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819 results for “Gender;”
Global Gender Gap Index
<p>The Global Gender Gap Index (by World Economic Forum) benchmarks the evolution of gender-based gaps among four key dimensions. The following information are visualized for each country: Economic Participation and Opportunity, Educational Attainment, Health and Survival, and Political Empowerment.</p> <p>The Global Gender Gap index benchmarks 156 countries, providing a tool for cross-country comparison and to prioritize the most effective policies needed to close gender gaps. The methodology of the index has remained stable since its original conception in 2006, providing a basis for robust cross-country and time-series analysis. The Global Gender Gap Index measures scores on a 0 to 100 scale and scores can be interpreted as the distance to parity (i.e. the percentage of the gender gap that has been closed).</p> <p>This dataset includes indicators from 2006 to 2021.</p>
Accompanying material for Article: No cash, no food. Gendered reorganization of livelihoods and food security in Cambodia
<p>Results of the two questionnaire-base surveys carried on in 2016 and 2019 on rural livelihoods in Cambodia</p>
Supporting material for "Impact of gender on the formation and outcome of formal mentoring relationships in the life sciences"
<p>This repository contains data and analysis code associated with the manuscript: L.P. Schwartz, J. Liénard, S. V. David. (2022) "Impact of gender on formation and outcome of formal mentoring relationships in the life sciences." Figures and tables in the manuscript can be produced by running the make_figures.ipynb notebook. Figures have been marked with headings indicating their position in the manuscript (Figure 1, Figure S1, etc.). In addition, the notebook contains code to reproduce regression analyses that are cited in the text but not directly associated with a figure.</p> <p>Data on mentoring relationships derives from Academic Family Tree (AFT, www.academictree.org) and public data sources on funding, publications, and awards. Inclusion criteria, public data sources, and procedures for linking across sources are described in the manuscript. Personal identifiers for researchers have been anonymized, but remain consistent across all data in the repository. In other words, the personal identifier "1" refers to the same person in all dataframes in the repository. But, that person is *not* the same researcher identified as "1" on the public AFT website.</p> <p><strong>Installation</strong></p> <p>Requires Python 3.x. and Pandas. To load required libraries using Anaconda, run:</p> <p>`conda create --name aft -c conda-forge pandas numpy scipy ipython jupyterlab scipy scikit-learn pandas matplotlib numpy statsmodels seaborn pytables`</p> <p><strong>Dataframes</strong></p> <p>Data is stored as a series of Pandas dataframes within HDF5 or CSV files:</p> <p>* cng_tc: The primary dataset used in the analysis. The name is an acronym for "connections" (i.e. training relationships, "cn"), "gender" ("g"), and "trainee count" ("tc"). Each row contains data on the mentor and trainee in one training relationship. See manuscript for inclusion criteria.</p> <p>* mentors: Data on mentors. Each row contains data on one mentor. See manunscript for inclusion criteria.</p> <p>* mentors_grants, mentors_hindex, mentors_locs_ranked: Subset of mentors with data available for funding (mentors_grants), citation (mentors_hindex), and institution rank (mentors_locs_ranked).</p> <p>* mentors_nobel, mentors_hhmi, mentors_nas: Subsets of mentors that received a Nobel (mentors_nobel), Howard Hughes Medical Institute grants (mentors_hhmi), or membership in the National Academy of Sciences (mentors_nas). See manuscript for details of data sources and linking procedures.</p> <p>* cn, cng, first_names, gn, gn_all, locs: Partial data (connections only, inferred gender only, connections and gender only, location only, first names and inferred gender only) for more inclusive sets of researchers in AFT. They are generally not used used for analysis, but have been included here to calculate statistics on the total amount of data included and to screen for data from U.S. locations.</p> <p>* nsf_gender_phds, nsf_gender_pds: National Science Foundation survey data on gender and fraction PhDs conferred per year (nsf_gender_phds) or fraction postdocs employed per year (nsf_gender_pds). See manuscript for details of data source.