With the rise of open science, and in particular after the publication of the Second National Plan for Open Science, research data are now at the heart of the academic community's concerns. For this reason, researchers and laboratories are looking at how to manage their data. Like an article, research data is intended to be shared, cited and reused.
- But what is search data?
In 2007,INIST (Institut de l'Information Scientifique et Technique) defined research data as "factual records (figures, texts, images and sounds), which are used as primary sources for scientific research and are generally recognized by the scientific community as necessary to validate research results".
- What is not included in the definition of research data?
Preliminary analyses, programs for future work, peer reviews, personal communications (e.g. e-mails), physical objects, media from academics, administrative data.
The aim of the FAIR principles is to promote the discovery, access, interoperability and reuse of shared data. Each FAIR principle is broken down into a set of characteristics that data and metadata must have to facilitate their discovery and use by humans and machines alike. The four main principles :
- Easy to find
The Accessible principle makes it easy to access and download data. It encourages long-term storage of data and metadata, and facilitates access and/or downloading by specifying the conditions of access (open or restricted) and use (license).
The Interoperable principle aims to exploit data regardless of the computing environment used. It can be broken down into: downloadable, usable, intelligible and combinable with other data, by humans and machines.
The Reusable principle aims to reuse data for future research, and highlights the characteristics that make data reusable for future research or other purposes (teaching, innovation, reproduction/transparency of science).
Interactive visualization of the 4 FAIR principles proposed by the DoRANum service platform: