8 datasets found
  1. f

    Displays the means, standard deviations (SD), coefficient of variance (CV),...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Yuhsin Chen; Paoyu Huang; Min-Yuh Day; Yensen Ni; Mei-Chu Liang (2023). Displays the means, standard deviations (SD), coefficient of variance (CV), median, minimum (Min), and maximum (Max) for DJ30 and NASDAQ100 indices based on weekly data from 2001 to 2020. [Dataset]. http://doi.org/10.1371/journal.pone.0284918.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yuhsin Chen; Paoyu Huang; Min-Yuh Day; Yensen Ni; Mei-Chu Liang
    License

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

    Description

    Displays the means, standard deviations (SD), coefficient of variance (CV), median, minimum (Min), and maximum (Max) for DJ30 and NASDAQ100 indices based on weekly data from 2001 to 2020.

  2. f

    VMA trading strategies for DJ30 and NASDAQ100 from 2001 to 2020 (weekly...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Yuhsin Chen; Paoyu Huang; Min-Yuh Day; Yensen Ni; Mei-Chu Liang (2023). VMA trading strategies for DJ30 and NASDAQ100 from 2001 to 2020 (weekly data). [Dataset]. http://doi.org/10.1371/journal.pone.0284918.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Yuhsin Chen; Paoyu Huang; Min-Yuh Day; Yensen Ni; Mei-Chu Liang
    License

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

    Description

    VMA trading strategies for DJ30 and NASDAQ100 from 2001 to 2020 (weekly data).

  3. T

    Israel Stock Market (TA-125) Data

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +17more
    csv, excel, json, xml
    Updated Feb 9, 2017
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    TRADING ECONOMICS (2017). Israel Stock Market (TA-125) Data [Dataset]. https://tradingeconomics.com/israel/stock-market
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    excel, json, csv, xmlAvailable download formats
    Dataset updated
    Feb 9, 2017
    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
    Oct 8, 1992 - Mar 27, 2025
    Area covered
    Israel
    Description

    The main stock market index in Israel (TA-125) increased 34 points or 1.41% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on March of 2025.

  4. f

    The relative heat map shows the distances between solutions measured in SRD...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    The relative heat map shows the distances between solutions measured in SRD score, when the reference is set one-by-one as one of the solutions. [Dataset]. https://plos.figshare.com/articles/dataset/The_relative_heat_map_shows_the_distances_between_solutions_measured_in_SRD_score_when_the_reference_is_set_one-by-one_as_one_of_the_solutions_/12019572
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Balázs R. Sziklai; Károly Héberger
    License

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

    Description

    The relative heat map shows the distances between solutions measured in SRD score, when the reference is set one-by-one as one of the solutions.

  5. f

    Table1_High spatial and temporal resolution cerebrovascular reactivity for...

    • figshare.com
    docx
    Updated Jun 21, 2023
    + more versions
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    Amanjyot Singh Sainbhi; Logan Froese; Alwyn Gomez; Izzy Marquez; Fiorella Amenta; Carleen Batson; Kevin Y. Stein; Frederick A. Zeiler (2023). Table1_High spatial and temporal resolution cerebrovascular reactivity for humans and large mammals: A technological description of integrated fNIRS and niABP mapping system.docx [Dataset]. http://doi.org/10.3389/fphys.2023.1124268.s001
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    docxAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    Frontiers
    Authors
    Amanjyot Singh Sainbhi; Logan Froese; Alwyn Gomez; Izzy Marquez; Fiorella Amenta; Carleen Batson; Kevin Y. Stein; Frederick A. Zeiler
    License

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

    Description

    Introduction: The process of cerebral vessels maintaining cerebral blood flow (CBF) fairly constant over a wide range of arterial blood pressure is referred to as cerebral autoregulation (CA). Cerebrovascular reactivity is the mechanism behind this process, which maintains CBF through constriction and dilation of cerebral vessels. Traditionally CA has been assessed statistically, limited by large, immobile, and costly neuroimaging platforms. However, with recent technology advancement, dynamic autoregulation assessment is able to provide more detailed information on the evolution of CA over long periods of time with continuous assessment. Yet, to date, such continuous assessments have been hampered by low temporal and spatial resolution systems, that are typically reliant on invasive point estimations of pulsatile CBF or cerebral blood volume using commercially available technology.Methods: Using a combination of multi-channel functional near-infrared spectroscopy and non-invasive arterial blood pressure devices, we were able to create a system that visualizes CA metrics by converting them to heat maps drawn on a template of human brain.Results: The custom Python heat map module works in “offline” mode to visually portray the CA index per channel with the use of colourmap. The module was tested on two different mapping grids, 8 channel and 24 channel, using data from two separate recordings and the Python heat map module was able read the CA indices file and represent the data visually at a preselected rate of 10 s.Conclusion: The generation of the heat maps are entirely non-invasive, with high temporal and spatial resolution by leveraging the recent advances in NIRS technology along with niABP. The CA mapping system is in its initial stage and development plans are ready to transform it from “offline” to real-time heat map generation.

