Resultados de investigación

This dataset includes: (1) snow-covered area (SCA) measurements (km²) obtained between 2000 and 2020 in the Toconao sub-basin, located within the Salar de Atacama Basin (Chile); and (2) meteorological and hydrological input and output data corresponding to three hydrological years (from 1 May 2016 to 30 April 2019), generated using the Cold Regions Hydrological Modelling Platform (CRHM). The dataset is analysed and interpreted in the article entitled “Snowmelt and Groundwater Recharge in an Arid Andean Basin: The Case of the Salar de Atacama, Chile.”, MICIU/AEI/ 10.13039/501100011033 and by SE+ JDC2023-051090 Centre of Excellence Severo Ochoa: CEX2018-000794-S, With funding form the Spanish government through the "Severa Ochoa Centre of Excellence" accreditation (CEX2018-000794-S)., Peer reviewed

This dataset corresponds to monitoring conducted in the upper basin of the Llobregat and Cardener rivers during 2024 and 2025. It includes data from ten sampling campaigns carried out to collect precipitation, surface water, and groundwater samples from 30 locations across the study area. The dataset is analyzed and interpreted in the article entitled 'Isotopic Characterization and Recharge Dynamics of Karst Aquifers in a Mediterranean Basin.'", Consejo Superior de Investigaciones Científicas: CEX2018-000794-S Ministerio de Ciencia, Innovación y Universidades: JDC2023-051090 Ministerio de Ciencia, Innovación y Universidades: RYC2023-04523-I, Peer reviewed

This work was supported by MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR, Grant no. FJC2020-043762-I to M.B; J.O. was supported by Universitat de Barcelona through the PREDOCS-UB grant (2021); A.F.-A. was supported by Ministerio de Ciencia e Innovación, Grant no. PRE2021-099287 from the project ProOeans (PID2020-118097RB-I00); F.R. was supported by the Ramón y Cajal program (RYC2020-030078-I); D.M. was supported by the CIDEGENT program of the Generalitat Valenciana (CIDEGENT/2021/058); This research was also supported by the projects SOSPEN (PID2021-124831OA-I00), SEASentinels (CNS2022-135631) and ProOceans (PID2020-118097RB-I00). This research contributes to the objectives of Q-MARE (a PAGES working group). This work acknowledges the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). This research is also part of the Integrated Marine Ecosystem Assessments (iMARES) research group funded by Agència de Gestió d'Ajuts Universitaris i de Recerca (Generalitat de Catalunya) Grant no. 2021 SGR 00435, 1. Scripts classified in the following folders: 1. Presences and Absences: Scripts for creating pseudo-absence points for each study area (other data is also included, e.g., bathymetry). 2. Environmental Data: Scripts for extracting, cleaning, and preparing environmental data for analysis. 3. Fitting: Scripts used to fit species distribution models, calculate the cutoff, and analyze spatial autocorrelation. 4. Predict: Scripts for making predictions based on the fitted models. 2. Datasets: Occurrence Data: The datasets containing species occurrence records after cleaning. Bas_etal_2025/1. Presences and absences/1. NZAU/CSV_ocurrences_NZAU/ Bas_etal_2025/1. Presences and absences/2. South_America/CSV_ocurrences_South_America/ Bas_etal_2025/1. Presences and absences/3. Southern_Africa/CSV_ocurrences_Southern_Africa/ Pseudo-absence Data: The datasets created after the generation of pseudo-absences for modeling species distributions. Bas_etal_2025/1. Presences and absences/1. NZAU/CSV_pseudoabsences_NZAU/ Bas_etal_2025/1. Presences and absences/2. South_America/CSV_pseudoabsences_South_America/ Bas_etal_2025/1. Presences and absences/3. Southern_Africa/CSV_pseudoabsences_Southern_Africa/ Environmental Data: The datasets consisting of the extraction of the environmental variables for each occurrence and pseudo-absence. Bas_etal_2025/2. Environmental data/Dataset after 6. extract_environmentalData_SH_SDM/NZAU/ Bas_etal_2025/2. Environmental data/Dataset after 6. extract_environmentalData_SH_SDM/South_America/ Bas_etal_2025/2. Environmental data/Dataset after 6. extract_environmentalData_SH_SDM/Southern_Africa/, Peer reviewed

More than 3000 physical, chemical and morphological data of soils described between December 1980 and July 1981 in the southernmost part of the province of Lérida, NE Spain, are collected in this dataset. The soils are located between the coordinates 41.62°N 1.06°E, and 41.28°N 0.34°E. Most of the soil samples were taken in backhoe dug pits where their profiles were described. The data —presented here in an Excel spreadsheet— come from the book by J. Porta & R. Julià (eds.) published in 1982 with I.S.B.N. 84-5008771-6. Moreover, two chapters were scanned from this book and included in this dataset as “Description.pdf” and “FormationClassification.pdf”. The author did not proofread the galley proofs of these chapters. The data contained in “Description.pdf” were manually transcribed in 2025 to the excel file “ProfilesLerida04clas.xlsx”. Care must be taken with the location coordinates given the non-correction of the galley proofs and because the resources available to us 45 years ago were quite rudimentary. The excel file “ProfilesLerida04clas.xlsx” incorporates the correction of obvious errors in the coordinates and their transformation to ETRS89 from the notations used in 1980-81. The help of Ms. Rosa Gómez-Báguena with the coordinates is greatly appreciated. We also present the file "1989 XVI Reun.SECS Lérida suelos secano", a report including discussion and data about the studied soils., This research was possible thanks to the grant PID2021-127170OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and the grant TED2021-130303B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGeneration EU/PRTR”., Peer reviewed

