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193 results for “socioeconomic”
Federation of Vehicular Data in Smart Villages with Socioeconomic Information
<p>We present a dataset tailored for monitoring vehicle activity in a rural environment, specifically the Barranco de Poqueira region, covering the municipalities of Pampaneira, Bubión, and Capileira within the Sierra Nevada National Park, Granada, Spain. The dataset is generated by four Hikvision License Plate Recognition (LPR) cameras, capturing vehicle entries and exits in each village. To enrich the dataset, we include additional contextual details such as vacation calendars, vehicle origins, and socio-demographic information. Spanning from February 2022 to August 2023, the dataset is organized into three files: one with raw data directly from the cameras, another aggregated at the visit level with contextual information, and a third aggregated by vehicles with context details. With potential applications in mobility studies, urban planning, tourism, and socio-demographic analysis, the dataset is structured into three distinct files, encompassing a total of 43 different variables.</p> <p>The <strong>RAW_SMART_POQUEIRA.csv</strong> file contains information about 4 variables: num_plate_ID, camera_ID, date, and direction.</p> <p>The file <strong>VEHICLES_SMART_POQUEIRA.csv</strong> contains information about 33 variables: num_plate_ID, visit_time, distance, num_holiday, num_workday, num_high_season, num_low_season, entry_in_high_season, entry_in_holiday, nights, visits_dif_weeks, visits_dif_months, total_entries, avg_visit, std_visit, avg_nights, std_nights, avg_holiday, std_holiday, avg_workday, std_workday, avg_high_season, std_high_season, avg_low_season, std_low_season, route, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p> <p>The file <strong>VISITS_SMART_POQUEIRA.csv</strong> contains information about 26 variables: num_plate_ID, entry_cam, entry_date, entry_time, exit_cam, exit_date, exit_time, visit_time, route, distance, num_holiday, num_workday, num_high_season, num_low_season, nights, visits_dif_weeks, visits_dif_months, entry_in_holiday, entry_in_high_season, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p>
German Index of Socioeconomic Deprivation (GISD)
<p>Der German Index of Socioeconomic Deprivation (GISD) ist ein am Robert Koch-Institut entwickelter Index zur Erfassung regionaler sozioökonomischer Benachteiligung. Er wird verwendet, um regionale sozioökonomische Ungleichheiten in der Gesundheit sichtbar zu machen und Ansatzpunkte zur Erklärung regionaler Unterschiede in der Gesundheit aufzeigen zu können. Mit dem GISD wird es möglich, sozioökonomische Unterschiede in den Gesundheitschancen, Krankheits- und Sterberisiken in Deutschland auch dann zu untersuchen, wenn die betreffenden Gesundheitsdaten auf individueller Ebene keine Information zum sozioökonomischen Status enthalten. Für die Generierung des GISD werden Information der Bildungs-, Beschäftigungs- und Einkommenssituation in Kreisen und Gemeinden aus der Datenbank INKAR verwendet. Er wird auf der Ebene der Gemeinden generiert und wird für die Raumbezüge Gemeinden, Gemeindeverbände, Stadt- und Landkreise, Raumordnungsregionen, NUTS-2 und Postleitzahlbereiche bevölkerungsgewichtet aggregiert bereitgestellt. Die Gewichtung der Indikatoren wird über Hauptkomponentenanalysen innerhalb der Teildimensionen vorgenommen. Die aktuell verfügbaren Daten beziehen sich auf den Gebietsstand 31.12.2021 und enthalten Werte von 1998 bis 2021.</p>
Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis
<p>This data repository is associated with the paper:</p> <p>Morris,J., A. Sokolov, J. Reilly, A. Libardoni, C. Forest, S. Paltsev, A Schlosser, R. Prinn and H. Jacoby (2025). Quantifying Both Socioeconomic and Climate Uncertainty in Coupled Human-Earth Systems Analysis. <em>Nature Communications </em><strong>16</strong>, 2703. https://doi.org/10.1038/s41467-025-57897-1</p> <p>This paper quantifies key socio-economic and climate uncertainties using the MIT Integrated Global System Model. </p>
Dataset for the manuscript Marmet, Wicki, Gmel, Gachoud, Daeppen, Bertholet, Studer (2021). The psychological impact of the COVID-19 crisis is higher among young Swiss men with a lower socioeconomic status: evidence from a cohort study. Plos One. DOI:10.1371/journal.pone.0255050
<p>Dataset for the manuscript Marmet, Wicki, Gmel, Gachoud, Daeppen, Bertholet, Studer (2021). The psychological impact of the COVID-19 crisis is higher among young Swiss men with a lower socioeconomic status: evidence from a cohort study. Plos One DOI:10.1371/journal.pone.0255050</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p> </p>
Neighborhood Socioeconomic and demographic changes in Baltimore's (BES) Neighborhoods: 1930 to 2010
This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.
Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits
<p>Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.</p>
Socioeconomic status and adiposity in childhood cancer survivors: A cross-sectional retrospective study
<p>This dataset contains information on selected indicators of socioeconomic status and anthropometric indicators of adiposity in a population of childhood cancer survivors from the Late Effect Outpatient Clinic at St. Anne's Hospital in Brno, Czech Republic. </p>
Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.
<h2>Description (V2 - Latest):</h2> <p>To enable easy integration in the workflows, we have provided the main datasets in the following formats:</p> <p> </p> <ul> <li><strong><em>Vector dataset:</em><code> Folder - Vector</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>Geopackage (.gpkg)</em></code> file <strong>(</strong><strong><em>Results_Vis.gpkg</em></strong><strong>)</strong> with polygon geometries at 1/8-degree spatial resolution in an <strong>EPSG:4326 </strong>coordinate system. The <em>attribute table</em> of this file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with <em>Y</em><strong> </strong>representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em> and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <ul> <li><strong><em>Raster datasets:</em></strong><strong> <code> Folder - Raster</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>geotiff (.tif)</em></code> files with <strong>LZW</strong> compression in an <strong>EPSG:4326</strong> coordinate system. The assessed gross rooftop area datasets are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5 </em>for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with<strong> </strong><em>Y</em> representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units.</li> </ul> <p> </p> <ul> <li><strong><em>Numerical dataset:</em></strong> <strong><code> Folder - Numerical</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>parquet (.parquet)</em></code> file <strong><em>(Results.parquet).</em></strong> This file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em> narratives with <em>Y </em>representing the assessment year having values as<strong> </strong><em>20, 30, 40, and 50</em><strong> </strong>for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a <em>CF</em> column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <p>In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <strong><code> Folder - Models</code></strong></p> <ul> <li><strong><em>M2_Model.json:</em></strong><strong> </strong>This file contains the frozen parameters of the M2 model in <code><em>.json</em></code> format generated from <code>XGBoost version 2.0.3</code></li> <li><strong><em>SSP_drivers.parquet:</em><em> </em></strong>This file contains the driver data used for generating the main dataset in our study</li> <li><strong><em>FN_MAP.parquet:</em></strong><strong> </strong>This file contains the boundary information for each fishnet grid tile in a Well Known Text <em>(WKT)</em> format.</li> <li><strong><em>Prediction.ipynb:</em></strong><strong> </strong>This file provides a python notebook interface to generate inferencing from <em><code>M2_Model.json</code> </em>using <code><em>SSP_drivers.parquet</em></code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code><em>FN_MAP.parquet</em></code><code> </code>file.</li> <li><strong><em>environment.yaml:</em></strong><strong> </strong>This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul> <p> </p> <h2><strong>Version history:</strong></h2> <p><strong>This version corresponds to the revised journal submission (Round 1). <em>The version will be updated upon the completion of the review of the main manuscript.</em></strong></p> <ul> <li><em>This version <strong>V2</strong> is supersedes <strong>V1</strong> to correspond with round 1 of review.</em></li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " <em>Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 </em>", submitted to <em>Scientific Reports</em> Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul> <p> </p> <h2>Changelog:</h2> <p>The following files from version <strong>V1</strong> of this dataset are now <strong><em>archived</em></strong> based on the reviews (Round 1).</p> <ol> <li> <blockquote><em><strong>1_Geospatial_Dataset_V1.gpkg</strong></em></blockquote> </li> <li> <blockquote><em><strong>2_Countrylevel_gross_rooftop_area_V1.parquet</strong></em></blockquote> </li> <li> <blockquote><em><strong>3_Analytics_Scripts_V1.ipynb</strong></em></blockquote> </li> </ol>
Geospatial, biophysical and socioeconomic data for the Athens municipality on a zipcode resolution
<p>A collated dataset from various remote sensing, local and national sources. </p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
EcoTrends-Socioeconomic Catalog data for the VCR/LTER Airshed 1820-2000
The "data" sheet contains the county x variable matrix, while the "metadata" sheet contains the variable definitions and sources. For a complete description of sources and the content of variables you should consult the Data Dictionary on the EcoTrends-Socioeconomic Catalog (http://coweeta.ecology.uga.edu/trends/catalog_trends_base2.php) since the data collection procedure and sources used where the same. Nichole Rosamilia compiled all the data for the airshed as well as EcoTrends in case you have any particular questions that are not addressed in the data catalog or below. A few points to note on the attached data: 1) '-9999' indicates a null value - prior to a given county coming into existence there is no data so it is recorded as null; periodically, and typically for no recorded reason, no data will be recorded or available for a given county so again it is recorded as null. 2) the units of measure on certain variables, e.g., CWAGE (commercial establishments), changes over time from dollars, to thousands of dollars, to millions of dollars. Nichole left the data as it appears in the original rather than modify it on entry. For the on-line version we run the data through a script to standardize the units of measure, but that happens later in our data processing. The approximate airshed for the VCR/LTER was based on translation (of the oxidized nitrogen airshed for Shenandoah National Park created by R. Dennis of the Atmospheric Sciences Modeling Division of ARL, NOAA and NERL USEPA). The original Shenandoah National Park airshed was digitized from a printed copy, then the centroid of the airshed polygon was moved to from the park to the Virginia Coast Reserve. This was then intersected with ESRI counties data to generate the list of needed counties. A copy of the shapefile and a PDF is included in the "vcrairshed.zip" file in the data directory.
