Research & Scholarly Publications Team: Creating and Managing Data

Organising your data

Once you create, gather, or start manipulating data and files, they can quickly become disorganised. To save time and prevent errors  later on, you and your colleagues should decide how you will name and structure files and folder. Including documentation (or 'metadata') will allow you to add context to your data so that you and others can understand it in the short, medium, and long-term. Good metadata should be both computer and human-readable.

Naming and Organising Files

Agreeing on a logical and consistent naming convention at the beginning of your project will make it easier to find and correctly identify your files, prevent version control problems when working on files collaboratively, and generally prevent errors in research. Organising your files carefully will save you time and frustration and prevent duplication or errors by helping you and your colleagues find what you need when you need it.

• Use folders ‐ group files within folders so information on a particular topic is located in one place.
• Adhere to existing procedures ‐ check for established approaches in your team or department which you can adopt.
• Name folders appropriately ‐ name folders after the areas of work to which they relate and not after individual researchers or students. This avoids confusion in shared workspaces if a member of staff leaves, and makes the file system easier for new staff or subsequent projects to navigate.
• Be consistent - When developing a naming scheme for your folders it’s important that once you’ve decided on a method, you stick to it. If you can, try to agree on a naming scheme from the outset of your research project.
• Structure folders hierarchically ‐ start with a limited number of folders for the broader topics, and then create more specific folders within these.
• Separate ongoing and completed work ‐ As you start to amass lots of folders and files, it’s a good idea to start thinking about separating your old documents, from those you are currently working on.
• Try to keep your ‘My Documents’ folder for files you're actively working on, and every month or so, move the files you're no longer working on to a different folder or location, such as a folder on your desktop, a special archive folder or an external hard drive.
• Backup - Ensure that your files, whether they are on your local drive or on a network drive, are backed up.
• Review records ‐ assess materials regularly or at the end of a project to ensure files aren’t kept needlessly. Put a reminder in your calendar so you don't forget!

What do I need to consider when creating a file name?

It is useful if your department/project agrees on the following elements of a file name:

• Vocabulary – choose a standard vocabulary for file names, so that everyone uses a common language.
• Punctuation – decide on conventions for if and when to use punctuation symbols, capitals, hyphens and spaces.
• Dates – agree on a logical use of dates so that they display chronologically i.e. YYYY-MM-DD. Order - confirm which element should go first, so that files on the same theme are listed together and can therefore be found easily.
• Numbers – specify the amount of digits that will be used in numbering so that files are listed numerically e.g. 01, 002, etc.

How should I name my files, so that I know which document is the most recent version?

Very few documents are drafted by one person in one sitting. More often there will be several people involved in the process and it will occur over an extended period of time. Without proper controls this can quickly lead to confusion as to which version is the most recent. Here is a suggestion of one way to avoid this happening:

Use a 'revision' numbering system. Any major changes to a file can be indicated by whole numbers, for example, v1 would be the first version, v2 the second version. Minor changes can be indicated by increasing the decimal figure for example, v1.01 indicates a minor change has been made to the first version, and v3.01 a minor change has been made to the third version.

When draft documents are sent out for amendment, they should return carry additional information to identify the individual who has made the amendments. Example: a file with the name 20100816_dataman_v1_sj indicates that a colleague (sj) has made amendments to the first version on the 16th August 2010. The lead author would then add those amendments to version v1 and rename the file following the revision numbering system.

Include a 'version control table' each important document, noting changes and their dates alongside the appropriate version number of the document. If helpful, you can include the file names themselves along with (or instead of) the version number.

Agree who will finalise documents, marking them as 'final.'

What are the benefits of sharing my data?

Many researchers fear that by sharing their data they will lose their competitive edge, that others will misinterpret or misuse their data or that their research methods will be open to scrutiny. However, there also benefits to be gained though sharing your data.  For example it:

• Allows independent validation of results
• Increases the impact and visibility of research makes best use of investment by avoiding replication
• leads to new collaborations and partnerships
• advances research when datasets are combined in new and innovative ways

If you plan for data sharing from the beginning of your project, you can decide on a method of providing access that you are comfortable with.

Are there occasions when I shouldn’t share my data?

Issues of intellectual property rights, commercial potential or of privacy can all affect whether you can or should share your data.

Sensitive and confidential data can, however, often be shared ethically if informed consent for data sharing has been given, subjects' identities are anonymised (if needed) or consideration is given to access restrictions.

These measures should be planned from the beginning of your research to ensure that you are not limiting future opportunities to share your data.

The UK Data Archive has an excellent guide on consent, confidentiality and ethics as part of their Managing and Sharing Data guide, and they provide brief guidance and tool reommendations for Anonymisation.

