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193 results for “socioeconomic”
Figure 4 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 4. Schooling of commercial fishermen in the municipality of Barcelos, in the middle Negro River basin, Amazonas. I.B.E.: incomplete basic education; C.B.E.: complete basic education; I.H.S.: incomplete high school and C.H.S.: complete high school.
Figure 3 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 3. Length of residence of commercial fishermen in the municipality of Barcelos, in the middle Negro River basin, Amazonas.
Figure 2 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 2. Age range of commercial fishermen in the municipality of Barcelos, in the middle Negro River basin, Amazonas.
Figure 9 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 9. Transports used by fishermen in commercial fishing activities in the municipality of Barcelos, in the middle Negro River basin, Amazonas. Table 4. Monitoring of fisheries according to fishers of commercial edible fish in rural and urban areas of the municipality of Barcelos, middle Negro River, Amazonas state, Brazil.
Figure 6 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 6. Fishing instruments used by commercial fishermen in the municipality of Barcelos, in the middle Negro River basin, Amazonas. Harpoon: long instrument, similar to a spear, used in fishing to catch larger fish; Trolling fishing: hook fishing which consists of casting the line while the boat is drifting in the river; Longline fishing: fishing line composed of a main line and secondary lines that are registered at regular intervals with hooks at the ends and Fishing fork (zagaia): harpoon consisting of three to five teeth fixed to a wooden pole.
Data archive for Allen and Nettle, 'Hunger and socioeconomic background additively predict impulsivity in humans'
<p>This archive contains the raw data from Allen and Nettle, 'Hunger and socioeconomic background additively predict impulsivity in humans', plus R code for the data analyses. The impulsivity measure (HMDT) used in studies 2 and 3 is also included here. </p> <p>The R script 'IndividualAnalysis.R' performs the analysis of each study individually. 'MetaAnalysis.R' performs the meta-analysis. The three .csv files are the raw data from the three studies respectively. </p>
Data for Age and socioeconomic status in relation to risk of maternal anemia among the Ariaal of northern Kenya
<p>Data used for a paper entitled, "Age and socioeconomic status in relation to risk of maternal anemia among the Ariaal agropastoralists of northern Kenya"</p>
Urban Tree Canopy Cover, environmental and socioeconomic data to São Paulo city, Brazil
<p>Urban Tree Canopy Cover from 2017, elevation and roughness from 2011 and socioeconomic data from 2010, to São Paulo city, Brazil. These data are analyzed in: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128497" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ufug.2024.128497</a></p>
Rising Environmental Inequalities and Their Relationship to Racial and Socioeconomic Disparities in the US Southwest
<p><em><strong>Note: These datasets are part of our research, which was published in Environmental Science & Technology. </strong></em></p> <p><em>Please cite this paper when you use these datasets:</em></p> <p><strong><em>Yuanhui Zhu, Soe W. Myint, Danica Schaffer-Smith, Daoqin Tong, Yuyu Zhou, Yubin Li, and Rebecca L. Muenich. </em>Rising Environmental Inequalities and Their Relationship to Racial and Socioeconomic Disparities in the US Southwest, 2025, <em>Environmental Science & Technology. <a title="DOI URL" href="https://doi.org/10.1021/acs.est.4c14369">https://doi.org/10.1021/acs.est.4c14369</a></em></strong></p> <p><em><strong>For any questions or concerns, contact Yuanhui Zhu at <a href="mailto:jta90@txstate.edu">jta90@txstate.edu</a></strong></em></p> <p> </p> <p>This study aims to provide insight into the US Southwest social and environmental inequality problems by combining high-resolution ECOSTRESS data, including Land Surface Temperature (LST), Evaporative Stress Index (ESI), and actual Evapotranspiration (ETa) with sociodemographic data at the block group level acquired from US Census. ESI and ETa represent drought and consumptive water use, respectively. Further, disparities of environmental changes over the past two decades in connection with races/ethnicities are explored using Landsat-based LST and ET from 2000 to 2020 across major US Southwest cities in light of global climate changes. We narrow our investigations to the summer months, including June, July, and August, when environmental issues are more pronounced during the day, such as heat-related mortality and morbidity and higher water consumption.