100+ datasets found
  1. f

    Data from: Mean and Variance Corrected Test Statistics for Structural...

    • tandf.figshare.com
    txt
    Updated May 31, 2023
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    Yubin Tian; Ke-Hai Yuan (2023). Mean and Variance Corrected Test Statistics for Structural Equation Modeling with Many Variables [Dataset]. http://doi.org/10.6084/m9.figshare.10012976.v1
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    txtAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Yubin Tian; Ke-Hai Yuan
    License

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

    Description

    Data in social and behavioral sciences are routinely collected using questionnaires, and each domain of interest is tapped by multiple indicators. Structural equation modeling (SEM) is one of the most widely used methods to analyze such data. However, conventional methods for SEM face difficulty when the number of variables (p) is large even when the sample size (N) is also rather large. This article addresses the issue of model inference with the likelihood ratio statistic Tml. Using the method of empirical modeling, mean-and-variance corrected statistics for SEM with many variables are developed. Results show that the new statistics not only perform much better than Tml but also are substantial improvements over other corrections to Tml. When combined with a robust transformation, the new statistics also perform well with non-normally distributed data.

  2. B

    Birth weight and economic growth data sets, Boston Lying-in (inpatient...

    • borealisdata.ca
    • search.dataone.org
    Updated Oct 17, 2024
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    Monique Gagné; W. Peter Ward (2024). Birth weight and economic growth data sets, Boston Lying-in (inpatient services), 1886-1900, [2012] [Dataset]. http://doi.org/10.5683/SP2/FUKFBY
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 17, 2024
    Dataset provided by
    Borealis
    Authors
    Monique Gagné; W. Peter Ward
    License

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

    Time period covered
    1886 - 1900
    Area covered
    Boston, United States
    Description

    The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The clinical records of the Boston Lying-in inpatient and outpatient services, and those of the New England Hospital maternity unit, are housed in the Rare Book Room, Francis A. Countway Library of Medicine, Harvard University, Boston, Massachusetts. While the information found in these records varied somewhat from one hospital to the next, each set of records was consistent throughout the period under review. Four data bases were established, one consisting exclusively of white patients for each of the three clinics and one composed of all black patients from both services of the Boston Lying-in. The four sample populations were constituted in the following ways. The clinical records of the New England Hospital’s maternity clinic exist in continuous series from 1872 to 1900. All births were recorded because there were fewer than 200 deliveries annually. The patient registers of the Boston Lying-in inpatient service span the years 1886-1900, with a gap in 1893 and 1894. A random sample of 200 cases was chosen for each year. The same procedure was followed at the outpatient clinic, whose case files extend from 1884 to 1900, excepting those years in which all were recorded because fewer births occurred, and a short period when all cases were noted even though they totaled more than 200. Because the number of black patients was small, and because the birth weight experience of blacks was distinctive in some important respects, a fourth file was created consisting of all blacks in the Lying-in inpatient and outpatient records. The preliminary data bases consisted of 3480, 2503, 3654, and 373 cases, respectively. The birth weight means in the Lying-in inpatient sample are accurate to 79 grams, and those of the outpatient clinic sample to 65 grams, at the 95 percent confidence level.

  3. Data from: Comparisons Between Boys and Girls in Zulliger - Comprehensive...

    • scielo.figshare.com
    xls
    Updated Jun 1, 2023
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    Anna Elisa Villemor Amaral; Ana Carolina Zuanazzi; Fabiano Koich Miguel; André Pereira Gonçalves (2023). Comparisons Between Boys and Girls in Zulliger - Comprehensive System [Dataset]. http://doi.org/10.6084/m9.figshare.20006078.v1
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Anna Elisa Villemor Amaral; Ana Carolina Zuanazzi; Fabiano Koich Miguel; André Pereira Gonçalves
    License

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

    Description

    Abstract The objective of this study was to verify possible differences in performance between boys and girls in the Zulliger Comprehensive System (ZSC). The sample consisted of 623 children aged from 6 to 14, from the Southeast region of Brazil, divided into four age groups: six to seven years, eight to nine, ten to eleven, and twelve to fourteen years. The means were compared using the t-test. The results indicated that some differences remained significant even after the Bonferroni correction, although the number of variables was reduced considerably when compared to the literature. The findings are discussed together with studies with projective techniques as well as other personality techniques. It was concluded that, although many variables were corroborated in the literature, more studies with more homogenous samples are needed, including, for example, control for the cognitive level and sociodemographic variables.

