100+ datasets found
  1. Standardize Data

    • figshare.com
    csv
    Updated Jul 17, 2025
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    Zekun Lu (2025). Standardize Data [Dataset]. http://doi.org/10.6084/m9.figshare.29590574.v1
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    csvAvailable download formats
    Dataset updated
    Jul 17, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    Zekun Lu
    License

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

    Description

    Standardize Data

  2. d

    Sea Surface Temperature (SST) Standard Deviation of Long-term Mean,...

    • catalog.data.gov
    • data.ioos.us
    • +2more
    Updated Jan 27, 2025
    + more versions
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    National Center for Ecological Analysis and Synthesis (NCEAS) (Point of Contact) (2025). Sea Surface Temperature (SST) Standard Deviation of Long-term Mean, 2000-2013 - Hawaii [Dataset]. https://catalog.data.gov/dataset/sea-surface-temperature-sst-standard-deviation-of-long-term-mean-2000-2013-hawaii
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    Dataset updated
    Jan 27, 2025
    Dataset provided by
    National Center for Ecological Analysis and Synthesis (NCEAS) (Point of Contact)
    Area covered
    Hawaii
    Description

    Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 2000-2013 for each pixel.

  3. n

    Methods for normalizing microbiome data: an ecological perspective

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated Oct 30, 2018
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    Donald T. McKnight; Roger Huerlimann; Deborah S. Bower; Lin Schwarzkopf; Ross A. Alford; Kyall R. Zenger (2018). Methods for normalizing microbiome data: an ecological perspective [Dataset]. http://doi.org/10.5061/dryad.tn8qs35
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    zipAvailable download formats
    Dataset updated
    Oct 30, 2018
    Dataset provided by
    James Cook University
    University of New England
    Authors
    Donald T. McKnight; Roger Huerlimann; Deborah S. Bower; Lin Schwarzkopf; Ross A. Alford; Kyall R. Zenger
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description
    1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity), Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting community-level patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs. 2. We tested these theoretical predictions via simulations and a real-world data set. 3. Proportions and rarefying produced more accurate comparisons among communities and were the only methods that fully normalized read depths across samples. Additionally, upper quartile, CSS, edgeR-TMM, and DESeq-VS often masked differences among communities when common OTUs differed, and they produced false positives when rare OTUs differed. 4. Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.
  4. a

    Standardized Precipitation Index

    • hub.arcgis.com
    • catalogue.arctic-sdi.org
    • +4more
    Updated Mar 16, 2016
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    Ontario Ministry of Natural Resources and Forestry (2016). Standardized Precipitation Index [Dataset]. https://hub.arcgis.com/documents/ce2a10c9eea04a10bb514a303acc676b
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    Dataset updated
    Mar 16, 2016
    Dataset authored and provided by
    Ontario Ministry of Natural Resources and Forestry
    License

    https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario

    Area covered
    Description

    The Standardized Precipitation Index (SPI) was generated for certain Environment Canada long-term climate stations in Ontario.

    The SPI quantifies the precipitation deficit and surplus for multiple time scales, including:

    one month three months six months nine months 12 months 24 months

    You can use the SPI to study the impact of dry and wet weather conditions to create comprehensive water management approaches.

    The SPI data package is distributed as a Microsoft Access Geodatabase.

    This is a legacy dataset that we no longer maintain or support.

    The documents referenced in this record may contain URLs (links) that were valid when published, but now link to sites or pages that no longer exist.

    Additional Documentation

    Standardized Precipitation Index - User Guide (PDF)

    Status Completed: production of the data has been completed

    Maintenance and Update Frequency
    Not planned: there are no plans to update the data

    Contact
    Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

  5. Data from: Standardizing Research Methods for Prognostics

    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    • s.cnmilf.com
    • +3more
    Updated Feb 18, 2025
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    nasa.gov (2025). Standardizing Research Methods for Prognostics [Dataset]. https://data.staging.idas-ds1.appdat.jsc.nasa.gov/dataset/standardizing-research-methods-for-prognostics
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    Dataset updated
    Feb 18, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.

  6. n

    Data from: Development of Data Dictionary for neonatal intensive care unit:...