</p> <p>This dataset covers major US Southwest cities. The dataset includes social-environmental data at the block level and remotely sensed environmental data. The description for each individual dataset is listed as follows,</p> <p><em><strong>T01(T01_dataset_meta_race_ethnicity_economy_env_southwest.csv)</strong></em>: This dataset provides statistics on race/ethnicity, social economy, and arithmetic mean of environmental variables at the block group level.</p> <p><em><strong>T02</strong></em>: These datasets provide processed environmental data, including Daytime Land Surface Temperature (LST), Actual Evapotranspiration (ETa), and Evaporative Stress Index (ESI) images at 70m resolution.</p> <p><em><strong>T03</strong></em>: These datasets include annual Landsat-based summer LST, and ETa means at 30m resolution from 2000 to 2020. The images of LST and ETa in 2012 are missing because Landsat datasets in 2012 are unavailable. We collected all available Landsat images with cloud cover lower than 75% in the summer from 2000 to 2020 to produce the dataset. The high-resolution (30m) LST and ETa are processed on Google Earth Engine (GEE). There are a total of 8860 scenes (including Landsat 4, 5, 7, and 8) from 2000 to 2020 to cover all major US Southwest urban areas. We also include two sample images of 2020 LST and ETa for demonstration.</p> <p><em><strong>T04</strong></em>: These datasets contain LST and ETa Sen's slope results, which represent the median rate of change of LST and ETa from 2000 to 2020 on an annual basis.</p> <p><em><strong>T05</strong></em>: The boundary of the major US Southwest urban areas</p>
A synthesis of hydroclimatic, ecological, and socioeconomic data for transdisciplinary research in the Mekong
<p>Various climate, hydro-meteorological, ecological, and socio-economic datasets are synthesized and made available for the Mekong River Basin. The sources of each dataset are also mentioned in the associated readme file.</p> <p>Dam attribute data, inundation data, and Cambodia census data can be made available upon request to the authors.</p>
Combined impacts of environmental and socioeconomic covariates on HFMD risk in China
<p>Supplement to "Combined impacts of environmental and socioeconomic covariates on HFMD risk in China"</p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</p>
SOCIOECONOMIC, DEMOGRAPHIC AND ENVIRONMENTAL FACTORS AND COVID - 19 VACCINATION: INTERACTIONS AFFECTING EFFECTIVENESS
<p>This study analyses the relation between people fully vaccinated and mortality to assess the effectiveness of this health policy to cope with COVID-19 pandemic between a sample of 150 countries. Statistical analyses show a positive correlation between share of people fully vaccinated and total COVID-19 mortality in early 2022 (r= 0.65, p-value <.01). These results suggest that COVID-19 vaccinations cannot be a sufficient policy response to eradicate the overall negative impact of the new infectious disease in society. Although high levels of vaccinations in some countries, many demographic (density of population), environmental (air pollution), technological (equipment of non-invasive ventilators), biological (new variants), socioeconomic (health expenditures) factors, etc., influence the diffusion and negative effects of COVID-19 pandemic society. This study can provide new knowledge to improve crisis management and the preparedness of countries to cope with or prevent future pandemic crisis and negative effects in socioeconomic systems.</p>
Data from: Breaking Barriers? Ethnicity and socioeconomic background impact on early career progression in the fields of ecology and evolution
Open the record for dataset details and reuse information.
Nonbreeding distributions of four declining Nearctic-Neotropical migrants are predicted to contract under future climate and socioeconomic scenarios
Open the record for dataset details and reuse information.
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
Open the record for dataset details and reuse information.
