52 datasets found
  1. f

    Data_Sheet_1_Graph schema and best graph type to compare discrete groups:...

    • figshare.com
    • frontiersin.figshare.com
    docx
    Updated Jun 4, 2023
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    Fang Zhao; Robert Gaschler (2023). Data_Sheet_1_Graph schema and best graph type to compare discrete groups: Bar, line, and pie.docx [Dataset]. http://doi.org/10.3389/fpsyg.2022.991420.s001
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    docxAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    Frontiers
    Authors
    Fang Zhao; Robert Gaschler
    License

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

    Description

    Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

  2. g

    Data from: United States Geological Survey Digital Cartographic Data...

    • datasearch.gesis.org
    • icpsr.umich.edu
    v1
    Updated Aug 5, 2015
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    United States Department of the Interior. United States Geological Survey (2015). United States Geological Survey Digital Cartographic Data Standards: Digital Line Graphs from 1:2,000,000-Scale Maps [Dataset]. http://doi.org/10.3886/ICPSR08379.v1
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    v1Available download formats
    Dataset updated
    Aug 5, 2015
    Dataset provided by
    da|ra (Registration agency for social science and economic data)
    Authors
    United States Department of the Interior. United States Geological Survey
    Description

    This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

  3. f

    Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm

    • plos.figshare.com
    docx
    Updated May 31, 2023
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    Tracey L. Weissgerber; Natasa M. Milic; Stacey J. Winham; Vesna D. Garovic (2023). Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm [Dataset]. http://doi.org/10.1371/journal.pbio.1002128
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS Biology
    Authors
    Tracey L. Weissgerber; Natasa M. Milic; Stacey J. Winham; Vesna D. Garovic
    License

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

    Description

    Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

  4. d

    1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey

    • catalog.data.gov
    • cmr.earthdata.nasa.gov
    • +1more
    Updated Apr 11, 2025
    + more versions
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    DOI/USGS/EROS (2025). 1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey [Dataset]. https://catalog.data.gov/dataset/1-100000-scale-digital-line-graphs-dlg-from-the-u-s-geological-survey
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    Dataset updated
    Apr 11, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

  5. 1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey -...

    • data.nasa.gov
    Updated Mar 31, 2025
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    nasa.gov (2025). 1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/1-100000-scale-digital-line-graphs-dlg-from-the-u-s-geological-survey
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    Dataset updated
    Mar 31, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

  6. Annual Cash or EZPass Usage Line Graph: Beginning 2008

    • data.ny.gov
    csv, xlsx, xml
    Updated Jan 3, 2025
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    NYS Thruway Authority (2025). Annual Cash or EZPass Usage Line Graph: Beginning 2008 [Dataset]. https://data.ny.gov/w/tpik-mfed/caer-yrtv?cur=8nX2S7AR7xh
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    xlsx, xml, csvAvailable download formats
    Dataset updated
    Jan 3, 2025
    Dataset provided by
    New York State Thruway Authorityhttp://www.thruway.ny.gov/
    Authors
    NYS Thruway Authority
    Description

    This data set contains the number and percentage of vehicles that used E-ZPass or Cash by year by plaza on the NYS Thruway beginning in 2008.

  7. f

    From Static to Interactive: Transforming Data Visualization to Improve...

    • plos.figshare.com
    xml
    Updated Jun 4, 2023
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    Tracey L. Weissgerber; Vesna D. Garovic; Marko Savic; Stacey J. Winham; Natasa M. Milic (2023). From Static to Interactive: Transforming Data Visualization to Improve Transparency [Dataset]. http://doi.org/10.1371/journal.pbio.1002484
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    xmlAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS Biology
    Authors
    Tracey L. Weissgerber; Vesna D. Garovic; Marko Savic; Stacey J. Winham; Natasa M. Milic
    License

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

    Description

    Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.

