About this dataset

Context

The dataset tabulates the population of Grass Range by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Grass Range. The dataset can be utilized to understand the population distribution of Grass Range by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Grass Range. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Grass Range.

Key observations

Largest age group (population): Male # 35-39 years (7) | Female # 70-74 years (36). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Grass Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Grass Range is shown in the following column.
• Population (Female): The female population in the Grass Range is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Grass Range for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Gender. You can refer the same here

The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types.

The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance.

If you share or use this dataset, please cite [4] and [5] in any relevant documentation.

In addition, an image dataset for crack classification has also been published at [6].

References:

[1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873

[2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605

[3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434

[4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678

5 Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044

[6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78

This geodatabase of point, line and polygon features is an effort to consolidate all of the range improvement locations on BLM-managed land in Idaho into one database. Currently, the point feature class has some data for all of the BLM field offices except the Coeur d'Alene and Cottonwood field offices. Range improvements are structures intended to enhance rangeland resources, including wildlife, watershed, and livestock management. Examples of range improvements include water troughs, spring headboxes, culverts, fences, water pipelines, gates, wildlife guzzlers, artificial nest structures, reservoirs, developed springs, corrals, exclosures, etc. These structures were first tracked by the Bureau of Land Management (BLM) in the Job Documentation Report (JDR) System in the early 1960s, which was predominately a paper-based tracking system. In 1988 the JDRs were migrated into and replaced by the automated Range Improvement Project System (RIPS), and version 2.0 is currently being used today. It tracks inventory, status, objectives, treatment, maintenance cycle, maintenance inspection, monetary contributions and reporting. Not all range improvements are documented in the RIPS database; there may be some older range improvements that were built before the JDR tracking system was established. There also may be unauthorized projects that are not in RIPS. Official project files of paper maps, reports, NEPA documents, checklists, etc., document the status of each project and are physically kept in the office with management authority for that project area. In addition, project data is entered into the RIPS system to enable managers to access the data to track progress, run reports, analyze the data, etc. Before Geographic Information System technology most offices kept paper atlases or overlay systems that mapped the locations of the range improvements. The objective of this geodatabase is to migrate the _location of historic range improvement projects into a GIS for geospatial use with other data and to centralize the range improvement data for the state. This data set is a work in progress and does not have all range improvement projects that are on BLM lands. Some field offices have not migrated their data into this database, and others are partially completed. New projects may have been built but have not been entered into the system. Historic or unauthorized projects may not have case files and are being mapped and documented as they are found. Many field offices are trying to verify the locations and status of range improvements with GPS, and locations may change or projects that have been abandoned or removed on the ground may be deleted. Attributes may be incomplete or inaccurate. This data was created using the standard for range improvements set forth in Idaho IM 2009-044, dated 6/30/2009. However, it does not have all of the fields the standard requires. Fields that are missing from the point feature class that are in the standard are: ALLOT_NO, MGMT_AGCY, ADMIN_ST, ADMIN_OFF, SRCE_AGCY, MAX_PDOP, MAX_HDOP, CORR_TYPE, RCVR_TYPE, GPS_TIME, UPDATE_STA, UNFILT_POS, FILT_POS, DATA_DICTI, GPS_HEIGHT, VERT_PREC, HORZ_PREC, and CONF_LEVEL. Several additional fields have been added that are not part of the standard: wrpt_idwrn, bird_laddr, and year_checked. There is no National BLM standard for GIS range improvement data at this time. For more information contact us at blm_id_stateoffice@blm.gov.

This dataset contains maps showing the principal attributes of tides around the Australian coast. It has been derived from data published in the Australian National Tide Tables. CAMRIS, standing for the Coastal and Marine Resources Information System, is a small-scale spatial analysis system developed in collaboration by several divisions of Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO), as part of the CSIRO Coastal Zone Program. CSIRO Division of Wildlife and Ecology is the custodian of the 'coastal' subset of the Australian Resources Information System (ARIS). Coastal ARIS became the core dataset of the CAMRIS project. The Coastal ARIS database was developed from a coastal inventory developed by Galloway et al. This inventory contained relatively large scale data including landform, geology, vegetation, soil, land use, climate and population information for each of 3027 3x10km sections around the coastline of mainland Australia and Tasmania, but excluding offshore islands.CC - Attribution (CC BY) This data has been licensed under the Creative Commons Attribution 3.0 Australia Licence. More information can be found at http://www.ausgoal.gov.au/creative-commons.

