Data Centred Intelligent Geosciences: Research Agenda and Opportunities, Position Paper

TL;DR

This paper outlines a vision for developing best practices and innovative methods for managing and reasoning about data-centric knowledge in geosciences using statistical, machine learning, and data analytics techniques.

Contribution

It introduces a research agenda focusing on data curation, methodological challenges, and workflow improvements in geosciences through advanced data-driven approaches.

Findings

Identifies open methodological questions in model building and assessment.

Proposes a framework for data-centric knowledge management.

Highlights the importance of integrating statistical and machine learning methods.

Abstract

This paper describes and discusses our vision to develop and reason about best practices and novel ways of curating data-centric geosciences knowledge (data, experiments, models, methods, conclusions, and interpretations). This knowledge is produced from applying statistical modelling, Machine Learning, and modern data analytics methods on geo-data collections. The problems address open methodological questions in model building, models' assessment, prediction, and forecasting workflows.