    • data.niaid.nih.gov
    • search.dataone.org
    • +1more
    zip
    Updated Dec 27, 2020
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    Harpreet Singh; Ravneet Kaur; Satish Saluja; Su Cho; Avneet Kaur; Ashish Pandey; Shubham Gupta; Ritu Das; Praveen Kumar; Jonathan Palma; Gautam Yadav; Yao Sun (2020). Development of Data Dictionary for neonatal intensive care unit: advancement towards a better critical care unit [Dataset]. http://doi.org/10.5061/dryad.zkh18936f
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    zipAvailable download formats
    Dataset updated
    Dec 27, 2020
    Dataset provided by
    Indraprastha Institute of Information Technology Delhi
    UCSF Benioff Children's Hospital
    Lucile Packard Children's Hospital
    Apollo Cradle For Women & Children
    KLKH
    CHIL
    Sir Ganga Ram Hospital
    Post Graduate Institute of Medical Education and Research
    Ewha Womans University
    Authors
    Harpreet Singh; Ravneet Kaur; Satish Saluja; Su Cho; Avneet Kaur; Ashish Pandey; Shubham Gupta; Ritu Das; Praveen Kumar; Jonathan Palma; Gautam Yadav; Yao Sun
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Background: Critical care units (CCUs) with wide use of various monitoring devices generate massive data. To utilize the valuable information of these devices; data are collected and stored using systems like Clinical Information System (CIS), Laboratory Information Management System (LIMS), etc. These systems are proprietary in nature, allow limited access to their database and have vendor specific clinical implementation. In this study we focus on developing an open source web-based meta-data repository for CCU representing stay of patient with relevant details.

    Methods: After developing the web-based open source repository we analyzed prospective data from two sites for four months for data quality dimensions (completeness, timeliness, validity, accuracy and consistency), morbidity and clinical outcomes. We used a regression model to highlight the significance of practice variations linked with various quality indicators. Results: Data dictionary (DD) with 1447 fields (90.39% categorical and 9.6% text fields) is presented to cover clinical workflow of NICU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 82% completeness, 97% accuracy, 91% timeliness and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicator and practice variations are strongly correlated (p-value < 0.05).

    Results: Data dictionary (DD) with 1555 fields (89.6% categorical and 11.4% text fields) is presented to cover clinical workflow of a CCU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 82% completeness, 97% accuracy, 91% timeliness and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicators and practice variations are strongly correlated (p-value < 0.05).

    Conclusion: This study documents DD for standardized data collection in CCU. This provides robust data and insights for audit purposes and pathways for CCU to target practice improvements leading to specific quality improvements.

  7. d

    Development Standard Variance

    • catalog.data.gov
    • data.montgomerycountymd.gov
    • +1more
    Updated Jun 29, 2025
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    data.montgomerycountymd.gov (2025). Development Standard Variance [Dataset]. https://catalog.data.gov/dataset/development-standard-variance
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    Dataset updated
    Jun 29, 2025
    Dataset provided by
    data.montgomerycountymd.gov
    Description

    A variance is required when an application has submitted a proposed project to the Department of Permitting Services and it is determined that the construction, alteration or extension does not conform to the development standards (in the zoning ordinance) for the zone in which the subject property is located. A variance may be required in any zone and includes accessory structures as well as primary buildings or dwellings. Update Frequency : Daily

  8. Pattern of Human Concerns Data, 1957-1963

    • icpsr.umich.edu
    ascii, sas, spss
    Updated Jan 12, 2006
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    Cantril, Hadley (2006). Pattern of Human Concerns Data, 1957-1963 [Dataset]. http://doi.org/10.3886/ICPSR07023.v1
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    ascii, spss, sasAvailable download formats
    Dataset updated
    Jan 12, 2006
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Cantril, Hadley
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/7023/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/7023/terms

    Time period covered
    1957 - 1963
    Area covered
    Cuba, Germany, Brazil, Israel, India, United States, Nigeria, Yugoslavia, Panama, Global
    Description

    Of the 14 nations included in the original study, these data cover the following ten: Brazil, Cuba, Dominican Republic, India, Israel, Nigeria, Panama, United States, West Germany, and Yugoslavia. (The data for Egypt, Japan, the Philippines, and Poland are not available through ICPSR.) In India and Israel the interviews were conducted in two waves, with different samples. Besides ascertaining the usual personal information, the study employed a "Self-Anchoring Striving Scale," an open-ended scale asking the respondent to define hopes and fears for self and the nation, to determine the two extremes of a self-defined spectrum on each of several variables. After these subjective ratings were obtained, the respondents indicated their perceptions of where they and their nations stood on a hypothetical ladder at three different points in time. Demographic variables include the respondents' age, gender, marital status, and level of education. For more information on the samples, coding, and the means of measurement, see the related publication listed below.

