The objective of the international network is to create a French - Brazilian International Emerging Action on Data-Centred Intelligent GeoSciences (IEA) to promote scientific cooperation and exchange between French and Brazilian researchers interested in the design and implementation of large-scale data centric Earth Science experiments. Initially, the project will focus on solid earth data (passive seismology mostly) but the framework can be expanded to other earth science data observations.

ADAGEO will be to develop and reason about best practices and novel ways of curating data-centric geosciences knowledge (data, experiments, models, methods, conclusions, and interpretations) produced from the application of statistical modelling, machine learning, and modern data analytics methods on geo data collections, e.g., Spatio-temporal modelling, probabilistic modelling, and uncertainty quantification; open methodological questions in model building, model assessment, prediction, and forecasting workflows.

Expected Results

The network will interact to reason about the following objectives:

  • Objective 1 (O1): Acquired data, models and knowledge integration
  • Objective 2 (O2): Curation, maintenance, exploration of data collections
  • Objective 3 (O3): Modelling and simulating experiments to answer questions in geoscience and make timely decisions

Coordination actions

  • (CA1-CA2) Summit & Thematic School on intelligent data-centric geosciences (hybrid 4 days event)

  • (CA3) Kaggle challenges in (2)

  • (CA4) Agenda on intelligent data-centric geosciences white paper

  • (CA5-CA6) 2 scientific visit 15 days (FR <-> BR) / 2 PhD internship of 12 months (1 Brazil -> France)

  • (CA7) Proposal for a scientific project of data-centric intelligent geosciences (ANR or CAPES-COFECUB or IRN)


  • (S1) Geoscience Brazilian data curation portal with exploration tools including examples of applications and analytics results (GitHub for encouraging reproducible experiments)
  • (S2) Use case statements with geosciences questions and associated data descriptions and inhouse experiments.
  • (S3) Position Paper about intelligent geosciences using data science methods.