  4. B

    Data from: Birth weight and economic growth data sets, Allgemeines...

    • borealisdata.ca
    Updated Oct 17, 2024
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    Monique Gagné; W. Peter Ward (2024). Birth weight and economic growth data sets, Allgemeines Krankenhaus, Vienna, 1865-1930, [2012] [Dataset]. http://doi.org/10.5683/SP2/Z7NRRK
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 17, 2024
    Dataset provided by
    Borealis
    Authors
    Monique Gagné; W. Peter Ward
    License

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

    Time period covered
    1865 - 1930
    Area covered
    Vienna, Austria
    Description

    The variables contained in the data sets are primarily concerned with perinatal outcomes and maternal health. A number of variables with respect to the social and economic status of the mothers and their families were also included (ie. Occupation, Marital status, Region). While all nine data sets are centered around these common themes and hold many variables in common, each data set has a unique combination of variables. The types of fields are wide-ranging but are primarily concerned with infant birth, maternal health, and socioeconomic status. The Geburtsprotokolle of the Allgemeines Krankenhaus are preserved in the Wiener Stadt- und Landesarchiv in Vienna. The primary data base for 1872 to 1930 consists of an annual sample of 200 cases chosen randomly from the records of Clinic I. (The single exception was 1882, when weights were missing for a three-month period and, in order to preserve the same seasonal distribution found elsewhere in the data base, only 150 cases were selected.) The patient records of Clinic I exist in continuous series with no significant changes in content throughout this period. The birth weight and length means in the sample are accurate to 76 grams and 0.4 centimeter at the 95 percent confidence level. In order to extend the span of time investigated, the records of Clinic I were supplemented by those of Clinic III, the first obstetric unit in the hospital in which birth dimensions were recorded routinely. It functioned only for the academic year (October to June) and it also accommodated far fewer patients annually than did Clinic I. The smaller number of patients attending Clinic III and the gaps in its records necessitated a different sampling procedure. For clinic years 1865-66 to 1868-69 the first two of every three cases in which live births occurred and for which a weight was listed were recorded. In 1869-70 cases were selected on the same basis as for Clinic I. For these reasons the Clinic III records have significant deficiencies not found in those of Clinic I and may be somewhat less reliable.

  5. n

    Data from: Data reuse and the open data citation advantage

    • data.niaid.nih.gov
    • data-staging.niaid.nih.gov
    • +1more
    zip
    Updated Oct 1, 2013
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    Heather A. Piwowar; Todd J. Vision (2013). Data reuse and the open data citation advantage [Dataset]. http://doi.org/10.5061/dryad.781pv
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    zipAvailable download formats
    Dataset updated
    Oct 1, 2013
    Dataset provided by
    National Evolutionary Synthesis Center
    Authors
    Heather A. Piwowar; Todd J. Vision
    License

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

    Description

    Background: Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the "citation benefit". Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results: Here, we look at citation rates while controlling for many known citation predictors, and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties. Conclusion: After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered.We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.

  6. 2

    HBAI

    • datacatalogue.ukdataservice.ac.uk
    Updated Apr 16, 2025
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    Department for Work and Pensions (2025). HBAI [Dataset]. http://doi.org/10.5255/UKDA-SN-5828-17
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    Dataset updated
    Apr 16, 2025
    Dataset provided by
    UK Data Servicehttps://ukdataservice.ac.uk/
    Authors
    Department for Work and Pensions
    Time period covered
    Mar 31, 1994 - Mar 31, 2024
    Area covered
    United Kingdom
    Description

    The Households Below Average Income (HBAI) data presents information on living standards in the UK based on household income measures for the financial year.

