Turn multispectral, hyperspectral and lidar observations into analysis-ready datasets with R. Using forest and ecological examples, participants assess metadata and data quality, correct geometric and radiometric errors, reduce noise, and derive spectral indices and structural features. Exercises connect temporal change analysis and classification with scale, accuracy and uncertainty, producing reproducible workflows for spatial research. 2026-11-10–2026-11-13, 12:00–16:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €420. Participants need basic competence and confidence in R an
In Data Science and Computer Science
Workshop · Data Engineering
Collecting data from public repositories: Web scraping in R
Dec 10–11, 2026
Develop reusable R workflows for collecting research data from public websites. Learn HTML structure and selectors, extract relevant text and tables with R packages, and clean, transform and export the results into analysis-ready datasets. Demonstrations, live coding, quizzes and individual or group exercises develop adaptable scripts. The course prioritises official APIs and addresses scraping when no suitable API exists, including respect for terms of service, copyright and server load. 10–11 December 2026, daily 16:00–19:30, Europe/Madrid (CET, UTC+1). Seven live hours plus three hours of