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Dataset for: Water, environment, and socioeconomic justice in California: A multi-benefit cropland repurposing framework
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Dateset The "blue eyed" in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups
<p>Dateset The “blue eyed” in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups</p>
Data Supplement: U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways.
<p>These data are to supplement the following in-press publication: </p> <p>Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.</p> <p>The data herein were generated using the `population_gravity` model which can be found here: <a href="https://github.com/IMMM-SFA/population_gravity">https://github.com/IMMM-SFA/population_gravity</a></p> <p>CONTENTS:</p> <p><strong>zoraghein-oneill_population_gravity_inputs_outputs.zip</strong></p> <ul> <li>contains a directory for each U.S. state for inputs and outputs</li> <li><strong>inputs</strong> contain the following: <ul> <li><strong><state-name>_<urban or rural>_<yr>_1km.tif: </strong> Urban and Rural population GeoTIF rasters at a 1km resolution <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_mask_short_term.tif: </strong> Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics <ul> <li>value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_<ssp>_popproj.csv: </strong> Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100</li> <li><strong><state-name>_coordinates.csv: </strong>CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)</li> <li><strong><state-name>_within_indices.txt: </strong>text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.</li> <li><strong><state-name>_<ssp>_params.csv: </strong>CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the `population_gravity` model for each year from 2010-2100 in 10-year time-steps as described in the companion publication</li> </ul> </li> <li><strong>outputs</strong> contain the following: <ul> <li><strong>jones_oneill</strong> directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication <ul> <li>contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100. <ul> <li><strong><state-name>_1km_<ssp>_total_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>model</strong> directory; these are the model outputs from `population_gravity` for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps. <ul> <li><strong><state-name>_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>zoraghein-oneill_population_gravity_national-ssp-maps.zip</strong></p> <ul> <li>Results of the `population_gravity` model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper <ul> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> <p> </p>
Data from: Mental health in individuals with spinal cord injury: the role of socioeconomic conditions and social relationships
Objectives: To evaluate socioeconomic inequalities in social relationships, and to assess whether socioeconomic conditions and social relationships are independently related to mental health problems in individuals with a physical disability due to spinal cord injury (SCI). Methods: We analyzed cross-sectional data from 511 individuals with SCI aged over 16 years who participated in the community survey of the Swiss SCI Cohort Study. Indicators for socioeconomic conditions included years of formal education, household income, and financial strain. Social relationships were operationalized by three structural (partner status, social contact frequency, number of supportive relationships) and four functional aspects (satisfaction with: overall social support, family relationships, contacts to friends, partner relationship). General mental health was assessed by the Mental Health Inventory (MHI-5) of SF-36 and depressive symptoms were measured by the Hospital Anxiety and Depression Scale (depression subscale, HADS-D). Established cut-offs for general mental health problems (MHI-5 ≤56) and depressive symptomatology (HADS-D ≥8) were used to dichotomize outcomes. Associations were assessed using logistic regressions. Results: Lower household income was predominately associated with poor structural social relationships, whereas financial strain was robustly linked to poor functional social relationships. Financial strain was associated with general mental health problems and depressive symptomatology, even after controlling for social relationships. Education and household income were not linked to mental health. Poor structural and functional social relationships were related to general mental health problems and depressive symptomatology. Notably, trends remained stable after accounting for socioeconomic conditions. Conclusion: This study provides evidence for socioeconomic inequalities in social relationships as well as for independent associations of financial strain and poor social relationships with mental health problems in individuals with SCI. Further research may develop strategies to improve mental health in SCI by strengthening social relationships. Such interventions may be especially beneficial for individuals in unfavorable socioeconomic conditions.