    HBAI uses equivalised disposable household income as a proxy for living standards in order to allow comparisons of the living standards of different types of households (that is, income is adjusted to take into account variations in the size and composition of the households in a process known as equivalisation). A key assumption made in HBAI is that all individuals in the household benefit equally from the combined income of the household. This enables the total equivalised income of the household to be used as a proxy for the standard of living of each household member.

    In line with international best practice, the income measures used in HBAI are subject to several statistical adjustments and, as such, are not always directly relatable to income amounts as they might be understood by people on a day-to-day basis. These adjustments, however, allow consistent comparison over time and across households of different sizes and compositions. HBAI uses variants of CPI inflation when estimating how incomes are changing in real terms over time.

    The main data source used in this study is the Family Resources Survey (FRS), a continuous cross-sectional survey. The FRS normally has a sample of 19,000 - 20,000 UK households. The use of survey data means that HBAI estimates are subject to uncertainty, which can affect how changes should be interpreted, especially in the short term. Analysis of geographies below the regional level is not recommended from this data.

    Further information and the latest publication can be found on the gov.uk HBAI webpage. The HBAI team want to provide user-friendly datasets and clearer documentation, so please contact team.hbai@dwp.gov.uk if you have any suggestions or feedback on the new harmonised datasets and documentation.

    An earlier HBAI study, Institute for Fiscal Studies Households Below Average Income Dataset, 1961-1991, is held under SN 3300.

    Latest Edition Information

    For the 19th edition (April 2025), resamples data have been added to the study alongside supporting documentation. Main data back to 1994/95 have been updated to latest-year prices, and the documentation has been updated accordingly.

    Using the HBAI files

    Users should note that either 7-Zip or a recent version of WinZip is needed to unzip the HBAI download zip files, due to their size. The inbuilt Windows compression software will not handle them correctly.

    Labelling of variables
    Users should note that many variables across the resamples files do not include full variable or value labels. This information can be found easily in the documentation - see the Harmonised Data Variables Guide.

    HBAI versions

    The HBAI datasets are available in two versions at the UKDS:

    1. End User Licence (EUL) (Anonymised) Datasets:

    These datasets contain no names, addresses, telephone numbers, bank account details, NINOs or any personal details that can be considered disclosive under the terms of the ONS Disclosure Control guidance. Changes made to the datasets are as follows:

    • All ages above 80 are instead top-coded to 80 years of age.
    • The variable for the amount of Council Tax liability for the household and pensioner flags for the head and spouse have been removed.
    • All amount variables have been rounded to the nearest £1.
    • A very small number of large households (with 10 or more individuals) have been removed from the dataset.

    2. Secure Access Datasets:

    Secure Access datasets for HBAI are held under SN 7196. The Secure Access data are not subject to the same edits as the EUL version and are, therefore, more disclosive and subject to strict access conditions. They are currently only available to UK HE/FE applicants. Prospective users of the Secure Access version of the HBAI must fulfil additional requirements beyond those associated with the EUL datasets.

  7. d

    CESM1-CAM5.1-Finite volume 2 degree model output prepared for CMIP5 1...

    • demo-b2find.dkrz.de
    Updated Oct 12, 2017
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    (2017). CESM1-CAM5.1-Finite volume 2 degree model output prepared for CMIP5 1 percent per year CO2, served by ESGF - Dataset - B2FIND [Dataset]. http://demo-b2find.dkrz.de/dataset/78cd7b71-46a5-5593-9883-92d2f050b19b
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    Dataset updated
    Oct 12, 2017
    Description

    '1pctCo2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 6.1 1pctCo2 (6.1 1 percent per year CO2) - Version 1: Idealized 1% per year increase in atmospheric CO2 to quadrupling. Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

