsh climates where toxins exist, then this innovative technique of splitting the carbon dioxide into useful products surely will have the ability to stay in industrial cities like these; and if they have capability to withstand the worst toxins, they surely will have the staying power in the international market.

In addition to its efficiency, the mere utilization of such a technology will sincerely resonate with the scientific community. Since numerous attempts have been made by scientists to find sustainable solutions to the greenhouse effect, the community — and more so the public — are desperate for a panacea. This solution not only thrives off the absorption of carbon dioxide, but it also creates several efficient products including but not limited to gaseous compounds that can provide liquid fuel or diesel, thereby acting as a detriment to further carbon emissions. Now, the world has seen this technology exist in one small laboratory. Through extensive research on maximizing the utility of the materials, the next massive breakthrough will be attempting to scale this technology to the international market, while ensuring that this device can be inexpensive as possible so that the scientific community can make some slot of profit. For this effective cost and efficient design, this device can essentially gain international acclaim after scientists give their approval to showcase a brand of these carbon emission combatants, all of which exist in different shape or form but run on renewable, green energy.

Without a cast of a doubt, the renewably-energized devices will completely revolutionize our approach to global warming. By developing a method that can concurrently reduce the carbon dioxide emissions and generating “split” products that promote green energy, the scientific community would absolutely gain the same recognition of this breakthrough as, for instance, circulating two men around the moon. This ideology, in effect, prompts people to question who they really are. Scientists are curious and explorative. But can they halt this mindset and instead focus on a more impeding dynamic: introspection of our character. Thus, it is only ethically sound that we as humans understand one blatant reality: our curiosity has, in essence, disrupted the nature of our Earth. So, it is only morally correct that we humans disband from our brigades in space, leave the hospital’s dissections and illnesses, and truly save our only home known to man.

Congratulations to the winners of the third Yale Scientific Synapse High School Essay Contest!

This year’s essay prompt was: “How does bias affect the course of scientific research? Discuss how public and personal bias has hindered and facilitated scientific progress.”

The Duality of Bias

By rocel beatriz balmes 1st place winner, yale scientific magazine national essay competition 2014 haines city high school lake alfred, florida.

Traditionally defined as a partiality towards particular people, objects, or beliefs, bias has developed a rather negative connotation—particularly in science—of resulting in unfair advantages and, thus, inaccurate results. Though this has, in effect, rendered it equivalent to a social pariah to the scientific community, throughout the years, it has persisted as a definitive barrier to scientific and social progress.

Take, for example, the emergence of “Social Darwinism” in the late 1800s. Despite the fact that Darwin focused only on biological evidence in animals and seldom mentioned ramifications for humans, public bias took the words of famed eugenicist Francis Galton and perpetuated the idea of a biologically superior race. Observing and dissecting the differences between their own fair features and the large lips and dark skin of their slaves, Americans came to the conclusion that they were the de facto superior race in all aspects of humanity, despite the lack of scientific empiricism. Instead of obtaining impartial evidence for their superiority—of which, they would actually find none—they focused their efforts on finding justification for their enslavement and systematic dehumanization of African Americans for centuries to come. Though this pseudoscience was nothing but a gross perversion of Darwin’s widely supported Theory of Evolution and Natural Selection, the concept of a harsher eugenics outlined by Vacher de Lapouge based on this very theory and the idea of white supremacy became the underpinnings of Nazi Germany’s eugenics agenda. This form of scientific racism, verified only by the bias of a racist, ethnocentric society led to the creation of global selective breeding programs that eliminated—and, in fact, continue to eliminate—millions of innocent people leaving only masses of unrealized potential for scientific and social progress.

Unfortunately, such bias is not unique to eras of the past. From the very dawn of its conception in the mid-to-late 1900s, stem cell research has been influenced by bias. Though the utilization of the cells as transformative tissues has been revolutionary, this was only possible with the extraction of the inner cell mass in a human embryo. Such procedures, when first introduced, shocked the public as a process strikingly similar to the very destruction of human life, regardless of the undeveloped status of said human. Researchers were swayed by some of the strongest proponents of the ban of such procedures. Rather than specific religious denominations or political parties, the conflict attracted masses of people from differing backgrounds to forge a formidable opposition to the progression of health science. Consequently, some research institutions succumbed to the period’s public and private moral bias and halted experimentation. That is not to say, of course, that this bias was in any way intended with malice or aimed to deprive severely ill people of life-saving stem cells. Bias—public bias in particular—is oftentimes muddled with the fear of the unorthodox and the unconventional. In this case, though the bias did prevent scientific progression, it is important to note that it was influenced by a people that was, perhaps, not quite ready for such progression.

Alternatively, bias can provide the push that some societies need in order to develop and revolutionize. Just as most words in the English language, the word bias is double-faceted by nature. Far from the unscrupulous reputation it usually holds in science, it can also be defined as a predilection or a fondness for something—an emotion that all scientists must have in order to undertake the challenges of their satisfying yet simultaneously grating careers. Thus, through the years, bias has had the dual role of barrier and catalyst to major scientific breakthroughs.

Take, for example, the conflict with stem cell research. Stem-cell pioneer James Thomson was a researcher in one of only two laboratories in 1998 to successfully extract stem cells and, at the same time, destroy the human embryo from which they were plucked. In a New York Times Article titled “Man Who Helped Start Stem Cell War May End It”, Thomson says that he knew of the social stigma that surrounded his research and that he himself was, at first, very skeptical of the moral implications and had even worked with ethicists before he unknowingly detonated a moral bomb with his ground-breaking scientific research. When public opinion proved to be a seemingly significant barrier biased against his progress, however, instead of backing down and raising the metaphorical white flag of surrender, Thomson’s determination was only fueled by this bias against him. Working with researchers from Kyoto University, Thomson helped developed a new technique of adding a few genes to ordinary skin cells to make them function like stem cells. The scientific ramifications of this ethically sound method are infinite. Aside from the obvious benefits in research, the medical world is now bombarded with revolutionary new methods and treatments as vital tissue generation without the need to wait for donors becomes a possibility. Though the road ahead may still be paved with challenges in production for Thomson, without the public and his own personal bias of morality pressuring him, his systematic search for and discovery of an ethical method would not have become a reality.

Though one might be tempted to label the above example as the exemption to the rule of bias’ role in science, it is important to note that some of the greatest innovations and fundamental truths of our world were conceived under researchers’ personal bias of belief in their ideas. From Galileo Galilei and Louis Pasteur, to Marie Curie and Jane Goodall, these scientists lived during eras during which they were ridiculed by a public inexorably biased against them for daring to have an alternative model of the world and, in the latter individuals’ cases, a gender unorthodox for a scientist. Yet, personal conviction, determination and, yes, bias led these three scientists to international acclaim. Indeed, bias possesses a dual dynamism that allows it to stand as an obstruction to and creator of scientific progress. Suspended between these two polarities is where revolution, innovation, and true science emerge.

Everything is Awesome

By marina tinone 2nd place winner, yale scientific magazine national essay competition 2014 william h. hall high school west hartford, connecticut.

My brother and I were blessed to have our own Lego collections. Our rooms were lined with shelves and shelves of our own creations, some of them built using the instructions from the Lego sets, most of them made by ourselves. We ditched the boring booklets in the box and just made what we needed.

For my brother, his bricks were used to build complex helicopters and submarines, usually creating machines significantly more complicated than the ones designed by Lego. When I asked him about his submarine, and why all the pieces he used weren’t the same color, he told me that the submarine was supposed to be invisible, so the colors didn’t need to match. Besides, the hinges, the pulleys, the contraptions he made by himself– those were the important parts.

In my world, my Lego creations weren’t invisible. My stuffed animals needed sleds to play in the snow, houses to sleep in, school buses to go to school in the morning and come back in the evening. My machines were not as complex as my brother’s, but they worked, and my colors matched. The stuffed animals needed their yellow school buses, and I thought a sled would look nice in blue.

My brother’s Legos always impressed our parents. He definitely had the eyes of an engineer, a scientist. Now, when Mom and Dad looked into my room and watched their daughter raise a blue sled loaded with stuffed rabbits into the air, well… the kids were different, that’s for sure.

Watching my brother receive praise for his creations from our chemist and engineer parents, I thought that science was restricted to those interests. Science was for the ones who made Legos for the sake of the machine, not for the ones whose stuffed rabbits wore scarves.

I wonder– did the world think the same way I did when Rosalind Picard introduced affective computing in 1997? Upon learning more about the limbic system and its role in shaping perception, Picard realized that it was not enough to simply create new microprocessors and develop energy-efficient chips if they didn’t interact with the user’s emotions and social cues. Technology needed a more human touch to develop. When she created this novel field and opened it to the world, did her peers find such emotion-based studies unworthy? Did they believe that such “science” was an aberration to the disciplines that touted rational, sentiment-free thinking?

As Picard explained to Adam Higginbotham of Wired magazine, “I realized we’re not going to build intelligent machines until we build, if not something we call emotion, then something that functions like our emotion systems.”

Today, there is an international conference and a journal dedicated to affective computing, and labs around the world continue to further the field by finding applications for their “intelligent machines” to shape how we interact with technology every day.

What about those who supported computer science in the 1970s, back when computer science looked like a pile of hole-punched papers? Computer scientists once had to suade others of the viability of a field that would later become one of the most relevant and lucrative areas of study.

What about Gregor Mendel’s investigation with pea plants in 1866? Mendel’s contemporaries criticizing his work surely did not know that he would be credited for fathering the ever-evolving field of genetics.

What about Edward Jenner’s smallpox vaccine in 1798? No one believed that the ungodly idea of infecting someone to treat someone would save millions of lives.

Did those biased against the potential, the validity of these new fields and scientific pursuits, really understand their purposes and merits? With their closed interpretations of science, did they really understand what science is and can be? Over time, scientists have attempted to define science. Astronomer Carl Sagan asserted that “Science is a way of thinking much more than it is a body of knowledge.” Physicist Stephen Hawking describes science as “not only a disciple of reason but, also, one of romance and passion.”

Although both eloquently stated their thoughts, I am convinced by the words of chemist Marie Curie –

“I am among those who think that science has great beauty. A scientist in his laboratory is not only a technician; he is also a child placed before natural phenomena which impress him like a fairy tale. We should not allow it to be believed that all scientific progress can be reduced to mechanism, machines, gearings, even though such machinery also has its own beauty.”

I remember comparing my blue sled to my brother’s invisible submarine, and I hold onto my creation a little tighter. Maybe there is something more to science than my brother’s sophisticated machines. When my younger self stood in her room, surrounded by her Lego bricks, she shouldn’t have diminished the progress she had made in her Lego laboratory, just because she didn’t use pulleys or interlocking gears.

I shouldn’t have been so close-minded against my own science, just because the world around me was biased against my ideas. From my studies, I hypothesized, I tested, I built upon my past results. My world needed science, but it didn’t need what had already been done, or was already deemed acceptable. It needed my own input. Call my ideas biased, call them faulted. But without the individuals interpreting and solving their world’s struggles using their own definitions, science would cease to develop.

Scientists continue to stand in their laboratories in child-like wonder, enraptured by the phenomena that enchant them, in all shapes and forms. Science is about discovering what you find beautiful in your world, and working, playing, in order to fulfill your personal curiosity and the needs of your imagination.

Let’s sit down. Let’s open up those boxes filled with possibilities. Throw away the instructions.

Let’s play.

The Good and Bad of Bias and Prejudice in Science

By jonathan chan 3rd place winner, yale scientific magazine national essay competition 2014 milton academy milton, massachusetts.

Scientists take pride in using the scientific method that dictates testing a hypothesis dispassionately with objective experiments, scrutinizing that the results are replicable, presenting all the data for independent peer review, and addressing any dissenting views vigorously. Over the years, scientists have been very successful in creating the public myth that they love second guessing their own hypotheses to safeguard themselves from unintentional bias and prejudice. This rigorous process has enabled science to become exalted as an arbiter of truth by most people. In reality, however, scientists behave very differently and bias in scientific research is in fact quite common; a steadily growing number of published papers have been found to be not replicable, calling into question the validity of many widely accepted hypotheses.

Scientists are humans, with personal beliefs and values. It is human nature to look for evidence to support one’s beliefs. A fundamental flaw of human nature is its love for being proven right and hate for being proven wrong. This flaw causes scientists to unconsciously find data to confirm their preferred hypotheses or preconceptions, and they overlook – even disregard – evidence that is contrary. This phenomenon is known to psychologists as “confirmation bias”. A study of the efficacy of Chinese acupuncture is an interesting example of how cultural beliefs of scientists affect their research. Clinical experiments on acupuncture performed in Asia overwhelmingly support its therapeutic effectiveness, while trials implemented in the West show inconclusive results.

