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475 results for “income”
Survey data on climate policy in three countries (Peru, Ghana, Philippines) within the project "Sustainable Middle Classes in Middle Income Countries: Transforming Carbon Consumption Patterns (SMMICC)"
<p>The unprecedented growth of the new middle classes in middle income developing countries implies a strong growth in both consumption and carbon emissions. The research project Sustainable Middle Classes in Middle Income Countries (SMMICC) investigates the drivers of carbon consumption choices of the new middle classes and policy options to decrease their carbon footprints, including the implementation of carbon taxes</p> <p>The research of the authors generated quantitative data on the acceptability of carbon taxes in three countries (Peru, Ghana, Philippines).</p> <p> </p> <p><strong>The data is provided in the following formats:</strong></p> <p>- 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.csv<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_peru.csv<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.csv</p> <p>- 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.dta<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_peru.dta<br>- 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.dta</p> <p>Additionally, the codebooks on variables of questionnaire and political parties in each country are attached in a csv format.</p>
Data for: The circular economy potential of urban organic waste streams in low- and middle-income countries
<p>This dataset includes the research data and supporting information for the publication "The circular economy potential of urban organic waste streams in low- and middle-income countries" which was published in the Journal of Environment, Development and Sustainability (DOI: 10.1007/s10668-021-01487-w).</p> <p>This dataset and the associated publication are the basis upon which the REVAMP (Resource Value Mapping) tool has been developed. See more info about the REVAMP tool here: https://www.sei.org/revamp</p> <p> </p>
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface
<p>Experimental Record of a Longwave-Evaporation experiment. The record to be referenced in a forthcoming scientific paper.</p>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
Quality controlled observations of hourly incoming shortwave radiation data at the surface for solar resource mapping in Norway (2016-2020).
<p>Observed hourly incoming shortwave radiation data at the surface of Norway for the years 2016-2020 along with quality control flags, visualization plots and a descriptive report. The data has been collected, visually inspected and quality controlled within the SunPoint project (SUn in Norway - POtential and INTegration of the solar energy resource, Norwegian Research Council project 320750). The main data source is frost.met.no but some gaps were filled with data directly obtained by the station holders.</p> <p>There are three NetDCF files for 47 stations selected after quality control:</p> <ul> <li>rsds_1hr_selection_v5_2016-2020.nc: Raw data</li> <li>rsds_flagged_1hr_selection_v5_2016-2020.nc: Raw data with flags</li> <li>rsds_cleaned_1hr_selection_v5_2016-2020.nc: Filtered data (i.e. all flagged data has been removed)</li> </ul> <p>and one NetCDF file for all available stations (106 stations)</p> <ul> <li>rsds_1hr_frost_and_more_2016-2020.nc</li> </ul> <p>Version 3.5 of the McClear clear-sky model is used for flagging which reduces the bias to ground measurements compared to earlier versions. </p> <p>The visualization and automated quality control routines are available in the Scripts.zip file (python).</p>
Replication package for: "Is Secessionism Mostly About Income or Identity? A Global Analysis of 3,153 Subnational Regions"
<p>This repository contains the data and code to replicate the analyses performed in <a href="https://academic.oup.com/ej/article/135/668/1261/7918442?utm_source=authortollfreelink&utm_campaign=ej&utm_medium=email&guestAccessKey=d6c8adb1-257c-47ad-827c-79b94cf86664" target="_blank" rel="noopener">"Is Secessionism Mostly About Income or Identity? A Global Analysis of 3,153 Subnational Regions"</a> by <a href="https://people.smu.edu/kdesmet/">Klaus </a><a href="https://people.smu.edu/kdesmet/" target="_blank" rel="noopener">Desmet</a>, <a href="https://sites.google.com/view/ignacioortuno" target="_blank" rel="noopener">Ignacio Ortuño-Ortín</a>, and <a href="http://omerozak.com">Ömer </a><a href="http://omerozak.com" target="_blank" rel="noopener">Özak</a>. If you use the code or data in this repository, please cite both the original paper and the dataset.<br><br>Citation:</p> <p>Desmet, Klaus, Ortuño-Ortín, Ignacio, and Özak, Ömer. (2024) "<a href="https://academic.oup.com/ej/article/135/668/1261/7918442?utm_source=authortollfreelink&utm_campaign=ej&utm_medium=email&guestAccessKey=d6c8adb1-257c-47ad-827c-79b94cf86664" target="_blank" rel="noopener">Is Secessionism Mostly About Income or Identity? A Global Analysis of 3,153 Subnational Regions</a>", Economic Journal, Volume 135, Issue 668, May 2025, Pages 1261–1299.</p>
