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25,418 results for “Brazil.”
Ants in an Induced Drought Experiment at the Caxiuana National Forest in Brazil 2011-2012
Environmental change scenarios caused by low precipitation forecast species loss in tropical regions. We used one year of data from a rainwater exclusion experiment in primary Amazonian rainforest to test whether induced water stress, and covarying changes in humidity, soil respiration, and tree species richness, size, and total biomass and its diversity affected species richness and composition (relative abundance) of ground-dwelling ants. Induced drought reduced ant richness, whereas increased humidity and variability in biomass increased it. Species composition differed between control and rainfall-excluded plots. Occurrence of many ant species was strongly reduced, but some generalist groups of ants were favored by induced drought. The expected loss of ant species and changes in ant species composition in tropical forests likely will lead to cascading effects on ecosystem processes and services they mediate.
Ant Resource Detection Distance at the Caxiuana National Forest in Brazil 2017-2018
Environmental change scenarios of low precipitation forecast species loss in tropical regions. These losses can affect generalist species that provide important ecosystem services, such as controlling the rate at which nutrients become available for uptake by other organisms in tropical forests. Here, we use a long-term rainwater exclusion experiment in primary Amazonian rainforest to test whether induced water stress affects the detection distance of food resources (baits) in a generalist ant guild (number of colonies, richness, and composition) that remove resources on the ground. We found that (i) overall the distance of resource removal by generalist ants did not change with drought; (ii) however, comparing ant species that occurred in drought-induced and control environments, workers walked shorter distances in the drought habitat; (iii) the number of resources detected by ant colonies in the drought-induced habitat decreased by 50%. Although generalist ants are considered resilient to habitat disturbance, the effect of reduced rainfall can negatively affect the services mediated by them. The rate of removal and consumption of resources in tropical forests may be related to abundance of generalist ants; losses in both nest density and walking distances may cause cascading effects on ecosystem processes and the services they mediate.
Assessing Plant Phenological Patterns in Tropical Brazil 1901–2020
Phenology is a key biological trait of an organism’s success and is one of the best indicators of its response to recent climate change. Plants are among the most well-studied organisms in this regard, but observational data bearing on this topic are largely restricted to species of the northern hemisphere, mostly from ca. the last three decades. Phenological data from tropical latitudes are especially lacking. Recent research has demonstrated that mobilized online herbarium specimens provide important, albeit mostly neglected, information on plant phenology. Here, we use the web tool CrowdCurio to crowdsource phenological data from nearly 35,000 herbarium specimens representing 260 flowering plant species broadly distributed across tropical Brazil. Our results, spanning 120 years and generated from over 1000 crowdsourcers, clarify numerous aspects of tropical plant phenology. First, they reveal that plant reproductive timing is exceptionally diverse across tropical biomes and taxa. Second, they identify that phenological responses to climate are variable across taxa and biomes. Third, among those species with broad latitudinal ranges, populations from more southern latitudes are significantly more phenologically sensitive to precipitation than those from northern populations. Our results are robust to a variety of confounding factors and span large phylogenetic distances and various life histories. These may represent more global trends in the latitudinal gradient of tropical phenological response with myriad potential ecological and evolutionary consequences. This dataset may be used for non-commercial purposes. Please provide the following attribution: Davis, C., Lyra, G., Park, D., Zhang, H., Asprino, R., Maruyama, R., Torquato, D., Cook, B., Xie, J., Ellison, A. 2022. Assessing plant phenological patterns in tropical Brazil 1901–2020. Harvard Forest Data Archive: HF427. Please note that the license we provide does not apply to images linked from the data set. P
Copper mineralization at Carajás mineral province - Brazil: geological, structural, and geophysical data
<p>Gridded geological, structural, and geophysical data at the Carajás mineral province. A number of known Cu occurrences are provided. This dataset is suitable for experimenting with machine learning methods.</p>
