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.
|Using survey data||
This guide aims to help researchers utilise extensive survey data available. In particular, this guide is designed to support those starting small research projects, especially students doing dissertations.
The guide includes materials to read, worksheets for getting started and questions to think about and answer.
|Mapping Census Data in QGIS||
This guide will cover how to map census data in QGIS. The example used in this guide creates a choropleth map showing the percentage of males who work in the manufacturing service using the QGIS package.
|Mapping Census Microdata using R||
This guide aims to show the strength of using Census Microdata for a variety of research purposes, via a worked example taken from real-life research. This guide assumes some familiarity with microdata, mapping and statistical software.
|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.