Data from: Socioeconomic determinants of antibiotic consumption in the state of São Paulo, Brazil: the effect of restricting over-the-counter sales
Background: Improper antibiotic use is one of the main drivers of bacterial resistance to antibiotics, increasing infectious diseases morbidity and mortality and raising costs of healthcare. The level of antibiotic consumption has been shown to vary according to socioeconomic determinants (SED) such as income and access to education. In many Latin American countries, antibiotics could be easily purchased without a medical prescription in private pharmacies before enforcement of restrictions on over-the-counter (OTC) sales in recent years. Brazil issued a law abolishing OTC sales in October 2010. This study seeks to find SED of antibiotic consumption in the Brazilian state of São Paulo (SSP) and to estimate the impact of the 2010 law. Methods: Data on all oral antibiotic sales having occurred in the private sector in SSP from 2008 to 2012 were pooled into the 645 municipalities of SSP. Linear regression was performed to estimate consumption levels that would have occurred in 2011 and 2012 if no law regulating OTC sales had been issued in 2010. These values were compared to actual observed levels, estimating the effect of this law. Linear regression was performed to find association of antibiotic consumption levels and of a greater effect of the law with municipality level data on SED obtained from a nationwide census. Results: Oral antibiotic consumption in SSP rose from 8.44 defined daily doses per 1,000 inhabitants per day (DID) in 2008 to 9.95 in 2010, and fell to 8.06 DID in 2012. Determinants of a higher consumption were higher human development index, percentage of urban population, density of private health establishments, life expectancy and percentage of females; lower illiteracy levels and lower percentage of population between 5 and 15 years old. A higher percentage of females was associated with a stronger effect of the law. Conclusions: SSP had similar antibiotic consumption levels as the whole country of Brazil, and they were effectively reduced by the policy.
Data from: Socioeconomic determinants of outcome after childhood arterial ischemic stroke
Objective: To determine whether lower socioeconomic status (SES) is associated with worse one-year neurological outcomes and reduced access to rehabilitation services in children with arterial ischemic stroke (AIS). Methods: From 2010-2014, Vascular Effects of Infection in Pediatric Stroke observational study prospectively enrolled and confirmed 355 children (29 days -18 years) with AIS at 37 international centers. SES markers measured via parental interview included annual household income (US dollars (USD)) at time of enrollment, maternal education level, and rural/suburban/urban residence. Receipt of rehabilitation services was measured by parental report. Pediatric Stroke Outcome Measure (PSOM) scores were categorized as 0-1, 1.5-3, 3.5-6, 6.5-10. Univariate and multivariable ordinal logistic regression models examined potential predictors of outcome. Results: At 12±3 months post-stroke, 320 children had documented outcome measurements, including 15 who had died. In univariate analysis, very low income (<$10,000 USD), but not other markers of SES, was associated with worse outcomes (OR 3.13, 95% CI:1.43-6.88; p=0.004). In multivariable analysis, including adjustment for stroke etiology, this association persisted (OR 3.17, 95% CI:1.18-8.47; p=0.02). Income did not correlate with receiving rehabilitation services at one-year post-stroke; however, quality and quantity of services were not assessed. Conclusions: In a large multinational, prospective cohort of children with AIS, low income was associated with worse neurological outcomes when compared to those with higher income levels. This difference was not explained by stroke type, neurological comorbidities, or reported use of rehabilitation services. The root causes of this disparity are not clear, and warrant further investigation.
