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.
|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.
|Voices of the Parliament: A Corpus Approach to Parliamentary Discourse Research||
While corpus methods are widely used in linguistics, including gender analysis, this tutorial shows the potential of richly annotated language corpora for research of the socio-cultural context and changes over time that are reflected through language use. The tutorial encourages students and scholars of modern languages, as well as users from other fields of digital humanities and social sciences who are interested in the study of socio-cultural phenomena through language, to engage with user-friendly digital tools for the analysis of large text collections. The tutorial is designed in such a way that it takes full advantage of both linguistic annotations and the available speaker and text metadata to formulate powerful quantitative queries that are then further extended with manual qualitative analysis in order to ensure adequate framing and interpretation of the results.
The tutorial demonstrates the potential of parliamentary corpora research via concordancers without the need for programming skills. No prior experience in using language corpora and corpus querying tools is required in order to follow this tutorial. While the same analysis could be carried out on any parliamentary corpus with similar annotations and metadata, in this tutorial we will use the siParl 2.0 corpus which contains parliamentary debates of the National Assembly of the Republic of Slovenia from 1990 to 2018. Knowledge of Slovenian is not required to follow the tutorial. To reproduce the analyses in other languages, we invite you to explore a parliamentary corpus of your choice from those available through CLARIN.
Taken from: Teaching with CLARIN:
|Introduction to Speech Analysis||
This course offers a general picture of managing speech corpora and of the methods that are available for the acoustic-phonetic study of speech. During the course, students use a speech analysis program called Praat and learn to apply the main features of the program in their own work with speech recordings. In addition, students will learn the basics of another program called ELAN that can be used for transcribing and annotating audio as well as video material.
Taken from: Teaching with CLARIN: https://www.clarin.eu/content/introduction-speech-analysis
|Computational Morphology with HFST||
The course demonstrates how HFST tools can be used for generating finite-state morphologies. Through practical exercises, students will learn how to use finite-state methods to develop a morphology for a language. This online course is suitable as a complement to a more theory or linguistics-oriented course on morphology.
After successfully completing the course:
- you can explain the basic theory on finite-state automata and transducers,
- you can design morphological lexica using finite-state technology,
- you know how to write morpho-phonological rules in a finite-state framework,
- you understand the diversity of morphological structure in different languages
and you know how to take these differences into account when designing computational models of morphology.
Taken from Teaching with CLARIN: https://www.clarin.eu/content/computational-morphology-hfst
|UK Data Service: Survey and Census Data||
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.
|UK Data Service: Secure Lab||
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.