Orchestrate Geospatial (Meta)Data Management Workflows and Manage FAIR Services

An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with 'FAIR' (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the 'DublinCore' standard (< https://dublincore.org/>), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with 'ISO/TC211' (< https://committee.iso.org/home/tc211>) and 'OGC' (< https://www.ogc.org/standards/>) geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. 'INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. 'PostGIS'), metadata catalogues (eg. 'GeoNetwork', 'CSW' servers), data servers (eg. 'GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of 'EML' metadata, and their management with 'DataOne' servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of 'CF' conventions, 'NetCDF' data format and 'OPeNDAP' access protocol, and their management with 'Thredds' servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', 'DataCite'), to facilitate the publication of data within (meta)data repositories such as 'Zenodo' (< https://zenodo.org>) or DataVerse (< https://dataverse.org/>). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference 'Zenodo' 'DOI' in 'GeoNetwork'/'GeoServer' metadata, or vice versa reference 'GeoNetwork'/'GeoServer' links in 'Zenodo' or 'EML' metadata). The use of standardized configuration files ('JSON' or 'YAML' formats) allow fully reproducible workflows to facilitate the work of data and information managers.

Spatial Parallel Computing by Hierarchical Data Partitioning

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on 'future' (Bengtsson, 2024 ) and 'mirai' (Gao et al., 2025 ) parallel back-ends, 'terra' (Hijmans et al., 2025 ) and 'sf' (Pebesma et al., 2024 ) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in 'terra' or 'sf' packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses 'exactextractr' (Baston, 2023 ), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.

Terrestrial Water Cycle

An open-access tool/framework that constitutes the core functions to analyze terrestrial water cycle data across various spatio-temporal scales.

Evapotranspiration R Recipes

An R-based application for exploratory data analysis of global EvapoTranspiration (ET) datasets. 'evapoRe' enables users to download, validate, visualize, and analyze multi-source ET data across various spatio-temporal scales. Also, the package offers calculation methods for estimating potential ET (PET), including temperature-based, combined type, and radiation-based approaches described in : Oudin et al., (2005) . 'evapoRe' supports hydrological modeling, climate studies, agricultural research, and other data-driven fields by facilitating access to ET data and offering powerful analysis capabilities. Users can seamlessly integrate the package into their research applications and explore diverse ET data at different resolutions.

Precipitation R Recipes

An open-access tool/framework to download, validate, visualize, and analyze multi-source precipitation data. More information and an example of implementation can be found in Vargas Godoy and Markonis (2023, ).

Create Future 'EnergyPlus' Weather Files using 'CMIP6' Data

Query, download climate change projection data from the 'CMIP6' (Coupled Model Intercomparison Project Phase 6) project < https://pcmdi.llnl.gov/CMIP6/> in the 'ESGF' (Earth System Grid Federation) platform < https://esgf.llnl.gov>, and create future 'EnergyPlus' < https://energyplus.net> Weather ('EPW') files adjusted from climate changes using data from Global Climate Models ('GCM').