Data from: Relationship between socioeconomic status and HIV infection: findings from a survey in the Free State and Western Cape Provinces of South Africa
Background Studies have shown a mixed association between socioeconomic status [SES] and prevalent HIV infection across and within settings in sub-Saharan Africa. In general, the relationship between years of formal education and HIV infection changed from a positive to a negative association with maturity of the HIV epidemic. Our objective was to determine the association between SES and HIV in women of reproductive age in the Free State [FSP] and Western Cape Provinces [WCP] of South Africa [SA]. Study design Cross sectional Setting South Africa Methods We conducted secondary analysis on 1906 women of reproductive age from a 2007-2008 survey that evaluated effectiveness of Prevention of Mother-to-Child HIV Transmission programs. SES was measured by household wealth quintiles, years of formal education and employment status. Our analysis principally utilized logistic regression for survey data. Results There was a significant negative trend between prevalent HIV infection and wealth quintile in WCP [p-value<0.001] and FSP [p-value=0.025]. In adjusted analysis, every additional year of formal education was associated with a 10% [adjusted Odds Ratio [aOR] 0.90 [95% Confidence Interval [CI]: 0.85-0.96] significant reduction in risk of prevalent HIV infection in WCP but no significant association was observed in FSP [aOR 0.99 CI: 0.89-1.11]. There was no significant association between employment and prevalent HIV in each province: [aOR 1.54 CI: 0.84-2.84] in WCP and [aOR 0.96 CI: 0.71-1.30] in FSP. Conclusion The association between HIV infection and SES differed by province and by measure of SES and underscores the disproportionately higher burden of prevalent HIV infection among poorer and lowly educated women. Our findings suggest the need for re-evaluation of whether current HIV prevention efforts meet needs of the least educated [in WCP] and the poorest women [both WCP and FSP], and point to the need to investigate additional or tailored strategies for these women.
Data from: Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery
Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter resolution aerial imagery, and identify social-ecological relationships of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to: i) map vegetation cover using sub-meter spatial resolution (0.3 m) imagery; ii) quantify the amount of residential and non-residential vegetation; and iii) investigate the relationship between patterns of urban vegetation versus socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green, and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planning in tropical cities, and emphasizes the need for more studies in tropical cities.
Linking socioeconomic inequalities and type 2 diabetes through obesity and lifestyle factors among Mexican adults: a structural equations modeling approach
<p><strong>Objective. </strong>To assess the association between type 2 diabetes (DM2) and socioeconomic inequalities, mediated by the contribution of body mass index (BMI), physical activity (PA), and diet (diet-DII). <strong>Materials and methods</strong>. We conducted a cross-sectional analysis using data of adults participating in the Diabetes Mellitus Survey of Mexico City. Socioeconomic and demographic characteristics as well as height and weight, dietary intake, leisure time activity and the presence of DM2 were measured. We fitted a structural equation model (SEM) with DM2 as the main outcome, and BMI, diet-DII and PA served as mediator variables between socioeconomic inequalities index (SII) and DM2. <strong>Results. </strong>The prevalence of DM2 was 13.6%. From the fitted SEM, each standard deviation increases in the SII was associated with increased scores of DM2 (β=0.174, <em>P</em><0.001). <strong>Conclusion. </strong>The results in the present study show how high scores in the index of SII may influence the presence of DM2.</p>
Philippines - Open, validated health, climate, environment and socioeconomic data
<p>The <strong>Project Climate Change, Health, and Artificial Intelligence (Project CCHAIN)</strong> dataset is a validated, open-sourced linked dataset containing 20 years (2003-2022) of climate, environmental, socioeconomic, and health dimensions at the barangay (village) level across twelve Philippine cities (Dagupan, Palayan, Navotas, Mandaluyong, Muntinlupa, Legazpi, Iloilo, Mandaue, Tacloban, Zamboanga, Cagayan de Oro, Davao).</p> <p>The full documentation can be accessed <a href="https://thinkingmachines.github.io/project-cchain">here</a>.</p> <p>The tables are designed in a way that users can <strong>choose variables that are most relevant</strong> to their focus city and use case, and <strong>link these variables</strong> to form a single dataset by merging using standard geography codes and calendar dates. This can be done using the provided <a href="https://drive.google.com/drive/folders/123MrMXSVT6149MvJ8hGoDgN1s-YlQoH_">linking notebook</a>, or offline using the user's own code.