Population scenarios for U.S. states consistent with Shared Socioeconomic Pathways
<p>This is a data record of the input and output data associated with the following publication:</p> <p>Jiang, L., B.C. O'Neill, H. Zoraghein, and S. Dahlke. 2020. Population scenarios for U.S. states consistent with Shared Socioeconomic Pathways. Environmental Research Letters, <a href="https://doi.org/10.1088/1748-9326/aba5b1">https://doi.org/10.1088/1748-9326/aba5b1</a>.</p> <p>The accompanying code can be found here: </p> <p>Zoraghein, H., R. Nawrotzki, L. Jiang, and S. Dahlke (2020). IMMM-SFA/statepop: v0.1.0 (Version v0.1.0). Zenodo. <a href="https://protect2.fireeye.com/v1/url?k=dbf32d36-8746128f-dbf30723-0cc47adc5fce-abb2658d1d55b41e&q=1&e=1c8eff6c-b006-48cb-8ff3-fd95c41a8980&u=https%3A%2F%2Fprotect-us.mimecast.com%2Fs%2FbxLNCXD7VRHXoAlVcVcXsa%3Fdomain%3Dprotect2.fireeye.com">http://doi.org/10.5281/zenodo.3956703</a> </p> <p>The following detail the contents:</p> <ul> <li>SSP<ssp_number>.zip <ul> <li><state_number>_<state_abbreviation> <ul> <li><state_number>_<state_abbreviation>_In_Mig.jpg <ul> <li>output figure for in-migration change through time for the target state</li> </ul> </li> <li><state_number>_<state_abbreviation>_Net_Mig.jpg <ul> <li>output figure for net-migration change through time for the target state</li> </ul> </li> <li><state_number>_<state_abbreviation>_Out_Mig.jpg <ul> <li>output figure for out-migration change through time for the target state</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_in_mig.csv <ul> <li>projected age and gender specific number of domestic in-migration for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_net_mig.csv <ul> <li>projected age and gender specific number of domestic net-migration for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_out_mig.csv <ul> <li>projected age and gender specific number of domestic out-migration for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_proj_pop.csv <ul> <li>projected age and gender specific number of population for the state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_total_in_mig.csv <ul> <li>projected age and gender specific in-migration from other states to the current state over time</li> </ul> </li> <li><state_number>_<state_abbreviation>_total_out_mig.csv <ul> <li>projected age and gender specific out-migration from the current state to other states over time</li> </ul> </li> <li><state_number>_<state_abbreviation>.jpg <ul> <li>figure for projected change of population for the state</li> </ul> </li> </ul> </li> </ul> </li> <li>State_inputs.zip <ul> <li><state_number>_<state_abbreviation> <ul> <li><state_number>_<state_abbreviation>_in_mig.csv <ul> <li>a table containing gender- and age-specific migration rates from all states to the current state at the base year.</li> </ul> </li> <li><state_number>_<state_abbreviation>_out_mig.csv <ul> <li>a table containing gender- and age-specific migration rates from the current state to all other states at the base year.</li> </ul> </li> <li>basePop.csv <ul> <li>a table containing gender- and age-specific counts of people in the current state at the base year (2010).</li> </ul> </li> <li>Constant_rate.csv <ul> <li>a table that constructs a constant scenario for fertility, mortality (life expectancy), urbanization level (required if we differentiate urban and rural), sex ratio at birth and net estimates of international migrants for the current state. The table is from 2010 to 2100 at 5 year intervals used for scenario building. The presented scenario, for instance, keeps everything constant at its historical record estimated from the data.</li> </ul> </li> <li>fertility.csv <ul> <li>a table containing age-specific fertility rates for the current state at the base year.</li> </ul> </li> <li>intMig.csv <ul> <li>a table containing gender- and age-specific proportions of international migrants to the current state at the base year.</li> </ul> </li> <li>mortality.csv <ul> <li>a table containing gender- and age-specific life expectancy and mortality rates for the current state at the base year (life table).</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p>
Athens Socioeconomic Heat Vulnerability Index - Features and Index per region
<p>Athens Socioeconomic Heat Vulnerability Index - Features and Index per region. Socioeconomic variables used and the composite vulnerability index per census block region of central Athens, 490 areas. All data is in percentage form [0-100] and the census block polygons are included.</p>
Structural and socioeconomic features of cities predict migratory bird species richness
<p>Cities are aggregates of human activities where our decisions shape the environment creating heterogeneity across urban centers that can have significant ecological effects on wildlife. Many bird species are found in cities during the breeding season, which implies they find sufficient resources in cities to support them during this energetically costly time. As populations of many migratory bird species are declining, knowledge of how they are affected by urbanization is needed. Yet, we know little about how the species richness of migratory birds varies across different types of cities. Here we ask if cities' structural and socioeconomic features can predict the species richness of migratory birds that generally select different breeding habitats during the breeding season. We used eBird data from census-designated urban areas in the United States to model the relationship between features of cities (housing density, median income, city age, and commuting time), environmental disturbance (measured by the human footprint index) and species richness by fitting generalized linear models to data. We show that commuting time was the most important factor determining species richness across cities and the rest of the city features were weakly associated with species richness. Overall species were responding to city variation in similar ways. While we expected that cities with more disturbance would have lower species richness, our results indicate that some species are able to tolerate even highly disturbed cities and that cities in certain regions may act as a refuge to birds. This knowledge is important for our general understanding of cities as habitat for birds and how migratory birds respond to across-city variation during the breeding season.</p>
Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
<p>The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.</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.