The file contains native and alien ranges of 1380 species worldwide obtained from the Global Invasive Species Database (http://www.iucngisd.org/gisd/) and CABI Invasive Species Compendium (http://www.cabi.org/isc/). The data are used to produce the results shown in Seebens, Essl & Blasius: The intermediate distance hypothesis of biological invasions, which is accepted for publication in Ecology Letters. The file is in csv format containing six columns: Species name, life form, native range, alien range, distance (great circle distance between the centroids of the respective regions) and species weights. More details about the data and the analysis can be found in Seebens et al.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

The current range represents the boundaries for Laguna Mountains skipper ( Pyrgus ruralis lagunae ) as understood by the United States Fish and Wildlife Service. The critical data identifies, in general, the areas of Final critical habitat for the species. Critical habitat constitutes areas considered essential for the conservation of a listed species. These areas provide notice to the public and land managers of the importance of the areas to the conservation of this species. Special protections and/or restrictions are possible in areas where Federal funding, permits, licenses, authorizations, or actions occur or are required. This species is listed as an endangered species by the USFWS. This species is only found in the Southern California Region.

This dataset contains ERA5 initial release (ERA5t) model level analysis parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Surface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.

The Geographic Names Information System (GNIS) actively seeks data from and partnerships with Government agencies at all levels and other interested organizations. The GNIS is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types. See http://geonames.usgs.gov for additional information.

cdo -sub -yearmax TX.nc -yearmin TN.nc out.nc Difference between the maximum of the maximum temperature and the minimum of the minimum temperature: Let TXi be the daily maximum temperature on day i and TNj be the daily minimum temperature on day j. For Etr build the difference between the maximum value of TXi per year and the minimum value of TNj per year. Climate Modell Data More information about the climate model data source and methods can be found in the text files of the head data set (DOI: 10.58160/gGzexcbDikobkyvK, see "IsPartOf-DOI").

The dataset collection in question consists of a range of data tables, all of which are interconnected. These tables contain intricate data, diligently compiled from the service interface of the Statistics Centre, also known in Finnish as 'Tilastokeskus'. This data is sourced directly from Finland. The dataset is comprehensive, providing a wide scope of statistical areas and greater regions, making it an invaluable resource for those seeking detailed demographic and regional data. The year 2023 is specifically highlighted in this collection, indicating the data's relevance to that particular year. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

Techsalerator’s News Event Data in Asia offers a detailed and expansive dataset designed to provide businesses, analysts, journalists, and researchers with comprehensive insights into significant news events across the Asian continent. This dataset captures and categorizes major events reported from a diverse range of news sources, including press releases, industry news sites, blogs, and PR platforms, offering valuable perspectives on regional developments, economic shifts, political changes, and cultural occurrences.

Key Features of the Dataset: Extensive Coverage:

The dataset aggregates news events from a wide range of sources such as company press releases, industry-specific news outlets, blogs, PR sites, and traditional media. This broad coverage ensures a diverse array of information from multiple reporting channels. Categorization of Events:

News events are categorized into various types including business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly find and analyze information relevant to their interests or sectors. Real-Time Updates:

The dataset is updated regularly to include the most current events, ensuring users have access to the latest news and can stay informed about recent developments as they happen. Geographic Segmentation:

Events are tagged with their respective countries and regions within Asia. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:

Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps users understand the context and significance of each event. Historical Data:

The dataset includes historical news event data, enabling users to track trends and perform comparative analysis over time. This feature supports longitudinal studies and provides insights into the evolution of news events. Advanced Search and Filter Options:

Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Asian Countries and Territories Covered: Central Asia: Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan East Asia: China Hong Kong (Special Administrative Region of China) Japan Mongolia North Korea South Korea Taiwan South Asia: Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Southeast Asia: Brunei Cambodia East Timor (Timor-Leste) Indonesia Laos Malaysia Myanmar (Burma) Philippines Singapore Thailand Vietnam Western Asia (Middle East): Armenia Azerbaijan Bahrain Cyprus Georgia Iraq Israel Jordan Kuwait Lebanon Oman Palestine Qatar Saudi Arabia Syria Turkey (partly in Europe, but often included in Asia contextually) United Arab Emirates Yemen Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and identify emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across Asia, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Asian news and events. Techsalerator’s News Event Data in Asia is a crucial resource for accessing and analyzing significant news events across the continent. By offering detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.

