Research data management

Scientific research is built on data, and managing this data in a structured manner is absolutely essential for high-quality, effective and transparent research. To that end, Rhine-Waal University of Applied Sciences supports its researchers in building sustainable and practice-oriented services within the framework of the FDMScouts.nrw funding line, which is coordinated under the broader state initiative fdm.nrw. The position of RDMScout at HSRW is embedded in the Centre for Research, Innovation and Transfer (ZFIT). A broad portfolio of internal RDM services for researchers at HSRW, supplemented by external services such as events, training seminars etc., will be developed and rolled out by the end of 2023. For more information, please contact Martin Reiter, RDMScout, at Forschungsdaten@hochschule-rhein-waal.de.

For more information (in German) about the aims and objectives of the funding line, please see this website.

Research data and research data management

The term research data refers to all data generated or used in connection with research processes and which also serve to validate research findings regardless of the scientific field, the methods applied, or the form or format of the data. Research data management (RDM) on the other hand refers to the sum total of all conceptual, organisational and technical measures intended to produce a structured, transparent and sustainable approach to managing research data. It includes the planning, collection, processing, documentation, secure storage, permanent archiving, suitable publication and, in some cases, erasure of data. In short, it comprises the entire research data lifecycle.

Why bother with research data management? Read more about how a structured approach to managing research data can benefit your research project.

Implementing research data management: getting started, support, contacts

The information portal forschungsdaten.info (in German) offers a beginner-friendly, detailed and practice-oriented overview of the various aspects of research data management as well information on new developments and upcoming events related to RDM. The wiki forschungsdaten.org (in German) also offers a wide range of information on the subject of RDMs.

It is a good idea to consider how you will manage your data as early as a project's planning phase. Many research funding institutions have their own requirements for research data, and being able to comply with these requirements can be essential to securing a grant. Thus you should familiarise yourself with the guidelines of leading research funding institution or specific funding programmes. The website forschungsdaten.info offers a good overview (in German) of the RDM requirements of key German and European funding institutions. Grant applications must frequently include a data management plan (link in German), or DMP, outlining how researchers will handle data both during and after the project. The Humboldt University of Berlin offers sample DMPs for various funding institutions in both German and English. For guidance on writing a data management plan, please consult the ZFIT DMP Guide. Finally, the Centre for Research, Innovation and Transfer can gladly advise you on applying for grants and implementing your R&D ideas.

Secure storage and collaborative use of research data are important considerations during the data collection and analysis phases of your project. Research data are a valuable commodity. In addition to choosing the right basic strategy for avoiding data loss (for example: the 3-2-1 principle) (in German), the choice of storage system also boils down to aspects such as data security, the amount of data collected, data privacy regulations etc. HSRW recommends that researchers use the non-commercial and free cloud service sciebo, which is operated by 22 higher education institutions in North Rhine-Westphalia. IT-Support can assist you with any questions you may have here.

Archiving and publishing data are the main topics to consider in the final phase of a project. Research data can be archived in online repositories and made available to the public or a restricted pool of users. Repositories are curated storage spaces where digital objects and any changes made to them are documented clearly and permanently. You can find a guide to selecting a suitable repository for your research data (including search services and DMP guidance) here. Data under 1 GB in size can also be published via HSRW’s own OPUS Publication Server. For more information, please contact the University Library’s Publication Service. In addition, research data can also be published as independent, citable work in speciality data journals. The site forschungsdaten.org offers a long list of these types of journals. However, before publishing any research data, HSRW strongly urges researchers to consult with ZFIT as to whether their research findings or inventions may be eligible for patents and thus protection under intellectual property laws. For more information on intellectual property laws, HSRW’s legal department offers guidelines for researchers.