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193 results for “socioeconomic”
Current and Future Flood maps for Flood risk assessment under the Shared Socioeconomic Pathways in the Greater Accra region, Ghana
<p>The study used 15 flood conditioning factors in simulating current and future flood conditions under the SSP scenarios using the Frequency Ratio (FR) model </p>
Data and Code for "Urban socioeconomic variation influences the ecology and evolution of trophic interactions"
<p>Data and code required for all analyses in <em>Urban socioeconomic variation influences the ecology and evolution of trophic interactions</em>. </p> <p><a href="../api/files/5ca97a29-9947-482a-9095-5a54c40c57f0/Gall_Data_2022_new.csv">Gall_Data_2022_new.csv</a> contains data for gall predation and diameter measurements. <a href="../api/files/5ca97a29-9947-482a-9095-5a54c40c57f0/Goldrod_Gall_Density.csv">Goldrod_Gall_Density.csv</a> contains goldenrod and gall density measurements for each site. <a href="../api/files/5ca97a29-9947-482a-9095-5a54c40c57f0/GallSitesFinal.csv">GallSitesFinal.csv</a> contains location data for all study sites. <a href="../api/files/5ca97a29-9947-482a-9095-5a54c40c57f0/DisseminationAreaCodes.csv">DisseminationAreaCodes.csv</a> contains codes for each site location needed to obtain census data. Full descriptions of data are included in <a href="../api/records/10702694/draft/files/README.txt/content" target="_blank" rel="noopener noreferrer">README.txt</a></p> <p>The script <a href="../api/records/10640975/draft/files/DataCleaning.R/content" target="_blank" rel="noopener noreferrer">DataCleaning.R</a> assembles the above four datasets with environmental and census data to produce the final dataset: <a href="../api/files/5ca97a29-9947-482a-9095-5a54c40c57f0/MartinElGalmady%26Johnson2023_cleandataset.csv">MartinElGalmady&Johnson2023_cleandataset.csv</a> and the supplemental dataset with galls with early larval death removed: <a href="../api/records/10640975/draft/files/NoELD_dataset.csv/content" target="_blank" rel="noopener noreferrer">NoELD_dataset.csv</a></p> <p><a href="../api/records/10640975/draft/files/Analysis.R/content" target="_blank" rel="noopener noreferrer">Analysis.R</a> provides the code for conducting analyses and producing figures using the <a href="../api/files/5ca97a29-9947-482a-9095-5a54c40c57f0/MartinElGalmady%26Johnson2023_cleandataset.csv">MartinElGalmady&Johnson2023_cleandataset.csv</a> dataset (or the <a href="../api/records/10640975/draft/files/NoELD_dataset.csv/content" target="_blank" rel="noopener noreferrer">NoELD_dataset.csv</a> for supplemental analyses with early larval death removed).</p> <p>Detailed descriptions of the final dataset are included in <a href="../api/records/10702694/draft/files/cleandataset_metadata.csv/content" target="_blank" rel="noopener noreferrer">cleandataset_metadata.csv</a> (NoELD_dataset has the same rows and columns as the full dataset). </p> <p>All code was run in R verison 4.2.2</p>
Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach - Data Supplement
<p>This contribution contains data and analysis scripts for the manuscript "Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach" by Lenin Del Rio Amador, Mathieu Boudreault and David A. Carozza.</p>
Socioeconomic status affects the incidence of COVID-19 in Chilean multiple sclerosis patients
<p><span><b><span>Objective: </span></b>To investigate the frequency of coronavirus disease (COVID-19) in patients with multiple sclerosis (pwMSs) living in a high socioeconomic vulnerability area in Chile.<b> </b></span></p> <p><span><b><span>Methods:</span></b> In this prospective cohort study, we compared the frequency of COVID-19 in 52 Chilean pwMSs on disease-modifying treatments (DMTs), living in urban municipalities with low-income/high-poverty levels, with that previously reported in pwMSs living in municipalities with high-income/low-poverty rates in Santiago, Chile. Demographic and clinical features of the pwMSs were obtained from their last consultation between March 3, 2020, and August 29, 2020.