100+ datasets found
  1. f

    Orange dataset table

    • figshare.com
    xlsx
    Updated Mar 4, 2022
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    Rui Simões (2022). Orange dataset table [Dataset]. http://doi.org/10.6084/m9.figshare.19146410.v1
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    xlsxAvailable download formats
    Dataset updated
    Mar 4, 2022
    Dataset provided by
    figshare
    Authors
    Rui Simões
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

    Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

  2. w

    Dataset of books about Commercial statistics

    • workwithdata.com
    Updated Apr 17, 2025
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    Work With Data (2025). Dataset of books about Commercial statistics [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=j0-book_subject&fop0=%3D&fval0=Commercial+statistics&j=1&j0=book_subjects
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    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is about books. It has 226 rows and is filtered where the book subjects is Commercial statistics. It features 9 columns including author, publication date, language, and book publisher.

  3. d

    ThirdGrade ELA Math Scores byTract 08032017

    • catalog.data.gov
    • detroitdata.org
    • +4more
    Updated Sep 21, 2024
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    Data Driven Detroit (2024). ThirdGrade ELA Math Scores byTract 08032017 [Dataset]. https://catalog.data.gov/dataset/thirdgrade-ela-math-scores-bytract-08032017-eca07
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    Dataset updated
    Sep 21, 2024
    Dataset provided by
    Data Driven Detroit
    Description

    Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by census tract for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to census tract by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

  4. r

    HI- Demographic Data

    • redivis.com
    Updated Dec 19, 2023
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    Columbia Population Research Center (2023). HI- Demographic Data [Dataset]. https://redivis.com/datasets/fh74-90v3ge9m2
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    Dataset updated
    Dec 19, 2023
    Dataset authored and provided by
    Columbia Population Research Center
    Description

    The table HI- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 767560 rows across 699 variables.

  5. N

    Makanda, IL Population Breakdown by Gender and Age Dataset: Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
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    Neilsberg Research (2025). Makanda, IL Population Breakdown by Gender and Age Dataset: Male and Female Population Distribution Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/e1eeddcc-f25d-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Makanda, Illinois
    Variables measured
    Male and Female Population Under 5 Years, Male and Female Population over 85 years, Male and Female Population Between 5 and 9 years, Male and Female Population Between 10 and 14 years, Male and Female Population Between 15 and 19 years, Male and Female Population Between 20 and 24 years, Male and Female Population Between 25 and 29 years, Male and Female Population Between 30 and 34 years, Male and Female Population Between 35 and 39 years, Male and Female Population Between 40 and 44 years, and 8 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the three variables, namely (a) Population (Male), (b) Population (Female), and (c) Gender Ratio (Males per 100 Females), we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau across 18 age groups, ranging from under 5 years to 85 years and above. These age groups are described above in the variables section. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the population of Makanda by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Makanda. The dataset can be utilized to understand the population distribution of Makanda by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Makanda. 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 Makanda.

    Key observations

    Largest age group (population): Male # 50-54 years (44) | Female # 60-64 years (32). 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 Makanda population analysis. Total expected values are 18 and are define above in the age groups section.
    • Population (Male): The male population in the Makanda is shown in the following column.
    • Population (Female): The female population in the Makanda 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 Makanda 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 Makanda Population by Gender. You can refer the same here

  6. m

    Data from: Predicting Long-term Dynamics of Soil Salinity and Sodicity on a...

    • data.mendeley.com
    Updated Nov 26, 2020
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    Amirhossein Hassani (2020). Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale [Dataset]. http://doi.org/10.17632/v9mgbmtnf2.1
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    Dataset updated
    Nov 26, 2020
    Authors
    Amirhossein Hassani
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset globally (excluding frigid/polar zones) quantifies the different facets of variability in surface soil (0 – 30 cm) salinity and sodicity for the period between 1980 and 2018. This is realised by developing 4-D predictive models of Electrical Conductivity of saturated soil Extract (ECe) and soil Exchangeable Sodium Percentage (ESP) as indicators of soil salinity and sodicity. These machine learning-based models make predictions for ECe and ESP at different times, locations, and depths and by extracting meaningful statistics form those predictions, different facets of variability in the surface soil salinity and sodicity are quantified. The dataset includes 10 maps documenting different aspects of soil salinity and sodicity variations, and auxiliary data required for generation of those maps. Users are referred to the corresponding "READ_ME" file for more information about this dataset.