The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil
<p>This video shows de simulation of scenarios presented in the article "The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil"</p>
Data from: Breaking Barriers? Ethnicity and socioeconomic background impact on early career progression in the fields of ecology and evolution
<p>The academic disciplines of Science, Technology, Engineering and Mathematics (STEM) have long suffered from a lack of diversity. While in recent years there has been some progress in addressing the underrepresentation of women in STEM subjects, other characteristics that have the potential to impact on equality of opportunity have received less attention. In this study, we surveyed 188 early career scientists (ECRs), defined as within ten years of completing their PhD, in the fields of ecology, evolutionary biology, behaviour, and related disciplines. We examined associations between ethnicity, age, sexual orientation, sex, socioeconomic background, and disability, with measures of career progression, namely publication record, number of applications made before obtaining a postdoc, type of contract, and number of grant applications made. We also queried respondents on perceived barriers to progression, and potential ways of overcoming them. Our key finding was that socioeconomic background and ethnicity were associated with measures of career progression. While there was no difference in the number of reported first-authored papers on PhD completion, ethnic minority respondents reported fewer other-authored papers. In addition, ECRs from a lower socioeconomic background were more likely to report being in teaching and research positions, rather than research only positions, the latter being perceived as more prestigious by some institutions. We discuss our findings in the context of possible inequality of opportunity. We hope that this study will stimulate wider discussion, and help to inform strategies to address the underrepresentation of minority groups in the fields of ecology and evolution, and STEM subjects more widely.</p>
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
<p>Using data from New York City, we found that there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare essential workers had greater mobility during the pandemic. When adjusted for the percent of essential workers, these associations do not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percent older than 75 years old and percent of healthcare essential workers) was associated with a higher rate of COVID-19 cases per 100k, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged are not only at an increased risk for COVID-19 infection, but lack the privilege to fully engage in social distancing interventions.</p>
Data and literature repository for "Climate Futures are Political Futures: Integrating Political Development Into the Shared Socioeconomic Pathways (SSPs)"
<p>The datasets provided in the repository (listed in Table 1 of the manuscript):</p> <ul> <li>Governance (Andrijevic et al., 2020)*</li> <li>Government effectiveness (Andrijevic et al., 2020)*</li> <li>Violent conflict (Hegre et al., 2016)</li> <li>Rule of law (update to the Soergel et al., 2021)</li> </ul> <p>*Please note that these two variables can be found in the same data file.<br><br></p> <p>The indicators can also be retrieved through the <a href="https://ssp-extensions.apps.ece.iiasa.ac.at/">SSP Extensions Explorer.</a> <br><br><strong><br>For applications of the projections of political indicators in further analyses, please consult the following references: </strong> </p> <p>Brutschin, E., Pianta, S., Tavoni, M., Riahi, K., Bosetti, V., Marangoni, G., & Van Ruijven, B. J. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abf0ce/meta">A multidimensional feasibility evaluation of low-carbon scenarios.</a> <em>Environmental Research Letters </em>2021, <em>16</em>(6), 064069.</p> <p>Gidden MJ, Brutschin E, Ganti G, Unlu G, Zakeri B, Fricko O<em>, et al. </em><a title="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5" href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d5">Fairness and feasibility in deep mitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate</a>. <em>Environmental Research Letters </em>2023, <strong>18</strong>(7)<strong>: </strong>074006. </p> <p>Hoch JM, de Bruin SP, Buhaug H, Von Uexkull N, van Beek R, Wanders N. <a title="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2" href="https://iopscience.iop.org/article/10.1088/1748-9326/ac3db2">Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach</a>. <em>Environmental Research Letters </em>2021, <strong>16</strong>(12)<strong>: </strong>124068. </p> <p>Joshi DK, Hughes BB, Sisk TD. <a title="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145" href="https://www.sciencedirect.com/science/article/abs/pii/S0305750X15000145">Improving governance for the Post-2015 Sustainable