Ben Abbes A. auteur aut IRD Machicao J. auteur aut IRD Meneguzzi L. auteur aut IRD Corrêa P.L.P. auteur aut IRD Specht A. auteur aut IRD David R. auteur aut IRD Subsol G. auteur aut IRD Vellenich D.F. auteur aut IRD Devillers Rodolphe auteur aut IRD Stall S. auteur aut IRD Mouquet N. auteur aut IRD Chaumont L. auteur aut IRD Berti-Equille Laure auteur aut IRD Mouillot D. auteur aut IRD RDA.Research Data Alliance, 19., Séoul (KOR), 2022/06/20-23 text conferencePaper eng text/pdf reformatted digital access The challenges of Reproducibility and Replicability (R&R) have become a focus of attention in order to promote open and accessible research. Therefore, efforts have been made to develop good practices for R&R in the area of computer science. Nevertheless, Deep Learning (DL) based experiments remain difficult to reproduce by others due to the complexity of these techniques. In addition, several challenges concern the use of massive and heterogeneous data that contribute to the complexity of this R&R. Firstly, we compiled three different aspects to help researchers to improve R&R. This compilation was based on machine learning checklists, guidelines, and principles from FAIR. Therefore, this compilation is useful for a (1) researcher seeking to reproduce a paper, (2) an author reporting on an experiment, and (3) a reviewer seeking to qualify the scientific contributions of the work. Secondly, we illustrate the compilation of three recent DL experiments for socio-economic estimation using remotely sensed data. specialized 122 126 021 RDA.Research Data Alliance, 19., Séoul (KOR), 2022/06/20-23 1 multigr. (s.n.) 2022 https://www.documentation.ird.fr/hor/fdi:010087182 10.5281/zenodo.6587702 [F B010087182] https://www.documentation.ird.fr/hor/fdi:010087182 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2023-07/010087182.pdf IRD - Base Horizon / Pleins textes 2023-02-03 2025-02-24 fdi:010087182 fre