Data 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.

This webinar is intended for everyone who wants to learn about ways of finding and reusing research data. Managing your research data in a FAIR and transparent manner is important. It helps researchers to meet requirements of funding institutions and ensures long-term re-usability of their data. The webinar introduces the CESSDA ERIC Data Catalogue as a means of finding and accessing research data. It enables participants to understand conditions for reuse (licenses) and introduces use-cases. This event is part of a workshop/webinar series organised by members of the SERISS project.

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.

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 presentation was delivered virtually for Botswana Open University Library on 17th May 2021 as part of a Foundational Data Stewardship Workshop. It is primarily aimed at data stewards but can also be useful to researchers and RDM service providers and should be viewed in conjunction with these two other presentations that were part of the same workshop:

• DOI:10.5281/zenodo.4665390 (Open and Responsible Research: Roles and Responsibilities for Data Stewards)
• DOI:10.5281/zenodo.4561728 (Developing and Implementing a Research Data Policy)

The training materials are all based around teaching the use of GATE, a freely available open-source toolkit for Natural Language Processing that has been widely used in both academia and industry for many different tasks.

The modules provide instruction on how to get to grips with the GATE toolkit for basic language processing, as well as more advanced techniques, and include a number of different scenarios, such as processing social media, hate speech and misinformation detection. They include modules both for programmers who want to further develop their own tools within the toolkit, and for non-programmers who want to just make use of existing tools. The modules teach not only the use of GATE itself, but also how to adapt it to one’s own needs (for example, to adapt English tools to a different language, or how to customise existing tools), and also the basic concepts around a number of language processing tasks including both low-level (tokenisation, POS tagging, parsing) to more sophisticated (information extraction, social media analysis, hate speech detection, misinformation detection), as well as how to interpret and integrate the results of the processing. Finally, it teaches programmers how to extend the toolkit itself, by adding new tools or integrating it into other systems.

Taken from Teaching with CLARIN: https://www.clarin.eu/content/gate-training-course 

The course includes a series of recorded videos, quizes, and practical assignments that will allow you to go through the course at your own pace. It invites researchers, students, trainers and data professionals and any other individual that is looking to gain basic knowledge on Open Science, EOSC and best practices for FAIR data.