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3,421 results for “Amazon”
Resilience estimates of Amazon and Congo rainforests based on mean annual precipitation and root zone storage capacity
<p>Resilience refers to the capacity of the ecosystem to absorb perturbations and remain in its native stable state. Here, we quantified forest resilience of South American and African ecosystems using mean annual precipitation and root zone storage capacity (2000-2019). We adopted Hirota et al. (2011) methodology for calculating resilience using logistic regression. This logistic regression predicts the probability of forest (tree cover > 50%) as a function of the independent variable. The predicted resilience estimates range between 0 to 1, where 1 represents the highest probability of finding forest – interpreted as highly resilient forest ecosystems.</p> <p>For more information, check: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115">https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16115</a></p>
Word Embedding of Amazon Product Review Corpus
<p>A word embedding of the <a href="https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets">Amazon Product Review Corpus</a> (<a href="https://www.doi.org/10.1145/1341531.1341560">Jindal and Liu, 2008</a>).</p> <p>Created using <a href="https://code.google.com/archive/p/word2vec/">Word2Vec</a> in CBOW mode, 500 dimensions and window size 5.</p> <p>Words have been lemmatised and particle verbs have been merged into a single token (e.g. <code>calm_down</code>).</p> <ul> </ul> <p> </p> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Marc Schulder, Michael Wiegand, Josef Ruppenhofer and Benjamin Roth (2017). <strong>"Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features"</strong>. Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP). Taipei, Taiwan, November 27 - December 3, 2017. <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p>
Sustainable Tourism Survey Dataset for the Northern Ecuadorian Amazon Region, 2024-2025
This dataset is based on a sustainability perception survey conducted at Perla Ecological Park, located in the Northern Ecuadorian Amazon, during December 2024 and January 2025. A total of 383 visitors participated in the study, which aimed to: (1) quantify and compare key sustainability indicators across PERLA’s management zones, (2) identify the most influential predictors of overall sustainable performance, and (3) derive and prioritize a set of integrated strategies that balance ecological conservation with economic viability. The survey instrument included Likert-scale, dichotomous, and thematic categorical items designed to assess public perceptions on tourism sustainability, natural and cultural resource management, institutional support, and visitor satisfaction. The research was carried out through a collaborative effort among multiple public universities and independent researchers, including two international institutions—one of them based in Ecuador—as part of a broader scientific initiative to inform evidence-based sustainability planning in protected areas. This dataset provides valuable insight for researchers, practitioners, and policymakers interested in sustainable tourism, visitor management, and participatory planning in biodiversity-rich environments.
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Dataset for assessing amazon rainforest regrowth with GEDI and ICESat-2 data
<p>This dataset includes GEDI data, ICESat-2 data, auxiliary data, and intermediate results necessary to reproduce results in <a href="https://doi.org/10.1016/j.srs.2022.100051">Milenkovic et al. 2022</a>. The code required to process the data is on: <a href="https://github.com/MilutinMM/SecFor-Regrowth.git">https://github.com/MilutinMM/SecFor-Regrowth.git</a>.</p> <p>Short descriptions of files:</p> <ul> <li><strong>ATL08_gdf.json</strong> - ICESat-2 ATL08 segments in Rondonia </li> <li><strong>ATL08_gdf_Para_MG.json</strong> - ICESat-2 ATL08 segments intersecting the two calibration sites</li> <li><strong>ATL08_h5_fileNames_Rondonia.