</p> <p>Here are some tips on how make most use of this dataset:</p> <ul> <li> <p><strong>Focus on one location.</strong> Starting with a detailed analysis of one location allows for a better understanding of the local dynamics, which may differ across locations.</p> </li> <li> <p><strong>Choose one health data source.</strong> Pick one of either a central or local data source. Using two different data health sources is not advised because it will lead to double/overcounting of disease cases.</p> </li> <li> <p><strong>Do not use all variables at once- do a literature review first to identify possible key variables</strong>. to identify possible key variables. More often than not, using all variables is not necessary and may even yield subpar results.</p> </li> <li> <p><strong>Check data availability</strong> on your focus location and make sure they fit the requirements of your study.</p> </li> </ul>
Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States (Supplementary Material)
<p>Supplementary Material to the manuscript entitled "Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States" by the same authors.</p>
Global terrestrial moisture recycling in Shared Socioeconomic Pathways
<p>Data generated for the preprint "Global terrestrial moisture recycling in Shared Socioeconomic Pathways" by Staal et al.:</p> <p>Staal, A., Meijer, P., Nyasulu, M.K., Tuinenburg, O.A. & Dekker, S.C. (preprint). Global terrestrial moisture recycling in Shared Socioeconomic Pathways. [link and info to be added later] </p> <p>Data are stored as netcdf files and zipped. The global files contain monthly global terrestrial precipitation recycling in mm/month. The basin files contain monthly basin precipitation recycling in mm/month. For SSPs 1-2.6, 2-4.5, 3-7.0, and 5-8.5 the monthly results are given for 2050-2059 and 2090-2099. For SSP2-4.5, also monthly results for 2015-2024 are provided. Please see Staal et al. for further details.</p>
The HEC method processing code and output for constrained precipitation extremes reveal unequal future socioeconomic exposure
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Map of the proportion of threatened endemic species per country in relation with environmental and socioeconomic drivers
<p>This dataset is a shapefile representing the proportion of threatened endemic species (both plants and animals) in 247 countries along with associated environmental and socioeconomic drivers. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326).</p>
Global Transportation Demand Dataset using the Shared Socioeconomic Pathways (SSPs) Scenario Framework
<p>We use historical data for the land-based passenger (in passenger-kilometers (km)) across 38 countries and freight transport (in tonne-km) for 43 countries between 1990 and 2018 from the Transport Outlook of the International Transport Forum (ITF) transport database, to investigate the key drivers of transport energy demand <em><strong>source</strong>: ITF. (2019). ITF Transport Outlook 2019. ITF Transport Outlook 2019. <a href="https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en">https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019_transp_outlook-en-2019-en</a></em></p> <p>We collect the historical socioeconomic variables from the World Bank’s global open data bank <em><strong>source</strong>: World Bank. (2020). Data Bank: World Development Indicators. <a href="https://databank.worldbank.org/source/world-development-indicators">https://databank.worldbank.org/source/world-development-indicators</a></em></p> <p>For this scenario analysis, we rely on the shared socioeconomic pathways (SSPs) from the IIASA database (Riahi et al., 2017). <em><strong>source: </strong>Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. <a href="https://doi.org/10.1016/j.gloenvcha.2016.05.009">https://doi.org/10.1016/j.gloenvcha.2016.05.009</a> Available Online: <a href="https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about">https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about</a></em></p> <p>The lack of data disaggregated by country and end-use sector in countries of interest was a significant drawback in the data collection process. We make a crucial assumption in this modeling exercise that historical demand profiles in developing countries track the global average per capita transport trends. Therefore, the resulting estimates are indicative and must be interpreted within this analysis's scope given the future is unknown and highly uncertain.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Input data for the Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>This repository contains large input data files for generating the RFF-SPs, and small accompanying files with data necessary for interpreting the larger files.