</p> <p>* photo: Data for validation of gender inference method.</p> <p><strong>Dataframe columns</strong></p> <p>* amount: Mentor's total funding<br> * amount_adj: Mentor's total funding (adjusted to 2020 dollars)<br> * broad_field: Mentor's general research area (e.g., life sciences, engineering, based on National Science Foundation classifications)<br> * continue: Whether trainee went on to become a mentor (i.e., has trainees listed in AFT)<br> * country: Country in which mentor's current institution is located<br> * firstname: First name of researcher (table of first names is not aligned with tables containing anonymized personal identifiers)<br> * first_grant_year: Year of mentor's first grant<br> * funding_rate: Mentor's annual funding rate (since first grant)<br> * funding_rate_adj: Mentor's annual funding rate (since first grant) adjusted to 2020 dollars<br> * hhmi: Whether mentor was granted HHMI funding<br> * hindex: Mentor's hindex<br> * location: Name of mentor's current institution<br> * locid: Identifier for mentor's institution<br> * locid_rank: Postion of mentor's institution in 2015 Quacquarelli-Symonds rankings (lower numbers are better)<br> * locid_rank_rev: Reversed version of "locid_rank" (i.e., higher numbers are better)<br> * majorarea: Mentor's specific research area (e.g, neuroscience)<br> * male_mentor, male trainee: Whether the probability that a researcher's first name is used by a person identifying as a man meets threshold (see manuscript for details on gender inference using first names)<br> * match_score: Score for string match between institution or name of awardee and researcher<br> * mentor_career_start: The date at which the mentor's academic career began<br> * mentor_continue_rate: Fraction of mentor's trainees that become mentors<br> * mentor_continue_rate_ft: Fraction of mentor's woman trainees that become mentors<br> * mentor_continue_rate_mt: Fraction of mentor's man trainees that become mentors<br> * mentor_t_p_male0: Fraction of mentor's trainees that are men<br> * mentor_t_p_male0_gs: Fraction of mentor's trainees that are men (graduate students only)<br> * mentor_t_p_male0_pd: Fraction of mentor's trainees that are men (postdocs only)<br> * mentor_tcount0: Mentor's total number of trainees<br> * nas: Whether mentor is a member of the National Academy of Sciences<br> * nobel: Whether mentor is a Nobel laureate<br> * p_male_mentor, p_male_trainee: Probability that a researcher's first name is used by a person identifying as a man<br> * pid: Anonymized identifier of researcher<br> * pid_mentor: Anonymized identifier of mentor in training relationship<br> * pid_trainee: Anonymized identifier of trainee in training relationship<br> * pq: "1" if data on training relationship is drawn from ProQuest database and has not been manually edited a human AFT user<br> * relation: Type of training relationship (1: graduate student, 2: postdoc)<br> * scorer1, scorer2, scorer3: Results of photo validation of gender inference for each scorer<br> * start: Training start year<br> * stop: Training end year<br> * trainee_tcount: Total people that the trainee has trained<br> * triad: Whether trainee has participated in both a graduate-level and postdoctoral training relationship</p> <p>The cn dataframe follows slightly different naming conventions, but is not generally used in the analysis (pid1 = pid_trainee, pid2 = pid_mentor, startdate = start, stopdate = stop).</p>
Gender policies in sports organizations
<p> </p> <p> The data has been collected from the national sports federations of Italy, Portugal, Turkey, Spain, and the United Kingdom in 2021 and 2022.</p> <p>With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP</p>
Data from the MA Thesis "Does She Talk Differently?: Exploring Implications of Gender in US Presidential and Vice Presidential Debates" and Coded Transcriptions of the 7 Analyzed Debates
<p>The raw data collected for the master's thesis "Does She Talk Differently?: Exploring Implications of Gender in US Presidential and Vice Presidential Debates", the tables and graphs created based on the data as well as the transcriptions for the seven debates analyzed for the research paper can be found in the files.</p>
Miniaturization and expansion of the contactless temperature measurement system. Facial temperatures in relation to age, pulse and gender.