“Confirmation bias” can influence every step of any scientific experiment set up to test a hypothesis, from how the experiment is designed, to how the results are measured, to how the data are interpreted. Scientific research today is highly competitive and involves significant financial resources; a culture of publish or perish is pervasive. There is constant pressure on scientists to generate groundbreaking discoveries in drugs, materials, and technologies. The experimental methods are highly complex, and as a result, “positive results” are extremely difficult to produce, measure, and assess. No wonder many researchers become overly excited over the first piece of positive data, giving it biased prominence over the mundane, negative results and subsequently “shoe- horning” the flawed data that eventuate a faulty conclusion.

In theory, peer review by independent professionals and publications should provide an effective defense against these subtle biases. In practice, however, this process is just as prone to the same kind of confirmation biases which favors positive results over null data and negative hypotheses. A recent study on the selection process of scientific publications concludes that papers are less likely to be published and to be cited if they report “negative” results. A prominent example of this institutional bias involves a high-profile study which linked child MMR vaccination with increased incidences of autism. This study caused widespread panic and resulted in a detrimental decade-long decrease in child immunization. Although numerous studies were conducted at the same time supporting a contrary conclusion, these “negative-result” papers failed to gain the level of attention of the “positive-result” paper the retraction of which took ten years.

History is replete with incidences where biases and prejudices have not only steered scientific research, but also fostered malicious prejudice of the research on an unsuspecting public. The prejudicial practice of eugenics in the early 1900’s caused thousands of innocent people to be labeled as inferior and unjustly persecuted for no scientific reason. Lysenkoism in the 1930’s in the Soviet Union advocated bias and useless “scientific” methods to increase crop yields for political purpose, resulting in the deaths of millions of starving peasants. On the other hand, bias has not always hindered scientific progress. Scientists in the past could not have known whether their brilliant ideas were right or wrong. Many of the problems they were trying to solve were not only difficult but also inductive due to a lack of evidence. These ideas necessarily originated as wild guesses encompassing the scientists’ individual biases and prevailing societal values.

Astrophysicist Mario Livio in his book “Brilliant Blunders” provides a litany of bias- induced scientific blunders which in time transformed into breakthrough scientific discoveries. Linus Pauling was a protein specialist and was likely to be biased in favor of proteins, which fueled his erroneous prediction of the DNA structure. Charles Darwin came out with the flawed theory of inheritance because he was likely influenced by the biases of the plant and animal breeders prevalent during his career. Lord Kelvin’s inordinate devotion to tidy mathematics and his bias against messiness resulted in his inaccurate calculation of earth’s age.

However, as these unconscious personal biases and societal prejudices are “uncovered” and properly understood, this development can actually facilitate the pursuit of true scientific knowledge. Bias and prejudice in science have caused unfortunate setbacks but at the same time have generated clarity for decisive shifts in thinking and accelerated advances. The scientific process is complex, messy, and at times even boring, full of starts and stops. Yet, this system of inquiry encompasses a self-correcting tendency which has withstood the test of time and remains a stunning success in understanding nature and improving lives. As influential German philosopher Hans-Gerog Gadamer writes: a researcher “cannot separate in advance the productive prejudices that enable understanding from the prejudices that hinder it”. Preconceptions can spur as well as blind in scientific research.

Unfortunately, scientific research today may have become overly zealous in guarding itself against biases and prejudices, succumbing to politically correct social forces and avoiding tackling sensitive problems and issues which may offend the prevailing public morality. Scientific research is increasingly constrained by these forces dictating what topics can be studied, how we study them, why we need to study them, and who gets to do the studying. A bigger crisis looms should science lose its relevance and importance due to excessive fear of unavoidable bias and prejudice in scientific research. As the Wright brothers said: “If a man is in too big a hurry to give up an error he is liable to give up some truth with it.”[/vc_column_text][vc_button2 title=”Go back” style=”square” color=”sky” size=”sm” link=”url:http%3A%2F%2Fwww.yalescientific.org%2Fsynapse%2Fcontest-winners%2F|title:Contest%20Winners|”][/vc_column][/vc_row]

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

THE NATION'S OLDEST COLLEGE SCIENCE PUBLICATION

• School Guide
• English Grammar Free Course
• English Grammar Tutorial
• Parts of Speech
• Figure of Speech
• Tenses Chart
• Essay Writing
• Email Writing
• NCERT English Solutions
• English Difference Between
• SSC CGL English Syllabus
• SBI PO English Syllabus
• SBI Clerk English Syllabus
• IBPS PO English Syllabus
• IBPS CLERK English Syllabus

Essay on Science in English: Check 200, 300 & 500 Words Essay

Science is the study of logic. It explains why the world is round, why stars twinkle, why light travels faster than sound, why hawks soar higher than crows, why sunflowers face the sun and other phenomena. Science answers every question logically rather than offering mystical interpretations. Students are very interested in science as a topic. This subject is indeed crucial for those hoping to pursue careers in science and related professions.

People who are knowledgeable in science are more self-assured and aware of their environment. Knowing the cause and origin of natural events, a person knowledgeable in science will not be afraid of them.

However, science also has a big impact on a country’s technological advancement and illiteracy.

Table of Content

English-language Long and Short Science Essay

Essay on science  (200 words), essay on science (300 words), essay on science (400 words), essay on science (500 words), essay on science (600 words).

We have included a brief and lengthy English essay on science below for your knowledge and convenience. The writings have been thoughtfully crafted to impart to you the relevance and meaning of science. You will understand what science is, why it matters in daily life, and how it advances national progress after reading the writings. These science essays can be used for essay writing, debate, and other related activities at your institution or school.

Science entails a thorough examination of the behavior of the physical and natural world. Research, experimentation, and observation are used in the study.

The scientific disciplines are diverse. The social sciences, formal sciences, and natural sciences are some of them. Subcategories and sub-sub-categories have been created from these basic categories. The natural sciences include physics, chemistry, biology, earth science, and astronomy; the social sciences include history, geography, economics, political science, sociology, psychology, social studies, and anthropology; and the formal sciences include computer science, logic, statistics, decision theory, and mathematics.

The world has positively transformed because of science. Throughout history, science has produced several inventions that have improved human convenience. We cannot fathom our lives without several of these inventions since they have become essential parts of them.

Global scientists persist in their experiments and occasionally produce more advanced innovations, some of which spark global revolutions. Even if science is helpful, some people have abused knowledge, usually those in positions of authority, to drive an arms race and destroy the environment.

There is no common ground between the ideologies of science and religion. These seeming opposite viewpoints have historically led to a number of confrontations and still do.

Science is a way to learn about, comprehend, examine, and experiment with the physical and natural features of the world in order to apply it to the development of newer technologies that improve human convenience. In science, observation and experimentation are broad and not restricted to a specific concept or area of study.

Applications of Science

Science has given us almost everything we use on a daily basis. Everything, from laptops to washing machines, microwaves to cell phones, and refrigerators to cars, is the result of scientific experimentation. Here are some ways that science affects our daily lives:

Not only are refrigerators, grills, and microwaves examples of scientific inventions, but gas stoves, which are frequently used for food preparation, are as well.

Medical Interventions

Scientific advancements have made it feasible to treat a number of illnesses and conditions. Thus, science encourages healthy living and has helped people live longer.

Interaction

These days, mobile phones and internet connections are necessities in our life and were all made possible by scientific advancements. These innovations have lowered barriers to communication and widened global connections.

E nergy Source

The creation and application of numerous energy forms have been facilitated by the discovery of atomic energy. One of its greatest innovations is electricity, and everyone is aware of the effects it has on daily life.

Variety in Cuisine

There has also been an increase in food diversity. These days, a wide variety of fruits and vegetables are available year-round. It’s not necessary to wait for a given season to enjoy a certain meal. This modification is the result of scientific experimentation.

So, science is a part of our daily existence. Without scientific advancements, our lives would have been considerably more challenging and varied. Nonetheless, we cannot ignore the fact that a great deal of scientific innovation has contributed to environmental deterioration and a host of health issues for humankind.

There are essentially three main disciplines of science. The Natural Sciences, Social Sciences, and Formal Sciences are some of them. To examine different aspects, these branches are further divided into subcategories. This is a thorough examination of these groups and their subgroups.

Scientific Subdisciplines

Natural Science

This is the study of natural phenomena, as the name implies. It investigates how the cosmos and the world function. Physical science and life science are subcategories of natural science.

a) Science of Physics

The subcategories of physical science comprise the following:

• Physics is the study of matter’s and energy’s properties.
• Chemistry is the study of the materials that make up matter.
• The study of space and celestial bodies is called astronomy.
• Ecology is the study of how living things interact with their natural environments and with one another.
• Geology: It studies the composition and physical makeup of Earth.
• Earth science is the study of the atmosphere and the physical makeup of the planet.
• The study of the physical and biological components and phenomena of the ocean is known as oceanography.
• Meteorology: It studies the atmospheric processes.

The subcategories of life science include the following:

• The study of living things is called biology.
• The study of plants is known as botany.
• The study of animals is known as zoology.

c) Social Science

This includes examining social patterns and behavioral patterns in people. It is broken down into more than one subcategory. Among them are:

• History: The examination of past occurrences
• Political science is the study of political processes and governmental structures.
• Geographic: Study of the atmospheric and physical characteristics of Earth.
• Human society is studied in social studies.
• Sociology: The study of how societies form and operate.

Academic Sciences

It is the area of study that examines formal systems like logic and mathematics. It encompasses the subsequent subcategories:

• Numbers are studied in mathematics.
• Reasoning is the subject of logic.
• Statistics: It is the study of numerical data analysis.
• Mathematical analysis of decision-making in relation to profit and loss is known as decision theory.
• The study of abstract organization is known as systems theory.
• Computer science is the study of engineering and experimentation as a foundation for computer design and use.

Scientists from several fields have been doing in-depth research and testing numerous facets of the subject matter in order to generate novel ideas, innovations, and breakthroughs. Although these discoveries and technologies have made life easier for us, they have also permanently harmed both the environment and living things.

Introduction

Science is the study of various physical and natural phenomena’ structures and behaviors. Before drawing any conclusions, scientists investigate these factors, make extensive observations, and conduct experiments. In the past, science has produced a number of inventions and discoveries that have been beneficial to humanity.

I deas in Religion and Science

In science, new ideas and technologies are developed through a methodical and rational process; in religion, however, beliefs and faith are the only factors considered. In science, conclusions are reached by careful observation, analysis, and experimentation; in religion, however, conclusions are rarely reached through reason. As a result, they have very different perspectives on things.

Science and Religion at Odds

Because science and religion hold different opinions on many issues, they are frequently perceived as being at odds. Unfortunately, these disputes occasionally cause social unrest and innocent people to suffer. These are a few of the most significant disputes that have happened.

The World’s Creation

The world was formed in six days, according to many conservative Christians, sometime between 4004 and 8000 BCE. However, cosmologists assert that the Earth originated about 4.5 billion years ago and that the cosmos may be as old as 13.7 billion years.

The Earth as the Universe’s Center

Among the most well-known clashes is this one. Earth was considered to be the center of the universe by the Roman Catholic Church. They say that it is surrounded by the Sun, Moon, stars, and other planets. Famous Italian mathematician and astronomer Galileo Galilei’s discovery of the heliocentric system—in which the Sun is at the center of the solar system and the Earth and other planets orbit it—led to the conflict.

Eclipses of the Sun and Moon

Iraq was the scene of one of the first wars. The locals were informed by the priests that the moon eclipse was caused by the gods’ restlessness. These were seen as foreboding and intended to overthrow the kings. When the local astronomers proposed a scientific explanation for the eclipse, a disagreement arose.

There are still many myths and superstitions concerning solar and lunar eclipses around the world, despite astronomers providing a compelling and rational explanation for their occurrence.

In addition to these, there are a number of other fields in which religious supporters and scientists hold divergent opinions. While scientists, astronomers, and biologists have evidence to support their claims, the majority of people adhere closely to religious beliefs.

Not only do religious activists frequently oppose scientific methods and ideas, but many other facets of society have also taken issue with science since its discoveries are leading to a host of social, political, environmental, and health problems. Nuclear weapons are one example of a scientific invention that threatens humanity. In addition, the processes involved in preparation and the utilization of the majority of scientifically created equipment contribute to pollution, making life more difficult for all.

In the previous few decades, a number of scientific advancements and discoveries have greatly eased people’s lives. The previous ten years were not an anomaly. A good number of important scientific discoveries were acknowledged. The top ten most amazing recent scientific inventions are shown below.

New Developments and Findings in Science

Amputee Gains Control of Biomechanical Hand via Mental After a tragic accident took away his forearm, Pierpaolo Petruzziello, an Italian, used his mind to control a biomechanical hand attached to his arm. The hand used wires and electrodes to connect to the nerves in his arm. He became the first to become skilled at doing motions like gripping objects, wriggling his fingers, and moving.

