Getting started: Handling research data at the University of Kassel

We highly value sustainable research practices and safeguarding good scientific and artistic/creative practices. For this reason, you will find links to our Research Data Guideline (DE) and our Principles for Ensuring Good Academic Practice (GAP|DE) below. Additionally, we provide a comprehensive overview of the key offerings and tools available for support, which can be downloaded as a PDF. However, we also invite you to explore our website for further insights, information, and access to our online courses.

Research Data Guideline

Principles to ensure good scientific and artistic / creative practice at the University of Kassel [german]

Offers, tools and support compact

Current events

You can find our live formats on the university's event pages.

Current events: Zu Campus Events

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.

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.

Online learning module on research data management [german]

The interactive online introduction to research data management is available in the Moodle system of the University of Kassel. An englisch translation will be available soon. Please message us, if you want to be informed as soon as the course is available.

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

 

What are FAIR DATA?

One of the major challenges in storing research data is the optimal preparation for humans and machines. The FAIR Data principles are intended to help with this.

What are FAIR DATA?: Read More

Requirements research funders

Many research funders, publishers, and the University of Kassel itself demand a planned approach to research data, and in some cases the publication of data sets.


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