In 2016, a Nature article "The FAIR Guiding Principles for scientific data management and stewardship" launched the FAIR concept.
FAIR stands for Findable, Accessible, Interoperable, Re-usable principles (The FAIR principles as published by FORCE11). The FAIR Data principles act as an international guideline for high quality data stewardship. Throughout the FAIR Principles, we use the phrase ‘(meta)data’ in cases where the Principle should be applied to both metadata and data.
“Percentage of time spent finding and organising data according to research data specialists: 79%” (#RDAPlenary). Processing and curating data according to FAIR principles both save time and have impact through information and knowledge build upon this data.
In a move to sharp its focus on the processing data according to FAIR principles rationales, the Research Data Alliance, for example, keeps identifying different parameters of FAIRness helpful to establish nodes (e.g. ELIXIR nodes: the national implementation of a harmonised FAIR Data Management programmes) for FAIRifying data, important in maximizing the discovery and reusability of digital resources in long term goal.
|Research Data Alliance :
Data Management Framework : What Makes It So Special?
FAIR Data Principles apply not only to data but also to metadata, and are supporting infrastructures (e.g., search engines). Most of the requirements for findability and accessibility can be achieved at the metadata level, but interoperability and reuse require more efforts at the data level. This scheme depicts the FAIRification process adopted by GO FAIR.
Even though Open Data and FAIR Data are different, they can be overlapping concepts; FAIR data doesn’t not automatically imply that it needs to be accessible - there can be limitations to access, for example, for sensitive data. Accessibility of FAIR data means “how-to-access” and is defined in a human- and machine-readable way.
FAIRness should be assessed before and after the work with data is done. Recently, a prototype software infrastructure and a set of metrics to assess the FAIRness of digital resources were developed (take a look at: FAIR Metrics; 1 May, 2018: RDA Workshop: Improving Reproducibility In Research: The Role Of Measurement Science).
FAIR data is not a platitude and is not a goal; it is a process. Besides the importance of FAIR compliance, FAIR data requires a paradigm shift, investments at the Data Management Planning stage, incentive structures and cultural change.
FAIR data practices have arrived in the communities because they have been shown to increase the quality of scientific findings. Getting more FAIRdata and moving its trust forward is also not just about FAIR SCIENCE, it's about FAIR SOCIETY...
"Implementing FAIR requires a model for #FAIRData Objects which have a PID linked to different types of essential metadata, including provenance and licencing. Use of community standards and sharing of code is fundamental for interoperability and reuse." (Twitter)
From 5-8 November 2018, data professionals and researchers from all disciplines and from across the globe will convene in for International Data Week (IDW). The theme of this landmark event is ‘Digital Frontiers of Global Science’ - REGISTER NOW
The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains
- FAIRsharing : Find, Register, Claim your Standard, Database, and Policy
- A FAIR guide for data providers to maximise sharing of human genomic data, 2018, by M.Corpas et al., PLOS
- Frictionless Data and FAIR Research Principles (OKFN)
- FAIR (Findable, Interoperable, Accessible, Reusable) (ANDS webinar series)
- Recorded Webinar : FAIR Principles and Data Management Planning (AIMS webinar)
- All the flavours of FAIR, fair & F.A.I.R (AOASG webinar series)
- FAIR Metrics : Framework to understand how increasing the FAIRness is ...
- Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework (BiorXiv, 2018)
- FAIR principles and metrics for evaluation (SlideShare, 2017)
- Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud (IOS Press, 2017)
- Towards Digital Object Cloud (DOC) compliant with the FAIR principles
- Make FAIR your Datasets using Springer Nature RESEARCH DATA SUPPORT
- The 13th International Digital Curation Conference “Beyond FAIR – from principles to practice to global join up” (19-22 February 2018, Barcelona)
- A Global Data Ecosystem for Agriculture and Food (by D. Allemang, B.Teegarden, GODAN Policy Document, 2017)
- About FAIR Data (DTL)
- Research Data Management : Training Materials
- Machine-actionable data management plans (maDMPs)
- Machine-actionable Data Management Plans (maDMPs) are happening...
- Enabling FAIR Data Project (COPDESS: Coalition on Publishing Data in the Earth and Space Sciences)
- GDPR & What It Means For Researchers (recorded LIBER webinar, 2018)
- LIBER Webinar: Turning FAIR Data Into Reality (recorded LIBER webinar)
- Guidelines for Data Management Plan from SNSF : learning from each other
Open access to scientific publications must become a reality by 2020 - by Robert-Jan Smits, 2018, interview, HORIZON
How to deal with Persistent Identifiers in the coming years: CESSDA's PID Policy
Data’s value: how and why should we measure it? (ODI, 2018)
European Open Science Cloud (EOSC) and FAIR data principles:
- Turning FAIR data into reality (GoogleDoc, Interim report from the European Commission Expert Group on FAIR data, 2018)
- FAIR Data Action Plan (ZENODO, Interim recommendations and actions from the European Commission Expert Group on FAIR data, 2018)
- Interoperability in practice and FAIR data principles
- How to make EOSC services FAIR? Experience and challenges
- Revisiting the FAIR principles for the European Open Science Cloud (2017)
- Contributions are open on Github to help develop FAIR metrics for EOSC
And, thanks again for your interest !