<b> </b></span></p> <p><span><b><span>Results:</span></b> In the low-income pwMSs, the mean patient age was 34 years, 69% were women, mean disease duration was 3 years, and mean Expanded Disability Status Scale score was 1.6. Of these, 61.5% pwMSs (32/52) underwent quarantine during the study period. <span>COVID-19 diagnosis was confirmed by reverse transcriptase polymerase chain reaction in five patients (10%): two were on glatiramer acetate, one was on fingolimod, and two were on alemtuzumab. All pwMSs with COVID-19 recovered fully. </span>The previously reported frequency of confirmed COVID-19 in middle‒upper income pwMSs living in Santiago was 1%. The frequency of COVID-19 among pwMSs in the low- and middle‒high income inhabitants of Santiago differed significantly (z = -4.3235, p < 0.00001; one-tailed Fisher exact test, p < 0.01).</span></p> <p><span><b>Conclusion: </b>The frequency of COVID-19 in the low-income/high-poverty cohort in Santiago, Chile, was markedly high. Accordingly, high socioeconomic vulnerability should be considered as an important risk factor for COVID-19 in pwMSs.</span></p>
Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>RFF-SPs Monte Carlo output data:</p> <p>This dataset includes the socioeconomic and emissions data generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al. (forthcoming) (<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a>). The data take the form of a Monte Carlo simulation with n = 10,000 draws. File structure and column metadata are described here:</p> <p>---</p> <p>death_rates/</p> <p>---</p> <ul> <li>rffsp_death_rates_run_1.feather</li> <li>rffsp_death_rates_run_2.feather</li> <li>rffsp_death_rates_run_2.feather</li> </ul> <p> ...</p> <ul> <li>rffsp_death_rates_run_999.feather</li> <li>rffsp_death_rates_run_1000.feather</li> </ul> <p>This folder contains 1,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file in this folder contains 3 columns: ISO3, Year, and DeathRate. For each row:</p> <p>- ISO3 contains the ISO numeric-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions.</p> <p>- year contains the calendar year of the prediction.</p> <p>- DeathRate contains a death rate in average deaths per 1000 people.</p> <p>Baseline mortality data found in data/mortality was derived from the death_rates.csv provided to the RFF team on October 7th from Hana Sevcikova. In that source file, the column DeathRate is the annual deaths per 1000 people. PopAvg is the denominator (average between two time periods) and PopStart is population at the start of the time interval. Values are average deaths per 1000 people.</p> <p>There is one mortality death rate trajectory for each population trajectory, and each RFF-SP is matched to one of these 1000 trajectories. The file sampled_pop_trajectory_numbers.csv described below maps each of the 10,000 RFF SP scenarios to the baseline mortality scenario (out of 1000) matched to its population draw.</p> <p>---</p> <p>emissions/</p> <p>---</p> <ul> <li>rffsp_co2_emissions.csv</li> <li>rffsp_ch4_emissions.csv</li> <li>rffsp_n2o_emissions.csv</li> </ul> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row:</p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000).</p> <p>- year contains the calendar year of the prediction.</p> <p>- value contains the projected annual global emissions of the gas specified in the filename.</p> <p>The units are as follows:</p> <ul> <li>rffsp_co2_emissions.csv is in gigatons C</li> <li>rffsp_ch4_emissions.csv is in megatons CH4</li> <li>rffsp_n2o_emissions.csv is in megatons N2</li> </ul> <p>---</p> <p>pop_income/</p> <p>---</p> <ul> <li>rffsp_pop_income_run_1.feather</li> <li>rffsp_pop_income_run_2.feather</li> <li>rffsp_pop_income_run_3.feather</li> </ul> <p> ...</p> <ul> <li>rffsp_pop_income_run_9999.feather</li> <li>rffsp_pop_income_run_10000.feather</li> </ul> <p> </p> <p>This folder contains 10,000 files in the .feather file format (<a href="https://arrow.apache.org/docs/python/feather.html">https://arrow.apache.org/docs/python/feather.html</a>), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>) of the country whose GDP and population are projected. - Year contains the calendar year of the predictions.</p> <p>- Pop contains the projected population for a given country and year, in units of thousands of people.</p> <p>- GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD.</p> <p>---</p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936.</p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), which in turn are based in part on Müller, Stock, and Watson (forthcoming).