Sharing data with collaborators

Please note:  The University does not authorise or approve the use of DropBox.  It should never be used for confidential, personal or sensitive data.

Digital Services recommend Microsoft Teams as a secure space for data(set) and document storage, and as an online  location to interact with colleagues.  For example, a Teams site can help you:

• Coordinate projects, calendars and schedules.
• Discuss ideas, and review documents or proposals.
• Store and share research data with international collaborators

Contact Digital Services for advice on using the service to safely collaborate with research partners.

Data preservation

Your data are only truly open if other people can access them, understand them and reuse them.  It is recognised good research practice by researchers and institutions to manage and retain data, fulfilling any legal requirements that may exist following the conclusion of research projects.  This requires active preservation to ensure that the files continue to be readable over the long term, making this an important feature of the research data lifecycle.  You should ensure that the repository you choose has active preservation procedures for digital data curation.  The Digital Curation Centre highlight data preservation as a key aspect to consider when planning a new research project, particularly with data that are unique and irreplaceable if destroyed or lost.  Without the ability to refer to verifiable data, your research may not be judged as sound.

Digital Repositories

There are numerous Digital Repositories and data centres with varying content types (e.g. articles, data sets, images, etc) and disciplinary foci. The majority of them share data openly with the public, or the research community.

OpenDOAR (Directory of Open Access Repositories) maintains an online list of open access digital repositories, and has a content search tool.

re3data.org is the Registry of Research Data Repositories, providing a global registry of data repositories from different academic disciplines, and its use is particularly recommended in the European Commission’s “Guidelines on Open Access to Scientific Publications and Research Data in Horizon 2020”.

Online stores of discipline or subject-specific data ('data centres') abound, but there is currently no definitive list of these.

Some examples of popular data centres include:

• The National Environment Research Council (NERC) Data Centres
• The Archaeology Data Service (ADS)
• The UK Data Archive (UKDA)
• The United States Geological Survey (USGS)

Preserving data at Coventry University

At Coventry University, we have the option of utilising the institutional repository for the storage of data and datasets. A short demonstration video 'Uploading Datasets to the Pure Repository' shows the process of adding data to the repository. On validation, deposited datasets will be automatically archived for long-term storage by Preservica, which is connected to the repository. View the Adding a Dataset to Pure tab for the Repository's Terms of Use and Take Down Notice.

Why would I put my data in a digital repository?

Raise the impact of your research. Digital repositories allow you to make data easily accessible to more people than ever before. The more people who can use your data, the more public good it can do and the more it can do to enrich your field of research. Open online access makes new collaborations and uses of data possible. In some areas (e.g. Archaeological excavation data), the data is often unique and many researchers feel a moral compunction to make it available to others (and, of course, to ensure its long-term preservation).

Raise your research profile. The more other researchers cite your data, the more they will know and admire your work. As the trend toward online open access rises, the prestige associated with data citations is growing.  In addition, making some data available can increase the credibility of your analyses.

Keep your data safe and readable in the long term. Many researchers hold on to an old computer from a decade or two ago because it is the only way to access their old files, created in formats that are now obsolete. Once these computers break, the files are essentially lost.  Many repositories store and back up your treasured research products and will, if appropriate file formats are used, attempt to move the data into new file formats as the original formats become obsolete.  So long as the repository exists, your materials will remain readable and usable.

Your funder may require it. This is more and more common. You can find summary of funders’ open access requirements using the SHERPA/JULIET database. Even if your funder does not require that you deposit your data, a plan to deposit your data may strengthen your bid.

If I published my paper/data in a peer-reviewed journal, can I still deposit it in an open digital repository?
This depends on the journal (especially for papers), but the majority do allow it. Contact your journal for more information, or, you can find summary information on journals’ copyright policies using the SHERPA/RoMEO database.

You can also ask your repository support team for help with this. Coventry University's Research and Scholarly Publications Team is happy to help you find an answer, regardless of your target repository.

Selection- Choosing what to keep

Choosing what to keep and what can be disposed of or deleted is always going to involve a subjective judgement, as nobody knows exactly what information is going to be wanted in the future.

All we can do is think the matter through carefully, abide by the policies we need to (e.g. from funders) and document decisions made and the reasons for them. It won’t be a perfect process, but should at least be a sensible one.

There are some good reasons why selection is worth doing:

• Because storage costs money;
• Storage requires effort / staff hours;
• Storing massive amounts of data complicate finding and access of truly useful stuff.
• Because Freedom of Information laws mean that what you keep on file may have to be disclosed, if requested.

How do I know what to keep and what to delete?