Graphs of data for elderly individuals (aged 60 to 120 years) with syphilis in Brazil
<p>A set of data graphs containing informations on elderly people with syphilis in Brazil, aged between 60-120 years with syphilis in Brazil, aged between 60-120 years and contains spreadsheet results of trend analysis of acquired syphilis, by regions of Brazil, in the period 2010-2020, referring to the article entitled "<strong>ACQUIRED SYPHILIS IN OLDER PEOPLE IN BRAZIL FROM 2010-2020".<br><br><br></strong>The dataset used to plot the graphs can be found at: <a href="https://doi.org/10.5281/zenodo.10086131">https://doi.org/10.5281/zenodo.10086131</a></p>
Scholarly journals publishing articles by family and community physicians in Brazil, up to December 2018
<p>This is the dataset of manuscript titled "In which journals do family and community physicians in Brazil publish? The <em>Trajetórias MFC</em> project". There are two spreadsheets: the dataset proper and the data dictionary. See the manuscript for background.</p> <p>All spreadsheets are in the CSV (comma-separated values) format, delimited with semicolons and encoded in UTF-8 with the byte-order mark (BOM). The spreadsheets can be opened with desktop or Web application software (LibreOffice Calc, Microsoft Excel, Google Sheets) or with statistical software such as R.</p> <p>A <a href="https://zenodo.org/record/3905255">previous version</a> of this dataset was used in a <a href="https://doi.org/10.1101/19005744">preprint</a>. This version should be cited by an upcoming article.</p> <p>See also the <a href="https://doi.org/10.1136/fmch-2020-000321">article</a>, <a href="https://doi.org/10.5281/zenodo.3376310">dataset</a> and <a href="https://doi.org/10.5281/zenodo.3381576">supplementary table</a> for an earlier milestone, about the postgraduate education of family and community physicians in Brazil.</p>
Data from Yellow Sigatoka monitoring methods in the subtropical climate of southern Brazil
<h2>Description of the data and file structure</h2> <p>In this study four methods of disease monitoring were tested under field conditions: Biological Pre Warning (BPW); Stage of Evolution (SE); youngest Leaf Spotted (YLS); Infection Index (II). The BPW system evaluates the youngest leaves (2, 3, and 4), assigning a value for each type of lesion present, as well as for intensity of the lesion on the leaves (BUREAU et al., 1992). In the dataset is cited as the variable gross sum (points).</p> <p>The SE evaluates more leaves (1, 2, 3, 4, and 5) and scores only the most advanced symptoms of leaf disease, but without considering lesion intensity (GANRY et al., 2008). The SE calculation also corrects the gross sum of the disease according to leaf emission. The leaf emission rate was calculated using the Brun scale, which evaluates cigar leaf growth in decimals from 0.0 to 0.8. In the dataset is cited as the variable corrected gross sum (points).</p> <p>YLS is evaluated as the first leaf that has 10 spots with gray centers (CARLIER et al., 2003). In the dataset is cited as the variable YLS, which means the leaf position counted from the top to the botton of the plant (leaf number 3, leaf number 4...).</p> <p>Sigatoka Infection Index is quantified by assessing the severity of banana leaf disease using the Stover scale, with indexes from 0 to 50%, by means of the following formula: Infection Index =% (IF): [Σn × b / (N- 1) × T] × 100, in which: n = the number of leaves at each Stover scale level; b = degree according to the scale; N = the number of degrees employed in the scale (6); T = the total number of leaves evaluated (CARLIER et al., 2003). In the dataset is cited as the variable Infection index that should be understood like the severity of this leaf disease.</p> <p>In the second phase of the study, two monitoring methods were applied in commercial orchards in order to compare the standard model (Biological Pre-Warning – BPW) with the alternative method selected in the experimental phase (Youngest Leaf Spotted – YLS). The methods were applied, as described before in three sites in Criciúma (site 1) and Siderópolis (sites 2 e 3), municipalities in the southern coast of the state of Santa Catarina, from March 2016 to November 2018. During this period, 37 disease evaluations were performed at each location.</p> <p>Disease data of the experimental area were submitted to descriptive analysis and Pearson correlation at 5% probability of error. Disease progress curves were also plotted. The disease development data in commercial orchards were analyzed by plotting disease progress curves for BPW and by frequency distribution (%) for the YLS variable during all period of the experiment.</p>
Database from: Developing a lateral topographic density model for Brazil.
