Courses

Dive into the world of research data management with the HeFDI Data School! Our introductory course provides crucial foundations to make your scientific work more efficient and sustainable. Discover how smart data management can maximise the quality, discoverability, and reusability of your research data. Join a movement towards greater transparency and integrity in science!

Discover how to comply with legal and ethical standards when handling personal research data. This course covers practical solutions from the GDPR and obtaining informed consent to anonymisation and creating data use agreements. Obtain the skills needed to organise your research in a legally compliant and responsible manner!

Planning well is half the battle won! A data management plan (DMP) describes data handling during and after a project and serves as a living document, adjusted throughout the project’s lifecycle. It helps not only to meet funder requirements but also optimises workflow and collaboration. Learn how a DMP can structure your research projects and manage your data effectively!

Documenting means understanding! Whether for data reuse, team collaboration, or personal orientation, excellent organisation and documentation of your research data is essential. In this course, learn various tools and techniques to manage your data effectively and increase its traceability, including insights into metadata and versioning.

Learn about the copyright situation surrounding research data and how you can achieve openness in its publication. Find out when and how licensing with Creative Commons licences makes sense. You will also gain insights into current legal issues regarding the use of generative AI in research.

Data is the new gold—or perhaps even more valuable! For others to benefit from your research data, it must be made known! In this module, learn what you can achieve with your data even after the project ends. Discover how to publish and archive data effectively to maximise their value.

Getting started with good research data management (RDM) is difficult—but what if RDM became a “by-product” of analysis and the barriers to entry for researchers were lowered for both digital analysis and fair RDM? Then perhaps the open-source platform Galaxy could be the answer.

In the introductory part of the workshop series, we will teach you how you can benefit from professional research data management and practice 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).

• What is FDM and why is it important? (Funding, reuse, FAIR)
• Research data life cycle
• Good scientific practice (GWP) & DFG Codex
• FDM infrastructures (HeFDI, NFDI)
• Outlook for the following events

Speaker

Dr. Beate La Sala, Goethe University Frankfurt am Main

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 agencies and also supports better structuring of your own working methods and collaboration in projects.

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

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 organization and documentation of your research data makes your work easier and increases traceability.

Description|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 a data repository as an example.

Legal and ethical requirements for handling (sensitive) personal research data are well known, but how can they be implemented in practice? This presentation provides tips on practical approaches.

The presentation will show how to store research data more securely using an encryption program. It will also answer the questions: What is encryption and how does it work?

In many areas of today’s scientific practice, it is crucial to document information accurately and in a machine-readable format, and to search it efficiently. This talk provides a basic introduction to two essential tools that can greatly facilitate this work: Markdown and regular expressions.

eLabFTW is a powerful and flexible electronic lab notebook (ELN) that facilitates the organization and documentation of scientific experiments. This introduction provides an overview of key features such as experiment management, sample linking, and team collaboration. It also introduces the latest features that make eLabFTW even more user-friendly and efficient.

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?

In the introductory part of the workshop series, we will show you how you can benefit from professional research data management and 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).

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.

Consistent versioning of research data is an important cornerstone of research data management, ensuring the traceability of the provenance of research data. In this presentation, we will explain the basic principles of versioning and show you ways in which you can version your data. In addition to simple versioning methods, we will also introduce you to the version control software ‘Git’ and the web application ‘GitLab’.

The right software makes data management easier, especially when a team is working on research data in parallel and collaborative work processes are necessary. This workshop will present examples of tools and methods that can support project and data management while facilitating effective teamwork.

Laboratory notebooks are part of everyday research life: they contain measurement results, sketches of experimental setups, observations and evaluations, among other things. But what if this data is increasingly digital? Should it be printed out and pasted into the notebook, or is there a better solution? This is where electronic laboratory notebooks (ELNs) come in. They also offer completely new possibilities. There is a wide range of possible solutions with a variety of approaches and functionalities.

The handling of digital research data often raises legal questions. How can I process personal data in a legally compliant manner? Is research data subject to copyright? Who decides whether data can be published? What happens to research data when changing institutions? There are many uncertainties in everyday scientific life, especially when it comes to copyright, data protection, service and liability issues in the context of processing, sharing and publishing research data. During the workshop, we will address these issues through presentations, practical elements and discussion rounds and, if possible, resolve them.

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

Good planning is half the battle! Whether it’s to meet the requirements of research funding bodies or simply to better structure your own working methods, a well-designed data management plan is an integral part of any modern research project. It helps you to consider data types, data processing, storage, data publication, legal issues and other key questions relating to research organisation at an early stage. In this module, you will learn how to create an individual data management plan (DMP) for your research project under guidance. Using tried-and-tested sample plans, you will gain an insight into what elements a data management plan should contain and how you can use the digital DMP tool ‘RDMO’ to write it in a structured and customisable way.

The unit aims to present the requirements of funding bodies in terms of data management. The experiences of the participants will be taken into account. Participants will gain an overview of the requirements of third-party funding bodies, in particular the DFG. They will be able to contribute their experiences and expectations and reflect on these in relation to the requirements. They will learn where and how they can obtain support.

A journal publication is often the goal of research work, but the data itself can and should also be published. In the last basic module of the event series, we will discuss how this can be done, what publication options are available, and what needs to be considered. You will gain practical insights through the presentation of individual data repositories for quantitative and qualitative data as well as the test publication of data in a repository. The archiving of research data is also part of good scientific practice. Specifically, we will address the following questions: What do we mean by archiving as opposed to storage and long-term archiving? Where can this be done?

One of the most important goals of open access publication of research data is to ensure that this data is prepared in such a way that it can be reused by other researchers as easily as possible. In reality, however, both in the field of economics and in the field of research, much more data is still being generated than is being reused. This is often due to a lack of knowledge about how and where to search for this data. This presentation will therefore show you ways to search for and find existing research data, as well as rules you should follow when reusing research data.

In many social science and humanities disciplines, qualitative research data – such as interview transcripts, observation logs, field diaries, documents, or audio and video recordings – play an important role. The openness of qualitative research and the low level of standardisation of research materials also result in specific requirements for research data management. This focus module offers practical recommendations for handling qualitative research data. In addition to an overview, three topics that often raise many questions are dealt with in depth: implementation of informed consent, anonymisation, and documentation/contexts.