<p><span>The dataset contains temperature measurements on the surface of the face taken on 109 people. Each patient (identified by Patient ID in dataset) acclimatized in a room with a temperature of 22-24 degrees Celsius. Then the person completed a survey, during which they provided their:</span></p> <ul> <li><span>age (column Survey - age [years]),</span></li> <li><span>gender (column Survey - Gender),</span></li> <li><span>temperature measurement using a pyrometer thermometer (column Survey - temperature [°C]),</span></li> <li><span>and pulse measurement using a pulse oximeter (column Survey - measured pulse [BPM]).</span></li> </ul> <p><span>After that, the examined person stood in front of the contactless temperature measurement system (using a thermal camera), which was continuously calibrated to the black body at a distance of 1.5-3 meters (column Distance between camera and patient [m]). Then, several hundred temperature measurements were taken on each person in the following ways:</span></p> <ul> <li><span>Median temperature on face [°C]</span></li> <li><span>Median temperature on face, 1% of pixels with max temperature [°C]</span></li> <li><span>Median temperature on face, 5% of pixels with max temperature [°C]</span></li> <li><span>Median temperature on face, 10% of pixels with max temperature [°C]</span></li> <li><span>Median temperature in the center of the eyes (3x3 pixels) [°C]</span></li> <li><span>Median temperature measured at the corners of the eyes (3x3 pixels) [°C]</span></li> </ul> <p><span>Additionally, the system automatically estimated:</span></p> <ul> <li><span>the age of the examined person (column Estimated Age [years]),</span></li> <li><span>the pulse of the examined person (column Estimated Pulse [BPM]),</span></li> <li><span>and gender (Estimated Gender).</span></li> </ul> <p><span>According to [1], the measured temperature on the surface of the face is influenced by the age of the measured person. As part of the project, a Binary Regression Tree was developed, which considers (estimated) age when calculating the temperature on the surface of the face (column Temperature calculated by Binary Tree Regression algorithm [°C]).</span></p> <p><span>[1] Cheung, Ming & Chan, Lung & Lauder, I & Kumana, Cyrus. (2012). Detection of body temperature with infrared thermography: accuracy in detection of fever. Hong Kong medical journal = Xianggang yi xue za zhi / Hong Kong Academy of Medicine. 18 Suppl 3. 31-4.</span></p>
Trends in research approaches and gender in plant ecology dissertations over four decades
<p>Dissertations are a foundational scientific product; they are the formative product that early-career scientists create and share original knowledge. Methodological approaches used in dissertations vary depending on the research field. In plant ecology, these approaches include observations, experiments (field or controlled-environment), literature reviews, theoretical approaches, or analyses of existing data (including 'big data'). This dataset was created to examine how the emphasis on each of these categories has changed over time, and whether male and female authors differ in the methods employed. The dissertations used for this study were gathered from the <em>Proquest Dissertations and Theses Global</em> (<a href="https://www.proquest.com/pqdtglobal)">https://www.proquest.com/pqdtglobal</a><a href="https://www.proquest.com/pqdtglobal)">)</a> database.</p>
Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)
<p>Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)</p> <p>Contains publicly available data from:</p> <ul> <li>Higher Education Research Institute. 1966–2006. “Cooperative Institutional Research Program(CIRP) Data Archives of the Freshman Survey Trends from 1966 to 2006.”</li> <li>National Center for Science and Engineering Statistics. 1972–1990. “Higher Education Research and Development Survey (HERD).</li> <li>U.S. National Center for Education Statistics. 1993. 120 Years of American Education: A Statistical Portrait. Washington, DC:U.S. Department of Education.</li> <li>U.S. National Center for Education Statistics. 2005. “Digest of Education Statistics, 2005.”</li> <li>U.S. National Center for Education Statistics. 2019. “Digest of Education Statistics, 2019.”