  9. i

    Standardized World Income Inequality Database , SWIID

    • ingridportal.eu
    Updated May 4, 2019
    + more versions
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    (2019). Standardized World Income Inequality Database , SWIID [Dataset]. http://doi.org/10.23728/b2share.d85fbdaf194c4a78aa79438e95a051fe
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    Dataset updated
    May 4, 2019
    Description

    Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality datasets: greater coverage across countries and over time is available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to overcome these limitations. A custom missing-data algorithm was used to standardize the United Nations University's World Income Inequality Database and data from other sources; data collected by the Luxembourg Income Study served as the standard. The SWIID provides comparable Gini indices of gross and net income inequality for 192 countries for as many years as possible from 1960 to the present along with estimates of uncertainty in these statistics. By maximizing comparability for the largest possible sample of countries and years, the SWIID is better suited to broadly cross-national research on income inequality than previously available sources: it offers coverage double that of the next largest income inequality dataset, and its record of comparability is three to eight times better than those of alternate datasets.

  10. e

    Raw data from external antibody databases and scripts to homogenize and...

    • b2find.eudat.eu
    Updated Oct 11, 2024
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    (2024). Raw data from external antibody databases and scripts to homogenize and standardize them used to build AntiBody Sequence Database (for reproducibility) - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/3b295aa7-3b08-5ef6-a208-fd43a9a66633
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    Dataset updated
    Oct 11, 2024
    Description

    Reproducibility data for the AntiBody Sequence Database (ABSD) article. This dataset contains the raw data (antibody sequences) extracted on June 20, 2024, from various databases, as well as the several scripts, to ensure the reproducibility of our results. External databases used: ABDB, AbPDB, CoV-AbDab, Genbank, IMGT, PDB, SACS, SAbDab, TheraSAbDab, UniProt, KABAT Scripts usage: each external database has a corresponding script to format all antibody sequences extracted from it. A last script enable merging all extracted antibody sequences while removing redundancy, standardizing and cleaning data.

  11. e

    Subjective wellbeing, 'Life Satisfaction', standard deviation

    • data.europa.eu
    • opendatacommunities.org
    • +1more
    html, sparql
    Updated Oct 11, 2021
    + more versions
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    Ministry of Housing, Communities and Local Government (2021). Subjective wellbeing, 'Life Satisfaction', standard deviation [Dataset]. https://data.europa.eu/data/datasets/subjective-wellbeing-life-satisfaction-standard-deviation
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    sparql, htmlAvailable download formats
    Dataset updated
    Oct 11, 2021
    Dataset authored and provided by
    Ministry of Housing, Communities and Local Government
    License

    http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence

    Description

    Standard deviation of responses for 'Life Satisfaction' in the First ONS Annual Experimental Subjective Wellbeing survey.

    The Office for National Statistics has included the four subjective well-being questions below on the Annual Population Survey (APS), the largest of their household surveys.

    • Overall, how satisfied are you with your life nowadays?
    • Overall, to what extent do you feel the things you do in your life are worthwhile?
    • Overall, how happy did you feel yesterday?
    • Overall, how anxious did you feel yesterday?

    This dataset presents results from the first of these questions, "Overall, how satisfied are you with your life nowadays?". Respondents answer these questions on an 11 point scale from 0 to 10 where 0 is ‘not at all’ and 10 is ‘completely’. The well-being questions were asked of adults aged 16 and older.

    Well-being estimates for each unitary authority or county are derived using data from those respondents who live in that place. Responses are weighted to the estimated population of adults (aged 16 and older) as at end of September 2011.

    The data cabinet also makes available the proportion of people in each county and unitary authority that answer with ‘low wellbeing’ values. For the ‘life satisfaction’ question answers in the range 0-6 are taken to be low wellbeing.

    This dataset contains the standard deviation of the responses, alongside the corresponding sample size.

    The ONS survey covers the whole of the UK, but this dataset only includes results for counties and unitary authorities in England, for consistency with other statistics available at this website.

    At this stage the estimates are considered ‘experimental statistics’, published at an early stage to involve users in their development and to allow feedback. Feedback can be provided to the ONS via this email address.

    The APS is a continuous household survey administered by the Office for National Statistics. It covers the UK, with the chief aim of providing between-census estimates of key social and labour market variables at a local area level. Apart from employment and unemployment, the topics covered in the survey include housing, ethnicity, religion, health and education. When a household is surveyed all adults (aged 16+) are asked the four subjective well-being questions.

    The 12 month Subjective Well-being APS dataset is a sub-set of the general APS as the well-being questions are only asked of persons aged 16 and above, who gave a personal interview and proxy answers are not accepted. This reduces the size of the achieved sample to approximately 120,000 adult respondents in England.

    The original data is available from the ONS website.