The Global Positioning System

In 2005, the Global Positioning System, or GPS as it is more often known, went into commercial use. It was incorporated into mobile devices and worked wonders for tourists all over the world. Traveling to more recent locations and needing instructions couldn’t be simpler.

The Self-Driving Car Toyota debuted Prius shortly after Google launched its own self-driving car experiment in 2008. The accelerator, steering wheel, and brake pedals are absent from this vehicle. It runs without the need for user input because it is driven by an electric motor. To guarantee that the driverless experience is seamless and secure, it is integrated with specialized software, a collection of sensors, and precise digital maps.

Android, widely regarded as one of the most significant innovations of the decade, revolutionized the market by flooding it with devices running Java and Symbian earlier on. These days, Android is the operating system used by the majority of smartphones. Millions of applications are supported by it.

c) Computer Vision

A number of sub-domains fall under the umbrella of computer vision, including learning, video tracking, object recognition, object pose estimation, event detection, indexing, picture restoration, and scene reconstruction. In order to produce symbolic information, the field includes methods for processing, analyzing, obtaining, and understanding images in high-dimensional data from the real world.

d) Touch Screen Technology

It appears that touch screen technology has taken over the planet. The popularity of touch screen gadgets can be attributed to their ease of use. These gadgets are becoming quite popular everywhere.

e) Method of 3D Printing

The 3D printer is capable of producing a wide range of items, such as lamps, cookware, accessories, and much more. Alternatively referred to as additive manufacturing, this process uses digital model data from electronic data sources like Additive Manufacturing Files (AMF) to construct three-dimensional items of any shape.

Git Hub is an online hosting service and version control repository that was founded in 2008. It provides features including bug tracking, task management, feature requests, and the sharing of codes, apps, and other materials. The GitHub platform was first developed in 2007, and the website went live in 2008.

f) Smart Timepieces

The market for smart watches has been around for a while. The more recent models, like the one introduced by Apple, have garnered enormous popularity and come with a number of extra capabilities. Nearly all of the functionality found on smartphones are included in these watches, which are also more convenient to wear and use.

g) Websites for Crowdfunding

The emergence of crowdsourcing websites like Indiegogo, Kickstarter, and GoFundMe has been a blessing for innovators. Inventors, artists, and other creative people can share their ideas and gain the funding they need to put them into action by using these websites.

Global scientists constantly observe and experiment to develop new scientific discoveries that improve people’s lives. Not only do they consistently create new technologies, but they also adapt the ones that already exist whenever there is an opportunity. Even while these innovations have made life easier for humans, you are all aware of the numerous environmental, social, and political risks they have brought about.

500+ Words Essay on Mother Teresa in English For Students 500+ Words Essay on Swami Vivekananda in English for Students Rabindranath Tagore Essay in English For Students APJ Abdul Kalam Essay For Students: Check 500 Words Essay

Essay on Science- FAQs

Who is father of science.

Galileo is the father of science.

Why is it called science?

The word “scientia” has Latin origins and originally meant “knowledge,” “an expertness,” or “experience.”

What is science for students?

Science is the study of the world by observation, recording, listening, and watching. Science is the application of intellectual inquiry into the nature of the world and its behavior. Think like a scientist, anyone can.

What is science’s primary goal or objective?

Science’s primary goal is to provide an explanation for the facts. Moreover, science does not prohibit the explanation of facts in an arbitrary manner. Additionally, science organizes the data and develops theories to explain the data.

Describe what a scientific fact is.

Repeatable, meticulous observations or measurements made through experiments or other methods are referred to as scientific facts. Furthermore, empirical evidence is another name for a scientific fact. Most importantly, the development of scientific hypotheses depends on scientific facts.

Please Login to comment...

Similar reads.

• School English
• English Blogs
• How to Get a Free SSL Certificate
• Best SSL Certificates Provider in India
• Elon Musk's xAI releases Grok-2 AI assistant
• What is OpenAI SearchGPT? How it works and How to Get it?
• Content Improvement League 2024: From Good To A Great Article

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

10 recent scientific breakthroughs

From cell reparation to reef restoration

• Newsletter sign up Newsletter

1. Finding the root cause of lupus

2. restoring brain cells, 3. menstrual blood as a diagnostic tool, 4. cell therapy for melanoma, 5. rhino ivf, 6. pristine configuration, 7. restoring reefs, 8. ai to find aliens, 9. inverse vaccines, 10. sequencing the y-chromosome.

Scientists in many fields received little recognition for the last couple of years, as the world focused on the emergency push to develop vaccines and treatments for Covid-19. But that doesn't mean they weren't still busy researching a dizzying series of developments that are now being reported as major discoveries and achievements.

Scientists have discovered a cause of lupus and a possible way to reverse it. A study published in the journal Nature points to abnormalities in the immune system of  lupus patients that is caused by a molecular abnormality. "What we found was this fundamental imbalance in the types of T cells that patients with lupus make," Deepak Rao, one of the study authors, said to NBC News . Specifically, "people with lupus have too much of a particular T cell associated with damage in healthy cells and too little of another T cell associated with repair," NBC News said.

The good news is that this could be reversed. A protein called interferon is mainly to blame for the T-cell imbalance. Too much interferon blocks another protein called the aryl hydrocarbon receptor, which helps regulate how the body responds to bacteria or environmental pollutants. In turn, too many T-cells are produced that attack the body itself. "The study found that giving people with lupus anifrolumab, a drug that blocks interferon, prevented the T-cell imbalance that likely leads to the disease," said NBC News.

Subscribe to The Week

Escape your echo chamber. Get the facts behind the news, plus analysis from multiple perspectives.

Sign up for The Week's Free Newsletters

From our morning news briefing to a weekly Good News Newsletter, get the best of The Week delivered directly to your inbox.

Scientists have found a way to repair brain cells impaired by a rare genetic disorder. A study published in the journal Nature found that a drug called antisense oligonucleotide allowed human neurons to develop normally despite carrying a mutation due to a genetic disorder called Timothy syndrome. "It's the beginning of a new era for many of these diseases that we first thought were untreatable," Dr. Huda Zoghbi, a professor at Baylor College of Medicine, said to NPR . 

Timothy syndrome is caused by a mutation of a single gene in a person's DNA. The new drug develops an "antisense nucleotide, a small piece of synthetic genetic material that alters the proteins made by a cell," said NPR. The antisense nucleotide for Timothy syndrome was designed to replace a defective protein with a healthy version — "in effect counteracting the mutation responsible for the disorder." This same approach could potentially be used to treat other genetic disorders, "including some that cause schizophrenia, epilepsy, ADHD and autism spectrum disorder."

Menstrual blood can potentially be used to measure blood sugar. In early 2024, the U.S. Food and Drug Administration (FDA) approved a new diagnostic menstrual pad called the Q-Pad and A1C Test by the biotechnology research company Qvin. The Q-Pad is an organic cotton period pad that "collects the blood, which a laboratory then uses to analyze the individual's average blood sugar over three weeks through the A1C biomarker," said Forbes .

"There is a lot of clinically relevant information in this bodily fluid that comes every month," Sara Naseri, the CEO and co-founder of Qvin, said to Axios . "We've built a way for women to get insights about their health regularly. Non-invasively, using blood that comes every month, the menstrual blood." Diagnostic capabilities can potentially be extended to diagnose HPV or endometriosis. 

The U.S. Food and Drug Administration (FDA) approved the first cellular therapy for aggressive forms of melanoma. The treatment, called Amtagvi, is "designed to fight off advanced forms of melanoma by extracting and replicating T cells derived from a patient's tumor," said NPR . These cells are also called tumor-infiltrating lymphocytes (TIL). T cells are integral in the immune system but can become "dysfunctional inside tumors." 

"The approval of Amtagvi represents the culmination of scientific and clinical research efforts leading to a novel T cell immunotherapy for patients with limited treatment options," Dr. Peter Marks, the director of the FDA's Center for Biologics Evaluation and Research, said in a statement . The treatment won't work for everyone, but research by the National Institutes of Health showed a "56% response rate among patients with melanoma, and 24% of patients had a complete disappearance of their melanoma, regardless of where it was," Axios said. "This is the tip of the iceberg of what TIL can bring to the future of medicine," Patrick Hwu, CEO of Moffitt Cancer Center, said to Axios .

Scientists were able to impregnate a southern white rhino using in-vitro fertilization (IVF).  Researchers in Kenya implanted a southern white rhino embryo into another of the same species using the technique in September 2023, resulting in a successful pregnancy. The technique could be used to save the northern white rhino from total extinction. "We achieved together something which was not believed to be possible," Thomas Hildebrandt , head of the reproduction management department at the Leibniz Institute for Zoo and Wildlife Research, said in a press conference. 

There are two species of white rhinos: northern and southern. The northern white rhino is on the verge of extinction due to poaching, with only two females remaining. Luckily, scientists have sperm preserved from the last male rhino, which could be combined with an egg from the female and implanted into a southern white rhino female to act as a surrogate. Using a white rhino embryo to test the procedure was a "proof of concept" which is a "milestone to allow us to produce northern white rhino calves in the next two, two and a half years," Hildebrandt said.

Scientists discovered six exoplanets that revolve around a star in a rare pattern called orbital resonance, said a study published in the journal Nature . This means that "for every six orbits completed by planet b, the closest planet to the star, the outermost planet g completes one," CNN said, adding that "as planet c makes three revolutions around the star, planet d does two, and when planet e completes four orbits, planet f does three."

The system was deemed a "rare fossil" by Rafael Luque, a postdoctoral scholar in the University of Chicago's Department of Astronomy and Astrophysics. "We think only about one percent of all systems stay in resonance," Luque said in a statement . "It shows us the pristine configuration of a planetary system that has survived untouched." The discovery could help further the study of sub-Neptunes, which are planets larger than Earth but smaller than Neptune. They are not present in our solar system. "There is little agreement among astronomers about how these planets form and what they're made of — so an entire system consisting of sub-Neptunes could help scientists determine more about their origin," Luque said.

Coral bleaching has been a rapidly growing problem as climate change worsens. Without intervention, the reefs will continue to deteriorate. To counter this, scientists have explored the idea of a "coral gym," essentially a "laboratory to make corals stronger," NPR said. The goal is to "train" coral to survive more extreme conditions.

Warming oceans and rising temperatures are the largest contributors to coral degradation. "One of the things that we do in this lab is subject them to different environmental conditions and evaluate who's a little bit stronger," Ian Enochs, lead of the Coral Program at the Atlantic Oceanographic and Meteorological Laboratory at the National Oceanic and Atmospheric Administration, said to NPR. Researchers created a "complex matrix of aquariums" where they can "subject different types of corals to different environments and not only understand how they might survive, but perhaps help them to do so."

Scientists have created an artificial intelligence model that can detect alien life , said a study published in the journal PNAS . The algorithm can "distinguish between samples of biological and nonbiological origin 90% of the time," after being "trained using living cells, fossils, meteorites and lab-made chemicals," Live Science said. "Put another way, the method should be able to detect alien biochemistries, as well as Earth life," Robert Hazen, co-author of the study, said in a statement .

The AI "does not involve a machine having to look for specific things," but rather "looks for differences between samples," BBC said. "These results mean that we may be able to find a lifeform from another planet, another biosphere, even if it is very different from the life we know on Earth," Hazen said. "And, if we do find signs of life elsewhere, we can tell if life on Earth and other planets derived from a common or different origin."

Scientists may have found a way to calm immune responses for those with autoimmune disorders using an " inverse vaccine ," said a study published in the journal Nature Biomedical Engineering . The immune system responds to specific identifying markers on invaders like viruses and bacteria called antigens, "but some immune cells react to self-antigens," which are "molecules from our own cells," said Science . "In autoimmune diseases, these misguided immune cells turn against patients' own tissues."

The new research worked by "directing potential self-antigens to the liver," where "immune cells there pick up self-antigens and then stifle T cells that could target these molecules." The experiment was performed on mice. "The method they use is promising and potentially can induce better tolerance," neurologist and neuroimmunologist A.M. Rostami said to Science, adding that "we don't know" whether this approach is "applicable to human disease in which we don't know the antigen."

Scientists have finally sequenced the entire Y chromosome, one of the human sex chromosomes present in those assigned male at birth. The feat has been "notoriously difficult" because of the Y chromosome's "complex repeat structure," said a research paper published in the journal Nature .

"Just a few years ago, half of the human Y chromosome was missing" from knowledge of the human genome, Monika Cechova, co-lead author on the paper, said to CNN . "I would credit new sequencing technologies and computational methods for this," Arang Rhie, who also worked on the paper, said to Reuters . The X chromosome was fully sequenced back in 2020.

Understanding the Y chromosome can help with a number of health issues, including fertility. Genes have also "been shown to be required for the prevention of cancer and cardiovascular disease," Kenneth Walsh, a professor of biochemistry and molecular genetics at the University of Virginia School of Medicine, said to CNN.

