research paper on indices

What Is a Journal Index, and Why is Indexation Important?

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A journal index, or a list of journals organized by discipline, subject, region and other factors, can be used by other researchers to search for studies and data on certain topics. As an author, publishing your research in an indexed journal increases the credibility and visibility of your work. Here we help you to understand journal indexing better - as well as benefit from it.

Updated on May 13, 2022

A journal index, also called a ‘bibliographic index' or ‘bibliographic database', is a list of journals organized by discipline, subject, region or other factors.

Journal indexes can be used to search for studies and data on certain topics. Both scholars and the general public can search journal indexes.

Journals in indexes have been reviewed to ensure they meet certain criteria. These criteria may include:

• Ethics and peer review policies
• Assessment criteria for submitted articles
• Editorial board transparency

What is a journal index?

Indexed journals are important, because they are often considered to be of higher scientific quality than non-indexed journals. You should aim for publication in an indexed journal for this reason. AJE's Journal Guide journal selection tool can help you find one.

Journal indexes are created by different organizations, such as:

• Public bodies- For example, PubMed is maintained by the United States National Library of Medicine. PubMed is the largest index for biomedical publications.
• Analytic companies- For example: the Web of Science Core Collection is maintained by Clarivate Analytics. The WOS Core Collection includes journals indexed in the following sub-indexes: (1) Science Citation Index Expanded (SCIE); (2) Social Sciences Citation Index (SSCI); (3) Arts & Humanities Citation Index (AHCI); (4) Emerging Sources Citation Index.
• Publishers- For example, Scopus is owned by Elsevier and maintained by the Scopus Content Selection and Advisory Board . Scopus includes journals in all disciplines, but the majority are science and technology journals.

Key types of journal indexes

You can choose from a range of journal indexes. Some are broad and are considered “general indexes”. Others are specific to certain fields and are considered “specialized indexes”.

For example:

• The Science Citation Index Expanded includes mostly science and technology journals
• The Arts & Humanities Citation Index includes mostly arts and humanities journals
• PubMed includes mostly biomedical journals
• The Emerging Sources Citation Index includes journals in all disciplines

Which index you choose will depend on your research subject area.

Some indexes, such as Web of Science , include journals from many countries. Others, such as the Chinese Academy of Science indexing system , are specific to certain countries or regions.

Choosing the type of index may depend on factors such as university or grant requirements.

Some indexes are open to the public, while others require a subscription. Many people searching for research papers will start with free search engines, such as Google Scholar , or free journal indexes, such as the Web of Science Master Journal List . Publishing in a journal in one or more free indexes increases the chance of your article being seen.

Journals in subscription-based indexes are generally considered high-quality journals. If the status of the journal is important, choose a journal in one or more subscription-based indexes.

Most journals belong to more than one index. To improve the visibility and impact of your article, choose a journal featured in multiple indexes.

How does journal indexing work?

All journals are checked for certain criteria before being added to an index. Each index has its own set of rules, but basic publishing standards include the following:

• An International Standard Serial Number (ISSN). ISSNs are unique to each journal and indicate that the journal publishes issues on a recurring basis.
• An established publishing schedule.
• Digital Object Identifiers (DOIs) . DOIs are unique letter/number codes assigned to digital objects. The benefit of a DOI is that it will never change, unlike a website link.
• Copyright requirements. A copyright policy helps protect your work and outlines the rules for the use or sharing of your work, whether it's copyrighted or has some form of creative commons licensing .
• Other requirements can include conflict of interest statements, ethical approval statements, an editorial board listed on the website, and published peer review policies.

To be included in an index, a journal must submit an application and undergo an audit by the indexation board. Index board members (called auditors) will confirm certain information, such as the full listing of the editorial board on the website, the inclusion of ethics statements in published articles, established appeal and retraction processes, and more.

Why is journal indexing important?

As an author, publishing your research in an indexed journal increases the credibility and visibility of your work. Indexed journals are generally considered to be of higher scientific quality than non-indexed journals.

With the growth of fully open access journals and online-only journals, recognizing “predatory” journals and their publishers has become difficult. Indexing a journal in one or more well-known databases is a good sign the journal is credible.

Moreover, more and more institutions are requiring publication in an indexed journal as a requirement for graduation, promotion, or grant funding.

As an author, it is important to ensure that your research is seen by as many eyes as possible. Index databases are often the first places scholars and the public will search for specific information. Publishing a paper in a non-indexed journal could be harmful in this context.

However, there are some exceptions, such as medical case reports.

Many journals don't accept medical case reports because they don't have high citation rates. However, several primary and secondary journals have been created specifically for case reports. Examples include the primary journal, BMC Medical Case Reports, and the secondary journal, European Heart Journal - Case Reports.

While many of these journals are indexed, they may not be indexed in the major indexes, though they are still highly acceptable journals.

Open access and indexation

With the recent increase in open access publishing, many journals have started offering an open access option. Other journals are completely open access, meaning they do not offer a traditional subscription service.

Open access journals have many benefits, such as:

• High visibility. Anyone can access and read your paper.
• Publication speed. It is generally quicker to post an article online than to publish it in a traditional journal format.

Identifying credible open access journals

Open access has made it easier for predatory journal publishers to attract unsuspecting or new authors. These predatory journal publishers often publish any article for a fee without peer review and with questionable ethical and copyright policies. Here we show you eight ways to spot predatory open access journals .

One way to identify credible open access journals is their index status. However, be aware that some predatory journals will falsely list indexes or display logos on their website. It is good practice to make sure the journal is indexed on the index's website before submitting your article to that journal.

Major journal indexing services

There are several journal indexes out there. Some of the most popular indexes are as follows:

Life Sciences and Hard Sciences

• Science Citation Index Expanded (SCIE) Master Journal List
• Engineering Index
• Web of Science (now published by Clarivate Analytics, formerly by ISI and Thomson Reuters)
• Chinese Academy of Sciences (CAS)

Humanities and Social Sciences

• Arts & Humanities Citation Index (AHCI) Master Journal List
• Social Sciences Citation Index (SSCI) Master Journal List

Indexation and impact factors

It is easy to assume that indexed journals will have higher impact factors, but indexation and impact factor are unrelated.

Many credible journals don't have impact factors, but they are indexed in several well-known indexes. Therefore, the lack of an impact factor may not accurately represent the credibility of a journal.

Of course, impact factors may be important for other reasons, such as institutional requirements or grant funding. Read this authoritative piece on the uses, importance, and limitations of impact factors .

Final Thoughts

Selecting an indexed journal is an important part of the publication journey. Indexation can tell you a lot about a journal. Publishing in an indexed journal can increase the visibility and credibility of your research. If you're having trouble selecting a journal for publication, consider learning more about AJE's journal recommendation service .

Catherine Zettel Nalen, MS

Academic Editor, Specialist, and Journal Recommendation Team Lead

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Guide to academic journal indexing: Where, when, and how to get indexed

Researchers overwhelmingly rely on scholarly indexes to find vetted academic content online. So to develop and improve the reputation and discoverability of any journal, getting it added to trusted abstracting and indexing (A&I) databases is essential.

Most journal publishers and editors know this, but how to go about seeking inclusion in indexes isn’t always as clear.

Which indexes should you add your journal articles to? What are the indexing criteria you’ll need to fulfill? When should you apply for target indexes? In what order? And how can you keep improving your content discoverability once admitted to indexes?

In this blog post, we answer these common indexing questions and more, covering everything you need to know to initiate and keep building upon a successful journal indexing strategy. Feel free to use the section links below to skip ahead based on where you are in your indexing journey.

Getting started: Understanding academic journal indexes

Key journal index types to consider and the benefits of each, how to develop an indexing strategy for one or more titles, key journal indexing criteria, navigating the journal indexing application process, tips for optimizing your article indexing outcomes, putting it all together.

Before we get into the nitty gritty of indexing, let’s start with some basics. What are journal indexes? Or, more specifically, how are we defining journal indexes for the purposes of this blog post?

Per this Walden University Library guide :

“An index is a list of items pulled together for a purpose. Journal indexes (also called bibliographic indexes or bibliographic databases) are lists of journals, organized by discipline, subject, or type of publication.”

Of course, mainstream search engines like Google and Bing also index content, but they do not fit the definition of an academic journal index. So we won’t get into them here.

However, with that said, many scholars use mainstream search engines in their research and want to know that their articles will be discoverable from them. So don’t forget to prioritize search engine optimization (SEO) with scholarly indexing. We cover everything you need to know about journal SEO for mainstream and academic search engines in this blog post .

Now, on to the primary types of academic journal indexes to consider (per the definition above).

Before embarking on any journal indexing initiative, we recommend developing a target list of the indexes you’d like your journal or journals to be part of to get a bird’s-eye view of your ultimate goal. The more quality indexes you identify, the better, as inclusion in multiple indexes will help expand your articles’ reach and potential impacts while boosting the reputation of your journal(s).

From there, you can map out an indexing strategy based on your discovery goals and the specific criteria of the indexes you’re interested in ( more on how to do this later ). For example, Scopus requires journals to have a 2+ year publication history. So if you’re working with a new journal, you’ll logistically have to wait for at least two years before applying to that index, whereas; you’ll be able to seek inclusion in other indexes like Google Scholar sooner.

Below we outline the index types to consider and the benefits of each.

Scholarly search engines and aggregators

One of the best starting points for journal indexing is scholarly search engines and aggregators, many of which are freely available to researchers and the general public and often have less stringent inclusion criteria with regard to publication history, citation counts, and so forth.

The top academic search engine to focus on is Google Scholar , Google’s free crawler-based academic index. You can find our complete guide to Google Scholar indexing here . We also cover how to improve your chances of showing up higher in Google Scholar search results in our guide to journal SEO . You may have also heard of the Microsoft Academic search engine, but that was discontinued in December 2021.

Scholarly aggregator options with search functionality include Semantic Scholar , Dimensions , Lens , and CORE . Aggregators like these pull in content from other trusted academic databases, with the Crossref content registration agency being a prime source. So one of the best starting points for getting a new journal indexed is applying for Crossref membership and registering Digital Object Identifiers or DOIs for all the articles you publish. We cover how to apply for DOIs here and how to leverage the discovery benefits of Crossref in this webinar .

Registering DOIs for all articles is among the most common indexing application requirements, as discussed below. So it’s a good idea to apply for DOIs early on for new journals.

General scholarly archiving and indexing databases

In addition to getting indexed in scholarly search engines and aggregators, you’ll obviously want to seek inclusion in dedicated academic indexing databases, also known as abstracting and indexing databases or A&Is. You can apply to add your journal(s) to indexing databases that cover multiple disciplines as well as discipline-specific or “specialized” A&Is.

Many aggregators also ingest content directly from partner A&Is or require journals to be admitted to specific A&Is before being eligible to be included in their search results as a means of quality control, so applying to A&Is can help your articles appear in aggregator search results as well. For example, Semantic Scholar only indexes journals already in the Directory of Open Access Journals (DOAJ), and the National Institute of Health’s (NIH) free scholarly search engine PubMed pulls in all of its content from the NIH’s archiving and indexing databases MEDLINE and PubMed Central (PMC). So to be included in PubMed search, journals must be accepted to one of those databases first. You can learn more about the relationship between the NIH’s databases and how to apply to PMC and/or MEDLINE to be added to PubMed Search in this blog post .

There will be myriad indexing options for every journal, ranging from government and institutional indexes to commercial indexes run by publishers and data analytics companies. We obviously can’t cover every possible index in this blog post. But we’ve done our best to compile a list of some of the most widely-used and reputable general scholarly A&Is below (we cover top discipline-specific A&Is in the next section):

• The Directory of Open Access Journals (DOAJ): The DOAJ is a non-profit community-curated online directory of peer-reviewed open-access journals. If you’re jumpstarting indexing for a new OA journal, we recommend beginning with the DOAJ because it’s a trusted OA index that various scholarly aggregators use as a data source. We compiled a complete guide to DOAJ indexing here . The DOAJ is a free-access index.
• Ulrich’s Periodicals Directory : Ulrich’s is a leading library directory and database with information about academic journals and serial publications around the world that is part of Clarivate. Ulrich’s is a subscription-access index.
• Scopus : Scopus is Elsevier’s abstract and citation database. It covers over 36,000 titles, spanning the life sciences, social sciences, physical sciences, and health sciences. You can read our guide to Scopus indexing here and a case study with the editors of Precision Nanomedicine , a Scholastica customer, about their experience getting indexed in Scopus here . Scopus is a subscription-access index.
• Web of Science : WoS is Clarivate’s abstract and citation database. Its Core Collection encompasses six citation indexes in the sciences, social sciences, and humanities and collectively contains more than a billion searchable citations spanning over 250 disciplines. We compiled a complete guide to WoS indexing here . WoS is a subscription-access index.
• EBSCO Information Services : EBSCO is a commercial index and aggregator that includes titles compiled by the company and journals from other databases, such as MEDLINE . EBSCO is a subscription-access index.
• JSTOR : JSTOR is a digital library database that covers over 12 million journal articles, books, images, and primary sources in 75 disciplines. They are best known for hosting digitized content from journal back files, books, and other resources. They now publish journals willing to host articles solely on the JSTOR platform.
• SciELO (Scientific Electronic Library Online) : SciELO is a bibliographic database, digital library, and cooperative electronic publishing model for OA journals created to support the publication and increase the visibility of OA research in developing countries. SciELO is a free-access index.
• Cabell’s : This last one is a little different. Rather than being an index readers use to find content, Cabell’s is a directory researchers use to determine which journals will be the best fit to publish in. Of course, attracting more high-quality submissions can also help journals expand their impact and reach, so it’s a good idea to pursue Cabell’s indexing. Cabell’s is a subscription-access index.

Discipline-specific or “specialized” indexes and search engines

Of course, the discipline-specific indexes you choose to apply to will depend on the subject area(s) your journals cover. If you’re unsure which indexes are the most widely used in a given journal’s discipline or across interdisciplinary areas, start to ask around. Query your authors, editors, reviewers, and readers to learn which discipline-specific databases they use.

There are many discipline-specific databases out there to look into. And some broader databases contain discipline-specific segments. For example, the Web of Science Core Collection includes the Science Citation Index Expanded , Social Sciences Citation Index , and Arts & Humanities Citation Index .

Other top discipline-specific or “specialized” indexes include the ones listed below.

STEM journals:

• PubMed Central (PMC): PMC is a digital repository that archives OA full-text articles published in biomedical and life sciences journals. PMC is a free-access index with search functionality. PubMed aggregates articles from PMC. So Applying to PMC is the fastest way to be included in PubMed Search, as explained in this guide .
• MEDLINE : This is the National Library of Medicine’s (NLM) bibliographic database of life sciences and biomedical research. MEDLINE is a free-access index searchable via PubMed.
• PsycInfo : PsycInfo is the American Psychological Association’s abstracting and indexing database, with over three million records of peer-reviewed literature in the behavioral sciences and mental health fields. PsycInfo is a subscription-access index.
• MathSciNet : MathSciNet is the American Mathematical Society’s searchable online bibliographic database containing over three million records of peer-reviewed literature. It is a subscription-access index.

Humanities and Social Sciences (HSS) journals:

• Project MUSE : MUSE is an index of humanities and social sciences content, including journals, which only indexes content published by a not-for-profit press or scholarly society.
• MLA Directory of Periodicals : This is a searchable list of publication information about the journals included in the MLA International Bibliography.
• EconLit : The American Economic Association’s A&I focused on literature in the field of economics.

