1. PhD-course Research Data Management (RDM) Expert Centre Research Data

2. INTRODUCTION Welcome Structure of the course: focus on data life cycle Last week: - Creating data, processing data, analysing data - Data management plan in keywords: 1 to 11 Today: - Discussion issues and bottlenecks, dmp 1 to 11 - Preserving data, giving access to data, re-using data - Data management plan in keywords: 12 to 14 Afterwards: - Work on your data management plan - Send it in for feedback

3. PLENARY DISCUSSION Reflecting on last week’s session & writing your data management plan. What were issues and bottlenecks in writing your data management plan? Planning: 1. Organisational context 2. Define data management roles Creating data: 3. Short description of your research project 4. Privacy and security 5. Loss of data Processing & analysing data: 6. Privacy and security 7. Overview of research data 8. Short term storage 9. Structuring your data 10. Sharing during research 11. Documentation

4. PRESERVING DATA

5. PRESERVING DATA: ARCHIVES National facilities: DANS: alpha, gamma and life sciences.DANS 3TU.Datacentrum: technical and exact sciences.3TU.Datacentrum CLARIN-NL: linguisticsCLARIN-NL Local facilities: Donders initiative (under construction) Radboud initiative: Research Data Services (under construction) Guidelines / initiatives from research institutes International: Re3data.org: overview of more than 1.000 data archives. You can select archives by discipline, type of data or countryRe3data.org Quality is an issue  look for trusted repositories

6. PRESERVING DATA: LONG-TERM STORAGE Which data should be stored? Two possibilities: From the perspective of reuse: Final (definitive) versions of data used for analysis, possibly also raw and processed data. Documentation/codebooks necessary for understanding the data. Read me.txt for understanding the structure and content of the deposit. From the perspective of scientific integrity: Approval ethical committee Informed consent & information sheet Raw, processed and analyzed data Documentation/codebooks Read me.txt Data Management Plan Audit trails and query trails

7. PRESERVING DATA: LONG-TERM STORAGE How should your data be stored? Formats: Choose a format which has a long-term guarantee. Some repositories (f.i. DANS) know preferred formats: they guarantee availability of the data in these formats in the far future. Privacy: Interview data and other privacy sensitive data must be anonymised. Removal of names is not sufficient for anonymisation in most cases. Several legal documents to guide you. Codelist and study data should be stored seperately File and folder structure: When you have multiple files and / or folders, design a structure which is easy to use, also in the future.

8. PRESERVING DATA: PREFERED FORMATS DANS

9. PRESERVING DATA: PRIVACY & ANONIMITY These are issues especially with interview data and medical data Relevant are: -Dutch Data Protection ActDutch Data Protection Act -Code of conduct VSNUCode of conduct VSNU -Commissie Mensgebonden onderzoek (Committee on Research involving Human Subjects)Commissie Mensgebonden onderzoek -Ethical Committees (Ethische toetsingscommissies) on faculty level If possible: data must be anonymised. Removal of names is not sufficient for anonymisation in most cases

10. PRESERVING DATA: FOLDER STRUCTURE Example: Longitudinal study on family relationships and personality: Questionnaires for four members in each family Three measurement waves Several content themes, for example problem behaviour, family relations, identity

11. PRESERVING DATA: ACCESSIBILITY How to make your data accessible? Use good metadata Who collected the data, where, when, what kind of data, subjects etc. General standards (Dublin Core) and standards for disciplines. Use a persistent identifier In most data archives a persistent identifier (DOI or other) is assigned to your stored data You can use this identifier in your publications to refer to your dataset

12. PRESERVING DATA: USE When you start your project, think about how you are going to manage your research data. Write a data management plan. It will save you a lot of time in the end. Preserving your data in the right way will make sure that you can always use your data whenever you want. Furthermore, also other researchers can easily use your data!

13. GIVING ACCESS TO DATA

14. GIVING ACCESS TO DATA: WHY Why share data with other researchers? Promote innovation and potential new data uses. Build on each others work, which is (in most cases) funded by public money. No duplication of data creation. Prevent fraud and improve research integrity. Increase visibility of research and therefore citations. Make possible new collaborations and (possibly) publications. Encourage scientific debate. Meet requirements of funders, journals and universities. Preparing data for sharing makes it also suitable for long term preservation.

15. GIVING ACCESS TO DATA Questions to consider when you want to share data: Are there ethical and legal reasons not to share my data? Must all data be shared? Where is my data safe? Is my data in an easy to use format? Will my data be accessible in the long term? Do I have sufficient documentation and metadata?

16. GIVING ACCESS TO DATA: TERMS You can set the terms of use of your data: Levels of access: -Open (with or without registration) -Restricted (request to depositor when someone wants to use the data) -Closed, but visible -Dark archive Citations Co-authorship Etc.

17. RE-USING DATA

18. RE-USING DATA: CITATION In citing data mention: -author -title -year of publication -publisher (for data this is often the archive where it is housed) -edition or version -access information (a URL or other persistent identifier). Bibliographical styles often have templates for citing datasets (f.i. APA 6 th ) And you can always re-use your own data!

19. DEMO DANS-EASY Dans Easy

20. SUPPORT Expert Centre Research Data www.ru.nl/researchdata researchdata@ubn.ru.nl 024-3612863 Clinical Research Centre Nijmegen (for questions concerning clinical data management) Radboudumc CRNC intranet website crcn@crcn.umcn.nl 024-3668333

21. WRITING YOUR DATA MANAGEMENT PLAN Format Radboud University: www.ru.nl/researchdatawww.ru.nl/researchdata (the Behavioural Science Institute uses its own format) Preserving data:12. Long-term storage 13. Metadata Giving access to data: 14. Sharing data after research

22. WRITING YOUR DATA MANAGEMENT PLAN Plenary discussion: What are issues and bottlenecks in writing your data management plan? Continue writing / adjusting your data management plan (don’t forget versioning) Do you have questions? Do you want our feedback on your data management plan? Email us at researchdata@ubn.ru.nl!researchdata@ubn.ru.nl Discuss your data management plan with your supervisor / (co-)promoter Evaluation form & feedback Thank you for your attention