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148 results for “Inequality”
Dataset for Manuscript "Evaluating Inequalities in Access to Evidence Based Surgical Care: A Systematic Review of Surgical Literature Publication and Consumption Opportunities Across Resource Settings"
<p>Dataset for a global access surgical literature systematic review. registered on Propsero (<strong>CRD42021240227) </strong>and titled <strong>Evaluating Inequalities in Access to Evidence Based Surgical Care: A Systematic Review of Surgical Literature Publication and Consumption Opportunities Across Resource Settings</strong></p>
FEATURES OF UKRAINIAN STUDENTS` VERBAL REPRESENTATION OF THE GENDER INEQUALITY`S CONCEPT
<p>The study aimed to determine Ukrainian students' verbal representation of the gender inequality's concept. Verbal representations were obtained based on the use of a directed associative experiment. The study involved 309 students (199 females and 110 males) from 17 to 25 years. Gender analysis showed: women provided 539 reactions, including 530 verbal (176 originals) reactions and nine rejections; men provided 319 reactions: 310 verbal reactions (103 original) and nine rejections. The most frequent reactions to the stimulus “gender inequality" were revealed: жінка / woman (10,6%), чоловік / man (9,4%), фемінізм / feminism (4,3%), несправедливість / injustice (3,7%), права / rights (2,9%), нерівність / inequality (2,8%), стать / gender (2,7%), сексизм / sexism (2,4%), дискримінація / discrimination (2,3%), насильство / violence (2%). It was determined that the concept of "gender inequality" has a negative connotation among Ukrainian students. Cognitive interpretation of the data showed that the concept has a more negative emotional connotation for women than for men. For a significant number of women, gender inequality includes experiences associated with sexism, discrimination, and violence. Analysis of male associations has shown that men's concept has a less emotional response and is presented at a more abstract (theoretical) level.</p>
Carbon dioxide removal could perpetuate community-scale inequalities in health impacts of U.S. air pollution
<p>Data for paper titled "Carbon dioxide removal could perpetuate community-scale inequalities in health impacts of U.S. air pollution". Corresponding code is in GitHub (https://github.com/CandeBergero/CDR_PM2.5_distribution_paper). Download, unzip, and replace the three folders from here into the GitHub repository. Additionally, folder "output_data" can be used to run code to generate paper figures without having to rerun all previous code.</p>
INEQUITIES AND DISPARITIES: AN INVESTIGATION OF ANTENATAL VISITS IN MOZAMBIQUE
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Global subnational Gini coefficient (income inequality) and gross national income (GNI) per capita PPP datasets for 1990-2023
<p>This dataset provides a gridded subnational datasets for</p> <ul> <li>Income inequality (Gini coefficient) at admin 1 level</li> <li>Gross national income (GNI) per capita PPP at admin 1 level</li> </ul> <p>The datasets are based on reported subnational admin data and spans three decades from 1990 to 2023 </p> <p>The datasets are presented in details in the following publication. <strong><em>Please cite this paper when using data. </em></strong></p> <p>Chrisendo D, Niva V, Hoffman R, Sayyar SM, Rocha J, Sandström V, Solt F, Kummu M. 2024. Income inequality has increased for over two-thirds of the global population. Preprint. doi: <a href="https://doi.org/10.21203/rs.3.rs-5548291/v1" target="_blank" rel="noopener">https://doi.org/10.21203/rs.3.rs-5548291/v1</a></p> <p><strong>Code is available</strong> at following repositories:</p> <ul> <li>Gini coefficient data creation: <a href="https://github.com/mattikummu/subnatGini" target="_blank" rel="noopener">https://github.com/mattikummu/subnatGini</a> </li> <li>GNI per capita data creation: <a href="https://github.com/mattikummu/subnatGNI" target="_blank" rel="noopener">https://github.com/mattikummu/subnatGNI</a> </li> <li>analyses for the article: <a href="https://github.com/mattikummu/gini_gni_analyses">https://github.com/mattikummu/gini_gni_analyses</a> </li> </ul> <p><strong>The following data is given (formats in brackets)</strong></p> <p>Gini coefficient:</p> <p>Please note, two distinct datasets for Gini cofficient are given. One based on SWIID national dataset, and another for WID national dataset. These are separated in file names as follows: _disp_ for SWIID (disposable income); _WID_ for WID. </p> <ul> <li>Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>Income inequality (Gini coefficient) at admin 1 level (subnational) (GeoTIFF, gpkg, csv)</li> <li>Slope for Gini coefficient at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)</li> <li>Uncertainty (standard deviation for each year and admin area; uncertainty for slope) for Gini (based on SWIID) at admin 1 level (gpkg)</li> <li>Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip)</li> </ul> <p>Gross national income (GNI) pear capita PPP (in 2021 USD):</p> <ul> <li>Gross national income (GNI) per capita PPP at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>Gross national income (GNI) per capita PPP at admin 1 level (subnational) (GeoTIFF, gpkg, csv)</li> <li>Slope for GNI per capita (log10) at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)</li> <li>Input data for the script that was used to generate the GNI per