For more journal indexing options, check out Wikipedia’s list here and Nature’s list of indexes that their journals are part of here . The University of Miami Library also has a comprehensive list of indexes here .

Pro Tip: As a rule of thumb, if your journal is an OA publication, it’s a good idea to make getting added to the DOAJ a priority. The DOAJ is one of the top general indexing databases in terms of use and reputability that journals can usually apply for relatively early in their publication life. With over 12,000 journal members, over 1.2 million visitors a month, and a continually updating stream of journal metadata ingested by leading discovery services across disciplines, the DOAJ is a powerful platform for journal awareness.

Once you know the indexes you want to pursue, it’s time to map out your indexing strategy. Indexes will have varying levels of inclusion criteria (e.g., publication and technical standards journals must fulfill), so it’s a good idea to make a gradual indexing plan. Start with low-hanging fruit indexes that you can have your journal(s) added to early on, and then build up to more selective cross-discipline and discipline-specific/“specialized” scholarly databases such as Scopus and MEDLINE.

Of course, the more highly vetted an index is, the more trustworthy it will be to scholars. So journals should keep working to apply to more stringent databases as they mature and become eligible. Don’t just stop at a few!

When weighing your indexing options, consider the level of article discovery benefits different indexes will offer. For example, some databases only index article titles, abstracts, and/or references. Whereas some index entire article files. Generally, indexes that ingest more article details or the full text will be better for expanding content discoverability since they’ll have more information to go off of when deciding if and when to show your articles in search results. They also offer a more direct search experience for researchers.

As seen in the previous section, indexes also offer different levels of accessibility, with some being freely available to anyone interested in searching them, like Google Scholar and PubMed search, and others requiring a subscription, like Scopus and Web of Science. For open access (OA) journals especially, ensuring articles are easy to find via free online indexes, not just subscription databases, is paramount to maximizing content accessibility.

Pro Tip: When developing your indexing strategy, don’t forget to account for application review timelines. While some indexes review journals on a rolling basis, others only review applications at certain times throughout the year. So that will also factor into when you’ll be eligible for different indexes.

As you’re considering possible indexes to apply to, you’ll obviously want to start by visiting their application requirements pages and reading them thoroughly. Doing a quick Google search for “[index name] application criteria” or “how to get indexed in [index name]” will usually get you there. If you’re having trouble finding an index’s application criteria, you can also always visit their help/contact page to find a support email to write to for guidance.

Now, onto indexing application criteria journals should fulfill.

As noted, reputable scholarly search engines, aggregators, and A&Is have admittance standards and often require journals to undergo an application process before being eligible for inclusion.

Here, we cover the most common indexing application criteria moving from basics to more stringent requirements. These are all publishing best practices, so you should aim to fulfill them regardless of which indexes you decide to pursue.

Publication standards

Starting with publication standards (e.g., journal details, editorial policies, etc.), in good news, many requirements will essentially be the same across scholarly indexes. Some of the most common publication criteria include that all journals should have:

• An International Standard Serial Number (ISSN)
• Digital Object Identifiers or DOIs for all articles ( Crossref is the leading DOI registration agency for journals)
• A dedicated editorial board page with the names, titles, and institutional affiliations of all editors
• Clearly stated peer review policies , including an overview of the journal’s peer review process (e.g., type, stages of review) and statements on publication ethics
• An established publishing schedule (e.g., bi-monthly, rolling)
• An established copyright policy (e.g., CC BY for fully OA journals)

From there, indexes may have more specific additional guidelines. For example, some indexes require journals, particularly those that publish online only, to show that their articles are being added to an archive (this is also a general best practice !). Other specific indexing requirements may include guidelines around:

• Publication scope: While many indexes accept journals in all disciplines or within a broad set of disciplinary areas, such as the humanities and social sciences, some only accept journals that publish within a particular subject area.
• Minimum publication history: Some indexes require publishers and/or journals to be around for a minimum amount of time before applying. For example, MEDLINE only accepts applications from organizations that have published scholarly content for two years or more.
• Level of publishing professionalization: Some indexes also look at the readability of published articles (e.g., level of editing) and production quality.
• Geographic diversity: Some indexes look to see that journals have geographically diverse editorial boards and authors.
• Adequate citations: Some indexes will not accept journals until they meet a certain citation-level threshold to demonstrate impact.

Technical requirements

In addition to publication standards like those outlined above, many scholarly search engines, aggregators, and indexes require or encourage journals to meet specific technical criteria for content ingestion.

First, it’s helpful to understand the three main ways scholarly search engines, aggregators, and A&Is ingest content:

• Web crawlers: Some scholarly search engines like Google Scholar index journal articles via web crawlers or bots that systematically scan websites for content. For crawlers to be able to find and index articles, publishers must apply machine-readable metadata to all article pages via HTML meta tags and maintain a website structure that complies with the search engine’s requirements. For example, Google Scholar will only index articles hosted on their own webpage with HTML meta tags. You can learn more about Google Scholar’s technical inclusion criteria here .
• Metadata/content deposits: Many indexes do not have web crawlers and instead require content deposits. In this case, publishers must submit article-level metadata and/or full-text article files to the index. Some indexes have forms for making manual metadata deposits. However, many require journals to directly deposit machine-readable metadata and/or full-text article files into the index via an FTP server or API integration. Making machine-readable metadata/article deposits is also a best practice because machine-readable metadata files are generally richer, more uniform, and less prone to inaccuracies than metadata input manually. JATS XML is the standard machine-readable format for journal metadata/article files. JATS, which was developed by the National Information Standards Organization (NISO), stands for Journal Article Tag Suite.
• Cascading metadata: As noted above, some scholarly aggregators automatically ingest content from other trusted academic databases such as the Crossref content registration service and DOAJ index.

At a minimum, journals should aim to produce front-matter JATS XML article-level metadata files that include:

• Journal publisher
• Journal issue details (e.g., publication date and volume/issue number)
• Journal title
• Article title
• Author names
• Copyright license
• Persistent Identifiers or PIDs (e.g., Digital Object Identifier, ORCID iD, ROR ID)

Once journals have the above core metadata fields, they can work to continue enriching their metadata outputs. We cover five elements of “richer” metadata to prioritize in this blog post .

Some databases also require full-text XML article files like PMC, which has specific JATS XML formatting guidelines.

We cover JATS XML 101 in this blog post and the what, why, and how of producing JATS XML in this webinar .

Producing XML in the JATS standard can get quite technical, but thankfully, there are software and services that can help. For example, Scholastica’s digital-first production service generates full-text JATS XML articles with rich metadata, and our fully-OA journal publishing platform features JATS XML metadata on all article pages.

From publication standards to technical requirements, most indexing criteria will be straightforward in nature. But fulfilling them will require a high level of attention to detail. That’s why, in all of your indexing endeavors, it’s so so so important to take your time!

Read indexing applications carefully, then re-read them again — we can’t emphasize this enough. And if you’re already in one index, don’t assume the criteria will be the same for the others. You’ll need to account for variables, big and small.

Also, if you have to update or add information to your journal website to fulfill indexing criteria, be sure to do so in all relevant places and to make required indexing information as specific and explicit as possible. For example, you don’t want your DOAJ application denied because you have missing or inconsistent copyright information on one of your website pages. (Yes, one page can make or break an application!).

Of course, it’s not the end of the world if an index denies your application! All indexes will allow you to reapply. But many require a waiting period for re-application (e.g., the DOAJ has a 6-month wait), so it pays to take some extra time to get your application right on the first round.

If you’re unsure whether one of your journals meets the criteria for a particular database, you can visit their website or contact their support staff to find out what you need to do to be eligible. Another great indexing resource is university libraries. Reach out to scholarly communication or subject-specific librarians to find out what they recommend. Many libraries are well-versed in helping journals get indexed.

A good indexing strategy extends beyond your initial application. Once admitted to indexes, adhering to the highest technical standards is critical to maximizing their discovery benefits.

Start by focusing on producing and enriching machine-readable article-level metadata to have more article details to send to indexes. That means including descriptive HTML meta tags on article website pages for crawler-based search engines and producing rich machine-readable metadata files for deposit-based A&Is. As noted, the machine-readable format standard for academic journals is JATS XML. JATS is preferred or required by many academic indexes, including all National Library of Medicine indexes (i.e., PubMed, PubMed Central, and MEDLINE).

For deposit-based indexes, it’s also important not to lose sight that content won’t be discoverable from those channels until it’s added. So the sooner you can make metadata/article file deposits for new or updated articles, the better. Ideally, you should automate index deposits where possible. Journal publishing platforms can help you here. For example, Scholastica’s OA publishing platform includes integrations with leading discovery services, including Crossref, the DOAJ, and PubMed.

As you can see from this blog post, journal indexing is a process — and it will take time . But it’s well worth the effort to seek inclusion in various relevant indexes and to work to optimize your indexing outcomes. Adding journals to indexes helps expand their reach, reputation, and, consequently, their impacts.

We hope you’ve found this guide helpful! You can learn more about how Scholastica is helping journal publishers automate indexing steps here and how we’re helping journals produce machine-readable metadata to make articles more discoverable here .

Update note: This blog post was originally published on the 21st of June 2017 and updated on the 13th of April 2023.

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What is Journal Indexing and the Types of Journal Indexing in Research

Just as an index is a list of items put together for a specific purpose, journal indexing is the process of listing  journals, organized by discipline, type of publication, region, etc. Journal indexes are also known as bibliographic or citation indexes. The online discovery of research articles relies heavily on journal indexing . And so, researchers and journals alike must know the types of journal indexing to get the best out of it.  

Table of Contents

What is journal indexing and how does it work?  

Journal indexing in research serves as a guide for relevant scholarly content and seeks to make the information widely available and easy to access. It can function as an information retrieval tool for libraries and archives. Journal indexing allows users to familiarize themselves with an article and decide if they want to read it further.  

The process of inclusion in a journal index involves scrutiny and assessment to ensure that a journal meets basic scholarly publishing standards. A journal applies to a relevant journal indexing service, requesting its integration in their database. The journal indexing service will follow a thorough vetting process for industry standards, some of which are as follows:  

• Scope of the journal (especially if the index is subject specific)  
• Registration of its International Standard Serial Number (ISSN)  
• Commitment to a publishing schedule  
• Provision of transparent Editorial Board information
• Provision of information on peer review, copyrights, ethics, etc.  
• Digital object identifiers (DOIs)  
• Basic article-level metadata (persistent identifiers, copyright licenses, open abstracts, etc.).  

Once the evaluation is complete and the journal is indexed by a database, it becomes available to the users of that journal indexing database .  

Types of index ing in journals  

There are many rich options for researchers to tap into during their literature search. To understand these options better, let’s take a look at the types of indexing in journals and how journal indexing databases can be classified.  

Specificity  

Broad or general indexes are, as the name indicates, broad in scope and coverage. Examples of such indexing databases in research are Directory of Open Access Journals (DOAJ), Scopus, and Web of Science.  

Free search engines like Google Scholar also fall in this category. Google Scholar indexes the full text or metadata of scholarly literature across the breadth of disciplines and publishing formats. This is where a researcher typically launches preliminary searches before a deep dive into specialized indexes.  

Specialized indexes are indexes specific to certain fields or subject areas. Examples of specialized indexes are Science Citation Index Expanded (SCIE), which includes mainly science and technology journals; PubMed, which contains mostly biomedical journals; and Arts & Humanities Citation Index, which includes mostly arts and humanities journals.  

Geographical coverage  

While indexes like Web of Science include journals from many countries, some are specific to a country or region, e.g., Korean Citation Index.  

Many journal indexing databases are free and open to the public (e.g., Web of Science Master Journal List), while some are subscription based. Journals in subscription-based indexes are generally considered higher in status, perhaps because of the stronger assessment for inclusion in such journal indexing databases . However, their access would be limited to subscribers only.  

What is indexed  

Different indexes even provide different levels of “discovery potential.” Some databases index article titles, abstracts, and references, whereas some index the full article. It is obvious that journal indexes offering more article information or full text will have a higher potential for discovery.  

Journal indexing and the importance of indexing in research  

Now that it is clear that there are so many indexing databases in research to choose from, how can users maximize their benefits?  

For researchers: at the literature search stage  

Researchers can search a list of journal indexing databases to find studies on specific topics. You can search for specific journals and even browse by subject or database. Journal indexes also help you save time because they simplify the search for relevant information.  

For researchers: at the journal selection stage  

As an author, publishing in an indexed journal increases the visibility and credibility of your work. Most institutions and funders require publications to appear in indexed journals. Many high-quality and high-impact journals are indexed in multiple databases. If you want to know where all your target journal is indexed, go to the “About the journal” section on a journal’s website. You might find an “Abstracting & Indexing” tab, under which you can view a list of journal indexing databases the journal appears in.  

For journals/publishers  

Being indexed in several well-known bibliographic databases points to the quality of a journal. Moreover, journal indexing makes a journal accessible to a wide audience. This increases its visibility and translates into better reach and impact of the journal, which boosts its reputation and sets the ball rolling for an even wider readership. Journals can also benefit from being added to general search engines, besides scholarly databases, to make their articles highly discoverable.  

Concluding notes  

Indexed journals are reliable sources of high-quality research. They can be used for efficient literature searches. Further, when choosing where to publish, authors should prioritize journals that are indexed in several general and specialized indexes to improve the visibility and impact of their work.  

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

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Measuring Your Impact: Impact Factor, Citation Analysis, and other Metrics: Citation Analysis

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About Citation Analysis

What is Citation Analysis?

The process whereby the impact or "quality" of an article is assessed by counting the number of times other authors mention it in their work.

Citation analysis invovles counting the number of times an article is cited by other works to measure the impact of a publicaton or author.  The caviat however, there is no single citation analysis tools that collects all publications and their cited references.  For a thorough analysis of the impact of an author or a publication, one needs to look in multiple databases to find all possible cited references. A number of resources are available at UIC  that identify cited works including: Web of Science, Scopus, Google Scholar, and other databases with limited citation data.

Citation Analysis - Why use it?

To find out how much impact a particular article or author has had, by showing which other authors cited the work within their own papers.  The H-Index is one specific method utilizing citation analysis to determine an individuals impact.

Web of Science

Web of Science provides citation counts for articles indexed within it.  It i ndexes over 10,000 journals in the arts, humanities,  sciences, and social sciences.

• Enter the name of the author in the top search box (e.g. Smith JT).  
• Select Author from the drop-down menu on the right.
• To ensure accuracy for popular names, enter Univ Illinois in the middle search box, then select “Address” from the field drop down menu on the right.  (You might have to add the second search box by clicking "add another field" before you enter the address)
• Click on Search
• a list of publications by that author name will appear.   To the right of each citation, the number of times the article has been cited will appear.   Click the number next to "times cited" to view the articles that have cited your article

Scopus provide citation counts for articles indexed within it (limited to article written in 1996 and after).   It indexes o ver 15,000 journals from over 4,000 international publishers across the disciplines.

