Data management for students: Storing and archiving

Safe storage and sharing is important, whether you are working with personal data or not, because it prevents loss of data and data leaks. Sharing personal data should be avoided in general. If your storage solution has no built-in sharing solution, you can use SURFfilesender. Enable file encryption in case it is absolutely necessary to share personal data through SURFfilesender.

Preferred:

• Radboud University Microsoft 365 Teams: You can ask your supervisor to create a Team for you. They may need to request permission to create a Team, after which they can create a Team with a private channel for you, and disable the synchronization function. Use the Microsoft 365 account given to you and your supervisor by the Radboud University, do not use a private Microsoft 365 account!
  Features: automatic backup, designed for sharing, accessible from anywhere
• Workgroup folder: You can ask your supervisor to create a workgroup folder for you via the account portal. Your supervisor should choose the option “Workgroup folder (with students): folder request”.
  Features: automatic backup, limited sharing, accessible via eduVPN and RU connect

Alternatives:

• Radboud University Microsoft 365 Teams: You can create a Team to which you add your supervisor(s). Use the Microsoft 365 account given to you by the Radboud University, request permission via the account portal, create a Team to which you add your supervisor(s), and disable the synchronization function.
  Features: automatic backup, designed for sharing, accessible from anywhere

• Local drive on personal pc/laptop: You can use the local drive on your personal pc or laptop if it is password protected and (when you have personal data) encrypted. This is a less preferred option because it has no automatic backups – if you lose your computer or it crashes, you can lose all your data.
  Features: no automatic backup, no built-in sharing

Do NOT use:

• A commercial cloud storage: Avoid cloud storage, such as Google Drive and Dropbox, especially for personal data! Also, avoid these as backups!
• Portable drives: Try to avoid portable drives, such as a USB drive or an external hard drive, to store or backup your data since they are easily lost.

Why should you archive?

Once your research project is completed, it is important to archive your data for the sake of scientific integrity, making it verifiable for others (e.g., for your supervisor or during audits). These datasets are not made public or shared with other researchers, but are merely accessible to people such as your supervisor.  Once you have archived your data, remove all other copies of the data you have stored elsewhere, for example on your local drive on a personal computer.

What should be archived?

You should archive all data that are relevant and necessary for an outsider to be able to reproduce your analysis and conclusions. If you have any doubts about what to archive, you should discuss this with your supervisor.

It is good practice to include documentation with your data. This documentation explains your data and makes sure that your dataset is still understandable in a few years from now. The files that should ideally be included in a dataset are:

• Research data files: This may include the collection materials (e.g., experiment and stimuli, questionnaires) the raw data (e.g., Qualtrics export, responses to interviews such as audio files, output from an experiment), (pre-)processed data (cleaned up data used for the analyses), and the analysed data consisting of the results (tables, charts, statistical outputs) as well as the analysis scripts (e.g., SPSS, R files)
• Documentation: The goal is to provide information about the context, content and structure of the dataset.
  • A readme file provides some context for the dataset. It also lists the data files that you uploaded by name and briefly describes the content of each file. You can find an example of a readme file in the Appendix.
  • A codebook describing the variables in your research as well as a methodology file can be useful additions, too.
• Supplementary files: You may additionally include your data management plan if you have one. And if you had human participants you could include ethical approval, blank informed consent forms, information sheets, and debriefing forms.

NB: Personal data which you did not need for your conclusions should be deleted as soon as possible (e.g., administrative data such as email addresses used to contact participants) and should not be archived in order to protect your participants’ privacy.

Where to archive?

Check with your supervisor or teacher to see where you should archive your data.

• RIS for students: Some study programmes ask students to use RIS for Students to archive their datasets. You can find a manual on the website.
• A workgroup folder: Your supervisor will need to create a workgroup folder and give you access. This can be done here.

NB: Data from RadboudUMC cannot be archived in RIS for Students nor in a workgroup folder.

Sharing data publicly is not standard procedure for student projects. If, however, your research gets published and/or your supervisor and you think that your data could be valuable to others for reuse, you should discuss the following points with your supervisor.

DO’S

• Ask your supervisor if they think it’s a good idea to share your data publicly.
• Ask your supervisor what data to share exactly.
• Ask your supervisor what an appropriate license and access level for your dataset is.
• Ask your supervisor about an appropriate archive in your field. Otherwise DANS EASY and DANS Data Stations are good options.

DON’TS

• Do not share personal data publicly!

A dataset that you share for reuse must not contain any personal data (unless this is required for a journal publication and you got specific consent from participants to do so and if your local ethics committee approved this). Thus, a dataset that you share publicly will often be different from the dataset you archived for scientific integrity. For example, you usually have to archive raw data for scientific integrity. However, you are often not allowed to share these data publicly. For example, when collecting audio recordings of participants, you will archive these for scientific integrity. However, if you want to share your data publicly for reuse, you will very likely only be able to share an anonymous transcript of the audio and not the audio files themselves.