  8. o

    Data from: Learning To Detect Patterns In 2x2 Graphs

    • osf.io
    url
    Updated Aug 21, 2024
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    Nestor Matthews; Megan Broderick; Sam Kozlowski (2024). Learning To Detect Patterns In 2x2 Graphs [Dataset]. http://doi.org/10.17605/OSF.IO/QWSFC
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    urlAvailable download formats
    Dataset updated
    Aug 21, 2024
    Dataset provided by
    Center For Open Science
    Authors
    Nestor Matthews; Megan Broderick; Sam Kozlowski
    License

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

    Description

    Background: Numerous academic disciplines commonly require students to develop skills in comprehending so-called 2x2 graphs. These graphs show a dependent variable, and two predictor variables -each with two levels (hence 2x2). Prior studies on 2x2 graph comprehension had participants either "think aloud" (Ali & Peebles, 2013; Peebles & Ali, 2015) or provide written descriptions (Shah & Freedman, 2011) about 2x2 main effects and interaction effects. Those methods privilege declarative knowledge (what can be stated). Here, we will measure perceptual learning in 2x2 graph-pattern-detection using a trial-and-error task that does not require declarative knowledge.

    Stimuli, Task, and Research Design: Participants will view variations on eight 2x2 graph patterns, shown as either line graphs or bar graphs. Each participant will classify 192 graphs into two initially unknown categories, Category “N” vs Category “Y”. These categories map to non-significant vs significant effects in one of three randomly assigned target factors: Factor A (left vs right height differences); Factor B (black vs white height differences); Interaction (black vs white slope differences). This psychophysical experiment has a 3x2 between-subject research design: Target Factor (“A”, “B”, “Interaction”) by Graph Type (Line versus Bar).

    Significance Statement: Across our Target Factor and Graph Type conditions, the mathematical information in our stimuli will remain identical. Consequently, statistically significant effects in our data will reveal biases in perceptual organization when naive participants learn to detect 2x2 graph patterns. These biases in perceptual organization have the potential to inform the applied fields of data visualization and statistics education, while furthering our knowledge about perceptual learning and ensemble perception.

  9. d

    Power Line 1986 (line)

    • catalog.data.gov
    Updated Nov 7, 2024
    + more versions
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    Earth Science Information Center, U.S. Geological Survey 507 National Center Reston, VA USA 20192 (Point of Contact) (2024). Power Line 1986 (line) [Dataset]. https://catalog.data.gov/dataset/power-line-1986-line
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    Dataset updated
    Nov 7, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    Data available online through the Arkansas Spatial Data Infrastructure (ASDI) at http://gis.arkansas.gov. The subject file represents the Arkansas portion of power line routes derived from 1:100,000-scale (""intermediate-scale"") Digital Line Graph data created by the USGS. Digital Line Graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1:100,000 are used. Intermediate-scale DLG's are broken down into five categories: 1. Public Land survey, 2. boundaries, 3. transportation 4. hydrography, and 5. hypsography.

  10. H

    Data from: Kids Count Data Center

    • dataverse.harvard.edu
    Updated Feb 23, 2011
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    Harvard Dataverse (2011). Kids Count Data Center [Dataset]. http://doi.org/10.7910/DVN/DLA2Q2
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 23, 2011
    Dataset provided by
    Harvard Dataverse
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Users can customize tables, graphs and maps on data related to children in a specific state or in the United States as a whole. Comparisons can be made between states. Background KIDS COUNT Data Center is part of the Annie E. Casey Foundation and serves to provide information on the status of children in America. The ten core indicators of interest under "Data by State" are: percent of low birth weight babies, infant mortality rate, child death rate, rate of teen deaths by accident, suicide and homicide, teen birth rate, percent of children living with parents who do not have full-time year-round employment, percent of teens who are high school drop outs, percent of teens not working and not in school, percent of children in poverty, and percent of families with children headed by a single parent. A number of other indicators, plus demographic and income information, are also included. "Data across States" is grouped into the following broad categories: demographics, education, economic well-being, family and community, health, safety and risk behaviors, and other. User Functionality Users can determine the view of the data- by table, line graph or map and can print or email the results. Data is available by state and across states. Data Across States allows users to access the raw data. Data is often present over a number of years. For a number of indicators under "Data Across States," users can view results by age, gender/ sex, or race/ ethnicity. Data Notes KIDS COUNT started in 1990. The most recent year of data is 2009 (or 2008 depending on the state, with some data available from 2010). Data is available on the national and state level, and for some states, at the county and city level.

  11. i

    Public Safety Arrests Data - Dataset - The Indiana Data Hub

    • hub.mph.in.gov
    Updated May 27, 2021
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    (2021). Public Safety Arrests Data - Dataset - The Indiana Data Hub [Dataset]. https://hub.mph.in.gov/dataset/public-safety-data-arrests
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    Dataset updated
    May 27, 2021
    License

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

    Area covered
    Indiana
    Description

    The bar chart shows the percentage of Indiana’s total arrests by racial category. The arrest percentage is calculated by dividing the number of arrests of people within a specific racial category by the total number of statewide arrests. The baseline of “per 1000” allows for comparison of rates across categories. Selecting the “rate per 1000” view produces a line graph that shows the number of arrests per 1,000 individuals by race. The number of arrests per county and by race are compared to 2010 Census population 2014-2020. Additional facts to note: 1. This dashboard shows data from the Criminal History Records Information System (CHRIS), which comes from three main sources. Arrest data comes from the Live Scan system, which is used for finger printing and capturing other pertinent information at the time of the arrest. Criminal disposition data are maintained by prosecutors in the ProsLink system, and by courts in the Odyssey system. Arrest county is determined by the location of the booking agency. If the booking agency is missing, then the arresting agency is used. The % of IN Population will not equal 100% because we are excluding non-represented racial category "Two or More Races," which accounts for ~1.7% of Indiana's population. Because some arrests are not included in the individual race categories shown here, total counts and percentages from the individual race categories add up to less than the totals for “All” races. While most dashboards in the Data Portal use Census estimates from 2019, this dashboard uses 2010 Census data.

  12. Amount of data created, consumed, and stored 2010-2023, with forecasts to...

    • statista.com
    • tokrwards.com
    Updated Jun 30, 2025
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    Statista (2025). Amount of data created, consumed, and stored 2010-2023, with forecasts to 2028 [Dataset]. https://www.statista.com/statistics/871513/worldwide-data-created/
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    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 2024
    Area covered
    Worldwide
    Description

    The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

  13. d

    Estimates of herbicide use for the forty-first through the sixtieth...

    • catalog.data.gov
    • data.usgs.gov
    • +2more
    Updated Oct 8, 2025
    + more versions
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    U.S. Geological Survey (2025). Estimates of herbicide use for the forty-first through the sixtieth most-used herbicides in the conterminous United States [Dataset]. https://catalog.data.gov/dataset/estimates-of-herbicide-use-for-the-forty-first-through-the-sixtieth-most-used-herbicides-i
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    Dataset updated
    Oct 8, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    United States, Contiguous United States
    Description

    This coverage contains estimates of herbicide use for the forty-first through the sixtieth most-used herbicides in the conterminous United States as reported in Gianessi and Puffer (1991). Herbicide-use estimates in this coverage are reported for each county polygon as acres treated, pounds of active ingredient used, and pounds used per square mile. The herbicide-use estimates provided by Gianessi and Puffer (1991) list acres treated and pounds of active ingredient applied for a given crop in each county for which use has been estimated. Cropping data are from the 1987 Census of Agriculture, and are subject to occasional suppressions of acreage estimates at the county level due to problems of confidentiality and census disclosure rules. The herbicide-use estimates included in this coverage are totals of use on all crops treated in a given county. The polygons representing county boundaries in the conterminous United States, as well as lakes, estuaries, and other nonland-area features were derived from the Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States (1970). Herbicides Herbicide use Counties United States

