LIBER’s Research Data Management Working Group carried out two data repositories surveys. The responses for both questionnaires have been merged and analyzed to gain a comprehensive picture about FAIRness at the level of repositories and their data. The report, which can be downloaded from Zenodo, summarises the answers given by managers, librarians and technical staff… Lire la suite FAIRness of repositories and their data
The journal Insights has just published a very informative article on how to distinguish FAIR principles (Findable, Accessible, Interoperable, Reusable), Open Data and Research Data Management (RDM) practices. A good synthesis to clarify your ideas. Abstract: Open data, FAIR (findable, accessible, interoperable and reusable) and research data management (RDM) are three overlapping but distinct concepts,… Lire la suite Three camps, one destination: the intersections of research data management, FAIR and Open
How to know if my dataset is FAIR (Findable, Accessible, Interoperable, Reusable)? The Australian Research Data Commons (ARDC) provides you with a FAIR self-assessment tool. Using this tool you will be able to assess the 'FAIRness' of a dataset and determine how to enhance its FAIRness (where applicable). You will be asked questions related to… Lire la suite The FAIR self-assessment tool: a tool to assess the ‘FAIRness’ of a dataset
The FAIRsFAIR project started on March 1st, 2019 and addresses, in a 36 months timeplan, the development and concrete realisation of an overall knowledge infrastructure on academic quality data management, procedures, standards, metrics and related matters, based on the FAIR principles. FAIRsFAIR aims to supply practical solutions for the use of the FAIR data principles… Lire la suite FAIRsFAIR: a new H2020 project about FAIR principles
FAIR research data encompasses the way to create, store and publish research data in a way that they are findable, accessible, interoperable and reusable. In order to be FAIR, research data published should meet certain criteria described by the FAIR principles. Despite this, many research performing organisations and infrastructures are still reluctant to apply the… Lire la suite Cost-benefit analysis for FAIR research data: EU policy recommendations
FAIR principles originated from the current patchy data management practices in EU, which are not optimal yet. Several local initiatives, as well as global ones, are making the move towards an infrastructure supporting the FAIR principles in order to get the most of research data. This report aims to estimate the cost of not having… Lire la suite EU publication: Cost of not having FAIR research data
Is FAIR the same as open data? What exactly is metadata? Why does FAIR data need a license? Why do we need data standards? Where should FAIR data be stored? What’s in it for researchers? Two research data specialists answer these questions in this article: Kate LeMay, senior research data specialist at the Australian Research… Lire la suite « A love letter to your future self »: What scientists need to know about FAIR data
SPARC Europe a publié fin décembre 2018 une note d’information sur les données à destination des décideurs : FAIR and Open Data, a briefing for policymakers and senior managers. Cette note explique clairement la différence entre Open Data et FAIR data. Un petit récapitulatif : Les données ouvertes (open data) couvrent des contenus librement accessibles… Lire la suite FAIR data et Open data : quelle différence ?
A FAIRy tale: a fake story in a trustworthy guide to the FAIR principles for research data. Table of contents: Introduction. Findable #1: (Meta)data are assigned globally unique and persistent identifiers. Findable #2: Data are described with rich metadata. Findable #3: Metadata clearly and explicitly include the identifier of the data they describe. Findable #4:… Lire la suite A FAIRy tale to understand the FAIR principles, published by the FAIR project
To take advantage of the digital revolution, to accelerate research, to engage the power of machine analysis at scale while ensuring transparency, reproducibility and societal utility, data and other digital objects created by and used for research need to be FAIR. Advancing the global Open Science movement and the development of the European Open Science… Lire la suite Turning FAIR data into reality : final report and action plan from the European Commission expert group on FAIR data.