Fair Data

According to FORCE11 [1], FAIR data stands for Findable, Accessible, Interoperable, and Re-usable. It is a concept promoted as a way to facilitate data sharing among scientists. We discuss hereafter the meaning of each aspect and how we suggest to address them.

1. Making data findable

including provisions for metadata

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

1.2. What naming conventions do Feel++ follow?

1.3. Will search keywords be provided that optimize possibilities for re-use?

1.4. Do we provide clear version numbers?

1.5. What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how.

2. Making data openly accessible

2.1. 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, clearly separating legal and contractual reasons from voluntary restrictions.
that in multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if relevant provisions are made in the consortium agreement and are in line with the reasons for opting out.

2.2. How will the data be made accessible?

e.g. by deposition in a repository

2.3. What methods or software tools are needed to access the data?

2.4. Is documentation about the software needed to access the data included?

2.5. Is it possible to include the relevant software ?

e.g. in open source code

2.6. 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.

2.7. Have you explored appropriate arrangements with the identified repository?

If there are restrictions on use, how will access be provided?

2.8. Is there a need for a data access committee?

2.9. 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?

3. Making data interoperable

3.1. Are the data produced in the project interoperable, that is 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)?

3.2. What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable?

3.3. Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability?

3.4. In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies?

4. Making data re-usable

4.1. How will the data be licensed to permit the widest re-use possible?

4.2. 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.

4.3. 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.

4.4. How long is it intended that the data remains re-usable?

4.5. Are data quality assurance processes described?

5. Bibliography

FORCE11, Guiding Principles for Findable, Accessible, Interoperable and Re-usable Data; Version B1.0. www.force11.org/fairprinciples