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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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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Spring School 2025 in Aachen – Research Data Management in Materials Science and Engineering

Enabling a consistent and unobstructed standardization of research data will lead to substantial improvements in efficiency and synergies/collaboration among individual researchers and well-designed IT systems. In the era of digital infrastructures, this measure holds paramount importance in shaping the future of research. For the data to be sustainable and reusable, it must adhere to the “FAIR Data Principles”, meaning it should be “Findable, Accessible, Interoperable, and Re-usable”. The FAIR principles will be a cornerstone of the European Open Science Cloud (EOSC) implementation. Nowadays, Data Management Plans (DMPs) have become a requirement for project applications in Horizon Europe and are increasingly being demanded by various funding organizations, including the German Science Foundation (DFG), among others. The scientific consortium NFDI-MatWerk (National Research Data Infrastructure) focuses on the research area Materials Science & Materials Engineering (MSE). The central challenge is the digital representation of materials and their relevant process and load parameters. NFDI-MatWerk will provide in this summer school tailored information and solutions for dealing with research data in MSE.

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