data management dissertation

Dissertations and major projects

• Planning your dissertation
• Researching your dissertation
• Introduction

Before collecting your data

During your data collection, after your project.

• Writing up your dissertation

Useful links for dissertations and major projects

• Study Advice Helping students to achieve study success with guides, video tutorials, seminars and appointments.
• Maths Support A guide to Maths Support resources which may help if you're finding any mathematical or statistical topic difficult during the transition to University study.
• Academic writing LibGuide Expert guidance on punctuation, grammar, writing style and proof-reading.
• Guide to citing references Includes guidance on why, when and how to use references correctly in your academic writing.
• The Final Chapter An excellent guide from the University of Leeds on all aspects of research projects
• Royal Literary Fund: Writing a Literature Review A guide to writing literature reviews from the Royal Literary Fund
• Academic Phrasebank Use this site for examples of linking phrases and ways to refer to sources.

But it is also really important to consider how you will organise , store , and keep track of your data as you collect it. Good data management strategies:

• Prevent you from losing data
• Increase your efficiency when analysing the data
• Show trends, patterns, and themes more clearly
• Ensure your findings are based on robust, comprehensive results
• Demonstrate that you are a rigorous researcher

What do I need to collect?

Good data management starts by collecting suitable data to answer your research questions. Gathering data that is fit for purpose means your analysis will be more efficient, and prevents you from becoming overwhelmed by having to process a lot of irrelevant information. When designing your data collection methods, look back at your research question(s) and keep asking yourself: How will the information I plan to collect help me answer these questions? 

For further information about different research approaches and how to write the method chapter, have a look at our methodologies video below

Ethics forms

If you are gathering data that involves human subjects, it is likely you'll need to fill in an ethics form which will ask you to consider issues such as the confidentiality of your participants. Your project supervisor or department should be able to advise you on the type of ethics form you need to complete. Plan ahead to complete the ethics form in good time as it may need to be approved by a departmental committee, and you won't be able to start collecting your data without it.

• Research approaches and the methodology chapter Study Advice video on research approaches and the methodology chapter to help you navigate this important stage. Log in with your student account to watch

Keep your electronic files on the University network (N drive) as it is reliable and backed up.

If you are storing data directly on your own laptop or PC outside the University network, make sure you have a rigorous backup system in case your device crashes, or is lost or stolen. Use an external hard drive or USB stick and save your data regularly. Have a safe place to keep your USB stick or hard drive and remember to take it with you when you leave the library!

Collect the minimum amount of personal data necessary and avoid collecting any personal information that you don't need.

Store any personal data in an appropriate, secure location, e.g. a locked filing cabinet, or password-protected or encrypted online files.

Avoid sending or storing personal data over unsecure networks such as via email or in cloud services like Dropbox.

Process and safely destroy any personal data as soon as they are no longer needed, for example promptly downloading and saving interview recordings from your phone or recording device into a password protected file.

If you have said on your ethics form that you will be annonymising data (e.g. interview responses) to protect participants' confidentiality, make sure you do this. Have a system for anonymously labelling each response such as assigning a letter, number, or changing their name (Participant A, Interviewee 1, 'Johnny').

Organisation

Have a systematic and clear way of naming your online files and, most importantly, stick to it!

You should be able to tell what's in a file without opening it. Including a date formatted like YYYY-MM-DD means you can sort files chronologically Having a version control number means you can easily distinguish between your 1st, 2nd, or 10th draft!

Store your electronic files in a logical folder structure to make them easier to locate and manage, e.g. creating folders to group files according to content type, activity, or date. For further examples see guidance from the UK Data Service (link below).

Also have a system for safely storing any field notes. You don't want to lose vital parts of your research on site or in an unfamiliar library that you won't be returning to. Simple systems are the best, for example putting things in box files is easier than having to find a hole-punch and ring binders.

Documentation

As well as making good notes from the books and journal articles you read (including the full bibliographic details for your references) it is also important to keep clear records of other parts of your research process:

• Record your search strategy: Note down the combinations of keywords you use and the library databases you have searched to avoid duplication and confusion later.
• Keep your lab book up to date: If you are doing primary scientific research, a good lab book helps you record what you did whilst it is fresh in your mind; it makes writing your methods and results much easier.
• Label your equipment and any work in progress: If you are using a shared research space, clearly identify your work, as you don't want people accidentally moving it or throwing it away!
• UK Data Service: organising data Guidance on file formats and organisation.

If you have the opportunity to continue with similar research, for example in a postgraduate degree, or present it to a public audience, such as at a conference or in a journal paper, it is good practice to keep your data in case fellow researchers want to access it; your project supervisor can help advise you about this.

In most cases, though, for undergraduate research projects it is very unlikely you will have to store your data after you have graduated. However, before you rush off to burn your notes, it is a good idea to keep everything safely until you have your final marks, just in case!

Advice adapted from the University of Reading's Research Data Management pages.

• Research data management website (University of Reading) Information about what you need to consider when collecting and storing data.
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What is research data?

Research data are the evidence that underpins the answer to your research question and can support the findings or outputs of your research. Research data takes many different forms. They may include for example, statistics, digital images, sound recordings, films, transcripts of interviews, survey data, artworks, published texts or manuscripts, or fieldwork observations. The term 'data' is more familiar to researchers in Science, Technology, Engineering and Mathematics (STEM), but any outputs from research could be considered data. For example, Humanities, Arts and Social Sciences (HASS) researchers might create data in the form of presentations, spreadsheets, documents, images, works of art, or musical scores. The Research Data Management Team in the University Library aim to help you plan, create, organise, share, and look after your research materials, whatever form they take. For more information about the Research data Management Team,  visit their website .

Data Management Plans

Research Data Management is a complex issue, but if done correctly from the start, could save you a lot of time and hassle when you are writing up your thesis. We advise all students to consider data management as early as possible and create a Data Management Plan (DMP). The Research Data Management Team offer help in creating your DMP and can offer advice and training on how to do this. There are some departments that have joined a pilot project to include Data Management Plans in the registration reviews of PhD students. As part of the pilot, students are asked to complete a brief Data Management Plan (DMP) and supervisors and assessors ensure that the student has thought about all the issues and their responses are reasonable. If your department is taking part in the pilot or would like to, see the Data Management Plans for Pilot for Cambridge PhD Students page. The Research Data Management Team will provide support for any students, supervisors or assessors that are in need.

Submitting your digital thesis and depositing your data

If you have created data that is connected to your thesis and the data is in a format separate to the thesis file itself, we recommend that you deposit it in the data repository and make it open access to improve discoverability. We will accept data that either does not contain third party copyright, or contains third party copyright that has been cleared and is data of the following types:

•     computer code written by the researcher
•     software written by the researcher
•     statistical data
•     raw data from experiments

If you have created a research output which is not one of those listed above, please contact us on the  [email protected]  address and we will advise whether you should deposit this with your thesis, or separately in the data repository. If you are ready to deposit your data in the data repository, please do so via symplectic elements. More information on  how to deposit can be found on the Research Data Management pages . If you wish to cite your data in your thesis, we can arranged for placeholder DOIs to be created in the data repository before your thesis is submitted. For further information, please email:  [email protected]  

Third party copyright in your data

For an explanation of what is third party copyright, please see the  OSC third party copyright page . If your data is based on, or contains third party copyright you will need to obtain clearance to make your data open access in the data repository. It is possible to apply a 12 month embargo to datasets while clearance is obtained if you need extra time to do this. However, if it is not possible to clear the third party copyrighted material, it is not possible to deposit your data in the data repository. In these cases, it might be preferable to deposit your data with your thesis instead, under controlled access, but this can be complicated if you wish to deposit the thesis itself under a different access level. Please email  [email protected]  with any queries and we can advise on the best solution.

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Research data management and the FAIR principles

Stages of data management, data protection (gdpr).

• Interview and Survey Data
• Research ethics
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Research data refers to any data with which the analysis and results of a study can be repeated and validated. The data may have been collected by the researcher, generated during the study or consist of pre-existing archival data, and include various measurement results, survey and interview data, notes, research diaries, software or source codes.

Data management refers to the systematic collection, processing, storing and description of research data. Students are encouraged to learn about data management early in their studies, because good data management skills are beneficial to study progress and to adopting suitable data management practices during the thesis-writing process.

Data management practices should seek to comply with the FAIR principles, ensuring that the data is

• Accessible,
• Interoperable and

This is achieved, for example, through the use of open file formats, comprehensive metadata, and persistent identifiers (e.g., DOI, URN, ORCID), and defining ownership, terms of use and licenses. Learn more about the FAIR principles and the policy component for open access to research data .

1. Planning

Before you collect any data, record the most suitable data practices in a Data Management Plan (DMP) that can be supplemented as the work progresses and plans become more accurate. Formulating a plan will help you identify potential data protection risks, as well as solutions suitable for storing and describing your data. Careful data management also allows you to make the data accessible for potential reuse and thus improve the reliability of your research and the repeatability of the results.

