Program

Sharing research results and outcomes has always been an integral part of the scientific tradition, but sharing datasets on a large scale is a relatively recent development. The standards for doing good research prescribe transparency and reproducibility of research data, however, making data available in a way that allows outside researchers to make use of it, comes with technical, practical and ethical challenges. Luckily, as the Open Science movement and its data practices has become more mainstream, there has also been a rapid development of community guidelines and standards to aid in the curation of research data.
This course introduces the basics of research data management (RDM), Open Science (OS) and FAIR data. The FAIR (Findable, Accessible, Interoperable, Reusable) principles form the core of good data practices and as such they are becoming increasingly important for sharing data, especially in the scientific field. The choices you make regarding the collection, storage and archivation of your research data are documented in a Data Management Plan (DMP). A DMP is a common requirement for funding programmes and universities, and with this these practices and principles have become an integral part of any research, linguistic or otherwise.
This course will address questions such as "Why should the data be shared in a FAIR way? What stands behind this concept on the technology side? How does FAIR data look like, how to use, and how to create it? Which relevant platforms and tools exist and how to use them?". During the course we will be using the AlpiLinK (Alpine languages in contact) project as a case study and students are encouraged to bring their own datasets as well. There will be time in class to work on a data management plan (DMP) for your own PhD research using European funding templates in which we will discuss the options for the creation of FAIR datasets, data storage and data citation.