capita PPP (input_data_GNI.zip)</li> </ul> <p><strong>Files are named as follows</strong><br><em>Format</em>: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. <br><em>Admin levels:</em> adm0 for admin 0 level, adm1 for admin 1 level<br><em>Product type:</em> </p> <ul> <li>_gini_disp_ for gini coefficient based on SWIID national dataset (disposable income)</li> <li>_gini_WID_ for gini coefficient based on WID national dataset</li> <li>_uncertainty_slope_gini_disp_ for slope uncertainty of SWIID based Gini</li> <li>_uncertainty_SD_gini_disp_ for standard deviation of SWIID based Gini</li> <li>_gni_perCapita_ for GNI per capita PPP</li> </ul> <p> </p> <p><strong>Metadata </strong></p> <p><em>Grids </em></p> <p>Resolution: 5 arc-min (0.083333333 degrees) </p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; one band for each year over 1990-2021</p> <p>Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita</p> <p> </p> <p><em>Geospatial polygon (gpkg) files: </em></p> <p>Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) </p> <p>Temporal extent: annual over 1990-2021</p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: gkpk </p> <p>Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita</p> <p> </p> <p><strong>Version 3 changes (24.07.2025)</strong></p> <ul> <li>both datasets updated to cover 1990-2023 (previously 1990-2021)</li> <li>extrapolation method updated</li> <li>Gini data now produced also using WID national data as a base (previously only one based on SWIID was provided)</li> <li>uncertainty analysis done for Gini (SWIID)</li> </ul>
Hepatitis Inequalities Study
ClinicalTrials.gov study NCT06596213. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Additional Gluteal Control Training for Low Back Pain With Functional Leg Length Inequality
ClinicalTrials.gov study NCT03554746. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Tools for Reducing Inequity in Acute Leukemia (TRIAL): Beta Testing
ClinicalTrials.gov study NCT06907797. IPD Sharing: YES. Countries: 1. Publications: 0.
Shoe Lifts for Leg Length Inequality in Adults With Knee or Hip Symptoms
ClinicalTrials.gov study NCT01894100. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Children reject inequity out of spite
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Data from: Predicting the outcome of competition when fitness inequality is variable
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Data from: Human punishment is not primarily motivated by inequality
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Data from: Mycorrhizal fungi respond to resource inequality by moving phosphorus from rich to poor patches across networks
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Data from: Social disappointment explains chimpanzees' behaviour in the inequity aversion task
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Data from: Individual size inequality links forest diversity and above-ground biomass
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Data from: Group size elevates inequality in cooperative behaviour
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Reflecting aspects of gender inequality in the brazilian software development sector
<p><strong>Overview</strong><br>Welcome to the repository for the paper "Reflecting aspects of gender inequality in the brazilian software development sector"! This repository contains the Supplementary Material, which includes a spreadsheet with the general data collected in the research, a descriptive report of the results obtained, and the set of questions addressed in the questionnaire used in the survey. The raw data presented in Section 5 of the paper are also accessible. The content here is intended to support the research and facilitate further exploration and replication of the study.</p> <p><strong>Repository Organization</strong><br>The repository is organized as follows: Includes supplementary materials such as additional data and documents referenced in the paper.</p> <p><strong>File Formats:</strong> .csv and .xlsx, .pdf<br>Software Requirements: Spreadsheet editor that supports .csv and .xlsx files.</p> <p><strong>License</strong><br>This repository is licensed under the Creative Commons Attribution 4.0 International License. You are free to use, share, and adapt the materials as long as appropriate credit is given.</p> <p> </p> <p><span dir="auto"><span dir="auto">This paper has not yet been published.</span></span></p>
Replication Package for "Wealth Inequality in a Low Rate Environment"
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OKG: A Knowledge Graph for Fine-grained Understanding of Social Media Discourse on Inequality
<p>The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality.</p> <p>We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023.</p> <p>In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization. </p> <p>Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person (l.stork@uva.nl) with the following information: </p> <ol> <li>A description of the use case </li> <li>Affiliation of the researchers involved</li> <li>How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d</li> </ol>
Understanding and Overcoming the Racial/Ethnic Inequalities in COVID-19 Vaccination Acceptance
ClinicalTrials.gov study NCT05238428. IPD Sharing: YES. Countries: 1. Publications: 0.
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OpenNeuro
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