• Once in Scopus, click on the Author search tab.
• Enter the name of the author in the search box.  If you are using initials for the first and/or middle name, be sure to enter periods after the initials (e.g. Smith J.T.). 
• To ensure accuracy if it is a popular name, you may enter University of Illinois in the affiliation field.  
• If more than one profile appears, click on your profile (or the profile of the person you are examining). 
• Once you click on the author's profile, a list of the publications will appear and to the right of each ctation, the number of times the article has been cited will appear.  
• Click the number to view the articles that have cited your article

 Dimensions (UIC does not subscribe but parts are free to use)

• Indexes over 28000 journals
• Does not display h-index in Dimensions but can calculate or if faculty, look in MyActivities
• Includes Altmetrics score
• Google Scholar

Google Scholar provides citation counts for articles found within Google Scholar.  Depending on the discipline and cited article, it may find more cited references than Web of Science or Scopus because overall, Google Scholar is indexing more journals and more publication types than other databases. Google Scholar is not specific about what is included in its tool but information is available on how Google obtains its content .   Limiting searches to only publications by a specific author name is complicated in Google Scholar.  Using Google Scholar Citations and creating your own profile will make it easy for you to create a list of publications included in Google Scholar.   Using your Google Scholar Citations account, you can see the citation counts for your publications and have GS calculate your h-index.  (You can also search Google Scholar by author name and the title of an article to retrieve citation information for a specific article.)

• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the scrren to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.

Try Harzing's Publish or Perish Tool in order to more selectively examine published works by a specific author.

Databases containing limited citation counts:

• PubMed Central
• Science Direct
• SciFinder Scholar

About the H-index

The h-index is an index to quantify an individual’s scientific research output ( J.E. Hirsch )   The h-index is an index that attempts to measure both the scientific productivity and the apparent scientific impact of a scientist. The index is based on the set of the researcher's most cited papers and the number of citations that they have received in other people's publications ( Wikipedia )  A scientist has index h if h of [his/her] Np papers have at least h citations each, and the other (Np − h) papers have at most h citations each.

Find your h-index at:

Below are instructions for obtaining your h-index from Web of Science, Scopus, and Google Scholar.

Web of Science provides citation counts for articles indexed within it.  It indexes over 12,000 journals in the arts, humanities,  sciences, and social sciences.  To find an author's h-index in WOS:

• To ensure accuracy for popular names, add an additional search box and enter "Univ Illinois" and then select “Address” from the field drop down menu on the right.
• Click on Citation Report on the right hand corner of the results page.  The H-index is on the right of the screen.
• If more than one profile appears, click on your profile (or the profile of the person you are examining).  Under the Research section, you will see the h-index listed.
• If you have worked at more than one place, your name may appear twice with 2 separate h-index ratings.  Select the check box next to each relevent profile, and click show documents.

  Google Scholar

• Using your google (gmail) account, create a profile of all your articles captured in Google Scholar.  Follow the prompt on the screen to set up your profile.   Once complete, this will show all the times the articles have been cited by other documents in Google Scholar and your h-index will be provided.  Its your choice whether you make your profile public or private but if you make it public, you can link to it from your own webpages.
• See  Albert Einstein's
• Harzing’s Publish or Perish (POP) 
• Publish or Perish Searches Google Scholar.  After searching by your name, deselect from the list of articles retrieved those that you did not author.  Your h-index will appear at the top of the tool.  Note:This tool must be downloaded to use
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A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

• Review paper
• Published: 11 March 2023
• Volume 22 , pages 349–395, ( 2023 )

Cite this article

• Sandra Chidiac   ORCID: orcid.org/0000-0002-1822-119X 1 ,
• Paula El Najjar 1 , 2 ,
• Naim Ouaini 1 ,
• Youssef El Rayess 1 &
• Desiree El Azzi 1 , 3  

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Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

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1 Introduction

Water is the vital natural resource with social and economic values for human beings (Kumar 2018 ). Without water, existence of man would be threatened (Zhang 2017 ). The most important drinking sources in the world are surface water and groundwater (Paun et al. 2016 ).

Currently, more than 1.1 billion people do not have access to clean drinking water and it is estimated that nearly two-thirds of all nations will experience water stress by the year 2025 (Kumar 2018 ).

With the extensive social and economic growth, such as human factors, climate and hydrology may lead to accumulation of pollutants in the surface water that may result in gradual change of the water source quality (Shan 2011 ).

The optimal quantity and acceptable quality of water is one of the essential needs to survive as mentioned earlier, but the maintenance of an acceptable quality of water is a challenge in the sector of water resources management (Mukate et al. 2019 ). Accordingly, the water quality of water bodies can be tested through changes in physical, chemical and biological characteristics related to anthropogenic or natural phenomena (Britto et al. 2018 ).

Therefore, water quality of any specific water body can be tested using physical, chemical and biological parameters also called variables, by collecting samples and obtaining data at specific locations (Britto et al. 2018 ; Tyagi et al. 2013 ).

To that end, the suitability of water sources for human consumption has been described in terms of Water Quality Index (WQI), which is one of the most effective ways to describe the quality of water, by reducing the bulk of information into a single value ranging between 0 and 100 (Tyagi et al. 2013 ).

Hence, the objective of the study is to review the WQI concept by listing some of the important water quality indices used worldwide for water quality assessment, listing the advantages and disadvantages of the selected indices and finally reviewing some water quality studies worldwide.

2 Water quality index

2.1 history of water quality concept.

In the last decade of the twentieth century, many organizations involved in water control, used the water quality indices for water quality assessment (Paun et al. 2016 ). In the 1960’s, the water quality indices was introduced to assess the water quality in rivers (Hamlat et al. 2017 ).

Horton ( 1965 ), initially developed a system for rating water quality through index numbers, offering a tool for water pollution abatement, since the terms “water quality” and “pollution” are related. The first step to develop an index is to select a list of 10 variables for the index’s construction, which are: sewage treatment, dissolved oxygen (DO), pH, coliforms, electroconductivity (EC), carbon chloroform extract (CCE), alkalinity, chloride, temperature and obvious pollution. The next step is to assign a scale value between zero and 100 for each variable depending on the quality or concentration. The last step, is to designate to each variable is a relative weighting factor to show their importance and influence on the quality index (the higher the assigned weight, the more impact it has on the water quality index, consequently it is more important) (Horton 1965 ).

Later on, Brown et al. ( 1970 ) established a new water quality index (WQI) with nine variables: DO, coliforms, pH, temperature, biochemical oxygen demand (BOD), total phosphate, nitrate concentrations, turbidity and solid content based on a basic arithmetic weighting using arithmetic mean to calculate the rating of each variable. These rates are then converted not temporary weights. Finally, each temporary weight is divided by the sum of all the temporary weights in order to get the final weight of each variable (Kachroud et al. 2019a ; Shah and Joshi 2017 ). In 1973, Brown et al., considered that a geometric aggregation (a way to aggregate variables, and being more sensitive when a variable exceeds the norm) is better than an arithmetic one. The National Sanitation Foundation (NSF) supported this effort (Kachroud et al. 2019a ; Shah and Joshi 2017 ).

Steinhart et al. ( 1982 ) developed a novel environmental quality index (EQI) for the Great Lakes ecosystem in North America. Nine variables were selected for this index: biological, physical, chemical and toxic. These variables were: specific conductance or electroconductivity, chloride, total phosphorus, fecal Coliforms, chlorophyll a , suspended solids, obvious pollution (aesthetic state), toxic inorganic contaminants, and toxic organic contaminants. Raw data were converted to subindex and each subindex was multiplied by a weighting factor (a value of 0.1 for chemical, physical and biological factors but 0.15 for toxic substances). The final score ranged between 0 (poor quality) and 100 (best quality) (Lumb et al. 2011a ; Tirkey et al. 2015 ).

Dinius ( 1987 ), developed a WQI based on multiplicative aggregation having a scale expressed with values as percentage, where 100% expressed a perfect water quality (Shah and Joshi 2017 ).

In the mid 90’s, a new WQI was introduced to Canada by the province of British Columbia, and used as an increasing index to evaluate water quality (Lumb et al. 2011b ; Shah and Joshi 2017 ). A while after, the Water Quality Guidelines Task Group of the Canadian Council of Ministers of the Environment (CCME) modified the original British Columbia Water Quality Index (BCWQI) and endorsed it as the CCME WQI in 2001(Bharti and Katyal 2011 ; Lumb et al. 2011b ).

In 1996, the Watershed Enhancement Program (WEPWQI) was established in Dayton Ohio, including water quality variables, flow measurements and water clarity or turbidity. Taking into consideration pesticide and Polycyclic Aromatic Hydrocarbon (PAH) contamination, is what distinguished this index from the NSFWQI (Kachroud et al. 2019a , b ).

Liou et al. (2003) established a WQI in Taiwan on the Keya River. The index employed thirteen variables: Fecal coliforms, DO, ammonia nitrogen, BOD, suspended solids, turbidity, temperature, pH, toxicity, cadmium (Cd), lead (Pb), copper (Cu) and zinc (Zn). These variables were downsized to nine based on environmental and health significance: Fecal coliforms, DO, ammonia nitrogen, BOD, suspended solids, turbidity, temperature, pH and toxicity. Each variable was converted into an actual value ranging on a scale from 0 to 100 (worst to highest). This index is based on the geometric means (an aggregation function that could eliminate the ambiguous caused from smaller weightings) of the standardized values (Akhtar et al. 2021 ; Liou et al. 2004 ; Uddin et al. 2021 ).

Said et al. ( 2004 ) implemented a new WQI using the logarithmic aggregation applied in streams waterbodies in Florida (USA), based on only 5 variables: DO, total phosphate, turbidity, fecal coliforms and specific conductance. The main idea was to decrease the number of variables and change the aggregation method using the logarithmic aggregation (this function does not require any sub-indices and any standardization of the variables). This index ranged from 0 to 3, the latter being the ideal value (Akhtar et al. 2021 ; Kachroud et al. 2019a , b ; Said et al. 2004 ; Uddin et al. 2021 ).

The Malaysian WQI (MWQI) was carried out in 2007, including six variables: DO, BOD, Chemical Oxygen Demand (COD), Ammonia Nitrogen, suspended solids and pH. For each variable, a curve was established to transform the actual value of the variable into a non-dimensional sub-index value.

The next step is to determine the weighting of the variables by considering the experts panel opinions. The final score is determined using the additive aggregation formula (where sub-indices values and their weightings are summed), extending from 0 (polluted) to 100 (clean) (Uddin et al. 2021 ).

The Hanh and Almeida indices were established respectively in 2010 on surface water in Vietnam and 2012 on the Potrero de los Funes in Argentina, based on 8 (color, suspended solids, DO, BOD, COD, chloride, total coliforms and orthophosphate) and 10 (color, pH, COD, fecal coliforms, total coliforms, total phosphate, nitrates, detergent, enterococci and Escherichia coli .) water quality variables. Both indices were based on rating curve- based sum-indexing system (Uddin et al. 2021 ).

The most recent developed WQI model in the literature was carried out in 2017. This index tried to reduce uncertainty present in other water quality indices. The West Java Water Quality Index (WJWQI) applied in the Java Sea in Indonesia was based on thirteen crucial water quality variables: temperature, suspended solids, COD, DO, nitrite, total phosphate, detergent, phenol, chloride, Zn, Pb, mercury (Hg) and fecal coliforms. Using two screening steps (based on statistical assessment), parameter (variable) redundancy was determined to only 9: temperature, suspended solids, COD, DO, nitrite, total phosphate, detergent, phenol and chloride. Sub-indices were obtained for those nine variables and weights were allocated based on expert opinions, using the same multiplicative aggregation as the NSFWQI. The WJWQI suggested 5 quality classes ranging from poor (5–25) to excellent (90–100) (Uddin et al. 2021 ).

2.2 Phases of WQI development

Mainly, WQI concept is based on many factors as displayed in Fig.  1 and described in the following steps:

Phases of WQI development

Parameter selection for measurement of water quality (Shah and Joshi 2017 ):

The selection is carried out based on the management objectives and the environmental characteristics of the research area (Yan et al. 2015 ). Many variables are recommended, since they have a considerable impact on water quality and derive from 5 classes namely, oxygen level, eutrophication, health aspects, physical characteristics and dissolved substances (Tyagi et al. 2013 ).

Transformation of the raw data parameter into a common scale (Paun et al. 2016 ):

Different statistical approach can be used for transformation, all parameters are transformed from raw data that have different dimensions and units (ppm, saturation, percentage etc.) into a common scale, a non-dimensional scale and sub-indices are generated (Poonam et al. 2013 ; Tirkey et al. 2015 ).

Providing weights to the parameters (Tripathi and Singal 2019 ):

Weights are assigned to each parameter according to their importance and their impact on water quality, expert opinion is needed to assign weights (Tirkey et al. 2015 ). Weightage depends on the permissible limits assigned by International and National agencies in water drinking (Shah and Joshi 2017 ).

Aggregation of sub-index values to obtain the final WQI:

WQI is the sum of rating and weightage of all the parameters (Tripathi and Singal 2019 ).

It is important to note that in some indices, statistical approaches are commonly used such as factor analysis (FA), principal component analysis (PCA), discriminant analysis (DA) and cluster analysis (CA). Using these statistical approaches improves accuracy of the index and reduce subjective assumptions (Tirkey et al. 2015 ).

2.3 Evolution of WQI research

2.3.1 per year.

According to Scopus ( 2022 ), the yearly evolution of WQI's research is illustrated in Fig.  2 (from 1978 till 2022).

Evolution of WQI research per year (Scopus 2022 )

Overall, it is clear that the number of research has grown over time, especially in the most recent years. The number of studies remained shy between 1975 and 1988 (ranging from 1 to 13 research). In 1998, the number improved to 46 studies and increased gradually to 466 publications in 2011.The WQI's studies have grown significantly over the past decade, demonstrating that the WQI has become a significant research topic with the goal of reaching its maximum in 2022 (1316 studies) (Scopus, 2022 ).

2.3.2 Per country

In Fig.  3 , the development of WQI research is depicted visually per country from 1975 to 2022.

Evolution of WQI research per country (Scopus 2022 )

According to Scopus ( 2022 ), the top three countries were China, India and the United States, with 2356, 1678 and 1241 studies, respectively. Iran, Brazil, and Italy occupy the fourth, fifth, and sixth spots, respectively (409, 375 and 336 study). Malaysia and Spain have approximately the same number of studies, respectively 321 and 320 study. The studies in the remaining countries decrease gradually from 303 document in Spain to 210 documents in Turkey. This demonstrates that developing nations, like India, place a high value on the development of water quality protection even though they lack strong economic power, cutting-edge technology, and a top-notch scientific research team. This is because water quality is crucial to the long-term social and economic development of those nations (Zhang 2019 ).

2.4 Different methods for WQI determination

Water quality indices are tools to determine water quality. Those indices demand basic concepts and knowledge about water issues (Singh et al. 2013 ). There are many water quality indices such as the: National Sanitation Foundation Water Quality Index (NSFWQI), Canadian Council of Ministers of Environment Water Quality Index (CCMEWQI), Oregon Water Quality Index (OWQI), and Weight Arithmetic Water Quality Index (WAWQI) (Paun et al. 2016 ).

These water quality indices are applied in particular areas, based on many parameters compared to specific regional standards. Moreover, they are used to illustrate annual cycles, spatio-temporal variations and trends in water quality (Paun et al. 2016 ). That is to say that, these indices reflect the rank of water quality in lakes, streams, rivers, and reservoirs (Kizar 2018 ).

Accordingly, in this section a general review of available worldwide used indices is presented.

2.4.1 National sanitation foundation (NSFWQI)

The NSFWQI was developed in 1970 by the National Sanitation Foundation (NSF) of the United States (Hamlat et al. 2017 ; Samadi et al. 2015 ). This WQI has been widely field tested and is used to calculate and evaluate the WQI of many water bodies (Hamlat et al. 2017 ). However, this index belongs to the public indices group. It represents a general water quality and does not take into account the water’s use capacities, furthermore, it ignores all types of water consumption in the evaluation process (Bharti and Katyal 2011 ; Ewaid 2017 ).