    Detailed information on the APS and the Subjective Wellbeing dataset is available here.

    As well as collecting data on well-being, the Office for National Statistics has published widely on the topic of wellbeing. Papers and further information can be found here.

  12. f

    Data from: FLiPPR: A Processor for Limited Proteolysis (LiP) Mass...

    • acs.figshare.com
    xlsx
    Updated May 24, 2024
    + more versions
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    Edgar Manriquez-Sandoval; Joy Brewer; Gabriela Lule; Samanta Lopez; Stephen D. Fried (2024). FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe [Dataset]. http://doi.org/10.1021/acs.jproteome.3c00887.s002
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    xlsxAvailable download formats
    Dataset updated
    May 24, 2024
    Dataset provided by
    ACS Publications
    Authors
    Edgar Manriquez-Sandoval; Joy Brewer; Gabriela Lule; Samanta Lopez; Stephen D. Fried
    License

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

    Description

    Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

  13. o

    Nominal and adversarial synthetic PMU data for standard IEEE test systems

    • osti.gov
    Updated Jun 15, 2021
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    Pacific Northwest National Laboratory 2 (2021). Nominal and adversarial synthetic PMU data for standard IEEE test systems [Dataset]. http://doi.org/10.25584/DataHub/1788186
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    Dataset updated
    Jun 15, 2021
    Dataset provided by
    PNNL
    US
    Pacific Northwest National Laboratory 2
    Description

    GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

  14. f

    Data from: CULTURAL ADAPTATION AND STANDARDIZATION OF A MULTIPLE...

    • figshare.com
    pdf
    Updated Jun 2, 2023
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    Nijairul Islam; Subhalakshmi Nandi (2023). CULTURAL ADAPTATION AND STANDARDIZATION OF A MULTIPLE INTELLIGENCES INVENTORY [Dataset]. http://doi.org/10.6084/m9.figshare.1054468.v1
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    pdfAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    figshare
    Authors
    Nijairul Islam; Subhalakshmi Nandi
    License

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

    Description

    :-The present study is an attempt to develop and standardize a multiple intelligences inventorywith an aim to its cultural adaptation. The original inventory, found on line, was in American Englishlanguage and hence not suitable to apply on Indian Bengalee population. Therefore, the test wasculturally adapted with the intention to assess the nature of eight multiple intelligences of schoolchildren, particularly adolescents, who study higher secondary level curriculum under West BengalCouncil of Higher Secondary Education. The inventory consists of 80 items in total, divided into eightsub-scales, each containing ten items. After try-out (N=20) and pilot study (N=50), the final study wasconducted with a sample of 100 higher secondary level students from exclusively Bengali mediumschools. Besides split-half reliability of the test, construct validity and internal consistency of the testitems were determined following conventional procedure.

  15. Standard terms and definitions applicable to the quality assurance of...

    • data.aeronomie.be
    • data.europa.eu
    pdf
    Updated Jan 30, 2025
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    Royal Belgian Institute for Space Aeronomy (2025). Standard terms and definitions applicable to the quality assurance of Essential Climate Variable data records [Dataset]. https://data.aeronomie.be/dataset/standard-terms-and-definitions-applicable-to-the-quality-assurance-of-essential-climate-variable-da
    Explore at:
    pdf(863196), pdfAvailable download formats
    Dataset updated
    Jan 30, 2025
    Dataset authored and provided by
    Royal Belgian Institute for Space Aeronomy
    License

    http://publications.europa.eu/resource/authority/licence/CC_BY_4_0http://publications.europa.eu/resource/authority/licence/CC_BY_4_0

    Description

    This document contains a selection of standard terms and definitions relevant to the quality assurance of Essential Climate Variable (ECVs) data records. It reproduces appropriate terms and definitions published by normalization bodies, mainly by BIPM/JCGM/ISO in their International Vocabulary of Metrology (VIM) and Guide to the Expression of Uncertainties (GUM). It also reproduces selected terms and definitions related to the quality assurance and validation of Earth Observation (EO) data, available publicly on the ISO website and on the Cal/Val portal of the Committee on Earth Observation Satellites (CEOS).

    Several of those terms have been recommended by CEOS in the GEO-CEOS Quality Assurance framework for Earth Observation (QA4EO) and, as such, are applicable to virtually all Copernicus data sets of EO origin. Terms and definitions are expected to evolve as normalization organisations regularly update their standards.

  16. C

    NODC Standard Format Fish Pathology Format (F013) Data (1975-1980) (NODC...