  8. d

    cmip5 output1 NCC NorESM1-ME esmControl, served by ESGF - Dataset - B2FIND

    • demo-b2find.dkrz.de
    Updated Sep 20, 2025
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    (2025). cmip5 output1 NCC NorESM1-ME esmControl, served by ESGF - Dataset - B2FIND [Dataset]. http://demo-b2find.dkrz.de/dataset/031f9ac8-64ff-5f45-b937-6c4adaa90c49
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    Dataset updated
    Sep 20, 2025
    Description

    "esmControl" is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 5.1 esmControl (5.1 ESM pre-industrial control): Impose non-evolving pre-industrial conditions as in experiment 3.1_piControl but emissions-forced (with atmosperhic CO2 determined by the model itself) Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

  9. e

    cmip5 output1 BCC bcc-csm1-1 historicalGHG

    • data.europa.eu
    Updated Oct 12, 2017
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    (2017). cmip5 output1 BCC bcc-csm1-1 historicalGHG [Dataset]. https://data.europa.eu/data/datasets/de-dkrz-wdcc-iso3050205?locale=fi
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    Dataset updated
    Oct 12, 2017
    Description

    'historicalGHG' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    7.2 historicalGHG (7.2 GHG-only historical) - Version 1: Historical simulation but with greenhouse gas forcing only.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

  10. e

    cmip5 output1 MPI-M MPI-ESM-P midHolocene

    • data.europa.eu
    Updated Apr 28, 2021
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    (2021). cmip5 output1 MPI-M MPI-ESM-P midHolocene [Dataset]. https://data.europa.eu/data/datasets/de-dkrz-wdcc-iso2287600?locale=et
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    Dataset updated
    Apr 28, 2021
    Description

    midHolocene is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    midHolocene (3.4 mid-Holocene) - Version 2: Consistent with PMIP (Paleo Model Intercomparison Project) specifications. Impose Mid-Holocene (6 kyrs ago) conditions including Orbital parameters and Atmospheric concentrations of well-mixed greenhouse gasses.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

  11. e

    cmip5 output1 NOAA-GFDL GFDL-ESM2M rcp45

    • data.europa.eu
    Updated Oct 13, 2017
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    (2017). cmip5 output1 NOAA-GFDL GFDL-ESM2M rcp45 [Dataset]. https://data.europa.eu/data/datasets/de-dkrz-wdcc-iso3205551?locale=en
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    Dataset updated
    Oct 13, 2017
    Description

    ‘rcp45’ is an experiment of the CMIP5 — Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    4.1 rcp45 (4.1 RCP4.5) — Version 1: Future projection (2006-2100) forced by RCP4.5. RCP4.5 is a representative concentration pathway which approximately results in a radiative forcing of 4.5 W m-2 at year 2100, relative to pre-industrial conditions. RCPs are time-dependent, consistent projections of emissions and concentrations of radiatively active gases and particles.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble Member/version number/variable name/cMor filename.nc.

  12. d

    CSIRO-Mk3-6-0 model output prepared for CMIP5 piControl

    • data.gov.au
    • researchdata.edu.au
    html
    Updated May 16, 2019
    + more versions
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    NCI Australia (2019). CSIRO-Mk3-6-0 model output prepared for CMIP5 piControl [Dataset]. https://data.gov.au/dataset/ds-aodn-f3262_8737_7115_1092
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    htmlAvailable download formats
    Dataset updated
    May 16, 2019
    Dataset provided by
    NCI Australia
    Description

    piControl is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( http://cmip-pcmdi.llnl.gov/cmip5/ ). CMIP5 is meant to provide a framework for coordinated climate change …Show full descriptionpiControl is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( http://cmip-pcmdi.llnl.gov/cmip5/ ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 3.1 piControl (3.1 Pre-Industrial Control) - Version 1: Pre-Industrial coupled atmosphere/ocean control run. Imposes non-evolving pre-industrial conditions. Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax ( http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

  13. cmip5 output1 NCAR CCSM4 decadal1966 mon land Lmon r7i2p1 v20130210...

