2022/08/20 by Nascimento, Aderson Farias do, Musicante, Martin A., da Costa, Umberto Souza +3
#Databases (cs.DB) #FOS: Computer and information sciences #FOS: Physical sciences #Geophysics (physics.geo-ph) #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2209.02384
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.