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4,462 results for “South America”
ConFire Model input/output for South America
<p>ConFIRE input and output used in the submission of Kelley et al. "Low Climatic Influence found in 2019 Amazonia Fires".</p> <p>"input" dir including "amazon_inference_data-2002-MCDBA_obs-TERRA_M__T.csv", just contains a csv file of all common grid data in other "input" subdirectories, which can be used in "optimise_run_model/bayesian_inference.ipynb" notebook. The files in the subdirectories are then used to make gridded model output using "optimise_run_model/make_model_output.ipynb".</p> <p>Output, also provided, is summerized in the files in "outputs/sampled_posterior_ConFire_solutions/constant_post_2018_full_2002_BG2020/"</p> <p>The three files contain:</p> <ul> <li>fire_summary_frequancy_of_counts.nc - Contains one spatial variable (long name "firecount frequency of occurrence") is the probability of a fire count (along the model_level_number dimension) at a particular month (time dimension) according to the models full posterior, <span class="math-tex">\(P(y_j)\)</span>(see papers supplementary). All months start on Jan 2001. </li> <li>fire_summary_precentile.nc - Contains one spatial variable (long name "firecount at percentile"), the fire count at each percentile of the full posterior in 1% increments from 1-99% (along the model_level_number dimension). Note, by definition, the 0a nd 100% percentile is 0 and <span class="math-tex">\(\infty \)</span> .</li> <li>fire_summary_observed_liklihood.nc - the position ( "variable_0") and p-value ("variable") of MCD64A1 in the model posterior.</li> <li>model_summary.nc - Summary of each variable of ConFire: <ul> <li>"burnt_area": burnt area or fire count summary, described as percentiles as per "fire_summary_precentile", but this time just assessing parameter uncertainty, i.e <span class="math-tex">\(P(\beta | Y_s)\)</span> in supplementary of paper.</li> <li>All other variables describe different model controls in the same "burnt_area". Full information on how controls are constructed can be found in Kelley et al. (2019) with updates listed in supplementary of this paper. <ul> <li>"fuel_continuity", "moisture_content", "ignitions", "suppression" are the actual values of the control</li> <li>"standard_<<control>>" is the standard limitation imposed by the control.</li> <li>"potential_<<control>> is the potential limitation</li> <li>"sensitivity_<<control>> is the senstivity of fire to a particular control.<br> <br> See Kelley et al. 2019 for the definition of limitation types and sensitivity</li> </ul> </li> </ul> </li> </ul> <p>Kelley, D.I., Bistinas, I., Whitley, R. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9, </strong>690–696 (2019) doi:10.1038/s41558-019-0540-7</p> <p> </p>
Paleomagnetic data for Beaver, Kent & Dalziel in Tectonics (2022), "Paleomagnetic Constraints From South Georgia On The Tectonic Reconstruction Of The Early Cretaceous Rocas Verdes Marginal Basin System Of Southernmost South America"
<p>Text data files of paleomagnetic data from Tables in: Beaver, D. G., D. V. Kent, and I. W. D. Dalziel (2022), Paleomagnetic Constraints From South Georgia On The Tectonic Reconstruction Of The Early Cretaceous Rocas Verdes Marginal Basin System Of Southernmost South America: Tectonics, in press.</p> <p><strong>Table 1.</strong> Site Mean Stable Paleomagnetic Directions from South Georgia.</p> <p><strong>Table 2.</strong> Site Mean Stable Directions for Differential Tilt Test of South Georgia Sites With Structural Control.</p> <p><strong>Table 3.</strong> Tectonic Rotations Inferred from Available Paleomagnetic Results from Rocas Verde Rock Units of Late Cretaceous Age in Fuegian Andes and South Georgia.<br> </p>
The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America
<p><strong>Title: </strong>The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America.</p> <p><strong>Authors:</strong> Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz.</p> <p><strong>Contact:</strong> Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>27 Jan 2022 - MANVI v2 was released!</strong> All data were reprocessed and improved. It is advised to re-download the whole series instead of combining v1 and v2. The dataset now covers years 2000-2021.</p> <p><strong>23 May 2019 - MANVI v1 was released.</strong> It covers years 2000-2018.