The NSFWQI has been widely applied and accepted in Asian, African and European countries (Singh et al. 2013 ), and is based on the analysis of nine variables or parameters, such as, BOD, DO, Nitrate (NO 3 ), Total Phosphate (PO 4 ), Temperature, Turbidity, Total Solids(TS), pH, and Fecal Coliforms (FC).

Some of the index parameters have different importance, therefore, a weighted mean for each parameter is assigned, based on expert opinion which have grounded their opinions on the environmental significance, the recommended principles and uses of water body and the sum of these weights is equal to 1 (Table 1 ) (Ewaid 2017 ; Uddin et al. 2021 ).

Due to environmental issues, the NSFWQI has changed overtime. The TS parameter was substituted by the Total Dissolved Solids (TDS) or Total Suspended Solids (TSS), the Total Phosphate by orthophosphate, and the FC by E. coli (Oliveira et al. 2019 ).

The mathematical expression of the NSFWQI is given by the following Eq. ( 1 ) (Tyagi et al. 2013 ):

where, Qi is the sub-index for ith water quality parameter. Wi is the weight associated with ith water quality parameter. n is the number of water quality parameters.

This method ranges from 0 to 100, where 100 represents perfect water quality conditions, while zero indicates water that is not suitable for the use and needs further treatment (Samadi et al. 2015 ).

The ratings are defined in the following Table 2 .

In 1972, the Dinius index (DWQI) happened to be the second modified version of the NSF (USA). Expended in 1987 using the Delphi method, the DWQI included twelve parameters (with their assigned weights): Temperature (0.077), color (0.063), pH (0.077), DO (0.109), BOD (0.097), EC (0.079), alkalinity (0.063), chloride (0.074), coliform count (0.090), E. coli (0.116). total hardness (0.065) and nitrate (0.090). Without any conversion process, the DWQI used the measured variable concentrations directly as the sub-index values (Kachroud et al. 2019b ; Uddin et al. 2021 ).

Sukmawati and Rusni assessed in 2018 the water quality in Beratan lake (Bali), choosing five representative stations for water sampling representing each side of the lake, using the NSFWQI. NSFWQI’s nine parameters mentioned above were measured in each station. The findings indicated that the NSFWQI for the Beratan lake was seventy-eight suggesting a good water quality. Despite this, both pH and FC were below the required score (Sukmawati and Rusni 2019 ).

The NSFWQI indicated a good water quality while having an inadequate value for fecal coliforms and pH. For that reason, WQIs must be adapted and developed so that any minor change in the value of any parameter affects the total value of the water quality index.

A study conducted by Zhan et al. ( 2021 ) , concerning the monitoring of water quality and examining WQI trends of raw water in Macao (China) was established from 2002 to 2019 adopting the NSFWQI. NSFWQI's initial model included nine parameters (DO, FC, pH, BOD, temperature, total phosphates, and nitrates), each parameter was given a weight and the parameters used had a significant impact on the WQI calculation outcomes. Two sets of possible parameters were investigated in this study in order to determine the impact of various parameters. The first option was to keep the original 9-parameter model, however, in the second scenario, up to twenty-one parameters were chosen, selected by Principal Component Analysis (PCA).

The latter statistical method was used to learn more about the primary elements that contributed to water quality variations, and to calculate the impact of each attribute on the quality of raw water. Based on the PCA results, the 21-parameter model was chosen. The results showed that the quality of raw water in Macao has been relatively stable in the period of interest and appeared an upward trend overall. Furthermore, the outcome of environmental elements, such as natural events, the region's hydrology and meteorology, can have a significant impact on water quality. On the other hand, Macao's raw water quality met China's Class III water quality requirements and the raw water pollution was relatively low. Consequently, human activities didn’t have a significant impact on water quality due to effective treatment and protection measures (Zhan et al. 2021 ).

Tampo et al. ( 2022 ) undertook a recent study in Adjougba (Togo), in the valley of Zio River. Water samples were collected from the surface water (SW), ground water (GW) and treated wastewater (TWW), intending to compare the water quality of these resources for irrigation and domestic use.

Hence, WQIs, water suitability indicators for irrigation purposes (WSI-IPs) and raw water quality parameters were compared using statistical analysis (factor analysis and Spearman’s correlation).

Moreover, the results proposed that he water resources are suitable for irrigation and domestic use: TWW suitable for irrigation use, GW suitable for domestic use and SW suitable for irrigation use.

The NSFWQI and overall index of pollution (OPI) parameters were tested, and the results demonstrated that the sodium absorption ratio, EC, residual sodium carbonate, Chloride and FC are the most effective parameters for determining if water is suitable for irrigation.

On the other hand, EC, DO, pH, turbidity, COD, hardness, FC, nitrates, national sanitation foundation's water quality index (NSFWQI), and overall index of pollution (OPI) are the most reliable in the detection of water suitability for domestic use (Tampo et al. 2022 ).

Following these studies, it is worth examining the NSFWQI. This index can be used with other WQI models in studies on rivers, lakes etc., since one index can show different results than another index, in view of the fact that some indices might be affected by other variations such as seasonal variation.

Additionally, the NSFWQI should be developed and adapted to each river, so that any change in any value will affect the entire water quality. It is unhelpful to have a good water quality yet a low score of a parameter that can affect human health (case of FC).

2.4.2 Canadian council of ministers of the environment water quality index (CCMEWQI)

The Canadian Water Quality Index adopted the conceptual model of the British Colombia Water Quality Index (BCWQI), based on relative sub-indices (Kizar 2018 ).

The CCMEWQI provides a water quality assessment for the suitability of water bodies, to support aquatic life in specific monitoring sites in Canada (Paun et al. 2016 ). In addition, this index gives information about the water quality for both management and the public. It can furthermore be applied in many water agencies in various countries with slight modification (Tyagi et al. 2013 ).

The CCMEWQI method simplifies the complex and technical data. It tests the multi-variable water quality data and compares the data to benchmarks determined by the user (Tirkey et al. 2015 ). The sampling protocol requires at least four parameters sampled at least four times but does not indicate which ones should be used; the user must decide ( Uddin et al. 2021 ). Yet, the parameters may vary from one station to another (Tyagi et al. 2013 ).

After the water body, the objective and the period of time have been defined the three factors of the CWQI are calculated (Baghapour et al. 2013 ; Canadian Council of Ministers of the Environment 1999 ):

The scope (F1) represents the percentage of variables that failed to meet the objective (above or below the acceptable range of the selected parameter) at least once (failed variables), relative to the total number of variables.

The frequency (F2) represents the percentage of tests which do not meet the objectives (above or below the acceptable range of the selected parameter) (failed tests).

The amplitude represents the amount by which failed tests values did not meet their objectives (above or below the acceptable range of the selected parameter). It is calculated in three steps.

The excursion is termed each time the number of an individual parameter is further than (when the objective is a minimum, less than) the objective and is calculated by two Eqs. ( 4 , 5 ) referring to two cases. In case the test value must not exceed the objective:

For the cases in which the test value must not fall below the objective:

The normalized sum of excursions, or nse , is calculated by summing the excursions of individual tests from their objectives and diving by the total number of tests (both meetings and not meeting their objectives):

F3 is then calculated an asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a range between 0 and 100:

Finally, the CMEWQI can be obtained from the following equation, where the index changes in direct proportion to changes in all three factors.

where 1.732 is a scaling factor and normalizes the resultant values to a range between 0 and 100, where 0 refers to the worst quality and one hundred represents the best water quality.

Once the CCME WQI value has been determined, water quality in ranked as shown in Table 3

Ramírez-Morales et al. ( 2021 ) investigated in their study the measuring of pesticides and water quality indices in three agriculturally impacted micro catchments in Costa Rica between 2012 and 2014. Surface water and sediment samples were obtained during the monitoring experiment.

The specifications of the water included: Pesticides, temperature, DO, oxygen saturation, BOD, TP, NO3, sulfate, ammonium, COD, conductivity, pH and TSS.

Sediment parameters included forty-two pesticides with different families including carbamate, triazine, organophosphate, phthalimide, pyrethroid, uracil, benzimidazole, substituted urea, organochlorine, imidazole, oxadiazole, diphenyl ether and bridged diphenyl.

WQIs are effective tools since they combine information from several variables into a broad picture of the water body's state. Two WQIs were calculated using the physicochemical parameters: The Canadian Council of Ministers of the Environment (CCME) WQI and the National Sanitation Foundation (NSF) WQI.

These were chosen since they are both extensively used and use different criteria to determine water quality: The NSF WQI has fixed parameters, weights, and threshold values, whereas the CCME has parameters and threshold values that are customizable.

The assessment of water quality using physico-chemical characteristics and the WQI revealed that the CCME WQI and the NSF WQI have distinct criteria. CCME WQI categorized sampling point as marginal/bad quality, while most sampling locations were categorized as good quality in the NSF WQI. Seemingly, the water quality classifications appeared to be affected by seasonal variations: during the wet season, the majority of the CCME WQI values deteriorated, implying that precipitation and runoff introduced debris into the riverbed. Thus, it’s crucial to compare WQIs because they use various factors, criteria, and threshold values, which might lead to different outcomes (Ramírez-Morales et al. 2021 ).

Yotova et al. ( 2021 ) directed an analysis on the Mesta River located between Greece and Bulgaria. The Bulgarian section of the Mesta River basin, which is under the supervision of the West-Aegean Region Basin Directorate, was being researched. The goal was to evaluate the surface water quality of ten points of the river using a novel approach that combines composite WQI developed by the CCME and Self organizing map (SOM) on the required monitoring data that include: DO, pH, EC, ammonium, nitrite, nitrate, total phosphate, BOD and TSS.

The use of WQI factors in SOM calculations allows for the identification of specific WQI profiles for various object groups and identifying groupings of river basin which have similar sampling conditions. The use of both could reveal and estimate the origin and magnitude of anthropogenic pressure. In addition, it might be determined that untreated residential wastewaters are to blame for deviations from high quality requirements in the Mesta River catchment.

Interestingly, this study reveals that WQI appear more accurate and specific when combined with a statistical test such as the SOM (Yotova et al. 2021 ).

2.4.3 Oregon water quality index (OWQI)

The Oregon Water Quality Index is a single number that creates a score to evaluate the water quality of Oregon’s stream and apply this method in other geographical region (Hamlat et al. 2017 ; Singh et al. 2013 ). The OWQI was widely accepted and applied in Oregon (USA) and Idaho (USA) (Sutadian et al. 2016 ).

Additionally, the OWQI is a variant of the NSFWQI, and is used to assess water quality for swimming and fishing, it is also used to manage major streams (Lumb et al. 2011b ). Since the introduction of the OWQI in 1970, the science of water quality has improved noticeably, and since 1978, index developers have benefited from increasing understanding of stream functionality (Bharti and Katyal 2011 ). The Oregon index belongs to the specific consumption indices group. It is a water classification based on the kind of consumption and application such as drinking, industrial, etc. (Shah and Joshi 2017 ).

The original OWQI dropped off in 1983, due to excessive resources required for calculating and reporting results. However, improvement in software and computer hardware availability, in addition to the desire for an accessible water quality information, renewed interest in the index (Cude 2001 ).

Simplicity, availability of required quality parameters, and the determination of sub-indexes by curve or analytical relations are some advantages of this approach (Darvishi et al. 2016a ). The process combines eight variables including temperature, dissolved oxygen (percent saturation and concentration), biochemical oxygen demand (BOD), pH, total solids, ammonia and nitrate nitrogen, total phosphorous and bacteria (Brown 2019 ). Equal weight parameters were used for this index and has the same effect on the final factor (Darvishi et al. 2016a ; Sutadian et al. 2016 ).

The Oregon index is calculated by the following Eq.  9 (Darvishi et al. 2016a ):

where,n is the number of parameters (n = 8) SI i is the value of parameter i.

Furthermore, the OWQI scores range from 10 for the worse case to 100 as the ideal water quality illustrated in the following Table 4 (Brown 2019 ).

Kareem et al. ( 2021 ) using three water quality indices, attempted to analyze the Euphrates River (Iraq) water quality for irrigation purposes in three different stations: WAWQI, CCMEWQI AND OWQI.

For fifteen parameters, the annual average value was calculated, which included: pH, BOD, Turbidity, orthophosphate, Total Hardness, Sulphate, Nitrate, Alkalinity, Potassium Sodium, Magnesium, Chloride, DO, Calcium and TDS.

The OWQI showed that the river is “very poor”, and since the sub-index of the OWQI does not rely on standard-parameter compliance, there are no differences between the two inclusion and exclusion scenarios, which is not the case in both WAWQI and CCMEWQI (Kareem et al. 2021 ).

Similarly, the OWQI showed a very bad quality category, and it is unfit for human consumption, compared to the NSFWQI and Wilcox indices who both showed a better quality of water in Darvishi et al., study conducted on the Talar River (Iran) (Darvishi et al. 2016b ).

2.4.4 Weighted arithmetic water quality index (WAWQI)

The weighted arithmetic index is used to calculate the treated water quality index, in other terms, this method classifies the water quality according to the degree of purity by using the most commonly measured water quality variables (Kizar 2018 ; Paun et al. 2016 ).This procedure has been widely used by scientists (Singh et al. 2013 ).

Three steps are essential in order to calculate the WAWQI:

Further quality rating or sub-index was calculated using the following equation (Jena et al. 2013 ):

Qn is the quality rating for the nth water quality parameter.

Vn is the observed value of the nth parameter at a given sampling station.

Vo is the ideal value of the nth parameter in a pure water.

Sn is the standard permissible value of the nth parameter.

The quality rating or sub index corresponding to nth parameter is a number reflecting the relative value of this parameter in polluted water with respect to its permissible standard value (Yogendra & Puttaiah 2008 ).

The unit weight was calculated by a value inversely proportional to the recommended standard values (Sn) of the corresponding parameters (Jena et al. 2013 ):

Wn is the unit weight for the nth parameter.

K is the constant of proportionality.

Sn is the standard value of the nth parameter.

The overall WQI is the aggregation of the quality rating (Qn) and the unit weight (Wn) linearly (Jena et al. 2013 ):

After calculating the WQI, the measurement scale classifies the water quality from “unsuitable water” to “excellent water quality” as given in the following Table 5 .

Sarwar et al. ( 2020 ) carried out a study in Chaugachcha and Manirampur Upazila of Jashore District (Bangladesh). The goal of this study was to determine the quality of groundwater and its appropriateness for drinking, using the WAWQI including nine parameters: turbidity, EC, pH, TDS, nitrate, ammonium, sodium, potassium and iron. Many samplings point was taken from Chaugachcha and Manirampur, and WQI differences were indicated (ranging from very poor to excellent). These variations in WQI were very certainly attributable to variances in geographical location. Another possibility could be variations in the parent materials from which the soil was created, which should be confirmed using experimental data. It is worth mentioning that every selected parameter was taken into consideration during calculation. Similarly, the water quality differed in Manirampur due to the elements contained in the water samples that had a big impact on the water quality (Sarwar et al. 2020 ).

In 2021, García-Ávila et al. undertook a comparative study between the CCMEWQI and WAWQI for the purpose of determining the water quality in the city of Azogues (Ecuador). Twelve parameters were analyzed: pH, turbidity, color, total dissolved solids, electrical conductivity, total hardness, alkalinity, nitrates, phosphates, sulfates, chlorides and residual chlorine over 6 months. The average WAWQI value was calculated suggesting that 16.67% of the distribution system was of 'excellent' quality and 83.33% was of 'good' quality, while the CCMEWQI indicated that 100% of the system was of ‘excellent’ quality.

This difference designated that the parameters having a low maximum allowable concentration have an impact on WAWQI and that WAWQI is a valuable tool to determine the quality of drinking water and have a better understanding of it (García-Ávila et al. 2022a , b ).

2.4.5 Additional water quality indices

The earliest WQI was based on a mathematical function that sums up all sub-indices, as detailed in the 2.1. History of water quality concept section (Aljanabi et al. 2021 ). The Dinius index (1972), the OWQI (1980), and the West Java index (2017) were later modified from the Horton index, which served as a paradigm for later WQI development (Banda and Kumarasamy 2020 ).

Based on eleven physical, chemical, organic, and microbiological factors, the Scottish Research Development Department (SRDDWQI) created in 1976 was based on the NSFWQI and Delphi methods used in Iran, Romania, and Portugal. Modified into the Bascaron index (1979) in Spain, which was based on 26 parameters that were unevenly weighted with a subjective representation that allowed an overestimation of the contamination level. The House index (1989) in the UK valued the parameters directly as sub-indices. The altered version was adopted as Croatia's Dalmatian index in 1999.

The Ross WQI (1977) was created in the USA using only 4 parameters and did not develop into any further indices.

In 1982, the Dalmatian and House WQI were used to create the Environmental Quality Index, which is detailed in Sect.  2.1 . This index continues to be difficult to understand and less powerful than other indices (Lumb et al. 2011a ; Uddin et al. 2021 ).

The Smith index (1990), is based on 7 factors and the Delphi technique in New Zealand, attempts to eliminate eclipsing difficulties and does not apply any weighting, raising concerns about the index's accuracy (Aljanabi et al. 2021 ; Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

The Dojildo index (1994) was based on 26 flexible, unweighted parameters and does not represent the water's total quality.

With the absence of essential parameters, the eclipse problem is a type of fixed-parameter selection. The Liou index (2004) was established in Taiwan to evaluate the Keya River based on 6 water characteristics that were immediately used into sub-index values. Additionally, because of the aggregation function, uncertainty is unrelated to the lowest sub-index ranking (Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

Said index (2004) assessed water quality using only 4 parameters, which is thought to be a deficient number for accuracy and a comprehensive picture of the water quality. Furthermore, a fixed parameter system prevents the addition of any new parameters.

Later, the Hanh index (2010), which used hybrid aggregation methods and gave an ambiguous final result, was developed from the Said index.

In addition to eliminating hazardous and biological indicators, the Malaysia River WQI (MRWQI developed in the 2.1 section) (2007) was an unfair and closed system that was relied on an expert's judgment, which is seen as being subjective and may produce ambiguous findings (Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

Table illustrated the main data of the studies published during 2020–2022 on water quality assessments and their major findings:

2.5 Advantages and disadvantages of the selected water quality indices

A comparison of the selected indices is done by listing the advantages and disadvantages of every index listed in the Table 7 below.

2.6 New attempts of WQI studies

Many studies were conducted to test the water quality of rivers, dams, groundwater, etc. using multiple water quality indices throughout the years. Various studies have been portrayed here in.

Massoud ( 2012 ) observed during a 5-year monitoring period, in order to classify the spatial and temporal variability and classify the water quality along a recreational section of the Damour river using a weighted WQI from nine physicochemical parameters measured during dry season. The WWQI scale ranged between “very bad” if the WQI falls in the range 0–25, to “excellent” if it falls in the range 91–100. The results revealed that the water quality of the Damour river if generally affected by the activities taking place along the watershed. The best quality was found in the upper sites and the worst at the estuary, due to recreational activities. If the Damour river is to be utilized it will require treatment prior any utilization (Massoud 2012 ).

Rubio-Arias et al. ( 2012 ) conducted a study in the Luis L. Leon dam located in Mexico. Monthly samples were collected at 10 random points of the dam at different depths, a total of 220 samples were collected and analyzed. Eleven parameters were considered for the WQI calculation, and WQI was calculated using the Weighted WQI equation and could be classified according to the following ranges: < 2.3 poor; from 2.3 to 2.8 good; and > 2.8 excellent. Rubio-Arias et al., remarked that the water could be categorized as good during the entire year. Nonetheless, some water points could be classified as poor due to some anthropogenic activities such as intensive farming, agricultural practices, dynamic urban growth, etc. This study confirms that water quality declined after the rainy season (Rubio-Arias et al. 2012 ).

In the same way, Haydar et al. ( 2014 ) evaluated the physical, chemical and microbiological characteristics of water in the upper and lower Litani basin, as well as in the lake of Qaraaoun. The samples were collected during the seasons of 2011–2012 from the determined sites and analyzed by PCA and the statistical computations of the physico-chemical parameters to extract correlation between variables. Thus, the statistical computations of the physico-chemical parameters showed a correlation between some parameters such as TDS, EC, Ammonium, Nitrate, Potassium and Phosphate. Different seasons revealed the presence of either mineral or anthropogenic or both sources of pollution caused by human interference from municipal wastewater and agricultural purposes discharged into the river. In addition, temporal effects were associated with seasonal variations of river flow, which caused the dilution if pollutants and, hence, variations in water quality (Haydar et al. 2014 ).

Another study conducted by Chaurasia et al., ( 2018 ), proposed a groundwater quality assessment in India using the WAWQI. Twenty-two parameters were taken into consideration for this assessment, however, only eight important parameters were chosen to calculate the WQI. The rating of water quality shows that the ground water in 20% of the study area is not suitable for drinking purpose and pollution load is comparatively high during rainy and summer seasons. Additionally, the study suggests that priority should be given to water quality monitoring and its management to protect the groundwater resource from contamination as well as provide technology to make the groundwater fit for domestic and drinking (Chaurasia et al. 2018 ).

Daou et al. ( 2018 ) evaluated the water quality of four major Lebanese rivers located in the four corners of Lebanon: Damour, Ibrahim, Kadisha and Orontes during the four seasons of the year 2010–2011. The assessment was done through the monitoring of a wide range of physical, chemical and microbiological parameters, these parameters were screened using PCA. PCA was able to discriminate each of the four rivers according to a different trophic state. The Ibrahim River polluted by mineral discharge from marble industries in its surroundings, as well as anthropogenic pollutants, and the Kadisha river polluted by anthropogenic wastes seemed to have the worst water quality. This large-scale evaluation of these four Lebanese rivers can serve as a water mass reference model (Daou et al. 2018 ).

Moreover, some studies compared many WQI methods. Kizar ( 2018 ), carried out a study on Shatt Al-Kufa in Iraq, nine locations and twelve parameters were selected. The water quality was calculated using two methods, the WAWQI and CWQI. The results revealed the same ranking of the river for both methods, in both methods the index decreased in winter and improved in other seasons (Kizar 2018 ).

On the other hand, Zotou et al. ( 2018 ), undertook a research on the Polyphytos Reservoir in Greece, taking into consideration thirteen water parameters and applying 5 WQIs: Prati’s Index of Pollution (developed in 1971, based on thirteen parameter and mathematical functions to convert the pollution concentration into new units. The results of PI classified water quality into medium classes (Gupta and Gupta 2021 ). Bhargava’s WQI (established in 1983, the BWQI categorize the parameters according to their type: bacterial indicators, heavy metals and toxins, physical parameters and organic and inorganic substances. The BWQI tends to classify the water quality into higher quality classes, which is the case in the mentioned study (Gupta and Gupta 2021 ). Oregon WQI, Dinius second index, Weighted Arithmetic WQI, in addition to the NSF and CCMEWQI. The results showed that Bhargava and NSF indices tend to classify the reservoir into superior quality classes, Prati’s and Dinius indices fall mainly into the middle classes of the quality ranking, while CCME and Oregon could be considered as “stricter” since they give results which range steadily between the lower quality classes (Zotou et al. 2018 ).

In their study, Ugochukwu et al. ( 2019 ) investigated the effects of acid mine drainage, waste discharge into the Ekulu River in Nigeria and other anthropogenic activities on the water quality of the river. The study was performed between two seasons, the rainy and dry season. Samples were collected in both seasons, furthermore, the physic-chemistry parameters and the heavy metals were analyzed. WQI procedure was estimated by assigning weights and relative weights to the parameters, ranking from “excellent water” (< 50) to “unsuitable for drinking” (> 300). The results showed the presence of heavy metals such as lead and cadmium deriving from acid mine drainage. In addition, the water quality index for all the locations in both seasons showed that the water ranked from “very poor” to “unsuitable for drinking”, therefore the water should be treated before any consumption, and that enough information to guide new implementations for river protection and public health was provided (Ugochukwu et al. 2019 ).

The latest study in Lebanon related to WQI was carried out by El Najjar et al. ( 2019 ), the purpose of the study was to evaluate the water quality of the Ibrahim River, one of the main Lebanese rivers. The samples were collected during fifteen months, and a total of twenty-eight physico-chemical and microbiological parameters were tested. The parameters were reduced to nine using the Principal Component Analysis (PCA) and Pearson Correlation. The Ibrahim WQI (IWQI) was finally calculated using these nine parameters and ranged between 0 and 25 referring to a “very bad” water quality, and between 91 and 100 referring to an “excellent” water quality. The IWQI showed a seasonal variation, with a medium quality during low -water periods and a good one during high-water periods (El Najjar et al. 2019 ).

3 Conclusion

WQI is a simple tool that gives a single value to water quality taking into consideration a specific number of physical, chemical, and biological parameters also called variables in order to represent water quality in an easy and understandable way. Water quality indices are used to assess water quality of different water bodies, and different sources. Each index is used according to the purpose of the assessment. The study reviewed the most important indices used in water quality, their mathematical forms and composition along with their advantages and disadvantages. These indices utilize parameters and are carried out by experts and government agencies globally. Nevertheless, there is no index so far that can be universally applied by water agencies, users and administrators from different countries, despite the efforts of researchers around the world (Paun et al. 2016 ). The study also reviewed some attempts on different water bodies utilizing different water quality indices, and the main studies performed in Lebanon on Lebanese rivers in order to determine the quality of the rivers (Table 6 ).

As mentioned in the article (Table 7 ); WQIs may undergo some limitations. Some indices could be biased, others are not specific, and they may not get affected by the value of an important parameter. Therefore, there is no interaction between the parameters.

Moreover, many studies exhibited a combination between WQIs and statistical techniques and analysis (such as the PCA, Pearson’s correlation etc.). with a view to obtain the relation between the parameters and which parameter might affect the water quality.

In other research, authors compared many WQIs to check the difference of water quality according to each index. Each index can provide different values depending on the sensitivity of the parameter. For that reason, WQIs should be connected to scientific advancements to develop and elaborate the index in many ways (example: ecologically). Therefore, an advanced WQI should be developed including first statistical techniques, such as Pearson correlation and multivariate statistical approach mainly Principal Component Analysis (PCA) and Cluster Analysis (CA), in order to determine secondly the interactions and correlations between the parameters such as TDS and EC, TDS and total alkalinity, total alkalinity and chloride, temperature and bacteriological parameters, consequently, a single parameter could be selected as representative of others. Finally, scientific and technological advancement for future studies such as GIS techniques, fuzzy logic technology to assess and enhance the water quality indices and cellphone-based sensors for water quality monitoring should be used.

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Chidiac, S., El Najjar, P., Ouaini, N. et al. A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives. Rev Environ Sci Biotechnol 22 , 349–395 (2023). https://doi.org/10.1007/s11157-023-09650-7

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Received : 07 December 2022

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DOI : https://doi.org/10.1007/s11157-023-09650-7

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Comparative Study of Various Research Indices Used to Measure Quality of Research Publications

International Journal of Applied and Advanced Scientific Research (IJAASR) - 2(1), 81-89. DOI: 10.5281/zenodo.569763

9 Pages Posted: 1 May 2017

P. S. Aithal

Poornaprajna College

Date Written: April 28, 2017

The success of research projects funded by various agencies can be evaluated by studying the research publications generated from those projects and the research publications can be evaluated using impact factors and citation indices. There are several citation indices commonly used to assess the value/quality of a research publication or the research impact of an author or a journal. Research indices are calculated based on either citation values of research publications of a research scholar or the number of research papers published by a research scholar for a given period. There are many research indices developed by many types of research which include H-index, i10-index, G-index, H(2)-index, HG-index, Q2-index, AR-index, M-quotient, M-index, W-index, Hw-index, E-index, A-index, R-index, W-index, J-index, etc. Out of these citation based research indices, h-index, G-index and i10-index are commonly used in some of the citation databases. Researchers have also studied the problems and limitations associated with these indices. In this paper, we have discussed the most popular research indices presently used which include h-index, G-index, and i-10-index along with their advantages, benefits, constraints, and disadvantages. Most of the research indices are calculated based on number of citations a paper receives. The major limitation of this model is that the citations usually increase with an increase in time even after the researcher dies, the citations and hence the indices continue to grow. It is argued that due to various reasons, a research publication may not attract citations initially for some years and after ten to twenty years some papers may attract citations. The best method of identifying the contribution to research is calculating the annual research index for an author by considering the annual research publications.Accordingly, based on annual research index of an author, his average research contribution for five years, or ten years, or twenty years or any desired period can be determined. Here, we have suggested some of the new research indices to be used for calculating research productivity of individuals as well as a team of people in an organization. The paper also contains some of our newly proposed indices including ARP-Index – (Annual Research Publication Index), RC-Index – (Research Continuation Index), RE-Index (Research expansion Index), Project Productivity Index, and Cost Index and the method of calculating these indices along with their advantages and limitations.

Keywords: ARP-Index, RC-Index, RE-Index, Project Productivity Index, Cost Index

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P. S. Aithal (Contact Author)

Poornaprajna college ( email ).

Poornaprajna Institute of Management Udupi District Karnataka India +919343348392 (Phone)

HOME PAGE: http://www.pim.ac.in

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Research Impact

• Author Identification
• Citation Indexes

Citation Indexes Overview

Other citation index numbers.

• Journal Impact
• More Resources

What are index numbers?

Citation index numbers provide a way to measure impact beyond raw citation counts. Index numbers can be calculated for individual articles, a group/list of publications, or even all the articles published in a journal or field (see our Journal Impact page).

What is the "best" index number?

Generally, the "best" measurement depends on what matters to you. The h-index is the most widely known index measurement. Some alternative measurements, like the g-index, address specific issues with the h-index. Other measurements target recent publications and citations, such as the the contemporary h-index. 

Alternatives to the h-index include:

• g-index :  Gives more weight to highly cited publications. The original h-index is insensitive to high "outliers" -- a few papers that have very high citation counts will not sway the h-index score (much). The g-index allows highly cited papers to play a larger role in the index, and tends to emphasize visibility and "lifetime achievement."
• hc-index (contemporary h-index) :  Gives more weight to recent publications. The original h-index favors senior researchers with extensive publication records, even if they have ceased publishing. The hc-index attempts to correct this and favors researchers currently publishing.
• i10-index: Measures the number of papers that have at least 10 citations. Introduced (and used) by Google Scholar.
• m-quotient: Divides the h-index by the number of years since the researcher's first published paper. m-quotient was proposed as a way to help younger researchers who may not have long publication lists.

For more index measurements, we suggest " Reflections on the  h- index ," by Prof. Anne-Wil Harzing, University of Melbourne.

What is the h-index?