</li> </ul> <p>To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", unzip all folders in this repository and place all files and folders (except "PapersFieldsofStudy.txt.gz") in "data/raw". AEA Data and Code Repository openicpsr-204361 shows the expected file structure. </p> <p>Additionally, this repository contains "PapersFieldsofStudy.txt.gz" from Microsoft Academic Graph data (April 2018 Extract) used for "Undergraduate Gender Diversity and the Direction of Scientific Research." To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", please decompress and place "PapersFieldsofStudy.txt.gz" into "data/raw/MAG/". The associated code repository can be found AEA Data and Code Repository openicpsr-204361.</p> <p>The attribution license of the Microsoft Academic Graph is <a href="https://opendatacommons.org/licenses/by/1-0/">ODC-BY</a></p> <p>Please cite the following paper in publications and reports using the Microsoft Academic Graph:</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p> <p> </p>
Evaluation of the Impact of the Educational Revolution in Peru and the Gender Wage Gap, 2017-2021
<p><strong><span>Background:</span></strong><strong><span> </span></strong><span>Women's educational attainment and their generation of value through education has increased the prospects for achieving economic equality between men and women. However, women continue to earn lower wages than men, reflecting growing inequality in several countries. Therefore, the objective of the study is to estimate the impact of education on the gender wage gap in Peru over the period 2017-2021.</span></p> <p><strong><span>Methods:</span></strong><span> Quantitative, explanatory study aimed at identifying the impact of education on the gender wage gap in Peru during the period 2017-2021. The research design is non-experimental and uses a time series that analyses the influence of the latent variable of education on the gender wage gap. This is a continuous variable to estimate the Tobit model.</span></p> <p><strong><span>Results:</span></strong><strong><span> </span></strong><span>The results show that the gender gap in Peru exhibited a decreasing trend between men and women during the period 2017-2020, with an average reduction of 10% until 2020 due to the health crisis. The highest average salary was achieved by men in 2019, reaching S/2289.97 soles, while women reached an average salary of S/1368.85 soles. In the post-pandemic scenario for 2021, the gender gap increased by 3%, with men earning an average salary of S/1999.63 soles and women earning an average salary of S/1281.16 soles. The analysis from 2017-2021 shows that years of education had a positive impact on the gender wage gap in Peru based on the Tobit model estimation.</span></p> <p><strong><span>Conclusions:</span></strong><strong><span> </span></strong><span>During the analysis period of 2017-2021, years of education had a positive impact on the gender wage gap in Peru, with the greatest impact occurring during the health crisis. The probability of women's incomes improving with an increase in years of education was 2.35%, while for men, the highest impact was in 2018, with a probability of income improvement of 2.16% in terms of marginal effect.</span></p>
Dataset Thesis of Diskriminasi Gender Berita Kriminalitas Online Di Indonesia
<p>Dataset pada file ZIP berisi data ringkasan berita kriminalitas di indonesia pada 1 Januari - 31 Desember 2023, data hasil pra-pemrosesan, dan data ekstraksi fitur vektor menggunakan word embedding sebelum dan sesudah debiasing.</p>
Survey data on the perceptions and impacts of gender inequality in the geosciences
<p>The data stem from an anonymous, online survey conducted from March 25 to April 11, 2018 using Google Surveys. The link to the survey was distributed by the authors via emails and social media (Twitter and Facebook). Among the 1415 participants, we analyzed the responses of those who identified as either female or male (leaving out seven non-binary respondents due to the small sample size), and currently work in academia (i.e., universities or research institutes, including emeritus and adjunct professors, research support staff, and research assistants). We thereby retained 1220 respondents with a gender distribution of 67.0% female to 33.0% male survey participants.</p> <p>Based on this survey, we published to following freely accessible article: Popp, A. L.; Lutz, S. R.; Khatami, S.; van Emmerik, T. H. M.; Knoben, W. J. M. (2019) A global survey on the perceptions and impacts of gender inequality in the earth and space sciences, <em>Earth and Space Science</em>, 6(8), 1460-1468, <a href="http://doi.org/10.1029/2019EA000706">doi:10.1029/2019EA000706</a>.</p>
Gender Specific Cannibalism in male Phytoseiulus persimilis.
