Data management plans

What types of files are you creating in your project? Will you be transforming the data for analysis? Outlining all the types, sources, and estimated size of data in your project will help you identify potential issues relating to storage, sharing, and preservation:

• List the characteristics of the data to be collected (e.g. quantitative spreadsheets or databases, text, audio, video, code, samples, physical items, etc.)
• Include the file formats/software versions and if they are open or proprietary. List relevant physical formats like lab notebooks, diaries, transcripts, etc. 
• Consider outlining the file types (e.g. publications, conference posters, blogs, datasets, etc.) you’ll be creating or transforming during collection and analysis
• Include the anticipated size of data, especially if it will require additional resources beyond the standard cloud storage allocation (currently 1 TB).

For more on file management see managing your data.

Good file organisation and documentation is good scholarship. Anyone should be able to understand your project, data collection, analysis, and files just by looking at your documentation. Remember, you may want to or be expected to share your data, and someone may want to verify, replicate, or reuse your data.

Describe the documentation and quality assurance strategies for each type of data during collection and analysis. Consider using a file naming convention and using built in documentation capabilities, like taking notes in code scripts.

• Outline what documentation you will create, collect or measure
• Think about how you will add, update, and maintain the data and documentation
• Decide how you will track multiple files or versions
• Choose how non-digital documentation will be managed and stored
• Establish if there is a relevant disciplinary standard for documentation and metadata* you could use
• Consider what documentation will be needed for shared/preserved data to facilitate data reuse
• Consider creating a README document for shared/preserved data.

*’Metadata’ sometimes refers to a specific type of disciplinary metadata, which is a community agreed upon specification for structuring data and documentation. Digital Curation Centre has a list of disciplinary metadata plans.

For more on documentation, see managing your data.

• Describe where you will store your data during collection, measurement and analysis
• Be specific about the journey your data will take and how it will be transferred from one device, platform or physical location to another
• How you will keep your data safe from accidental loss, corruption, being overwritten and unauthorised access
• Decide if you will transfer data from a collection tool or specific equipment to another, maybe in a different format, to do your analysis, e.g. voice recorder or video, field measurements or log book, or online survey or participant questionnaire
• How and when will you do this? Every day/week? After data collection ends?

In almost all cases, research data should be kept on University storage. Avoid using local hard drives, portable storage devices, laptops, and tablets for storage to reduce the risk of accidental loss. Do not use third party storage like Dropbox, Google drive, etc. They offer less protection and are less secure than University storage. If your project has special requirements like high performance computing, highly sensitive data, or commercially owned data then consult IT Services, Ethics, or your sponsor/funding agency for an appropriate set-up. 

• Outline where you will store the data at every stage of collection, measurement and analysis
• Describe how and when you will transfer data if necessary, including deleting data off collection tools/measurement equipment and their associated storage
• Identify any ethical, legal or commercial issues with your data, e.g. identifiable data, copyrighted materials, patents, etc. How will you protect the data? (This could include transforming, de-identifying, or anonymising the data, or using collaborative agreements)
• Identify who will have access to the data. How will collaborators have access to the data?
• Identify any special storage or computing requirements you or your funder may have
• Describe how you will securely store and maintain any non-digital data.

For more on storage, see managing your data.

Data sharing for verification, reproduction and reuse is an increasingly important marker of academic integrity. Researchers are encouraged to make their data as open as possible. Your plan should identify what data will or will not be shared from the project. For data that can’t be shared, you should include a justification for why not.

For shareable data, you should outline where, when, and how others can access it. Often data is released following publication or at the close of a project. Be aware: some funders and publishers require data to be shared within specific timelines. You can describe any restrictions or terms of use for the data, including any licenses you might want to apply:

• Make sure your consent forms don’t prohibit sharing/retention, and even better, request permission for de-identified data to be shared/archived in an open repository
• Outline what parts of your data can and cannot be shared or published
• Describe and justify any restrictions, secure permissions needed or terms of access (restricted, registration form, Non Disclosure Agreement, etc.)
• When will the data be released?
• Is there non-digital data that needs to be made available? How will people request access (e.g. by using a publicly discoverable metadata record)?
• If the data cannot be shared, explain why (e.g. don’t own, national security, copyrighted)
• Will you transform the data? (e.g. de-identify or convert to an open format)
• Identify how you will share your data, such as depositing in a repository
• Consider applying a Creative Commons license to your shared data or code
• Check out the FAIR principles of data sharing.

Best practice is to deposit the data into a data repository. Repositories provide the best visibility, tracking, and safe keeping for your data:

• Identify a suitable repository. Consider a discipline specific repository that is most appropriate for your data. Check out PLOS’ list of recommended repositories or Scientific Data’s recommended data repositories
• You can also use the University’s Open Research repository.

For more information on sharing your data, please see open data.

The aim is to be "As open as possible, as closed as necessary"

Retaining data is an important part of the academic process. Even if data cannot be shared now, it may still have important historical value for future researchers, especially for non-replicable observational data. You should identify what data will be deleted and when deletion will occur, what will be retained, where it will be stored/archived, and who will oversee its deletion, or safe keeping after the project has finished.

Consider the long-term viability of your file formats. Open formats (e.g. rich text or CSV files) are more likely to survive over time than proprietary formats (e.g. Word doc or Excel files). If your data is small enough, you may want to preserve a copy of your data in its original format alongside an open format. Be sure to include adequate documentation alongside preserved data.

• Identify which data should or should not be preserved. This should be anything that underpins the conclusions of your project and any published works
• Identify what documentation you will include with the data to facilitate verification/reuse
• Consider transforming your data to an open format for preservation
• Identify where, when and who will be preserving or deleting the data and for how long will it be kept
• If you shared your data in a repository, it may have a preservation policy you can link/refer to.

For more on preservation, see open data.

There are some real benefits to writing a plan as it encourages a more structured approach to thinking through your research processes and outputs. This provides you with an opportunity to discover and make use of several Open Research practices in your field and to chat to your supervisor about professional expectations. 

Training on data management plans and research data management is available through the Doctoral College.

DMPOnline is an online tool designed to help researchers write their data management plans. They have templates for all the major funders and specific guidance based on funders’ policies. If your funder hasn’t provided a template, or there is no funder, use the generic template, which also has plenty of advice.

If you’d like to use DMPOnline, all you need to do is:

1. Sign in to DMPOnline using your University of Surrey credentials
2. Click on 'create plan' and select your funder on the dropdown (if your funder isn’t listed, or you don’t have one, tick “no funder” and use the generic template).

Once you’ve set up your plan, you can share it with collaborators to edit or view. When it’s finished, you can export it to use in your grant application, confirmation report, or for your own records.