This dataset includes georeferenced data relevant to four Mediterranean gorgonians* Eunicella singularis, Eunicella cavolini, Paramuricea clavata,* and* Corallium rubrum*. It was used to model their habitat suitability index and uncertainty within the Mediterranean shallow and mesophotic depths (< 150 m) together with environmental predictors selected according to their potential relevance for the ecology of these species, This research was funded by the following I+D+i projects: HEATMED project (RTI2018-095346-B-I00), funded by MICIU/AEI/ 10.13039/501100011033/and by “ERDF/EU”, CORFUN project (TED2021-131622B-I00) funded by MICIU/AEI/ 10.13039/501100011033 and by European Union NextGenerationEU/PRTR, UNDRESCORAL project (PID2022-137539OB-C21) funded by MCIN/AEI/ 10.13039/501100011033 and FEDER/UE and, OCEAN CITIZEN project (Grant agreement 101093010), funded by the European Union under Horizon Europe Research and Innovative programme. L.F.-F. was supported by FI SDUR grant (2020 FISDU 00482) from the “Generalitat de Catalunya”. C.L. gratefully acknowledges the financial support by ICREA under the ICREA Academia program. J.G. acknowledge the funding of the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). Y.Z. was supported by an FPU grant (FPU20/03574). F.L.M. acknowledges Fundação para a Ciência e a Tecnologia (FCT/MCTES) for the financial support to CESAM (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020) through national funds. D.A. acknowledge the financial support by a Catalan Government Grups de Recerca Consolidats grant (2021 SGR 01195). L.F.-F., C.L., Y.Z. and J.G. are part of the Marine Conservation research group—MedRecover (2021 SGR 01073) from the “Generalitat de Catalunya”, Peer reviewed

A number of references about the study of hydrology of soils have been put together in the zip file "Separatas Sense 01". Granulometries have become superfluous for many applications of soil hydrology., Consejo Superior de Investigaciones Científicas, Peer reviewed

[Steps to reproduce] check the article entited: Double volnerability of active-NRF2 lung squamous cell carcinoma to NRF2 and TRIM24, Consejo Superior de Investigaciones Cientificas, Peer reviewed

This data release contains carbon stocks data and down-core profiles of bulk density and organic matter or carbon content for 1284 sediment cores from blue carbon ecosystems along the Pacific coast of North America compiled from 69 data sources. Ecosystem types are tideflats, seagrass meadows, emergent marsh, mangroves, and tidal swamps. In addition to carbon data, the data release includes metadata on each of the 69 data sources, the geographic positions and standardized tidal elevation (z*) of sediment cores, and core-level environmental data such as ecoregion, Köppen-Geiger climate zone, sediment grain size data, and vegetation type. These data form the basis for the publication: Janousek et al. (2025), “Blue carbon stocks along the Pacific coast of North America are mainly driven by local rather than regional factors”, Major funding for development of the Northeast Pacific Blue Carbon Database and this paper was provided by the NERRS Science Collaborative (Grants NA14NOS4190145 and NA19NOS4190058), the National Oceanic and Atmospheric Administration (Grant NA19NOS4780176), and the Pew Charitable Trusts. A. Gray was supported in part by USDA NIFA Hatch Project CA-R-ENS-5120-H and USDA Multi-State Project W4188. M. Hessing-Lewis and C. Prentice were supported by the Tula Foundation and the Hakai Institute, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

