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Training

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Research Data Lifecycle Graph Research Data Lifecycle Graph Image: UK Data Service

Our team of professionals with backgrounds in various academic disciplines offers trainings in research data management. The central aspects of the successful management of research data are captured in a research data lifecycle model (see right). Knowledge and skills of data management and curation are essential for any researcher who aims to optimize their outputs, increase the impact of their work and support scientific transparency.

Our courses and workshops are available for researchers of all university faculties and at any stage of their academic career. The participants of our trainings can get familiar with the entire research data lifecycle or choose to focus on specific stages of it.

A summary of our training offers is presented below. All courses are offered in  English and German language. 

Training Offers

Type of Training Duration and Frequency Content Target Group
Workshops at the Graduate Academy 1-2 days, every term Overview of research data lifecycle PhD students, early career reseachers
Sessions within existing courses 90 mins, once per course Introduction to research data management B.Sc./M.Sc., B.A./M.A. Students
Module "Management of scientific data" 90 mins, weekly, every 2nd term Detailed account of every step of the research data lifecycle M.Sc. Students 
Training sessions upon request min. 90 mins Overview of research data lifecycle with focus on any of the steps most relevant for individual groups Researchers from any backgrounds

Contents and Objectives

Overview
  • What is research data and why is research data management necessary?
  • Guidelines for research data at universities and funding agencies
  • What is the Research Data Lifecycle?
  • What is a data management plan?
Research data
  • Data, files, and their formats
  • The Fair Data Principles
  • Sustainable data storage
  • Discipline-specific requirements and questions
Metadata
  • What is metadata?
  • What are metadata standards?
  • Which discipline-specific standards exist?
  • How does metadata look like?
Data Security and Protection
  • Why is it important?
  • Which kinds and options exist?
  • Advice for your particular projects
Data archiving
  • Which options exist?
  • What are the costs?
  • Who are your contact points?
Data publication
  • How and where can data be published?
  • What is Open Access?
Legal and ethical aspects
  • Who owns the data?
  • Potential legal conflicts concerning data protection and ownership
  • Which ethical issues should be considered?
  • Copyright licenses
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