<p>This dataset is part of the article entitled "DEVELOPING A LATERAL TOPOGRAPHIC DENSITY MODEL FOR BRAZIL".</p> <p>This dataset includes the topographic Lateral Topographic Density model for Brazil (LTDBrasil) and standard deviations (sdLTDBrasil), in Kg/m³, with 30 arc-seconds grid spacing.</p> <p>The files are in *tif and *.tfw format.</p> <p>Reference: Medeiros D.F., Marotta G.S., Yokoyama E., Franz I.B., Fuck R.A. 2021. Developing a lateral topographic density model for Brazil. Journal of South American Earth Sciences, v. 110, p. 103425. https://doi.org/10.1016/j.jsames.2021.103425</p>
Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil
<p><strong>The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019). </strong></p>
Vectorized Hydrogeology Map of Rondônia - Brazil
<p>Vectorized Hydrogeology Map for the State of Rondônia - Brazil.</p> <p>Hydrogeology map was vectorized from CPRM [SERVIÇO GEOLÓGICO DO BRASIL]. 1998. State of Rondônia Hydrogeological Map. [Porto Velho]. Map. Scale: 1:1.000.000. Programa de Recursos Hídricos - PRH. Available on https://rigeo.sgb.gov.br/handle/doc/5364.<br><br>Citation: CPRM [SERVIÇO GEOLÓGICO DO BRASIL]. 1998. State of Rondônia Hydrogeological Map. [Porto Velho]. Map. Scale: 1:1.000.000. Programa de Recursos Hídricos - PRH.</p>
Geochemistry of soils and eroded suspended sediments from two large rural catchments in southern Brazil for studies on Suspended Sediment Fingerprinting
<p> <strong>1. Introduction</strong></p> <p>This dataset comes from a research project entitled "Water and pollutants, from cropfields to cities: evaluation and improved of soil management technologies in a catchment network " supported by the Foundation for Research Support of the State of Rio Grande do Sul (FAPERGS) and National Council for Scientific and Technological Development (CNPq) (process n°10/0034-0). The project was carried out between 2010 and 2014 under the coordination of José Miguel Reichert and Danilo Rheinheimer dos Santos, professors at the Federal University of Santa Maria. One of the aims of this project was to understand the main pollutant transfer process from hillslopes to fluvial systems in large rural catchments representative of the agricultural production system in Southern Brazil. In this context, the Suspended Sediment Fingerprinting (SSF) was extremely useful for quantifying the origin of the sediment yield monitored at the outlet of these catchments. Among the various works carried out in this project, we highlight Tales Tiecher's doctoral thesis (Tiecher, 2015) that explored the SSF in many catchments, including the Conceição and Guaporé river basins.</p> <p><strong>2. Material and Methods</strong></p> <p>The catchments represent the magnitude of erosive and hydrological processes representative of Southern Brazil. The Conceição catchment has a drainage area of 804 km<sup>2</sup> (28°27′22″S and 53°58′24″ W). According to Köppen, the climate is Cfa type, with an annual rainfall between 1,750 and 2,000 mm. Geology is riodacithe basalt, with a formation of deep and highly weathered soils (Oxisols, Ultisols, and Alfisols). The relief is characterized by gentle slopes (6–9 %) on top and hillside slopes and higher steepness (10–14%) near the drainage channels. Farming based on the production of soybeans (<em>Glycine max</em>) in summer and wheat (<em>Triticumspp.