Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

5,117

datasets available to search

ShareScore release 0.9.0

Dataset results

5,117 results for “argentina”

Learn how ShareScore rates datasets ↗

zenodo52/100

Dataset: Seasonal field trials of single-seed removal by desert birds from experimental devices in Ñacuñan Reserve (Mendoza, Argentina)

Dataset for the paper: Milesi FA, Lopez de Casenave J & Cueto VR (2018) Which food patches are worth exploring? Foraging desert birds do not follow environmental indicators of seed abundance at small scales: a field experiment. bioRxiv 295923. doi: https://doi.org/10.1101/295923 Metadata included within the tab-delimited text file

opencc-by-4.0Apr 2018View details →

edi52/100

Water quality and watershed attributes of 41 Pampean streams in Argentina, 12 years later (2003-2015).

This database consists of water chemistry (pH, conductivity, dissolved oxygen, nutrients, and carbonates) and catchment attributes for 41 streams of Buenos Aires province, Argentina. Water quality was measured in 2003/4 and 12 years later (2015/16). Sampling were made in May (autumn), November (spring), and February (summer) at baseflow condition. Some physico-chemical parameters were measured in situ. Parameters determined at laboratory were nutrients and salts. And catchment attributes were determined (physiographic parameters, land use, soil type and geology).

openCC (other)Oct 2022View details →

zenodo48/100

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the LPAR site (Argentina)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Rio de La Plata, LPAR, in Argentina. It is a subset of the complete data record which consists of the best quality LPAR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances (without NIR Similarity Correction, see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full LPAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 400-900 nm range 2. The water reflectance at 500 nm is below 0.1

opencc-by-4.0Jun 2023View details →

zenodo48/100

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the IFEVA site in Argentina

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the  HYPERNETS site at IFEVA in Buenos Aires  Argentina (IFAR). It is a subset of the complete data record which consists of the best quality IFAR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as: HDRF = π L / E where L is the directional upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The IFAR site is a temporary test site located in the Agronomy Faculty campus in Buenos Aires city, Argentina (34.592322°S, 58.479017°W). The venue is managed by the IFEVA (Agricultural Physiology and Ecology Research Institute) and characterized by natural pastures with different treatments distributed in 16 patches of 7mx7m. The HYPSTAR®-XR sensor has been deployed in June 2021 at the top of a 2.4 m high tripod that is pointing to one of the patches where the vegetation has no specific treatment (natural) and is cut regularly every year in February. Data is collected every 30 min between 14:00 and 18:00 hs UTC (11:00 to 15:00 local time). The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full IFAR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to remove outliers and only supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely.

opencc-by-4.0Jun 2023View details →

edi48/100

Supervised land cover classification using Google Earth Engine in Córdoba, Argentina, 2018-2020

Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.

openCC (other)Dec 2023View details →

edi48/100

Sampling sites where ecological indices were used to assess the impact of different environmental stressors in aquatic environments in Argentina

Dataset is a compilation of all sampling sites of articles where ecological indices were used to assess the impact of different environmental stressors in aquatic environments from Argentina. Points of this dataset were extracted from 78 papers published between 1996 and 2018. We selected articles that use ecological indices to analyze some local environmental problematics or stressors. Using the type of index from each article we performed the kml file, which contained the categorized sampling sites by different symbols according to the ecological index: physico-chemical, biological, geomorphological and multimetric. We carried out a map (shapefile) with all the sampling sites referenced to the ecoregions of Argentina proposed by Burkart (1999).

openCC (other)Jul 2019View details →

zenodo44/100

Supplementary Information and data for: "Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina"

This repository contains the supplementary information and raw data annexed to the manuscript "Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina", authored  by Karla Rubio-Sandoval et al. and submitted for consideration in the journal Quaternary Science Reviews. The folder contains the following items. 1. Raw_data.xlsx This is an excel file that includes all survey and analytical data in several sheets, briefly described hereafter. - GNSS data. Data surveyed with differential GNSS in the field. - Sea level index points. Datapoints used as sea-level index points, and associated calculations of paleo Relative Sea Level. - AAR Summary. Table summarising the main results of the AAR analyses. - AAR complete sheet. The complete set of analytical data done for the Amino Acid Racemization dating. - Radiocarbon data. The analytical results of radiocarbon dating. - Literature ages. A compilation of the Electron Spin Resonance and U-series ages published for the Camarones site. - Transects. Topographical transects extracted from the TanDEM-X Digital Elevation model and referred to the GEOIDEAR 16 geoid. - Distance plot. Data for plotting Relative Sea Level vs distance along the coast of the sea-level index points described in the manuscript. 2. Holocene (folder) This folder contains two excel files ("Area_Camarones_Accepted.xlsx" and "Area_Camarones_Rejected.xlsx") that include the Holocene data described in the paper compiled following the standard HOLSEA template. 3. Runup_modelling This folder contains three folders, each with a Jupyter notebook (.ipynb) and datasets to perform the runup calculations described in the manuscript.

