Research Data Management
How can we improve data and code management in order to enhance reusability 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 - illustrated with examples of the challenges and perils of real-life biodiversity datasets.
- The data life cycle
- Open science and the FAIR principles
- iDiv's Data & Code Sharing Policy: your responsibilities
- Producing a good data management plan (DMP)
- Working with synthesis datasets
- Nominally vs actually reusable: writing rich metadata
- Reproducibility & transparency: version control, repeatable workflows
- Publishing data & code: best practice in using public repositories
- Internal storage and long-term archiving
Prerequisites and expectations: Basic knowledge of R might be helpful.
The course aims to be interactive - participants will be expected to contribute to discussions, complete written work during the course hours, take part in simple games and give mini-presentations. Afternoons will usually devoted to a practical assignment, after which students will be invited to evaluate each other's work.
Note: there will be some homework between the course days.
Please bring your laptop.