The dataset includes an Excel spreadsheet “shape_attribute_info.xlsx” where the head columns of the attribute table of the shapes “livestock” and “crop” are explained., Apart from the basic information from the whole basins, we include the compressed folder named “Farnaca.zip” containing the geographic files used for the specific study of this area in the “Saladas de Sástago-Bujaraloz”: the shape (polygon) delimitating the study area (“area_farnaca”), and the slope of this polygon, We presented the background information for the study “An assessment of the N load from animal farms in saline wetland catchments in the Ebro Basin, NE Spain”. The studied wetlands basins are the: Saladas de Alcañiz-Calanda, Gallocanta Lake, Saladas de Sástago-Bujaraloz and Sariñena Lake. All the areas are catalogued in the Natura 2000 European Network, under the European Directives Birds (2009/147/EC) and Habitats (92/43/EEC), and are nitrates vulnerable areas according to the European Water Framework Directive (WFD, 91/676/EC) designation. The data presented here belong to the research projects AGROWET, funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGeneration EU/PRTR”, and ISABEL, funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. The dataset includes 4 compressed folders: “Saladas de Alcañiz-Calanda (ALC).zip”, “Gallocanta Lake (GA).zip”, “Saladas de Sastago-Bujaraloz (BU).zip” and “Sariñena Lake (SA).zip”. Each folder comprises fives GIS files generated in ArcGIS v.10.8: the “Watershed_name_of_basin”, “dem_name_of_basin”, “NVZ_name_of_basin” (Nitrate Vulnerable Zone), “Livestock_name_of_basin” and “Crop_name_of_basin”. “Watershed_name_of_basin” is a shape (polygon) delimited following the drainage divides with the help of the digital elevation model (DEM). “DEM_name_of_basin” is a raster file generated from the LiDAR data with a vertical accuracy of 2 m and a density of 0.5 points m2. “NVZ_name_of_basin” is a shape (polygon) downloaded from the National Spatial Data Infrastructure of the MITECO website (https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/agua/zonas-vulnerables.html). “Livestock_name_of_basin” is a shape (point) of the livestock census collected as Table from the official administrative Register of Livestock Farms of Aragon, Spain (REGA), and it was available in the Open Data platform (https://opendata.aragon.es/). This shape presents a legend layer (.lyr). “Crop_name_of_basin” is a shape (polygon) based on the information provided by the farmer declarations in the context of the Common Agricultural Policy (CAP). This shape presents a legend layer with the declared crops grouped for a better spatial representation. The abovementioned geographic layers are map projected in the European Datum ETRS89/UTM zone 30N., Agencia Estatal de Investigación: PID2021-127170OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” Agencia Estatal de Investigación: TED2021-130303B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGeneration EU/PRTR”, Peer reviewed

[Raw Data ] The raw_data folder contains tracking data from food and no-food experiments, as well as simulations using the default parameter set (see Table S1 in the manuscript and the script parameters.py in the linked GitHub repository for details)., [Experimental data are stored as .csv files. Each file contains:] *N-ind: identity of each individual *Frame: time in frames *Xmm, Ymm: x and y coordinates in millimeters *Crossings: whether an ant is interacting (1) or not (0) *Time_sec: time in seconds (Frame / 2) *node: the closest intersection in the Y-maze to the (x, y) position, [Simulation data are divided into two folders, each containing 100 files, one per realization of the model:] The main folder, data, contains tracking data analogous to the experiments. These files are in Parquet format and should be read using libraries such as arrow (R) or pyarrow (Python). Each file includes: *T: time in seconds *Frame: time in frames (T x 2, as integer) *N: number of ants in the arena *pos: ant positions *food_target: whether ants are being directed to a food source (0 for no, 1 for the first food patch, 2 for the second food patch), [The files in the food_data folder have the following columns:] *node: location of the food in the lattice *t: time of detection by an ant *origin: currently unused (contains no meaningful data), [Processed Data] This folder contains RData files with pre-processed data for immediate use in reproducing analyses: *det.RData: data from food experiments *nf.RData: data from no-food experiments *hex.csv: Y-maze intersection coordinates (experiment) *hex_sims.csv: Y-maze intersection coordinates (simulation), [Regarding simulations:] rho_eps.RData contains a summary of results across the parameter space defined by "rho" (scout proportion) and "epsilon" (social copying proportion), as used in Figure 5 of the manuscript. simulations_n contains .csv files showing the number of ants in the arena (N) over time (Frame) for each simulation. Although this can be derived from the raw data, these files are included here to directly support Figure 4 reproduction., [Steps to reproduce] These data result from the processing of video recordings of ants during foraging experiments. Full protocols are described in the related article (Fernández-López et al., 2025) and in Cristin et al. (2024): https://doi.org/10.1038/s41598-024-63307-1. Experimental Setup We conducted foraging experiments in a 2×2 meter arena, structured as a regular Y-maze (honeycomb) pattern. Each trial involved a colony of Aphaenogaster senilis ants exploring the environment for 3 hours. A total of 10 trials were conducted under experimental conditions (with food present), and 12 trials under control conditions (without food). Video Recording and Tracking All trials (with and without food) were video recorded using twelve synchronized cameras, capturing at 2 Hz and in 4K resolution. We processed the footage using a background subtraction method implemented in custom software to extract ant positions. This method involves subtracting consecutive frames to detect moving objects, in this case, the ants. The processed video tiles were then stitched together to reconstruct the full 2×2 meter maze. Tracking was performed using the Hungarian algorithm (based on distance minimization), along with additional custom heuristics to refine trajectories. Data Analysis The resulting tracking data served as the basis for all analyses. Due to the discrete structure of the Y-maze, we conducted both discrete (node-based) and continuous (position-based) analyses. Full results and relevant statistics are provided in the cited publications. For the simulations, please refer to the instructions provided in the associated GitHub repository., The data presented here were used to generate the figures and results of the research article titled "Foraging Ants as Liquid Brains: Movement Heterogeneity Shapes Collective Efficiency" (Fernández-López et al., 2025). To fully reproduce the analyses and figures, you will need to download the related code and instructions available in the GitHub repository cited below., Ministerio de Ciencia, Innovación y Universidades: PID2021-122893NB-C21 Agencia Estatal de Investigación: CGL2016-78156-C2-1-R, Peer reviewed