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25,418 results for “Brazil”
ARTICLE DATASET - ANTHROPOGENIC MICROPARTICLES ACCUMULATION IN SMALL-BODIED SEAGRASS MEADOWS: THE CASE OF TROPICAL ESTUARINE SPECIES IN BRAZIL
<p>This dataset with the data analysis script refers to the publication <a href="https://www.sciencedirect.com/science/article/abs/pii/S0025326X24007768?via%3Dihub"><strong>https://doi.org/10.1016/j.marpolbul.2024.116799</strong></a>.</p> <p>The dataset consists of a spreadsheet containing 9 tabs with survey data described below and their respective captions, found in the first line of each tab.</p> <p>The script for statistical analysis in the R language contains descriptive and statistical analyses, in addition to the functions for creating the graphs displayed in the article.</p> <p><strong>Description of the spreadsheet dataset tabs --------------------------------------------------------------------------------</strong></p> <ol> <li>description - Contains general information about the article</li> <li>meadows - Contains properties related to the characteristics of the multispecific grassland.</li> <li>ap_samples - Contains properties related to the abundances of anthropogenic microparticles, as to classifications by shape, size (mm) and color in units, kg and frequency of occurrence.</li> <li>ap_categories - Contains raw data related to the classifications of anthropogenic microparticles, in terms of shape, size (mm) and color.</li> <li>sediment - Contains raw data related to the classifications of sediment particles.</li> <li>granulometry - Contains properties related to sediment particles in µm, and their classifications by predominance, frequency and kg.</li> <li>shape - Contains raw data related to the classification of the shapes of anthropogenic microparticles in units and kg.</li> <li>size - Contains raw data related to the classification of the sizes of anthropogenic microparticles in units and kg.</li> <li>color - Contains raw data related to the classification of the colors of anthropogenic microparticles in units and kg.</li> </ol>
Soil Properties Summarized by Lots in Ouro Preto D'Oeste, Rondônia - Brazil (2019 surveyed lots V2)
<p>Soil properties data, summarized (weighted average by area with the given property on the lots surveyed on 2019 - V2) over small properties lots in Ouro Preto d'Oeste, Rondônia - Brazil:<br> AWHC:Available Water Holding Capacity: Water content at “field capacity” minus Water content at wilting point. Water content is % by weight. (50% = 50% of the weight of a lump of soil is water). (%)<br> PBS: Percent Base Saturation (%)<br> CLAY: Clay content (%)</p>
Review of Polydora species from Brazil, with identification key and description of two new species (Annelida: Spionidae)
<p>Supplementary Material for the article published by the <strong><em>Ocean and Coastal Research</em> Journal</strong></p> <p>Complete information on the material examined during this study and records by other authors is given in Supplementary Tables S1−11. A list of the museums and other collections (and their acronyms) holding the samples which are reported in this study is given in Table ESM12.</p>
Multi-decade land use and land cover samples for Brazil based in a stratified sampling design and visual interpretation of Landsat data (1985 — 2018)
<p>This dataset is composed by 85,152 random points throughout the Brazilian territory selected according to a stratified sampling design, based in 127 regular regions and six slope classes (<a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1-arc?qt-science_center_objects=0#qt-science_center_objects">SRTM</a>). Each sample was visually inspected by three independent interpreters, which associated all the land use and land cover (LULC) changes between 1985 and 2018, on a <strong>yearly basis</strong>, using as reference two <strong>Landsat</strong> images per year, a <strong>MODIS</strong> NDVI time series and high resolution images from <strong>Google Earth</strong>. </p> <p>This process was guided by a <a href="https://www.lapig.iesa.ufg.br/chave/">reference labeling protocol</a> which established the follow LULC classes:</p> <ul> <li><strong>Annual crop:</strong> Areas occupied with short to medium-term crops, usually with a vegetative cycle of less than one year, which after harvest needs to be re-planted. </li> <li><strong>Aquaculture:</strong> Artificial lakes, where aquaculture and/or salt production activities predominate</li> <li><strong>Beach and dune (Other):</strong> Sandy areas, with bright white color, where there is no vegetation predominance of any kind.