What does this mean?
It is easier to reuse data sets if they are similar: same type of data, data organised in a standardised way, well-established and sustainable file formats, documentation (metadata) following a common template and using common vocabulary. If community standards or best practices for data archiving and sharing exist, they should be followed. For instance, many communities have minimal information standards (e.g., MIAME, MIAPE). FAIR data should at least meet those standards. Other community standards may be less formal, but nevertheless, publishing (meta)data in a manner that increases its use(ability) for the community is the primary objective of FAIRness. In some situations, a submitter may have valid and specified reasons to divert from the standard good practice for the type of data to be submitted. This should be addressed in the metadata. Note that quality issues are not addressed by the FAIR principles. The data’s reliability lies in the eye of the beholder and depends on the intended application.
Examples
Links to Resources
- http://schema.datacite.org/ [for general purpose, not domain-specific]
- http://dublincore.org/specifications/ [for general purpose, not domain-specific]
- https://www.ncbi.nlm.nih.gov/geo/info/MIAME.html [microarrays]
- http://cds.u-strasbg.fr/doc/catstd.htx [astrophysics]
- https://www.iso.org/standard/53798.html [geographic information and services]
- http://cfconventions.org/ [climate and forecast]
- http://www.iucr.org/resources/cif [crystallographic information]
- http://www.nexusformat.org/ [neutron, x-ray, and muon experiment data]
- http://www.ddialliance.org/Specification [social, behavioral, and economic sciences]
- https://sdmx.org/ [statistical data]
- https://knb.ecoinformatics.org/#tools/eml [ecology]