All disciplines

In an effort to make open access for publications and data simple for everyone, OpenAIRE is creating factsheets with a brief overview of how to comply with H2020 OA mandates and how to use OpenAIRE services where available.

Collaboratively developed MOOC by RDNL, DCC and the University of Edinburgh, published on FutureLearn

MOOC on delivering RDM services

Video is a talk by Anna Schwickerath (GESIS) on how to make your data findable, accessible, interoperable and reusable (FAIR).

YouTube Video: 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. This webinar aims to raise awareness about relevant key data management practices for sharing, specifically regarding data documentation, gaining consent, and data anonymisation. 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 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.

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.

German version available here: https://doi.org/10.5281/zenodo.1215376 

Guidelines to help researchers make their research data findable, accessible, interoperable and reusable (FAIR), to ensure sound management. 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.

Training kit for librarians who wish to gain confidence and understanding of research data management, based on open educational materials, covering five topics:

• Data management planning
• Organising & documenting data
• Data storage & security
• Ethics & copyright
• Data sharing

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.

Content for a three-hour introductory RDM session for librarians. The course covers:

• Research data and RDM
• Data management planning
• Data sharing
• Skills

The materials consist of presentation slides and an accompanying handbook.

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).