Planning can be done using the DMPTuuli tool that is accessible with your HAKA credentials. DMPTuuli contains templates and instructions that can be applied to the data management plan of a thesis.

2. Storing and Organising Data

Choose a secure storage solution for your data, based on the demand for the data and its confidentiality level. Secure storage, version control and backup help prevent any unintentional deletion of data. Open file formats, logical file naming and folder structure, as well as rich content descriptions facilitate the findability, intelligibility and sharing of data. Consider the following questions when choosing the storage solution:

• Ensure secure access control so that only authorised parties have access to the data.
• Utilise the university’s cloud storage solutions (e.g., Owncloud, SharePoint) suitable for data sharing.
• Protect confidential data with a password or encryption.
• Confidential data should not be stored in commercial cloud services, such as Dropbox or Google Drive.
• If you store data on your personal devices, ensure backups, device password protection and anti-virus protection.
• If you use services provided by the university, using automatic backup solutions is recommended.

NB: External storage media, such as flash drives, are not recommended as primary storage solutions, because data stored on them is susceptible to becoming lost, deleted and unintentionally shared with outsiders.

Backing up helps you decrease the risk of irreparable damage to or deletion of data. Always keep separate working and backup copies of the research data. Choose storage solutions that include automatic backup. Backing up should be based on the 3-2-1 Rule, meaning that data is stored in the following way:

• in at least three copies
• on two different types of storage media
• one of which is kept physically separate from the others.

File formats

To ensure the usability of your data on a variety of devices and software, using open, non-commercial file formats is recommended. Most software supports the following common file formats:

• Text: txt, .odt., .rtf, .csv, PDF/A, .html,.xml
• Images: jpeg, tiff, png, dng
• Video: MPEG-4 (.mp4), dpx
• Sound:  FLAC, aif, aac.

Naming conventions and Folder structure

Systematic file naming practices and folder structures ensure the identifiability and findability of your data, even when there are time lapses in processing it. Clear file naming also simplifies file sharing. When you name a file:

• Choose a descriptive name
• Avoid names that are too long or short
• Avoid special characters and spaces
• To separate parts of the name, use the underscore (_), hyphen (-) or initial capital letters
• Add dates, version numbers and/or modifier initials to distinguish between different file versions
• Avoid overlap in folder and file names

NB: If you use abbreviations, remember to define them in writing so that they can be understood.

Documentation

Document the basics of your data during the thesis writing process to ensure the findability and usability of your data. Documenting makes it easy to check the contents of your data, how it has been processed and where it is stored. The simplest option is to record the descriptive data (or metadata) related to your data in a text file (a.k.a. README file) that you save as a separate file along with your data. Metadata may also be published according to the description guidelines of the particular publishing service. Record at least the following information in the file:

• Data name, size and file format
• Data content and descriptions of variables (abbreviations, measuring scales, coding)
• Data collection (who, where, when, how)
• Data processing (who, how, when)
• Storing data and terms and conditions

Read more about storing, file naming , recommended file formats and documenting in the Data Management Guidelines of the Finnish Social Science Data Archive.

3. Publishing, archiving or deleting

Take care of your research data even after the completion of the thesis. Electronic data requires further measures to stay up-to-date, and not all data needs to be archived for long periods of time. Based on the reuse value of your data, choose appropriate measures, such as data archiving, publishing or deletion. Keep in mind that your right to use the University of Vaasa IT services expires after graduation, unless you continue in another university role, such as a position of doctoral researcher or employee. If the data is stored in the University of Vaasa systems, remember to transfer or delete it before your access rights expire.

If your data contains personal details, it is usually deleted after the thesis has been accepted. Keep in mind that moving a file to the recycle bin does not sufficiently delete the data. More thorough measures, such as overwriting a drive or mechanically destroying a flash drive, are needed. Further information on deleting the data: Office of the Data Protection Ombudsman or Data Management Guidelines of the Finnish Social Science Data Archive .

If the data has reuse value and you have permission to reuse or publish the data, you may publish or archive your data in a chosen data archive. Keep in mind that you may need permission for data reuse or publishing from your research subjects or potential customer, and that data anonymisation may be a condition for publishing. For example, the Finnish Social Science Data Archive , The Language Bank of Finland , and Fairdata’s IDA and Qvain offer domestic solutions for publishing data and related metadata, while Zenodo or EUDAT  B2Share are some of the international service provider options.

Data protection refers to the safeguarding of personal data. The notion of personal data is broad, and what qualifies as personal data is any information that either directly or indirectly enables the identification of a person, for example by connecting an individual piece of information to another piece of information. More information on personal data: Office of the Data Protection Ombudsman . Personal data processing related to studies must adhere to the principles of the University of Vaasa data protection policy: University of Vaasa Information Security Policy .

Before collecting and processing personal data:

• Discuss the issue with your thesis supervisor or the teacher in charge of the course
• Familiarise yourself with the University of Vaasa personal data processing instructions: Data Processing Instructions (NB: The instructions can only be accessed by university personnel) and University of Vaasa Data Protection Statement (NB: currently available only in Finnish).
• Be sure to provide the research participants with a privacy notice. Further reading available in this guide under Privacy Notice .

While collecting and processing personal data:

• Limit the processing of personal data to what is necessary to achieve the aims of the thesis. Do not collect personal data “just in case”!
• Store personal data in a secure way and ensure that third parties do not accidentally or intentionally gain access to personal data.

The student collecting personal data acts as the data controller.

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• Last Updated: Oct 2, 2024 9:58 AM
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• Data Management Guide overview
• Creating your data overview

Organising your data

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Looking after your data

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• Choosing a software licence
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Data Management Guide

• Research Data Management

Creating your data

Accessing your data

• Electronic Research Notebooks
• Data Repository
• Data Policies
• Data Champions

Various forms of research data

Research data management is a complex issue, but if done correctly from the start it could save you a lot of time and hassle at the end of the project, when preparing your data for a publication or writing up your thesis. Research data takes many forms, ranging from measurements, numbers and images to documents and publications. The resources on these pages were prepared for you by the University Library to help you plan, create, organise, share and look after your research materials, whatever form they take.

Research data management guidelines

• Data Management Plan
• Choosing Formats
• Intellectual Property Rights
• Data Protection and Ethics
• Naming and Organising Files
• Documentation and Metadata
• Managing References
• Organising E-mail
• Remote Access
• Sharing your data with Collaborators
• Long-term storage and preservation
• Selection - choosing what to keep
• Sharing - what, why, and how to share data
• Digital repositories 
• What data do I share?
• Why share your data?
• How to share your data?
• Data access statements
• Additional resources

Electronic Research Notebooks (ERNs)

• Features and functionality
• Storage and security
• Forward planning
• How to pick a notebook
• Further reading

Related links

• Training and workshops on data management
• Where can I deposit my data?

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The project is a joint initiative of Cambridge University Library and the Research Operations Office .

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If your PhD contains research data , you will have to think about how to deal with those data. In this section, you will learn about

• principles for data management
• data management plans
• how to store and archive your data
• how to provide good and sufficient metadata
• how to structure data files
• data security  

The FAIR principles were originally introduced in 2016 (Wilkinson et al., 2016), and the Research Council of Norway (NFR) states:

The FAIR guiding principles for scientific data management and stewardship are included as a main principle... (NFR, 2017)

The same principles govern the data policy in the Horizon 2020 framework, and are followed by more and more academic publishers, such as Nature Publishers . The Norwegian government also states that the FAIR principles should govern all publicly funded research in Norway (Meld. St. 25 (2016–2017) The Humanities in Norway , summary in English)

In order to comply with the FAIR guidelines, data should be

• a ccessible
• i nteroperable

One of the keys to complying with this principle is to use a decent data management plan . We will present the necessities of a good data management plan below, but first some information on the FAIR guidelines.

F indable data

To be findable, data should be uniquely and persistently identifiable, which means that it should be possible to find the same object at any point in time by using persistent links. The data should also minimally include enough basic machine-readable metadata to separate it from other data.

A ccessible data

Accessible data are those obtainable by machines and humans after appropriate authorization. Access must be granted through a well-defined protocol.

I nteroperable data

This means that data and metadata are machine-readable and formatted according to well-known vocabularies or ontologies. In other words, data must be both correct and understandable for a machine in order to be interoperable.

R e-usable data

A further requirement is re-usability, which can only be ensured if the FAI-part above are followed. In addition: metadata should be described sufficiently well to allow it to be automatically linked or integrated with other data sources. Published data should have enough metadata to enable correct referencing.

Treating your data according to the FAIR-principles will make sure that the data you collect are re-usable and verifiable for fellow researchers according to the national strategy on transparent and reproducible data . One key element in this is to make sure you think ahead when it comes to data management.