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting Rondonia (primary input)</li> <li><strong>calibartionModels.zip</strong> - GEDI and ICESat-2 calibration models and statistics (xlsx files)</li> <li><strong>deforested_poligons_2018_2019.zip</strong> - SPH file of a deforested polygon in the calibration site</li> <li><strong>gedi_L2A_allTime_gdf_Para_MG.json</strong> - GEDI shots intersecting the two calibration sites</li> <li><strong>gedi_L2A_allTime_MG_all.csv</strong> - GEDI shots within the FN calibration site</li> <li><strong>gedi_L2A_allTime_Para_all.csv</strong> - GEDI shots within the TNF calibration site</li> <li><strong>GEDI_L2A_fileNames_Rondonia.txt </strong> - A list of GEDI orbits (L2A h5 files) intersecting Rondonia (primary input)</li> <li><strong>gedi_L2A_gdf_Para_MG_sens_a2.json</strong> - GEDI shots intersecting the two calibration sites with sensitivities derived from the algorithm setting group 2</li> <li><strong>gedi_L2A_gdf_sens_a2.json</strong> - GEDI shots in Rondonia</li> <li><strong>gedi_L2A_MG_all_sens_a2.csv </strong>- GEDI shots within the FN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Para_all_sens_a2.csv</strong> - GEDI shots within the TFN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Rondonia_all_sens_a2.csv</strong> - GEDI shots in Rondonia </li> <li><strong>MG_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the FN calibration site</li> <li><strong>Para_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the TFN calibration site</li> <li><strong>svbr-rondonia-2018.tif</strong> - Forest age map for Rondonia (Silva Junior et al. 2020)</li> <li><strong>svbr-rondonia-2018_bw_eroded.tif</strong> - a secondary forest extent mask with removed border pixels</li> </ul> <p>References:</p> <p>Milenković, M., Reiche, J., Armston, J., Neuenschwander, A., De Keersmaecker, W., Herold, M., Verbesselt, J., Assessing amazon rainforest regrowth with GEDI and ICESat-2 data, <em>Science of Remote Sensing</em>, 2022, 100051, ISSN 2666-0172, <a href="https://doi.org/10.1016/j.srs.2022.100051">https://doi.org/10.1016/j.srs.2022.100051</a>.</p> <p>Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G. <em>et al.</em> Benchmark maps of 33 years of secondary forest age for Brazil. <em>Sci Data</em> <strong>7, </strong>269 (2020). <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.1038/s41597-020-00600-4</a></p>
L2C - Canopy height models across the Brazilian Amazon
<p>Canopy height models derived from LiDAR data collected across the Brazilian Amazon. The files are provided in .tiff format in 7 zip folders. A full description of the data set is available here: https://zenodo.org/record/4968706#.YzB693ZKg5s</p> <p>We also provide the summary data used for statistical analysis in the associated publication: </p> <p>Reis and Jackson et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology.</p> <p>Each transect covered 375 ha (12.5 km × 300 m) by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per square meters, the field of view was equal to 30°, the flying altitude was 600 m, and transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was set to be below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and under 0.5 m, respectively.</p> <p>The data collection was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvimento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1)</p> <p>The research project was funded by the UK Natural Environment Research Council project number <strong>NE/S010750/1</strong></p>
AMnrGC - Amazon river non-reduntant microbial genes catalogue
<p> </p> <p><strong>AMnrGC : Amazon river basin non-redundant microbial gene catalogue</strong></p> <p> </p> <p> RELEASE 2018/01<br> --------------------------------------</p> <p> </p> <p>1. INTRODUCTION</p> <p> AMnrGC is a collection of genes and proteins which were constructed<br> by use of Amazon river basin openly available metagenomes from<br> sequencing projects (SRP044326, PRJEB25171 and SRP039390). Briefly,<br> metagenomes were coassembled by groups made up their geographical<br> location with Megahit v.1.0 and the contigs were used to gene predictions<br> by Prodigal v.2.6.3. Genes sequences were length filtered (> 150 bp) and<br> clustered by CD-HIT-EST (version 4.6) at 95% of nucleotide identity and<br> 90% of overlap of the shorter gene. Theorical protein products were annotated<br> by the most completes databases up to date and their complete information<br> is available here.</p> <p> </p> <p>2. LOCATION</p> <p> AMnrGC versions will be available on the web only under the current ZENODO<br> repository: 10.5281/zenodo.1484504</p> <p> </p> <p>3. FORMAT</p> <p> Gene entries were named as ">AM_AGSSY_XXX" where XXX represents an unique numerical<br> identifier. Genes were deposited in their coding phase, because of this, all of them<br> can be used to generate the protein sequences by transeq function at ORF+1.