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Mon Jun 30 2025 - Wed Jul 2 Past

Mon - Wed 30 Jun 2025 - 2 Jul 2025Past

By WiNoDa, NFDI4ING, NFDI4Objects & DKZ.2R: DataLad Workshop and Hackathon

This event takes place in English

Organizing decentrally stored data requires more than just a good overview and discipline. The right tool plays a crucial role in successful data management and productive collaboration in research teams. One such tool is DataLad, whose powerful features make it much easier to manage, version and distribute large amounts of data and promote the reproducibility of research processes. To help researchers use DataLad efficiently and deepen their knowledge, the data competence centers DKZ.2R, WiNoDa and the NFDI consortia NFDI4ING and NFDI4Objects are organizing a three-day workshop. From June 30 to July 2, there will be many opportunities to try out practice-oriented content and intensive collaboration with DataLad at the IT Center of RWTH Aachen University.

The first day of the workshop is dedicated to the basics and an introduction to the subject matter. Starting with a compact introduction to Git, the basic concepts and functions of DataLad will then be explained. On the second day, the focus will be on practice: in a “hackathon” session, participants will work in teams on their own projects or use cases. At the end of the workshop, the teams will present their results, providing a comprehensive overview of the various application areas of datalad and stimulating valuable discussions.

The workshop is aimed specifically at FDM experts who want to expand their skills in the field of data management and act as contact persons (“multipliers”) for DataLad in their teams and institutions in the future. Basic knowledge of Git is helpful, but not a prerequisite for participation.

If you are interested, please contact info@dkz2r.de.
The participation for this event is free of charge. Up to 5 people from each organizing institution can take part in the workshop.

Do's and Don'ts in Research Data Management

C. Hillen
Data , RDM

Research Data Management Do’s and Don’ts - Step up your RDM skills!

1. Structuring and naming your folders There is an easy way to make your data findable for you and your team: establish a folder structure which makes sense for you and your working group as well as naming conventions for your folders.

Don’t:

Paul and Suzie
»Guideline
>application
»version2_final
»v.3
»review
»3rd.version
>JD
»qn
»0-1

Instead do:

000_int_orga
»01_application
»02_review 120_questionaires
»01_qualitative »02_quantitative 130_data
»01_qualitative »02_quantitative
Read More

How To: Open Science

A. Mielke
Data , RDM , Open Science

Tired of Recreating someone else’s work? - How Open Science can accelerate research and overcome reinvention

Have you ever found papers on algorithms but their implementation is missing? Found an interesting analysis but there is no way to check the results, as you don’t have access to the data they were derived from? Ever thought you had a great idea for a project, just to find out a year later that you are not the only research group following that specific idea? Not having access to other people’s code, data, metrics or even their plans for research projects often leads to unnecessary delays and scientific redundancies. There is an easy solution to overcome (almost) all of these issues. It’s called Open Science! What is Open Science? The UNESCO defines Open Science as a construct of “movements and practices aiming to make multilingual scientific knowledge openly available, accessible and reusable for everyone, to increase scientific collaborations and sharing of information for the benefits of science and society, and to open the processes of scientific knowledge creation, evaluation and communication to societal actors […]”. To ensure that everyone has access to scientific knowledge and infrastructure, Open Science focuses on four main concepts.

How To: Good Scientific Practice

C. Hillen
Data , RDM , good scientific practice

“Scientific integrity forms the basis for trustworthy research”, so it says in the Guidelines for Safeguarding Good Research Practice of the DFG, the German Research Foundation. As a major funder of research in Germany the DFG, as well as many other funders of research in Germany and the European Union, requires researchers to follow a certain set of rules conducting their research. These rules are called “good scientific practice” and have to be followed by researchers to be viable for funding. According to the guidelines researchers are required to “document all information relevant to the production of a research result as clearly as is required by and is appropriate for the relevant subject area to allow the result to be reviewed and assessed”. But good scientific practice is not done by documenting your research. It also includes i.a. protecting the personality rights of your subjects and handling research data in an appropriate manner by e.g. “back(-ing) up research data and results made publicly available, as well as the central materials on which they are based and the research software used, by adequate means according to the standards of the relevant subject area, and retain them for an appropriate period of time.” This is where Research Data Management (RDM) comes in. Of course RDM is much more than just creating a backup of your data on a USB-Stick and handing it over to anyone asking for it. “Good scientific practice” in RDM follows the FAIR principles: