|Research Data Management and Sharing||
This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories.
|Train-the-trainer concept for research data management||
As part of the project FDMentor, a German-language train-the-trainer program on research data management was created and piloted in a series of workshops. The comments and tips from the participants in the two pilot phases and the feedback from the relevant community were gradually incorporated over 2019. The second version of the train-the-trainer concept now available offers a revised script with the contents of the teaching units, detailed teaching scripts, working materials, lecture slides and numerous worksheets and templates that are intended to support teaching. The topics covered include both aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts.
|Guidelines on FAIR Data Management in Horizon 2020||
Guidelines to help researchers make their research data findable, accessible, interoperable and reusable (FAIR), to ensure sound managementd. Good research data management is not a goal in itself, but rather the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse.
|DIY Research Data Management Training Kit for Librarians||
Training kit for librarians who wish to gain confidence and understanding of research data management, based on open educational materials, covering five topics:
The kit uses the Research Data Mantra online course and selected exercises from the UK Data Archive. It further contains a training schedule, podcasts for short talks, presentation slides, evaluation forms, data curation profiles and reflective writing questions based on the experience of academic librarians who have taken the course.
Data Curation Profiles provide a complete framework for interviewing a researcher in any discipline about their research data and their data management practices.
|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.
easySHARE is a simplified HRS-adapted dataset for student training, and for researchers who have little experience in quantitative analyses of complex survey data. While the main release of SHARE is stored in more than 100 single data files, easySHARE stores information on all respondents and of all currently released data collection waves in one single dataset. Moreover, for the subset of variables covered in easySHARE, the complexity was considerably reduced. For example the information collected only from one person of a couple or in a household was transferred to all respective respondents; time constant information collected only in the first interview was transferred to all later interviews; the coding of missing values was enriched to provide an easier understanding of the routing and filtering of the interviews; etc. In addition, several ready to analyse variables have been added, such as health indexes, demographic information, or economic measures. When possible measures have been selected or recoded to facilitate comparative analyses with the US Health and Retirement Study (HRS).
|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.
|Open Research Software and Open Source||
Software and technology underpin modern science. There is an increasing demand for more sophisticated open source software, matched by an increasing willingness for researchers to openly collaborate on new tools. These developments come with a specific ethical, legal and economic challenges that impact upon research workflows. This module will introduce the necessary tools required for transforming software into something that can be openly accessed and re-used by others.