</li> <li><strong>Forest formation:</strong> Vegetation types with predominance of tree species, with continuous canopy formation</li> <li><strong>Grassland formation:</strong> Grassland formations with predominance of herbaceous stratum</li> <li><strong>Mangrove (Other):</strong> Dense and Evergreen Forest formations, often flooded by tide and associated with the mangrove coastal ecosystem.</li> <li><strong>Mining (Other):</strong> Areas where clear signs of extensive mineral extractions are present, shows clear exposure of the soil by the action of heavy machinery. Only regions surrounding the AhkBrasilien (AHK) and the CPRM digital reference data were considered.</li> <li><strong>Not observed:</strong> Areas blocked by clouds or atmospheric noise, or with absence of ground observation masked out from analysis.</li> <li><strong>Other non-forest natural formations:</strong> Marshes (with fluvio-marine influence).</li> <li><strong>Other non-vegetated area (Other):</strong> Non-permeable surface areas (infrastructure, urban expansion or mining) not mapped into their classes</li> <li><strong>Pasture:</strong> Pasture areas, natural or planted, related with farming activity. In particular in the Pampa and Pantanal biomes part of the area classified as Grassland Formation also includes pasture areas.</li> <li><strong>Perennial crop:</strong> Areas occupied with crops with a long cycle (more than one year), which allow successive harvests without the need for new crop. </li> <li><strong>Rocky outcrop (Other)</strong>: Naturally exposed rocks without soil cover, often with the partial presence of rupicolous vegetation and high slope. </li> <li><strong>Salt flat (Other):</strong> "Apicuns" or Salt flats are formations often without tree vegetation, associated to a higher, hypersaline and less flooded area in the mangrove, generally in the transition between this area and the continent.</li> <li><strong>Savanna formation:</strong> Savanna formations with defined tree and shrub-herbaceous stratum</li> <li><strong>Semi-perennial crop:</strong> Cultivated areas with sugar cane</li> <li><strong>Tree plantation:</strong> Planted tree species for commercial use (e.g. Eucalyptus, Pinus and Araucaria)</li> <li><strong>Urban infrastructure:</strong> Urban areas with predominance of non-vegetated surfaces, including roads, highways and constructions.</li> <li><strong>Water:</strong> Rivers, lakes, dams, reservoir and other water bodies</li> <li><strong>Wetland:</strong> Wetlands with fluvial influence or swampy areas</li> </ul> <p>To enable a proper area estimation and accuracy assessment (<a href="https://www.tandfonline.com/doi/abs/10.1080/01431161.2014.930207">Stehman, 2014</a>) the dataset is provided with the <strong>sampling probability</strong> for each sample (<em>brazil_lulc_samples_1985_2018</em> and <em>brazil_lulc_samples_1985_2018_row_wise</em>) and the <strong>sampling weight</strong> (<em>brazil_lulc_samples_1985_2018_row_wise</em>), which was adjusted to disregard the "<strong>Not observed" </strong>class. The number of votes for the associated LULC class (visual interpretation agreement) and an indication if the sample is between two different LULC<strong> </strong>classes (<strong>border flag</strong>) are also provided.</p> <p>The samples were used to produce several <strong><a href="https://github.com/lapig-ufg/tvi-analysis">area estimation analyses</a></strong>, including land use and land cover dynamics, historical deforestation and agricultural expansion of Brazil. A publication describing in detail the methodology and the analysis is under preparation.</p>
Dataset on the dental morphology and occlusal dental wear of pre-colonial societies of the South and Southeast Coast of Brazil
