phd-course research data management (rdm) expert centre research data
TRANSCRIPT
PhD-course Research Data Management (RDM)
Expert Centre Research Data
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
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 context2. Define data management roles
Creating data: 3. Short description of your research project
4. Privacy and security5. Loss of data
Processing & analysing data: 6. Privacy and security7. Overview of research data8. Short term storage9. Structuring your data10. Sharing during research11. Documentation
PRESERVING DATA
PRESERVING DATA: ARCHIVES
National facilities:• DANS: alpha, gamma and life sciences.• 3TU.Datacentrum: technical and exact sciences.• CLARIN-NL: linguistics
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 country
Quality is an issue look for trusted repositories
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
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.
PRESERVING DATA: PREFERED FORMATS DANS
PRESERVING DATA: PRIVACY & ANONIMITY
• These are issues especially with interview data and medical data
• Relevant are:- Dutch Data Protection Act- Code of conduct VSNU- Commissie Mensgebonden onderzoek (Committee on Research
involving Human Subjects)- Ethical Committees (Ethische toetsingscommissies) on faculty
level
• If possible: data must be anonymised. Removal of names is not sufficient for anonymisation in most cases
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
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
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 makesure that you can always use your data whenever you want.
Furthermore, also other researchers can easily use your data!
GIVING ACCESS TO DATA
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.
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?
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.
RE-USING DATA
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 6th)
And you can always re-use your own data!
SUPPORT
Expert Centre Research Data
• www.ru.nl/researchdata• [email protected]• 024-3612863
Clinical Research Centre Nijmegen(for questions concerning clinical data management)
• Radboudumc CRNC intranet website• [email protected]• 024-3668333
WRITING YOUR DATA MANAGEMENT PLAN
Format Radboud University: www.ru.nl/researchdata (the Behavioural Science Institute uses its own format)
Preserving data: 12. Long-term storage13. Metadata
Giving access to data: 14. Sharing data after research
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 [email protected]!
• Discuss your data management plan with your supervisor / (co-)promoter
Evaluation form & feedbackThank you for your attention