</em>), oats (<em>Avena strigosa</em>), and ryegrass (<em>Lolium multiflorum</em>) in winter. The Guaporé catchment has a drainage area of 1,980 km<sup>2</sup> (28°54′41″S and 51°57′10″W), it covers part of the meridional plateau border. The climate is classified as Cfa, with annual rainfall varies between 1,400 and 2,000 mm. Geology is characterized by volcanic lava flows, and topography is undulating to hilly. Due to variations in landscape, several classes of soils (Entisols, Luvisol, Cambisol, Oxisol, Ultisol, and Chernosol). The land use is highly heterogeneous. In the upper third of the catchment, there is a predominance of soybean cultivated under no-tillage soil management. In the other two-thirds (middle and lower parts), land use and soil management are very heterogeneous. The main land uses are tobacco (<em>Nicotiana tabacum</em>) and maize (<em>Zea mays</em>) crops, Eucalyptus (<em>Eucalyptus</em> spp.), as well as pastures for dairy cattle. The contribution of unpaved roads is relevant to the sediment yield in both catchments (Didoné et al., 2014). Composite samples of potential sediment sources (cropland, unpaved roads, and stream channel banks) were collected. Sediment source samples were taken from the surface soil layer (0–0.05 m) of cropland and unpaved roads and on exposed sites located along the river channel network. Each sample was composed of at least 10 subsamples. To obtain representative samples of suspended sediment transported in the catchment’s outlet were used three strategies: (1) to collect flood suspended sediments (FSS) through the manual sampling (USDH-48) at different periods during the rising and falling stages of floods; (2) to deploy time-integrated suspended sediment samplers (TISS), by installing the device developed by Phillips et al. (2000) at different sites within the catchments; to collect fine-bed sediment (FBS) with a suction stainless sampler limiting the loss of fine material at the bed river. Source and sediment samples were oven‐dried at 50 °C, gently disaggregated using a pestle and mortar, and then sieved to 62,5 μm. The geochemical tracers evaluated were total organic carbon estimated by wet oxidation (K<sub>2</sub>Cr<sub>2</sub>O<sub>7</sub> + H<sub>2</sub>SO<sub>4</sub>) and the total concentration of Al, Ba, Be, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, V, and Zn using inductively coupled plasma optical emission spectrometry after microwave‐assisted digestion with concentrated HCl and HNO<sub>3</sub> (ratio 3:1) for 9.5 min at 182 °C (Tiecher, 2015; Tiecher et al. 2017, 2018).</p> <p> <strong>3. Final remarks</strong></p> <p> The SSF results provided by this dataset (Tiecher, 2015) combined with sediment yield monitoring were very important for the assessment and modeling studies in these two catchments that took place after that (Didoné et al., 2015; 2017). In addition, other studies have explored the same sample bank, expanding upon the array of tracer properties and increasing our understanding about the mechanisms of sediment and pollutant transfer in these catchments (Le Gall et al. 2017; Zafar et al., 2017; Ramon et al., 2020).</p> <p> <strong>4. References</strong></p> <p> Didoné, E. J., Minella, J. P. G., Reichert, J. M., Merten G. H., Dalbianco, L., Barros, C. A. P., Ramon, R. (2014) Impact of no-tillage agricultural systems on sediment yield in two large catchments in southern Brazil. J Soils Sediments 14:1287–1297.</p> <p>Didoné, E.J., Minella, J.P.G., Evrard, O. (2017). Measuring and modelling soil erosion and sediment yields in a large cultivated catchment under no-till of Southern Brazil. Soil Tillage Res. 174, 24-33. https://doi.org/10.1016/j.still.2017.05.011</p> <p>Didoné, E. J.; Minela, J. P. G.; Merten, G. H. (2015). Quantifying soil erosion and sediment yield in a catchment in southern Brazil and implications for land conservation. J. Soils Sediments 11, 2334-2346. https://doi.org/10.1007/s11368-015-1160-0</p> <p>le Gall, M., Evrard, O., Dapoigny, A., Tiecher, T., Zafar, M., Minella, J. P. G., Laceby, J. P., & Ayrault, S. (2017). Tracing sediment sources in a subtropical agricultural catchment of southern Brazil cultivated with conventional