RDM Curriculum of the UA Ruhr

The latest call for proposals from a funding body sounds just right, but it asks for statements on the handling of research data? The colleague has left the institution and has not documented his knowledge so that nobody knows exactly which data can be found where? Can’t find the data just collected last week?

Do these examples sound familiar to you and you are looking for a solution? A data management plan (DMP) can help.

In this 2-hour online workshop, you will learn

• how to recognize that the funding body requires statements on the handling of research data.
• how to use checklists and templates, and what information belongs in a data management plan.
• how to work with the tool RDMO (Research Data Management Organizer), which you can use to create DMPs.
• why it is worth preparing a DMP - even if the funding body does not require it, so that the situations described no longer occur.

Why should I reuse data and how do I find suitable data? Why should I make my data reusable and what is needed to create reusable and reproducible research data? In the workshop we will address these questions and how you can save time and gain visibility for your research by reusing data and making your own data reusable.

When are data protected by copyright? To what extent and in what way may they be passed on to other researchers or published? These are questions that will be addressed in the workshop and explained in more detail using case studies. The focus will be on § 60c of the German Copyright Act (UrhG, Scientific Research) and § 60d UrhG (Text and Data Mining) in particular, but also on other topics such as the level of creation, the scope of the work, and the subsequent use of research data under Creative Commons licenses. In addition to the case studies presented, participants can also contribute and discuss their own examples.

Please note that the contents of the workshop are for information purposes only and do not constitute legally binding information.

Are you interested in integrating Open Science into your research but don't know where or how to start? This workshop is designed for researchers at all career stages who want to maximize the validity, credibility, and impact of their research through Open Science practices.

You will gain hands-on experience with practices that make scientific research more transparent and reproducible, focusing on:

– Preregistrations: Understand the added value of preregistrations, learn how to easily create one on the most suitable platform, and publish it to your advantage.

– Open Software: Discover best practices for developing and sharing open-source software, with practical guidance on using GitLab.

– Open Data and Materials: Learn where and how to effectively publish your research data and materials, including strategies for finding discipline-specific repositories and using RUB’s research data repository, ReSeeD.

The aim of this course is to help you identify your optimal entry points into Open Science and to equip you with the tools and knowledge needed to take your first steps in implementing Open Science in your research.

To participate, you should have GitLab access via your institute instance. In order to test ReSeeD, we will use the demo version. RUB members can log in via their user ID, members of the UDE and TUDo can log in using their ORCID.

With the Open Science Framework (OSF) you can organize your whole project on one platform. In the workshop, we will show you how to create projects, upload and manage materials, assign access permissions and licenses, preregister planned studies, and publish data and papers.

In a hands-on exercise, we will put theory into practice together with you.

To prepare for the workshop, please register for free on the platform  https://osf.io/  -

In this workshop you will learn how to design your qualitative research data along the FAIR principles. In addition to the theory of the FAIR principles with special consideration of the challenges of qualitative research, alternative approaches (e.g. through codebooks) for particularly sensitive data are also presented. At the heart of the workshop is a practical part in which participants are given the opportunity to prepare a data set themselves.

No prior knowledge is required for this workshop.

Effectively publishing and securely archiving research data is a critical challenge for scientists of all disciplines. Selecting the appropriate repository is particularly crucial for data that is not intended or not yet ready for publication. It's essential to use a platform that ensures your data can be managed according to the Guidelines of Good Scientific Practice and eventually be archived or published.

ReSeeD, the central research data repository of Ruhr-Universität Bochum, offers a comprehensive infrastructure for research data management. The repository covers various use cases (e.g. metadata assignment, role and rights management, collaborative work) and is suitable for different types of data.

In this workshop, you will explore

• the basic functionalities of ReSeeD.
• how to manage and store your data in ReSeeD - from the creation of datasets, collaboration and sharing of datasets, to their archiving and publication.
• You will gain hands-on experience with ReSeeD, allowing you to directly apply what you've learned.

No RDM basic knowledge is required for this workshop.

This course provides an introduction to the Python programming language and initial applications for your research data. We will deal with loading and analyzing tabular data, displaying results graphically and saving them as text or Excel files. No knowledge of Python is required; Python beginners are particularly welcome.

In order to participate in the workshop, please install Python and a development environment. Installation instructions (in German):  https://fdm.tu-dortmund.de/informationen/2024-03-05-workshop-einfuehrung-in-python/

Would you like to document your work with the statistical software R more comprehensibly and clearly? R Notebooks offer an easy way to summarize your code, explanations and results in one document and share it with other people, regardless of their R knowledge. In this hands-on workshop, we will guide you through your first steps with R Notebooks on the way to reproducible analyses in R.

Requirements for participation in the workshop: Basic knowledge of R and computer with installation of the latest versions of R and RStudio.

Coscine is a research data management platform for FAIRe (Findable, Accessible, Interoperable, Reusable) data in research projects. Here you can store, describe with metadata, share and archive your research data. Coscine integrates functions ranging from metadata management to the automatic assignment of persistent identifiers. In collaborative projects, researchers from different institutions can work together on their data in Coscine. UDE researchers automatically receive 100 GB of S3-compatible object storage in Coscine, which can be expanded upon request. In addition, linking of other data sources is possible to display all project-relevant research data at a glance. In the workshop we will give you a hands-on introduction to Coscine.

eLabFTW is a web application in which electronic lab books can be maintained collaboratively as a research team or group. Experiments and database elements can be added, commented and saved.

In contrast to the paper lab book, a full text search as well as the insertion and editing of images is also possible. The immutability of completed experiments is guaranteed by time stamps.

Previous knowledge is not required.

As a project management tool, GitLab offers a large selection of tools for project organization. This includes wikis for documentation, a shared and versioned data storage and a ticket system that can be used to structure work packages and manage them, for example, in the form of a Kanban board. In this workshop we will give an insight into how these functions of GitLab can be used in the organization of scientific projects.

For practical exercises you need a university ID (Uni-Kennung) of the UDE.

further prerequisites: none