Data management plans

A data management plan (DMP) describes the data management life cycle for data to be collected, processed and/or generated. The data management plan will state how you treat data from project start to end. Note that the data management plan can be regarded as part of the research process, and should be included in the final project publication.

Public funders such as the European Commission or the Research Council of Norway require you to provide a data management plan as part of your project description, and there could very well be a note on data management in your PhD agreement. There are guidelines on what the plan should contain; here are two examples:

• The European Commission H2020 Programme Guidelines for data management plans
• The Research Council of Norway's recommendations for data management plans

As there is a growing tendency to use the FAIR-guidelines in more areas of research, you should try to use them to create a plan for your data even if your current research is not funded directly by an external party. Following the FAIR-guidelines is recommended by many of the Norwegian higher educational institutions, for example the University of Oslo data management , the University of Bergen , UiT The Arctic University of Norway or the Norwegian University of Science and Technology .

Templates for data management plans

A general data management plan contains information such as this:

• The research project, people involved, and whether it is part of a larger project.
• Who is responsible for following up the DMP and who has rights to manage and/or access the data. This could be just you, your research group, or other collaborators.
• Details of the data you are going to collect or generate. These could be observations, simulations, interviews, etc. This part could also include information on existing data, which requires that you have searched for research data created by others.
• How documentation and metadata will be created, and what file formats will be used.
• Where the data will be stored during the project, expected file size, procedures for backup, storage and archiving. The local IT department can probably support you here.
• Where the data will be archived at the end of the project, and if they will be shared.
• If the data are sensitive, how this will be handled.

It is usually best to use a DMP template and generate a plan containing the necessary information. A few different templates exist, and you could of course use the Horizon2020 template also for non-EU funded research. If your PhD needs to be registered with NSD or preapproved by REK (personal/sensitive data), the DMP generator published by NSD is probably the better choice. Note that your institution may also recommend certain templates for creating data management plans.

• The Norwegian Centre for Research Data DMP generator
• Horizon 2020 template (pages 7 - 9)

Storage and archiving

In many PhDs, the amount of data produced is small enough to be stored on your own computer, or a shared area provided by your institution if you collaborate with others. Most universities and university colleges provide an institutional home area for you to use, which is automatically backed up regularly, usually every night. If you need more disc space, or have special requirements for data storage, your local IT department can help you. Note that if you work with sensitive data, there are stricter requirements for safe storage, e.g. where to store data, encryption, passwords for access, etc.

Most institutions provide their own services for collaborative use. This is recommended to ensure storage for as long as you need, and to limit access to those who have the right of access. Dropbox and Google docs should be avoided.

You may have different needs for storage during your PhD, and for archiving when it is finished. Some data can be shared , while other data should be archived in a closed repository. If you do not have clear requirements for what archive to use, you can search research data or data repositories to find the one that best suits your data.

Documentation and metadata

Data documentation is an essential part of data management. The documentation should include what data you have collected, what methods were used, and what the research context is. If there are any limitations, this should be stated. This is especially important if you plan to share data, but is also useful for yourself, so that you are in complete control of your data sets, and do not risk having to re-do the data collection. Provide information on what the data represent and if they have been processed in any way.

Metadata are data about the data. They are often provided in addition to more extensive documentation, to give brief information on the data. This could include the author, a descriptive title of the data set, date of collection, keywords, etc. There should be enough metadata to enable you and others to understand how to use the data in the future. Some data archives or repositories have metadata forms with required fields. If at all possible you should use metadata from a widely accepted standard in your field of study; this will make your data easier to find through data search engines.

Structuring data files

It may seem obvious that data should be structured so that they are easy to understand and reuse. However, there are common mistakes like using acronyms and abbreviations that may seem easy to understand now, but turn out to make little sense later. Try to follow the guidelines below:

• Provide units of measurement when applicable.
• Use naming conventions that are easy to understand, and be consistent, with e.g. file names on the form datatype_date_location.ext. If you use acronyms and abbreviations in file names, headers, etc, explain them in the metadata.
• Avoid names that are too long.
• Avoid special characters.
• Use underscore (_) instead of space in file names, as some programmes have difficulty reading files with spaces in the file name.
• Use persistent file formats.

Technical security measures

There is no such thing as too much data security , but there is no need to make your data management more cumbersome than needed. In a low-risk setting such as this example of sensitive data , your smartphone could be the perfect tool for the job; high quality, digital files, unlimited storage, small size, high power capacity, easy to transfer the data for processing, etc. Setting up a rigorous system with rotating passwords, ever-changing encryption keys and file transfer through encrypted channels is not needed if the data are not considered as sensitive enough.

The key point is that you need to think about this before you start on the data collection. As is obvious from the examples above, there are no fixed 'security categories' when dealing with personal and sensitive data, and you will have to define your need for security yourself.

Recording of sound and video

Video and sound recordings can be highly personally identifiable, remember that even the voice of the interviewees can be enough to identify them! Cartooning, censoring and voice distortion are the only ways to anonymise such data, but this option is not viable for all kinds of projects, such as those studying spoken dialect or facial expressions. Note that anonymization should be done on a computer without internet connection. Anonymised data can be processed on a computer with internet but remember that the content of any dialogue still remains; what is said can be enough to identify people.

You need to be in full control of the equipment used for recording sound or video; you should not leave it unattended or lend it to unauthorised persons. Note that equipment with an internet connection, such as a smartphone, should never be used to record personal information unless you can ensure that all communications are shut off. Equipment without an internet connection and which uses removable storage units is generally the best option. If encryption of the storage media is not an option, make sure that the physical media are securely stored in a safe or similar, and transfer any sensitive or personal data to more secure storage, such as a computer without internet connection and encrypted harddrive. Long-time storage of such data for the purpose of further research will need specific secure long-time storage facilities, and the approval of such storage from the relevant authorities.

Transferring data

Pay attention to the way you transfer your data between units. The transfer of data should be as safe as the storage unit; for example, you cannot send highly sensitive data in emails. At the highest levels of security, an encrypted connection is needed, but a physical transfer using a wire or card-reader would also work well. If you transcribe sensitive recorded data, make sure that there is no one in the room with you, looking over your shoulder.

In cases where email is the best solution for transferring personal data, use software such as 7zip for encryption before sending the file. Sensitive data should as a general rule never be sent as attachments to email going over a normal mail server. Ask your IT department if sending emails internally in you institution is regarded as safe or not.

Processing and storing

Data sometimes need to be transferred to your desktop computer for processing. Anonymization of data from an interview is one example, but there could be numerous reasons for needing to process the raw data before analysing them. If you have data in the high-risk category, you should probably have a designated computer with permanently deactivated communications for the job. No internet or similar connections should be available on a computer used for processing high-risk data. You also need to make sure that the processing can take place in a secure place; a public reading- room or internet cafe with people looking over your shoulder should never be used.

Low-risk data could be stored on removable units, such as memory cards or USB-sticks, but there must at least be some basic level of security for personal data. Encryption of the removable storage is a possibility, or you could store media in a safe location. Higher-risk data need specific storage units using high-level security measures such as secure servers with encrypted disks and strictly regulated access, even physical access to the login-terminals themselves.

Some data can be shared, and some should indeed be shared! Sharing data is generally done through uploading, or archiving , the data into a repository . Even if your funder or your institution is a strong supporter of the FAIR principles , you are still responsible for not sharing data that should not be shared. Remember: "data should be as open as possible and as closed as necessary". Read through the short section on when not to share your data before you upload your data into an open repository. Note that uploading data into a database is not the same as sharing them; there are many options for secure storing of your data, which is not the same as sharing them. Sharing and storing are not the same.

Two of the key notions behind data sharing is that they should allow both new research and that conclusions can be verified. With this in mind, you should make a decision regarding the rawness of the data you share; for very large datasets a certain degree of processing of the data before sharing is the obvious choice, but every step of processing you take could limit the possibilities of future use.

Encryption of mobile storage media is possible on a normal Windows computer by turning on 'BitLocker' for the selected drive. If you use a Mac, encryption can be done in 'disk utility' after creating an image out of the folder you wish to encrypt. Encryption is necessary for all removable storage media containing high-risk data. The encryption key (the password) must never be accessible by any unauthorised persons and should not be stored in connection with the encrypted disk itself.

Data management vocabulary

Below you can find some commonly used Norwegian and English terms used in data management and research activities in the higher education institutions in Norway.

Words and expressions used in privacy and data protection

The following list is adapted from the Norwegian Data Protection Authority's list of words and expressions used in privacy and data protection. You can find the complete list, including an English-Norwegian version, on the site of the Data Protection Authority .

The UHR dictionary of academic terms

The Norwegian Association of Higher Education Institutions (UHR) has created a short dictionary (termbase). In this dictionary, you will find translations of more than 2000 administrative terms from the two written languages in Norway to English, and vice versa.

