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Science is an incremental process that builds on prior findings and technical advancements, as such it relies on the correcteness of previous and current results. To contribute responsably with our research, we need to guarantee replicable results and transparency regarding the processes that led to those results. This is not an easy task, it requires meticulous work, time and especially shared conventions for managing our data within a community.

What is Research Data Management?

Research Data Management (RDM) is the collection of processes (e.g. organization, documentation, publication and archiving) that researchers adopt to govern their data, before data collection, during data analysis and after data have been published.
Although data differ significantly across disciplines, general principles for an open and transparent data management are shared among different fields and find their definition in the so called FAIR data principles.

What are the FAIR principles?

In recent years, in order to provide a common ground for an open a transparent research, scientists from different fields have put forward general guidelines known as FAIR data principles.

FAIR is an acronym that stands for: Findable, Accessable, Interoperable and Reusable

Such principles have been codified by Wilkinson et al. (2016) to provide a concise and domain agnostic guide for open and reproducible science.

Findable

(Data should be findable by human and machines, thus machine readable metadata are crucial for findability)

  • 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

Accessible

(It should be clear to the user how to access the data)

  • 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

Interoperable

(Data need to be integrable with other datasets, analyses and/or workflows)

  • 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

Reusable

(In order to be resuable, data and metadata need to have clear and intelligible descriptions)

  • 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