  7. e

    Data from: World Mineral Statistics Dataset

    • data.europa.eu
    html
    Updated Oct 11, 2021
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    Bath and North East Somerset Council (2021). World Mineral Statistics Dataset [Dataset]. https://data.europa.eu/set/data/world-mineral-statistics-dataset1
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    htmlAvailable download formats
    Dataset updated
    Oct 11, 2021
    Dataset authored and provided by
    Bath and North East Somerset Council
    Description

    The Bath and North East Somerset Council has one of the largest databases in the world on the production and trade of minerals. The dataset contains annual production statistics by mass for more than 70 mineral commodities covering the majority of economically important and internationally-traded minerals, metals and mineral-based materials. For each commodity the annual production statistics are recorded for individual countries, grouped by continent. Import and export statistics are also available for years up to 2002. Maintenance of the database is funded by the Science Budget and output is used by government, private industry and others in support of policy, economic analysis and commercial strategy. As far as possible the production data are compiled from primary, official sources. Quality assurance is maintained by participation in such groups as the International Consultative Group on Non-ferrous Metal Statistics. Individual commodity and country tables are available for sale on request.

  8. Local authority housing statistics data returns for 2017 to 2018

    • gov.uk
    Updated Jul 16, 2020
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    Ministry of Housing, Communities and Local Government (2020). Local authority housing statistics data returns for 2017 to 2018 [Dataset]. https://www.gov.uk/government/statistical-data-sets/local-authority-housing-statistics-data-returns-for-2017-to-2018
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    Dataset updated
    Jul 16, 2020
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Ministry of Housing, Communities and Local Government
    Description

    Dataset of all the data supplied by each local authority and imputed figures used for national estimates.

    This file is no longer being updated to include any late revisions local authorities may have reported to the department. Please use instead the Local authority housing statistics open data file for the latest data.

    https://assets.publishing.service.gov.uk/media/60e580d4e90e0764d3614396/Local_Authority_Housing_Statistics_data_returns_2017_to_2018_final.xlsx">Local authority housing statistics data returns for 2017 to 2018

    MS Excel Spreadsheet, 1.26 MB

    This file may not be suitable for users of assistive technology.

    Request an accessible format.
    If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email alternativeformats@communities.gov.uk. Please tell us what format you need. It will help us if you say what assistive technology you use.
  9. d

    IFHEADS01 - Family Units

    • datasalsa.com
    • data.europa.eu
    csv, json-stat, px +1
    Updated Jan 3, 2025
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    Central Statistics Office (2025). IFHEADS01 - Family Units [Dataset]. https://datasalsa.com/dataset/?catalogue=data.gov.ie&name=ifheads01-family-units
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    xlsx, json-stat, px, csvAvailable download formats
    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    Central Statistics Office
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jul 13, 2025
    Description

    IFHEADS01 - Family Units. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Family Units...

  10. Hydrographic and Impairment Statistics Database: THRB

    • catalog.data.gov
    • datasets.ai
    Updated Jun 5, 2024
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    National Park Service (2024). Hydrographic and Impairment Statistics Database: THRB [Dataset]. https://catalog.data.gov/dataset/hydrographic-and-impairment-statistics-database-thrb
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    Dataset updated
    Jun 5, 2024
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Description

    Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

  11. N

    Dataset for Kiawah Island, SC Census Bureau Demographics and Population...

    • neilsberg.com
    Updated Jul 24, 2024
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    Neilsberg Research (2024). Dataset for Kiawah Island, SC Census Bureau Demographics and Population Distribution Across Age // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/b79be6a5-5460-11ee-804b-3860777c1fe6/
    Explore at:
    Dataset updated
    Jul 24, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Carolina, Kiawah Island
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Kiawah Island population by age. The dataset can be utilized to understand the age distribution and demographics of Kiawah Island.

    Content

    The dataset constitues the following three datasets

    • Kiawah Island, SC Age Group Population Dataset: A complete breakdown of Kiawah Island age demographics from 0 to 85 years, distributed across 18 age groups
    • Kiawah Island, SC Age Cohorts Dataset: Children, Working Adults, and Seniors in Kiawah Island - Population and Percentage Analysis
    • Kiawah Island, SC Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

    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/.