</p> <p>--------------------------------------------------------------------------------<br> /MSW_GDP/<br> --------------------------------------------------------------------------------</p> <p>-MSW.mat is a MATLAB data file based on Müller, Stock, and Watson (forthcoming) containing two series:<br> 1) Year-by-sample draws "F-paths" representing frontier values of ln(GDP/capita) for the OECD, and <br> 2) Year-by-country-by-sample deviations from those F-paths, denoted as the U-path.</p> <p>A country's ln(GDP/capita) is equal to the sum of the sample's F-path and the country-sample's U-path. There are 418 years representing 1900-2317, 113 countries with names in country_names.txt, and 2000 sample trajectories. GDP/capita is measured in 2011$.</p> <p>-country_names.txt contain iso codes for the 113 countries in the order in which they appear in MSW.mat's U-path object. OECD countries are indicated with an "x" for reference.</p> <p>This information was transmitted via email from Ulrich Mueller (Princeton) to Kevin Rennert (RFF) on January 28, 2020, with a link to the file on Mueller's website at http://www.princeton.edu/~umueller/matlabJan2020.mat.</p> <p>--------------------------------------------------------------------------------<br> /Raftery population/<br> --------------------------------------------------------------------------------</p> <p>-pop_trajectories_probmig.csv contains country-by-year-by-sample (here, a sample is labelled a "Trajectory") population estimates based on Raftery and Ševčíková (2021). Population is in units of thousands of people, with 1000 samples. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on August 11, 2021.</p> <p>-death_rates.csv contains country-by-year-by-sample death rates in units of annual deaths per one thousand people, with 1000 samples corresponding to the sample order of samples in pop_trajectories_probmig.csv. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on October 7, 2021.</p> <p>-iso_key.csv contains a key mapping ISO alpha-3 country codes ("ISO3") to ISO numeric-3 country codes (denoted "NumericCode" in this file and "LocID" in pop_trajectories_probmig.csv). This data was acquired from a search on https://www.iso.org/obp/ui#search in 2021. The "country codes" box was checked and no keywords were used. The list was trimmed to the 184 countries included in the current version of the GIVE model.</p> <p>--------------------------------------------------------------------------------<br> Information<br> --------------------------------------------------------------------------------</p> <p>The probabilistic economic and emissions projections are from Müller, Stock, and Watson (forthcoming). </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>--------------------------------------------------------------------------------<br> References <br> --------------------------------------------------------------------------------</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: https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: https://www.sciencedirect.com/science/article/pii/S0169207021001394 </p>
Dataset for "Sensitivity of subregional distribution of socioeconomic conditions to the global assessment of water scarcity"
<p>This dataset contains the data related to the final analysis for "Sensitivity of subregional distribution of socioeconomic conditions to the global assessment of water scarcity".</p>
Socioeconomic impacts on Andean adolescents' growth: variation between households, between communities, and over time
<p>Background/Objectives</p> <p>We evaluated potential socioeconomic contributors to variation in Andean adolescents' growth between households within a peri-urban community undergoing rapid demographic and economic change, between different community types (rural, peri-urban, urban), and over time. Because growth monitoring is widely used for assessing community needs and progress, we compared the prevalences of stunting, underweight, and overweight estimated by three different growth references.</p> <p>Results</p> <p>Alteños' height averaged ∼0.8SD/∼0.6SD/∼2SDs greater than adolescents' height in urban and rural communities measured in 1983/1998/1977, respectively. Overweight prevalence was comparable to the WHO, and lower than MESA and Puno, references. Stunting was 8.5/2.5/0.5 times WHO/MESA/Puno samples, respectively. Female Alteños' growth was positively associated with household and maternal income indices.</p> <p>Conclusions/Implications</p> <p>Both peri-urban conditions and temporal trends contributed to gains in Alteños' growth. Rural out-migration can alleviate migrants' poverty, partly because of more diverse economic options in urbanized communities, especially for women. Nonetheless, Alteños averaged below WHO and MESA height and weight medians. Evolved biological adaptations to environmental challenges, and the consequent variability in growth trajectories, favors using multiple growth references. Growth monitoring should be informed by community- and household-level studies to detect and understand local factors causing or alleviating health disparities. Lay Summary Andean adolescents exhibited elevated rates of stunting; their height varied with socioeconomic status within and between high-altitude communities. Unlike some lowland populations, the prevalence of overweight/obesity was not elevated. Population-specific growth assessments and targeted interventions (e.g., maternal education and employment) are likely to help alleviate disparities in Andean children's growth.</p>
