HeFDI Data School 2024/25
The HeFDI Data School offers cross-location and interdisciplinary courses on research data management. It is aimed equally at doctoral students and academic staff at Hessian universities and beyond.
The basic modules provide (initial) orientation in research data management (RDM). They focus on basic concepts, standard methods and best practices. The additional modules offer an in-depth insight into selected sub-areas.
HeFDI Data School 2024/25: Zur Modulbeschreibung und Anmeldung
HiPERCH 16 / HeFDI Code School (EN)
Software for the evaluation and creation of research data is now being developed in almost all research areas, but systematic training is rarely part of the curriculum or further training in non-computer science subjects. Without sustainable, high quality research software, the evaluation and analysis of research data is limited in many places and the traceability and reproducibility of research results is jeopardized. This is where HiPerCH / HeFDI Code School comes in and develops formats for further training in sustainable and qualitative research software for doctoral students and postdocs from all disciplines. The offer fills a gap, and demand is rising continuously.
HiPERCH 16 / HeFDI Code School (EN): Agenda and registration
Research data are all data that are generated, processed or used in the course of a scientific process or are its result. Research data can exist in different formats depending on the scientific discipline.
Research data management is the process in which the generation, management and securing of this data is described or planned. It covers all areas of data management, in particular the planning of data collection, the generation and preparation of data, data integrity, its documentation and sustainable storage, and making the data accessible. This process is developed and documented using a data management plan, which is or should be part of any research project.
The data management plan is a "living document" that initially represents the central planning tool for data management in the research project and develops into the project documentation tool during the course of the project.
Data security: Professional handling of research data protects against
- data loss,
- misuse,
- enables a later comprehension of the research results and a future
- a future re-use of the data!
If the principles of research data management are observed during the planning and implementation of the research project, the risk of data loss can be minimized.
Physical data loss is prevented by the required number of copies, storage media and backup intervals. Long-term availability of data is ensured by using long-term readable file formats and backing up on suitable storage media.
Loss of data content is prevented by professional documentation of data collection, data preparation and description via metadata. This ensures that even after years, people not originally involved in the research project can interpret the collected data and thus reuse it if necessary. It is important that metadata which may not seem relevant to the immediate research interests but which are indispensable for the subsequent use of the data - also and especially by persons who were not involved in the original collection - are also taken into account from the outset.
The need for professional research data management may result from subject-specific requirements, requirements of your own research institution, research funders, or journals. Find out about the requirements of your subject, your university or institute, your third-party funder, or the journal you wish to publish with, e.g.:
Research data guidelines of the University of Kassel
Guidelines for handling research data of the DFG
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