Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
121
datasets available to search
ShareScore release 0.9.0
Dataset results
121 results for “gender differences”
Dataset - paper: Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors
<p>Dataset corresponding to a paper that has been accepted for publication in Appetite (Philippe, K., Chabanet, C., Issanchou, S., Grønhøj, A., Aschemann-Witzel, J., & Monnery-Patris, S. (2022, in press). <em>Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors</em>. Appetite).</p> <p>The objectives of the study were (1) to examine possible differences between Danish mothers and fathers with regard to their involvement in child feeding and their feeding practices, and (2) to identify possible parent-related predictors of parental feeding practices and of parental involvement in child feeding at home.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-Denmark.docx".</p>
Gender Differences in Public Code Contributions: a 50-year Perspective - Replication Package
<p>This page details the steps needed to replicate the findings of the paper: <a href="https://upsilon.cc/~zack/">Stefano Zacchiroli</a>, <em>Gender Differences in Public Code Contributions: a 50-year Perspective</em>, <a href="https://www.computer.org/csdl/magazine/so">IEEE Software</a>, 2021.</p> <p>After retrieving the replication package, follow the instruction described in the README.html file.</p>
Integration of data sets from different sources for modeling gender violence and perception of insecurity
<p>The dataset is composed of three distinct files which aggregate processed data derived from open datasets of three cities: Dublin, San Francisco, and Valencia. The data has been mapped to a grid of 25m² for Valencia and 50m² for Dublin and San Francisco. The respective files are named DATA_ES_VLC.csv, DATA_IE_DUB.csv, and DATA_US_SFO.csv. Additionally, there is a dataset for tweets named DATA_TWT.csv, which contains tweets collected through web scraping and analysed using natural language processing (NLP) algorithms and neural networks. The aim is to identify and classify tweets that discuss gender-based violence in the city of Valencia. Another file, MAP_ES_VLC.csv, includes points collected during various mapathons conducted by the Polytechnic University of Valencia campus for a science project aimed at identifying potentially insecure locations.</p>
Gender differences in preferences for mental health apps in the general popu-lation – a Choice-based Conjoint Analysis from Germany
<p><em><span>Background:</span></em><span> Men and women differ in the mental health issues they typically face. This study aims to describe gender differences in preferences for mental health treatment options and specifically tries to identify participants who prefer AI-based therapy over traditional face-to-face therapy. </span></p> <p><em><span>Method:</span></em><span> A nationally representative sample of 2</span><span>,</span><span>108 participants (53% female) aged 18 to 74 </span><span>years </span><span>completed a </span><span>CBCAs</span><span>. Within the CBCA</span><span>,</span><span> participants evaluated twenty choice sets, each describing three treatment variants in terms of provider, content, costs, and waiting time. </span></p> <p><em><span>Results:</span></em><span> Costs (</span><span>relative importance</span><span> [RI] = 55%) emerged as the most critical factor when choosing between treatment options, followed by provider (RI= 31%), content (RI = 10%), and waiting time (RI = 4%). Small yet statistically significant differences were observed between women and men. Women placed </span><span>greater</span><span> importance on the provider</span><span>,</span><span> while men placed </span><span>greater</span><span> importance on cost and waiting time. Age and previous experience with psychotherapy and with mental health apps were systematically related to individual preferences but did not alter gender effects. Only a minority </span><span>(approximately 8%)</span><span> of participants preferred AI-based treatment to traditional therapy. </span></p> <p><em><span>Conclusions:</span></em><span> Overall, affordable mental health treatments </span><span>performed</span><span> by human therapists are consistently favored by both men and women. AI-driven mental health apps should align with user preferences to address psychologist shortages. However, it is uncertain whether they alone can meet the rising demand, highlighting the need for alternative solutions.</span></p> <p><em><span>Keywords: </span></em><span>gender preferences; discrete choice experiment; mental health treatment; artificial intelligence</span></p>
Worldwide Gender Differences in Public Code Contributions - Replication Package
