Storing Your Data

Even with the best planning and preparation research projects can take unexpected turns. You might have to leave your research project for some time due to professional obligations or other life events. Also, not all research projects that have been started will be finished and not all datasets that have been collected make it into a final publication. How can good data management practice help to mitigate the effects of any (involuntary) interruptions? And how can you advance science even if you decide to not continue with your project?

It’s all about organisation and documentation. Apart from rules and disciplinary requirements, good data management includes being kind towards your future self. Data management requires time, patience and discipline, but it will certainly pay off in case you’ll have to leave the research for a while.

Try to test yourself by going back to a dataset that you used 5 or 10 years ago. Check files or papers you used for an article or chapter. Looking at the folder structure and the names of the files and folders, does everything still make sense to you? If you would do the same research today, what would you change?

Keep in mind that other researchers might also want to use a dataset that you’ve collected. This is why it can be very valuable to engage a fellow researcher in reviewing the organization and documentation of your dataset. Ask them to look at your files. Can s/he understand what you did and why, based on the publication and the additional files?

If third parties can understand your storage system, you can advance science with your dataset even if you don’t end up using it yourself. To that end, you could consider depositing the data in a dedicated data archive (repository), such as DANS in the Netherlands. If you’ve collected personal data, you would have to pay special attention to privacy and the relevant procedures, e.g. if and how consent has been obtained.

Depending on the dataset, it might also be useful to publish a data paper that provides information on the data, the research setup, the methods used and the characteristics of the dataset. This way, the work invested in the data collection and documentation becomes visible, and can be used and cited by other researchers.

Change your habits!
If your self-assessment reveals room for improvement, you can change your data management habits step by step. For the topic of organization and documentation, the Data Management Expert Guide by CESSDA ERIC is a great starting point.

This entry was based on the following blog post: