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

2. INTRODUCTION Welcome & introduce yourself Structure of the course: focus on data life cycle Today: - Creating data, processing data, analysing data - Data management plan in keywords: 1 to 11 Next week: - Preserving data, giving access to data, reusing data - Data management plan in keywords: 12 to 14 In between & afterwards: - Work on your data management plan - Send it in for feedback Questionnaires: aspects we do & do not address Ask questions & give feedback

3. WHY RESEARCH DATA MANAGEMENT Safe time Increase efficiency Keep your data safe Share and reuse your data Link publication and data set Transparency and integrity Funder, journal and university policies International laws and guidelines (WMO, WBP, GCP) Data is research output!

4. FUNDER & JOURNAL POLICIES Horizon 2020 -Open research data pilot, including data management plan NWO (& data archive DANS) -Data management paragraph & pilot for data management plan ZonMw -Pilot “Toegang tot data”, including data management plan and data paragraph Journals can have a policy regarding research data availability, e.g. Plos, Nature

5. RADBOUD UNIVERSITY POLICY Data are stored at the latest at the time of publication of the research Data must be accompanied by the information necessary for understanding and potential reuse of the data Data management plan is recommended The minimum term of storage is ten years Responsibility: researcher & director of the research institute Details are worked out by the research institutes Radboudumc has more stringent guidelines for research that falls under WMO (medical research involving human subjects act)

6. RADBOUD UNIVERSITY PILOTS The University will set preconditions (infrastructure, support) Expert Centre Research Data: developing a support service at the University Library regarding research data management (DMP, storage during and after your research, legal and ethical advice, training etc.) Research data services (RDS): linking existing infrastructures for data storage for the long tail data (Radboud Repository, Metis, DANS / 3TU.DC) (RIS interface) Repository: building a repository for big data at the Donders Institute for Brain, Cognition and Behaviour Digital Research Environment: developing a system at the UMC which manages the data from start to end of a research project.

7. WHAT IS DATA Researchers use multiple forms of data. Ideas in your head and notes on paper Structured analyses

8. WHAT IS DATA Types of research data Qualitative Quantitative Text Questionnaires Images Videos Audio files Architecture Interviews Observations Patient data Measurements Lab results Everything a researcher needs to answer his/her research question!

9. DATA LIFE CYCLE Source: UK data archive

10. DATA MANAGEMENT PLAN: FUNCTIONS You think and decide timely about research data management issues Use it as a dynamic document (mention date / version) Use it as a discussion document Useful in meetings for monitoring progress of your research For WMO, investigator initiated studies within Radboudumc, a DMP is obligatory for obtaining RvB approval

11. DATA MANAGEMENT PLAN: FORMAT Several formats: Funder (NWO, Horizon2020, ZonMW…) University: - Expert Centre Research Data - Radboud UMC (for WMO studies) - Behavioural Science Institute DCC DMP online tool

12. CREATING DATA

13. The process starts with deciding what kind of data you need in order to answer your research question. This can go two ways: You can use existing data. Find out if you need permission and informed consent to use the data. Find out what your rights are concerning long term storage of the data. You can collect your own data. Write down how the collection process will take place. Find out who has the ownership of this data.

14. CREATING DATA Before you will use the existing data or before you will begin your collection process, you also need to think about: Privacy and legal issues. -Do you need informed consent forms? -Do you need to anonymise your data? -Who can have access to the data? Documentation -What do I need to understand my data collection process ten years from now? Practical issues -Storage space -Folder structure -Versioning -Data management system (audit trail) -Etc. Write a data management plan!

15. PROCESSING DATA

16. PROCESSING DATA: STORAGE Decide where to store your data Paper documents: Closet, shelf, drawer, desk etc. Do you have enough space? Digital documents: USB stick is not sufficient, so: -Use the university network -Use storing possibilities within the research institute -For medical research (privacy issues, WBP) access should be restricted to research personnel only -SurfdriveSurfdrive Often used, but not recommended: -Google docs -Dropbox (data are stored in the US)

17. Classification/ CriticalSensitiveStandard Facility Designated RU storage Suitable Mobile media (usb/laptop) Not permittedEncrypted only FileSender Not permittedPermitted*Permitted Edugroups Not permittedPermitted*Permitted SURFdrive Not permittedPermitted*Permitted * Encryption recommended Critical = personal data; Sensitive= competition-sensitive or confidential PROCESSING DATA: STORAGE

18. Think about a good backup strategy. Paper documents: Make copies and store them separated from your originals. Scan important papers and safe them on the network drive. Digital documents: Backup your files regularly, preferably at a fixed moment. Consider where to save your backed up files. Consider what to back up: files and/or folders or also software? Make sure that the backed up file is always the same as the original. Think about long term storage. Think about safety and privacy (pseudonimisation). A backup is a backup! PROCESSING DATA: BACKUPS

19. PROCESSING DATA: STORAGE Decide how to store your data Tips for filing paper documents: -Keep your filing system simple (alphabetical, numerical, thematic, type). -Make sure you have enough space. -Make sure everything is kept safe. -Think about the long term (will you understand your filling system ten years from now?) -Make a content file and give every document a code.

20. Tips for filing digital documents: Don’t use long and complicated file names and make them meaningful. Use folders to create shorter file names. Mention which version is concerned. Preferably: don’t go deeper than three or four levels. Make categories which are logical for your research. Separate ongoing and complete work. Don’t Dataset1.xls Maaikedataset5version10.xls Do Mortalitygirls20140501.xls Mortalitygirls20140501_v01.xls PROCESSING DATA: FILING

21. Versioning, a few tips: A good versioning strategy depends on who is using the files and where they are stored. Make sure that if information in one file is altered, the information in related files is also altered. Decide how many versions you want to save, which versions to keep and for how long. Identify milestone versions and a raw data version, which can never be altered or deleted. Record the changes that are made in a new version and the status of the version. For more tips, read “Managing and sharing data” from the UK data archive page 14-15. PROCESSING DATA: VERSIONING

22. PROCESSED DATA: VERSIONING

23. After you have decided where and how to store your data, you can enter, digitise, translate, transcribe, clean, validate, check and anonymise your data. It is very important to describe this process for yourself and others: (Lab)journal/methodology Start from the beginning of the data collection process Record all your steps and choices Record the reasons behind your choices Codebooks The manual for your dataset What does the variable name mean? What do the variable values mean? Work in progress PROCESSING DATA: DOCUMENTATION

24. ANALYSING DATA

25. 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

26. WRITING YOUR DATA MANAGEMENT PLAN Format Radboud University: www.ru.nl/researchdatawww.ru.nl/researchdata 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

27. WRITING YOUR DATA MANAGEMENT PLAN Fill in Data Management Plan in keywords: Concentrate on points 1 to 11 Mutual feedback * Continue writing on your Data Management Plan * Choose a partner / decide on language