<p><strong>Worldwide Gender Differences in Public Code Contributions - Replication Package</strong></p> <p>This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022, <em>Worldwide Gender Differences in Public Code Contributions</em>. In Software Engineering in Society (ICSE-SEIS'22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 12 pages. <a href="https://doi.org/10.1145/3510458.3513011">https://doi.org/10.1145/3510458.3513011</a></p> <p>This document comes with the software needed to mine and analyze the data presented in the paper.</p> <p><strong>Prerequisites</strong></p> <p>These instructions assume the use of the <a href="https://www.gnu.org/software/bash/">bash</a> shell, the <a href="https://www.python.org/">Python</a> programming language, the <a href="https://www.postgresql.org/">PosgreSQL</a> DBMS (version 11 or later), the <a href="https://facebook.github.io/zstd/">zstd</a> compression utility and various usual *nix shell utilities (cat, pv, ...), all of which are available for multiple architectures and OSs.<br> It is advisable to create a <a href="https://docs.python.org/3/tutorial/venv.html">Python virtual environment</a> and install the following PyPI packages: <code>click==8.0.3 cycler==0.10.0 gender-guesser==0.4.0 kiwisolver==1.3.2 matplotlib==3.4.3 numpy==1.21.3 pandas==1.3.4 patsy==0.5.2 Pillow==8.4.0 pyparsing==2.4.7 python-dateutil==2.8.2 pytz==2021.3 scipy==1.7.1 six==1.16.0 statsmodels==0.13.0</code></p> <p><strong>Initial data</strong></p> <ul> <li><code>swh-replica</code>, a PostgreSQL database containing a copy of Software Heritage data. The schema for the database is available at <a href="https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/">https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/</a>.<br> We retrieved these data from <a href="https://www.softwareheritage.org">Software Heritage</a>, in collaboration with the archive operators, taking an archive snapshot as of 2021-07-07. We cannot make these data available in full as part of the replication package due to both its volume and the presence in it of personal information such as user email addresses. However, equivalent data (stripped of email addresses) can be obtained from the Software Heritage archive dataset, as documented in the article: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli, <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Pages 138-142, IEEE 2019. <a href="http://dx.doi.org/10.1109/MSR.2019.00030">http://dx.doi.org/10.1109/MSR.2019.00030</a>.<br> Once retrieved, the data can be loaded in PostgreSQL to populate <code>swh-replica</code>.</li> <li><code>names.tab</code> - forenames and surnames per country with their frequency</li> <li><code>zones.acc.tab</code> - countries/territories, timezones, population and world zones</li> <li><code>c_c.tab</code> - ccTDL entities - world zones matches</li> </ul> <p><strong>Data preparation</strong></p> <ul> <li>Export data from the <code>swh-replica</code> database to create <code>commits.csv.zst</code> and <code>authors.csv.zst</code> <code>sh> ./export.sh</code></li> <li>Run the authors cleanup script to create <code>authors--clean.csv.zst</code> <code>sh> ./cleanup.sh authors.csv.zst</code></li> <li>Filter out implausible names and create <code>authors--plausible.csv.zst</code> <code>sh> pv authors--clean.csv.zst | unzstd | ./filter_names.py 2> authors--plausible.csv.log | zstdmt > authors--plausible.csv.zst</code></li> </ul> <p><strong>Gender detection</strong></p> <ul> <li>Run the gender guessing script to create <code>author-fullnames-gender.csv.zst</code> <code>sh> pv authors--plausible.csv.zst | unzstd | ./guess_gender.py --fullname --field 2 | zstdmt > author-fullnames-gender.csv.zst</code></li> </ul> <p><strong>Database creation and data ingestion</strong></p> <ul> <li> <p>Create the PostgreSQL DB <code>sh> createdb gender-commit </code>Notice that from now on when prepending the <code>psql></code> prompt we assume the execution of psql on the <code>gender-commit</code> database.</p> </li> <li> <p>Import data into PostgreSQL DB <code>sh> ./import_data.sh</code></p> </li> </ul> <p><strong>Zone detection</strong></p> <ul> <li>Extract commits data from the DB and create <code>commits.tab</code>, that is used as input for the gender detection script<br> <code>sh> psql -f extract_commits.sql gender-commit</code></li> <li>Run the world zone detection script to create <code>commit_zones.tab.zst</code> <code>sh> pv commits.tab | ./assign_world_zone.py -a -n names.tab -p zones.acc.tab -x -w 8 | zstdmt > commit_zones.tab.zst </code>Use <code>./assign_world_zone.py --help</code> if you are interested in changing the script parameters.