opencc-by-4.0Dec 2023View details →

zenodo44/100

[[Deprecated]] DIGITAL SOIL TEXTURE MAPS OF ARGENTINA

A new version has been uploaded by Guillermo Schulz.

opencc-by-4.0Jan 2022View details →

zenodo44/100

DIGITAL SOIL TEXTURE MAPS OF ARGENTINA

Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest. Global accuracy based on cross-validation <table> <tbody> <tr> <td> sp </td> <td> RMSE </td> <td> Rsquared </td> <td> MAE </td> </tr> <tr> <td> Sand 0-15 cm </td> <td> 16.189 </td> <td> 0.640 </td> <td> 11.069 </td> </tr> <tr> <td> Sand 15-30 cm </td> <td> 16.320 </td> <td> 0.629 </td> <td> 11.213 </td> </tr> <tr> <td> Sand 30-60 cm </td> <td> 16.676 </td> <td> 0.618 </td> <td> 11.364 </td> </tr> <tr> <td> Sand 60-100 cm </td> <td> 16.762 </td> <td> 0.587 </td> <td> 11.472 </td> </tr> <tr> <td> silt 0-15 cm </td> <td> 12.011 </td> <td> 0.638 </td> <td> 8.352 </td> </tr> <tr> <td> silt 15-30 cm </td> <td> 11.807 </td> <td> 0.608 </td> <td> 8.388 </td> </tr> <tr> <td> silt 30-60 cm </td> <td> 11.504 </td> <td> 0.561 </td> <td> 8.168 </td> </tr> <tr> <td> silt 60-100 cm </td> <td> 11.728 </td> <td> 0.583 </td> <td> 8.263 </td> </tr> <tr> <td> clay 0-15 cm </td> <td> 8.766 </td> <td> 0.475 </td> <td> 5.721 </td> </tr> <tr> <td> clay 15-30 cm </td> <td> 10.723 </td> <td> 0.452 </td> <td> 7.432 </td> </tr> <tr> <td> clay 30-60 cm </td> <td> 11.211 </td> <td> 0.557 </td> <td> 7.842 </td> </tr> <tr> <td> clay 60-100 cm </td> <td> 11.005 </td> <td> 0.536 </td> <td> 7.734 </td> </tr> </tbody> </table>

opencc-by-4.0Feb 2022View details →

zenodo44/100

Prediction stock of soil organic carbon in Argentina

We standardized the Stocks soil organic carbon (SOC) at 0-30 cm depth for 5,073 soil samples. We spatially predicted SOC stock (kg/m2) using regression forest and associated prediction uncertainties using quantile regression forest at 1000 m resolution. Global accuracy based on cross-validation. We obtained a RMSE 2.624 and Rsquared 0.464.

opencc-by-4.0Jun 2018View details →

zenodo44/100

Salt-affected soils in Argentina

Maps of pH on water, electrical conductivity (dS/m), exchangeable sodium percent (%) and soils affected by salts in Argentina at different depth intervals (0-30 and 30-100 cm) at 1000 m resolution. Including the tree standard layers and error estimation using random forest. Global accuracy based on cross-validation. <table> <tbody> <tr> <td> sp </td> <td> RMSE </td> <td> Rsquared </td> </tr> <tr> <td> EC (dS/m) 0-30 cm </td> <td> 5.53 </td> <td> 0.134 </td> </tr> <tr> <td> EC (dS/m) 30-100 cm </td> <td> 5.77 </td> <td> 0.208 </td> </tr> <tr> <td> pH on water 0-30 cm </td> <td> 0.84 </td> <td> 0.456 </td> </tr> <tr> <td> pH on water  30-100 cm </td> <td> 0.80 </td> <td> 0.523 </td> </tr> <tr> <td> Exchangeable sodium percent (%) 0-30 cm </td> <td> 13.08 </td> <td> 0.169 </td> </tr> <tr> <td> Exchangeable sodium percent (%) 30-100 cm </td> <td> 15.40 </td> <td> 0.187 </td> </tr> </tbody> </table>

opencc-by-4.0Oct 2020View details →

zenodo44/100

Black soils in Argentina

Spatial prediction was done following a digital soil mapping framework. Soil profiles were classified into black soil (833) or non black soil (3007) according to the BS definitions. We used soil horizons up to 25 cm. Soil organic carbon (obtained by wet combustion), Munsell colour and horizon boundaries were used for the analysis. We filtered out organic horizons as well as those profiles with missing data. Model calibration was carried out using a classification random forest model. We predicted the probability black soil occurrence and took 0,5 as the splitting threshold. Validation was done by 10-fold 20 times repeated cross-validation. We obtained a 75% global accuracy and a kappa of 0,52.