  14. f

    S1 Data -

    • figshare.com
    xlsx
    Updated Jan 24, 2024
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    Erick Jacob Okek; Fredrick Joshua Masembe; Jocelyn Kiconco; John Kayiwa; Esther Amwine; Daniel Obote; Stephen Alele; Charles Nahabwe; Jackson Were; Bernard Bagaya; Stephen Balinandi; Julius Lutwama; Pontiano Kaleebu (2024). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0287272.s001
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    xlsxAvailable download formats
    Dataset updated
    Jan 24, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Erick Jacob Okek; Fredrick Joshua Masembe; Jocelyn Kiconco; John Kayiwa; Esther Amwine; Daniel Obote; Stephen Alele; Charles Nahabwe; Jackson Were; Bernard Bagaya; Stephen Balinandi; Julius Lutwama; Pontiano Kaleebu
    License

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

    Description

    BackgroundSignificant milestones have been made in the development of COVID19 diagnostics Technologies. Government of the republic of Uganda and the line Ministry of Health mandated Uganda Virus Research Institute to ensure quality of COVID19 diagnostics. Re-testing was one of the methods initiated by the UVRI to implement External Quality assessment of COVID19 molecular diagnostics.Methodparticipating laboratories were required by UVRI to submit their already tested and archived nasopharyngeal samples and corresponding meta data. These were then re-tested at UVRI using the WHO Berlin protocol, the UVRI results were compared to those of the primary testing laboratories in order to ascertain performance agreement for the qualitative & quantitative results obtained. Ms Excel window 12 and GraphPad prism ver 15 was used in the analysis. Bar graphs, pie charts and line graphs were used to compare performance agreement between the reference Laboratory and primary testing Laboratories.ResultsEleven (11) Ministry of Health/Uganda Virus Research Institute COVID19 accredited laboratories participated in the re-testing of quality control samples. 5/11 (45%) of the primary testing laboratories had 100% performance agreement with that of the National Reference Laboratory for the final test result. Even where there was concordance in the final test outcome (negative or positive) between UVRI and primary testing laboratories, there were still differences in CT values. The differences in the Cycle Threshold (CT) values were insignificant except for Tenna & Pharma Laboratory and the UVRI(p = 0.0296). The difference in the CT values were not skewed to either the National reference Laboratory(UVRI) or the primary testing laboratory but varied from one laboratory to another. In the remaining 6/11 (55%) laboratories where there were discrepancies in the aggregate test results, only samples initially tested and reported as positive by the primary laboratories were tested and found to be false positives by the UVRI COVID19 National Reference Laboratory.ConclusionFalse positives were detected from public, private not for profit and private testing laboratories in almost equal proportion. There is need for standardization of molecular testing platforms in Uganda. There is also urgent need to improve on the Laboratory quality management systems of the molecular testing laboratories in order to minimize such discrepancies.

  15. d

    Permafrost

    • catalog.data.gov
    Updated Aug 13, 2021
    + more versions
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    US Geological Survey, EROS Alaska Field Office (Point of Contact) (2021). Permafrost [Dataset]. https://catalog.data.gov/dataset/permafrost3
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    Dataset updated
    Aug 13, 2021
    Dataset provided by
    US Geological Survey, EROS Alaska Field Office (Point of Contact)
    Description