<p>This dataset compiles information on dental morphology and occlusal dental wear of 431 individuals exhumed from coastal and riverine sites of the South and Southeast Coast of Brazil, dated between approximately 10,000 to 1,000 years before present. Dental traits were scored according to the Arizona State University Dental Anthropology System (ASUDAS) (Scott and Irish, 2017; Turner II et al., 1991). Few additional mandibular traits were added following Hauser and Stefano (1989). Occlusal dental wear was scored according to the method described in Smith (1984).</p> <p>Sex and age at death estimations derive from previous studies (Estevam, 2020; Fischer, 2012; Neves et al., 2005; Silva, 2005; Tognoli, 2016; Wesolowski, 2007). When this information was not available from previous studies, it was assessed by the first author (Fidalgo, 2021) using standard protocol methods (Buikstra and Ubelaker, 1994). Further detailed information and description of each variable can be consulted within the “description” and “dental grades” sheets in the excel file.</p> <p>The dataset is part of a PhD project developed at the Museu de Arqueologia e Etnologia da Universidade de São Paulo, carried by Daniel Fidalgo and advised by Veronica Wesolowski and Mark Hubbe (Fidalgo, 2021). Manuscripts have already been published using this data (Fidalgo et al., 2021a, 2021b). All research was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grants 17/20637-4 and 19/18289-3. It was also supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) grant 001.</p>
Flow-topography interactions in the western tropical Atlantic boundary off Northeast Brazil.
<p>Figures and other media files of the paper Flow-topography interactions in the western tropical Atlantic boundary 2off Northeast Brazil.</p>
PLANET4B coding of 29 interviews Trade & GVCs case study Brazil & EU 20250708 v2 data
<p>This file contains the coding of 29 interviews collected between 2023 and 2025, in the context of the case study "Trade & global value chains", part of the Horizon Europe Project PLANET4B. The interviews include participants from academia, environmental NGOs, Indigenous peoples and local communities, government and businesses in Brazil and in the Netherlands, connected to the global value chains of soy and beef between Brazil-Netherlands. Some of these interviews discuss the recent European Union Regulation on Deforestation-Free Products (EUDR). </p> <table> <tbody> <tr> <td> </td> </tr> </tbody> </table>
Harmfull algae bloom monitoring program dataset; ERDDAP, ERA5 and ONI datasets; and R script for multicriteria analisys in Santa Catarina coastal zone, Brazil.
<p>Project Harmful Algae Bloom (HAB) Monitoring Network in Santa Catarina, Brazil - Database and R script with data analysis. This project was funded by the Foundation for Research Support of the State of Santa Catarina – FAPESC and generated a database combining a HAB monitoring dataset with oceanographic (from ERDDAP) and climatic (from ERA5 and ONI) data which was submitted to multicriteria analysis using R. The HAB monitoring dataset was obtained from Cidasc/SC State Government (http://www.cidasc.sc.gov.br/defesasanitariaanimal/monitoramento-de-algas-nocivas/) and contains results of phytoplankton counts in water samples and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline. Oceanographic data were obtained from the ERDDAP/NOAA website (https://coastwatch.pfeg.noaa.gov/erddap/index.html), including the variables mean chlorophyll concentration (mg.m-3) and mean sea surface temperature (ºC); Climate data were obtained from Copernicus/ERA5 (https://cds.climate.copernicus.eu/) including the variables mean air temperature (ºC), mean pressure (Pasc.), mean cloud cover (%), mean precipitation (kg.m-2), radiation (Einsteins.m-2.day-1), mean U wind (m.s-1), and mean V wind (m.s-1).; Oceanic Niño Index (ONI) data were obtained from the NOAA website (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php); The R script involves a pre-processing routine aimed at summarizing and integrating all datasets and the subsequent data analyses carried out to evidence temporal patterns related to different type of algal blooms. Detailed methods will be provided in a scientific article.</p>
SVAD Age Data: Brazil