Useful resources

CESSDA ERIC (the Consortium of European Social Science Data Archives European Infrastructure Consortium) provides an expert tour guide on data management . The guide aims to help researchers make their data findable, understandable, sustainably accessible, and reusable.

Wilkinson, M.D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A.,...Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data , 3 , Article 160018. https://doi.org/10.1038/sdata.2016.18

• Types of reviews
• Search techniques
• The Dissertation
• Where to publish
• Submitting articles
• Co – authorship
• Registering research results
• Citation impact

PhD on Track

Examples of data management plans

These examples of data management plans (DMPs) were provided by University of Minnesota researchers. They feature different elements. One is concise and the other is detailed. One utilizes secondary data, while the other collects primary data. Both have explicit plans for how the data is handled through the life cycle of the project.

School of Public Health featuring data use agreements and secondary data analysis

All data to be used in the proposed study will be obtained from XXXXXX; only completely de-identified data will be obtained. No new data collection is planned. The pre-analysis data obtained from the XXX should be requested from the XXX directly. Below is the contact information provided with the funding opportunity announcement (PAR_XXX).

Types of data : Appendix # contains the specific variable list that will be used in the proposed study. The data specification including the size, file format, number of files, data dictionary and codebook will be documented upon receipt of the data from the XXX. Any newly created variables from the process of data management and analyses will be updated to the data specification.

Data use for others : The post-analysis data may be useful for researchers who plan to conduct a study in WTC related injuries and personal economic status and quality of life change. The Injury Exposure Index that will be created from this project will also be useful for causal analysis between WTC exposure and injuries among WTC general responders.

Data limitations for secondary use : While the data involve human subjects, only completely de-identified data will be available and used in the proposed study. Secondary data use is not expected to be limited, given the permission obtained to use the data from the XXX, through the data use agreement (Appendix #).

Data preparation for transformations, preservation and sharing : The pre-analysis data will be delivered in Stata format. The post-analysis data will also be stored in Stata format. If requested, other data formats, including comma-separated-values (CSV), Excel, SAS, R, and SPSS can be transformed.

Metadata documentation : The Data Use Log will document all data-related activities. The proposed study investigators will have access to a highly secured network drive controlled by the University of Minnesota that requires logging of any data use. For specific data management activities, Stata “log” function will record all activities and store in relevant designated folders. Standard file naming convention will be used with a format: “WTCINJ_[six letter of data indication]_mmddyy_[initial of personnel]”.

Data sharing agreement : Data sharing will require two steps of permission. 1) data use agreement from the XXXXXX for pre-analysis data use, and 2) data use agreement from the Principal Investigator, Dr. XXX XXX ([email protected] and 612-xxx-xxxx) for post-analysis data use.

Data repository/sharing/archiving : A long-term data sharing and preservation plan will be used to store and make publicly accessible the data beyond the life of the project. The data will be deposited into the Data Repository for the University of Minnesota (DRUM), http://hdl.handle.net/11299/166578. This University Libraries’ hosted institutional data repository is an open access platform for dissemination and archiving of university research data. Date files in DRUM are written to an Isilon storage system with two copies, one local to ​each of the two geographically separated University of Minnesota Data Centers​. The local Isilon cluster stores the data in such a way that the data can survive the loss of any two disks or any one node of the cluster. Within two hours of the initial write, data replication to the 2nd Isilon cluster commences. The 2nd cluster employs the same protections as the local cluster, and both verify with a checksum procedure that data has not altered on write. In addition, DRUM provides long-term preservation of digital data files for at least 10 years using services such as migration (limited format types), secure backup, bit-level checksums, and maintains a persistent DOIs for data sets, facilitating data citations. In accordance to DRUM policies, the de-identified data will be accompanied by the appropriate documentation, metadata, and code to facilitate reuse and provide the potential for interoperability with similar data sets.

Expected timeline : Preparation for data sharing will begin with completion of planned publications and anticipated data release date will be six months prior.

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College of Education and Human Development featuring quantitative and qualitative data

Types of data to be collected and shared The following quantitative and qualitative data (for which we have participant consent to share in de-identified form) will be collected as part of the project and will be available for sharing in raw or aggregate form. Specifically, any individual level data will be de-identified before sharing. Demographic data may only be shared at an aggregated level as needed to maintain confidentiality.

Student-level data including

• Pre- and posttest data from proximal and distal writing measures
• Demographic data (age, sex, race/ethnicity, free or reduced price lunch status, home language, special education and English language learning services status)
• Pre/post knowledge and skills data (collected via secure survey tools such as Qualtrics)
• Teacher efficacy data (collected via secure survey tools such as Qualtrics)
• Fidelity data (teachers’ accuracy of implementation of Data-Based Instruction; DBI)
• Teacher logs of time spent on DBI activities
• Demographic data (age, sex, race/ethnicity, degrees earned, teaching certification, years and nature of teaching experience)
• Qualitative field notes from classroom observations and transcribed teacher responses to semi-structured follow-up interview questions.
• Coded qualitative data
• Audio and video files from teacher observations and interviews (participants will sign a release form indicating that they understand that sharing of these files may reveal their identity)

Procedures for managing and for maintaining the confidentiality of the data to be shared

The following procedures will be used to maintain data confidentiality (for managing confidentiality of qualitative data, we will follow additional guidelines ).

• When participants give consent and are enrolled in the study, each will be assigned a unique (random) study identification number. This ID number will be associated with all participant data that are collected, entered, and analyzed for the study.
• All paper data will be stored in locked file cabinets in locked lab/storage space accessible only to research staff at the performance sites. Whenever possible, paper data will only be labeled with the participant’s study ID. Any direct identifiers will be redacted from paper data as soon as it is processed for data entry.
• All electronic data will be stripped of participant names and other identifiable information such as addresses, and emails.
• During the active project period (while data are being collected, coded, and analyzed), data from students and teachers will be entered remotely from the two performance sites into the University of Minnesota’s secure BOX storage (box.umn.edu), which is a highly secure online file-sharing system. Participants’ names and any other direct identifiers will not be entered into this system; rather, study ID numbers will be associated with the data entered into BOX.
• Data will be downloaded from BOX for analysis onto password protected computers and saved only on secure University servers. A log (saved in BOX) will be maintained to track when, at which site, and by whom data are entered as well as downloaded for analysis (including what data are downloaded and for what specific purpose).

Roles and responsibilities of project or institutional staff in the management and retention of research data

Key personnel on the project (PIs XXXXX and XXXXX; Co-Investigator XXXXX) will be the data stewards while the data are “active” (i.e., during data collection, coding, analysis, and publication phases of the project), and will be responsible for documenting and managing the data throughout this time. Additional project personnel (cost analyst, project coordinators, and graduate research assistants at each site) will receive human subjects and data management training at their institutions, and will also be responsible for adhering to the data management plan described above.

Project PIs will develop study-specific protocols and will train all project staff who handle data to follow these protocols. Protocols will include guidelines for managing confidentiality of data (described above), as well as protocols for naming, organizing, and sharing files and entering and downloading data. For example, we will establish file naming conventions and hierarchies for file and folder organization, as well as conventions for versioning files. We will also develop a directory that lists all types of data and where they are stored and entered. As described above, we will create a log to track data entry and downloads for analysis. We will designate one project staff member (e.g., UMN project coordinator) to ensure that these protocols are followed and documentation is maintained. This person will work closely with Co-Investigator XXXXX, who will oversee primary data analysis activities.

At the end of the grant and publication processes, the data will be archived and shared (see Access below) and the University of Minnesota Libraries will serve as the steward of the de-identified, archived dataset from that point forward.

Expected schedule for data access

The complete dataset is expected to be accessible after the study and all related publications are completed, and will remain accessible for at least 10 years after the data are made available publicly. The PIs and Co-Investigator acknowledge that each annual report must contain information about data accessibility, and that the timeframe of data accessibility will be reviewed as part of the annual progress reviews and revised as necessary for each publication.

Format of the final dataset

The format of the final dataset to be available for public access is as follows: De-identified raw paper data (e.g., student pre/posttest data) will be scanned into pdf files. Raw data collected electronically (e.g., via survey tools, field notes) will be available in MS Excel spreadsheets or pdf files. Raw data from audio/video files will be in .wav format. Audio/video materials and field notes from observations/interviews will also be transcribed and coded onto paper forms and scanned into pdf files. The final database will be in a .csv file that can be exported into MS Excel, SAS, SPSS, or ASCII files.

Dataset documentation to be provided

The final data file to be shared will include (a) raw item-level data (where applicable to recreate analyses) with appropriate variable and value labels, (b) all computed variables created during setup and scoring, and (c) all scale scores for the demographic, behavioral, and assessment data. These data will be the de-identified and individual- or aggregate-level data used for the final and published analyses.