Dataset Card for Fineweb Ultra Mini

Fineweb Ultra Mini is a dataset derived from the original Fineweb dataset made by huggingface (see here: https://huggingface.co/datasets/HuggingFaceFW/fineweb). The dataset focuses on extracting high quality data from the Fineweb dataset, from the 2-3% range. If you would like even more high-quality data, keep out for our next release, fineweb ultra mini pro, which focuses on the 0-1% of high quality data originally found in fineweb.… See the full description on the dataset page: https://huggingface.co/datasets/reflex-ai/fineweb-ultra-mini.

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

AbstractHome range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}_\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or 50.1\%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing $\hat{N}_\mathrm{area}$. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small $\hat{N}_\mathrm{area}$. While 72\% of the 369 empirical datasets had \textgreater1000 total observations, only 4\% had an $\hat{N}_\mathrm{area}$ \textgreater1000, where 30\% had an $\hat{N}_\mathrm{area}$ \textless30. In this frequently encountered scenario of small $\hat{N}_\mathrm{area}$, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data. Usage notesEmpirical GPS tracking dataAnonymised, empirical tracking data used to estimate home range areas based on various home range estimators.Anonymised_Data.zip

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

This is a dataset representing the boundaries for the Hermes copper butterfly ( Lycaena hermes ) as understood by the United States Fish and Wildlife Service. This species is listed as a threatened species by the USFWS. This species is only found in the Southern California Region (and in Baja California).

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Lake. The data include parameters of paleolimnology with a geographic location of Alaska, United States Of America. The time period coverage is from 14828 to 0 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Please read: This is the look-up table for Address Range Road Type and is part of the set of NZ Roads tables. The Address Range Road Type look-up table is used by the following tables; NZ Roads: Address Range Road. The NZ Roads dataset includes eight data tables and eleven lookup tables. The dataset has been sourced from LINZ’s NZ Roads database, a database for the management of national roads, including those managed for addressing purposes. This set of normalised tables replaces the Landonline: Road Centre Line layer and the Landonline: Road Name and Landonline: Road Name Association tables currently published on LDS. These centrelines are required to indicate the presence of an authoritative road name. Named centrelines are not intended to represent the exact location of a road formation. Named centrelines do not indicate the presence of legal access. For a simplified version of the data contained within these tables see NZ Roads (Addressing), which aggregates geometries based on road name, and NZ Roads Subsections (Addressing), which holds the individual geometries. Please refer to the NZ Roads Data Dictionary for detailed metadata and information about this layer.

The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an OOPSLA 2020 paper. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the IDoFT dataset.

The dataset will be presented at the 1st International Flaky Tests Workshop 2024 (FTW 2024). Please refer to our extended abstract for more details about the motivation for and context of this dataset.

The following table provides a summary of the data.

Slug (Module) FIC Hash Tests Commits Av. Commits/Test Flaky Tests Tests w/ Consistent Failures Total Distinct Histories

TooTallNate/Java-WebSocket 822d40 146 75 75 24 1 2.6x10^9

apereo/java-cas-client (cas-client-core) 5e3655 157 65 61.7 3 2 1.0x10^7

eclipse-ee4j/tyrus (tests/e2e/standard-config) ce3b8c 185 16 16 12 0 261

feroult/yawp (yawp-testing/yawp-testing-appengine) abae17 1 191 191 1 1 8

fluent/fluent-logger-java 5fd463 19 131 105.6 11 2 8.0x10^32

fluent/fluent-logger-java 87e957 19 160 122.4 11 3 2.1x10^31

javadelight/delight-nashorn-sandbox d0d651 81 113 100.6 2 5 4.2x10^10

javadelight/delight-nashorn-sandbox d19eee 81 93 83.5 1 5 2.6x10^9

sonatype-nexus-community/nexus-repository-helm 5517c8 18 32 32 0 0 18

spotify/helios (helios-services) 23260 190 448 448 0 37 190

spotify/helios (helios-testing) 78a864 43 474 474 0 7 43

The columns are composed of the following variables:

Slug (Module): The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.

FIC Hash: The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes.

Tests: The number of distinct test class and method combinations over the entire considered commit range.

Commits: The number of commits in the considered commit range

Av. Commits/Test: The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.

Flaky Tests: The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.

Tests w/ Consistent Failures: The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.

Total Distinct Histories: The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.