Sign up for Today's Best Articles in your inbox

A free daily email with the biggest news stories of the day – and the best features from TheWeek.com

 Devika Rao has worked as a staff writer at The Week since 2022, covering science, the environment, climate and business. She previously worked as a policy associate for a nonprofit organization advocating for environmental action from a business perspective.  

• Harold Maass, The Week US

The Week Recommends Channing Tatum and Naomi Ackie stun in this film on the hedonistic rich directed by Zoë Kravitz

By The Week UK Published 29 August 24

The Week Recommends The 'inventive, emotionally astute writing' leaves audiences gripped throughout

In The Spotlight Uefa says the new format offers 'greater excitement' but critics say boredom is guaranteed

By Chas Newkey-Burden, The Week UK Published 29 August 24

In the Spotlight Jared Isaacman takes an all-private crew to space

By Joel Mathis, The Week US Published 29 August 24

In the Spotlight Sunita Williams and Butch Wilmore's eight-day trip to the ISS has now stretched into weeks amid concerns over their Starliner spacecraft

By The Week UK Published 17 August 24

The Explainer Enough water has been found to cover the surface of the Red Planet – but there's a catch

By Chas Newkey-Burden, The Week UK Published 16 August 24

In Depth A running list of the space agency's most exciting developments

By Devika Rao, The Week US Published 13 August 24

The Explainer Meteor showers, eclipses and more are coming to the skies

By Devika Rao, The Week US Published 12 August 24

Under the Radar Curiosity rover discovers elemental sulphur rocks, adding to 'growing evidence' of life-sustaining elements on Red Planet

By Harriet Marsden, The Week UK Published 24 July 24

Speed Read Two astronauts are stuck on the International Space Station due to problems with Boeing’s Starliner

By Peter Weber, The Week US Published 11 July 24

Today's Big Question People could be living in lunar 'houses' by 2040, says Nasa

By Chas Newkey-Burden, The Week UK Published 11 July 24

• Contact Future's experts
• Terms and Conditions
• Privacy Policy
• Cookie Policy
• Advertise With Us

The Week is part of Future plc, an international media group and leading digital publisher. Visit our corporate site . © Future US, Inc. Full 7th Floor, 130 West 42nd Street, New York, NY 10036.

Famous Scientists

7 Great Examples of Scientific Discoveries Made in Dreams

While we sleep, our subconscious minds continue to work on problems our conscious minds have failed to solve.

Most people who have struggled with a crossword clue know that sometimes they have found the answer easily after sleeping on the problem.

‘Sleeping on it’ has also led to major scientific discoveries, such as the seven examples below.

Discovery of the Periodic Table

With his long hair, his beard, and his passion for chemistry, Dmitri Mendeleev was a charismatic professor. He had his beard cut once a year.

In 1869 he wrote the elements’ names on cards – one element on each card. He then wrote the properties of every element on its own card.

He saw that atomic weight was important in some way, but he could not find a pattern.

Convinced that he was close to discovering something significant, Mendeleev moved the cards about for many hours until finally he fell asleep at his desk.

When he awoke, he found that his subconscious mind had done his work for him! A logical arrangement of the elements had come to him. He later wrote:

Discovery of Evolution by Natural Selection

For years he asked how new species could arise, but could find no answer.

In 1858, he had an extreme dream, in the shape of hallucinations caused by a tropical fever. When the fever had gone, he found that the theory of evolution by natural selection had come to him.

Discovery of the Structure of Benzene and Aromatic Chemistry

It was a tough problem, because the ratio of carbon and hydrogen atoms was unlike that seen in other hydrocarbon compounds.

On a cold night in 1865, he worked on the problem in his room.

Unable to find a solution, he turned his chair to the fire and dozed.

He began dreaming of atoms dancing. Gradually the atoms arranged themselves into the shape of a snake. Then the snake turned around and bit its own tail.

The image of the snake, tail in its mouth, continued to dance before his eyes. When Kekulé awoke, he realized what the dream had been telling him:

Benzene molecules are made up of rings of carbon atoms.

Understanding these aromatic rings opened up an enormously important new field of chemistry – aromatic chemistry – and a new understanding of chemical bonding.

Thousands of New Mathematical Ideas

Although he died in 1920, the richness of his ideas and conjectures in fields such as Elliptic Functions and Number Theory – nearly all of which were correct – were ahead of his time, and continue to inspire and direct research carried out by mathematicians today.

The Cambridge University mathematician Godfrey H. Hardy, who worked with Ramanujan, expressed the thought that if mathematicians were rated on the basis of pure talent on a scale from 0 to 100, he himself would be worthy of 25, J.E. Littlewood 30, David Hilbert 80, and Srinivasa Ramanujan 100.

Ramanujan said that the Hindu goddess Namagiri would appear in his dreams, showing him mathematical proofs, which he would write down when he awoke. He described one of his dreams as follows:

Discovery of the Scientific Method

One of his main lines of thought was skepticism – that everything should be doubted until it could be proved.

His four main ideas for scientific progress were:

1. Never accept anything as true until all reasons for doubt can be ruled out.

2. Divide problems into as many parts as possible and necessary to provide an adequate solution.

3. Thoughts should be ordered, starting with the simplest and easiest to know, ascending little by little, and, step by step, to more complex knowledge.

4. Make enumerations so complete, and reviews so general, that nothing is omitted.

Descartes wrote that the basis of the Scientific Method came to him in dreams he had on November 10, 1619.

Proof that our Nerves Transmit Signals Chemically

In 1920 Loewi had a dream about the problem. He woke excitedly during the night and scribbled notes about the dream.

In the morning, he could not remember the dream, and he could not read his nocturnal notes either!

The following night, he dreamed about the problem again. The dream was about an experiment he could use to prove his idea, and this time he remembered it.

He carried out research based on his dream and published the work in 1921, establishing that signalling across synapses was indeed chemical, as he had suspected.

It’s ironic that it took 17 years for subconscious thoughts to come to the surface in the man often called the father of neuroscience!

Ironic or not, in 1936 the great man was awarded the Nobel Prize in Medicine for work that came to him in a dream.

The Fossil Fish

He had been trying to understand the structure of a fossil fish for two weeks, but could make no progress.

Agassiz’s wife wrote about how the solution came to him in the form of dreams over three nights:

He had been striving for two weeks to decipher the somewhat obscure impression of a fossil fish on the stone slab in which it was preserved. Weary and perplexed, he put his work aside at last, and tried to dismiss it from his mind. Shortly after, he woke one night persuaded that while asleep he had seen his fish with all the missing features perfectly restored. But when he tried to hold and make fast the image it escaped him. Nevertheless, he went early to work, thinking that on looking anew at the impression he should see something which would put him on the track of his vision. In vain the blurred record was as blank as ever. The next night he saw the fish again, but with no more satisfactory result. When he awoke it disappeared from his memory as before. Hoping that the same experience might be repeated, on the third night he placed a pencil and paper beside his bed before going to sleep. Towards morning the fish reappeared in his dream, confusedly at first, but at last with such distinctness that he had no longer any doubt as to its zoological characters. Still half dreaming, in perfect darkness, he traced these characters on the sheet of paper at the bedside. In the morning he was surprised to see in his nocturnal sketch features which he thought it impossible the fossil itself should reveal. He hastened to work, and, with his drawing as a guide, succeeded in cutting away the surface of the stone under which portions of the fish proved to be hidden. When wholly exposed it corresponded with his dream and his drawing, and he succeeded in classifying it with ease.

The subconscious mind is very powerful. Provided the conscious mind absorbs plenty of data while awake, the subconscious mind can process and make sense of the data while asleep. Some of the greatest scientific discoveries in history are testament to the importance of sleep and dreams in the operation of our minds.

More from FamousScientists.org:

April 12, 2020 at 1:31 pm

God is imagination

Ideas are immortal

March 1, 2020 at 8:42 am

I am not a scientist but I came to this page because last night I dreamt about how I descovered a entity as a living digital microorganism ( just so you know I’m 18 years old and I didnt even know what a entity or a living digital microorganism was until I googled it today) but I seached the internet and it said that it wasnt posible so I really dont know what to think because in my mind it feels so possible and I have the desire to find out the truth.

March 1, 2020 at 8:44 am

And its like I know everything and nothing about it at the same time. Its very weird.

September 30, 2020 at 9:26 pm

That is interesting, I definately think you should investigate

December 26, 2022 at 5:35 pm

Could this be it? https://sitn.hms.harvard.edu/uncategorized/2021/scientists-store-data-in-dna-of-living-bacteria/

June 3, 2019 at 9:43 pm

I am a theorist writing an article on the power of dreams in bringing forth transformative ideas. I would welcome any reports on dreams that proved revelatory to the dreamer. The more details the better. Thanks. [email protected]

November 11, 2020 at 10:51 am

Is precognition included? I would like to discuss with valid proofs. I’m a bit skeptical myself.

March 12, 2017 at 9:01 am

I am not a scientist but I took a nap during my flight and I dreamt about “Carbon Polyurethane dioxide”. Is there such a thing? It’s totally weird. I don’t recall reading any magazine or label that would show such words.

December 26, 2022 at 5:28 pm

https://www.imeche.org/news/news-article/carbon-dioxide-used-in-sustainable-polyurethane-production

February 14, 2017 at 10:38 pm

One other thing i learned about “sleeping on a problem” is that your subconscious can process data, circumstances, etc. without social influences and prejudices interfering or biasing the path to the solution/thesis/whatever.

December 28, 2016 at 1:44 pm

it is fascinating and thrilling to notice the power of subconscious mind .I also wish to dream one day .these great men really inspire me

October 7, 2016 at 1:01 pm

Very nice. I was looking for such information. God speaks to us through dreams indeed.

March 20, 2016 at 11:57 am

This article describes the fascinating! Thank you for compiling this information into such comprehensive, undeniable proof of the power of dreams and the subconscious mind!

September 16, 2015 at 4:30 pm

we have so much knowledge lurking below the level of conscious. releasing that knowledge is the key to a greater truth. often we are too uptight to see the truth that is under our noses. even scientists. dreams have a definite purpose, bringing hidden knowledge to the surface.

March 11, 2015 at 11:11 am

I first must say, I am no scientist…even remotely, but I love questioning the oddities of life. I am fascinated by the spiritual world and the connectedness of all things. I am constantly pulled back to energy, frequencies, and vibration. After seeing the effects vibration and frequencies have on water, salt, etc., and the “snowflake appearance” that is beautifully formed….it begs the question rather these vibrations and frequencies are a part of the snow flake’s artistry? Can you tell me if this has ever been considered? I also question if these…energy, frequency, and vibration, are part of the equation of how, or why each life form on earth from plant to human are different. This could account for why things like astrology, or the “state of the universe” ….such as the energies, frequencies, and vibrations at the time of our formation and birth…have some basis for truth. Any thoughts???

January 24, 2015 at 4:20 am

I love sleeping. I wish i could manage on 7-8 hrs a night, but I need more like 9-10. I’m sure I could achieve more in life if I didn’t need so much sleep. On the other hand, I won a science prize at university for an idea I had in my sleep, so I shouldn’t really complain. Hoping i’ll have another good idea soon.

May 16, 2015 at 4:52 pm

Just an update. I recently met a guy sitting next to me on a plane. He had read some book about humans only needing 4 hours sleep in 24, so that’s how he was now managing his life. I wish I’d been honest enough to tell him that he looked like death warmed up.