Dataset documentation will consist of electronic codebooks documenting the following information: (a) a description of the research questions, methodology, and sample, (b) a description of each specific data source (e.g., measures, observation protocols), and (c) a description of the raw data and derived variables, including variable lists and definitions.

To aid in final dataset documentation, throughout the project, we will maintain a log of when, where, and how data were collected, decisions related to methods, coding, and analysis, statistical analyses, software and instruments used, where data and corresponding documentation are stored, and future research ideas and plans.

Method of data access

Final peer-reviewed publications resulting from the study/grant will be accompanied by the dataset used at the time of publication, during and after the grant period. A long-term data sharing and preservation plan will be used to store and make publicly accessible the data beyond the life of the project. The data will be deposited into the Data Repository for the University of Minnesota (DRUM),  http://hdl.handle.net/11299/166578 . This University Libraries’ hosted institutional data repository is an open access platform for dissemination and archiving of university research data. Date files in DRUM are written to an Isilon storage system with two copies, one local to each of the two geographically separated University of Minnesota Data Centers. The local Isilon cluster stores the data in such a way that the data can survive the loss of any two disks or any one node of the cluster. Within two hours of the initial write, data replication to the 2nd Isilon cluster commences. The 2nd cluster employs the same protections as the local cluster, and both verify with a checksum procedure that data has not altered on write. In addition, DRUM provides long-term preservation of digital data files for at least 10 years using services such as migration (limited format types), secure backup, bit-level checksums, and maintains persistent DOIs for datasets, facilitating data citations. In accordance to DRUM policies, the de-identified data will be accompanied by the appropriate documentation, metadata, and code to facilitate reuse and provide the potential for interoperability with similar datasets.

The main benefit of DRUM is whatever is shared through this repository is public; however, a completely open system is not optimal if any of the data could be identifying (e.g., certain types of demographic data). We will work with the University of MN Library System to determine if DRUM is the best option. Another option available to the University of MN, ICPSR ( https://www.icpsr.umich.edu/icpsrweb/ ), would allow us to share data at different levels. Through ICPSR, data are available to researchers at member institutions of ICPSR rather than publicly. ICPSR allows for various mediated forms of sharing, where people interested in getting less de-identified individual level would sign data use agreements before receiving the data, or would need to use special software to access it directly from ICPSR rather than downloading it, for security proposes. ICPSR is a good option for sensitive or other kinds of data that are difficult to de-identify, but is not as open as DRUM. We expect that data for this project will be de-identifiable to a level that we can use DRUM, but will consider ICPSR as an option if needed.

Data agreement

No specific data sharing agreement will be needed if we use DRUM; however, DRUM does have a general end-user access policy ( conservancy.umn.edu/pages/drum/policies/#end-user-access-policy ). If we go with a less open access system such as ICPSR, we will work with ICPSR and the Un-funded Research Agreements (UFRA) coordinator at the University of Minnesota to develop necessary data sharing agreements.

Circumstances preventing data sharing

The data for this study fall under multiple statutes for confidentiality including multiple IRB requirements for confidentiality and FERPA. If it is not possible to meet all of the requirements of these agencies, data will not be shared.

For example, at the two sites where data will be collected, both universities (University of Minnesota and University of Missouri) and school districts have specific requirements for data confidentiality that will be described in consent forms. Participants will be informed of procedures used to maintain data confidentiality and that only de-identified data will be shared publicly. Some demographic data may not be sharable at the individual level and thus would only be provided in aggregate form.

When we collect audio/video data, participants will sign a release form that provides options to have data shared with project personnel only and/or for sharing purposes. We will not share audio/video data from people who do not consent to share it, and we will not publicly share any data that could identify an individual (these parameters will be specified in our IRB-approved informed consent forms). De-identifying is also required for FERPA data. The level of de-identification needed to meet these requirements is extensive, so it may not be possible to share all raw data exactly as collected in order to protect privacy of participants and maintain confidentiality of data.

Theses: Data Plan for your PhD

• Finding theses @ Soton
• Data Plan for your PhD
• Deposit - Faculty Office
• Deposit - PGR Manager & Pure
• Thesis Data Deposit
• Restricting Access
• Using your own publications in your thesis
• Info. for Faculty Admin
• Info. for Supervisors

DMP for your PhD research

All first year post graduate researchers should complete a data management plan for their research and include it as part of their first three month review.  There is also a Blackboard course  Data Management Plans for Doctoral Students -  mandatory for all new doctoral students - to introduce you to research data management and help you complete the plan. Log into Blackboard using your university username and password.

A data management plan or DMP is a living document that helps you consider how you will organise your data, files, research notes and other supporting documentation throughout the length of the project.  The aim is to help you find these easily, keep them safe and have sufficient documentation to be able to re-use throughout your research and beyond.

You will need to complete a preliminary data management plan in your first three months, along with your Academic Needs Analysis.  Your DMP will continue to develop as your research progresses and you will need to update and review your DMP at every progression review. ( Code of Practice for Research Degree Candidature and Supervision, )

All researchers will have data. Data can be broadly defined as 'Material intended for analysis'.  This covers many forms and formats, and is not just about digital data.

For example, 

Art History - high resolution reproductions of photographs, notebook describing context

English literature - research notes on text, textual analysis

Engineering - experimental measurements on the physical properties of liquid metals

The University also has a definition for “Research Data” in its  Research Data Management Policy  that you should consider.

A PhD DMP template and guidance on how to complete your Data Management Plan is available ( see below ). All new doctoral students should complete the Data Management Plans for Doctoral Students module on Blackboard. Contact us if you need further information or have feedback via [email protected]

Guidance on depositing your research data at the end of your doctorate can be found on the Thesis Data Deposit guide. Please also see our depositing research data videos at  https://library.soton.ac.uk/researchdata/datasetvideos

Creating your DMP

• Introduction
• DMP and Project Overview
• About your Project Data
• Making Data Findable
• Making Data Accessible
• Making Data Reusable
• Making Data Secure
• Implementing the Plan
• Example Plans

What are data management plans? A data management plan is a document that describes:

• What data will be created
• What policies will apply to the data  
• Who will own and have access to the data
• What data management practices will be used 
• What facilities and equipment will be required 
• Who will be responsible for each of these activities

Your data management plan should be written specifically for the research that you will be doing.  Our template is a guide to help you identify the key areas that you need to consider, but not all sections will apply to everyone.  You may need to seek further guidance from your supervisor, colleagues in your department or other sources on best practice in your discipline.  We provide some details of guidance available in our training section and on our general research data management pages.

Each of the tabs looks at the different topics that can be included in a data management plan.  You can move through the tabs in any order.

Describing your Project

At the start of your data management plan (DMP) it is useful to include some basic information about the research you are planning to do.  This may already exist in other documents in more detail, but for the purposes of the DMP try to summarise in as few sentences as possible.

What policies will apply?

It is important that you think about who is funding your research and whether there are any requirements that you need to meet.  Are you funded by a UK Research Council? What policies do they have on research data - see  Funder Guidance .  What does our University Research Data Management policy  and Code for Conduct for Research state is required?

Does the type of data you will be creating, using, collecting mean that you have to meet certain legal conditions?  Will you be collecting any form of personal data, (see ICO Personal Data Definition ), special category data (see ICS Special Category definition ) or is it commercially sensitive?  For example, if you are involved in population health and clinical studies research data and records minimum retention could be 20-25 years for certain types of data - see the MRC Retention framework for research data and records  for further details. 

Do you need Ethics Approval?

Anyone who is dealing with human subjects or cultural heritage (see University policies ) will require to obtain ethics approval and this must be done prior to collecting any data.  Your DMP should inform what you say in your ethics application about how you will collect, store and re-use your data.  It is important that your DMP and your ethics application are in agreement and you provide your participants with the correct information. Once you receive your ethics approval, review your data management plan and update as necessary.

Reviewing your Data Management Plan

A DMP should be a living document and should be updated as your research develops.  It should be reviewed on a regular basis and good practice would encourage that the dates of review are included in the plan itself.  Use of a version table in any document can be helpful.

What data will be created?

In your data management plan you need to provide some detail about the material you will be collecting to support your research.  This should cover how you will collect notes, supporting documentation and bibliographic management as well as your primary data. Will all your data be held electronically or will you require to maintain a print notebook to collect your observations?

Are you using Secondary Data?

Not everyone has to collect their own data, it may already have been collected and made available.  This data is known as secondary data.  Some secondary data are freely available, but other data are released with terms and conditions that you need to meet.  In some cases this may influence where you can store and analyse the data.  You need to be aware of this as you plan the work you intend to do.

How are you collecting or creating your data?

How you collect or gather the material for your research will influence what you need to do to manage them. The way you do this may alter as your research progresses and you should update your plan as required. Will you be collecting data by observing, note-taking in an archive, carrying out experiments or a mixture of these? 

How much data are you likely to have?