<br> The protein entries correspond to genes artifical translation used in the annotations,<br> and also available, codified in the same way, but containing the indication "_1" in<br> the end of the header. Example:</p> <p> Gene:<br> >AM_AGSSY_151515</p> <p> Protein:<br> >AM_AGSSY_151515_1</p> <p> Annotations were provided as separate tables for each database used to annotate the<br> sequences. The header of these tables indicates the meaning of each value.</p> <p> </p> <p>2. FUTURE FORMAT CHANGES</p> <p> No major changes are expected for the main general format of the database.<br> New versions should include updated versions of annotations or even additional sequences,<br> numbered as subsequent entries.</p> <p> </p> <p>3. ACKNOWLEDGEMENTS<br> <br> This work is a joint effort of Laboratory of molecular biology from Federal<br> University of São Carlos, São Paulo, Brazil (LBM/UFSCAR) and Protists group<br> of Institut del Ciencias del Mar, Barcelone, Spain (ICM). We are grateful to<br> Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), as well as, the spanish funding organ Consejo Superior de Investigaciones Científicas (CSIC).</p> <p> </p> <p> This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.</p> <p> </p> <p>4. THE AMnrGC TEAM</p> <p> AMnrGC is maintained by a group of researchers. You can contact<br> the AMnrGC consortium.<br> <br> Current curators:</p> <p> - Célio Dias Santos Júnior (celio.diasjunior@gmail.com)<br> - Flavio Henrique-Silva (dfhs@ufscar.br)<br> - Ramiro R. Logares (ramiro.logares@icm.csic.es)<br> </p> <p>5. COPYRIGHT NOTICE</p> <p> AMnrGC - Amazon river basin non-redundant microbial gene catalogue<br> Copyright (C) 2018 The AMnrGC consortium.</p> <p> This database is provided “as is” and without any warranty of any kind,<br> of openly available for non-commerical purposes. You can redistribute and/or modify it<br> as you wish, under the terms of the ODBL 1.0 license:</p> <p> https://opendatacommons.org/licenses/odbl/1.0/</p> <p> For commercial purposes, please contact us. </p> <p>___________________</p> <p>The AMnrGC Consortium<br> 2018</p>
Data for "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments"
<p>The processed data supporting the Global Environmental Change publication "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments".</p> <p>These data can be analyzed and visualized with the code at: <a href="https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare">https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare</a></p> <p>For a description of each file & the variables contained, please look to the README file.</p>
Lista de municípios prioritários no combate ao desmatamento na Amazônia e Cerrado (Deforestation priority list of municipalities in the Amazon and Cerrado)
<h4>Português</h4> <p>A Lista Negra do desmatamento, da Amazônia e do Cerrado, é atualizada pelo Diário Oficial da União (DOU), e determina a identificação de municípios com taxas elevadas de desmatamento. Mais detalhes sobre essa política pública podem ser encontrados em <a href="http://dx.doi.org/10.5380/dma.v42i0.53542" target="_blank" rel="noopener">Bizzo et al. (2017)</a>. </p> <p>Os critérios de inclusão de um município na lista da Amazônia são: área total de floresta desmatada; área total de floresta desmatada nos últimos três anos; e aumento da taxa de desmatamento em pelo menos três, dos últimos cinco anos. As consequências da inclusão na lista são: maior monitoramento e fiscalização; não aprovação de créditos para atividade agropecuária; embargo de atividades relacionadas ao desmatamento; aplicação de multas; divulgação de dados do imóvel rural em que ocorreu a infração, e seu titular. Para a remoção da lista, o município deve: Possuir 80% de seu território, em propriedades rurais, monitorado pelo Cadastro Ambiental Rural (CAR); e manter taxa de desmatamento anual abaixo do limite estabelecido em portaria do Ministério do Meio Ambiente.</p> <p>Os critérios de inclusão para os municípios do Cerrado são: média de desmatamento dos últimos dois anos superior a 25 quilômetros quadrados; desmatamento acima de 20% em áreas de vegetação nativa remanescente no município; ou a exploração da madeira em áreas protegidas. Para o Cerrado, não foram estabelecidos critérios para exclusão da lista.</p> <p>A série temporal contendo os municípios que foram incluídos na lista de prioridade e monitoramento para o controle do desmatamento. A série temporal vai de 2008 a 2022. Os dados foram coletados do Diário Oficial da União (DOU), publicado pelo governo brasileiro.</p> <p>A série temporal completa encontra-se no arquivo "priority_list.csv".</p> <h4>English</h4> <p>Time series containing the municipalities that were included in the priority and monitoring list for deforestation control. The time series ranges from 2008 and 2022. The data were gathered from the Diário Oficial da União (DOU), published by the Brazilian government.</p> <p>The complete time series is found in the "priority_list.csv" file.</p>