Me? I’m still sleeping much too much, and thriving on it. 😎

March 20, 2016 at 11:55 am

Lol that’s awesome

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Alphabetical List of Scientists

Louis Agassiz | Maria Gaetana Agnesi | Al-Battani Abu Nasr Al-Farabi | Alhazen | Jim Al-Khalili | Muhammad ibn Musa al-Khwarizmi | Mihailo Petrovic Alas | Angel Alcala | Salim Ali | Luis Alvarez | Andre Marie Ampère | Anaximander | Carl Anderson | Mary Anning | Virginia Apgar | Archimedes | Agnes Arber | Aristarchus | Aristotle | Svante Arrhenius | Oswald Avery | Amedeo Avogadro | Avicenna

Charles Babbage | Francis Bacon | Alexander Bain | John Logie Baird | Joseph Banks | Ramon Barba | John Bardeen | Charles Barkla | Ibn Battuta | William Bayliss | George Beadle | Arnold Orville Beckman | Henri Becquerel | Emil Adolf Behring | Alexander Graham Bell | Emile Berliner | Claude Bernard | Timothy John Berners-Lee | Daniel Bernoulli | Jacob Berzelius | Henry Bessemer | Hans Bethe | Homi Jehangir Bhabha | Alfred Binet | Clarence Birdseye | Kristian Birkeland | James Black | Elizabeth Blackwell | Alfred Blalock | Katharine Burr Blodgett | Franz Boas | David Bohm | Aage Bohr | Niels Bohr | Ludwig Boltzmann | Max Born | Carl Bosch | Robert Bosch | Jagadish Chandra Bose | Satyendra Nath Bose | Walther Wilhelm Georg Bothe | Robert Boyle | Lawrence Bragg | Tycho Brahe | Brahmagupta | Hennig Brand | Georg Brandt | Wernher Von Braun | J Harlen Bretz | Louis de Broglie | Alexander Brongniart | Robert Brown | Michael E. Brown | Lester R. Brown | Eduard Buchner | Linda Buck | William Buckland | Georges-Louis Leclerc, Comte de Buffon | Robert Bunsen | Luther Burbank | Jocelyn Bell Burnell | Macfarlane Burnet | Thomas Burnet

Benjamin Cabrera | Santiago Ramon y Cajal | Rachel Carson | George Washington Carver | Henry Cavendish | Anders Celsius | James Chadwick | Subrahmanyan Chandrasekhar | Erwin Chargaff | Noam Chomsky | Steven Chu | Leland Clark | John Cockcroft | Arthur Compton | Nicolaus Copernicus | Gerty Theresa Cori | Charles-Augustin de Coulomb | Jacques Cousteau | Brian Cox | Francis Crick | James Croll | Nicholas Culpeper | Marie Curie | Pierre Curie | Georges Cuvier | Adalbert Czerny

Gottlieb Daimler | John Dalton | James Dwight Dana | Charles Darwin | Humphry Davy | Peter Debye | Max Delbruck | Jean Andre Deluc | Democritus | René Descartes | Rudolf Christian Karl Diesel | Diophantus | Paul Dirac | Prokop Divis | Theodosius Dobzhansky | Frank Drake | K. Eric Drexler

John Eccles | Arthur Eddington | Thomas Edison | Paul Ehrlich | Albert Einstein | Gertrude Elion | Empedocles | Eratosthenes | Euclid | Eudoxus | Leonhard Euler

Michael Faraday | Pierre de Fermat | Enrico Fermi | Richard Feynman | Fibonacci – Leonardo of Pisa | Emil Fischer | Ronald Fisher | Alexander Fleming | John Ambrose Fleming | Howard Florey | Henry Ford | Lee De Forest | Dian Fossey | Leon Foucault | Benjamin Franklin | Rosalind Franklin | Sigmund Freud | Elizebeth Smith Friedman

Galen | Galileo Galilei | Francis Galton | Luigi Galvani | George Gamow | Martin Gardner | Carl Friedrich Gauss | Murray Gell-Mann | Sophie Germain | Willard Gibbs | William Gilbert | Sheldon Lee Glashow | Robert Goddard | Maria Goeppert-Mayer | Thomas Gold | Jane Goodall | Stephen Jay Gould | Otto von Guericke

Fritz Haber | Ernst Haeckel | Otto Hahn | Albrecht von Haller | Edmund Halley | Alister Hardy | Thomas Harriot | William Harvey | Stephen Hawking | Otto Haxel | Werner Heisenberg | Hermann von Helmholtz | Jan Baptist von Helmont | Joseph Henry | Caroline Herschel | John Herschel | William Herschel | Gustav Ludwig Hertz | Heinrich Hertz | Karl F. Herzfeld | George de Hevesy | Antony Hewish | David Hilbert | Maurice Hilleman | Hipparchus | Hippocrates | Shintaro Hirase | Dorothy Hodgkin | Robert Hooke | Frederick Gowland Hopkins | William Hopkins | Grace Murray Hopper | Frank Hornby | Jack Horner | Bernardo Houssay | Fred Hoyle | Edwin Hubble | Alexander von Humboldt | Zora Neale Hurston | James Hutton | Christiaan Huygens | Hypatia

Ernesto Illy | Jan Ingenhousz | Ernst Ising | Keisuke Ito

Mae Carol Jemison | Edward Jenner | J. Hans D. Jensen | Irene Joliot-Curie | James Prescott Joule | Percy Lavon Julian

Michio Kaku | Heike Kamerlingh Onnes | Pyotr Kapitsa | Friedrich August Kekulé | Frances Kelsey | Pearl Kendrick | Johannes Kepler | Abdul Qadeer Khan | Omar Khayyam | Alfred Kinsey | Gustav Kirchoff | Martin Klaproth | Robert Koch | Emil Kraepelin | Thomas Kuhn | Stephanie Kwolek

Joseph-Louis Lagrange | Jean-Baptiste Lamarck | Hedy Lamarr | Edwin Herbert Land | Karl Landsteiner | Pierre-Simon Laplace | Max von Laue | Antoine Lavoisier | Ernest Lawrence | Henrietta Leavitt | Antonie van Leeuwenhoek | Inge Lehmann | Gottfried Leibniz | Georges Lemaître | Leonardo da Vinci | Niccolo Leoniceno | Aldo Leopold | Rita Levi-Montalcini | Claude Levi-Strauss | Willard Frank Libby | Justus von Liebig | Carolus Linnaeus | Joseph Lister | John Locke | Hendrik Antoon Lorentz | Konrad Lorenz | Ada Lovelace | Percival Lowell | Lucretius | Charles Lyell | Trofim Lysenko

Ernst Mach | Marcello Malpighi | Jane Marcet | Guglielmo Marconi | Lynn Margulis | Barry Marshall | Polly Matzinger | Matthew Maury | James Clerk Maxwell | Ernst Mayr | Barbara McClintock | Lise Meitner | Gregor Mendel | Dmitri Mendeleev | Franz Mesmer | Antonio Meucci | John Michell | Albert Abraham Michelson | Thomas Midgeley Jr. | Milutin Milankovic | Maria Mitchell | Mario Molina | Thomas Hunt Morgan | Samuel Morse | Henry Moseley

Ukichiro Nakaya | John Napier | Giulio Natta | John Needham | John von Neumann | Thomas Newcomen | Isaac Newton | Charles Nicolle | Florence Nightingale | Tim Noakes | Alfred Nobel | Emmy Noether | Christiane Nusslein-Volhard | Bill Nye

Hans Christian Oersted | Georg Ohm | J. Robert Oppenheimer | Wilhelm Ostwald | William Oughtred

Blaise Pascal | Louis Pasteur | Wolfgang Ernst Pauli | Linus Pauling | Randy Pausch | Ivan Pavlov | Cecilia Payne-Gaposchkin | Wilder Penfield | Marguerite Perey | William Perkin | John Philoponus | Jean Piaget | Philippe Pinel | Max Planck | Pliny the Elder | Henri Poincaré | Karl Popper | Beatrix Potter | Joseph Priestley | Proclus | Claudius Ptolemy | Pythagoras

Adolphe Quetelet | Harriet Quimby | Thabit ibn Qurra

C. V. Raman | Srinivasa Ramanujan | William Ramsay | John Ray | Prafulla Chandra Ray | Francesco Redi | Sally Ride | Bernhard Riemann | Wilhelm Röntgen | Hermann Rorschach | Ronald Ross | Ibn Rushd | Ernest Rutherford

Carl Sagan | Abdus Salam | Jonas Salk | Frederick Sanger | Alberto Santos-Dumont | Walter Schottky | Erwin Schrödinger | Theodor Schwann | Glenn Seaborg | Hans Selye | Charles Sherrington | Gene Shoemaker | Ernst Werner von Siemens | George Gaylord Simpson | B. F. Skinner | William Smith | Frederick Soddy | Mary Somerville | Arnold Sommerfeld | Hermann Staudinger | Nicolas Steno | Nettie Stevens | William John Swainson | Leo Szilard

Niccolo Tartaglia | Edward Teller | Nikola Tesla | Thales of Miletus | Theon of Alexandria | Benjamin Thompson | J. J. Thomson | William Thomson | Henry David Thoreau | Kip S. Thorne | Clyde Tombaugh | Susumu Tonegawa | Evangelista Torricelli | Charles Townes | Youyou Tu | Alan Turing | Neil deGrasse Tyson

Harold Urey

Craig Venter | Vladimir Vernadsky | Andreas Vesalius | Rudolf Virchow | Artturi Virtanen | Alessandro Volta

Selman Waksman | George Wald | Alfred Russel Wallace | John Wallis | Ernest Walton | James Watson | James Watt | Alfred Wegener | John Archibald Wheeler | Maurice Wilkins | Thomas Willis | E. O. Wilson | Sven Wingqvist | Sergei Winogradsky | Carl Woese | Friedrich Wöhler | Wilbur and Orville Wright | Wilhelm Wundt

Chen-Ning Yang

Ahmed Zewail

Every print subscription comes with full digital access

Science News

Know your science history? Take our headline quiz to find out

Can you guess when Science News published these science headlines?

2 spacecraft caught the waves that might heat and accelerate the solar wind

Here’s how an arthropod pulls off the world’s fastest backflip.

A fluffy, orange fungus could transform food waste into tasty dishes

In a first, these bats were found to have toes that glow

Remote seamounts in the southeast Pacific may be home to 20 new species

People with food and other allergies have a new way to treat severe reactions

Trending stories.

The possibilities for dark matter have just shrunk — by a lot 

The historic ‘Wow!’ signal may finally have a source. Sorry, it’s not aliens

The Webb telescope’s peek into a stellar nursery finds baby planets too

Spotlight on Health

New COVID-19 booster shots have been approved. When should you get one?

The vaccines target the omicron variants currently circulating in the United States.

More than 100 bacteria species can flourish in microwave ovens

A newly approved ‘living drug’ could save more cancer patients’ lives, from the archives.

The Universe: Chaotic or Bioselective?

August 24, 1974 Vol. 106 No. #8

Science News Magazine

August 24, 2024 Vol. 206 No. 3

NASA’s Perseverance rover finds its first possible hint of ancient life on Mars

Paper cut physics pinpoints the most hazardous types of paper.

Featured Media

National Geographic’s ‘OceanXplorers’ dives into the ocean’s mysteries

National Geographic’s documentary series ‘OceanXplorers,’ produced by James Cameron, invites you aboard one of the most advanced research vessels in the world.

A risk-tolerant immune system may enable house sparrows’ wanderlust

A new element on the periodic table might be within reach 

Freeze-drying turned a woolly mammoth’s DNA into 3-D ‘chromoglass’

Twisters asks if you can 'tame' a tornado. We have the answer

Follow Science News

• Follow Science News on X
• Follow Science News on Facebook
• Follow Science News on Instagram

More Stories

This protist unfolds its ‘neck’ up to 30 times its body length to scout prey

The largest known genome belongs to a tiny fern, it’s a big year for cicadas. here’s what to know about this year’s emergence.

World record speeds for two Olympics events have fallen over time. We can go faster

Does social status shape height, rain bosworth studies how deaf children experience the world.

Zapping sand to create rock could help curb coastal erosion

The world’s record-breaking hot streak has lasted 14 months. when will it end, extraordinary heat waves have readers asking how a/c affects greenhouse gas emissions.

The nearest midsized black hole might instead be a horde of lightweights

A distant quasar may be zapping all galaxies around itself, some meteors leave trails lasting up to an hour. now we may know why.

The world’s fastest microscope makes its debut

Can light spark superconductivity a new study reignites debate, health & medicine.

Extreme heat and rain are fueling rising cases of mosquito-borne diseases

50 years ago, antibiotic resistant bacteria became a problem outside hospitals, expanding antibiotic treatment in sub-saharan africa could save kids’ lives.

Mantle waves buoy continents upward and bedeck them with diamonds

‘turning to stone’ paints rocks as storytellers and mentors, why japan issued its first-ever mega-earthquake alert, science & society.

‘After 1177 B.C.’ describes how societies fared when the Bronze Age ended

Scientists are fixing flawed forensics that can lead to wrongful convictions, language models may miss signs of depression in black people’s facebook posts.

Subscribers, enter your e-mail address for full access to the Science News archives and digital editions.

Not a subscriber? Become one now .

Essay on Science in English for Children and Students

Table of Contents

Essay on Science in English: Science is a systematic and logical study of occurrences, events, happenings etc.

Fill Out the Form for Expert Academic Guidance!

Please indicate your interest Live Classes Books Test Series Self Learning

Verify OTP Code (required)

I agree to the terms and conditions and privacy policy .

Fill complete details

Target Exam ---

Science is the study that logically explains the round shape of earth; it explains the twinkling of stars; why light travels faster than sound; why hawk flies higher than a crow; why the sunflower turns to the sunlight etc. Science doesn’t provide supernatural explanations; rather it gives logical conclusion to every question. Science as a subject is extremely popular with students. It’s indeed an essential subject for aspirants who want to make their career in science and related fields.

Knowledge of science makes people more confident and well aware of their surroundings. One who knows science will not be scared of natural occurrences, knowing their origin and reason.

On the other hand science also plays a significant role in technological development of a nation and hence also in removing growth impediments like unemployment and illiteracy.

Long and Short Essay on Science in English

We have provided below short and long essay on science in English for your knowledge and information.

