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

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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.

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Status Meeting of the German Data Literacy Centers

From September 19th to 20th the BMBF is inviting representatives of all eleven Data Literacy Centers in Germany to come together in Berlin and discuss the progress and the difficulties of the projects. There will be two days of workshops with topics ranging from discussions of domain-specific data literacy to strategies and concept for stabilizing data literacy projects. Furthermore, there will be plenty of time for networking with the other Data literacy centers, as well as invited speakers from other data literacy and research data management initiatives such as the NFDI. Our main coordinator, Alicia Janz, and Lukas Bossert from RWTH Aachen University will represent the DKZ.2R at the meeting. We are looking forward to gaining insights into the work of other data literacy centers and learning from their experiences and the measures they are taking to support scientist in their work with research data.

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