The h-index attempts to correlate a researcher's total publications and total citations. It was proposed by Jorge E. Hirsch in 2005 (" An index to quantify an individual's scientific research output ," PNAS November 15, 2005 vol. 102 no. 46 16569-16572). For more information, see the Wikipedia article .

Graph of the h-index, from Wikipedia.

How do I calculate my h-index?

• Web of Science or Google Scholar will automatically calculate the h-index for the list of publications in your profile. 
• Publish or Perish will calculate h-index (and many other index numbers) for an author's publications. 
• If you want to calculate an h-index manually, Hirsch defines the h-index as follows: "A scientist has index  h  if  h  of his or her  Np  papers have at least  h  citations each and the other ( Np – h ) papers have ≤h  citations each."
• << Previous: Citation Metrics
• Next: Journal Impact >>
• Last Updated: Jan 26, 2024 11:12 AM
• URL: https://guides.library.georgetown.edu/researchimpact

What is an index and do you need one?

Want to know former US president Bill Clinton’s thoughts on the Watergate scandal? The 1993 World Trade Center bombing? Monica Lewinsky? There’s no need to read all 957 pages of his autobiography,  My Life . Simply flick to the back of the book and check the index for the page number.

An index is a list of all the names, subjects and ideas in a piece of written work, designed to help readers quickly find where they are discussed in the text. Usually found at the end of the text, an index doesn’t just list the content (that’s what a table of contents is for), it analyses it.

Where are indexes used?

In addition to back-of-the-book indexes found in non-fiction books and technical reports, indexes are also used to make other sources of information – including journal articles, maps and atlases, art collections, online databases and websites – easier to navigate. Where books are published online, in PDF or e-book format, indexes link directly to points in the text.

Indexes are a common inclusion in many annual reports and are mandatory for annual reports produced by Australian Government departments, executive agencies and other non-corporate Commonwealth agencies.

What makes a good index?

An index provides a map to a report’s content. It does this through identifying key themes and ideas, grouping similar concepts, cross-referencing information and using clear formatting. A good index will:

• be arranged in alphabetical order
• include accurate page references that lead to useful information on a topic
• avoid listing every use of a word reor phrase
• be consistent across similar topics
• use sub-categories to break up long blocks of page numbers
• use italics for publications and Acts
• cross-reference information to point to other headings of interest or preferred terms.

For example, a back-of-the-book index might read:

sales, sales process, 147, 149, 158,  see also  strategy  (directs the reader to a related term)

scripts, 56–59  (grouping term)

podcasts, 56–57  (sub-term)

video, 58–59

search engine optimisation, 100, 156

Security Analysis  (David Dodd and Benjamin Graham), 89–90  (reference to a book)

spelling,  see  proofreading  (directs the reader to the word or phrase used in the text)

While software is available to help indexers arrange, format and edit entries, indexers will also use their judgement when deciding what to put into an index, what to leave out and how to organise it.

Don’t forget to add a table of contents

A good index may be the difference between people referring to a report regularly and it gathering dust on the bookshelf. If you don’t have an index, it’s important to at least have a good table of contents.

Located at the front of a report, a table of contents allows readers to easily see what the report is about and how sections of the text are arranged, in the order they appear.

A good table of contents will include headings, outlining the main sections or themes; sub-headings that indicate what each section of copy is about; and the page numbers they appear on. Additional content such as tables and boxes can also be added.

Want to make your report as easy to navigate as possible? Bookend it with a table of contents and an index – readers will have no excuse for not being able to find the information they’re after.

We can help create a roadmap for your reports, books and other larger documents. Learn more about  indexing  or  contact us here .

How to create an award-winning annual report

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Research Impact: Citation Indexes

• Journal Impact
• Citation Indexes
• ORCiD Researcher ID
• More Resources

Citation Indexes Overview

What are index numbers.

Citation index numbers provide a way to measure impact beyond raw citation counts. Index numbers can be calculated for individual articles, a group/list of publications, or even all the articles published in a journal or field (see our Journal Impact page).

What is the "best" index number?

Generally, the "best" measurement depends on what matters to you. The h-index is the most widely known index measurement. Some alternative measurements, like the g-index, address specific issues with the h-index. Other measurements target recent publications and citations, such as the the contemporary h-index. 

Other Citation Index Numbers

Alternatives to the h-index include:

• g-index :  Gives more weight to highly cited publications. The original h-index is insensitive to high "outliers" -- a few papers that have very high citation counts will not sway the h-index score (much). The g-index allows highly cited papers to play a larger role in the index, and tends to emphasize visibility and "lifetime achievement."
• hc-index (contemporary h-index) :  Gives more weight to recent publications. The original h-index favors senior researchers with extensive publication records, even if they have ceased publishing. The hc-index attempts to correct this and favors researchers currently publishing.
• i10-index: Measures the number of papers that have at least 10 citations. Introduced (and used) by Google Scholar.
• m-quotient: Divides the h-index by the number of years since the researcher's first published paper. m-quotient was proposed as a way to help younger researchers who may not have long publication lists.

For more index measurements, we suggest " Reflections on the  h- index ," by Prof. Anne-Wil Harzing, University of Melbourne.

What is the h-index?

The h-index attempts to correlate a researcher's total publications and total citations. It was proposed by Jorge E. Hirsch in 2005 (" An index to quantify an individual's scientific research output ," PNAS November 15, 2005 vol. 102 no. 46 16569-16572). For more information, see the Wikipedia article .

Graph of the h-index, from Wikipedia.

How do I calculate my h-index?

• Web of Science or Google Scholar will automatically calculate the h-index for the list of publications in your profile. 
• Publish or Perish will calculate h-index (and many other index numbers) for an author's publications. 
• If you want to calculate an h-index manually, Hirsch defines the h-index as follows: "A scientist has index  h  if  h  of his or her  Np  papers have at least  h  citations each and the other ( Np – h ) papers have ≤h  citations each."
• << Previous: Citation Metrics
• Next: Altmetrics >>
• Last Updated: Nov 17, 2023 1:28 PM
• URL: https://libguides.gwu.edu/researchimpact

The Differences Between Indexes and Scales

Definitions, Similarities, and Differences

• Research, Samples, and Statistics
• Key Concepts
• Major Sociologists
• News & Issues
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• Archaeology

Indexes and scales are important and useful tools in social science research. They have both similarities and differences among them. An index is a way of compiling one score from a variety of questions or statements that represents a belief, feeling, or attitude. Scales, on the other hand, measure levels of intensity at the variable level, like how much a person agrees or disagrees with a particular statement.

If you are conducting a social science research project, chances are good that you will encounter indexes and scales. If you are creating your own survey or using secondary data from another researcher’s survey, indexes and scales are almost guaranteed to be included in the data.

Indexes in Research

Indexes are very useful in quantitative social science research because they provide a researcher a way to create a composite measure that summarizes responses for multiple rank-ordered related questions or statements. In doing so, this composite measure gives the researcher data about a research participant's view on a certain belief, attitude, or experience.

For example, let’s say a researcher is interested in measuring job satisfaction and one of the key variables is job-related depression. This might be difficult to measure with simply one question. Instead, the researcher can create several different questions that deal with job-related depression and create an index of the included variables. To do this, one could use four questions to measure job-related depression, each with the response choices of "yes" or "no":

• "When I think about myself and my job, I feel downhearted and blue."
• "When I’m at work, I often get tired for no reason."
• "When I’m at work, I often find myself restless and can’t keep still."
• "When at work, I am more irritable than usual."

To create an index of job-related depression, the researcher would simply add up the number of "yes" responses for the four questions above. For example, if a respondent answered "yes" to three of the four questions, his or her index score would be three, meaning that job-related depression is high. If a respondent answered no to all four questions, his or her job-related depression score would be 0, indicating that he or she is not depressed in relation to work.

Scales in Research

A scale is a type of composite measure that is composed of several items that have a logical or empirical structure among them. In other words, scales take advantage of differences in intensity among the indicators of a variable. The most commonly used scale is the Likert scale , which contains response categories such as "strongly agree," "agree," "disagree," and "strongly disagree." Other scales used in social science research include the Thurstone scale, Guttman scale, Bogardus social distance scale, and the semantic differential scale.

For example, a researcher interested in measuring prejudice against women could use a Likert scale to do so. The researcher would first create a series of statements reflecting prejudiced ideas, each with the response categories of "strongly agree," "agree," "neither agree nor disagree," "disagree," and "strongly disagree." One of the items might be "women shouldn’t be allowed to vote," while another might be "women can’t drive as well as men." We would then assign each of the response categories a score of 0 to 4 (0 for "strongly disagree," 1 for "disagree," 2 for "neither agree or disagree," etc.). The scores for each of the statements would then be added for each respondent to create an overall score of prejudice. If a respondent answered "strongly agree" to five statements expressing prejudiced ideas, his or her overall prejudice score would be 20, indicating a very high degree of prejudice against women.

Compare and Contrast

Scales and indexes have several similarities. First, they are both ordinal measures of variables. That is, they both rank-order the units of analysis in terms of specific variables. For example, a person’s score on either a scale or index of religiosity gives an indication of his or her religiosity relative to other people. Both scales and indexes are composite measures of variables, meaning that the measurements are based on more than one data item. For instance, a person’s IQ score is determined by his or her responses to many test questions, not simply one question.

Even though scales and indexes are similar in many ways, they also have several differences. First, they are constructed differently. An index is constructed simply by accumulating the scores assigned to individual items. For example, we might measure religiosity by adding up the number of religious events the respondent engages in during an average month.

A scale, on the other hand, is constructed by assigning scores to patterns of responses with the idea that some items suggest a weak degree of the variable while other items reflect stronger degrees of the variable. For example, if we are constructing a scale of political activism, we might score "running for office" higher than simply "voting in the last election." "Contributing money to a political campaign " and "working on a political campaign" would likely score in between. We would then add up the scores for each individual based on how many items they participated in and then assign them an overall score for the scale.

Updated by Nicki Lisa Cole, Ph.D.

• How to Construct an Index for Research
• Scales Used in Social Science Research
• An Overview of Qualitative Research Methods
• Cluster Analysis and How Its Used in Research
• What Is Participant Observation Research?
• Definition and Overview of Grounded Theory
• Pros and Cons of Secondary Data Analysis
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• Deductive Versus Inductive Reasoning
• Data Sources For Sociological Research
• A Review of Software Tools for Quantitative Data Analysis
• Constructing a Deductive Theory
• How to Conduct a Sociology Research Interview
• Ethical Considerations in Sociological Research
• The Study of Cultural Artifacts via Content Analysis
• Science Says You Should Leave the Period Out of Text Messages

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	Title	Type
1		journal	106.094 Q1	211	49	124	4844	35427	89	381.89	98.86	43.95
2		journal	37.044 Q1	39	3	13	897	955	13	100.11	299.00	27.78
3		journal	35.910 Q1	508	123	336	11462	13599	153	34.50	93.19	29.41
4		journal	30.448 Q1	306	47	136	3645	2240	136	11.14	77.55	26.67
5		journal	26.837 Q1	505	105	304	10805	10951	163	31.23	102.90	44.33
6		journal	24.342 Q1	892	439	1496	32820	53447	1207	31.30	74.76	40.19
7		journal	22.399 Q1	391	239	731	8584	13091	153	19.72	35.92	34.15
8		journal	22.344 Q1	359	95	353	6242	4811	351	10.33	65.71	23.89
9		journal	21.836 Q1	184	117	335	8842	13775	196	31.17	75.57	26.86
10		journal	21.048 Q1	217	127	400	9888	10807	183	28.36	77.86	38.85
11		journal	20.544 Q1	1184	1388	4522	14603	107246	1824	21.69	10.52	38.26
12		journal	19.139 Q1	352	83	227	5043	1938	221	7.00	60.76	16.91
13		journal	19.045 Q1	630	595	1363	16478	36243	729	27.23	27.69	43.99
14		journal	18.663 Q1	71	0	19	0	963	19	0.00	0.00	0.00
15		journal	18.587 Q1	23	11	16	0	802	16	47.57	0.00	81.69
16		journal	18.530 Q1	215	83	261	4493	2531	258	7.04	54.13	17.80
17		journal	18.509 Q1	1391	3770	8037	74917	160102	3840	19.40	19.87	38.12
18		journal	18.117 Q1	511	485	1066	13393	17008	461	13.24	27.61	35.19
19		journal	17.828 Q1	833	271	851	115878	50519	819	49.76	427.59	30.50
20		journal	17.701 Q1	223	75	273	3371	1946	268	6.24	44.95	13.84
21		journal	17.654 Q1	234	108	410	6448	4495	409	8.04	59.70	16.43
22		journal	17.507 Q1	398	178	590	11546	12604	360	19.83	64.87	41.91
23		journal	17.497 Q1	229	229	609	6629	16808	379	26.18	28.95	29.53
24		journal	17.300 Q1	639	336	654	13672	13100	504	19.88	40.69	37.01
25		journal	16.061 Q1	388	36	103	14097	4303	99	42.66	391.58	14.94
26		journal	16.009 Q1	467	169	540	11148	13815	304	23.17	65.96	36.44
27		journal	15.966 Q1	264	102	252	19168	11266	244	38.64	187.92	24.30
28		journal	15.827 Q1	140	106	297	4359	4041	62	12.99	41.12	41.35
29		journal	15.620 Q1	328	23	84	4178	2696	83	27.02	181.65	40.68
30		journal	14.943 Q1	115	16	42	403	896	41	24.10	25.19	77.78
31		journal	14.796 Q1	388	400	978	11477	15900	588	17.52	28.69	33.83
32		journal	14.780 Q1	123	0	13	0	374	11	12.56	0.00	0.00
33		journal	14.707 Q1	32	46	35	4815	2160	34	61.71	104.67	36.44
34		journal	14.618 Q1	160	70	247	587	5353	230	21.11	8.39	58.79
35		journal	14.605 Q1	109	23	72	5797	1938	70	14.90	252.04	45.57
36		journal	14.577 Q1	419	262	637	10044	17562	466	27.42	38.34	28.93
37		journal	14.293 Q1	421	123	346	10202	6211	207	17.40	82.94	32.86
38		journal	14.231 Q1	558	306	834	9499	20730	593	24.08	31.04	24.85
39		journal	14.175 Q1	210	28	92	3163	1260	86	10.59	112.96	42.59
40		journal	13.942 Q1	294	144	670	5180	12698	362	18.81	35.97	39.02
41		book series	13.670 Q1	210	14	42	3772	1271	39	23.96	269.43	26.09
42		journal	13.655 Q1	311	89	563	4857	6315	559	11.14	54.57	23.11
43		journal	13.609 Q1	165	93	250	5332	1699	250	6.02	57.33	15.88
44		journal	13.578 Q1	455	233	688	15608	13409	550	16.89	66.99	40.35
45		journal	13.315 Q1	136	180	471	6682	12109	368	24.27	37.12	26.28
46		journal	13.080 Q1	260	243	827	1865	14374	679	16.58	7.67	62.53
47		journal	12.511 Q1	635	252	983	61439	44032	979	38.71	243.81	32.40
48		journal	12.324 Q1	81	55	137	2838	862	137	6.20	51.60	17.36
49		journal	12.294 Q1	46	62	154	8174	4174	86	27.10	131.84	31.14
50		journal	12.288 Q1	44	60	79	4858	3323	78	42.06	80.97	33.06

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Research guidance, Research Journals, Top Universities

Explaining H-index, i10-index, G-index & other research metrics

This blog post aims to explain various  research metrics like the h-index, i-10 index, and g-index . Moreover, we will also be explaining how you can increase these research metrics .

