1. Translating policy into RDM support at Radboud University (the Netherlands) Liber Workshop on Libraries and Research Data Liber 2015 conference Mijke Jetten (m.jetten@ubn.ru.nl) Expert Centre Research Data, Radboud University Nijmegen (www.ru.nl/researchdata)

2. Meet Nienke

3. Radboud University RDM policy Central √ Storage of data and metadata, latest at the moment of publication √ Also: storage of data of approved bachelor and master theses √ Minimally for 10 years √ Data management plan recommended √ Responsibility with researcher, research director, educational director √ University guarantees facilities Decentral √ General policy is supplemented by the research institutes (what, how, where etc.)

4. RDM facilities at Radboud University

5. Let’s get back to Nienke It seems so perfect, but... √ A central policy doesn’t answer Nienke’s questions √ A decentral policy isn’t easy to realise The library is... √ Stable factor √ Linking pin between pilots √ Aware of researchers’ needs Turning central policy into decentral policies and thus: tailored-made support

6. Checklist Example 1 (Data planning) Should a data management plan (DMP) be drafted prior to the commencement of data collection? If so, are there any (institutional) requirements regarding the content of a data management plan? (appendix 3. RU format DMP) (appendix 4. funder requirements) (appendix 5. journal requirements) Example 2 (data planning) Will data collection, data sharing and data storage costs be declared to funding agencies? (appendix 10. table with storing, sharing and archiving options and costs) Example 3 (Data collecting) Where are the data preferably stored during research? Are there (institutional) requirements by groups of researchers regarding storage of data during research? (big data, privacy, encrypting, sharing etc.) (appendix 12. decision tree storage during research)

7. Checklist Example 4 (Data processing) What data should be kept minimally during the research process (raw data, processed data, analysed data)? (appendix 14. types of data) (appendix 15. structuring and documenting data during research) Example 5 (data planning) What training on data management should be available? Which of these trainings should be mandatory? (appendix 9. training possibilities) Including possible data stewardship roles, such as: - Keeps overview of DMP’s - Checks filled-in DMP’s (in relation to (institutional) requirements) - Keeps an overview of data processing steps and where this information is stored - Keeps an overview of what tools are used for processing data - Keeps an overview of preferred formats for processing data - Checks whether processing data during research is stored in preferred formats