and conservation farming practices. Land Degradation and Development, 28(4). https://doi.org/10.1002/ldr.2662</p> <p>Ramon, R., Evrard, O., Laceby, J. P., Caner, L., Inda, A. v., Barros, C. A. P., Minella, J. P. G., & Tiecher, T. (2020). Combining spectroscopy and magnetism with geochemical tracers to improve the discrimination of sediment sources in a homogeneous subtropical catchment. Catena, 195, 104800. https://doi.org/10.1016/j.catena.2020.104800</p> <p>Tiecher, T. (2015). Fingerprinting sediment sources in agricultural catchments in Southern Brazil. Doctoral Dissertation in Soil Science. Universidade Federal de Santa Maria, Santa Maria, RS.</p> <p>Tiecher, T., Minella, J. P. G., Caner, L., Evrard, O., Zafar, M., Capoane, V., le Gall, M., & Santos, D. R. D. (2017). Quantifying land use contributions to suspended sediment in a large cultivated catchment of Southern Brazil (Guaporé River, Rio Grande do Sul). Agriculture, Ecosystems and Environment, 237. https://doi.org/10.1016/j.agee.2016.12.004</p> <p>Tiecher, T., Minella, J. P. G., Evrard, O., Caner, L., Merten, G. H., Capoane, V., Didoné, E. J., & dos Santos, D. R. (2018). Fingerprinting sediment sources in a large agricultural catchment under no-tillage in Southern Brazil (Conceição River). Land Degradation and Development, 29(4). https://doi.org/10.1002/ldr.2917.</p> <p>Zafar, M., Tiecher, T., Capoane, V., Troian, A., dos Santos, D.R. (2017). Characteristics, lability and distribution of phosphorus in suspended sediment from a subtropical catchment under diverse anthropic pressure in Southern Brazil. Ecol. Eng. 100, 28–45.</p>
Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).
<p>This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III.</p> <p>All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude.</p> <p><strong>ww3-2.inp</strong> and <strong>Bathymetry2.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. <strong>gfs-2.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. <strong>ww3-2.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain.</p> <p><strong>ww3-3.inp</strong> and <strong>Bathymetry3.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. <strong>gfs-3.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. <strong>ww3-3.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain.</p> <p>The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).</p> <p> </p> <p><strong>Other data that complement this dataset:</strong></p> <p><strong><a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375"><em>Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</em></a></strong></p> <p><em><strong><a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a></strong></em></p> <p> </p> <p> </p>
Maximum height for the native vegetation in Minas Gerais State, Brazil
<p>Maximum height for native vegetation of Minas Gerais State (Brazil) based on GEDI measurements and environmental factors. The environmental layers included annual average temperature, annual average precipitation, terrain elevation, slope, number of cloud free days, number of months with precipitation below 100 mm. The GEDI height records were overlapped to the environmental layers, and filtered, considered the efficiency frontier.</p>
Organic and inorganic data for soil cores from Brazil and Florida Bay seagrasses to support Howard et al 2018, CO2 released by carbonate sediment production in some coastal areas may offset the benefits of seagrass “Blue Carbon” storage, Limnology and Oceanography, DOI: 10.1002/lno.10621
Using piston corers, soils from Florida Bay and Brazilian seagrass meadows were collected to complete organic and inorganic carbon inventories for the top 1 m of soil. Instrumental analyses and loss on ignition at 500C were used to measure C content of downcore slices.