Nutritional table to estimate the availability of nutrients in households from the Mexican National Survey of Household Income and Expenditures (ENIGH) 2008-2020
<p>The database contains the amount of six nutrients (calories, proteins, vitamin A and C, iron, and zinc) per 100 grams/mililiters for each of the food categories used in the Mexican National Survey of Household Income and Expenditures 2008-2020.</p>
The dataset of Hong Kong Housing transaction records (1997-2018, after an incoming data quality processing)
<p>These detailed housing transaction records of over 2 million property rights entries in Hong Kong's property markets over the past 23 years (1997 - 2018).</p>
State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100)
<p>This dataset is documented in this manuscript here- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p> <p>Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. We project US state level income distributions using a PCA-based approach, applying a downscaled version of the approach employed by Narayan et al. (2022, in-prep). A state-level dataset had to be synthesized and projected based on existing sources. We apply a PC-based model to our derived state-level dataset, employing projected GINI’s from the SSP scenarios. We produce projected income distribution by income decile for three SSPs to year 2100. For the purpose of the projections, we developed a consistent set of tax adjusted net income deciles for all states from 2011 to 2014. This dataset was used for initialization of the projections and for validation.</p> <p>If/when using this dataset, please cite this paper- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p>
INCOME DISPARITIES AMONG MICRO AND SMALL ENTERPRISES: THE DIGITAL DIVIDE IN INDONESIA
<p>Data related to small scale enterprise in relations to the digital aspects by prinvince in Indonesia source from Badan Pusat Statistik, Republic of Indonesia</p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) </p>
EVFS incoming power lines KML shapefile and coordinates
The coordinates and shapefile (kml) of the incoming power lines near El Verde Field Station
Environmental and social factors influencing median income and BMI in the State of Geneva
<p>Hectometric grid (100m x 100m) covering the inhabited areas of the State of Geneva. It contains informations relative to the bmi and median income (GIREC) within the cells, together with a series of environmental and social factors, with which a correlation can be sought.</p>
Block-level & block group-level income projections for Washington state under different SSPs from 2020 to 2100
<p>The files "bg_binned_income_proj.csv" and "bk_binned_income_proj.csv" contain income projections in 2015 dollars for Washington state (under census geographic boundary 2020) from 2020 to 2100 at the block group and block level, respectively, based on different Shared Socioeconomic Pathways (SSP2, SSP3, and SSP5). The income projections are represented by the projected number of households for each of the three different income bins.</p><p>In the files, GISJOIN is the unique identifier for each block (or block group) . Each number of households projection is stored in a column, where the first four characters of the column name represent the projection year (e.g., 2020, 2030), the following four characters represent the SSP (e.g., SSP2, SSP3, SSP5), and the remaining characters indicate the income bin (Income1 represents annual household income less than $18,150, Income2 represents annual household income between $18,150 and $48,396, and Income3 represents annual household income greater than $48,396). For example, "2020SSP2Income1" indicates the household number projection for the first income bin in 2020 under SSP2.</p><p>"README.txt" describes the general steps for income data generation.</p>
Australian Statistical-Area (SA) Level Regions and Census Income Data (2011)
<p>The Australian Statistical Geography Standard (ASGS) defines a series of nested geographical areas in Australia known as Statistical Area (SA) Levels. SA3 regions are aggregations of SA2 regions, and SA2 regions are aggregations of SA1 regions. This data set contains the shapefiles of all SA1, SA2, and SA3 regions across Australia at the time of the 2011 census, originally downloaded from the Australian Bureau of Statistics (<a href="https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.001July\%20201.">ABS</a>).</p><p>This data set also contains income information from the 2011 census, at the SA1 and SA2 level in New South Wales (NSW). Specifically, it contains the number of families of various types within a range of weekly income brackets.</p><p>Sainsbury-Dale et al. (2023) used a subset of this data set in a study on poverty levels in an area of (NSW) surrounding Sydney. </p><p> </p><p><strong>References</strong></p><p>Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2023) "Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK", <i>Journal of Statistical Software</i>, to appear.</p>
