The immense advances in computer power achieved in the last decades have had a significant impact in Earth science, providing valuable research outputs that allow the simulation of complex natural processes and systems, and generating improved forecasts. The development and implementation of innovative geoscientific software is currently evolving towards a sustainable and efficient development by integrating models of different aspects of the Earth system. This will set the foundation for a future digital twin of the Earth. The codification and update of this software require great effort from research groups and therefore, it needs to be preserved for its reuse by future generations of geoscientists. Here, we report on Geo-Soft-CoRe, a Geoscientific Software & Code Repository, hosted at the archive DIGITAL.CSIC. This is an open source, multidisciplinary and multiscale collection of software and code developed to analyze different aspects of the Earth system, encompassing tools to: 1) analyze climate variability; 2) assess hazards, and 3) characterize the structure and dynamics of the solid Earth. Due to the broad range of applications of these software packages, this collection is useful not only for basic research in Earth science, but also for applied research and educational purposes, reducing the gap between the geosciences and the society. By providing each software and code with a permanent identifier (DOI), we ensure its self-sustainability and accomplish the FAIR (Findable, Accessible, Interoperable and Reusable) principles. Therefore, we aim for a more transparent science, transferring knowledge in an easier way to the geoscience community, and encouraging an integrated use of computational infrastructure. Systematic Review Registration: https://digital.csic.es/handle/10261/193580

This research has been funded by the Projects EPOS IP 676564, EPOS SP 871121, SERA 730900, GeoCAM (PGC2018-095154-B-I00, Spanish Government) and the Center of Excellence for Exascale in Solid Earth (ChEESE) under the Grant Agreement 823844. IDF was funded by a FEDER-Junta de Castilla y León Postdoctoral contract (SA0084P20). JA and M-GL are funded by the Spanish Ministry of Science and Innovation through the Juan de la Cierva fellowship (IJC 2018-036074-I and IJC 2018-036826-I, respectively), funded by MCIN/AEI /10.13039/501100011033. AH is grateful for his Ramón y Cajal contract (RYC 2020-029253-I). Additional funding was provided by the Spanish Ministry of Science and Innovation (RTI 2018-095594-B-I00, PGC 2018-095154-B-100) and the Generalitat de Catalunya (AGAUR, 2017SGR1022). AP’s work was supported by: a Science Foundation Ireland Career Development Award (17/CDA/4695); an investigator award (16/IA/4520); a Marine Research Programme funded by the Irish Government, co-financed by the European Regional Development Fund (Grant-Aid Agreement No. PBA/CC/18/01); European Union’s Horizon 2020 research and innovation programme InnoVar under grant agreement No 818144; SFI Centre for Research Training in Foundations of Data Science 18/CRT/6049, and SFI Research Centre awards I-Form 16/RC/3872 and Insight 12/RC/2289_P2. AH and SG thank the Spanish research project PaleoModes (CGL2016-75281-C2-1-R) which provided some of their financial support. JF is supported by an Atracción de Talento senior fellowship (2018-T1/AMB/11493) funded by Comunidad Autonoma de Madrid (Spain), and a project funded by the Spanish Ministry of Science and Innovation (PID2020-114854GB-C22).

Systematic Review Registration: https://digital.csic.es/handle/10261/193580

Peer reviewed