Why this short course
Earth and environmental sciences thrive on data diversity: from ocean temperatures to biodiversity records, from climate indicators to ecological observations and their interactions in ecosystems or relation to geophysical circumstances. Yet, this very diversity can also be a barrier: different datasets are described with different standards and stored in different formats.

Research Infrastructures (RI's) provide seamless access to environmental datasets, like the Environmental Research Infrastructure Community (ENVRI) that has developed the ENVRI-Hub. On top of that, RI's offer Vvirtual Rresearch Eenvironments as a service where scientists can integrateturn their own code into workflows and execute these workflows with this data in the cloud .

This short course will give researchers a practical introduction to how workflow builders like NaaVRE, as developed by LifeWatch ERIC, can directly support their own projects to reduce knowledge loss and build more reproducible and impactful science.

What researchers will learn
The interactive outline will engage participants by offering them an opportunity to:
Navigate the ENVRI-Hub and LifeWatch services and datasets: knowing what’s available and what fits their needs;
Understand how to integrate 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
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 workflows could advance their own research projects.

Interactive format
The course will provide demonstration of principles and methodology, and offer live excersises to experiment with finding data and processing it in a cloud environment.

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