Development Goals: Scenario forecasting the next 50 years</a>. <em>World Development </em>2015, <strong>70: </strong>286-302. </p> <p>Moyer JD. <a title="https://www.sciencedirect.com/science/article/pii/S0305750X23000062" href="https://www.sciencedirect.com/science/article/pii/S0305750X23000062">Blessed are the peacemakers: The future burden of intrastate conflict on poverty</a>. <em>World Development </em>2023, <strong>165: </strong>106188. </p> <p>Moyer JD, Turner SD, Meisel CJ. <a title="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740" href="https://journals.sagepub.com/doi/abs/10.1177/0022343320929740">What are the drivers of diplomacy? Introducing and testing new annual dyadic data measuring diplomatic exchange</a>. <em>Journal of Peace Research </em>2021, <strong>58</strong>(6)<strong>: </strong>1300-1310. </p> <p>Petrova, K, Olafsdottir, G, Hegre, H, Gilmore, EA (2023). <a title="https://iopscience.iop.org/article/10.1088/1748-9326/acb163" href="https://iopscience.iop.org/article/10.1088/1748-9326/acb163">The ‘conflict trap’ reduces economic growth in the shared socioeconomic pathways</a>. <em>Environmental Research Letters</em>, 2023, <strong>18</strong>(2), 024028. </p>
Biodiversity conservation and socioeconomic development
<p>This data set contains the data used to analyse the synergies and trade-offs between biodiversity conservation and local socioeconomic development in and around sample African and European PAs. The data were collected based on face-to-face survey with PA managers.</p>
Socioeconomic and climate scenarios for the Mar Menor-Campo de Cartagena socioecosystem
<p>The five scenarios describe plausible changes in 15 external drivers of the socioecosystem of the Mar Menor and surrounding Campo de Cartagena between 1964 and 2070. These scenarios were developed for evaluation of their impacts on Key Performance Indicators of sustainability using a simulation model based on System Dynamics. This model, developed by Martínez-López et al (2022) can be consulted <a href="http://doi.org/10.5281/zenodo.7142764">here</a>.</p> <p>Historic data combined with the Shared Socioeconomic Pathways (SSPs) from the IPCC report ‘Global warming of 1.5°C’ and the Representative Concentration Pathways (RCPs), were used as starting point to develop the model-specific scenarios. The five scenarios are based on SSP 1, SSP2, SSP4 and SSP5 in combination with emission scenarios that will keep global temperature rise below 1.5ºC. The BAU scenario represents a combination of SSP2 without any climate change.</p> <p>The detailed documentation of SSPs from O’Neil et al (<a href="http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004">http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004</a>) and subsequent expert interviews and input received during stakeholder workshops organised in the framework of the COASTAL project were used to prepare the region-specific time-series of the 15 variables for the Mar Menor and surrounding Campo de Cartagena.</p> <p>The 15 external drivers are:</p> <ol> <li>Agricultural revenue per hectare</li> <li>Growth rate of agriculture</li> <li>Percentage of nutrients that are metabolized by the native lagoon ecosystem</li> <li>Average excess of fertilizer use</li> <li>Yearly effectiveness in nutrients reduction of nutrients, soil and water retention measures</li> <li>Electricity Price</li> <li>Mean number of hours per day of photovoltaic electricity production</li> <li>Photovoltaic energy facilities growth rate in Megawatts installed</li> <li>Growth rate of tourism</li> <li>Average percentage of groundwater desalinated</li> <li>Agricultural water demand per hectare</li> <li>Catchment water sources</li> <li>Urban wastewater treatment plant effluents</li> <li>Yearly average of sea water desalination</li> <li>Amount of water transferred from the Tagus river (RCP15ATS)</li> </ol>
Dataset of "Social Robots and Sensors for Enhanced Ageing at Home: A Focus on Mobility and Socioeconomic Factors."
<p>This dataset supports the article:</p> <p>"Social Robots and Sensors for Enhanced Aging at Home: A Focus on Mobility and Socioeconomic Factors."</p> <p>For further details see the Readme.txt file.</p>
Figure 8 in Socioeconomic aspects and profile of fishing according to fishers of commercial edible fish in the municipality of Barcelos, middle Negro River, Amazonas, Brazil
Figure 8. Most preferred places for fishing by commercial fishermen in the municipality of Barcelos, in the middle Negro River basin, Amazonas.
Figure 7 in Socioeconomic aspects and profile of fishing according to fishers of commercial edible fish in the municipality of Barcelos, middle Negro River, Amazonas, Brazil
Figure 7. Aquatic environment where commercial fishing takes place in the municipality of Barcelos, in the middle Negro River basin, Amazonas.
ScienceDex guides
Understand access before you commit
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.