<p><strong>Description</strong></p> <p>The dental data were collected from anonymized radiographic images generated at the Universidade de São Paulo (FOUSP). Individuals are between 6 years and 15 years of age. As part of the SVAD age indicator template, there are 64 age variables (columns) in the dataset, which include diaphyseal lengths and breadths, epiphyseal fusion/appearance of ossification centers, and dental development. However, because this data is collected from two dimensional panoramic radiographs, only dental data are included; all other variables have a 'NA'. The data collection methodology can be found in Stull and Corron (2022) and Corron et al. (2021).</p> <p><strong>Basic Key Code:</strong></p> <ul> <li>*DL_L/R == diaphyseal lengths</li> <li>*DB_L/R == diapyseal lengths</li> <li>*EF_L/R == epiphyseal fusion</li> <li>*Oss == ossification </li> <li>*man_ and max_ == mandibular and maxillary dental development</li> </ul> <p><strong>References</strong></p> <p>Stull, K.E. and Corron, L.K. (<a href="https://www.mdpi.com/2673-6756/2/1/3"><em>2022</em></a>) The Subadult Virtual Anthropology Database (SVAD): An Accessible Repository of Contemporary Subadult Reference Data. Forensic. Sci. 2022, 2, 20–36. https://doi.org/10.3390/forensicsci2010003</p> <p>Corron, LK, <em>et al. (<a href="https://www.sciencedirect.com/science/article/pii/S0379073821000074">2021</a>)</em> Standardizing ordinal subadult age indicators: Testing for observer agreement and consistency across modalities. <em>Forensic Science International</em> <strong>320</strong>, 110687. </p>
Geospatial analysis of mining areas reclamation potential through Technosols in Brazil
<p>This repository contains two datasets:</p> <p>1. An update of metadata analysis with data published before 2021 resulting from the search equation "TS = (Technosol* AND (Organic carbon OR Organic matter)" in the Web of Science (WOS) database. Update from Allory 2022: https://doi.org/10.24396/ORDAR-60.</p> <p>2. A database containing geospatial datasets (inputs and outputs), R scripts, and other FOSS software files used for the geospatial analysis of land reclamation potential through Technosols in Brazil.</p>
Data on specialist and generalist herbivory, environmental variation, and phytochemical similarity from the Atlantic Rainforest of Brazil: 2013-2014
What: These are data on specialist and generalist herbivory from naturally occurring Piper plants as well as environmental data collected in 10 m diameter plots across sites in the Atlantic Rainforest of Brazil. Data also include chemical similarity calculated as the Morisita similarity index for species in a given plot and chemical modules that demonstrate classes of compounds that group together and influence herbivory. Why: Data were collected to understand factors that influence herbivory and tropical forest richness with a particular focus on secondary chemistry metabolomics. Where: Field sites include São Bento de Sapucaí (-22.8758, -45.8581), Parque Nacional de Itatiaia (-22.3698, -44.6285), Parque Estadual de Intervales (-24.3088, -48.2736), and Parque Estadual de Serra do Mar - Núcleo Pincinguaba (-23.6200, -46.7222). When: Data were collected in the field from Dec 2013 – May 2014. How: Field data were collected in 10 m diameter plots centered on randomly located Piper plants. Chemical data come from 1H-NMR metabolomics.
CO2 concentrations and emissions from subtropical headwater streams, São Carlos, Brazil, 2018
The data were collected in the municipalities of São Carlos, Itirapina, and Brotas in the state of São Paulo, southeastern Brazil. Six sandy/rocky-bottom headwater streams (1st to 2nd order) were selected based on the main land use in the catchment. Three streams drained sugarcane plantations, and three streams drained native vegetation catchments (Cerrado vegetation). The catchment drainage areas were determined using digital elevation models. Land use was classified based on satellite images from LANDSAT using ArcGIS software. The data were collected to study the impact of different land uses (sugarcane plantations vs. native vegetation) on the headwater streams. These streams have previously been studied for methane dynamics, indicating a focus on understanding environmental and ecological impacts. Three samples were collected from each stream during spring, summer, and winter using the headspace extraction technique. Due to access issues, samples from one stream were not collected in spring and summer 2018. Syringes filled with ultrapure nitrogen were used to collect stream water samples, which were then shaken to equilibrate gases. The gas was analyzed using a Shimadzu GC-2014 gas chromatograph equipped with various detectors. Concentrations were compared with standards to calculate CO2 levels, and CO2 emissions were calculated based on gas transfer velocity and dissolved concentrations.