</li> <li>Read zones assignment data from the file into the DB<br> <code>psql> \copy commit_culture from program 'zstdcat commit_zones.tab.zst | cut -f1,6 | grep -Ev ''\s$'''</code></li> </ul> <p><strong>Extraction and graphs</strong></p> <ul> <li>Run the script to execute the queries to extract the data to plot from the DB. This creates <code>commits_tz.tab</code>, <code>authors_tz.tab</code>, <code>commits_zones.tab</code>, <code>authors_zones.tab</code>, and <code>authors_zones_1620.tab</code>.<br> Edit <code>extract_data.sql</code> if you whish to modify extraction parameters (start/end year, sampling, ...). <code>sh> ./extract_data.sh</code></li> <li>Run the script to create the graphs from all the previously extracted tabfiles. This will generate <code>commits_tzs.pdf</code>, <code>authors_tzs.pdf</code>, <code>commits_zones.pdf</code>, <code>authors_zones.pdf</code>, and <code>authors_zones_1620.pdf</code>. <code>sh> ./create_charts.sh</code></li> </ul> <p><strong>Additional graphs</strong></p> <p>This package also includes some already-made graphs</p> <ul> <li><code>authors_zones_1.pdf</code>: stacked graphs showing the ratio of female authors per world zone through the years, considering all authors with at least one commit per period</li> <li><code>authors_zones_2.pdf</code>: ditto with at least two commits per period</li> <li><code>authors_zones_10.pdf</code>: ditto with at least ten commits per period</li> </ul>
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>
Investigating PhDs' early career occupational outcomes in Italy: individual motivations, role of supervisor and gender differences (Anonymized UniTo dataset)
<p>Anonymized dataset to replicate the UniTo analysis in Carriero <em>et al.</em> (2023).</p> <p><em>If you use the data, please cite</em>:</p> <p>Carriero, R., Coda Zabetta, M., Geuna, A., & Tomatis, F. (2023). Investigating PhDs’ early career occupational outcomes in Italy: Individual motivations, role of supervisor and gender differences. <em>Higher Education</em>. (<a href="https://doi.org/10.1007/s10734-023-01068-y">https://doi.org/10.1007/s10734-023-01068-y</a>)</p>
Gender differences in scientific careers: A large-scale bibliometric analysis
<p>Data set belonging to the paper 'Gender differences in scientific careers: A large-scale bibliometric analysis' (https://arxiv.org/abs/2106.12624).</p>
Gender and age-related differences of ccular biometric parameters in patients undergoing cataract surgery in Bosnia and Herzegovina
<p><strong>Purpose</strong>: To determine the distribution and mutual relationship of ocular biometric parameters, as well as to evaluate gender- and age-related differences in patients undergoing cataract surgery in Bosnia and Herzegovina.</p> <p><strong>Materials and methods</strong>: It was a retrospective cross-sectional study of consecutive patients who underwent cataract surgery between January 2017 and December 2021 in a tertiary care clinic. All biometric measurements were performed using the optical biometer OA-2000 (Tomey, Nagoya, Japan).</p> <p><strong>Results</strong>: The study evaluated 1278 eyes from 1278 consecutive cataract patients. The average age of all included patients was 69.4 ± 9.98 (range 40–96). A total of 672 eyes (52.58%) were from females. The mean axial length (AL), anterior chamber depth (ACD), lens thickness (LT), and mean keratometry were 23.46±1.18mm, 3.17±0.40mm, 4.54±0.48mm, 43.42±1.55D respectively. Corneal astigmatism of ≥1D, >2D and >3D was found in 33.4%, 7.8% and 2.5% patients, respectively. Females were found to have shorter AL (p<0.0001), shallower ACD (p<0.0001) and steeper corneas (p<0.0001). In both genders, AL, ACD and with-the-rule astigmatism showed a decreasing trend (p=0.0001), while keratometry, the average cylinder, and against-the-rule astigmatism showed an increasing trend (p=0.0001) with increasing age. Furthermore, in both genders, there was an increasing trend in ACD (p=0.0001), and a decreasing trend in keratometry (p=0.0001) and LT (p=0.0001) with increasing AL.</p> <p><strong>Conclusions</strong>: This study provides useful reference data on ocular biometry for cataract surgeons in Bosnia and Herzegovina. Female patients tend to have steeper corneas, shorter AL and shallower AC than males, and these differences are independent of age or AL.</p>
Supplementary Material for ICT Practitioners' Perception of Working from Home During the Covid-19 Pandemic: Exploring Gender Differences
<p>Supplementary Material for the paper <em>ICT Practitioners' Perception of Working from Home During the Covid-19 Pandemic: Exploring Gender Differences. </em></p> <p>The .<em>pdf</em> file contains the form questions used to conduct the survey and the other files contain responses from survey participants.</p>
Gender Differences in Switching From Smoking Regular Cigarettes to E-Cigarettes
ClinicalTrials.gov study NCT03856515. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Analyzing recreational fishing effort – Gender differences and the impact of Covid-19
Open the record for dataset details and reuse information.