Page Contents

Measuring your research impact

Researchers use different metrics to measure the quality of published papers in journals . It basically gives an idea of the impact of any research paper . These metrics can be applied to any publication on any subject across the world. Through research metrics, one can monitor and quantify published articles. These citation metrics ultimately help in getting a university’s ranking .

Research metrics are one of the most established ways to measure the quality of research work. It tells the importance of particular research. Nowadays, H-index, impact factor , G-index, i-10 index are commonly used research metrics. These metrics help in measuring how much a researcher’s article is cited by the co-researchers. It helps in increasing the impact of the research work.  Researchers can use these metrics for availing various fellowships and scholarships, and gaining job opportunities across the world. 

Also, read the following articles:

Difference between SCI, SCIE, and ESCI journals

Difference between Scopus and Web of Science (WoS)

What is the h-index?

It is commonly known as the Hirsch number or Hirsch index. It was developed by American physicist Jorge E. Hirsch in 2005. h-index can be defined as for a given value of h, the researchers should h number of published articles that are cited at least by h-times. Suppose the author has an h-index of 25, which means that each of his published articles is cited at least 25 times by other researchers. It mainly gives an idea of the quality of the research papers. Generally, the higher the h-index, the greater the impact of a research paper will be. Thus, the h-index can be used to measure the quality and quantity of the research paper simultaneously. The h-index for any author can be determined manually with the help of any citation database. Using Scopus or Web of Science data, the h-index can also be calculated.

What is the i-10 index?

It is another commonly used research metric by the authors/researchers. i-10 index is provided by Google Scholar . It can define as a measure of having publications with at least 10 citations. For example, if an author/researcher’s i-10 index is 6, it indicates that six of his/her publications are cited 10 times. i-10 index also helps in increasing the weightage of any student profile. The main advantage of the i-10 index is that it can be calculated very easily. Google Scholar provides easy and free access to find out these metrics. 

Charles Robert Darwin, a renowned scientist, has the highest number of citations to date. This scientist has 156678 citations with an h-index of 106 and an i-10 index of 526. This means this researcher has received at least 10 citations for each of the 526 published articles. An h-index of 106 means that, out of his total publications, his 106 articles have been cited at least 106 times by different researchers.

What is G-index?

It is another level of measuring research metrics. It was suggested by Leo Egge in 2006. In general, the h-index does include a citation count of highly cites papers. But g-index helps in boosting the profile of a researcher by giving preference to highly cited papers. G-index is basically an advanced version of the h-index.  G-index measures the citation performance for a set of articles. A g-index of 20 indicates that the top 20 publications in a researcher/author profile are cited by 400 times (20 2 ). Similarly, a g-index of 10 indicates that the top 10 publications in a researcher profile are cited by 100 times (10 2) . 

How to increase the h-index? 

In the present scenario, the quality of any published article is measured by the number of citations he/she received, research metrics like the impact factor of the journal he/she has published, and the h-index of any author profile. Generally, during the entire research career, if the researcher receives of h-index of 25 or more, it is considered to be an excellent researcher’s profile. However, on average most of the researchers have an h-index between 10-15.

• In order to increase the h-index, one must publish papers of high quality. The researcher should ensure that he/she has not published any article in predatory/fake journals . The researcher should publish more and more original research articles . Although, sometimes publishing more review articles receives a greater number of citations , that ultimately increases the h-index in a profile.  
• Secondly, another way of increasing the h-index is through proper communication of the published article. He/she can advertise through various social media platforms such as Twitter , and ResearchGate, and continuously update the Google scholar profile. This will mainly help in increasing the visibility of published articles. 
• Thirdly, the researchers while writing the manuscript , he/she should ensure that the title of the paper is simple, clear, short, and concise. He/she should use a maximum of 5-6 appropriate keywords in the abstract. The abstract should be written in a very informative manner. It should briefly describe the research study. The research paper should always explain the novelty/newness of his/her article. Usually, the first sentence of the article appears in the all-search engines. So, it should be written in a very attractive manner. The abstract should be written in a such way it gives an overall summary of the research findings. 
• Fourthly, if it is possible, the researcher should publish in open-access journals . OA journals also undergo a peer-review process. Generally, these journals are available on online platforms which are easy to access and free of charge. Through open-access journals, readers can get full-text access to published articles easily. It will ultimately draw the attention of more audiences, which will ultimately help in gaining citations, thus increasing the h-index. 

What is considered to be a good i-10 index? 

Similar to the h-index, if the author/researcher has an i-10 index of 25 or more, it is considered an excellent research profile. An i-10 index of 25 means that, out of total publications, the researcher has received at least 10 citations for every 25 published articles. The i-10 index differs from researcher to researcher. It mainly depends on the subject area and sub-section of the research area. Generally, publishing more articles related to solving practical problems receives a greater number of citations. Professors with arts and humanities backgrounds may not have a higher i-index as compared to professors with science backgrounds. However, the i-10 index is the second-well-recognized research metric after the h-index.

I Hope, this blog post will help you to understand various research metrics used in research.

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• DOI: 10.28918/ijibec.v8i1.7188
• Corpus ID: 271655429

Comparative Analysis of Indonesia and Malaysia Sharia Stock Index Performance Using Sharpe, Treynor, and Jensen Methods

• Fauziah Azizah , Sri Setya Handayani , +1 author Article Info
• Published in International Journal of… 6 June 2024
• Economics, Business
• International Journal of Islamic Business and Economics (IJIBEC)

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Practical publication metrics for academics

Bethany a. myers.

1 Louise M. Darling Biomedical Library, University of California, Los Angeles California, USA

Katherine L. Kahn

2 Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles California, USA

Research organizations are becoming more reliant on quantitative approaches to determine how to recruit and promote researchers, allocate funding, and evaluate the impact of prior allocations. Many of these quantitative metrics are based on research publications. Publication metrics are not only important for individual careers, but also affect the progress of science as a whole via their role in the funding award process. Understanding the origin and intended use of popular publication metrics can inform an evaluative strategy that balances the usefulness of publication metrics with the limitations of what they can convey about the productivity and quality of an author, a publication, or a journal. This paper serves as a brief introduction to citation networks like Google Scholar, Web of Science Core Collection, Scopus, Microsoft Academic, and Dimensions. It also explains two of the most popular publication metrics: the h‐index and the journal impact factor. The purpose of this paper is to provide practical information on using citation networks to generate publication metrics, and to discuss ideas for contextualizing and juxtaposing metrics, in order to help researchers in translational science and other disciplines document their impact in as favorable a light as may be justified.

INTRODUCTION

As the scale of global research continues to increase, research organizations are becoming more reliant on quantitative approaches to determine how to recruit and promote researchers, allocate funding, and evaluate the impact of prior allocations. It has been common practice for funders; appointment, tenure, and promotion committees; academic administrations; publishers; and others to apply a variety of quantitative metrics to rank researchers, papers, journals, and even institutions and countries. 1 , 2 , 3 Many of these quantitative metrics are based on research publications. The total number of publications can be used to infer scientific output or productivity, whereas the number of citations to those publications may be used to infer the impact of the research. In aggregate, these “publication metrics” have potential to serve both researchers and those evaluating the work of researchers. 4 For the researcher, metrics can highlight the scope and strengths of one’s work, forming a useful starting point to answer the question, “what have I done?” Researchers can use metrics to structure their review of their past work, design efficient summaries of their prior research trajectory, and inform future decision making. For an evaluator (and many researchers eventually find themselves in the position of evaluating the scientific achievements of others), understanding a researcher’s publication and citation record provides context for judging their achievements and future potential.

Publication metrics are not only important for individual careers, but also affect the progress of science as a whole via their role in the funding award process. Funders that receive many grant applications may see quantitative publication metrics as a shortcut to assess research quality and impact. Researchers competing for grants may in turn strive to achieve a perceived threshold for certain metrics. An understanding of the origin and intended use of popular publication metrics can inform an evaluative strategy that balances the usefulness of metrics with the limitations of what they can convey about the productivity and quality of an author, a paper, or a journal.

This is especially important in translational science, a discipline created to improve patient and population outcomes. Translational science researchers are iteratively called upon by peers, funders, and their institutions to use their publication records to document their progress toward these outcomes, and translational science evaluators use publication metrics in their assessments. 5 , 6 The purpose of this paper is to briefly describe the most frequently used quantitative publication metrics, provide practical information on generating metrics, and discuss ideas for contextualizing and juxtaposing metrics, in order to help researchers in translational science and other disciplines document their impact in as favorable a light as may be justified.

CITATION NETWORKS

Citation counts represent the number of times a publication has been cited by other publications. Because citation counts represent a key constituent of the most frequently used publication metrics, understanding their source is necessary to effectively utilize publication metrics. Citation counts are usually provided by citation networks or indices, which are systems that connect each publication to every publication it cites, as well as every publication that has cited it. No single citation network functions as the dominant source of citation data. Instead, several citation networks exist and vary according to which publications they include. As a consequence, citation networks also vary in the citation numbers they generate for any given publication. Citation networks include traditional indexed databases, which contain article metadata ingested from publisher sources and accessed by users via a searchable interface; academic search engines, which scrape the web for relevant content and allow users to search the content via a web interface; and metadata datasets that can only be accessed computationally (e.g., via API [Application Programming Interfaces]). However, citation data are compiled, networks are created by connecting each citation reference in the publication’s bibliography to that reference’s record in the database. For example, if paper A cites another paper B, the citation network “reads” that citation and adds one cited reference to the existing total citation count of paper A. Users have a choice among multiple science citation networks. Six of the largest are described in Table  1 .

Descriptions of common citation networks

Citation network	Accessibility	Type of database	Description	Owner	Number of publication records	Number of citation connections
Web of Science Core Collection	Subscription required	Indexed database (i.e., its publication metadata comes from publishers); citation network	Clarivate publishes several citation indices that cover publications in different disciplines and formats, but the largest is the Science Citation Index Expanded, which is included in the Web of Science Core Collection	Clarivate ​Analytics	53 million	1.1 billion
Scopus	Subscription required	Indexed database; citation network	Scopus’s essential functionality is very similar to Web of Science Core Collection	Elsevier	75 million	1.4 billion
Google Scholar	Freely available	Academic search engine (i.e., it crawls the web looking for scholarly content); citation network	Google Scholar contains many publications beyond journal articles, such as books, reports, patents, presentations, posters, and other materials. As it crawls the web, creating citation connections, sometimes it encounters incorrect or difficult‐to‐parse bibliographies and erroneously creates duplicate records for the same publication. Google Scholar’s broad and opaque definition of scholarly content, as well as its automated citation record creation process, usually results in higher citation count numbers than Web of Science and Scopus.	Google	Unknown, but was recently estimated at 389 million	Unknown
OpenCitations Index of Crossref Open DOI‐to‐DOI Citations	Freely available	Publication metadata and citation index dataset	The data are accessible through an API or a public website, but the search is limited and the interface may be difficult to navigate for users.	Crossref	58 million	720 million
Microsoft Academic	Freely available	Indexed database; academic search engine; citation network	Launched in 2016, Microsoft Academic’s citation index is unique in presenting citation counts not only as verified connections between papers in its own index, but also as an “estimated” citation count using a statistical prediction tool to compensate for possible citations that may exist outside of its own dataset.	Microsoft	240 million	2.2 billion (estimated)
Dimensions	Freely available	Indexed database that also leverages additional open and proprietary data; citation network	Dimensions is the newest publication index and citation data source, launched in 2018. Dimensions makes use of open data such as Crossref, its parent company Digital Science’s other research‐related products, and publisher partnerships to index and link its records.	Digital Science	106 million	1.2 billion

Abbreviation: API, Application Programming Interfaces.

Practical applications

To select one or more citation networks, users may consider (1) the network’s coverage of publication types and areas of research, (2) its number of citation linkages between publications, (3) the user‐friendliness of its interface, and (4) its functionality when it comes to automatically generating metrics. Researchers should be aware that most citation networks are primarily comprised of journal articles. Therefore, it may be more difficult to assess the citation impact of gray literature such as white papers, reports, clinical trials, or other nontraditional publications. 7 Recent studies comparing various citation networks for accuracy and completeness may help inform the decision to choose a particular citation network. 8 , 9 , 10 , 11 , 12 Because publication metrics are derived from citation counts within citation networks, and citation counts vary depending on the network’s publication coverage, metrics derived for a given author from one network will not necessarily be concordant with metrics for the same author but derived from another network. A researcher may find that one citation network contains records for most of their publications, whereas another network may only have records for some of their publications. Although it is generally advantageous for researchers to find a network containing records for all of their publications, 8 researchers selecting among networks must also consider that networks’ bibliometric data vary according to the quality of the included data, in addition to the quantity of publications reported. For example, a researcher is likely to find more of their publications, and therefore a higher citation count and h‐index, by using Google Scholar. However, as described in Table  1 , Google Scholar may contain erroneous or duplicate records due to the way it collects publication data from the web. Although at first glance the researcher may think that Google Scholar offers a higher number and therefore a “better” metric, the accuracy of that metric may be questionable if it is based upon faulty bibliographic data. Precise documentation by researchers of the citation network(s) they select to inform their publication metrics provides the opportunity for their evaluators to assess the accuracy of their analyses.

If a researcher determines that metrics from multiple citation networks are useful to show context for their work, two or more different citation networks can be documented clearly to avoid confusion in interpreting their metrics. For example, a researcher working on a tenure and promotion dossier may decide to primarily use Web of Science Core Collection to search for their journal articles, and use Web of Science Core Collection’s citation counts and calculated h‐index to document their career’s published articles. This researcher may also use Google Scholar to find their gray literature publications, and decide to include the citation counts of those publications to promote their research that resulted in a white paper, report, or other nonarticle publication type. In this example, the dossier should clearly indicate that the metrics presented for the journal articles came from Web of Science Core Collection, while the metrics presented for the gray literature publications came from Google Scholar.

PUBLICATION‐LEVEL METRICS

Publication‐level (including both articles and nonarticle publications) metrics represent any quantitative number relating to an individual publication. Most commonly, this takes the form of citation counts: the number of citations to any given publication. In addition to citation counts, the number of article views and downloads are frequently listed on journal articles hosted on publisher websites. Other emerging metrics known as “alternative metrics” often seek to indicate social impact rather than solely scientific impact. 13 , 14 Although they may theoretically be applied to authors, institutions, journals, or other entities, in practice, the most prevalent implementation of alternative metrics is publication‐level. Examples include the number of times a publication has been shared on social media or blogs, the number of comments or “likes” it has received, or the number of times it has been mentioned in mass media. Due to their loosely defined and rapidly changing nature, alternative metrics are difficult to locate, although one company, Altmetrics, 15 has monetized centralizing various indicators into an “attention score.” Alternative metrics can add societal context and diversity to a research evaluation, 16 but researchers and evaluators should keep in mind that metrics reflecting public engagement may not correlate with scientific impact. 13 , 17

Citation counts for an individual publication can be generated by searching a title or Digital Object Identifier (DOI) in any of the citation networks described in Table  1 . All six citation networks display the number of citations to a particular publication on the search results page for that publication. Individual publication citation counts may be used to highlight particularly impactful citations, but a more creative approach for a researcher’s dossier might be to group publications together and write about the citation impact of the group. For example, a researcher may aggregate citations by time period (e.g., before or after getting a prior promotion or being awarded a grant), by their different research fields or subfields (e.g., clinical and basic science), or by authorship type (e.g., first vs. senior [last] author). This facilitates discussion of publication impact in context, and may be useful to assert the value of a previous grant investment, explain impact variation within different fields, or provide evidence that research leadership affected impact. Another approach for a researcher or evaluator might be to selectively use comparative metrics by comparing a single or group of publications to any of the following: other articles published in the same field, other articles published within the same journal, or other articles published by peer researchers.