These following questions, based on material devised by the Digital Curation Centre, can help you decide what you should keep and what can be deleted:

• Does my funder or the University need me to keep this data and / or make it available for a certain amount of time?
• Does this data constitute the 'vital records' of a project, organisation or consortium and therefore need to be retained indefinitely?
• Do I have the legal and intellectual property rights to keep and re-use this data? If not, can these be negotiated?
• Does sufficient documentation and descriptive information (‘metadata’) exist to explain the data, and allow the data or record to be found wherever it ends up being stored?
• If I need to pay to keep the data, can I afford it?

Once you've sorted through your files and asked these questions you then need to:

• Check your data protection responsibilities.
• Prepare documentation for each file.
• Find out how to deposit in an institutional or subject-specific repository, as appropriate.

Uploading a Dataset to Pure 

Follow these instructions to upload your data to Coventry University's institutional repository.

1. Navigate to [link to be shared when the activity is in progress]

2. Log in with username and password.

3. To add a new dataset, click on the green 'Add new' button in the top right-hand corner.

4. Select 'Dataset' from the submission options.

5. Start adding metadata to the record, including the title of the data and a description.  If the data have been collected over a set period of time, add the dates in.

6. Add the names of the people involved in the data collection / creation to the record, including the role that they played in the research activity and the location in which they work.

7. Include the Organisational Unit by which the dataset is managed (this is usually the same as the location in which the researcher works).

8. Under 'Data availability', add the publisher(s) and if available, the Digital Object Identifier.

9. Upload the dataset and associated files in the 'Electronic data' box, any useful information to accompany the files, and the date that the dataset and files were made available.  Ensure to include the correct reuse license as required by the funder / university.

10. Select the access options to the dataset - this will depend on the funder's and/or the University's open data requirements so it is important to refer to their documentation.

11. Adding contact details will ensure others can can request further information about the dataset, if required.

12. Include other useful information, if it is appropriate to the research (temporal coverage, geo location, legal/ethical).

13. Ensure the Visibility is set to 'Public - No restriction' and save the record for validation.  It will then be checked by the Research and Scholarly Publications Team to ensure that the metadata details fulfil funder / university requirement.

Uploading a Dataset to Pure 

Documentation and Metadata

To ensure that you understand your own data and that others may find, use and properly cite your data, it helps to add 'documentation' or 'metadata' (data about data) to the documents and datasets you create. This encompasses all the information necessary to interpret, understand and use a given dataset or set of documents.

It is good practice to begin to document your data at the very beginning of your research project and continue to add information as the project progresses. Include procedures for documentation in your data planning.  There are a number of ways you can add documentation to your data:

Embedded documentation

Information about a file or dataset can be included within the data or document itself. For digital data sets, this means that the documentation can sit in separate files (for example text files) or be integrated into the data file(s), as a header or at specified locations in the file. Examples  include:

• Code, field and label descriptions
• Descriptive headers or summaries
• Transcripts
• Recording information in the Document Properties function of a file (Microsoft)

Supporting documentation

This is information in separate files that accompanies data in order to provide context, explanation, or instructions on confidentiality and data use or reuse. Examples include:

• Working papers or laboratory books
• Questionnaires or interview guides
• Final project reports and publications

The addition of a README file to a metadata record can supplement information relating to data. A template file is available via the Adding a Dataset to Pure tab.

Catalogue metadata

This is structured information which can be used to identify and locate the data that meet the user's requirements via a web browser or web based catalogue. Catalogue metadata is usually structured according to an international standard and associated with the data by repositories or data centres when materials are deposited with them. Examples are:

• Title
• Description
• Abstract
• Creator
• Geographic location
• Keywords

Finding Data

You may be creating and collecting entirely new data for your project, but you can often draw on a wealth of data already available to complement or enrich your own research. Given the proper attention to Intellectual Property Rights (IPR), Data Protection and ethics, you may be able to process existing raw data to create entirely new research outputs.

Benefits of reusing data

There are many reasons why you may wish to reuse data:  

• Reusing data increases work efficiency, as both time and resources are saved.  If data are already available and suitable for reuse, less money and time are required to collect new information.
• Using data that are already available can help to avoid duplication of data collection.
• Using other researchers' data can promote opportunities to expand on their research and broaden knowledge.
• Reusing data that already exists takes less effort to collect than engaging in collecting new data.

When reusing existing data, you must ensure that you use the data in the way that the data owner has specified.  A method which authors and creators of outputs use to provide a clear, regulated method of providing permissions for reuse of works is through Creative Commons licenses.  Those interested in reusing the data then follow the license conditions.

The following table visually explains Creative Commons Licenses and their permissions.

Locating data repositories

The following links will help you locate data repositories that may be of use to you.