Dataset and images for "Instantaneous R calculation for COVID-19 epidemic in Brazil"
<p>This dataset was generated from raw data obtained at </p> <ul> <li>Ceará State - <a href="https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv">https://indicadores.integrasus.saude.ce.gov.br/api/casos-coronavirus/export-csv</a></li> <li>São Paulo State - <a href="http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv">http://www.seade.gov.br/wp-content/uploads/2020/04/Dados-covid-19-estado.csv</a></li> <li>Brazil - <a href="https://covid.saude.gov.br/">https://covid.saude.gov.br/</a></li> </ul> <p>Data was processed with R package EpiEstim (methodology in the associated preprint). Briefly, instantaneous R was estimated within a 5 day time window. Prior mean and standard deviation values for R were set at 3 and 1. Serial interval was estimated using a parametric distribution with uncertainty (offset gamma). We compared the results at two time points (day 7 and day 21 after the first case was registered at each region) from different brazillian states in order to make inferences about the epidemic dynamics.</p>
Monthly reference evapotranspiration for Brazil
<p>The set of monthly ETo data referred to as monthly reference evapotranspiration for Brazil. It has a <strong>spatial resolution</strong> of <strong>30 seconds (~ 1 km²)</strong> and a <strong>temporal resolution of 1 month</strong>. The data set grid is in <strong>GeoTIFF format</strong>, and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection</strong> <strong>(EPSG: 4326)</strong>. The files are named as YEAR MONTH.</p> <p>Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard for estimating ETo and requires several meteorological elements. Free remote sensing products with evapotranspiration information are rare. The objective of this study was to estimate the monthly ETo from the potential evapotranspiration (PET) made available by the MOD16 product. The monthly ETo estimated by the Penman-Monteith method was considered the standard. For this, data were acquired from the 265 meteorological station of the National Institute of Meteorology (INMET), throughout Brazil, in the period from 2000 to 2014 (15 years). Using machine learning algorithms, MOD16 images and WorldClim information as covariates, the ETo. All machine learning models were effective in improving the performance of the metrics evaluated. Cubist was the model that presented the best metrics for r² (0.91), NSE (0.90) and nRMSE (8.54%) and should be the preferred one for ETo prediction. The use of monthly ETo is recommended, which opens up possibilities for its use in numerous other studies.</p> <p>The article: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245834">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245834</a></p>
REDB-BR: Rainfall Erosivity Database for Brazil
<p>This is REDB-BR, the Rainfall Erosivity Database for Brazil from the MSWEP rainfall dataset.</p> <p>It provides the R factor from the Universal Soil Loss Equation (USLE) in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset.</p> <p>The R factor was calculated trough 73 erosivity index regression equations, which mostly uses a relation between monthly precipitation and annual precipitation, the Modified Fournier Index (MFI), and represents a good approximation to locals with no sub-hourly data for long periods. </p> <p>The main product of REDB-BR is the R factor map, available also as a .tif raster. The database also includes the equations shapefile, Thiessen Polygons shapefile and the equations table. </p>
Landslides from Space - Bento Rodrigues Dam Failure, Brazil (5th November 2015)
<p>On 5th November 2015, an iron ore tailings dam in Bento Rodrigues suffered a failure. About 60 million cubic meter of iron waste flowed down the valley. Two villages were partly destroyed and the drinking water supply of a few hundred thousand people were effected. The river Doce will be affected by this disaster for many decades.</p> <p>The pre-event acquisition is from 5th November 2015 (Landsat-8) and the post-event acquisition is from 26th December 2015 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel and Landsat data (2015) </em></p>
Landsat-based dataset for mapping annual center-pivot irrigated cropland in Brazil
<p>Center-pivot irrigated cropland (CPIC) is a critical component of irrigation and plays an essential role in improving water use efficiency and increasing food production. To automatically extract the spatial distribution of CPIC in Brazil based on the remote sensing technology, we constructed a training dataset that supports the semantic segmentation models.</p><p>The dataset were built with the <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-5">Landsat 5</a> , 7 and 8<a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-7"> </a> images as well as the CPIC maps from <a href="https://metadados.snirh.gov.br/geonetwork/srv/por/catalog.search#/metadata/e2d38e3f-5e62-41ad-87ab-990490841073">ANA reference</a> data. We used the Landsat images in 2005, 2010 and 2015 to build the dataset.</p><p>The samples in train_images and train_masks were used to train and valid the Convolutional Neural Network models; </p><p>The samples in valid_data were used to test the model's prediction accuracy.</p><p>Pixels with values 255 and 0 in the mask samples represent the CPIC and background categories.</p><p><strong>For technical details that used to create the dataset, please refer to </strong><i><strong>https://doi.org/</strong></i><strong>10.1016/j.isprsjprs.2023.10.007.</strong></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.