Knowing how much data you might create is important as it will dictate where you can store your data and whether you need to ask for additional storage from iSolutions.  It is unlikely that you can say exactly what volume of data you might create, but you will have an idea of individual file sizes.  If you will be working with word, excel documents and a reference management software library then you are likely to be dealing with megabytes or gigabytes of data. If you will be collecting high resolution images then you may end up needing to store terabytes. Estimate as early as possible and if you think you may need additional space you should discuss this with your supervisor.

What formats will you be using?

A crucial factor in being able to share data is that it is in an open format or collected using disciplinary standard software that allow export to open formats.  Consider how open the format of your data will be when selecting the software, instruments, word processing packages that you use. See the Data formats section in  Introducing Research Data  Part III for points to consider.

Who will own the data?

If you have been sponsored by a research council, government, industry or commercial body the agreement you signed may cover ownership of the data that you create.  Being aware of this early is useful as it will influence what you are able to do when you come to writing papers, sharing and depositing your data when your finish. It may also impact on where you can store your data.  

How will you make your data findable?

Using standards to capture the essential metadata is a good way to help create data that will be easy to find.  It will also make preparing for deposit in the future more straightforward.  The Research Data Alliance has a helpful list of  disciplinary metadata  and use case examples.  You can make reference to these in your plan once you know what will be most appropriate to use.

Where will you store the data during your PhD?

Where you store your data will depend on things such as the type and size of data you are collecting.  Certain types of data, such as personal , special category data (formerly referred to as sensitive data) or commercially confidential data, will require to be stored more securely than others.  This type of data generally requires to be stored on University network drives that have additional protection and not on personal computers or cloud storage (for example, Office 365, One Drive). Where you are collecting less sensitive data your choice of storage is wider.  For all storage it should in a location with good back-up procedures in place. Consult iSolutions knowledge base  for further information.

How will you name your files and folders?

It can be helpful to think about creating a procedure on how you will name your files. This is a basic step where it is useful to consider how easy it will be to interpret the name in the future.  Abbreviations can be good, but ask yourself how someone else might understand the file name should you need to share it with them. What would make it easy to know what each file contains?  While it is possible to have quite longer file names this can cause problems when you zip files. 

How will you tell one version of a file from another?

How will you be able to tell whether you are dealing with the latest version of a file? How will you manage major versus minor changes?  What if you want to return to an earlier version?  Use the data management plan to investigate what would be the optimum method for you and establish a good procedure from the beginning.  Generally the use of 'draft', 'latest' or 'final' should be avoided.  Instead consider using the data (YYYY-MM-DD) or a version number, for example, v.1.0  where the nominal value increases with major changes and decimal for minor ones.  Adding a version table at the end of a document can also be helpful.

How can you share your data?

To make data accessible is not about doing something at the end of the project, but needs to be planned for from the beginning.  During your research you are likely to have colleagues or collaborators who will need to be able to access the data - how will you do this?  Will you need a collaborative space and if so what can you use?  Does it need to be is a protected location with restricted access due to the type of data you are using? By establishing good procedures on documentation, metadata collection, file-naming and using disciplinary standards this will assist you throughout your research, as well as helping at the end.

How do you handle personal, sensitive or commercially confidential data?

If the data you are collecting contains   personal ,  special category  data (formerly referred to as sensitive data) or commercially confidential data  then sharing or transferring the files needs to be carried out in a way that does not make the data vulnerable. Data should be anonymised or pseudo-anonymised as early as possible after collection, seek disciplinary guidance prior to collection. 

The medium of transfer must be secure and where necessary encryption should be used. You may want to consider one of the following:

There may be other software available and you should check if there is a standard in your discipline. 

Transferring data via USB or external drives is not recommended, but where required these should be encrypted. Avoid using email to send files and instead use our University SafeSend service.  This offers transfer of files up to 50GB and your files can be encrypted by ticking "Encrypt every file" when creating a new drop-off - see ' How secure is SaveSend'

What data do you need to keep and what do you need to destroy?

Not all the data from a project needs to be kept and the data you collect should be reviewed regularly.  The Digital Curation Centre (2014) guide  ' Five steps to decide what data to keep: a checklist for appraising research data v.1 ' may help you to decide what to retain. It is important that you retain or discard data in line with your ethics approval.

You also need to consider what data needs to be destroyed, how you will mark the data for destruction and when this needs to happen. Destroying paper based records is relatively easy through our confidential waste system.   Destroying digital data is less so as it may need to be done so that it cannot be forensically recovered. Guidance on destroying your data is available  or contact iSolutions for advice.

Why do you need to consider the long-term storage now?

At the end of your PhD you will be encouraged to share your data as openly as possible, and as closed as necessary. To do this safely consider what you need to do to enable your data to be accessible in the future.  Knowing where the best place to store your data may inform what you need to plan for in its creation or collection.  Are you aware of any disciplinary data repositories that hold similar data?  Examples are:

• Archaeology - Archaeology Data Service  
• ESRC - UK Data Archive
• STFC -  eData   
• NERC - data centres
• Biology - GenBank
• General repository - Zenodo

Investigate what requirements these repositories have on formats, documentation etc and incorporate these into your plan. Otherwise you should plan to deposit in the University Institutional Repository . 

There are currently no costs for depositing most dataset in our Institutional Repository unless the data requires specialist archive storage or is in excess of 1TB. External repositories may have charges for depositing data. 

Who will be creating the archive?

Generally as a PhD the job of drawing together your data into a dataset ready for deposit will fall to you as the researcher.  It is not the responsibility of your supervisor, although they may be able to advise on what needs to be done.  If you are part of a larger project there may be someone designated to curate the project data.  For further assistance contact [email protected] . 

How long should the data be kept?

This will depend on a number of factors.  Your funder may have a policy that requires the data to be held for a minimum of 10 years from last use.  If you are working in certain medical areas the data may need to be held for 25 years.  There may be some restrictions on how long you can retain personal data relating to Data Protection Act 2018 (GDPR).  Significant data that has been given a persistent identifier (DOI) will be kept permanently.

What documentation or additional information needs to accompany the data?

Keeping a record of what changes you have made, when data was collected, where data was collected from, observations, definitions of what has been collected are all crucial to allowing data to be used safely and with integrity. How do you plan to do this? How will you make sure that you can match up your notes with the files they refer to?  Some programming languages such as Python and R allow you to make notes in the files about what you are doing which is really helpful.  Where this is not an option then you will need to develop your own method to make sure that processes applied to the data are recorded and available to you to refer back to later.  Creating a register of your files by type using an excel spreadsheet may be worth considering, but it should be manageable and importantly kept up-to-date.

In order for data to be reusable it requires data provenance.  Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures.

• Research Data Management and Sharing - Documentation The importance of systematically documenting your research data. more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

What restrictions will need to apply?

Not all data can be made openly available.  Some data may only be shared once a data sharing agreement has been signed, while other data may not be suitable for sharing.  Funding councils encourage all data to be as open as possible and as closed as necessary. Where will your data fit with this?  What agreements do you need to be able to share your data?

When can data be made available?

Data can be deposited in our Institutional Repository  and kept as an 'entry in progress' until it is ready for publication. 

Not all data needs to be made immediately available at the end of your PhD.  It is possible to add an embargo to give yourself some additional time to find funding to continue your work and re-use your own data.  See Regulations on embargoes.

However, it is not always necessary for you to wait until the end of your PhD before depositing data.  If you write a conference or journal paper it is likely that you will be asked to make the underpinning data available.

How will you keep your data safe?

What would happen if your files became corrupted or your laptop was stolen, would you be able to restore them?  What would happen if someone was able to access your data without your knowledge or approval?  If you are holding personal  or   special category  data (formerly referred to as sensitive data) and these became public this would be a data breach with potentially serious consequences.

Dr Fitzgerald  Loss of seven years of Ebola research  

Consider carefully the impact to you and your research if these were to happen and what procedures you may need to put into place to reduce the risk of these happening.

• Research Data Management and Sharing - Data Security Ensuring your research data are kept safe from corruption, and that access is suitably controlled more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

How will you back up your data?

Good housing keeping of your data is important and this includes doing regular back ups of your data.  University storage is backed up regularly but it is important to have your own 'back up' folders, kept separately from your working files.  Back up should be done on as regular a basis as required.  This can be defined by the length of time you are prepared to repeat work lost.  You may need to back up daily, weekly or monthly depending on the nature of your research.  

• Research Data Management and Sharing - Backup Effective backup strategies for your research data. more... less... From the Coursera Research Data Management and Sharing course https://www.coursera.org/learn/data-management

As well as establishing a process for backing up your files, you should check the process of restoring your files.  You will need to check that the files restore correctly.  Having good documentation on what your files contain, what transformations or analysis has been carried out will be invaluable for this process.

How can you safely destroy data?

Destroying data, especially   personal ,  special category  data (formerly referred to as sensitive data) or commercially confidential data , is not as straightforward as just deleting the file.  Further action is required otherwise the data could be recovered.  Please read our guidance on destruction of data   and GDPR regulations .

