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.
819
datasets available to search
ShareScore release 0.9.0
Dataset results
819 results for “Gender;”
MOOD_2020_Worldpop_HumanPopulation_Age_Gender
<p>Worldpop Human 2020 population by Age and Gender. </p> <p><strong>Abstract:</strong></p> <p>Human population estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasets for the MOOD extent. Gender age categories were summed to provide datasets for all males, all females, and the total population. </p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>Filenames are as follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = mimimum age; OO= Maximum age</p> <p><br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999995960000092,81.9999997120000046</p> <p><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) </p> <p><strong>Temporal resolution:</strong><br> The year 2020</p> <p><strong>Pixel values:</strong><br>Human population estimates per pixel </p> <p><strong>Source: </strong><br>Worldpop (www.worldpop.org) datasets</p> <p><br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0</p> <p><br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</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>
Data on grammatical gender and numeral classifiers
<p>This material contains the dataset from the <a href="https://version.helsinki.fi/hals/sinnemaki/sinnemaki2019">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius. 2019. On the distribution and complexity of gender and numeral classifiers. In Di Garbo, Francesca, Bernhard Wälchli & Bruno Olsson (eds.), <em>Grammatical Gender and Linguistic Complexity, Volume II: World-wide Comparative Studies</em> (Studies in Diversity Linguistics 27), 133–200. Berlin: Language Science Press.</p>
Pinterest dataset for age and gender identification in author profiling
<p>This dataset was used for the experiments presented in the article "Reconstructive Classification for Age and Gender Identification in Social Networks" - IEEE Transactions on Computational Social Systems</p> <p>The dataset contains text data from 548,761 pins corresponding to 264 users of Pinterest.</p> <p>There are 7 files.</p> <p>The first 5 files correspond to the extracted textual features from the pins that are aggregated per user: ats, emojis/emoticons, hashtags, links, and words.</p> <p>There are 264 lines in each file (one per user), as the concatenation of the extracted features from all the pins corresponding to each user.</p> <p>The last 2 files are the labels for the age and gender of the users. There are also 264 lines (one per user).</p> <p>For age, there are 4 possible labels: 18-24, 25-34, 35-46, and 50+</p> <p>For gender, there are 2 possible labels: F and M</p>
Structural Gender Imbalances in Ballet Collaboration Networks
<p>Data contains node list of company artists and their artist type and gender. Edge list provides collaboration network of each company. </p> <p>Null model data provides metrics obtained from null models where assortativity preferences are removed by shuffling collaborations (edges) or artists' attributes (gender) in the collaboration network.</p> <p>For more details, please see documentation in <a href="/api/files/aa096f3e-bc2f-400e-94a9-6bd76ea4a324/Ballet_data_dict.rtf?versionId=7e58ba3a-caae-490b-9da6-9285bd7e4807">Ballet_data_dict.rtf</a></p> <p>For company abbreviations:</p> <p>ABT: American Ballet Theater; NYBC: New York City Ballet; NBC: National Ballet of Canada; ROH: The Royal Ballet of The Royal Opera House. </p>
Datasets for The Effect of COVID-19 on AGU Journal Authors by Gender and Geographical Location
<p>These files provide anonymized source data and tabular data on gender, age, and country of corresponding authors (submitting author) of American Geophysical Union (AGU) journals from January 2018 through June 2020. These datasets supplement an iposter presented at Japan Geosciences Union- American Geophysical Union joint 2020 meeting and supplement the corresponding preprint submission to ESSOAR.</p>
Corresponding spreadsheet to the Paper 'An intersectional approach to analyse gender productivity and open access: a bibliometric analysis of the Italian National Research Council' submitted to the Scientometric journal by Roberta Ruggieri, Fabrizio Pecoraro and Daniela Luzi from National Research Council, Italy.