Data - Mobile phones and their impact on the socioeconomic development of rural women in Peru
<p>Project database Mobile phones and their impact on the socioeconomic development of rural women in Peru</p>
Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection
<p>Reporting data from the Mosquito Alert citizen science system, active catch basin surveillance, and mosquito trap surveillance used in "Assessing and Correcting Neighborhood Socioeconomic Spatial Sampling Biases in Citizen Science Mosquito Data Collection."</p> <p>The file named mosquito_alert_adult_bite_reports_Barcelona_2014_2023.Rds includes all adult mosquito and mosquito bite reports received from Barcelona Municipality from the start of the Mosqiuto Alert project in 2014 through the end of 2023. The file named mosquito_alert_validated_albopictus_reports_Barcelona_2014_23.Rds includes all expert-validated <em>Ae. albopictus </em>reports received from Barcelona Municipality during the same time period. The data is stored as RDS files and contain the following fields:</p> <ul> <li><strong>year </strong>- the year in which the report was made. Class = dbl.</li> <li><strong>date </strong>- the date om which the report was made. Class = date.</li> <li><strong>type </strong>- the report type, either adult mosquito ("adult") or mosquito breeding site ("site"). Class = chr.</li> <li><strong>lon</strong> - the longitude of the report location. Class = dbl.</li> <li><strong>lat</strong> - the latitude of the report location. Class = dbl.</li> <li><strong>validation_score</strong> - Entolab validation score. Either 1 (possible <em>Ae. albopictus</em>) or 2 (probable <em>Ae. albopictus</em>). This field is present only in the validated reports data. </li> </ul> <p>The file named active_catch_basin_drain_data.Rds includes information about all catch basin drains in Barcelona Municipality in which the Barcelona Public Health Agency (ASPB) detected mosquito activity as part of its continuous monitoring and control of mosquitoes from 2019 through 2023. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>any_reports </strong>- dummy variable indicating whether any Mosquito Alert adult mosquito or mosquito bite reports were sent through Mosquito Alert from within 200 m of the catch basin drain during the year in which the ASPB detected mosquito activity in hte catch basin drain. Class = lgl.</li> <li><strong>se_expected</strong> - sampling effort for the 0.025 degree lon/lat sampling cell in which the catch basin drain lies during the year in which the ASPB detected mosquito activity in the drain. This value is taken from the SE_expected variable in the sampling_effort_daily_cellres_025.csv.gz file available at https://zenodo.org/records/12602985. Sampling effort is estimated as the expected number of participants sending at least one report from the cell during the day in question given the the number of participants recorded in the cell that day and the amount of time elapsed since each one began participating in the project. Class = dbl.</li> <li><strong>p_singlehh</strong> - proportion of single-member households in the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_age </strong>- mean age of the population of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>mean_rent_consumption_unit</strong> - mean income per consumption unit in the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>popd</strong> - population density of the census tract in which the catch basin drain is located. Class = dbl.</li> <li><strong>id_item </strong>- unique identifier given to the catch basin drain. Drain itentifiers appear multiple times in the data when the ASPB detected activity in the drain in multiple years. Class = dbl.</li> </ul> <p>The file named trap_data.Rds includes information on the adult mosquito trap surveillance analyzed in this article. The data is stored in an RDS file with the following fields:</p> <ul> <li><strong>females </strong>- number of Ae. albopictus females found in the trap. Class = dbl.</li> <li><strong>trap_name</strong> - unique identifier for the trap. Class = chr.</li> <li><strong>trapping_effort</strong> - number of days from when the trap was set to when it was checked. Class = dbl.</li> <li><strong>date</strong> - date on which the trap was checked. Class = date.</li> <li><strong>mean_tm30</strong> - mean temperature for the 30 days leading up to the date on which the trap was checked. Class = dbl.</li> <li><strong>mean_rent_consumption_unit </strong>- mean income per consumption unit for the census tract in which the trap was located. Class = dbl.</li> </ul>
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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.