opencc-by-4.0Mar 2021View details →

zenodo44/100

Structural Inheritance in the Eastern Cordillera, NW Argentina: Low‐Temperature Thermochronology of the Cianzo Basin - Supporting Information

Supporting information accompanying the publication "Structural Inheritance in the Eastern Cordillera, NW Argentina: Low‐Temperature Thermochronology of the Cianzo Basin" published in Tectonics. The dataset contains (U-Th-Sm)/He and apatite fission track data from the Cianzo Basin, Jujuy, Argentina, and accompanying figures. Table S1 contains full single-grain results from apatite (AHe) and zircon (ZHe) (U-Th-Sm)/He analyses. Table S2 and S3 contain AFT results including full counting data from apatite fission track (AFT) analyses. Figure S1 supports AHe and ZHe data with plots showing relationships between cooling ages, eU, Ft and ESR. Figures S2–S4 support AFT data with radial plots.

opencc-by-nc-nd-4.0May 2024View details →

zenodo44/100

Citizen Science projects in Argentina

Citizen science activities recognized in Argentina. The code for the Actions column are:  <ul> <li>e -Collect c - Hypothesis design d - Design collection strategies f - Sample analysis</li> <li>g - Data analysis h - Generate conclusions j - Generate new questions k - Digitalization i - Disseminate conclussions </li> </ul>

opencc-by-4.0Sep 2019View details →

zenodo44/100

National Checklists 2017: Argentina Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Argentina collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

National Checklists 2019: Argentina Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from Argentina collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

zenodo44/100

Temperature and Relative Humidity Data in Rooms of Historical Museum (Cabildo) in Salta, Argentina.

Temperature and Relative Humidity Data in Rooms of Historical Museum (Cabildo) in Salta, Argentina. Data was monitored over 15 consecutive days, with readings taken at 15-minute intervals during two periods: the cold season and the warm season. HOBO data loggers (model U12-12) were used, featuring a temperature accuracy of ±0.35°C and a resolution of 0.03°C at 25°C, as well as a humidity accuracy of ±2.5% and a resolution of 0.03%. The sensors were specifically calibrated for the expected temperature range, with a calibration age of less than one year and a calibration error within ±0.5°C. Additionally, a sensor was installed in the building's galleries in all cases to record outdoor temperature and relative humidity data, allowing for a comparative analysis between indoor and outdoor conditions.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Geographic Information System for marine aquaculture in Argentina

Planning the use of marine areas for aquaculture through the development of Geographic Information Systems (GIS) has taken on great importance recently . This is because GIS allows decision-making through the analysis and integration of a large amount of data of various kinds gathered in a single database. This system allows the incorporation of information on optimal environmental conditions for farm species and relevant data to develop strategies throughout the entire production chain, from service providers and inputs to the final marketing of the product. The recommended actions of the strategic guidelines for a more sustainable and competitive EU aquaculture in 2021–2030 (EC 2021) stated explicitly the need to “Develop a more detailed guidance document on the planning for space and access to water for marine, freshwater and land-based aquaculture”, highlighting the importance of the GIS. Here you will find 4 files with the following information: 1) Metadata.doc file with the details of the metadata used to diagram the GIS layers. 2) GIS.gpkg file with each of the layers in raster and vector format. 3) Land-based model.gpkg file with examples of GIS modeling for land-based facilities. 4) Open-water model.gpkg file with examples of GIS modeling for facilities in open systems.

opencc-by-4.0Aug 2024View details →

zenodo44/100

Miocene construction of the High Andes recorded by exhumation of the Frontal Cordillera, La Ramada Massif of western Argentina (32°S) (Supporting Information)

Supporting datasets for Howlett et al., "Miocene construction of the High Andes recorded by exhumation of the Frontal Cordillera, La Ramada Massif of western Argentina (32°S)" in TECTONICS.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Resource use strategies, resistance and tolerance to aerial biomass removal in Argentina mid-west native plants

Dataset of the PhD Thesis from Lucas D. Gorné:     - Gorné LD. 2018. Estrategias de uso de recursos, resistencia y tolerancia a la remoción de biomasa aérea en plantas nativas del centro-oeste de Argentina. Tesis del Doctorado en Ciencias Biológicas. Facultad de Ciencias Exactas, Físicas y Naturales. Universidad Nacional de Córdoba. Córdoba, Argentina. https://ri.conicet.gov.ar/handle/11336/87925.

opencc-by-4.0Sep 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record