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This report lays out the set-up of the FAIR core competence centre, including initial knowledge base design and tools, communications infrastructure, defined responsibilities, and expectations on service levels.  The document focuses on the design and functionality of the competence centre and how it will meet the needs of its user base.

This report advances the establishment of a FAIR Competence Centre as outlined in the previous two reports from WP6 of FAIRsFAIR, D6.1 “Overview of needs for Competence Centre” and D.6.2 “Initial core competence centre structure”, part of FAIRsFAIR WP6 deliverables which is concerned with the development of a competence centre as a model of engagement and support for research communities. Whilst the aforementioned reports focused, the first on the analysis of the landscape of available competence centres, and the second the set-up of the FAIR core competence centre, the present deliverable’s emphasis is put on the description of operations of the core competence centre, including initiatives aiming to identify synergies and areas of harmonisation that are required to support knowledge base development.

As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022.

FAIRsFAIR Work Package 7 “FAIR Data Science and Professionalisation” aims to develop resources and build communities that support the uptake of RDM and FAIR practice within higher education curricula.

To achieve these objectives, the present report aims to build a foundation for the identification of existing practices and needs of higher education institutions. Both a web-based questionnaire with 90 responses and two focus groups with a total of 50 participants were conducted between September and November 2019 as basis for the report.

“FAIR Competence Framework for Higher Education (Data Stewardship Professional Competence Framework)” is the third deliverable from Work Package 7 “FAIR Data Science and Professionalisation” of the FAIRsFAIR project (www.fairsfair.eu).

The report presents a proposed FAIR Competence Framework for Higher Education (FAIR4HE) that is defined as a part of the general Data Stewardship Professional Competence Framework (CF-DSP) presented in the deliverable. The proposed CF-DSP defines the set of competences that extend the competences initially defined in the EDISON Data Science Framework (EDSF). The proposed competence framework is defined based on a recent job market analysis for the Data Steward and related professions.

The presented CF-DSP has been validated against existing Data Stewardship competence frameworks defined primarily for the research community or practitioners. CF-DSP provides the competences definition structure that allows easy mapping to a Body of Knowledge and set of Learning Outcomes that can be used for defining academic curricula. The presented CF-DSP has been discussed with, and incorporated feedback from, several community events organised by the FAIRsFAIR project.

This report presents a collection of seven case studies describing how FAIR competences are being addressed through education and training programmes. The good practices offer examples of successful integration of Research Data Management (RDM) and FAIR data-related skills in university curricula and training to provide an up-to-date perspective on how these skills are being implemented by higher education institutions. This report provides universities with points of inspiration and practical examples of how fellow institutions and organisations in the higher education sector addressed the need for more RDM and FAIR data-related skills to be taught at the bachelor, master and doctoral levels. It does so by analysing external and internal drivers, steps for the implementation, invested capacity and the impact reached by the good practices.

The UK Data Service and the Office for National Statistics (UK) held a business data conference in 2021. This resource provides all slides, recordings and other useful materials from the conference. 

The conference included a mixture of presentations from the  Office for National Statistics  (ONS), UK Data Service, and researchers who have used data from the UK Surveys covering the areas of business, industry and trade. 

The UK Data Service is able to assist researchers wishing to link external business data to ONS business data in the Secure Lab. This guide provides all the information needed to guide users wishing to link external business data to ONS business data. 

This is a guide to the UKDS Secure Lab. Within this guide, users can find everything they need to know in order to access and use the highly detailed, sensitive data available via the UKDS SecureLab.

This source includes a series of resources in the form of guides and e-books dedicated to trainers and students interested in sample design, weighting, changes over time using cross sectional and longitudinal data and mapping census data using different software.

The UK Data Service SecureLab has enabled secure access to the most sensitive and confidential data in the collection since 2011. SecureLab provides controlled access to data that are too detailed, sensitive or confidential to be made available under less restrictive access levels.