Reusable Metadata

It will be much easier to find and reuse data if there are many labels are attached to the data. Principle R1 is related to F2, but R1 focuses on the ability of a user (machine or human) to decide if the data is actually USEFUL in a particular context.

To make this decision, the data publisher should provide not just metadata that allows discovery, but also metadata that richly describes the context under which the data was generated. This may include the experimental protocols, the manufacturer and brand of the machine or sensor that created the data, the species used, the drug regime, etc.

Moreover, R1 states that the data publisher should not attempt to predict the data consumer’s identity and needs. We chose the term ‘plurality’ to indicate that the metadata author should be as generous as possible in providing metadata, even including information that may seem irrelevant.

R1.1 - Licenses

A concern for data reusability is not only about technical interoperability but also covers legal interoperability. What usage rights do you attach to your data? This should be described clearly.

Ambiguity could severely limit the reuse of your data by organisations that struggle to comply with licensing restrictions. Clarity of licensing status will become more important with automated searches involving more licensing considerations. The conditions under which the data can be used should be clear to machines and humans.

R1.2 - Provenance

For others to reuse your data, they should know where the data came from (i.e., clear story of origin/history, see R1), who to cite and/or how you wish to be acknowledged.

Include a lineage and description of the workflow that led to your data. These concerns center around who generated or collected the data, how it has been processed, whether it has been published before and whether it contain data from someone else that you may have transformed or completed.

Ideally, this workflow is described in a machine-readable format.

R1.3 - Standards

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.

R1: (Meta)data are richly described with a plurality of accurate and relevant attributes