L4E - Maximum tree height extracted from LiDAR transects over the Brazilian Amazon
<p>Between 2016 and 2018, the EBA airborne missions (conducted by the Brazilian National Institute for Space Research (INPE) and funded by Amazon Fund) collected airborne lidar transects of 375 ha (12.5 x 0.3 km) each. A majority of the transects were flown over randomly selected locations of old growth and second growth as forests defined by the PRODES and TerraClass databases (PRODES, INPE, 2016; TerraClass, INPE, 2014). PRODES separates forests from non-forest while TerraClass identifies second growth forest and other land covers.</p>
Monthly SPEI dataset at 3-, 6-, and 12-month time scales in the Amazon River Basin
<p>SPEI datasets derived from the Global SPEI database webpage and developed by Vicente-Serrano et al. (2010). This product is disseminated by the Spanish National Research Council (CSIC) for the period from January 1901 to December 2018 (118 years, version 2.6). The SPEI datasets at 3-, 6-, and 12-month time scales are gridded over a 0.50-degree grid on the Amazon River Basin. A total of 1416 GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format, one per month, are provided.</p>
Large-scale attributed graph & hypergraph datasets: TWeibo, Amazon2M, Amazon, MAG-PM
<p>Here we provide additional large-scale datasets used in our work "A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation", along with the index files for constructing KNN graphs using ScaNN and Faiss.</p> <p>Usage:</p> <p>cd ANCKA/</p> <p>unzip ~/Download_path/ANCKA_data.zip -d data/</p>
Water surface occurrence and recurrence from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction"
<p>This data package contains the 10 m spatial resolution occurrence and recurrence water surface masks from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction" . These maps have been produced with Sentinel-1 images (10 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. Water surface occurrence is computed for the period 2022-2023 and indicates the percentage of time that a pixel is classified as water (100%: always water, 0%: never water, and values between 0 and 100 indicate seasonality). Water surface recurrence is computed for the period 2022-2023 and indicates the number of times that a pixel was classified as a water surface, i.e., 35 indicates that the pixel was classified 35 times as a water surface during the 2022-2023 period. The total size of the dataset is 158 Mo and is distributed in two Geotiffs, one for the water surface occurence and one for the water surface. When using this dataset, please cite the original article <a href="https://doi.org/10.3390/rs16061056">https://doi.org/10.3390/rs16061056</a></p>
L4A - Biomass map of the Brazilian Amazon and uncertainty
<p>The AGB final map (further referred as EBA - Estimativa de Biomassa para a Amazônia - map) presented a maximum AGB value of 518 Mg ha-1, a mean AGB of 174 Mg ha-1, and a standard deviation of 102 Mg ha-1. The map is provided in TIF format, projected using EPSG 4236. The uncertainty map is provided in TIF format, projected using EPSG 4236. The information is offered in Mg ha-1.</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Maps of the Sustainable Development Goal (SDG) indicator 15.3.1 with its sub-indicators for the entire Amazon River Basin
<p>Maps of the SDG indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) together with its sub-indicators for the Amazon River Basin for the period 2001-2020. The sub-indicators are trajectory (or trend), state, and performance. The SDG indicator 15.3.1 was calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution and the 16-day MOD13Q1 NDVI and SoilGrids dataset were used as inputs. In addition, annualized maps of drought severity derived from SPI12, SPEI12, and scPDSI are added. </p> <p>A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution.</p> <p>Coding for the SDG indicator 15.3.1, trajectory, state, and performance.</p> <p>-32768 is ‘No data’</p> <p>-1 is ‘Degraded’</p> <p>0 is ‘Stable’’</p> <p>1 is ‘Improvement’</p> <p>Coding for the drought severity.</p> <p>From 0 (minimum drought severity) to 1 (maximum drought severity).</p>
