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Publisher: NFDI4Biodiversity
This learning unit is based on the online learning unit "Forschungsdatenmanagement - eine Online-Einführung (HeFDI Data Learning Materials)" of the Hessian Research Data Infrastructures. In particular, it was supplemented by NFDI4Biodiversity to include information on managing biodiversity research data.
Authors
Marlen Fischer1, Juliane Röder1, Johannes Signer2, Daniel Tschink3, Tanja Weibulat4, Ortrun Brand1
Marlen Fischer1, Juliane Röder1, Johannes Signer2, Daniel Tschink3, Tanja Weibulat4, Ortrun Brand1
Affiliations
1Philipps-Universität Marburg, Biegenstraße 36, 35032 Marburg, Germany
2Georg-August-Universität Göttingen, Wilhelmsplatz 1, 37073 Göttingen, Germany
3German Federation for Biological Data (GFBio e.V.), Mary-Somerville-Str. 2-4, 28359 Bremen, Germany
4Bavarian Natural History Collections, Menzinger Str. 67, 80638, Munich, Germany
1Philipps-Universität Marburg, Biegenstraße 36, 35032 Marburg, Germany
2Georg-August-Universität Göttingen, Wilhelmsplatz 1, 37073 Göttingen, Germany
3German Federation for Biological Data (GFBio e.V.), Mary-Somerville-Str. 2-4, 28359 Bremen, Germany
4Bavarian Natural History Collections, Menzinger Str. 67, 80638, Munich, Germany
Review in alphabetical order
We would like to express our gratitude to everyone who has contributed to the success and excellence of our self-learning unit. Your dedication and expertise have truly made this educational endeavour possible.
We would like to thank our colleagues Aletta Bonn, Barbara Ebert, Judith Engel, Frank-Oliver Glöckner, Thomas Kneib, Birgitta König-Ries, Esther Krähwinkel, Jimena Linares, Hanna Prautzsch and Débora Raposo for their critical feedback and support. Your invaluable contributions have played an important role in shaping the self-learning unit.
We extend our thanks to all our colleagues within the team of the GFBio e.V., University of Marburg, University of Bremen, University Göttingen and particularly to the team of Task Area 1 Measure 4. Your collaboration, dedication, and hard work have been instrumental in the planning, organisation, and execution of our self-learning unit. To our partners and collaborators beyond our organisation, we extend our gratitude for your invaluable support. This journey of learning and growth has been made more meaningful by the strength of our collective efforts. Through collaboration and teamwork, we have built a community that thrives on knowledge sharing, mutual support, and a shared passion for advancing our field.
In conclusion, we are thankful to each and every individual who has contributed to the success of our self-learning unit. Your dedication and commitment have not only enriched our educational endeavours but have also contributed to the overall growth and development of our community.
Prerequisites
No previous knowledge is required for this module. The chapters build on each other thematically, but can also be worked through individually. If information from other chapters is needed, they are linked.
No previous knowledge is required for this module. The chapters build on each other thematically, but can also be worked through individually. If information from other chapters is needed, they are linked.
Target audience
Students at PhD-level and beyond and researchers in the field of biology and environmental sciences, especially those working with biodiversity data, who are looking for a first introduction to research data management.
Students at PhD-level and beyond and researchers in the field of biology and environmental sciences, especially those working with biodiversity data, who are looking for a first introduction to research data management.
Learning objectives
After completing this unit you will be able to understand the content and purpose of research data management and apply it in the field of biology. The detailed learning objectives are given at the beginning of each chapter.
After completing this unit you will be able to understand the content and purpose of research data management and apply it in the field of biology. The detailed learning objectives are given at the beginning of each chapter.
Content
If you prefer to read, you can access the content of this unit through the text only. However, the interspersed videos offer further access to the topic, so those who work well with video explanations will find the content presented here in a different way.
If you prefer to read, you can access the content of this unit through the text only. However, the interspersed videos offer further access to the topic, so those who work well with video explanations will find the content presented here in a different way.
Duration
If you concentrate on reading all the texts, you can expect to spend about x hours on the unit. If you also watch the videos or follow the links, you will need more time. And remember to take breaks. The approximate length of each chapter is given in the table of contents.
If you concentrate on reading all the texts, you can expect to spend about x hours on the unit. If you also watch the videos or follow the links, you will need more time. And remember to take breaks. The approximate length of each chapter is given in the table of contents.
Published or last modified
December 2023
December 2023
Licensing
This module is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
This module is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
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Privacy - embedded videos
Videos from YouTube are embedded in this learning module on the following pages. When the videos are called up, Google/YouTube uses, processes and possibly passes on cookies and other data. Information on data protection and terms of use of the service can be found following the link. The use of the learning module requires corresponding consent.