• Data Disposal Essential guidance from the UK Data Archive on data disposal

An important part of research data management is that your plan is implemented and part of your everyday good research practice.  The plan should be a living document and reflect your practice.  You may find that some parts become redundant or that there is a better way to carry out a process so your plan should be updated. As a PhD researcher it is likely that you will be the person responsible for implementing the plan.  If your research is part of a wider research project there may be someone in the team who has been given the role and you should discuss your data management plan with them.

Having written your plan consider what actions do you need to take in order to carry it out? What further information do you need to find? Investigate what training or briefing sessions are available via PGR Manager.  If you want to enhance your data analysis skills check out material on Linked in Learning 

Over time we will add plans to this section as we get permission to share them.

• PhD DMP Example (Web Science) This is an example PhD Data Management Plan for a research project looking at learner engagement and peer support in digital environments.
• Arts and Humanities
• Science, Medicine and Engineering
• Social Sciences
• Further Reading

Courses offered by the University:

Data Management Plans for Doctoral Students -  mandatory course on for all new doctoral students. Log into Blackboard using your university username and password.

Data Management Plan: Q&A Clinic - as a follow up to the compulsary online course, the Library is running twice weekly clinics to answer your DMP queries. Book PGR Development Hub .

Data Management Plan: Why Plan?  45 minute briefing.  A Panopto recording of this course  is  available

Research Data Management: What you need to know from the start .  45 minute briefing. Book via Gradbook

Research Data Management Workshop .180 minute workshop Book via Gradbook

• Introduction to research data (visual arts) Introduction to research data in the visual arts, wirtten by Marie-Therese Gramstadt as part of the Kultur project
• Manage, improve and open up your research and data PARTHENOS training module on various aspect on data management
• VADS4R Data Management Planning A toolkit developed by the Visual Arts Data Skills for Researchers (vads4R)
• Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics The Cross-Linguistic Data Formats initiative proposes new standards for two basic types of data in historical and typological language comparison (word lists, structural datasets) and a framework to incorporate more data types (e.g. parallel texts, and dictionaries). The new specification for cross-linguistic data formats comes along with a software package for validation and manipulation, a basic ontology which links to more general frameworks, and usage examples of best practices. [article]
• EMBL-EBI Training EMBL-EBI train scientists at all levels to get the most out of publicly available biological data.
• Datatree - Data Training A free online course, aimed at PhD and early career researchers, with all you need to know for research data management, along with ways to engage and share data with business, policymakers, media and the wider public. more... less... The course is for any scientist, whether you look after your own data or are guided by an organisation.
• Expert Tour Guide on Data Management A guide for social science researchers who are in an early stage of practising research data management.
• CESSDA ERIC RDM User Guides Brief guides on important topics in data management and a helpful checklist
• Guide to Social Science Data Preparation and Archiving An important guide covering the different stages of data management to enable the sharing and preserving of data in the Social Sciences
• Managing your dissertation data : Thinking ahead Maureen Haaker and Scott Summers from the UK Data Service gave this presentation. The session sought to help the students ensure transparency in the collection and writing up of their dissertation, whilst also ensuring that good practices in data management were followed. more... less... Although aimed at undergraduate dissertation it provides useful information for everyone.
• UK Data Service Prepare and Manage Data Good data management practices are essential in research, to make sure that research data are of high quality, are well organised, documented, preserved and accessible and their validity controlled at all times. This results in efficient and excelling research.
• FAIR Principlies Guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets
• How to Develop a Data Management and Sharing Plan Jones, S. (2011). ‘How to Develop a Data Management and Sharing Plan’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/how-guides
• MRC Retention framework for research data and records guidance on retention of research data an records resulting from population health and clinical studies
• Open Data Handbook Handbook that discusses the why, what and how of open data – why to go open, what open is, and the how to ‘open’ data.
• Open Research Data and Materials Open Science Training Handbook section on research data

Key Documents

• DMP Templates
• Deposit Guide
• Code of Conduct for Research University of Southampton policy - October 2017
• Data Protection Policy University of Southampton policy May 2018
• Data Sharing Protocol University of Southampton protocol - May 2018. [Login required]
• Ethics - Human participant policy University of Southampton policy - March 2012
• Ethics - Policy on Cultural Heritage University of Southampton policy - October 2018
• Research Data Management Policy University of Southampton policy - 2015

The template below has been provided to assist you in writing your data management plan.  Not all sections will be relevant, but you should consider carefully each section.

• Template for PhD DMP (pdf)
• Template for PhD DMP (Word)

When the time comes to deposit your data, follow the advice in our Thesis Data Deposit guide . 

Email us on: [email protected]

Who's Who in the Research Engagement Team

Research Support Guide

  research support.

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Study Skills

Research skills.

• Searching the literature
• Note making for dissertations

Research Data Management

• Copyright and licenses
• Publishing in journals
• Publishing academic books
• Depositing your thesis
• Research metrics
• Build your online profile
• Finding support

Welcome to this module, where we will cover all the main aspects of looking after your research data, including:

• how to store and backup up data
• how to organise data
• what to do with protected data (personal or commercially sensitive)
• why sharing data is important and how to do it
• writing Data Management Plans

Data can take many forms: not only spreadsheets, but also images, interview recordings and transcripts, old texts, survey results, protocols... the list goes on.

To complete this section, you will need:

• Approximately 60 minutes.
• Access to the internet. All the resources used here are available freely.
• Some equipment for jotting down your thoughts, a pen and paper will do, or your phone or another electronic device.

Where did it all go wrong?

Lack of planning at the start of a project can cause problems (and much more work!) later on. Think of data management as a time investment to make sure that the data you collect is used effectively and remains usable over time.  

Watch this video by the NYU Health Sciences Library as an example of poor data management and take some brief notes on any mistakes you spot. When you’re done, compare your notes with our answers underneath.  

Check your answers

What did this researcher do wrong?

Here are all the mistakes we spotted: -he did not consider how others may want to reuse his data -he did not share the data in a repository -he was not aware of his funder and publisher requirements -he did not have multiple backups -he did not keep the data in a safe place -data on a USB stick is easy to lose -he did not use a safe way to share data (the post could have been lost) -he did not save the data in a common format -he did not save instructions on how to open the data -he did not plan for long-term preservation -he did not give variables intuitive names -he did not save metadata on what the variable names mean -he relied on knowledge found only in the brain of one person, rather than writing metadata

Keeping your data safe and up to date

Ensuring your data are safe is crucial to any research project. A good storage and backup strategy will help prevent potential data loss. Explore this scenario to see if your choices align with good research practice. Click on the link below to begin

Note: scenario opens in new window. Please view the scenario in full-screen. Return to this window to continue with the module, or if you wish to restart the scenario

Data storage and backup - why bother?

• Please visit the UK Data Service for more detailed tips on Storage and Backup of research data
• Branching scenario built using YoScenario
• All photos are CC-0 from Pexels

Organising data

Once you are sure that your data is safe from accidental loss, you should be thinking about how to organise it. Are your computer files ‘an amorphous plethora of objects’? In this video by the University of Edinburgh Data Library, Professor Jeff Haywood talks about his experiences of organising data.  

If you want to read more about organising your data, including folder structures and file naming, there is a detailed guide on the Cambridge data website.  

If you are at the start of a project, spend some time now preparing an organisational structure for your data. Create all the folders you are likely to need and a few named placeholders for files you will create. If you would like some feedback on it, email me .

Actvity - What should a PhD student do with her data?

Follow Martha in our scenario and help her make the best choices! 

Sharing your data

Take a look a this video of Cambridge researchers talking about their experience of sharing data.

Using repositories 

So what does it mean in practice to share your data? All you have to do is upload your dataset and information about it on a repository, either a subject-specific one, an institutional one like Apollo, or a general one. The repository then lets people find and download the data. Find out more in the video below. 

Useful resources related to the video:

• Link to upload in Apollo  
• Cornell metadata guide 
• Funders policies on data website  
• License Selector Tool 
• Re3data 
• Blog post by Blair Fix 
• Bioinformatics Training Facility courses
• Slides for Sharing your data in repositories

Protected data 

If your research data is of a personal or sensitive nature, you must make sure you understand and respect the additional requirements associated with managing it. If possible, get in touch with your department’s ethics committee, or your industrial sponsor to check what they expect of you. Additional help can be sought from the Research Data team , the Research Integrity team , and the  Information Compliance Office .  

What are personal and sensitive data?

Personal data is data relating to a living individual, which allows the individual to be identified from the information itself or from the information plus any other information held by the 'data controller' (or from information available in the public domain). The University of Cambridge as a whole is the data controller. Sensitive data is personal data about: racial or ethnic origin, political opinions, religious beliefs, Trade Union membership, physical and mental health, sexual life, or criminal offences and court proceedings about these.

