Maturity Frameworks supporting the implementation of FAIR data principles

 
FAIR data is expected to add value to organisations among others by reducing time needed to find valuable information, facilitating interoperability, reusability and enabling AI initiatives. When starting to implement that FAIR data principles, many organisations realise they embark in a transformation journey which often goes beyond data itself.
 
How can we evaluate how “FAIR” data sets actually are? How do we evaluate, measure and support the maturation of organisations as they become increasingly adept at FAIR data?
 
This webinar will address the theme of FAIR data and organisational maturity frameworks including the FAIRplus Dataset Maturity (DSM) Model, FAIRassist.org and the Pistoia Alliance’s FAIR Maturity Matrix (fairmm.pistoiaalliance.org).
 

Speakers

Susanna Sansone, Mark Wilkinson and the authors of the FAIR Maturity Matrix
 
Prof. Susanna-Assunta Sansone (https://tinyurl.com/324t928x) is the Academic Lead of the Oxford University’s Research Practice Programme to define good practices for how research findings are published, disseminated, and evaluated. She is also Professor of Data Readiness in the Department of Engineering Science, the Director of the Oxford e-Research Centre. An author of the FAIR Principles, Susanna is part of international collaborative actives and funded projects to implement FAIR; in the European context she is a member of the EOSC FAIR Metrics and Digital Objects Task Force, and co-lead of the ELIXIR Interoperability Platform.
 
Mark Wilkinson has a B.Sc.(Hons) in Genetics from the University of Alberta, and a Ph.D. in Botany from the University of British Columbia.  He spent four years at the Max Planck Institut für Züchtungsforschung in Köln, Germany, pursuing studies in a mix of plant molecular and developmental biology and bioinformatics.  He then did a research associateship at the Plant Biotechnology Institute of the National Research Council Canada, focusing on the problem of biological data representation and integration for the purposes of automated data mining. In the subsequent 25 years, his laboratory has focused on designing biomedical data/tool representation, discovery, and automated reuse infrastructures – what are now called “FAIR Data” infrastructures. He is the lead author of the primary FAIR Data Principles paper, and lead author on the first paper describing a complete implementation of those principles over legacy data – what is now called the ‘FAIR Data Point’.  He is a founding member of the FAIR Metrics working group, tasked with defining the precise, measurable behaviors that FAIR resources should exhibit, and the author of the first software application capable of a fully-automated and objective evaluation of “FAIRness”. He was co-Chair of the EOSC Task Force on FAIR Metrics and Data Quality until it closed in 2024, continues to work with EOSC in the Opportunity Area 3 (FAIR Assessment and Alignment) and is founder of a spin-off company, FAIR Data Systems S.L., that provides consulting, training, and customized software solutions that help clients become FAIR.
 
Authors of the FAIR Maturity Matrix: See:https://pistoiaalliance.atlassian.net/wiki/spaces/PUB/pages/3367632927/FAIR+Maturity+Matrix+V1.0+Contributors