EXC2147 ct.qmat Data Management Policies and Publication Guidelines

Why Data Management publication guidelines?

Scientists in the ct.qmat Cluster of Excellence generate vast amounts of research data, including numerical data files, plots, pictures, protocols, and software. This research data is a precious asset. If managed efficiently in an open system, this data can be made permanently available to the scientific community. As scientists we have committed ourselves to the so-called FAIR principles for data and the FAIR4RS principles for software that is created during the research process. Looking forward, these principles enable easy reuse by future scientists and will be the foundation for future AI-driven developments methods, but - at the same time - will already today enhance the daily lives of ct.qmat's researchers by providing state-of-the-art infrastructure that, enables remote collaboration for the dislocated parts of the cluster, and provides computational resources and tools with a low entry barrier. Together with our partners the University of Würzburg's Information Technology Centre (RZUW) and the FAIRmat NFDI initiative that ct.qmat is an active supporter of, we strive to make the infrastructure components a reality. An overview of the cluster's data management infrastructure is here. To faciliate the organizational aspects we follow the guidelines in this document.

Open Access publication of primary research by default

ct.qmat is committed to the principles of reproducibility, scientific integrity, and open access. For the publication of our results, we will adhere to the following guidelines (last update: Feb. 2024).

To make all publications of the Cluster available and freely accessible worldwide, we encourage scientists to submit preprints of their research to arXiv.org. All the information required to reproduce scientific results is included in the publication and the accompanying (data-)supplements.

We encourage users to participate in our regular reproducability hackathons to experience themselves on how to improve reproducability. Our science is often data-driven and for this we encourage our user to submit their primary data files into data repositories and link them to their publication. We recommend to publish at least the primary data underlying all figures (incl. appendices) in common, open, and accessible formats, e.g. NeXus, HDF5, json or XML. ct.qmat has infrastructure for multiple data repositories available. A detailed guide for WueData, Opara, Zenodo, and NOMAD can be found in this document.

Open Source of software and everything that facilitates the research process

We encourage ct.qmat members to publish software and scripts that they have created on open development platforms such as GitHub or a GitLab. URLs to these repositories in primary publications are encouraged in publications and DOIs to specific source code versions can be generated by Zenodo. In contrast to data we encourage members to actively improve on their software after their publication and find uses in follow-up research. A guide for getting started on a distributed development platform can be found here

Contact / Help

You can reach the data management team through datamanagement.ct.qmat@listserv.dfn.de, we are always happy to help with data management challenges you encounter and open to suggestions on extending our infrastructure.

You might also get help on the Discord server of the Würzburg Chapter of DE-RSE, you can join the server through this invite link.