Subject guides: Research Data Management (RDM): Data Management Planning for FAIR Data

Data Management Planning for FAIR Data

In general, your research data should be 'FAIR', which means findable, accessible, interoperable and re-usable. These principles serve as a foundation for data management. However, the principles do not dictate any particular technology, standard, or implementation method, all aspects of FAIR should be addressed in the DMP, including all necessary provisions to make your data FAIR. If there are any specific issues for individual datasets (e.g. regarding openness), that should be clearly stated and explained in your plan.

When developing your DMP, carefully review the FAIR-ness of your data, and consider answering the following questions in your plan:

Making data Findable, including provisions for metadata.

✦ Is the data produced and/or used in the project discoverable with metadata, identifiable and locatable using a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)?

✦ What naming conventions do you follow? See the File Naming guide.

✦ Will search keywords be provided that optimise possibilities for re-use?

✦ Do you provide clear version numbers? See the Version Control guide.

✦ What metadata will be created? See the Metadata guide.

Making data

Openly Accessible

✦ Which data produced and/or used in the project will be made openly available as the default?

✦ If certain datasets cannot be shared (or need to be shared under restrictions), explain why?

✦ How will the data be made accessible (e.g. by deposition in a repository)?
✦ What methods or software tools are needed to access the data?

✦ Is documentation about the software needed to access the data included?

✦ Is it possible to include the relevant software (e.g. in open-source code)?
✦ Are there well-described conditions for access (i.e. a machine-readable license)?
✦ How will the identity of the person accessing the data be ascertained?
✦ Where will the data and associated metadata, documentation and code be deposited? Preference should be given to certified repositories which support open access where possible.
✦ Have you explored appropriate arrangements with the identified repository?
✦ If there are restrictions on use, how will access be provided?

Making data

Interoperable

✦ Are the data produced in the project interoperable, allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. (i.e. adhering to standards for formats, as much as possible compliant with available (open) software applications, and in particular facilitating re-combinations with different datasets from different origins)?

✦ In what file formats is your data? Are you using open, non-proprietary file formats?
✦ What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable?
✦ Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability?

Increase data

Re-use

(through clarifying licences)

✦ How will the data be licensed to permit the widest re-use possible?
✦ When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible.
✦ Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why.
✦ How long is it intended that the data remains re-usable?
✦ Is data quality assurance processes described?

Allocation of resources

✦ What are the costs for making data FAIR in your project?
✦ How will these be covered? Are costs related to open access to research data covert by your grant (if applicable).

✦ How will those costs be covered? Are there alternative free options available?
✦ Are the resources for long-term preservation discussed (costs and potential value, who decides and how what data will be kept and for how long)?
✦ Who will be responsible for data management in your project?

Ethical Aspects

✦ Are there any ethical or legal issues that can have an impact on data sharing? These can also be discussed in the context of the ethics review.

✦ Is informed consent for data sharing and long-term preservation included in questionnaires dealing with personal data?

(Sourced from: European Commission. (2016). H2020 Programme. Guidelines on FAIR Data Management in Horizon 2020. Version 3.0. https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf#page=10)

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