What are the legal requirements for data protection?

The The EU General Data Protection Regulation (GDPR), coupled with the UK Data Protection Act 2018 (DPA 2018) gives individuals certain rights and imposes obligations on those who record and use personal information to be open about how information is used and to follow eight data protection principles. Personal data must be: processed fairly, lawfully and transparently; obtained for specified, explicit and lawful purposes; adequate, relevant and not excessive; accurate and, where necessary, kept up-to-date; not kept for longer than necessary; processed in accordance with the subject's rights; kept secure; not transferred abroad without adequate protection

How should I store my sensitive or confidential data?

You should limit physical access to sensitive data or encrypt it (speak with your local IT/Computing Officer or the University Information Services Help Desk for help in doing this). To avoid accidentally compromising the data at some future date, you should always store information about the data's sensitivity and any available information on participants' consent or use agreements from your data provider with the data itself (i.e. put information about lawful and ethical data use in your data documentation or metadata description).

Data supporting my research is personal or sensitive. How do I share these data?

There can be a potential conflict between abiding by data protection legislation and ethical guidelines, whilst at the same time fulfilling funder's and individual's requirements to make research results available. Consult your ethics committee before deciding to share participants’ data. Your plans for research data processing, storage and sharing should be considered at the start of each project and reflected in both your data management plan and consent form. For example, you can inform your participants that anonymised data will be shared via the University of Cambridge data repository. There is good guidance on consent forms at the UK Data Archive (www.ukdataservice.ac.uk). The UK Data Archive also provides a sample consent form. Your Department’s Ethics Committee may also provide sample consent forms.

If you would like to learn more about personal and sensitive data and do some practical exercises on identifying these data types, the University of Cambridge offers short 30-mins long online courses on personal and sensitive data . 

You should also consider whether your data is commercially sensitive: do you or a sponsor plan to profit from the research in the future? There should be a collaboration agreement in place from the start to clarify the terms of any commercial collaboration. The  Research Operations Office can help with this. If you are working with both public funders and commercial partners, clarify early what data can be shared and what can’t, so you can make this clear to all parties.  

Data Management Plans 

Throughout this module we have seen how important it is to plan the way you will manage your data right at the start of a project. A Data Management Plan (DMP) is a document that captures that process.  

To end this module and pull together everything you have learnt, we recommend you write your own DMP for a project you are about to start or have recently started. Use these instuctions as a guide.

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Managing data across the research lifecycle

Learn more about using the research data lifecycle to inform your data management planning

• What are research data at UCL?

What is the research data lifecycle?

What is a data management plan, why are data management plans useful, before you get started, dmp training and review service, ucl research data policy, what are research data at ucl.

According to the UCL Research Data policy , data are:  “facts, observations or experiences on which an argument or theory is constructed or tested. Data may be numerical, descriptive, aural or visual. Data may be raw, abstracted or analysed, experimental or observational. Data include but are not limited to: laboratory notebooks; field notebooks; questionnaires; texts; audio files; video files; models; photographs; test responses”.

There are three kinds of data: 

• Open - data which are freely available online;
• Controlled/restricted   - data access is restricted on the basis of there being ethical, legal and/or commercial reasons prohitbiting their open release. Potential secondary users must meet certain criteria before access is given; 
• Closed - data which are permanently embargoed due to their nature.

The research data lifecycle models the different phases of the research process - from planning and preparation through to archiving and sharing - making your research and outputs discoverable to the wider research community and members of the public. There are four phases:

• Planning and preparation - You've had an idea for a research study so it's time to start making plans and getting prepared. It's usually during this phase you will write a data management plan and perhaps submit it as part of a grant application.
• Active research - You are now actively researching putting all those research plans into action.
• Archiving, curating and preserving - The research is complete and it's time to archive your research outputs to preserve them for the longer-term.
• Discovery, access and sharing - making your research discoverable to others for potential reuse can help to maximise research opportunities.

A Data Management Plan (DMP) describes your planned and/or actioned data management and sharing activities. It is generally 1-3 pages in length and should cover the four phases of the research data lifecycle. It is generally written at the start of a research project and should be revisted at different stages of the project and updated where necessary. DMPs may be published in the UCL Research Data Repository and assigned a DOI.

When writing your plan, remember to check if any  funder's policies and requirements  apply to your rseearch. A range of how-to guides  are also available to assist you in writing your plan.

UCL Data Management Plan template

Download our Data Management Plan template (MS Word)

Guidance is provided as comments in the margins.

In addition to often being a prerequisite to receiving certain grants, DMPs are useful for:

• maximising the research potential of existing research outputs by reusing and repurposing them
• thinking about and developing your strategy for issues such as data storage and long-term preservation , handling of sensitive data , data retention and sharing , early on in your research.
• anticipating legal, ethical and commercial exceptions to releasing data ; deciding who can have access to data in the short and long term.
• estimating the costs of your research project , which can then be included in your project budget.

Here are a few tips to help you start writing a DMP:

• Verify which data management and data sharing policies apply - these could be institutional, funder or journal publisher-led.
• Identify whether you will need to enter into a data sharing agreement before datasets and other study materials may be shared. There could also be legal frameworks and copyright issues to be mindful of. There is more information about material transfer agreements .
• Where research involves living human participants, it is recommended you speak with the Data Protection team to confirm which data protection legislation apply. Where you are collaborating with partners based globally, confirm whether international data protection legislation apply to your research.
• Verify submission deadlines.

The RDM team offers both face-to-face and online training courses on how to write a data management plan. Using the UCL DMP template, attendees have the opportnity to write a data management plan which they can take away with them and use as a basis for a more detailed plan of their data management and sharing activities.  

For more help and advice, contact your Research Data Support Officers who can also review drafted UCL Data Management Plans if you send them in advance of submission (allow 1 to 2 weeks at least before your submission deadline).

The UCL Research Data Policy describes UCL's expectations relating to data management and sharing within the wider Open Science context. 

DMPonline , a free tool created by the DCC, provides a framework for creating your Data Management Plan. UCL guidance is now incorporated into DMPonline; see our further guidance on using the tool.

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Introduction to research data management

What is research data management .

Research data management refers to how you will look after the data you collect or generate during your research. It covers activities such as planning for your data management needs at the start of your project, organising , storing and securing data during your project and ensuring long-term preservation , data sharing and reuse at the end of your project.

Why manage research data? 

Data management is increasingly recognised as an essential part of good research practice. Responsibly managed data is important for research integrity, transparency and open science. Many funders now expect data that supports published findings or has potential for reuse in future research to be made publicly available with as few restrictions as possible whenever legal and ethical restrictions allow .

The benefits of research data management include:   

• Reduces the risk of data loss
• Makes it easier to find and understand data
• Helps make data authentic, accurate and reliable
• Improves research integrity and reproducibility
• Facilitates data sharing and reuse
• Enables compliance with funder and publisher policies

What are research data?  

Research data are any materials that you collect or generate during your research project that can be used to support or verify your research findings . The UKRI Concordat on Open Research Data defines research data as ‘… the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form (e.g. print, digital, or physical)'. Research data can be generated or collected for different purposes and through different processes:  

• Observational: data captured in real-time, usually irreplaceable e.g. sensor data, survey data, sample data, neuroimages
• Experimental: data from laboratory equipment, often reproducible, but can be expensive to reproduce e.g. gene sequences, chromatograms, toroid magnetic field data
• Simulation: data generated from test models where the model and metadata are more important than output data e.g. climate models, economic models
• Derived or compiled: data is reproducible, but expensive e.g. text and data mining, compiled database, 3D models
• Reference or canonical: a (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets most probably published and curated e.g. gene sequence databanks, chemical structures, spatial data portals 

Examples of research data include:

• text documents
• spreadsheets
• audio and video recordings
• photographs, films
• collections of digital objects
• questionnaires, transcripts of interviews
• sensor readings
• models, algorithms, scripts

Having a clear understanding of the types of data you will collect or generate will help you make informed decisions about managing your data effectively.  

Where can I find additional help and support?  

Contact the Research Data Management team by booking a 1-2-1 consultation or send us an email at [email protected] .

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What does my funder require.

Find out what your funder requires in relation to research data management

What does my publisher require?

Find out what your publisher requires in relation to research data management

Imperial RDM Policy

Read the Imperial College London research data management policy

Research Topics & Ideas: Data Science

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

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Research Topics & Ideas: Automation & Robotics

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6 Chapter 6 Data Management and Analysis

• Published: December 2009
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This chapter examines issues related to quantitative and qualitative data including data collection, data management, data processing, data preparation and data analysis; as well as data storage and security in relation to HIPAA and other security requirements. The selection of appropriate statistical procedures including descriptive and inferential statistics is reviewed, as are the requirements and strategies for the collection and analysis of qualitative data including data coding and theme identification.

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