Research Data Management
How can we improve data and code management in order to enhance reproducibility and thus trust in science? This workshop will provide practical guidance on how to organize, structure, describe and publish your data/code in order to comply with good scientific practice - all illustrated with examples of the challenges and perils of real-life biodiversity datasets.
- The data life cycle
- Open science and the FAIR principles
- Expectations of a good data management plan (DMP)
- Nominally vs actually reusable: writing rich metadata
- Reproducibility & transparency: version control, repeatable workflows
- Storage and long-term archiving
- Publishing data & code: hands-on with the iDiv Data Portal
- More complex biodiversity datasets: working with spatial data and synthesis datasets.
Aim: A solid understanding of the range of activities involved in making one's data and code accessible and reproducible, in order to be both Open and FAIR. Practical experience in the tasks now required by funding organizations, research institutions, and publishers in order to fulfill this remit.
Participants bring: their own datasets to work on, and questions to share and resolve. Basic knowledge of R might be helpful for some aspects of the course; but in general participants can benefit from the majority of the course without prior knowledge.
Teachers: Anahita Kazem, Alban Sagouis
Co-teachers: Roel van Klink, Emma Ladouceur, Paola Barajas