Data steward

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Title Body
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):

  • Policies for Open Access and Open Science
  • Research Data Management
  • Legal Guidelines for Research Performing Organisations (RPOs)
Research Data Management Toolkit

This toolkit includes a number of resources on research data management. However, due to its broad scope, the toolkit is not structured as an online course.It contains courses, videos, infographics, books and other materials

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.

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:

  • 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.

RDM for librarians

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

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. 

ELIXIR Train the Trainer (ONLINE)

This course aims to provide trainers with guidance and tips for developing and delivering training in bioinformatics, exploring a range of methods appropriate to different learning styles and examining the requirements for a successful course (both scientific and logistic).

This event is organized as part of the Horizon 2020 ELIXIR-EXCELERATE project.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.''

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.