Welcome to
day three of Love Your Data Week 2017! Today’s topic is Good
Data Examples. What makes data “good” or “well managed?” The FAIR Data
Principles: —Findability, Accessibility, Interoperability, and Reusability
are a good place to start. Published by
Mark Wilkinson and his colleagues in 2016, these principles “put specific
emphasis on enhancing the ability of machines
to automatically find and use the data, in addition to supporting its reuse by
individuals.” 1A brief
description of the principles, excerpted from Wilkinson’s article, explains:
To be Findable:
- F1. (meta)data are assigned a globally unique and persistent identifier
- F2. data are described with rich metadata (defined by R1 below)
- F3. metadata clearly and explicitly include the identifier of the data it describes
- F4. (meta)data are registered or indexed in a searchable resource
To be Accessible:
- A1. (meta)data are retrievable by their identifier using a standardized communications protocol
- A1.1 the protocol is open, free, and universally implementable
- A1.2 the protocol allows for an authentication and authorization procedure, where necessary
- A2. metadata are accessible, even when the data are no longer available
To be Interoperable:
- I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
- I2. (meta)data use vocabularies that follow FAIR principles
- I3. (meta)data include qualified references to other (meta)data
To be Reusable:
- R1. meta(data) are richly described with a plurality of accurate and relevant attributes
- R1.1. (meta)data are released with a clear and accessible data usage license
- R1.2. (meta)data are associated with detailed provenance
- R1.3. (meta)data meet domain-relevant community standards”2
These guiding principles benefit all
stakeholders, including, as Wilkinson states, “researchers wanting to share, get credit, and reuse each other’s
data and interpretations; professional data
publishers offering their services; software
and tool-builders providing data analysis and processing services such as
reusable workflows; funding agencies
(private and public) increasingly concerned with long-term data stewardship;
and a data science community mining,
integrating and analyzing new and existing data to advance discovery.”3
Wilkinson
identifies several examples of FAIRness, including Dataverse, FAIRDOM, and Open PHACTS, and notes that the FAIR Guiding Principles have been adopted by a wide range of data
management organizations across the globe.
1-3Wilkinson MD, Dumontier M, Aalbersberg IJ,
Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne
PE, et al. The FAIR
Guiding Principles for scientific data management and stewardship. Sci Data.
2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18. PubMed PMID: 26978244; PubMed
Central PMCID: PMC4792175.
