- Recall the meaning of FAIR
- Understand why FAIR is a collection of principles (rather than rules)
- Use self-assessments to evaluate the FAIRness of your data
In 2016, the FAIR Guiding Principles for scientific data management and stewardship were published in Scientific Data. Read it.
- 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
- 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
- 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
- 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 standard
Open vs. Public vs. FAIR:
FAIR does not demand that data be open: See one definition of open: http://opendefinition.org/
FAIR is a collection of principles. Ultimately, different communities within different scientific disciplines must work to interpret and implement these principles. Because technologies change quickly, focusing on the desired end result allows FAIR to be applied to a variety of situations now and in the foreseeable future.
The CARE principles for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event “Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop,” 8 November 2018, Gaborone, Botswana.
- C1. For inclusive development and innovation
- C2. For improved governance and citizen engagement
- C3. For equitable outcomes
Authority to Control
- A1. Recognizing rights and interests
- A2. Data for governance
- A3. Governance of data
- R1. For positive relationships
- R2. For expanding capability and capacity
- R3. For Indigenous languages and worldviews
- E1. For minimizing harm and maximizing benefit
- E2. For justice
- E3. For future use
FAIR - TLC¶
Traceable, Licensed, and Connected
- The need for metrics: https://zenodo.org/record/203295#.XkrzTxNKjzI
How to get to FAIR?¶
This is a question that only you can answer, that is because it depends on (among other things)
- Your scientific discipline: Your datatypes and existing standards for what constitutes acceptable data management will vary.
- The extent to which your scientific community has implemented FAIR: Some disciplines have significant guidelines on FAIR, while others have not addressed the subject in any concerted way.
- Your level of technical skills: Some approaches to implementing FAIR may require technical skills you may not yet feel comfortable with.
While a lot is up to you, the first step is to evaluate how FAIR you think your data are:
Thinking about a dataset you work with, complete the ARDC FAIR assessment.