  12. d

    HES-DID Data Linkage Report

    • digital.nhs.uk
    pdf
    Updated Jul 7, 2016
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    (2016). HES-DID Data Linkage Report [Dataset]. https://digital.nhs.uk/data-and-information/publications/statistical/hes-did-data-linkage-report
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    pdf(210.8 kB), pdf(165.5 kB)Available download formats
    Dataset updated
    Jul 7, 2016
    License

    https://digital.nhs.uk/about-nhs-digital/terms-and-conditionshttps://digital.nhs.uk/about-nhs-digital/terms-and-conditions

    Time period covered
    Apr 1, 2015 - Feb 29, 2016
    Area covered
    England
    Description

    This is the latest statistical publication of linked HES (Hospital Episode Statistics) and DID (Diagnostic Imaging Dataset) data held by the Health and Social Care Information Centre. The HES-DID linkage provides the ability to undertake national (within England) analysis along acute patient pathways to understand typical imaging requirements for given procedures, and/or the outcomes after particular imaging has been undertaken, thereby enabling a much deeper understanding of outcomes of imaging and to allow assessment of variation in practice. This publication aims to highlight to users the availability of this updated linkage and provide users of the data with some standard information to assess their analysis approach against. The two data sets have been linked using specific patient identifiers collected in HES and DID. The linkage allows the data sets to be linked from April 2012 when the DID data was first collected; however this report focuses on patients who were present in either data set for the period April 2015-February 2016 only. For DID this is provisional 2015/16 data. For HES this is provisional 2015/16 data. The linkage used for this publication was created on 06 June 2016 and released together with this publication on 07 July 2016.

  13. Data Set Description for Hyperspectral Imagery

    • catalog.data.gov
    • cloud.csiss.gmu.edu
    • +1more
    Updated Nov 12, 2020
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    U.S. EPA Office of Research and Development (ORD) (2020). Data Set Description for Hyperspectral Imagery [Dataset]. https://catalog.data.gov/dataset/data-set-description-for-hyperspectral-imagery
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    Dataset updated
    Nov 12, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Description

    The data set description provides a detail account of the type of data that is used within the peer-reviewed literature. The data involves special instrumentation, such as hyperspectral imaging cameras to develop thousands of pixels, which form images, like on a television screen. Other data is used to develop absorbance spectra from infrared spectrometers and compared to reference data to confirm the presence of a desired, tested chemical. This dataset is associated with the following publication: Baseley, D., L. Wunderlich, G. Phillips, K. Gross, G. Perram, S. Willison, M. Magnuson, S. Lee, R. Phillips, and W. Harper Jr.. Hyperspectral Analysis for Standoff Detection of Dimethyl Methylphosphonate on Building Materials [HS7.52.01]. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 135-142, (2016).

  14. Trends in International Mathematics and Science Study, 2015

    • catalog.data.gov
    • datasets.ai
    • +3more
    Updated Aug 12, 2023
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    National Center for Education Statistics (NCES) (2023). Trends in International Mathematics and Science Study, 2015 [Dataset]. https://catalog.data.gov/dataset/trends-in-international-mathematics-and-science-study-2015-3ef9e
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    Dataset updated
    Aug 12, 2023
    Dataset provided by
    National Center for Education Statisticshttps://nces.ed.gov/
    Description

    The Trends in International Mathematics and Science Study, 2015 (TIMSS 2015) is a data collection that is part of the Trends in International Mathematics and Science Study (TIMSS) program; program data are available since 1999 at . TIMSS 2015 (https://nces.ed.gov/timss/) is a cross-sectional study that provides international comparative information of the mathematics and science literacy of fourth-, eighth-, and twelfth-grade students and examines factors that may be associated with the acquisition of math and science literacy in students. The study was conducted using direct assessments of students and questionnaires for students, teachers, and school administrators. Fourth-, eighth-, and twelfth-graders in the 2014-15 school year were sampled. Key statistics produced from TIMSS 2015 provide reliable and timely data on the mathematics and science achievement of U.S. students compared to that of students in other countries. Data are expected to be released in 2018.

  15. d

    Statistics on Malaysian Companies registered with MATRADE of Franchise...

    • archive.data.gov.my
    Updated Sep 5, 2018
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    (2018). Statistics on Malaysian Companies registered with MATRADE of Franchise Services - Dataset - MAMPU [Dataset]. https://archive.data.gov.my/data/dataset/statistics-on-malaysian-companies-registered-with-matrade-of-franchise-services
    Explore at:
    Dataset updated
    Sep 5, 2018
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malaysia
    Description

    Statistics on Malaysian Companies registered with MATRADE of Franchise Services No. of Views : 69

  16. Sheep statistics, supply and disposition of sheep and lambs

    • open.canada.ca
    • beta.data.urbandatacentre.ca
    csv, html, xml
    Updated Feb 25, 2025
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    Statistics Canada (2025). Sheep statistics, supply and disposition of sheep and lambs [Dataset]. https://open.canada.ca/data/en/dataset/12d4e931-c6b8-4df3-bf5b-0a25524fc6c1
    Explore at:
    xml, html, csvAvailable download formats
    Dataset updated
    Feb 25, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Sheep statistics, supply and disposition of sheep and lambs, Canada and provinces (head x 1,000). Data are available on an annual basis.