</p> <p>---</p> <p>sample_numbers/</p> <p>---</p> <ul> <li>sampled_gdp_trajectory_numbers.csv</li> <li>sampled_pop_trajectory_numbers.csv</li> </ul> <p>These two files hold the sample IDs corresponding to the 10,000 draws from the dataset described above in the pop_income/ section. For reproducibility, these sample sets are available as inputs to the scripts which created them and used when the script is run in deterministic mode. More information on the weighting and specifications of such sampling is available in the code.</p> <p>---</p> <p>ypc1990/</p> <p>---</p> <ul> <li>rffsp_ypc1990.csv</li> </ul> <p>This CSV file is a matrix of 10,000 rows, pertaining to the 10,000 draws, and 184 columns, pertaining to the 184 countries in this analysis. The columns are labeled with the ISO Alpha-3 code (<a href="https://www.iso.org/iso-3166-country-codes.html">https://www.iso.org/iso-3166-country-codes.html</a>). The values are the GDP for a given country and sample, in units of millions of 2011 USD.</p> <p><strong>References</strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a></p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. <em>International Journal of Forecasting</em>, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a></p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. <em>Brookings Papers on Economic Activity.</em> Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/</a></p> <p> </p>
Global analysis of environmental and socioeconomic factors associated with human burden of environmentally mediated pathogens
<p>This repository contains four datasets that support repeatability of the analyses in the Sokolow et al. paper published in <em>Lancet Planetary Health</em>. Descriptions of the four datasets are included in the metadata document. This study found that 80% of pathogen species known to infect humans are environmentally mediated, causing about 40% of contemporary infectious-disease burden (global loss of 130 million years of healthy life annually). More than 91% of this environmentally-mediated disease burden occurs in tropical countries, and the poorest countries carry the highest burdens across all latitudes. There were weak associations between disease burden and biodiversity or agricultural land use at the global scale. In contrast, the proportion of people with rural poor livelihoods in a country was a strong proximate indicator of environmentally mediated infectious disease burden there. Political stability and wealth were associated with improved sanitation, better health care, and lower proportions of rural poverty, indirectly resulting in lower burdens of environmentally mediated infections."</p>
Improved estimation of global soil moisture of different shared socioeconomic pathways for 2016-2099
<p>We design a novel Transformer SM Simulation Net (TSMSNet) to conduct global monthly 0.5°×0.5° SM simulation of SSP1-2.6, SSP2-4.5, and SSP5-8.5 from 2016 to 2099. Nine qualified future SM datasets, along with corresponding spatial distribution dataset of error parameters, and geographic background data are selected as model inputs. The learning target is calculated through merging the merits from Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM. The results indicate the TSMSNet simulated SM (R = 0.68, ubRMSE = 0.045 m<sup>3</sup>/m<sup>3</sup>) performs notable superiority in matching both temporal variation and absolute value of in-situ measurements across different landcover and climate regions. Besides, the TSMSNet simulated SM could favorably match the long-term trend of learning target. TSMSNet simulated SM has an overwhelming drying trend during 2016 to 2099. The decline magnitude rises accompanied by SSP changing from sustainable pathway to fossil-fueled development. The areas with significant drying trend mainly distributed in plateau and mid-latitude region. In terms of land cover types, evident drying trends are found in cropland and forest. SM shows faster descent rate in livable areas than unlivable areas. In summary, we develop a reliable future SM dataset, that is expected to act as a valuable reference for understanding future water cycle patterns.</p>
Biophysic and socioeconomic drivers of burned area and carbon emissions from fires in the Pantropical tropical dry forests
