Description: The Forecasting Challenge module provides students with practical experience in collaborative, interdisciplinary ecological
research through participation in a real-world forecasting challenge. Students plan and execute a complete research workflow as a single cohort, from data collection to model implementation and forecast evaluation.
Challenges may be designed internally or aligned with international initiatives such as the National Ecological Observatory Network (NEON) Forecasting Challenge, in which participants predict forthcoming ecosystem data based on past observations. Whenever feasible, the module includes a field campaign—potentially abroad—to broaden the students’ practical experience in ecological data collection and cross-cultural scientific collaboration.
Students are responsible, with academic support, for the financial, logistical, data management, and scientific design components of the challenge. The work is conducted in accordance with open science principles, including transparent data handling, reproducible analysis workflows, and open dissemination of results.
Learning outcomes:
Plan and execute a collaborative ecological forecasting project from data collection through analysis and evaluation.
Apply advanced forecasting and modelling techniques to real-world ecological data.
Manage the complete data life cycle in accordance with open science standards, including documentation, sharing, and reproducibility.
Coordinate logistical, financial, and scientific tasks within a multidisciplinary research team.
Communicate forecasting results effectively through scientific reports and presentations, including interpretation of uncertainty and limitations.
Module group: Research Module
Credits: 5 ECTS
Lecturers: Prof. Dr. Steven Higgins