Gender and age-related differences of ccular biometric parameters in patients undergoing cataract surgery in Bosnia and Herzegovina
Open the record for dataset details and reuse information.
Data from: Gender diversity of editorial boards and gender differences in the peer review process at six journals of ecology and evolution.
<p>Despite substantial progress for women in science, women remain underrepresented in many aspects of the scholarly publication process. We examined how the gender diversity of editors and reviewers changed over time for six journals in ecology and evolution (2003-2015 for four journals, 2007-2015 or 2009-2015 for the other two), and how several aspects of the peer review process differed between female and male editors and reviewers. We found that, for five of the six journals, women were either absent or very poorly represented as handling editors at the beginning of our dataset. The representation of women among handling editors increased gradually and consistently, with women making up 29% of the handling editors (averaged across journals) in 2015, similar to the representation of women as last authors on ecology papers (23% in 2015) but lower than the proportion of women among all authors (31%) and among members of the societies that own the journals (37-40%). The proportion of women among reviewers has also gradually but consistently increased over time, reaching 27% by 2015. Female editors invited more female reviewers than did male editors, and this difference increased with age of the editor. Men and women who were invited to review did not differ in whether they responded to the review invitation, but, of those that responded, women were slightly more likely to agree to review. In contrast, women were less likely than men to accept invitations to serve on journal editorial boards. Our analyses indicate that there has been progress in the representation of women as reviewers and editors in ecology and evolutionary biology, but women are still underrepresented among the gatekeepers of scholarly publishing relative to their representation among researchers.</p>
Assessing user stories: the influence of template differences and gender-related problem-solving styles - Supplemental material
<p>Here we include the supplementary material that may be used as a replication package for the quasi-experiment reported in the paper "Assessing user stories: the influence of template differences and gender-related problem-solving styles", submitted to REJ Special Issue - RE 2021.</p>
Gender Differences in Math Tests: The Role of Time Pressure
<p>Data and Code to replicate the empirical analysis in "Gender Differences in Math Tests: The Role fo Time Pressure"</p>
Manifestations of Gender-Bias in ChatGPT 3.5: Differences in Responses Aimed at Young Adults
<p>Eighteen pairs of prompt-response with different the LLM-based ChatGPT 3.5 chatbots exploring differences in responses aimed at young women vs young men, in the context of the STEM gap. Conversations were conducted in Spring 2023, exported as images and bundled in a zip file. The zip file also contains a spreadsheet with the text version of prompts and responses, as well as quantitative and qualitative comparative analysis.</p>
Scheffe Test Results for Differences in Parents' View on Family's Role in Preventing Gender-Based Violence by Number of Children in the Family
<p><strong><span>Scheffe Test Results for Differences in Parents’ View on </span><span>Family's Role in Preventing Gender-Based Violence</span><span> by Number of Children in the Family</span></strong></p>
Occupational Differences in Parental Perspectives on the Family's Role in Preventing Gender-Based Violence
<p><strong><em><span>Occupational Differences in Parental Perspectives on the Family's Role in Preventing Gender-Based Violence</span></em></strong></p>
Assesing gender differences for non-predictable Breakthrough Cancer Pain Phenomenon: a secondary analysis from IOPS-MS Study
<p>IOPS-MS was a big multicentric study with the aim of characterizing BTcP in a large number of patients belonging to different settings and assessing possible factors influencing its development. Moreover, NP-BTcP topic was addressed elsewhere [https://doi.org/10.3390/cancers13164018].</p> <p>One key difference is related to the hormonal (gender related characteristics) differences between men and women. For example, estrogen and progesterone can affect pain sensitivity, with some studies suggesting that women may be more sensitive to pain during certain stages of the menstrual cycle. Similarly, testosterone may have analgesic effects in men, which could contribute to differences in pain sensitivity between genders.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.