<p><span>The global burned area declined by nearly one-quarter between 1998 and 2015. Drylands contain a large proportion of these global fires but there are important differences within the drylands, e.g., savannas and tropical dry forests (TDF). Savannas, a biome fire-prone and fire-adapted, have reduced the burned area, while the fire in the TDF is one of the most critical factors impacting biodiversity and carbon emissions. Moreover, under climate change scenarios TDF is expected to increase its current extent and raise the risk of fires. Despite regional and global scale effects, and the influence of this ecosystem on the global carbon cycle, little effort has been dedicated to studying the influence of climate (seasonality and extreme events) and socioeconomic conditions of fire regimen in TDF. Here we use the Global Fire Emissions Database and, climate and socioeconomic metrics to better understand long-term factors explaining the variation in burned area and biomass in TDF at the Pantropical scale. On average, fires affected 1.4% of the total TDF' area (60,208 km<sup>2</sup>) and burned 24.4% (259.6 Tg) of the global burned biomass annually at Pantropical scales. Climate modulators largely influence local and regional fire regimes. Inter-annual variation in fire regime is shaped by El Niño and La Niña. During El Niño and the forthcoming year of La Niña, there is an increment in extension (35.2 and 10.3%) and carbon emissions (42.9 and 10.6%). Socioeconomic indicators such as land management and population were modulators of the size of both, burned area and carbon emissions. Moreover, fires may reduce the capability to reach the target of "half protected species" in the globe, i.e., high-severity fires are recorded in ecoregions classified as nature could reach half protected. These observations may contribute to improving fire management.</span></p>
Socioeconomic and environmental indicators of health and health condition for 77 districts of the Czech Republic (2001–2003 and 2016–2019)
<p>The dataset comprises 60 health indicators categorized into seven groups (A.1 to A.7), consisting of a total of 36 health determinants. Additionally, there is one category (B.1) specifically for health conditions, containing 24 health indicators. This dataset was created for 77 regions in the Czech Republic, including 76 districts (LAU1) and the capital city of Prague. The data were extracted from official sources, namely the Czech Statistical Office, the Institute of Health Information and Statistics of the Czech Republic, the Ministry of Labour and Social Affairs, and the Czech Hydrometeorological Institute. The dataset covers two time periods: 2001–2003 and 2016–2019.</p>
Mobile Screening for Major Depressive Disorder in Adults From an Ethnically and Socioeconomically Diverse Population.
ClinicalTrials.gov study NCT05989412. IPD Sharing: YES. Countries: 1. Publications: 18.
Reduced Nicotine Content Cigarettes in Smokers of Lower Socioeconomic Status
ClinicalTrials.gov study NCT01928719. IPD Sharing: NO. Countries: 1. Publications: 1.
Examining Racial and SocioEconomic Disparities (ERASED) in Chronic Low Back Pain Study
ClinicalTrials.gov study NCT03338192. IPD Sharing: NO. Countries: 1. Publications: 4.
Ecological and socioeconomic factors associated with reported tick-borne viruses
Open the record for dataset details and reuse information.
Data from: How Hurricanes Irma and Maria affected population dynamics and nutrient content of <em>Aedes aegypti</em> in San Juan, PR, USA: socioeconomic and temporal factors
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Global analysis of environmental and socioeconomic factors associated with human burden of environmentally mediated pathogens
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Biophysic and socioeconomic drivers of burned area and carbon emissions from fires in the Pantropical tropical dry forests
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Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
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Temperature impacts on dengue incidence are nonlinear and mediated by climatic and socioeconomic factors
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Structural and socioeconomic features of cities predict migratory bird species richness
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Socioeconomic status affects the incidence of COVID-19 in Chilean multiple sclerosis patients
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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.