How to draft a DMP from the perspective of the social sciences, using the SNSF template

data management, open data, best practices

This guide helps drafting a data management plan (DMP) from the perspective of the social sciences. Drawing on the model developed by the Swiss National Science Foundation, it provides recommendations and tips on how to address the main topics of the DMP so that it fulfils the funder’s requirements while at the same time producing high quality data.

• Keep in mind that good data management is not only a pre-requisite for data sharing: above all it allows you to conduct high quality research by strengthening your data and their analytic potential.
• The cost of managing your data only at the end of your research project is much higher than applying good practices throughout the research process. Data management planning strongly contributes to reducing costs, not only as it allows you to identify key actions and to make sure you implement them at the right moment, but also since it allows you to budget data management costs in your research proposal.
• While the Swiss National Science Foundation requires that the data underlying scientific publications must be made available provided that there are no legal, ethical or copyright issues, we strongly recommend that you share any data that are relevant for re-use. Data underlying publications must be made available at the time of the publication, while wider data can be made available after the project ends.
• Once your project gets funded, do not forget to review your DMP and further develop your data management strategy. A DMP should be a living document. It should be revised and developed as the research project is carried out.
• If you know in which archive/repository your data will be deposited, do get in touch with them at an early stage in the research process. This may help you save time by ensuring you handle the data in a way that will facilitate data sharing, but also by benefiting from their expertise and recommendations with respect to day-to-day data management practices.