Service provider

This introductory webinar is for anyone who is involved in the collection of data and is considering making (some of) their data available in accordance with funders’ requirements. More and more funders are requiring that research data be made available after completion of the research project, usually through the archiving of data in a trusted repository. However, research teams often still lack the appropriate skills and knowledge regarding how to properly prepare their data for archiving and sharing. This webinar aims to raise awareness about relevant key data management practices for sharing, specifically regarding data documentation, gaining consent, and data anonymisation. Addressing each of these three topics, it provides a short theoretical introduction, including what FAIR means and how it is implemented, as well as practical illustrations drawing on a large-scale cross-national survey (the European Social Survey). It also provides some practical tips with respect to data archiving, in particular how to choose an appropriate archive or repository.

Description

DDI-Codebook is a more light-weight version of the standard, intended primarily to document simple survey data. Originally DTD-based, DDI-C is also available as an XML Schema.

Applications

Documentation of a simple study. Basic descriptive content for variables, files, source material, and study level information. Supports discovery, preservation, and the informed use of data. 

DDI-Lifecycle is designed to document and manage data across the entire life cycle, from conceptualization to data publication, analysis and beyond. It encompasses all of the DDI-Codebook specification and extends it. Based on XML Schemas, DDI-Lifecycle is modular and extensible. This version also supports improvements in Classification management (based on GSIM / Neuchatel), non-survey data collection (Measurements), sampling, weighting, questionnaire Design and support for DDI as a Property Graph.

The DDI Controlled Vocabularies Group (CVG) has created a set of controlled vocabularies that can be used with DDI as well as for other purposes and applications. Select DDI Alliance vocabularies are already in use at organizations like the Finnish Social Science Data Archive (FSD), the GESIS - Leibniz Institute for the Social Sciences, the Inter-university Consortium for Political and Social Science (ICPSR), Mathematica Policy Research,  the UK Data Archive (UKDA), and the University at Bielefeld, Germany. Nesstar Publisher (http://www.nesstar.com/) now incorporates the controlled vocabularies for Analysis Unit and Time Method.

This handbook aims to support higher education institutions with the integration of FAIR-related content in their curricula and teaching.  It was written and edited by a group of about 40 collaborators in a series of six book sprint events that took place between 1 and 10 June 2021. The document provides practical material, such as competence profiles, learning outcomes and lesson plans, and supporting information. It incorporates community feedback received during the public consultation which ran from 27 July to 12 September 2021.

The overall objective of FAIRsFAIR is to accelerate the realization of the goals of the EOSC by opening up and sharing all knowledge, expertise, guidelines, implementations, new trajectories, courses and education on FAIR matters. To support this, FAIRsFAIR is tasked to set up a single FAIR Data Stewardship Competence Centre which this report defines as a shared hub of expertise in implementing FAIR data stewardship principles, offering leadership, coordination and cataloging services to connect relevant people, guidance, learning resources and curricula in different thematic areas.

Requirements for competence centres in general and a core competence centre for FAIR data stewardship in general were identified by interviewing other members of the FAIRsFAIR project to understand their expectations for a core competence centre as well as the resources they will contribute to the knowledge base. Furthermore, we carried out a broad characterisation of current competence centres enriched with case studies of good examples for certain aspects of a competence centre. We created user stories for how stakeholders might interact with the competence centres and refined them through an open consultation answered by 106 people, interviews with EOSC clusters, and feedback gathered in workshops at the Open Science Fair 2019.

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.