A Survival Guide to Research Data Sharing Services in the Rhine-Ruhr Region

• A. Janz
• Data , RDM , Data Sharing

A Survival Guide to Research Data Sharing Services in the Rhine-Ruhr Region

There are a lot of reasons why collaborating with other researchers on scientific projects is great! It provides new perspectives and gives you the chance to benefit from other people’s knowledge and input. When it comes to sharing and exchanging data across multiple locations and devices however, researchers are often disoriented and don’t know which tools, cloud services and so on are safe to share data in a secure and ethical way.

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Announcement - Call for participation

• A. Janz
• Consulting , Data , RDM , HPC , Data Science , Artificial Intelligence , Machine Learning

Update (June 24, 2024)

The call for participation is now open! Read more

Upcoming!

The Data Literacy Center Rhine-Rhur is issuing a call for participation in its “rent-an-expert” project! This is a great opportunity for PhD students and early postdocs who are working on research projects that involve data science, artificial intelligence, high performance computing and simulation, to get free support from our expert consultants.

Support can take the form of short- or long-term consulting, depending on the needs of the project. More info will be available shortly!

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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:

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