Managing Your Data

FAIR principles image by SangyaPundir, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons

In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.

Funder requirements, data sharing policies, and the interest in creating a culture of sharing and openness in research are all reasons why the FAIR principles are important. Interdisciplinary and collaborative research require the ability for researchers to share their data in a way that it can be used successfully. 

FAIR data can make it easier for yourself and others to understand and interpret data, increase visibility, increase citations, prevent data loss, and maximize the potential from your research.

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

• 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 they describe.
• F4. (Meta)data are registered or indexed in a searchable resource

Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.

• A1. (Meta)data are retrievable by their identifier using a standardised communications protocol.
• A1.1 The protocol is open, free, and universally implementable.
• A1.2 The protocol allows for an authentication and authorisation procedure, where necessary.
• A2. Metadata are accessible, even when the data are no longer available.

The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

• 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.

The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

• 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.