The ODE (Overview, Data, and Execution) protocol for a standardized use of machine learning in environmental, social and related interdisciplinary sciences

Abstract

Machine Learning (ML) is increasingly applied across environmental, social and interdisciplinary sciences to analyze complex systems and inform decision-making. Yet, this rapid growth has exposed significant gaps in methodological consistency, documentation, and reproducibility. The lack of standardized frameworks often leads to fragmented workflows and difficulties in interpreting or reproducing results across disciplines. To address these challenges, we introduce the ODE (Overview, Data, and Execution) protocol: a structured, accessible framework to support transparent documentation of ML workflows. Inspired by established standards such as ODD (Overview, Design concepts, Details), ODD + D (adding human Decision-making) for agent-based modeling, ODMAP (Overview, Data, Model, Assessment, Prediction) for species distribution models and FAIR (Findable, Accessible, Interoperable and Reusable) principles, ODE's novelty is to translate ML workflows into a standardized reporting format, specifying what must be described for transparency, reuse, and reproducibility. In practice, ODE is a reporting checklist, typically provided as supplementary material, supporting authors and reviewers.