This introductory session of our CLARIN-CH Training Sessions 2025 will provide an overview of the CLARIN-CH ecosystem of infrastructures, highlighting their role in supporting research involving language resources and language technology. Participants will gain insights into the various infrastructures available within CLARIN-CH and how they facilitate Open Research Data (ORD) practices in language research. Each of the infrastructures will be further adressed, both from a theoretical and a hands-on perspective, in decidated sessions during March-June 2025.

Topics covered in the introductory session:

1. What is CLARIN-CH?
2. The Importance of ORD in Language Research
   • What is ORD and the data life-cycle
   • How ORD practices enhance reproducibility, trust, collaboration and data sharing (e.g. linking your research data to your scientific paper)
3. Overview of the CLARIN-CH ecosystem of infrastructures
   • The LiRI Corpus Platform
   • The swissdox@LiRI media database
   • ZHAW Swiss-AL
   • Language Repository of Switzerland (LaRS) and the SWISSUbase repository
   • The CLARIN-CH Documentation Platform
   • Introduction to key ORD projects, such as UpLORD and FAIR-FI-LD, and their main outcomes
     • Metadata interoperability and harvesting by CLARIN Virtual Language Observatory
     • Curation workflow by LaRS team
     • Standard data formats
     • CLARIN Federated Content Search technology
     • Videoscope @ USI
4. Challenges of ORD in Language Data
   • Handling sensitive and complex linguistic data, including ethical and legal considerations.
   • Encouraging researchers and institutions to engage with CLARIN-CH

Who should attend?

This session is designed for researchers using language data, irrespective of scientific discipline, sub-field of linguistics or approach, as well as data stewards, who want to learn more about the CLARIN-CH research data ecosystem and its tools.

By the end of the session, participants will have a clearer understanding of how to navigate the CLARIN-CH infrastructures, apply ORD principles effectively, and address challenges in language data management. This will make your life much easier when publishing your data and when write your DMP.