The essays have been wisely written to deliver to you the meaning and significance of science.

After going through the essays you will know what is science and its importance in our day to day life, also how science helps in the development of a country.

You can use these science essay in your school’s or college’s essay writing, debate or other similar competitions.

Science Essay 1 (200 words)

Science involves extensive study of the behaviour of natural and physical world. The study is conducted by way of research, observation and experimentation.

There are several branches of science. These include the natural sciences, social sciences and formal sciences. These broad categories have further been divided into sub categories and sub-sub categories. Physics, chemistry, biology earth science and astronomy form a part of the natural sciences, history, geography, economics, political science, sociology, psychology, social studies and anthropology are a part of the social sciences and formal sciences include mathematics, logic, statistics, decision theory, system theory and computer science.

Science has changed the world for good. There have been several scientific inventions from time to time and these have made life convenient for the human beings. Several of these inventions have become an integral part of our lives and we cannot imagine our lives without them.

Scientists worldwide continue to experiment and keep coming up with newer inventions every now and then with some of them bringing revolution worldwide. However, as useful as it is, science has also been misused by some, mainly by those in power, for fuelling an arms race and degrading the environment.

The ideologies of science and religion have not found any meeting ground. These seemingly contrasting ideas have given rise to several conflicts in the past and continue to do so.

Science Essay 2 (300 words)

Introduction

Science is a means to study, understand, analyze and experiment with the natural and physical aspects of the world and put them to use to come up with newer inventions that make life more convenient for the mankind. The observation and experimentation in the field of science is not limited to a particular aspect or idea; it is widespread.

Uses of Science

Almost everything we use in our daily lives is a gift of science. From cars to washing machines, from mobile phones to microwaves, from refrigerators to laptops – everything is an outcome of scientific experimentation. Here is how science impacts our everyday life:

Not just microwaves, grillers and refrigerators, gas stoves that are commonly used to prepare food are also a scientific invention.

• Medical Treatments

The treatment of several diseases and ailments has been made possible because of the advancement in science. Science thus promotes healthy living and has contributed in the increase of life span.

• Communication

Mobile phones and internet connections that have become an integral part of our lives these days are all inventions of science. These inventions have made communication easier and brought the world closer.

• Source of Energy

The discovery of atomic energy has given way to the invention and deployment of various forms of energies. Electricity is one of its main inventions and the way it impacts our everyday life is known to all.

• Variety of Food

The variety of food has also increased. Many fruits and vegetables are now available all through the year. You do not require waiting for a particular season to enjoy a specific food. The experimentations in the field of science have led to this change.

Science is thus a part of our everyday life. Our life would have been very different and difficult without the advancement in science. However, we cannot deny the fact that many scientific inventions have led to the degradation of the environment and have also caused numerous health problems for the mankind.

Science Essay 3 (400 words)

Science is basically divided into three broad branches. These include Natural Sciences, Social Sciences and Formal Sciences. These branches are further classified into sub-categories to study various aspects. Here is a detailed look at these categories and sub categories.

Branches of Science

• Natural Sciences

As the name suggests, this is the study of the natural phenomena. It studies how the world and universe works. Natural Science is further categorized into Physical Science and Life Science.

• a) Physical Science

Physical science includes the following sub categories:

• Physics: The study of properties of energy and matter.
• Chemistry: The study of substances of which matter is made.
• Astronomy: The study of the space and celestial bodies.
• Ecology: The study of relation of organisms with their physical surroundings as well as with each other.
• Geology: It deals with Earth’s physical structure and substance.
• Earth Science: The study of Earth’s physical constitution and its atmosphere.
• Oceanography: The study of biological and physical elements and phenomena of the sea.
• Meteorology: It deals with the processes of the atmosphere
• b) Life Science

The following sub categories form a part of the life science:

• Biology: The study of living organisms.
• Botany: The study of plant life.
• Zoology: The study of animal life.
• Social Sciences

This involves the study of the social pattern and human behaviour. It is further divided into various sub-categories. These include:

• History: The study of events occurred in the past
• Political Science: Study of systems of government and political activities.
• Geography: Study of Earth’s physical features and atmosphere.
• Social Studies: Study of human society.
• Sociology: Study of development and functioning of the society.
• Psychology: Study of human behaviour.
• Anthropology: Study of different aspects of humans within present and past societies.
• Economics: Study of production, consumption and circulation of wealth.
• Formal Sciences

It is that branch of science that studies formal systems such as mathematics and logic. It involves the following sub-categories:

• Mathematics: The study of numbers.
• Logic: The study of reasoning.
• Statistics: It deals with the analysis of numerical data.
• Decision Theory: Mathematical study to enhance decision making ability when it comes to profit and loss.
• Systems Theory: The study of abstract organization.
• Computer Science: The study of experimentation and engineering to form basis for designing and use of computers.

The experts in various branches of science have continually been studying the subject deeply and experimenting with different aspects to come up with newer theories, inventions and discoveries. These discoveries and inventions have made life easier for us; however, at the same time these have also made an irreversible damage to the environment as well as the living beings.

Science Essay 4 (500 words)

Science is the study of structure and behaviour of different physical and natural aspects. Scientists study these aspects, observe them thoroughly and experiment before coming to a conclusion. There have been several scientific discoveries and inventions in the past that have proved to be a boon for the mankind.

Concepts of Science and Religion

While a logical and systematic approach is followed in the field of science to come up with new ideas and inventions, religion, on the other hand, is purely based on belief system and faith. In science, a thorough observation, analysis and experimentation is done to derive a result whereas there is hardly any logic when it comes to religion. Their view of looking at things is thus completely different from one another.

Conflict between Science and Religion

Science and religion are often seen at loggerheads due to their conflicting views on certain things. Sadly, at times these conflicts lead to disturbance in the society and causes suffering to the innocent. Here are some of the major conflicts that have occurred between the advocates of religion and the believers of scientific methodologies.

• The Creation of World

Many conservative Christians believe that God created the world in six days sometime between 4004 and 8000 BCE. On the other hand, the cosmologists state that the universe is as old as around 13.7 billion years and that the Earth emerged around 4.5 billion years ago.

• Earth as the Centre of the Universe

This is one of the most famous conflicts. The Roman Catholic Church regarded Earth as the centre of the universe. As per them, the Sun, Moon, stars and other planets revolve around it. The conflict arose when famous Italian astronomer and mathematician, Galileo Galilei discovered the heliocentric system wherein the Sun forms the centre of the solar system and the Earth and other planets revolve around it.

Unfortunately, Galileo was condemned as a heretic and put in house arrest for the rest of his life.

• Solar and Lunar Eclipse

One of the earliest conflicts occurred in Iraq. The priests there had told the locals that lunar eclipse was caused because of the restlessness of gods. These were thought to be ominous and aimed at destroying the kings. The conflict occurred when the local astronomers came up with the scientific reason behind the eclipse.

While the astronomers state a strong and logical reason about the occurrence of the solar and lunar eclipse, myths and superstitions surrounding the same still continue in various parts of the world.

• The Evolution of Species

Taking reference from the biblical book of Genesis, the conservative Christians believe that all the species of flora and fauna were created during the six days period when God created the world. The biologists, on the other hand, argue that the various species of plants and animals evolved over hundred and millions of years via the procedures of natural selection.

Apart from these, there are several other arenas wherein the scientists and religious advocates have contradictory views. Even though the scientists/ astronomers/ biologists have a backing for their theories most people deeply follow the religious views.

It is not only the religious advocates who often raise voice against the scientific methodologies and ideologies, science has also been criticized by many other sections of society because its inventions are giving way to various social, political, environmental and health issues. Scientific inventions such as nuclear weapons pose a threat to the mankind. Besides, the procedures of preparation as well as the use of most scientifically designed devices are adding to the pollution, thereby making life difficult for everyone.

Science Essay 5 (600 words)

There have been several scientific discoveries and inventions in the last couple of decades that have made life much easier. Last decade was no exception. There were quite a few significant scientific inventions that received appreciation. Here is a look at the 10 most remarkable recent scientific inventions.

Recent Scientific Inventions and Discoveries

• Control over Biomechanical Hand through Mind

Amputee Pierpaolo Petruzziello, an Italian who lost his forearm in an unfortunate accident, learned how to control a biomechanical hand connected to his arm by way of his thoughts. The hand connected to his arm nerves via electrodes and wires. He became the first person to master the art of making movements such as finger wiggling, grabbing objects and moving fist with his thoughts.

• Global Positioning System

Global Positioning System, popularly referred to as GPS, became commercially viable in the year 2005. It was embedded into the mobile devices and proved to be a boon for the travelers worldwide. Looking for directions while travelling to newer places couldn’t get easier.

• Prius – The Self-Driving Car

Google initiated the self-driving car project in the year 2008 and soon Toyota introduced Prius. This car does not have brake pedal, steering wheel or accelerator. It is powered by an electric motor and does not require any user interaction to operate. It is embedded with special software, a set of sensors and accurate digital maps to ensure that the driverless experience is smooth and safe.

Known to be one of the most noteworthy inventions of the decade, Android came as a revolution and took over the market that was earlier flooded with Symbian and Java powered devices. Most smart phones these days run on the Android operating system. It supports millions of applications.

• Computer Vision

Computer vision includes several sub-domains such as event detection, indexing, object recognition, object pose estimation, motion estimation, image restoration, scene reconstruction, learning and video tracking. The field encompasses techniques of processing, analyzing, acquiring and comprehending images in high-dimensional data from the actual world so as to come up with symbolic information.

• Touch Screen Technology

The touch screen technology seems to have taken over the world. The ease of operating makes for the popularity of the touch screen devices. These devices have become a rage worldwide.

• 3D Printing Technique

The 3D printing device can make a variety of stuff including kitchenware, accessories, lamps and much more. Also known as additive manufacturing, this technique creates three-dimensional objects of any shape with the use of digital model data from electronic data source such as Additive Manufacturing File (AMF).

Launched in the year 2008, Git Hub is a version control repository revision control and Internet hosting service that offers features such as bug tracking, task management, feature requests and sharing of codes, apps, etc. The development of GitHub platform started in 2007 and the site was launched in 2008.

• Smart Watches

Smart watches have been in the market for quite some time. However, the newer ones such as that launched by Apple have come with several added features and have gained immense popularity. These watches come with almost all the features of the smart phones and are easier to carry and operate.

• Crowd Funding Sites

The introduction of crowd-funding sites such as GoFundMe, Kickstarter and Indiegogo has been a boon for the creative minds. By way of these sites, inventors, artists and other creative people get a chance to share their ideas and receive financial help they require to implement the same.

Scientists worldwide observe and experiment continually to bring forth new scientific inventions, making life easier for people. They do not only keep coming up with newer inventions but also improvise the existing ones wherever there is a scope. While these inventions have made life easier for the man; however, the amount of environmental, social and political hazards these have caused are not hidden from you all.

Related Information:

• Essay on Science and Technology
• Paragraph on Wonders of Science
• Paragraph on Science
• National Science Day
• International Week of Science and Peace
• The National Council for Science and Technology Communication ( NCSTC ) is a scientific programme of the Government of India for the popularisation of science,

Related content

Get access to free Mock Test and Master Class

Register to Get Free Mock Test and Study Material

Offer Ends in 5:00

Select your Course

Please select class.

Essays on Science and Innovation

Journal Title

Journal issn, volume title, repository usage stats.

The commercialization of scientific discoveries into innovation has traditionally been the purview of large corporations operating central R&D laboratories through much of the past century. The past four decades have seen this model being gradually supplanted by a more decentralized system of universities and VC-backed startups that have displaced large corporations as the conductors of scientific research. This dissertation tries to understand how firms create and exploit scientific knowledge in this changing structure of American innovation. The first study examines how scientific knowledge can expand markets for technology and thereby encourage the entry of new science-based firms into invention. The argument is tested in the context of the U.S. patent market and finds that patents citing scientific articles tend to be traded more often, even after controlling for various proxies of patent quality. The second study explores why some American firms started investing in scientific research in the early twentieth century. The chapter relies on a newly assembled panel dataset of innovating firms consisting of their investments in science, patenting, financials and ownership between 1926 and 1940. The empirical patterns reveal that the beginnings of corporate research in America were driven by companies at the technological frontier attempting to take advantage of opportunities for innovation made possible by scientific advances. This investment was especially pronounced for firms based in scientific fields that were underdeveloped in the United States. The final study asks why startups are more likely to bring scientific advances to market. The existing literature has explained the higher innovative propensity of some startups by their superior scientific capabilities. However, it is also possible that the apparent innovativeness of startups may be a result of firm choice, rather than inherent capability gaps with respect to incumbents. Startups may choose novel products that are riskier but offer higher payoffs because they pay a higher entry cost in the form of investments in new factories, sales and distribution channels. I test this entry cost mechanism in the context of the American laser industry which responded to an exogenous influx of Soviet laser science following the end of the Cold War.