</p> <p> </p> <p><strong>Data:</strong> MODIS (MAIAC) EVI and NDVI indices</p> <p><strong>Scale factor</strong>: 10000</p> <p><strong>Coverage:</strong> South America land</p> <p><strong>Time period:</strong> 2000 to 2021 (starting in 2000, Julian day 64)</p> <p><strong>Spatial resolution:</strong> 1 km</p> <p><strong>Temporal resolution:</strong> 16 days</p> <p><strong>Coordinate reference system:</strong> geographic projection, datum WGS-84</p> <p><strong>Processing details:</strong></p> <ul> <li>The original MODIS (MAIAC) data were described by Lyasputin et al. 2011 (<a href="https://doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>). The daily MODIS (MAIAC) surface reflectance data from collection 6, acquired from Terra and Aqua satellites, are available from the MCD19A1 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1</a>). The Bidirectional Reflectance Distribution Function (BRDF) model parameters are available from MCD19A3 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3</a>)</li> <li>The daily MCD19A1 data at 1 km spatial resolution were normalized using the BRDF parameters and Ross-Thick Li-Sparse (RTLS) model considering a fixed nadir view and a 45 deg. solar zenith angle using the parameters from the MCD19A3 product</li> <li>The daily data were aggregated into 16-day composites by the pixel’s median. The 16-day composites always start from Day Of Year (DOY) 016 and end with DOY 352. Therefore, the remaining days from 352 to 365/366 were not used. This procedure was used to facilitate inter-annual comparisons</li> <li>The tiles that cover the South America were mosaicked and re-projected from sinusoidal to geographic projection</li> <li>The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated using standard formulas. The EVI parameters were: C1 = 6, C2 = 7.5, L = 1, G = 2.5</li> </ul> <p><strong>File(s) format:</strong></p> <ul> <li>Zip files for EVI and NDVI - one per year: <ul> <li>Inside them there are raster files with ".tif" format, one per 16-day window. The filename syntax is "maiac_southamerica_DATA_YYYYDOY.tif", where YYYY is the year (e.g. 2000), and the DOY is the Julian day of the last day of the composite window, i.e. YYYYDOY for January 2005 for DOY from 001 to 016 is 2005016, from DOY 017 to 032 is 2005032, etc.</li> </ul> </li> <li>Csv files with the YYYYDOY and "real" dates for the time period</li> </ul> <p><strong>Code:</strong> <a href="https://github.com/ricds/maiac_processing">https://github.com/ricds/maiac_processing</a></p> <p><strong>Acknowledgements:</strong> This work was funded by São Paulo Research Foundation – FAPESP, Brazil, grant 2015/22987-7. We thank NASA, and especially Yujie Wang and Alexei Lyapustin, for providing the freely available MODIS (MAIAC) data.</p> <p> </p> <p><strong>Dataset usage</strong>: This dataset is a product of the first author's PhD work and lots of hours of coding and patience. It is free to use, but if you use this dataset in your work, please make sure to properly cite the repository. We also welcome users to invite us for collaboration.</p> <p> </p> <p><strong>For use of this dataset please cite:</strong></p> <p>Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America". (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3159487">https://doi.org/10.5281/zenodo.3159487</a></p> <p> </p> <p><strong>More information: </strong>contact Ricardo Dalagnol (ricds@hotmail.com). We also have the MODIS (MAIAC) BRDF-corrected bands 1-8, EVI, NDVI at 1 km with 16-day and monthly aggregation composites.</p>
Last interglacial (MIS 5e) sea-level proxies in southeastern South America
<p>This dataset is a compilation of published last interglacial sea level indicators for the southeast coast of South America (including Uruguay, Argentina, and one site in southern Chile). We have documented 60 proxies, which includes 48 sea level index points, 11 marine limiting indicators and 1 terrestrial limiting indicator. All of these data have at least some chronological control, with radiocarbon, electron spin resonance, U/Th, amino acid racemization and OSL dating techniques. The vertical uncertainty values were assigned based on information given for measurements, and the type of deposit.</p>
Dataset of Georeferenced Dams in South America (DDSA) v1.0.2
<p><strong>Recommended citation</strong></p> <p>Paredes-Beltran, B., Sordo-Ward, A., and Garrote, L.: Dataset of Georeferenced Dams in South America (DDSA), Earth Syst. Sci. Data, 13, 213–229, https://doi.org/10.5194/essd-13-213-2021, 2021.</p> <p><strong>Updated version 1.0.2:</strong></p> <p>We present version 1.0.2 to the DDSA database, the improvements made to version 1.0.1 are described below:</p> <ol> <li>Supplementary table 1: Future Dams in South America has been updated and now 574 future projected dams in South America, 61 under construction for 2020 and 513 planned projects for the future.</li> </ol> <p><strong>Updates made in version 1.0.1:</strong></p> <p>Version 1.0.1 to the DDSA database, includes improvements made to version 1.0.0, which are described below:</p> <ol> <li>New hydrological information attributes have been included: <ol> <li>Aridity index</li> <li>Residence time</li> <li>Degree of regulation.</li> </ol> </li> <li>A shapefile of watersheds for each dam has been included.</li> <li> <ol> </ol> Supplementary table 1: Future Dams in South America has been included.