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,
- 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
+++ Dates / News +++
HeFDI Code School 'Sustainable Research Software' (EN)
Important information: All workshops are held online and in English.
|12 May 2023, 9h-13h||Scientific Software Development is not a Jenga game!|
|26 May 2023, 9h-13h||Clean Code and Refactoring|
|23 June 2023, 9h-13h||Introduction to Software Testing|
|7 July 2023, 9h-13h||Continuous Integration and Test Driven Development|
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