Description

Suh, Jungkyu (2022). Essays on Science and Innovation . Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/25166 .

Collections

Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license .

August 20, 2024

Science Improves When People Realize They Were Wrong

Science means being able to change your mind in light of new evidence

By Naomi Oreskes

Scott Brundage

Many traits that are expected of scientists—dispassion, detachment, prodigious attention to detail, putting caveats on everything, and always burying the lede—are less helpful in day-to-day life. The contrast between scientific and everyday conversation, for example, is one reason that so much scientific com­­munication fails to hit the mark with broader audiences. (One ob­server put it bluntly : “Scienti­­fic writing is all too often ... bad writing.”) One aspect of science, however, is a good model for our behavior, especially in times like these, when so many people seem to be sure that they are right and their opponents are wrong. It is the ability to say, “Wait—hold on. I might have been wrong.”

Not all scientists live up to this ideal, of course. But history offers admirable examples of scientists admitting they were wrong and changing their views in the face of new evidence and arguments. My favorite comes from the history of plate tectonics.

In the early 20th century German geophysicist and meteorologist Alfred Wegener proposed the theory of continental drift, suggesting that continents were not fixed on Earth’s surface but had mi­­grated widely during the planet’s history. Wegener was not a crank: he was a prominent scientist who had made important contributions to meteorology and polar re­­search. The idea that the now separate continents had once been somehow connected was supported by extensive evidence from stratigraphy and paleontology—evidence that had already inspired other theories of continental mobility. His proposal did not get ignored: it was discussed throughout Eur­ope, North America, South Africa and Australia in the 1920s and early 1930s. But a majority of scientists rejected it, particularly in the U.S., where geologists objected to the form of the theory and geophysicists clung to a model of Earth that seemed to be incompatible with moving continents.

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

In the late 1950s and 1960s the debate was reopened as new evidence flooded in, especially from the ocean floor. By the mid-1960s some leading scientists—including Patrick M. S. Blackett of Imperial College London, Harry Hammond Hess of Princeton University, John Tuzo Wilson of the University of Toronto and Edward Bullard of the University of Cambridge—endorsed the idea of continental motions. Between 1967 and 1968 this revival began to coalesce as the theory of plate tectonics.

Not, however, at what was then known as the Lamont Geological Laboratory, part of Columbia University. Under the direction of geophysicist Maurice Ewing, Lamont was one of the world’s most respected centers of marine geophysical research in the 1950s and 1960s. With financial and logistical support from the U.S. Navy, Lamont researchers amassed prodigious amounts of data on the heat flow, seis­micity, bathymetry and structure of the seafloor. But Lamont under Ewing was a bastion of resistance to the new theory.

It’s not clear why Ewing so strongly opposed continental drift. It may be that having trained in electrical engineering, physics and math, he never really warmed to geological questions. The evidence suggests that Ewing never engaged with Wegener’s work. In a grant proposal written in 1947, Ewing even confused “Wegener” with “Wagner,” referring to the “Wagner hypothesis of continental drift.”

And Ewing was not alone at Lamont in his ignorance of de­­bates in geology. One scientist recalled that in 1965 he personally “was only vaguely aware of the hy­­pothesis” [of continental drift] and that colleagues at Lamont who were familiar with it were mostly “skeptical and dis­missive.” Ewing was also known to be auto­cratic; one oceanographer called him the “oceanographic equivalent of General Patton.” It wasn’t an environment that en­­couraged dissent.

One scientist who did change his mind was Xavier Le Pichon . In the spring of 1966 Le Pichon had just defended his Ph.D. thesis, which denied the possibility of regional crustal mobility. After seeing some key data at Lamont—data that had been presented at a meeting of the American Geophysical Union just that week—he went home and asked his wife to pour him a drink, saying, “The conclusions of my thesis are wrong.”

Le Pichon had used heat-flow data to “prove” that Hess’s hypothesis of seafloor spreading—the idea that basaltic magma welled up from the mantle at the mid-­oceanic ridges, creating pressure that split the ocean floor and drove the two halves apart—was incorrect. Now new geomagnetic data convinced him that the hypothesis was correct and that something was wrong with either the heat-flow data or his interpretation of them.

Le Pichon has described this event as “extremely painful,” explaining in an essay that “during a period of 24 hours, I had the im­­pres­sion that my whole world was crumbling. I tried desperately to reject this new evidence.” But then he did what all good scientists should do: he set aside his bruised ego (presumably after polishing off that drink) and got back to work. Within two years he had co-­authored several key papers that helped to establish plate tectonics. By 1982 he was one of the world’s most cited scientists—one of only two geophysicists to earn that distinction.

In the years that followed, Lamont scientists made many crucial contributions to plate tectonics, and Le Pichon became one of the leading earth scientists of his generation, garnering numerous awards, distinctions and medals, including (ironically) the Maurice Ewing Medal from the American Geophysical Union. In science, as in life, it pays to be able to admit when you are wrong and change your mind.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

A new ‘AI scientist’ can write science papers without any human input. Here’s why that’s a problem

Dean, School of Computing Technologies, RMIT University, RMIT University

Disclosure statement

Karin Verspoor receives funding from the Australian Research Council, the Medical Research Future Fund, the National Health and Medical Research Council, and Elsevier BV. She is affiliated with BioGrid Australia and is a co-founder of the Australian Alliance for Artificial Intelligence in Healthcare.

RMIT University provides funding as a strategic partner of The Conversation AU.

View all partners

Scientific discovery is one of the most sophisticated human activities. First, scientists must understand the existing knowledge and identify a significant gap. Next, they must formulate a research question and design and conduct an experiment in pursuit of an answer. Then, they must analyse and interpret the results of the experiment, which may raise yet another research question.

Can a process this complex be automated? Last week, Sakana AI Labs announced the creation of an “AI scientist” – an artificial intelligence system they claim can make scientific discoveries in the area of machine learning in a fully automated way.

Using generative large language models (LLMs) like those behind ChatGPT and other AI chatbots, the system can brainstorm, select a promising idea, code new algorithms, plot results, and write a paper summarising the experiment and its findings, complete with references. Sakana claims the AI tool can undertake the complete lifecycle of a scientific experiment at a cost of just US$15 per paper – less than the cost of a scientist’s lunch.

These are some big claims. Do they stack up? And even if they do, would an army of AI scientists churning out research papers with inhuman speed really be good news for science?

How a computer can ‘do science’

A lot of science is done in the open, and almost all scientific knowledge has been written down somewhere (or we wouldn’t have a way to “know” it). Millions of scientific papers are freely available online in repositories such as arXiv and PubMed .

LLMs trained with this data capture the language of science and its patterns. It is therefore perhaps not at all surprising that a generative LLM can produce something that looks like a good scientific paper – it has ingested many examples that it can copy.

What is less clear is whether an AI system can produce an interesting scientific paper. Crucially, good science requires novelty.

But is it interesting?

Scientists don’t want to be told about things that are already known. Rather, they want to learn new things, especially new things that are significantly different from what is already known. This requires judgement about the scope and value of a contribution.

The Sakana system tries to address interestingness in two ways. First, it “scores” new paper ideas for similarity to existing research (indexed in the Semantic Scholar repository). Anything too similar is discarded.

Second, Sakana’s system introduces a “peer review” step – using another LLM to judge the quality and novelty of the generated paper. Here again, there are plenty of examples of peer review online on sites such as openreview.net that can guide how to critique a paper. LLMs have ingested these, too.

AI may be a poor judge of AI output

Feedback is mixed on Sakana AI’s output. Some have described it as producing “ endless scientific slop ”.

Even the system’s own review of its outputs judges the papers weak at best. This is likely to improve as the technology evolves, but the question of whether automated scientific papers are valuable remains.

The ability of LLMs to judge the quality of research is also an open question. My own work (soon to be published in Research Synthesis Methods ) shows LLMs are not great at judging the risk of bias in medical research studies, though this too may improve over time.

Sakana’s system automates discoveries in computational research, which is much easier than in other types of science that require physical experiments. Sakana’s experiments are done with code, which is also structured text that LLMs can be trained to generate.

AI tools to support scientists, not replace them

AI researchers have been developing systems to support science for decades. Given the huge volumes of published research, even finding publications relevant to a specific scientific question can be challenging.

Specialised search tools make use of AI to help scientists find and synthesise existing work. These include the above-mentioned Semantic Scholar, but also newer systems such as Elicit , Research Rabbit , scite and Consensus .

Text mining tools such as PubTator dig deeper into papers to identify key points of focus, such as specific genetic mutations and diseases, and their established relationships. This is especially useful for curating and organising scientific information.

Machine learning has also been used to support the synthesis and analysis of medical evidence, in tools such as Robot Reviewer . Summaries that compare and contrast claims in papers from Scholarcy help to perform literature reviews.

All these tools aim to help scientists do their jobs more effectively, not to replace them.

AI research may exacerbate existing problems

While Sakana AI states it doesn’t see the role of human scientists diminishing, the company’s vision of “a fully AI-driven scientific ecosystem” would have major implications for science.

One concern is that, if AI-generated papers flood the scientific literature, future AI systems may be trained on AI output and undergo model collapse . This means they may become increasingly ineffectual at innovating.

However, the implications for science go well beyond impacts on AI science systems themselves.

There are already bad actors in science, including “paper mills” churning out fake papers . This problem will only get worse when a scientific paper can be produced with US$15 and a vague initial prompt.

The need to check for errors in a mountain of automatically generated research could rapidly overwhelm the capacity of actual scientists. The peer review system is arguably already broken , and dumping more research of questionable quality into the system won’t fix it.

Science is fundamentally based on trust. Scientists emphasise the integrity of the scientific process so we can be confident our understanding of the world (and now, the world’s machines) is valid and improving.

A scientific ecosystem where AI systems are key players raises fundamental questions about the meaning and value of this process, and what level of trust we should have in AI scientists. Is this the kind of scientific ecosystem we want?

• Artificial intelligence (AI)
• Computer science
• Research integrity
• Paper mills

Director of STEM

Community member - Training Delivery and Development Committee (Volunteer part-time)

Chief Executive Officer

Finance Business Partner

Head of Evidence to Action

• Today's news
• Reviews and deals
• Climate change
• 2024 election
• Newsletters
• Fall allergies
• Health news
• Mental health
• Sexual health
• Family health
• So mini ways
• Unapologetically
• Buying guides
• Labor Day sales

Entertainment

• How to Watch
• My watchlist
• Stock market
• Biden economy
• Personal finance
• Stocks: most active
• Stocks: gainers
• Stocks: losers
• Trending tickers
• World indices
• US Treasury bonds
• Top mutual funds
• Highest open interest
• Highest implied volatility
• Currency converter
• Basic materials
• Communication services
• Consumer cyclical
• Consumer defensive
• Financial services
• Industrials
• Real estate
• Mutual funds
• Credit cards
• Balance transfer cards
• Cash back cards
• Rewards cards
• Travel cards
• Online checking
• High-yield savings
• Money market
• Home equity loan
• Personal loans
• Student loans
• Options pit
• Fantasy football
• Pro Pick 'Em
• College Pick 'Em
• Fantasy baseball
• Fantasy hockey
• Fantasy basketball
• Download the app
• Daily fantasy
• Scores and schedules
• GameChannel
• World Baseball Classic
• Premier League
• CONCACAF League
• Champions League
• Motorsports
• Horse racing

New on Yahoo

• Privacy Dashboard

Nasa makes discovery ‘as important as gravity’ about Earth

A new planet-wide electric field that is as fundamental to Earth as gravity has been discovered in a major scientific breakthrough.

The ambipolar electric field, which begins 150 miles above the planet, has been described as a “great invisible force” that lifts up the sky and is responsible for the polar winds.

The polar winds interact with the jet streams to help drive the majority of weather patterns across the globe.

Until now, the field had only been theorised, but a Nasa team, which includes scientists from the University of Leicester, has now sent a rocket into the field and measured it for the first time.

It means Earth now has three energy fields: gravity; the magnetic field , which shields the planet from cosmic radiation; and the ambipolar electric field.

Dr Glyn Collinson, the principal investigator of the Endurance Mission at Nasa Goddard Space Flight Centre in Greenbelt, Maryland, said: “Whenever spacecraft have flown over the poles of the Earth they have felt this supersonic wind of particles called the polar wind.

“There must be some invisible force lurking there responsible for this outflow, but we’ve never been able to measure it because we didn’t have the technology.

“This field is so fundamental to understanding the way the planet works. It’s been here since the beginning alongside gravity and magnetism. It’s been wafting particles to space and stretching up the sky since the beginning.”

The field has been hard to detect because it is extremely weak, just 0.55 volts. But it is enough to nearly treble the scale height of the ionosphere – part of the upper atmosphere that sits between 30 and 600 miles above sea level. The scale height describes how quickly the atmosphere fades away, meaning the ionosphere remains denser at greater heights than it would without it.