    • data.cnra.ca.gov
    • cloud.csiss.gmu.edu
    • +5more
    Updated May 9, 2019
    + more versions
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    Ocean Data Partners (2019). NODC Standard Format Fish Pathology Format (F013) Data (1975-1980) (NODC Accession 0014146) [Dataset]. https://data.cnra.ca.gov/dataset/nodc-standard-format-fish-pathology-format-f013-data-1975-1980-nodc-accession-0014146
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    Dataset updated
    May 9, 2019
    Dataset authored and provided by
    Ocean Data Partners
    Description

    The Fish Pathology (F013) data set contains data from examinations of diseased fishes. Although these data may be from field observations, they derive primarily from laboratory analyses. Data include: location, and fishing duration, distance, and gear; catch statistics (e.g., total weight, number of individuals, age group, identity of diseases, and number of diseased individuals) by species for any number of species; and biological condition of selected specimens. The size, affected organ, location, and frequency of lesions may be reported for individual specimens. Specimens are identified with the NODC Taxonomic Code. These data may be characteristics of individual lesions or average lesion statistics.

  17. N

    Standard City, IL Age Group Population Dataset: A Complete Breakdown of...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). Standard City, IL Age Group Population Dataset: A Complete Breakdown of Standard City Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/45489357-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 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
    Illinois, Standard City
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 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 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 age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. 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 Standard City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Standard City. The dataset can be utilized to understand the population distribution of Standard City by age. For example, using this dataset, we can identify the largest age group in Standard City.

    Key observations

    The largest age group in Standard City, IL was for the group of age 65 to 69 years years with a population of 35 (18.72%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Standard City, IL was the 40 to 44 years years with a population of 1 (0.53%). 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

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the Standard City is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of Standard City 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 Standard City Population by Age. You can refer the same here

  18. d

    Data from: 2024 Standard Scenarios: A U.S. Electricity Sector Outlook

    • catalog.data.gov
    • data.openei.org
    • +1more
    Updated Jan 23, 2025
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    National Renewable Energy Laboratory (NREL) (2025). 2024 Standard Scenarios: A U.S. Electricity Sector Outlook [Dataset]. https://catalog.data.gov/dataset/2024-standard-scenarios-a-u-s-electricity-sector-outlook
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    Dataset updated
    Jan 23, 2025
    Dataset provided by
    National Renewable Energy Laboratory (NREL)
    Area covered
    United States
    Description

    This data corresponds to the 2024 Standard Scenarios report, which contains a suite of forward-looking scenarios of the possible evolution of the U.S. electricity sector through 2050. These files contain modeled projections of the future. Although we strive to capture relevant phenomena as comprehensively as possible, the models used to create this data are unavoidably imperfect, and the future is highly uncertain. Consequentially, this data should not be the sole basis for making decisions. In addition to drawing from multiple scenarios within this set, we encourage analysts to also draw on projections from other sources, to benefit from diverse analytical frameworks and perspectives when forming their conclusions about the future of the power sector. For further discussions about the limitations of the models underlying this data, see section 1.4 of the "ReEDS Documentation" linked below. For scenario descriptions, input assumptions, and metric definitions for the data in these files, see the "2024 Standard Scenarios Report" linked below.

  19. o

    Standard Street Cross Street Data in El Segundo, CA

    • ownerly.com
    Updated Dec 9, 2021
    + more versions
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    Ownerly (2021). Standard Street Cross Street Data in El Segundo, CA [Dataset]. https://www.ownerly.com/ca/el-segundo/standard-st-home-details
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    Dataset updated
    Dec 9, 2021
    Dataset authored and provided by
    Ownerly
    Area covered
    Standard Street, El Segundo, California
    Description

    This dataset provides information about the number of properties, residents, and average property values for Standard Street cross streets in El Segundo, CA.

  20. Aquarius Official Release Level 3 Sea Surface Density Standard Mapped Image...

    • data.nasa.gov
    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    Updated Apr 1, 2025
    + more versions
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    nasa.gov (2025). Aquarius Official Release Level 3 Sea Surface Density Standard Mapped Image Seasonal Data V5.0 - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/aquarius-official-release-level-3-sea-surface-density-standard-mapped-image-seasonal-data--70d6b
    Explore at:
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, sea surface density product forversion 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

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Zekun Lu (2025). Standardize Data [Dataset]. http://doi.org/10.6084/m9.figshare.29590574.v1
Organization logoOrganization logo

Standardize Data

Explore at:
csvAvailable download formats
Dataset updated
Jul 17, 2025
Dataset provided by
Figsharehttp://figshare.com/
figshare
Authors
Zekun Lu
License

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

Description

Standardize Data

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