  17. d

    Department of Labor, Office of Research (Current Employment Statistics NSA...

    • catalog.data.gov
    • data.ct.gov
    • +3more
    Updated Aug 9, 2024
    + more versions
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    data.ct.gov (2024). Department of Labor, Office of Research (Current Employment Statistics NSA 1990 - Current) [Dataset]. https://catalog.data.gov/dataset/department-of-labor-office-of-research-current-employment-statistics-nsa-1990-current
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    Dataset updated
    Aug 9, 2024
    Dataset provided by
    data.ct.gov
    Description

    Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

  18. My NASA Data

    • data.nasa.gov
    • s.cnmilf.com
    • +4more
    Updated Mar 31, 2025
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    nasa.gov (2025). My NASA Data [Dataset]. https://data.nasa.gov/dataset/my-nasa-data
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    Dataset updated
    Mar 31, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    MY NASA DATA (MND) is a tool that allows anyone to make use of satellite data that was previously unavailable.Through the use of MND’s Live Access Server (LAS) a multitude of charts, plots and graphs can be generated using a wide variety of constraints. This site provides a large number of lesson plans with a wide variety of topics, all with the students in mind. Not only can you use our lesson plans, you can use the LAS to improve the ones that you are currently implementing in your classroom.

  19. N

    Richfield, UT Population Breakdown by Gender Dataset: Male and Female...

    • neilsberg.com
    csv, json
    Updated Feb 24, 2025
    + more versions
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    Neilsberg Research (2025). Richfield, UT Population Breakdown by Gender Dataset: Male and Female Population Distribution // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/b24f4718-f25d-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Richfield, Utah
    Variables measured
    Male Population, Female Population, Male Population as Percent of Total Population, Female Population as Percent of Total Population
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the gender classifications (biological sex) reported by the US Census Bureau. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the population of Richfield by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Richfield across both sexes and to determine which sex constitutes the majority.

    Key observations

    There is a slight majority of female population, with 50.72% of total population being female. 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.

    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. No further analysis is done on the data reported from the Census Bureau.

    Variables / Data Columns

    • Gender: This column displays the Gender (Male / Female)
    • Population: The population of the gender in the Richfield is shown in this column.
    • % of Total Population: This column displays the percentage distribution of each gender as a proportion of Richfield total population. Please note that the sum of all percentages may not equal one due to rounding of values.

    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 Richfield Population by Race & Ethnicity. You can refer the same here

  20. d

    PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic...

    • catalog.data.gov
    • gdr.openei.org
    • +2more
    Updated Jan 20, 2025
    + more versions
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    University of Wisconsin (2025). PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic Sensing Data [Dataset]. https://catalog.data.gov/dataset/porotomo-natural-laboratory-horizontal-and-vertical-distributed-acoustic-sensing-data-94fb4
    Explore at:
    Dataset updated
    Jan 20, 2025
    Dataset provided by
    University of Wisconsin
    Description

    This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also included are links to the documentation for the dataset, Jupyter Notebook tutorials for working with the data as it is stored in AWS S3, and links to data viewers in OEDI for the horizontal (DASH) and vertical (DASV) DAS datasets. Horizontal DAS (DASH) data collection began 3/8/16, paused, and then started again on 3/11/2016 and ended 3/26/2016 using zigzag trenched fiber optic cabels. Vertical DAS (DASV) data collection began 3/17/2016 and ended 3/28/16 using a fiber optic cable through the first 363 m of a vertical well. These are raw data files from the DAS deployment at (DASH) and below (DASV) the surface during testing at the PoroTomo Natural Laboratory at Brady Hot Spring in Nevada. SEG-Y and hdf5 files are stored in 30 second files organized into directories by day. The hdf5 files available via HSDS are stored in daily files. Metadata includes information on the timing of recording gaps and a file count is included that lists the number of files from each day of recording. These data are available for download without login credentials through the free and publicly accessible Open Energy Data Initiative (OEDI) data viewer which allows users to browse and download individual or groups of files.

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Rui Simões (2022). Orange dataset table [Dataset]. http://doi.org/10.6084/m9.figshare.19146410.v1

Orange dataset table

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
xlsxAvailable download formats
Dataset updated
Mar 4, 2022
Dataset provided by
figshare
Authors
Rui Simões
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

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