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.
Research data management/FAIR data
|RDM for librarians||
Content for a three-hour introductory RDM session for librarians. The course covers:
The materials consist of presentation slides and an accompanying handbook.
|Digital Curation 101||
Digital Curation 101 employs the curation lifecycle model sections as a means of presenting content to students. The DC 101 has been developed because the DCC, in its role as a source of expert advice and guidance to the community, identified a need for a contextual, theoretical introduction to the basics of digital curation with practical examples and exercises. The target audience is new grant holders with Research Council curation mandates to fulfil. The course indicates what should be considered in planning and implementing projects.
|Research Data Management in Life Sciences||
Upon completion of this course, participants should have an understanding of what Research Data Management is, and why it is important in academic research. They should have an understanding of the FAIR data principles, and how they can make data more FAIR. They should be able to successfully manage all types of research data and to document both the research itself, as well as the data in a comprehensive way.
|Best practices in research data management and stewardship||
This two-day course (27 and 28 May, 2020) is aimed at project managers, researchers and graduate students in the biomedical sciences who wish to improve their skills on data handling. The course will introduce the concepts of the FAIR principles for data, the concept and implementation of data stewardship as well as practical aspects of day to day data management and data management plans, which are required in many grant applications.
|Reproducible Research and Data Analysis||
Reproducible research is at the heart of science. There has been an increased need and willingness to open and share research from the data collection right through to the interpretations of results. This has come with its own set of challenges, which include designing workflows that can be adopted by collaborators in a way that does not compromise the integrity of their contribution. This module will introduce the necessary tools required for transparent reporting which is reproducible and readable.
|Managing Evidence: A CAEL Module||
In this era of evidence-driven reform, school leaders must learn to harness an array of data to drive improvement. In this module, you will explore key concepts in performance measurement, research design, and data analysis (qualitative and quantitative) to understand what can be gleaned from different sources. With your cohort, you will discuss the data you have available and learn how to draw on multiple forms of evidence to make more informed policy and programmatic decisions.
This Train-the-Trainers (TTT) package forms an addition to the CESSDA Data Management Expert Guide which was developed by the CESSDA Training Working Group in 2017 and 2018.
This package contains different materials that trainers can use in developing and giving Research Data Management and Discovery trainings for (social science) researchers.
Materials for instance include workshop outlines, slides and exercises that can be reused and adapted by local RDM trainers. The package contains five different types of materials: Workshop Outlines, Exercises, Presentations, Documents and Handouts, and Images. A full overview of all materials in this TTT Package is giving below.
Recordings of past webinars organised by the UK Data Service.
|Data management videos||
Short online video tutorials demonstrating practical data management aspects such as writing a data management plan, encrypting data files, backing up data, etc.
|Analytical Information Systems||
Learn about the use of programming for data analysis, data management, and statistical analysis techniques.