  6. n

    NSW Urban Heat Island to Modified Mesh Block 2016

    • datasets.seed.nsw.gov.au
    Updated Apr 30, 2016
    + more versions
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    (2016). NSW Urban Heat Island to Modified Mesh Block 2016 [Dataset]. https://datasets.seed.nsw.gov.au/dataset/nsw-urban-heat-island-to-modified-mesh-block-2016
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    Dataset updated
    Apr 30, 2016
    License

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

    Area covered
    New South Wales
    Description

    The Urban Heat Island (UHI) dataset measures the effects of urbanisation on land surface temperatures across Sydney Greater Metropolitan Area for the Summer of 2015-2016. UHI shows the variation of temperature to a non-urban vegetated reference, such as heavily wooded areas or national parks around Sydney. Derived from the analysis of thermal and infrared data from Landsat satellite, the dataset has been combined with the Australian Bureau of Statistics (ABS) Mesh Block polygon dataset to provide a mean UHI temperature that enables multi-scale spatial analysis of the relationship of heat to green cover. Data and Resources

  7. a

    Urban Heat Mapping – Morning Temperature

    • hub.arcgis.com
    • kingcounty.hub.arcgis.com
    • +1more
    Updated May 12, 2023
    + more versions
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    King County (2023). Urban Heat Mapping – Morning Temperature [Dataset]. https://hub.arcgis.com/documents/7f612e047fda4bb9ac14f09f44ffdd5d
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    Dataset updated
    May 12, 2023
    Dataset authored and provided by
    King County
    Description

    Area-wide modeled near-surface temperature for 6-7 am on July 27, 2020, based on temperature and humidity data collected for a one-day heat mapping project conducted by King County, Seattle Public Utilities, and the City of Seattle. Data collected on July 27, 2020 in partnership with project volunteers and CAPA Strategies. Data analysis and maps produced by CAPA strategies. This predictive temperature model was created from multi-band land cover rasters from Sentinel-2 satellite and raw heat data from sensor SD cards using the 70:30 holdout method.Heat maps also available for 6-7 am and 7-8 pm. Results can be viewed using this ArcGIS web app viewer. More information on the project available in Heat Watch Report for Seattle & King County. Contact CAPA Strategies for questions on the data, maps, and data analysis methods.

  8. Land surface temperature and urban heat island estimates for Australian...

    • data.gov.au
    pdf, tiff
    Updated Sep 24, 2019
    + more versions
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    The Commonwealth Scientific and Industrial Research Organisation (2019). Land surface temperature and urban heat island estimates for Australian capital cities, summer 2018-19 [Dataset]. https://data.gov.au/dataset/ds-dap-csiro%3A41378
    Explore at:
    tiff, pdfAvailable download formats
    Dataset updated
    Sep 24, 2019
    Dataset provided by
    CSIROhttp://www.csiro.au/
    License

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

    Area covered
    Australia
    Description

    Land surface temperature (LST) maps, and urban heat island (UHI) maps, for Australian capital cities, calculated over summer 2018-19. The metadata and files (if any) are available to the public. Land surface temperature (LST) maps, and urban heat island (UHI) maps, for Australian capital cities, calculated over summer 2018-19. The metadata and files (if any) are available to the public.

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Yuhsin Chen; Paoyu Huang; Min-Yuh Day; Yensen Ni; Mei-Chu Liang (2023). Displays the means, standard deviations (SD), coefficient of variance (CV), median, minimum (Min), and maximum (Max) for DJ30 and NASDAQ100 indices based on weekly data from 2001 to 2020. [Dataset]. http://doi.org/10.1371/journal.pone.0284918.t001

Displays the means, standard deviations (SD), coefficient of variance (CV), median, minimum (Min), and maximum (Max) for DJ30 and NASDAQ100 indices based on weekly data from 2001 to 2020.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
Jun 2, 2023
Dataset provided by
PLOS ONE
Authors
Yuhsin Chen; Paoyu Huang; Min-Yuh Day; Yensen Ni; Mei-Chu Liang
License

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

Description

Displays the means, standard deviations (SD), coefficient of variance (CV), median, minimum (Min), and maximum (Max) for DJ30 and NASDAQ100 indices based on weekly data from 2001 to 2020.

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