</li> </ol> <p><strong>Use of the dataset</strong></p> <p>Before using the dataset, please notify us (be.paredes@alumnos.upm.es; be.paredes@uta.edu.ec) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.</p> <p>When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using this dataset.</p> <p><strong>Description</strong></p> <p>Dams and their reservoirs generate major impacts on society and the environment. In general, its relevance relies on facilitating the management of water resources for anthropogenic purposes. However, dams could also generate many potential adverse impacts related to safety, ecology or biodiversity. These factors, and the additional effects that climate change could cause in these infrastructures and their surrounding environment, highlight the importance of dams and the necessity for their continuous monitoring and study. There are several studies examining dams both at regional and global scale, however, those that include the South America region focus mainly on the most renowned basins (primarily the Amazon basin), most likely due to the lack of records on the rest of the basins of the region. For this reason, a consistent database of georeferenced dams located in South America is presented: Dataset of georeferenced dams in South America DDSA. It contains 1,010 entries of dams with a combined reservoir volume of 1,017 cubic kilometres and it is presented in form of a list describing a total of 24 attributes that include the dams name, characteristics, purposes and georeferenced location. Also, hydrological information on the dams’ catchments is also included: catchment area, mean precipitation, mean near-surface temperature, mean potential evapotranspiration, mean runoff, catchment population, catchment equipped area for irrigation, aridity index, residence time and degree of regulation. Information was obtained from public records, governments records, existing international databases and from extensive internet research. Each register was validated individually and geolocated using public access online map browsers and then, hydrological and additional information was derived from a hydrological model computed using the HydroSHEDS dataset. With this database, we expect to contribute to the development of new research in this region.</p> <p><strong>Content</strong></p> <p>The files included in the Dataset of georeferenced dams in South America DDSA are:</p> <ul> <li><strong>1.</strong> Dam Information</li> <li><strong>2.1.</strong> Dam Hydrological Information - Catchment Area</li> <li><strong>2.2.</strong> Dam Hydrological Information - Catchment Mean Monthly Near Surface Temperature</li> <li><strong>2.3. </strong>Dam Hydrological Information - Catchment Mean Monthly Precipitation</li> <li><strong>2.4.</strong> Dam Hydrological Information - Catchment Mean Monthly Potential Evapotranspiration</li> <li><strong>2.5. </strong>Dam Hydrological Information - Catchment Mean Monthly Runoff</li> <li><strong>2.6.</strong> Dam Hydrological Information - Catchment Population</li> <li><strong>2.7. </strong>Dam Hydrological Information - Catchment Eqquiped Area for Irrigation</li> <li><strong>2.8. </strong>Dam Hydrological Information - Aridity Index</li> <li><strong>2.9. </strong>Dam Hydrological Information - Residence Time</li> <li><strong>2.10. </strong>Dam Hydrological Information - Degree of Regulation</li> <li><strong>3.</strong> Dataset Attribute Description</li> <li><strong>4.</strong> Dataset Data Source</li> <li><strong>5.</strong> Dataset in KMZ format </li> <li><strong>6.</strong> Dataset in SHAPEFILE format (dams)</li> <li><strong>7. </strong>Dataset in SHAPEFILE format (dams catchments)</li> <li><strong>8. </strong>Supplementary Table 1: Future Dams in South America v1.01</li> </ul>
Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020
<p>The dataset "Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020" contains the emissions analysed in the manuscript "Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates", published in Fire 2023, 6(11), 426; <a href="https://doi.org/10.3390/fire6110426">https://doi.org/10.3390/fire6110426</a>.</p>
Phlorest phylogeny derived from Walker & Ribeiro 2011 'Bayesian phylogeography of the Arawak expansion in lowland South America'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Walker, R. S., & Ribeiro, L. A. (2011). Bayesian phylogeography of the Arawak expansion in lowland South America. Proceedings of the Royal Society B: Biological Sciences, 278(1718), 2562–2567.</p> </blockquote>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
Regional model results (combined) for the six transition potentials (one for Africa, Australia, Asia, Europe, North America, and South America)
<p>Results for the six regional models showing areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition.</p>
Teleseismic P-wave Tomography Beneath the Pantanal, Paraná and Chaco-Paraná Basins, SE South America: Delimiting Lithospheric Blocks of the SW Gondwana Assemblage.