    • wdc-climate.de
    Updated Feb 25, 2014
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    Teng, Haiyan (2014). cmip5 output1 NCAR CCSM4 decadal1966 mon land Lmon r7i2p1 v20130210 cSoilSlow [Dataset]. https://www.wdc-climate.de/ui/entry?acronym=NRS466MLLcsoils721v130210
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    Dataset updated
    Feb 25, 2014
    Dataset provided by
    World Data Centerhttp://www.icsu-wds.org/
    Authors
    Teng, Haiyan
    Time period covered
    Jan 16, 1966 - Dec 16, 1975
    Area covered
    Variables measured
    slow_soil_pool_carbon_content
    Description

    'decadal1966' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    decadal1966 (10-year hindcast/prediction initialized in year 1966) - Version 2: The atmospheric composition (and other conditions) should be prescribed as in the historical run (expt. 3.2) and the RCP4.5 scenario (expt. 4.1) of the long-term suite of experiments. Ocean initial conditions should be in some way representative of the observed anomalies or full fields for the start date. Land, sea-ice and atmosphere initial conditions are left to the discretion of each group.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

  14. g

    CSIRO-Mk3-6-0 model output prepared for CMIP5 rcp60 | gimi9.com

    • gimi9.com
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    CSIRO-Mk3-6-0 model output prepared for CMIP5 rcp60 | gimi9.com [Dataset]. https://gimi9.com/dataset/au_csiro-mk3-6-0-model-output-prepared-for-cmip5-rcp60/
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    Description

    rcp60 is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (http://cmip-pcmdi.llnl.gov/cmip5/). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 4.4 rcp60 (4.4 RCP6) - Version 1: Future projection (2006-2100) forced by RCP6. RCP6 is a representative concentration pathway which approximately results in a radiative forcing of 6 W m-2 at year 2100, relative to pre-industrial conditions. Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

  15. e

    cmip5 output1 CCCma CanESM2 sstClim

    • data.europa.eu
    Updated Oct 13, 2017
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    (2017). cmip5 output1 CCCma CanESM2 sstClim [Dataset]. https://data.europa.eu/data/datasets/de-dkrz-wdcc-iso3104378?locale=en
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    Dataset updated
    Oct 13, 2017
    Description

    ‘sstClim’ is an experiment of the CMIP5 — Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    6.2a sstClim (6.2a Control SST Climatology) — Version 1: AMIP-style experiment with control run climatological SSTs and sea ice.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System Model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble Member/version number/variable name/CMOR filename.nc.

  16. m

    Met Office Hadley Centre

    • meta.meteo.ru
    mogex1
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    cmip5 output1 MOHC HadGEM2-ES esmFixClim1, Met Office Hadley Centre [Dataset]. http://meta.meteo.ru/geonetwork/srv/api/records/de.dkrz.wdcc.iso2977083
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    mogex1Available download formats
    Dataset provided by
    cmip5 output1 MOHC HadGEM2-ES esmFixClim1
    Area covered
    Description

    esmFixClim is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    5.4-1 esmFixClim (5.4-1 ESM fixed climate 1) - Version 1: Radiation code sees piControl CO2 concentration, but carbon cycle sees 1% per year rise.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

  17. e

    cmip5 output1 IPSL IPSL-CM5A-LR sstClim4xCO2

    • data.europa.eu
    Updated Oct 13, 2017
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    (2017). cmip5 output1 IPSL IPSL-CM5A-LR sstClim4xCO2 [Dataset]. https://data.europa.eu/data/datasets/de-dkrz-wdcc-iso2260818
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    Dataset updated
    Oct 13, 2017
    Area covered
    Campbeltown - Ardrossan
    Description

    ‘sstClim4xco2’ is an experiment of the CMIP5 — Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    6.2b sstClim4xco2 (6.2b SST Climatology With 4XCO2 Forcing) — Version 1: AMIP-style experiment with control run climatological SSTs and sea ice (as in 6.2a) but with quadrupled 4XCO2 imposed.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble Member/version number/variable name/cMor filename.nc.

  18. d

    HadGEM2-A model output prepared for CMIP5 amip4xCO2, served by ESGF -...