First-order vertebrated mortality due the 2020 wildfires in the Pantanal wetland, Brazil
We conducted ground surveys along line transects to estimate the first-order impact of the 2020 wildfires on vertebrates in the Pantanal wetlands, Brazil. We adopted the distance sampling technique (Burnham et al 1980) to estimate the densities and the number of dead vertebrates in the 39,030 square kilometers affected by fire. We covered 123 transects scattered in the floodplain, up to 72 hours after the fires, mostly within 24 or 48 hours. We recorded the perpendicular distance between each carcass found in the field and the line transect. The carcasses were identified at least at Order level, down to species level when possible. The surveys were conducted from August to November 2020.
Figure 4 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 4. Parastacus pilicarpus sp. nov. A, habitus dorsal view (holotype); B, cephalon dorsal view (holotype); C, cephalon lateral view (holotype); D, female pleon, dorsal view (paratype 1); E, male first to third pleonal pleura (holotype); F, female first to third pleonal pleura (paratype 1); G, telson and uropods dorsal view (holotype). Scale bars: A, D, F – 1 cm; E - 5 mm; C, G – 3.33 mm; B – 2.5 mm.
Figure 8 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 8. Distribution of Parastacus buckupi sp. nov. (star) and P. pilicarpus sp. nov. (triangle) in the states of Rio Grande do Sul and Santa Catarina, southern Brazil.
Figure 7 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 7. Comparative board of the chelipeds of selected species of genus Parastacus Huxley, 1879 with pilous cutting edge of fingers. A – P. buckupi sp. nov. (holotype); B – P. pilicarpus sp. nov. (holotype); C – P. fluviatilis Ribeiro & Buckup in Ribeiro et al. (2016) (UFRGS 2704); D – P. pilimanus (von Martens, 1869) (UFRGS 2413, CL 38.74). Scale bars: 1 cm.
Figure 6 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 6. Parastacus pilicarpus sp. nov., habitat and living specimens. A, B, Typical habitat, a first order stream in the municipality of Morro Grande, state of Santa Catarina; C, living specimen. Photographs by Caio R. M. Feltrin. No available information of scale in photograph C.
Figure 1 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 1. Parastacus buckupi sp. nov. A, habitus dorsal view (holotype); B, cephalon dorsal view (holotype); C, cephalon lateral view (holotype); D, female pleon (paratype 4); E, male first to third pleonal pleura (holotype); F, female first to third pleonal pleura (paratype 4); G, tailfan dorsal view (holotype). Scale bars: A – 1 cm; B–D, G – 5 mm; E, F – 3.33mm.
Figure 2 in A new species of Novamundoniscus Schultz, 1995 (Isopoda, Oniscidea, Dubioniscidae) from the state of Tocantins, Brazil
Figure 2. Novamundoniscus adhara Campos-Filho & Cardoso sp. nov., (female paratype). (A) right mandible; (B) left mandible; (C) maxillula outer endite; (D) maxilla; (E) maxilliped
Figure 1 in A new species of Novamundoniscus Schultz, 1995 (Isopoda, Oniscidea, Dubioniscidae) from the state of Tocantins, Brazil
Figure 1. Novamundoniscus adhara Campos-Filho & Cardoso sp. nov., (female paratype). (A) habitus; (B) dorsal scale-seta; (C) cephalon, frontal view; (D) pleonites 4 and 5, and telson; (E) antennula; (F) antenna
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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