|OpenAIRE CoP training coordinators||
The Community of Practice for Training Coordinators (CoP) is an informal network to share training experiences initiated by a group of people who coordinate training programmes of research and e-infrastructures. This initiative of starting a Community of Practice for training coordinators aims to map out the training activities of various pan-European, EOSC-related initiatives and strengthen their training capacity by improved alignment, sharing experiences and good practices, initiating cross-infrastructure training activities.
|OpenAIRE Task Forces||
The task forces are a new programme in OpenAIRE Advance to enable capacity building, competencies and awareness raising on different relevant open science topics, involving all partners and NOADs, demonstrating a vertical approach for knowledge exchange. NOADs with more engaged activity levels are involved throughout and are also encouraged to facilitate these groups, aiming to strengthen understanding, steer project activities and, mobilise community ties.
In OpenAIRE Advance there are three task forces (at the bottom of the page you find useful resources (guidelines, blogs, checklists, etc):
|Open Access and the Humanities: A Train the Trainer Perspective||
The goal of this ‘train the trainer’ Humanities Open Access event has three parts. First, to set a base level of knowledge and mutual understanding around the topic, the actors, and the tools and methods available. Secondly, to understand the specific challenges to training humanities researchers, as well as the specific issues and challenges faced by humanists. Thirdly, to imagine future collaborations, and a recurring programme of events and activities to enable them. This initiative arises from a collaboration between DARIAH, the Open Library of Humanities (OLH), EIFL, and FOSTER Open Science. It will consist of a series of talks establishing the context for humanities open access, the tools and resources available and their attendant issues, and the platforms and funding models that have the potential to deliver fee free, equitable and gold open access for humanists. Finally, we will end with a question and answer session and a discussion of further activities.
|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).
|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.