Research Data Management – Make your data count!
Course further information
Whatever stage of your project you are at, this workshop will help you identify your data management needs. It will give you guidance on how to organize, structure, describe and publish your data.
Due to the increasing digitization and datafication in all fields of research, the proper management of research data becomes increasingly important.
You spent months on collecting samples and measurements in the field or in the lab? You explored, analyzed, and interpreted this data and finally published your findings in a scientific journal? Well, then it is time to think about your data again and what to do with it now. Or are you just starting your PhD or your postdoc project and want to make sure not to overlook anything when it comes to obtaining and documenting your measurements?
According to the guidelines for safeguarding good scientific practice your results should be replicable and repeatable. Are you aware of the concept of FAIR data, that is mentioned in the research data policies of many funders, institutions, and journals? FAIR means that data are findable, accessible, interoperable, and re-usable. To ensure this, your data should be well documented, securely stored and available for later reuse. Publishing your research data through a dedicated data journal or repository may help you on this and may also get you an additional publication and further citations.
A few days before the course starts, you will be given access to the preparation material (Moodle). It is recommended that you work through the material beforehand as it will be referred to in the course.
Topics:
- Basic definitions in research data management and the data life cycle
- Data management plans (DMP)
- Documentation, data organization, metadata
- Storage and back-up
- Archiving
- Publication and re-use of research data
- Legal aspects
Course dates: May 28 and May 31, 9-13 h
Content focus
- Introduction to research data management and the data-life-cycle concept
- Preparing research data for re-use (data structure, data quality, metadata)
- Opportunities and requirements in data publication and long-term data archiving
Dr. Cora Assmann
Course instructor
Roman Gerlach
Course instructor
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