<p>Tomographic data set for different depths (CSV-files with Longitude, Latitude anda Velocity Perturbation in percentage), Interpreted limit of the São Francisco Paleocontinent and the Abstract for the paper "Teleseismic P-wave Tomography Beneath the Pantanal, Paraná and Chaco-Paraná Basins, SE South America: Delimiting Lithospheric Blocks of the SW Gondwana Assemblage." submitted to Journal of Geophysical Research: Solid Earth.</p>
National Checklists 2017: South America Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>List of species from South America inferred from individual country lists that were derived from effechecka and modified geonames polygons
National Checklists 2019: South America Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>List of species from South America inferred from individual country lists that were derived from effechecka and modified geonames polygons
National Checklists 2019: South America Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>List of species from South America inferred from individual country lists that were derived from effechecka and modified geonames polygons
DATASETS and OUTCOMES - Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the DRASTIC method: the case of South America
<p>A robust and comprehensive assessment of intrinsic aquifer vulnerability at continental scale map may represent an essential initial step towards a more sustainable land-use and water management.</p> <p>This repository contains the outcomes of an intrinsic aquifer vulnerability assessment of South America, performed by the DRASTIC method. The assets included in this repository are mainly raster maps (.tif, .geotif), created and georeferenced in QGIS (v3.16). Coordinate reference system (CRS) of the dataset is WGS84.</p> <p>Technical specifications of all graphical outcomes are stored in a dedicated file (README.txt).</p>
Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America.
<p>Köppen - Geiger scripts and resulting datasets for the publication entitled "Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America." This scripts can be adapted to any geographic scale and region. Works with climate change scenarios.</p> <p>Original publication: <a href="https://doi.org/10.1016/j.gloplacha.2023.104155">https://doi.org/10.1016/j.gloplacha.2023.104155</a></p> <p>Dataset description</p> <p><strong>Scripts.rar</strong>: R Scripts used in this publication, as well they are reproducible</p> <p><strong>Readme_Köppen.txt</strong>: README file that explain the requisites and data formatting to run the scripts</p> <p><strong>Output datasets.zip</strong>: Output GIS datasets of this publication. Coordinate system GCS WGS 1984</p> <p> </p>
Phytoplankton list of taxa and cell biovolume from a subtropical coastal lagoon, South America
<p>We share information about the phytoplankton community from a subtropical coastal lagoon, Laguna de Rocha, a Biosphere Reserve, located in Uruguay, South America (34°37'60" S, 54°18'0" W). The lagoon is ~ 72 km2 with a mean depth of 0.5 m, and is characterized by a strong South-North salinity gradient that reflects its intermittent connection to the Atlantic Ocean (in the South). The lagoon has been subject to eutrophication in the last several decades. The dataset presented here belongs to the publication of Bonilla et al., 2005 (doi.org/10.1007/BF02696017). We present the complete list of phytoplankton taxa identified in a survey (1996-2000) for two sites, South and North, locations described in figure 1 (Bonilla et al., 2005). We also presented the cellular linear measurements and the cell biovolume of the most representative taxa. We believe these data are useful for the scientific community interested in phytoplankton dynamics, diversity and calculation of biomass (biovolume).</p>
DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America
<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.</p>
Mechanisms influencing physically sequestered soil carbon in temperate restored grasslands in South Africa and North America
This dataset contains a measurement of physically protected carbon (microaggregate-within-macroaggregate C) and potential drivers of physically protected C accumulation during grassland restoration. The data were collected from three independent grassland restorations from agriculture in North America and South Africa. Northeast Kansas, USA data were collected in May 2013. Northeast Free State, RSA data were collected in September–November 2005. Southeast Nebraska, USA data were collected in October 2008–May 2008. Aggregate fractionations were performed by hierarchical wet sieving (Six et al 2000). Carbon and N quantification were done by flash combustion-gas chromatography. Microbial biomass C quantification was performed with chloroform fumigation-incubation (chloroform fumigation-extraction in the case of northeast Kansas). Phospholipid fatty acid biomass analysis was conducted using the methods of Bligh and Dyer (1959).
Figure 1. Neocherentes dilloniorum Tippmann, 1960, holotype male. a in Two new species of South America Neocherentes Tippmann, 1960 (Coleoptera: Cerambycidae: Lamiinae: Onciderini)
Figure 1. Neocherentes dilloniorum Tippmann, 1960, holotype male. a) Dorsal habitus. b) Lateral habitus. c) Close-up of head. d) Close-up of abdominal ventrites.
Figure 3 in Two new species of South America Neocherentes Tippmann, 1960 (Coleoptera: Cerambycidae: Lamiinae: Onciderini)
Figure 3. Neocherentes pergeri sp. nov., holotype male. a) Dorsal habitus. b) Lateral habitus. c) Close-up of head. d) Close-up of abdominal ventrites.
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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.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.
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.