    This data set consists of a geo-referenced digital map and attribute data derived from the publication 'Permafrost map of Alaska'. The map is presented at a scale of 1 to 2,500,000 and shows the correlation of physiographic province to presence of permafrost across the state of Alaska. The digital data were prepared under the U.S. Geological Survey Global Change Program, Land Data Systems - Arctic Land Processes Studies for display and analysis of terrain. The line work was captured by hand digitizing the source map, Ferrians, O.J., 1965, Permafrost map of Alaska - U.S. Geological Survey Miscellaneous Geologic Investigations Map I-445. Scale 1 to 2,500,000. The digital map was assembled and edited in ARC/INFO. The source map projection is polyconic. It is based on the Clarke 1866 ellipsoid with a central meridian of 150 W longitude. The data were geo-referenced from digitizer coordinates to the polyconic projection and then projected into an Albers Equal Area projection. The coastline was taken from the U.S Geological Survey, 1 to 2,000,000 scale Digital Line Graph data (U.S. Geological Survey, 1987). Attributes for the permafrost map were assigned. Metadata documentation was completed in 1996. The map units are closed polygons that are generalized in shape and size. They are defined in terms of their physiographic characteristics and association with permafrost. Each unit differs with respect to all other units and is uniquely identified as follows. 11 Mountainous Area underlain by continuous permafrost 12 Mountainous Area underlain by discontinuous permafrost 13 Mountainous Area underlain by isolated masses of permafrost 21 Lowland and Upland Area underlain by thick permafrost 22 Lowland and Upland Area underlain by moderately thick to thin permafrost 23 Lowland and Upland Area underlain by discontinuous permafrost 24 Lowland and Upland Area underlain by numerous isolated masses of permafrost 25 Lowland and Upland Area underlain by isolated masses of permafrost 26 Lowland and Upland Area generally free of permafrost Use constraints - The U.S. Geological Survey should be acknowledged as the data source in products derived from these data. The data are general in nature and should not be used at a scale larger than 1 to 2,500,000, that of the original map. Users must assume responsibility to determine the usability of this data for their purposes. The use of these data is not restricted and may be interpreted by organizations, agencies, units of government or others; however, they are responsible for its appropriate application. Digital data files are periodically updated. Files are dated and users are responsible for obtaining the latest revisions of the data. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made by the agency regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty. A copy of this map is presented on the CAPS Version 1.0 CD-ROM, June 1998.

  16. H

    Gender Info

    • dataverse.harvard.edu
    Updated Jul 7, 2011
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    Harvard Dataverse (2011). Gender Info [Dataset]. http://doi.org/10.7910/DVN/JL3DIQ
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 7, 2011
    Dataset provided by
    Harvard Dataverse
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Users can access data related to international gender issues including but not limited to data on population, families, health, education, work, and political participation Background Gender Info 2007 is maintained by the United Nations Statistic Division in collaboration with the United Nations Children’s Fund (UNICEF) and the United Nations Population Fund (UNFPA). Gender Info has been adapted by DevInfo technology and presents up to date country level data. User functionality Users have several options for retrieving data from Gender Info. Users can search by indicator or by area. Users are able to use the search bar to search or s croll through the listed indicators/areas and check those that apply. Once finished, users are able to view the data in map, table, or graph formats. Users are able to manipulate graphs into bar graphs, column graphs, pie charts, and line graphs. Users must then save their files to the diGalleray where they can be downloaded. There is a Help Box available in the right hand corner for assistanc e. Alternatively users can directly access the diGallery which contains premade graphs and charts that are available for download. Data Notes For every data query a year and resource reference is provided. There is no indication on the site when the data will be updated.

  17. g

    Submerged Area 1986 (polygon) | gimi9.com

    • gimi9.com
    Updated Jan 1, 2001
    + more versions
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    (2001). Submerged Area 1986 (polygon) | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_submerged-area-1986-polygon/
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    Dataset updated
    Jan 1, 2001
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Data available online through the Arkansas Spatial Data Infrastructure (ASDI) at http://gis.arkansas.gov. The subject file represents the Arkansas portion of areas to be submerged derived from 1:100,000-scale (""intermediate-scale"") Digital Line Graph data created by the USGS. Digital Line Graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1:100,000 are used. Intermediate-scale DLG's are broken down into five categories: 1. Public Land survey, 2. boundaries, 3. transportation 4. hydrography, and 5. hypsography.

  18. f

    Table_1_Raw Data Visualization for Common Factorial Designs Using SPSS: A...