Amazon product reviews (mock dataset)
<p>About</p> <p>This is a mock dataset with Amazon product reviews. Classes are structured: 6 "level 1" classes, 64 "level 2" classes, and 510 "level 3" classes.</p> <p><br> 3 files are shared:</p> <ul> <li>train_40k.csv - training 40k Amazon product reviews</li> <li>valid_10k.csv - 10k reviews left for validation</li> <li>unlabeled_150k.csv - raw 150k Amazon product reviews, these can be used for language model finetuning.</li> </ul> <p>Level 1 classes are: health personal care, toys games, beauty, pet supplies, baby products, and grocery gourmet food.</p> <p>Dataset originally from <a href="https://www.kaggle.com/datasets/kashnitsky/hierarchical-text-classification">https://www.kaggle.com/datasets/kashnitsky/hierarchical-text-classification</a></p>
Canopy gap sizes across the Brazilian Amazon
<p>This data frame contains the raw gap data used to study the gap size-frequency distributions across the Brazilian Amazon in:</p> <p>Reis, C., Jackson, T., et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology. </p> <p>They gaps were extracted from the EBA LiDAR data set https://zenodo.org/record/4968706#.YygSGHZKg5s</p>
Trajetorias dataset: environmental, epidemiological, and economic indicators for the Brazilian Amazon
<p>The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The Trajetorias dataset is organized in dimensions describing: environmental degradation, land use and land cover, human mobility, climate anomalies, the burden of vector-borne diseases, and poverty indices for rural and urban populations for each of the BLA municipalities. Characterizing the environmental, epidemiological, and socioeconomic profile of the municipalities. These indicators were designed to unveil the specificities of the Amazon region, so that the relationships between these dimensions can be explored regarding past and current enacted policies. The Trajetorias dataset relies on four surveys - the two demographic censuses conducted in 2000 and 2010, and the two agrarian censuses conducted in 2006 and 2017, from which were defined fixed timestamps for analysis. The demographic censuses are the source of data for the multidimensional poverty indices. Environmental data come from satellite images collected by several national and international programs, such as the Amazon Deforestation Monitoring Program (PRODES), DEGRAD, and DETER, accounting for changes in landscape that took place between each demographic census and the subsequent agrarian census. Lastly, disease control data was obtained from the National Disease Notification System and summarized for the 5-year period centered in the agrarian censuses.</p>
Data used for doi.org/10.1029/2012JD018338 : Baars et al, 2012: Aerosol profiling with lidar in the Amazon Basin during the wet and dry season
<p><span>This is the data whoch ahs been used for the publication:</span></p> <p><span><span>Baars, H.</span></span><span>, <span>A. Ansmann</span>, <span>D. Althausen</span>, <span>R. Engelmann</span>, <span>B. Heese</span>, <span>D. Müller</span>, <span>P. Artaxo</span>, <span>M. Paixao</span>, <span>T. Pauliquevis</span>, and <span>R. Souza</span> (<span>2012</span>), <span>Aerosol profiling with lidar in the Amazon Basin during the wet and dry season</span>, <em>J. Geophys. Res.</em>, <span>117</span>, D21201, doi:<a title="Link to external resource: 10.1029/2012JD018338" href="https://doi.org/10.1029/2012JD018338" target="_blank" rel="noopener">10.1029/2012JD018338</a>.</span></p> <p><span>For each of the Figures in the Publication the underlaying data is provided in a respective folder.</span></p> <p><span>The raw data (i.e,. the analyzed lidar data for several case during the one-year campaign in 2008) is provided separately.</span></p> <p><span>As the time of data creation is more than 10 years ago, the data description does not comply to current standards. Thus, in case of any questions, please contact the first author.</span></p> <p> </p> <p> </p> <p> </p>
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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)
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.