One strategy for utilizing publication‐level metrics for a grouped set of publications is to use the mean number of citations per publication. This number may be higher or lower than the same author's h‐index (see below) depending on the distribution of citations within the body of work. Supplementing the mean number of citations for a large list of articles with the median and/or the standard deviation would help evaluators to understand the spread of the citation counts. Such measures of central tendency and variability could be used alongside, or instead of, direct citation counts for individual publications when presenting any of the previously discussed methods of grouping publications.

AUTHOR‐LEVEL METRICS

The h‐index 18 is the number ( N ) for an author such that at least N of the author’s publications have a minimum of N citations each. For example, imagine an author with any number of total publications, and at least 10 of their total papers have 10 or more citations. This author’s h‐index would be 10 (Table  2 ). The h‐index is a widely used and easily understood metric that demonstrates the citation impact across an author’s career. However, users of the h‐index should recognize several major limitations of this metric. The h‐index is consistently skewed toward researchers’ older papers, which have had more time to accumulate citations. A high h‐index is challenging to achieve for early career researchers. The h‐index also weights all authors equally regardless of authorship position, meaning it does not provide information about the relative contribution of authors. Additionally, h‐indices may be lower for researchers who have published extensively, but have only a limited number of highly cited publications compared with researchers whose papers’ citations are more evenly distributed. The h‐index is also vulnerable to extreme instances of self‐citation, or in‐group citation, which artificially inflate it. 19 , 20 Finally, and importantly, the h‐index should not be used to compare researchers across fields, as citation rates vary widely between disciplines. 21 As long as the drawbacks are understood, the h‐index can be a useful tool in an analysis comparing the total publication output of an author with the distribution of citations to their work. Numerous alternatives to the h‐index have been proposed that attempt to correct for such drawbacks, including variations on the h‐index itself, 22 , 23 , 24 , 25 the e‐index, 26 the g‐index, 27 and the m‐quotient, 18 , 28 but none have reached the popularity of the original h‐index.

Two different patterns of the distribution of authors’ total number of publications

Author A’s 15 total publications, sorted in order of decreasing citation count	Author B’s 100 total publications, sorted in order of decreasing citation count
Publication #1: 270 citations	Publication #1: 5000 citations
Publication #2: 250 citations	Publication #2: 1000 citations
Publication #3: 210 citations	Publication #3: 800 citations
Publication #4: 170 citations	Publication #4: 685 citations
Publication #5: 120 citations	Publication #5: 469 citations
Publication #6: 116 citations	Publication #6: 371 citations
Publication #7: 101 citations	Publication #7: 196 citations
Publication #8: 29 citations	Publication #8: 82 citations
Publication #9: 17 citations	Publication #9: 57 citations
Publication #10: 10 citations	Publication #10: 11 citations
Publication #11: 9 citations	Publication #11: 9 citations
Publication #12: 8 citations	Publication #12: 8 citations
Publication #13: 5 citations	Publication #13: 8 citations
Publication #14: 0 citations	Publication #14: 7 citations
Publication #15: 0 citations	Publication #15: 6 citations
	Publications #16–100: 5 or fewer citations each
Summary: Author A has an h‐index of 10, because A has at least 10 papers with at least 10 citations each.	Summary: Author B also has an h‐index of 10, because B has at least 10 papers with at least 10 citations each, even though they have a more extensive publication history and more individual citation counts on their most highly cited papers.

An h‐index can be calculated manually from a list of an author's publications’ total citation counts. Ideally, citation counts should be generated from a single citation network; citation counts collected from multiple networks should be presented separately and not joined into a single h‐index. If the list of citation counts for each paper is sorted from highest to lowest, it is simple to spot the crossover point at which the number of citations meets or exceeds the number of publications (Table  2 ). If an author does not have a list of their publications at hand, an h‐index can also be generated by searching Web of Science Core Collection, Scopus, or Google Scholar. In Web of Science and Scopus, an author’s publications can be searched by author name, affiliation, or unique identifier (such as ORCID); and an h‐index may be generated from the result set. In Google Scholar, authors will need to create a profile page and add their publications to their account to have their h‐index displayed. Researchers should be aware that Google Scholar may display duplicate records for their publications. This can cause inflated citation counts, if duplicate records are counted separately as citing papers. It can also cause the total number of citations to one publication to be split across the duplicate records for that same publication, decreasing the author’s h‐index. Researchers are encouraged to verify their publication records in Google Scholar. As with the publication‐level metrics discussed previously, it may be useful to consider multiple h‐indices for groups of publications that represent temporal, thematic, or authorship responsibility to either argue for or evaluate specific impact.

JOURNAL‐LEVEL METRICS

The most popular journal metric is the journal impact factor (JIF), 29 created by the scientometrician Eugene Garfield. The JIF is the number of total “citable items” a journal published in a 2‐year period divided by the total number of citations over the same 2‐year period. The denominator is currently defined as articles, review articles, and proceedings papers, 30 whereas the numerator includes citations to all publications in a journal. The JIF is a proprietary metric owned by Clarivate Analytics, who publishes Journal Citation Reports (JCR; subscription required), a database of annually updated JIFs, journal rankings, and other journal‐level metrics. The JIF was originally designed to indicate a relationship between a journal’s publications and citations, but there have been many critiques of its evolution into a single‐number proxy for broad scientific value. 31 , 32 , 33

Responsible application of JIFs requires an understanding of how the impact factor is calculated. For example, because citable items are defined to include research papers but to exclude nonresearch publication types (e.g., letters and editorials), editors may restructure their publication types in order to publish research articles in sections that were classified by Clarivate as “editorial.” Similarly, reducing the number of items in JIF denominators increases the total JIF. To keep JIFs proprietary, Clarivate does not disclose information on journals’ citable item sections, making it impossible for users to know if the metric is fair or accurate. 34 Editors may also pursue a higher impact factor via their journal’s submissions by asking submitters to include more citations to the journal in their manuscripts, or by soliciting more highly cited article types. Review articles consistently receive more citations than original research articles, 35 so editors may be incentivized to focus on secondary rather than primary publications. The pursuit of citations contributes to publication bias wherein prestigious, and aspirational, journals reject incremental or replicative research in favor of novel results, whose findings may not be reliable. 36 As with h‐indices, JIFs are also susceptible to fraudulent citations. 37 Most importantly, the impact factor of a journal is not capable of conveying the quality, scientific accuracy, or impact of any particular article published within that journal. The impact factor reflects citation patterns to the journal title as a whole, not the impact of any individual publication. Other journal metrics include Eigenfactor, 38 Scopus CiteScore, 39 SciImago Journal Rank Indicator, 40 and various modifications of the JIF itself, that may be useful for researchers desiring to explore or verify journal metrics for a particular context. 41 , 42 However, the original JIF remains, by far, the most familiar journal‐level metric.

The journal impact factor for a particular journal title can be searched via JCR. Although some individual journals may list their impact factors on their websites, it is recommended that dates and JIFs be verified via JCR. Research evaluators may not be familiar with the relative prestige of journals outside their own discipline, so researchers may use this opportunity to make a compelling presentation of the JIFs of the journals where they have published. JCR contains journal ranking data, simplifying the process of comparative analysis. Researchers can compare the JIFs of the journals in which they have published to other journals in the same field. A journal without a sky‐high impact factor may still be in the top quartile of journals within one’s field. JCR also contains historical impact factor data, which may be useful for discussion of a researcher’s decision to publish in up‐and‐coming journals.

LIMITATIONS

Some of the key limitations of citation networks and their citation counts, the h‐index and the JIF, have been discussed in the present paper. However, other metric considerations, as well as the broader concept of quantitative publication metrics as a whole, should be further studied in the process of evaluation policies and procedures improvement. This paper is intended as an introduction to the most frequently used publication metrics in the context of research careers or grant evaluations, and not as a thorough analysis of all available metrics. Additionally, this paper seeks to present practical information on how to access and apply popular metrics and tools in the context of research evaluation. Many of the products mentioned in this paper require expensive subscriptions that may be beyond the budget of some institutions. Understanding how the “free” alternatives, which collect user data in lieu of subscriptions, compare to the major subscription databases may be helpful for researchers trying to understand their options for accessing and presenting their publication metrics. Those who wish to gain a deeper understanding of their local subscriptions, or who seek further information about scientometrics, are encouraged to contact their institution’s librarian.

The use of quantitative strategies as a proxy for the scientific productivity, impact, and quality of research publications has both strengths and limitations. 43 , 44 No metric can serve as a fully representative proxy for research quality. The research itself, which may include nonpublication outputs, must be evaluated based on scientific integrity, societal need, advancement of the field, and other potentialities that matter to the evaluators (such as emphasis on support for new or under‐represented researchers, or previously unfunded research topics). There is increasing recognition of the importance of utilizing publication metrics responsibly in research evaluation. 45 The San Francisco Declaration on Research Assessment and the Leiden Manifesto provide recommendations and principles for improving research assessment and the appropriate use of metrics. 46 , 47 Quantitative publication metrics may serve as one component of a holistic assessment. However, even when integrated into a peer‐reviewed evaluative process that also includes qualitative assessment, metrics can either overly inflate or miss the perceived “impact” of research. Nevertheless, publication metrics’ ubiquity demands that funders, authors, and the publishing industry have a solid grasp of the strengths and weaknesses of using numbers as a proxy for scientific impact. A prudent utilization of publication metrics requires a thoughtful approach that includes a realistic understanding of what individual and aggregate metrics are capable of conveying. When used as part of a larger narrative, publication metrics can provide insight into an article’s reach, a journal’s evolution, or a researcher’s career. Strategic application of metrics can empower researchers to tell a clearer and more holistic story of their work, and responsible interpretation of metrics can empower evaluators to more efficiently, fairly, and consistently determine the future of scientific funding and advancement. Future improvements in research evaluation strategies can incentivize Open Science and the greater dissemination of research outputs. 48 , 49 Ultimately, the considered and transparent application and interpretation of publication metrics may help address some of the social inequities in science, provide more opportunity for under‐represented researchers and research areas, improve the wellbeing of researchers caught in the burnout “publish or perish” cycle, and speed the most promising basic research to clinical and policy implementation, and improved outcomes.

CONFLICT OF INTEREST

The authors declared no competing interests for this work.

This research was supported in part by NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI Grant Number UL1TR001881.

These are the 10 best-performing stocks of all time

• New research identifies the 10 best-performing US stocks since 1926.
• Of 29,000 US stocks traded since 1926, most had negative returns, but top performers showed consistent gains.
• The study affirms the importance of "time in the market," according to its author, Hendrik Bessembinder.

A new research paper has examined all 29,078 US stocks that have existed since 1926 and identified the top 10 performers of all time.

These stocks, some of them household names, have generated enormous wealth for those who purchased even just one share of the company and held on to it for nearly 100 years.

Of course, identifying the 10 best-performing stocks for the next 100 years is nearly impossible, as the paper's author points out.

"The majority (51.6%) of these stocks had negative cumulative returns," finance professor Hendrik Bessembinder of Arizona State University wrote.

But with a healthy dose of luck, $1 invested in 1926 would have been worth $2.67 million at the end of 2023, assuming it was invested in the right company.

Bessembinder also observed that the 10 best-performing stocks delivered consistently modest annualized gains, reinforcing the slow and steady mantra championed by long-term investors.

"Annualized compound returns to these top performers relatively were modest, averaging 13.47% across the top seventeen stocks, thereby affirming the importance of 'time in the market," Bessembinder wrote.

From soda to cigarettes to computers and airplanes, these are the 10 top-performing US stocks since 1926, according to the paper, which assumed that dividends were reinvested.

Ticker: PEP Return of $1 invested : $86,360 Investment date: December 31, 1925 - December 29, 2023

Ticker: KO Return of $1 invested : $123,724 Investment date: December 31, 1925 - December 29, 2023

• S&P Global

Ticker: SPGI Return of $1 invested : $128,787 Investment date: February 14, 1929 - December 29, 2023

Ticker: ETN Return of $1 invested : $151,173 Investment date: December 31, 1925 - December 29, 2023

• International Business Machines

Ticker: IBM Return of $1 invested : $175,437 Investment date: December 31, 1925- December 29, 2023 5. Boeing

Ticker: BA Return of $1 invested : $212,206 Investment date: September 5, 1934 - December 29, 2023

• General Dynamics

Ticker: GD Return of $1 invested : $220,850 Investment date: January 28, 1926 - December 29, 2023

• Kansas City Southern

Ticker: Delisted after 2021 acquisition by Canadian Pacific Return of $1 invested : $361,757 Investment date: December 31, 1925 - December 13, 2021

• Vulcan Materials

Ticker: VMC Return of $1 invested : $393,492 Investment date: December 31, 1925- December 29, 2023

• Altria Group

Ticker: MO Return of $1 invested : $2,655,290 Investment date: December 31, 1925 - December 29, 2023

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About 400 Million People Worldwide Have Had Long Covid, Researchers Say

The condition has put significant strain on patients and society — at a global economic cost of about $1 trillion a year, a new report estimates.

By Pam Belluck

Pam Belluck has been reporting about long Covid since the condition first emerged.

• Share full article

About 400 million people worldwide have been afflicted with long Covid, according to a new report by scientists and other researchers who have studied the condition. The team estimated that the economic cost — from factors like health care services and patients unable to return to work — is about $1 trillion worldwide each year, or about 1 percent of the global economy.

The report, published Friday in the journal Nature Medicine, is an effort to summarize the knowledge about and effects of long Covid across the globe four years after it first emerged.

It also aims to “provide a road map for policy and research priorities,” said one author, Dr. Ziyad Al-Aly, the chief of research and development at the V.A. St. Louis Health Care System and a clinical epidemiologist at Washington University in St. Louis. He wrote the paper with several other leading long Covid researchers and three leaders of the Patient-Led Research Collaborative, an organization formed by long Covid patients who are also professional researchers.

Among the conclusions:

About 6 percent of adults globally have had long Covid.

The authors evaluated scores of studies and metrics to estimate that as of the end of 2023, about 6 percent of adults and about 1 percent of children — or about 400 million people — had ever had long Covid since the pandemic began. They said the estimate accounted for the fact that new cases slowed in 2022 and 2023 because of vaccines and the milder Omicron variant.

They suggested that the actual number might be higher because their estimate included only people who developed long Covid after they had symptoms during the infectious stage of the virus, and it did not include people who had more than one Covid infection.

Many people have not fully recovered.

The authors cited studies suggesting that only 7 percent to 10 percent of long Covid patients fully recovered two years after developing long Covid. They added that “some manifestations of long Covid, including heart disease, diabetes, myalgic encephalomyelitis and dysautonomia are chronic conditions that last a lifetime.”

The consequences are far-reaching, the authors wrote: “Long Covid drastically affects patients’ well-being and sense of self, as well as their ability to work, socialize, care for others, manage chores and engage in community activities — which also affects patients’ families, caregivers and their communities.”

The report cited estimates that between two million and four million adults were out of work because of long Covid in 2022 and that people with long Covid were 10 percent less likely to be employed than those who were never infected with the virus. Long Covid patients often have to reduce their work hours, and one in four limit activities outside work in order to continue working, the report said.

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