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

Didactic 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.

Prior knowledge needed:
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.

Dates and times:
17 March, 9:30am-5:30pm
19 March, 9:30am-5:30pm
21 March, 9:30am-5:30pm (3:30-5:30pm will be consultation hours)