    • frontiersin.figshare.com
    xlsx
    Updated Jun 15, 2023
    + more versions
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    Florian Loffing (2023). Table_1_Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial.XLSX [Dataset]. http://doi.org/10.3389/fpsyg.2022.808469.s002
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    xlsxAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    Frontiers
    Authors
    Florian Loffing
    License

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

    Description

    Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

  19. T

    United States Stock Market Index Data

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +12more
    csv, excel, json, xml
    Updated Oct 16, 2025
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    TRADING ECONOMICS (2025). United States Stock Market Index Data [Dataset]. https://tradingeconomics.com/united-states/stock-market
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    excel, xml, json, csvAvailable download formats
    Dataset updated
    Oct 16, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 3, 1928 - Oct 16, 2025
    Area covered
    United States
    Description

    The main stock market index of United States, the US500, rose to 6695 points on October 16, 2025, gaining 0.37% from the previous session. Over the past month, the index has climbed 1.44% and is up 14.62% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

  20. Summary for Policymakers of the Working Group I Contribution to the IPCC...

    • catalogue.ceda.ac.uk
    • data-search.nerc.ac.uk
    Updated Mar 9, 2024
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    Joeri Rogelj; Chris Smith; Gian-Kasper Plattner; Malte Meinshausen; Sophie Szopa; Sebastian Milinski; Jochem Marotzke (2024). Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.4 (v20210809) [Dataset]. https://catalogue.ceda.ac.uk/uuid/bd65331b1d344ccca44852e495d3a049
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    Dataset updated
    Mar 9, 2024
    Dataset provided by
    Centre for Environmental Data Analysishttp://www.ceda.ac.uk/
    Authors
    Joeri Rogelj; Chris Smith; Gian-Kasper Plattner; Malte Meinshausen; Sophie Szopa; Sebastian Milinski; Jochem Marotzke
    License

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

    Time period covered
    Jan 1, 2015 - Dec 31, 2100
    Area covered
    Earth
    Description

    Data for Figure SPM.4 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

    Figure SPM.4 panel a shows global emissions projections for CO2 and a set of key non-CO2 climate drivers, for the core set of five IPCC AR6 scenarios. Figure SPM.4 panel b shows attributed warming in 2081-2100 relative to 1850-1900 for total anthropogenic, CO2, other greenhouse gases, and other anthropogenic forcings for five Shared Socio-economic Pathway (SSP) scenarios.

    How to cite this dataset

    When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

    IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

    Figure subpanels

    The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b.

    List of data provided

    This dataset contains:

    • Projected emissions from 2015 to 2100 for the five scenarios of the AR6 WGI core scenario set (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5)
    • Projected warming for all anthropogenic forcers, CO2 only, non-CO2 greenhouse gases (GHGs) only, and other anthropogenic components for 2081-2100 relative to 1850-1900, for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.

    The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

    Data provided in relation to figure

    Panel a:

    The first column includes the years, while the next columns include the data per scenario and per climate forcer for the line graphs.

    • Data file: Carbon_dioxide_Gt_CO2_yr.csv. relates to Carbon dioxide emissions panel
    • Data file: Methane_Mt_CO2_yr.csv. relates to Methane emissions panel
    • Data file: Nitrous_oxide_Mt N2O_yr.csv. relates to Nitrous oxide emissions panel
    • Data file: Sulfur_dioxide_Mt SO2_yr.csv. relates to Sulfur dioxide emissions panel

      Panel b:

    • Data file: ts_warming_ranges_1850-1900_base_panel_b.csv. [Rows 2 to 5 relate to the first bar chart (cyan). Rows 6 to 9 relate to the second bar chart (blue). Rows 10 to 13 relate to the third bar chart (orange). Rows 14 to 17 relate to the fourth bar chart (red). Rows 18 to 21 relate to the fifth bar chart (brown).].

    Sources of additional information

    The following weblink are provided in the Related Documents section of this catalogue record: - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 1, which contains details on the input data used in Table 1.SM.1..(Cross-Chapter Box 1.4, Figure 2). - Link to related publication for input data used in panel a.

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Fang Zhao; Robert Gaschler (2023). Data_Sheet_1_Graph schema and best graph type to compare discrete groups: Bar, line, and pie.docx [Dataset]. http://doi.org/10.3389/fpsyg.2022.991420.s001

Data_Sheet_1_Graph schema and best graph type to compare discrete groups: Bar, line, and pie.docx

Related Article
Explore at:
docxAvailable download formats
Dataset updated
Jun 4, 2023
Dataset provided by
Frontiers
Authors
Fang Zhao; Robert Gaschler
License

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

Description

Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

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