WiSe 2023/24

In the introductory part of the workshop series, we will teach you how you can benefit from professional research data management and practise good scientific practice throughout the entire life cycle of your research data. You will learn about the range of options available for making your data findable, accessible, interoperable and reusable (FAIR).

Good planning is half the battle! A data management plan (DMP) describes the (planned) handling of research data during and after the project. Ideally, it is a living document that can be revised or refined as needed during the course of the project. A DMP can meet the requirements of research funding bodies and also supports better structuring of your own working methods and collaboration in projects.

Data: the new gold – or even more valuable? First, someone has to find out about your research data! This module focuses on what you can do with your data even after the project has ended. Specifically, it addresses the following questions: What does data publication mean? How and where can data be published, and what needs to be considered? Archiving is also part of good scientific practice. You will gain practical insights through the presentation of an example data repository.

Those who document understand! Do you want your data to be reused later? Do you work in a team? Is your research work likely to be interrupted at some point? Whether for yourself or for others, good organisation and documentation of your research data makes your work easier and increases traceability.

The module presents the copyright situation for research data. It focuses in particular on the question of how openness can be achieved in the publication of research data. In particular, it aims to clarify when licensing with Creative Commons licences is the right choice and what needs to be considered in this context. The lecture will also address current legal issues relating to the use of generative AI in research.

The module focuses on data protection requirements when handling (sensitive) personal data. What are the most important obligations of researchers when handling personal data (e.g. legal basis, informed consent)? What specific framework conditions have been established? What basic measures are necessary when working with personal data (e.g. anonymisation, pseudonymisation)? Which data is highlighted as particularly sensitive and therefore particularly worthy of protection? Are there special permissions for data processing for research purposes (e.g. right to information and objection, archiving, publication)? What documentation obligations exist and when is an additional data protection impact assessment required?

Open science? What is it? And if so, how much openness is good for my science?

Open science means making the entire research process transparent and accessible, from the conception of ideas to the publication of results. This includes open access to publications (open access), data (open data), materials (open materials) and educational content (open educational resources), the use of open source software and its comprehensible description and linking. This allows the output from the entire research process to be reused and the results to be replicated if necessary.

In the wake of the COVID-19 pandemic, it has become clear that open research data is essential for solving societal challenges. Your own research data is also interesting and potentially valuable to other researchers or the interested public. Sometimes research data also takes on central importance in completely new contexts: for example, decades-old recordings of animal voices are now opening up new possibilities for biodiversity research.

Open science and citizen science are often mentioned in the same breath. Initiatives on both sides, such as AG Open Science and Bürger schaffen Wissen (Citizens Create Knowledge), like to refer to the common overarching goal of democratising and opening up scientific activity to other sections of society. While citizen science appears from this perspective to be an embodiment of the open science principle, these are two complex concepts whose interaction in practice can also raise questions.

Whether it’s a small script for parsing web pages or complex programming languages, it’s nice not to have to reinvent the wheel. This workshop will explore the question of what the term ‘open source’ means in the context of software and when (and why) you should consider publishing your own code. How can we reuse existing code and where can I find it?