Why this short course
Earth and environmental sciences thrive on data diversity: from ocean temperatures to biodiversity records, from climate indicators to geological observations. Yet, this very diversity can also be a barrier: different datasets are described with different standards, stored in different formats, and are difficult to connect across research infrastructures. The ENVRI-Hub provides a set of tools to overcome these challenges. It offers researchers a unified framework to discover, access, and reuse complex and multidisciplinary data.

This short course will give researchers a practical introduction to how ENVRI-Hub workflows can directly support their own projects, to build more reproducible and impactful science.

What researchers will learn
By joining this short course, researchers will:
- Get a clear picture of why Essential Variables matter in Earth and environmental sciences and how variable harmonisation improves scientific collaboration;
- Explore datasets through different pathways, including LLM-based search;
- Draft a mini workflow using curated Jupyter notebooks to map and query essential variables and visualise results;
- Share ideas with peers on how ENVRI-Hub workflows could advance their own research projects.

Interactive format
This 1h45min researcher-focused applied training session will blend live demonstrations, guided practice with curated tools, and participation discussions.

The interactive outline will engage participants by offering them an opportunity to:
- Navigate the ENVRI-Hub services and datasets: knowing what’s available and what fits their needs;
- Understand how to integrate ENVRI-Hub analytical tools into their research workflows: from data discovery and annotation to analysis and sharing;
- Present research use cases by reflecting on common challenges and benefits across domains

Who should join
This short course is tailored for:
- Researchers in Earth and environmental sciences, project coordinators, and data scientists looking to improve their data workflows;
- Anyone interested in applying interoperable approaches to interdisciplinary research;
- Anyone with basic familiarity with Python/Jupyter.