“Despite being weak it’s incredibly important, it counters gravity and it lifts the skies up. It’s like this conveyor belt, lifting the atmosphere up into space,” added Dr Collinson.

“A half a volt is almost nothing – it’s only about as strong as a watch battery. But that’s just the right amount to explain the polar wind.”

Understanding the atmosphere is crucial to the evolution of Earth and could help scientists spot other planets that could be habitable. The team believes that any planet with an atmosphere is likely to have an ambipolar field.

To launch into the ambipolar electric field, scientists needed to travel to the world’s most northerly launch pad, on the site of Ny-Alesund in Svalbard, Norway, just a few hundred miles from the North Pole.

The mission, which began in 2016, was named Endurance after the ship that carried Ernest Shackleton on his voyage to Antarctica in 1914.

Prof Suzie Imber, a space physicist at the University of Leicester, and co-author of the paper, said: “Svalbard hosts the only rocket range in the world where you can fly through the polar wind and make the measurements we needed.”

The team found that hydrogen ions, the most abundant type of particle in the polar wind, experience an outward force from this field, which is 10.6 times stronger than gravity.

Alex Glocer, the Endurance project scientist at Nasa Goddard and co-author of the paper, said: “That’s more than enough to counter gravity – in fact, it’s enough to launch them upwards into space at supersonic speeds.”

The discovery of the field was announced in the journal Nature.

Dr Collinson added: “What makes Earth the special place that we all call home? One of the reasons may be to do with the energy fields that our planet creates.

“One of them is gravity. It’s important for life because it’s holding our atmosphere up. The second field is the magnetic field that’s protecting our planet from the stream of particles that comes from the sun

“Our rocket has discovered, and finally measured, number three. Now that we’ve finally measured it, we can begin learning how it’s shaped our planet as well as others over time.”

Broaden your horizons with award-winning British journalism. Try The Telegraph free for 3 months with unlimited access to our award-winning website, exclusive app, money-saving offers and more.

share this!

August 23, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Citizen science project identifies 20 new astronomical discoveries

by University of Portsmouth

A citizen science project, which invites members of the public to take part in identifying cosmic explosions, has already identified 20 new astronomical discoveries.

More than 2,000 volunteers across 105 different countries have worked on 600,000 classifications over a six-month period.

The project Kilonova Seekers aims to find kilonovae—the cosmic explosions of neutron stars and black holes colliding in distant galaxies.

Volunteers are asked to play "spot the difference" using data from the two Gravitational-wave Optical Transient Observer (GOTO) telescopes, which are located on opposite sides of the planet—on La Palma, in Spain's Canary Islands, and Australia's Siding Spring Observatory.

Dr. Lisa Kelsey, from the University of Portsmouth's Institute of Cosmology and Gravitation, said, "The success of Kilonova Seekers demonstrates the invaluable role of public participation in scientific discovery . The contribution of citizen scientists is really helping us push the boundaries of our understanding of the universe."

The first stage of Kilonova Seekers is presented in a paper published in Monthly Notices of the Royal Astronomical Society .

Although all of the 20 discoveries haven't been classified yet, the researchers have identified five as Type la Supernovae, which are powerful and bright explosions of stars.

Type la Supernovae are important in astronomy because they have a consistent peak brightness, which makes them useful as "standardizable candles" to measure distances in space. By knowing how bright these supernovae should be, astronomers can calculate how far away they are, which helps measure the accelerating expansion of the universe.

The other discovery that has been classified is a cataclysmic variable star. This is a binary star system consisting of a white dwarf star stealing matter from its companion star, which causes bright flashes of light.

Dr. Kelsey added, "The remaining 14 have not yet been classified, so we aren't sure exactly what they are."

One of the key accomplishments of the project is the speed of classification and consensus from the volunteers.

Dr. Kelsey said, "As we have volunteers from around the world, there is almost always someone online looking at the data in real-time."

Scientists monitor alerts from gravitational wave detectors LIGO, Virgo and KAGRA, which trigger GOTO telescopes within 30 seconds to begin searching the sky. Any images taken are then shared with the public via the Zooniverse, the world's largest and most popular platform for facilitating citizen science.

Kilonova Seekers launched publicly on Zooniverse on 11 July 2023 and there were 1,000 classifications within the first 30 minutes.

Based on data obtained from Google Analytics, there are participants from every continent, except Antarctica. The wide accessibility of Zooniverse projects enables researchers to reach countries that may be traditionally underrepresented in astronomical communities.

The United States is by far the largest contributor, with a total of 1,284 users. The United Kingdom has about half that, with 615 users. However, users from Portugal are the most active, with each person viewing more than 2,750 pages on average.

Dr. Kelsey added, "The project not only contributes to the discovery of transient phenomena but also enhances the development of next-generation classification algorithms. This means that with the help of the public, we can create better ways to sort and understand the information.

"This speed of human vetting is simply not sustainable without the dedication of our citizen scientists."

Dr. Tom Killestein, from the University of Turku in Finland, said, "Alongside all the discoveries the volunteers have made, they've created a list of over 20,000 gold standard examples that we've used to improve our machine learning classifiers. This powerful synergy between machine learning and citizen science will allow us to continually improve our algorithms, and directly increase the number of discoveries of supernovae and other exciting objects."

Journal information: Monthly Notices of the Royal Astronomical Society

Provided by University of Portsmouth

Explore further

Feedback to editors

Promising antibiotic candidates discovered in microbes deep in the Arctic Sea

3 hours ago

Molecularly imprinted polymers help get the stink out of smoke-tainted wine

11 hours ago

Scientists unlock the secrets of how a key protein converts DNA into RNA

12 hours ago

Chemists achieve PFAS-free synthesis of fluorinated pharmaceutical and agrochemical compounds

13 hours ago

Number of fish species at risk of extinction five-fold higher than previous estimates according to new prediction

14 hours ago

Tuberculosis under the sea: A marine sponge microbe provides insights into the bacterium's evolution

Ancient gene gives spiders their narrow waist, research reveals

Catalytic process vaporizes plastic bags and bottles, yielding gases to make new, recycled plastics

Bacteria able to overcome cost of vancomycin resistance in lab setting

Uncovering the secret communication of marmoset monkeys: They have names

Relevant physicsforums posts, j0524-0336, surprisingly high li concentration.

Aug 27, 2024

Can there be free electrons in interstellar gas?

Aug 26, 2024

Rock on Mars possibly indicating Life

Does modified gravity somehow explain galaxies that don't need it.

Aug 25, 2024

Regarding the accuracy of the description of a pulsar in a 1977 paper

Aug 24, 2024

Optimizing Exposure Times: Balancing Efficiency and Image Quality

Aug 23, 2024

More from Astronomy and Astrophysics

Related Stories

Volunteers invited to play 'spot the difference' to help scientists identify cosmic explosions

Jul 19, 2023

Astronomers ask public to help find newly formed black holes

Aug 12, 2024

Citizen science project classifying gamma-ray bursts

Apr 16, 2024

Citizen scientists find 10,000 new variable stars

Nov 10, 2021

Citizen astronomers and AI discover 30,000 ring galaxies

Mar 14, 2024

AI helps scientists understand cosmic explosions

May 31, 2024

Recommended for you

Using atomic excitations to measure the rotation of spacetime

18 hours ago

Astronomers detect extended ionized gas around Messier 77

19 hours ago

New Horizons spacecraft measurements shed light on the darkness of the universe

17 hours ago

Supercomputer simulations reveal the nature of turbulence in black hole accretion disks

Research investigates variable star population of globular cluster NGC 1851

Aug 28, 2024

Study provides a more nuanced understanding of black hole thermodynamics

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Phys.org in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

Blog The Education Hub

https://educationhub.blog.gov.uk/2024/08/20/gcse-results-day-2024-number-grading-system/

GCSE results day 2024: Everything you need to know including the number grading system

Thousands of students across the country will soon be finding out their GCSE results and thinking about the next steps in their education.   

Here we explain everything you need to know about the big day, from when results day is, to the current 9-1 grading scale, to what your options are if your results aren’t what you’re expecting.  

When is GCSE results day 2024?  

GCSE results day will be taking place on Thursday the 22 August.     

The results will be made available to schools on Wednesday and available to pick up from your school by 8am on Thursday morning.  

Schools will issue their own instructions on how and when to collect your results.   

When did we change to a number grading scale?  

The shift to the numerical grading system was introduced in England in 2017 firstly in English language, English literature, and maths.  

By 2020 all subjects were shifted to number grades. This means anyone with GCSE results from 2017-2020 will have a combination of both letters and numbers.  

The numerical grading system was to signal more challenging GCSEs and to better differentiate between students’ abilities - particularly at higher grades between the A *-C grades. There only used to be 4 grades between A* and C, now with the numerical grading scale there are 6.  

What do the number grades mean?  

The grades are ranked from 1, the lowest, to 9, the highest.  

The grades don’t exactly translate, but the two grading scales meet at three points as illustrated below.  

The bottom of grade 7 is aligned with the bottom of grade A, while the bottom of grade 4 is aligned to the bottom of grade C.    

Meanwhile, the bottom of grade 1 is aligned to the bottom of grade G.  

What to do if your results weren’t what you were expecting?  

If your results weren’t what you were expecting, firstly don’t panic. You have options.  

First things first, speak to your school or college – they could be flexible on entry requirements if you’ve just missed your grades.   

They’ll also be able to give you the best tailored advice on whether re-sitting while studying for your next qualifications is a possibility.   

If you’re really unhappy with your results you can enter to resit all GCSE subjects in summer 2025. You can also take autumn exams in GCSE English language and maths.  

Speak to your sixth form or college to decide when it’s the best time for you to resit a GCSE exam.  

Look for other courses with different grade requirements     

Entry requirements vary depending on the college and course. Ask your school for advice, and call your college or another one in your area to see if there’s a space on a course you’re interested in.    

Consider an apprenticeship    

Apprenticeships combine a practical training job with study too. They’re open to you if you’re 16 or over, living in England, and not in full time education.  

As an apprentice you’ll be a paid employee, have the opportunity to work alongside experienced staff, gain job-specific skills, and get time set aside for training and study related to your role.   

You can find out more about how to apply here .  

Talk to a National Careers Service (NCS) adviser    

The National Career Service is a free resource that can help you with your career planning. Give them a call to discuss potential routes into higher education, further education, or the workplace.   

Whatever your results, if you want to find out more about all your education and training options, as well as get practical advice about your exam results, visit the  National Careers Service page  and Skills for Careers to explore your study and work choices.   

You may also be interested in:

• Results day 2024: What's next after picking up your A level, T level and VTQ results?
• When is results day 2024? GCSEs, A levels, T Levels and VTQs

Tags: GCSE grade equivalent , gcse number grades , GCSE results , gcse results day 2024 , gsce grades old and new , new gcse grades

Sharing and comments

Share this page, related content and links, about the education hub.

The Education Hub is a site for parents, pupils, education professionals and the media that captures all you need to know about the education system. You’ll find accessible, straightforward information on popular topics, Q&As, interviews, case studies, and more.

Please note that for media enquiries, journalists should call our central Newsdesk on 020 7783 8300. This media-only line operates from Monday to Friday, 8am to 7pm. Outside of these hours the number will divert to the duty media officer.

Members of the public should call our general enquiries line on 0370 000 2288.

Sign up and manage updates

Follow us on social media, search by date.

Comments and moderation policy

A Hierarchical Context Augmentation Method to Improve Retrieval-Augmented LLMs on Scientific Papers

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, supplemental material, index terms.

Computing methodologies

Artificial intelligence

Natural language processing

Natural language generation

Information systems

Information retrieval

Document representation

Document structure

Retrieval models and ranking

Language models

Recommendations

On exploiting content and citations together to compute similarity of scientific papers.

In computing the similarity of scientific papers, previous text-based and link-based similarity measures look at only a single side of the content and citations. In this paper, we propose a novel approach called SimCC that effectively combines the ...

Recommending Scientific Papers: The Role of Citation Contexts

This paper addresses the problem of building recommender systems for scientific papers based on the linguistic and contextual analysis of citation contexts. We explain the importance of taking into consideration citation contexts and the different ...

On combining text-based and link-based similarity measures for scientific papers

In computing the similarity of scientific papers, text-based and link-based similarity measures look at only a single side of the content or citations. In this paper, we propose a new approach to compute the similarity of scientific papers accurately by ...

Information

Published in.

• General Chairs:

Northeastern University, USA

CENTAI / Eurecat, Italy

• SIGMOD: ACM Special Interest Group on Management of Data
• SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data

Association for Computing Machinery

New York, NY, United States

Publication History

Permissions, check for updates, author tags.

• context augmentation
• retrieval-augmented llms
• scientific papers
• structure information
• Research-article

Acceptance Rates

Contributors, other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

View or Download as a PDF file.

View online with eReader .

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.