The SSH Training Discovery Toolkit provides an inventory of training materials relevant for the Social Sciences and Humanities.
Use the search bar to discover materials or browse through the collections. The filters will help you identify your area of interest.
|Introduction to RDM, FAIR and Open Science
This module is a part of the introductory online course “Data Steward Training”. In this course learners will develop the skills and gain knowledge on how to be a successful data steward.
By the end of this module, learners will:
This module is suitable for data stewards and trainers seeking introductory learning material. It will take around 1 hour and 30 minutes to complete the module.
The materials include video presentations, full video transcripts, PowerPoint presentations and various learning activities and resources to support learning.
|How to be FAIR with your data. A teaching and training handbook for higher education institutions
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.
|Overview of needs for competence centres
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.
|Initial Core Competence Centre Structures
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.
|Established Competence Centre for Variety of Communities
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.
|FAIR in European Higher Education
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)
“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.
|Good Practices in FAIR Competence Education
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.
|Developing and Implementing a Research Data Policy
This presentation was given as part of the FAIRsFAIR-CODATA-RDA Data Steward Training Series which was held virtually on 11 December 2020 with the National University of Costa Rica.
The session explored issues to be considered when developing Research Data Management policies and related support services. The session aimed to ensure that participants were:
• able to develop realistic and well scoped RDM policies
• aware of free resources that are available to help build capacity for RDM service delivery and support
This presentation was prepared using materials that have been developed and/or adapted by the Digital Curation Centre (DCC). Attributions to reused open content are provided where relevant in the slides.
|Open and Responsible Research:Roles and Responsibilities for Data Stewards
An introduction for data stewards into open and responsible research, covering Responsible Conduct of Research, Open Science and Responsible Research and Innovation. This forms part of the CODATA/RDA/FAIRsFAIR data steward training.