    • demo-b2find.dkrz.de
    Updated Sep 20, 2025
    + more versions
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    (2025). HadGEM2-A model output prepared for CMIP5 amip4xCO2, served by ESGF - Dataset - B2FIND [Dataset]. http://demo-b2find.dkrz.de/dataset/ec78575d-07d9-5a56-bd27-0b8f53443b3b
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    Dataset updated
    Sep 20, 2025
    Description

    "amip4xco2" is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 6.5 amip4xco2 (6.5 4xCO2 AMIP) - Version 1: Identical to expt. 6.2b, but with AMIP SSTs prescribed as in expt. 3.3 (which is the control for this run). Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

  19. W

    cmip5 output1 NCAR CCSM4 decadal1966 mon ocean Omon r2i2p1 v20120525...

    • wdc-climate.de
    Updated Feb 25, 2014
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    Teng, Haiyan (2014). cmip5 output1 NCAR CCSM4 decadal1966 mon ocean Omon r2i2p1 v20120525 msftbarot [Dataset]. https://www.wdc-climate.de/ui/entry?acronym=NRS466MOOmsftba221v120525
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    Dataset updated
    Feb 25, 2014
    Dataset provided by
    World Data Center for Climate (WDCC) at DKRZ
    Authors
    Teng, Haiyan
    Time period covered
    Jan 16, 1966 - Dec 16, 1975
    Area covered
    Variables measured
    ocean_barotropic_mass_streamfunction
    Description

    'decadal1966' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    decadal1966 (10-year hindcast/prediction initialized in year 1966) - Version 2: The atmospheric composition (and other conditions) should be prescribed as in the historical run (expt. 3.2) and the RCP4.5 scenario (expt. 4.1) of the long-term suite of experiments. Ocean initial conditions should be in some way representative of the observed anomalies or full fields for the start date. Land, sea-ice and atmosphere initial conditions are left to the discretion of each group.

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

  20. W

    cmip5 output1 BCC bcc-csm1-1 amip4K mon landIce LImon r1i1p1 v20120910 snd

    • wdc-climate.de
    Updated Feb 27, 2014
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    Zhang, Jie (2014). cmip5 output1 BCC bcc-csm1-1 amip4K mon landIce LImon r1i1p1 v20120910 snd [Dataset]. https://www.wdc-climate.de/ui/entry?acronym=BCB1a4MJIsnd111v120910
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    Dataset updated
    Feb 27, 2014
    Dataset provided by
    World Data Center for Climate (WDCC) at DKRZ
    Authors
    Zhang, Jie
    Time period covered
    Jan 16, 1979 - Dec 16, 2008
    Area covered
    Variables measured
    surface_snow_thickness
    Description

    'amip4K' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5.

    6.8 amip4K (6.8 AMIP plus 4K anomaly) - Version 1: Consistent with CFMIP requirements, add a uniform +4 K SST to the AMIP SSTs of expt. 3.3 (which is the "control" for this run).

    Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository

    Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

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Yubin Tian; Ke-Hai Yuan (2023). Mean and Variance Corrected Test Statistics for Structural Equation Modeling with Many Variables [Dataset]. http://doi.org/10.6084/m9.figshare.10012976.v1

Data from: Mean and Variance Corrected Test Statistics for Structural Equation Modeling with Many Variables

Related Article
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txtAvailable download formats
Dataset updated
May 31, 2023
Dataset provided by
Taylor & Francis
Authors
Yubin Tian; Ke-Hai Yuan
License

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

Description

Data in social and behavioral sciences are routinely collected using questionnaires, and each domain of interest is tapped by multiple indicators. Structural equation modeling (SEM) is one of the most widely used methods to analyze such data. However, conventional methods for SEM face difficulty when the number of variables (p) is large even when the sample size (N) is also rather large. This article addresses the issue of model inference with the likelihood ratio statistic Tml. Using the method of empirical modeling, mean-and-variance corrected statistics for SEM with many variables are developed. Results show that the new statistics not only perform much better than Tml but also are substantial improvements over other corrections to Tml. When combined with a robust transformation, the new statistics also perform well with non-normally distributed data.

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