<p>Gender equality and Open Access (OA) are priorities within the European Research Area (ERA) and cross-cutting issues in European research program H2020. Gender and openness are also key elements of Responsible Research and Innovation (RRI). However, despite the common underlying targets of fostering an inclusive, transparent and sustainable research environment, both issues are analysed as independent, unrelated topics.<br> This paper represents a first exploration of the inter-linkages between gender and OA analysing the scientific production of researchers of the Italian National Research Council under a gender perspective integrated with the different OA publications modes. A bibliometric analysis was carried out for articles published in the period 2016-2018 and retrieved from the Web of Science. Results are presented constantly analysing CNR scientific production in relation to gender, disciplinary fields and OA publication modes. These variables are also used when analysing articles that receive financial support.<br> Our results indicate that gender disparities in scientific production still persist in particularly in STEM disciplines (Science, Technology, Engineering and Mathematics), while in medical and agricultural sciences the gender gap is the closest to parity. A positive dynamic toward OA publishing and women scientific production is shown when open disciplines with well-established practices are related to articles supported by funds. A slightly higher women propensity toward OA is shown when considering Gold OA,OA or authorships with women in the first and last article by-line position. Moreover, the prevalence of Italian funded articles with women’s contributions published in Gold OA journals seems to confirm this tendency, especially if considering the week enforcement of the Italian OA policies.</p>
FIG. 5 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 5. — Floral display size in Sedum praealtum A. DC. (Crassulaceae): A, number of inflorescences; B, number of flowers; C, sex ratio (female-phase flowers/ total number of flowers). Values are means ± SE.
FIG. 3 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 3. — Scheme of inflorescence architecture of Sedum praealtum A. DC. (Crassulaceae). A, Basal-positioned floral buds; B, Distal-positioned floral buds.
FIG. 4 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 4. — Comparison between basal-positioned floral buds (black circles) and distal-positioned floral buds (white circles) in its probability of reaching anthesis during observation period. The S(t) abbreviation is the probability that a floral bud has not opened. Time until floral bud reaches anthesis refers to the time elapsed since the begin of the observation period. Solid line shows mean of time that basal-positioned floral buds open, and dashed line shows mean of time that distal-positioned floral buds open.
FIG. 2 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 2. — Floral traits in Sedum praealtum A. DC. (Crassulaceae). A, Floral longevity (days); B, Onset of female-phase flowers (days); C, Female-phase duration (days). Values are means ± SE.
FIG. 1 in Gender expression in Sedum praealtum A. DC. (Crassulaceae) in Central Veracruz, Mexico
FIG. 1. — Male-phase and female-phase flowers of Sedum praealtum A. DC. (Crassulaceae). A, Male-phase flower with dehiscent anthers; B, Female-phase flower with expansion of the stigmas. Photos by Angélica Hernández-Ramírez. Scale bars: 0.5 cm.
Intersectionality Spectrum - when DEI initiatives target gender equity
<p>alt-text:</p> <p>Intersectionality spectrum with different categories of intersectionality along the x-axis and the degree of difficulty shown as a bar graph on the y-axis. There is a dotted vertical line that separate visiable spectrum from invisible spectrum. There is an orange box that says “Most DEI initiatives target gender equity” and there are three orange arrows pointing to the white women symbol. This is because they benefit the most, and other non-white women do not, even though other non-white women have a higher degree of difficulty.</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>
Microbial Load on Dental Students' Hands: A Gender and Academic Stage Analysis
<p>This dataset supports a study investigating microbial contamination on the hands of dental students at Al-Hadi University College. The data were collected using impression sampling of the index and thumb fingers of 35 students (17 males, 18 females) from the third, fourth, and fifth academic years. Sampling occurred at the end of academic or clinical activities, and bacterial load was measured using colony-forming units (CFUs) cultured on MacConkey agar. Variables in the dataset include gender, academic stage, handedness, and CFU counts.</p>
Data Set Analisis Perilaku dan Interaksi pada YouTuber Gaming berdasarkan Persebaran Gender
<p>Data Set Analisis Perilaku dan Interaksi pada YouTuber Gaming berdasarkan Persebaran Gender</p>
Fig. 1 in Diet and food consumption of the pearl cichlid Geophagus brasiliensis (Teleostei: Cichlidae): relationships with gender and sexual maturity
Fig. 1. Coastal plain of Rio Grande do Sul in southern Brazil showing the Patos-Mirim lagoon complex (a) and the location of the four sampling sites where the specimens of the pearl cichlid Geophagus brasiliensis were collected (b).
Fig. 3. a in Diet and food consumption of the pearl cichlid Geophagus brasiliensis (Teleostei: Cichlidae): relationships with gender and sexual maturity
Fig. 3. a: Percentage of empty stomachs (PES) according to sexual maturity (immature and mature) and gender (female and male). b: Mean values (+ standard error) of the total food content expressed in volume (log10(x+1) transformed) in the digestive tract of the pearl cichlid Geophagus brasiliensis.
Fig. 2 in Diet and food consumption of the pearl cichlid Geophagus brasiliensis (Teleostei: Cichlidae): relationships with gender and sexual maturity
Fig. 2. Volume (%, black bars) and frequency of occurrence (%, gray bars) of the main food categories found in the digestive tracts of the pearl cichlid Geophagus brasiliensis.
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>
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.