Ten practical advices to facilitate good research data management and compliancy to what funders expect

1. 10 commandments
in RDM funder
compliancy
Hannelore Vanhaverbeke
KU Leuven
https://orcid.org/0000-0002-8748-8808
A pragmatic approach

2. Tell me why?
• What funders say:
Data management is an integral part of sound scientific research
Data management plans (DMPs) are a key element of good data
management
• What you should tell yourself:
Data management is an integral part of sound scientific research
Data management plans (DMPs) are a key element of good data
management

3. 3
4
52
1
6
7
8
10
9

4. 1. Don’t wait for the manna, eh… money
• Go through all sections of a DMP template when writing your
proposal
• use DMPonline.(kuleuven).be, its templates cover all questions and offers
guidance
• You will
• write a better section on data management in the proposal
• detect possible costs that can be eligible for funding

5. 2. Do not wander for 40 years (or so)
• Know whom in your institution can help with data questions +
mention these persons/services in your application/reporting
• IT, Library, Research Office, …

6. 3. Don’t live on a prayer
• Designate a coordinator for all RDM stuff + say so in the application
• esp. in a consortium
• min. senior postdoc level
• Tasks:
• coordinates writing the initial DMP
• monitors whether updates to the DMP are called for
• coordinates writing the final DMP
• ensures uniformity or at least minimal harmonization in practices (documentation,
back up, …
(I would advice to do the same for Open Access)

7. 4. Split hairs (not the sea)
• Know precisely what kind of data you will use/produce (mind you:
‘data’ = very broad)
• The data description section is the most vital part of a DMP
• Make a list of all ‘input’ and ‘output’ data per WP or objective and
aggregate
• per WP/objective
• data type
• Reviewers love tabular forms & bullet style lists
• Incomplete/inaccurate overview = incomplete/inaccurate DMP

8. 5. Where there’s smoke… Mind burning bushes
• Special data categories
• Everything to do with humans (participants, interviewees, bits & pieces of
humans)
• Everything to do with possible IP (claimed by you or imposed by 3rd parties)
• Dual use issues
• For some you cannot start research before legal/ethical approval
• privacy registry/ethical committees
• For some of these categories you cannot just choose what you do
with the data (publishing, storing, …): read agreements carefully!

9. 6. Avoid plagues
• Storage & back up: best use managed services (managed by your
institution)
• “fool proof”: back up is automatic
• someone can retrieve the data
• many services are ‘GDPR’ proof: encryption, controlled access etc
Own devices: responsibility is yours
cost vs quality – when budgeted cost for storage are mostly
eligible
• Documentation, uniform description

10. 7. Be FAIR
• Findable – Accessible – Interoperable - Reusable
• FAIR is not Open
• FAIR also pertains to data you wish to reuse in the future
• FAIR is not to be ‘imposed’ at the end of the ride but from the
beginning
• https://www.go-fair.org/fair-principles/

11. 8. Do not speak in tongues
• Never ever write things you do not understand
• Never ever promise something you cannot guarantee
• rather say “at this moment we consider the use of…”, “if in the course of
research this changes we will update the DMP”, “in case of IP, TTO will be
contacted”, …
• Know about metadata (= “tongues”): metadata creation/application
of standards is the most difficult in RDM
• There are no standards
• It is not clear how to implement these in research workflow

12. 9. Go & multiply
• There are a limited nr of reasons for not sharing (privacy, contractual
agreements, IP, security issues)
In all other cases, consider sharing: “shared data live longer”
• Clearly specify how data will be shared & under what licence (CC-0?)
• Make use of good data repositories (license, PID, ...)
• Work is done for you (in terms of preservation & discovery)
• With good metadata your data will be found & possibly reused => data citation
• General repositories: Harvard's Dataverse ; EUDAT's B2Share ; Figshare ; Zenodo ; Open
Science Framework
• Discipline-specific repositories as advised by Nature, PLoS, F1000

13. 10. Lead the way
• preserve DMPs and share them; they can be useful to others
• within labs or research units: draw up a ‘generic DMP’

14. Self-assessment
• DCC H2020
compliance checklist
• EPSRC self-assessment
rubric