<p>This dataset was used to research if cannibalism by male <em>Phytoseiulus</em> <em>persimilis</em> was reared towards a specific gender. This was done by comparing the sex ratios in groups of individuals that experienced cannibalism in the larval stage to those who did not. </p>
Fig. 1 in Helminth's assemblage of Trachemys dorbigni (Testudines: Emydidae) in southern Brazil: implications of anthropogenic environments and host's genders
Fig. 1. Overall layout of the collection environments in the study of helminth assemblage of Trachemys dorbigni in southern Brazil: A, detail of the Centro Agropecuário da Palma (UFPel), rural area of CapÃo do LeÃo, State of Rio Grande do Sul, Brazil; B, detail of the urban Área of Pelotas, Rio Grande do Sul, Brazil. Source: extracted and modified the site Google® Earth (©2014 Google – Images ©2014 Digital Globe).
Figure 2 in Mollusk Foraging and Gendered Labor at Litekyan (Ritidian) During the Spanish Contact Period in Guam
Figure 2. Map of the Guam National Wildlife Refuge showing the location of the two excavated latte (near the West End Cave) (Fig. 60 in Carson 2017).
Figure 6 in Mollusk Foraging and Gendered Labor at Litekyan (Ritidian) During the Spanish Contact Period in Guam
Figure 6. Visual representation of Latte 1 and 2. The units that were sampled are highlighted with an X.
Figure 5 in Mollusk Foraging and Gendered Labor at Litekyan (Ritidian) During the Spanish Contact Period in Guam
Figure 5. Household excavation site (Miller et al. 2021). Latte Building 1 reveals cooking hearth features alongside its structure.
Given Name Prevalence for Cumulative Gender Analysis
<p>This dataset contains the 15000 most prominent given names from Wikidata and with their calculated prevalent gender based of the genders assigned to the people in Wikidata that have those names. As well as the software components and introduction to produce an updated version of this dataset at a later point.</p> <p>Within the description of the TETTRIs Task 3.2 "Automatic mapping of taxonomic expertise", it is stated that for the various expert groups gender balance should be one of the factors to profile for. Since the analysis on the various groups should be done automatically, it is necessary to estimate the gender balance of a group without manual curation. One approach that we are considering is to do this estimate based on the given names of the identified experts. This repository lays the ground work for such an approach.</p> <p>This is clearly a heuristical approach. <strong>The data from this repository is not to be used to assess the gender of any individual</strong>, but only to determine the gender balance amongst a group of people with room for statistical errors .</p> <p>We are aware that this approach relies on many oversimplifications as well as biases in the underlying data and some of those biases and oversimplifications are addressed in the file README.md, included in the data set.</p>
Questionnaire on gender and age-related peculiarities in informed consent to clinical trials – National legislation
<p>European experts from the six selected countries (Germany, Spain, Austria, France, Italy, and United Kingdom) included in research done within task 1.3 (Ethical and legal review of gender and age-related issues associated with the acquisition of informed consent) participated in a survey on gender and age-related peculiarities in informed consent to clinical trials within national legislations.</p> <p>Experts were selected for their high-level scientific expertise in the fields relevant to the objectives of task 1.3. A short questionnaire on “Gender and age-related peculiarities in informed consent to clinical trials within national legislations" has been prepared and circulated to contact experts. This questionnaire was meant to identify the legal review process and collect up-to-date data. It was structured in 10 queries, exclusively aimed at obtaining hard law and soft law information pertaining to the topics addressed in task 1.3.</p>
Database of Russian names, surnames and midnames for gender identification
<p>Database of names, surnames and midnames across the Russian federation used as source to teach algorithms for gender identification by fullname.</p> <p>Dataset prepared for MongoDB database. It has MongoDB dump and dump of tables as JSON lines files.</p> <p>Used in gender identification and fullname parsing software <a href="https://github.com/datacoon/russiannames">https://github.com/datacoon/russiannames</a></p> <p>Available under Creative Commons CC-BY SA by default.</p>
BIO4AFRICA_Survey Data Uganda - Analysis of the needs and contexts of target farmers and rural communities with a gender lens_D1.1_311022_1
<p>The results of this survey support the identification of the needs, challenges, and context of target farmers and rural communities and how, why and under which circumstances the social, economic, regulatory, institutional, market and political circumstances could act as a barrier or an enabler for the uptake of bio-based solutions.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.