Data for sensitivity analysis of Hübler, M., M. Wiese, M. Braun and J. Damster (2023): The distributional effects of CO2 pricing at home and at the border on German income groups
<p>This dataset contains the output files used in the sensitivity analysis of the computable general equilibrium (CGE) model developed in Hübler et al. (2023). Each folder is labeled with the respective set of sector-level elasticity of substitution parameters considered in the analysis: elasticities between domestically produced versus imported goods (esubd), Armington elasticities (esubm) and elasticities between production factors (esubva).</p> <p>For each set of parameters, we generate 1000 random draws from a +-10 % interval around each of the sector-specific elasticities, resulting in 1000 sets of sectoral parameter values. Each .xlsx output file located in a dedicated subfolder corresponds to a model run with a specific set of parameter values. In addition, we conduct sensitivity analyses of two individual parameters, namely the CO2 target (CO2factor) considered in our policy scenarios and the elasticity of substitution in consumption (esub_cons).</p> <p>The sensitivity analysis is carried out using the <a href="https://snakemake.readthedocs.io/en/stable/">Snakeflow</a> workflow management system, and R code for generating parameter spaces and processing the output files is available on <a href="https://github.com/mariuslbraun/climate-trade-distribution-sensitivity">GitHub</a>.</p>
Association between family income to poverty ratio and nocturia
<p>Data from the National Health and Nutrition Examination Survey (NHANES) in 2005-2010, including 6,662 adults aged 20 or older, were utilized for this cross-sectional study. The baseline data was used to display the distribution of each characteristic visually. Multiple linear regression and smooth curve fitting were used to study the linear and non-linear correlations between PIR and nocturia. Subgroup analysis and interaction tests were conducted to examine the stability of intergroup relationships.</p>
Data accompanying publication "High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide."
<p>This dataset accompanies the publication "How High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide." </p> <p>In our study, we combine income-based emission inequality data with an emulator-based modeling framework to thoroughly study the link between emissions of individual, wealthy emitter groups and climate extremes worldwide. Specifically, we assess individual contributions to current global temperature levels and systematically attribute changes in regional monthly heat and drought extremes across the globe.</p> <p>We focus on emissions of the top 10/1/0.1 wealthiest individuals globally and in the US, the EU27, India and China. The dataset contains results for 1-in-50/100/10'000 year extremes at grid-cell level and whenever imapcts are aggregated by region we refer to the regionmask AR6 regions. </p> <p>The file contents are the following:</p> <ol> <li>Attributed_GMT.csv: attributed global mean temperature levels by emitter group</li> <li>tas_frequency_hot.nc, spei_frequency_dry.nc, spi_frequency_dry.nc: attributed changes in the frequency of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>tas_intensity_hot.nc, spei_intensity_dry.nc, spi_intensity_dry.nc: attributed changes in the intensity of extreme events for extreme heat (tas), potential droughts (spei-3) and meteorological droughts (spi-3) on grid-cell level</li> <li>processed_extremes_frequency.csv: attributed changes in the frequency of extreme events aggregated to ar6 land regions </li> <li>processed_extremes_intensity.csv: attributed changes in the intensity of extreme events aggregated to ar6 land regions</li> </ol>
FIG. 3 in The canopy-forming alga Ericaria brachycarpa (J.Agardh) Molinari-Novoa & Guiry (Fucales, Phaeophyceae) shows seasonal and depth adaptation to the incoming light levels
FIG. 3. — Lineal fitting of the photosynthesis/PFD data at the lineal part of the P/PFD curve for the algal specimens collected at different depths.
FIG. 4 in The canopy-forming alga Ericaria brachycarpa (J.Agardh) Molinari-Novoa & Guiry (Fucales, Phaeophyceae) shows seasonal and depth adaptation to the incoming light levels
FIG. 4. — Photosynthesis at saturation (Psat), photosynthesis at low light levels (Pb) and dark respiration (Rd) for specimens thriving at 3 and 20 m (not transplanted: nt3 and nt20) and for those transplanted at the same depth (3to3 and 20to20) and at different depths (3to20 and 20to3) after 11 and 90 days after transplantation.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.