84 datasets found
  1. f

    Data from: A method for calculating BMI z-scores and percentiles above the...

    • tandf.figshare.com
    • datasetcatalog.nlm.nih.gov
    pdf
    Updated Jun 5, 2023
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    Rong Wei; Cynthia L. Ogden; Van L. Parsons; David S. Freedman; Craig M. Hales (2023). A method for calculating BMI z-scores and percentiles above the 95th percentile of the CDC growth charts [Dataset]. http://doi.org/10.6084/m9.figshare.12932858.v1
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    pdfAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Rong Wei; Cynthia L. Ogden; Van L. Parsons; David S. Freedman; Craig M. Hales
    License

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

    Description

    The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values. This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population. Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

  2. Table 3.1 Percentile points for total income before and after tax

    • gov.uk
    Updated Mar 12, 2025
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    HM Revenue & Customs (2025). Table 3.1 Percentile points for total income before and after tax [Dataset]. https://www.gov.uk/government/statistics/percentile-points-for-total-income-before-and-after-tax-1992-to-2011
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    Dataset updated
    Mar 12, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    HM Revenue & Customs
    Description

    The table only covers individuals who have some liability to Income Tax. The percentile points have been independently calculated on total income before tax and after tax.

    These statistics are classified as accredited official statistics.

    You can find more information about these statistics and collated tables for the latest and previous tax years on the Statistics about personal incomes page.

    Supporting documentation on the methodology used to produce these statistics is available in the release for each tax year.

    Note: comparisons over time may be affected by changes in methodology. Notably, there was a revision to the grossing factors in the 2018 to 2019 publication, which is discussed in the commentary and supporting documentation for that tax year. Further details, including a summary of significant methodological changes over time, data suitability and coverage, are included in the Background Quality Report.

  3. e

    Percentile Intervals in Bayesian Inference are Overconfident - Dataset -...

    • b2find.eudat.eu
    Updated Apr 30, 2024
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    (2024). Percentile Intervals in Bayesian Inference are Overconfident - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/1cde1faa-6752-531a-af3f-7dfa8610aa63
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    Dataset updated
    Apr 30, 2024
    Description

    This dataset demonstrates the difference in calculating percentile Intervals as approximation for Highest Density Intervals (HDI) vs. Highest Posterior Density (HPD). This is demonstrated with extended partial liver resection data (ZeLeR-study, ethical vote: 2018-1246-Material). The data includes Computed Tomography (CT) liver volume measurements of patients before (POD 0) and after partial hepatectomy. Liver volume was normalized per patient to the preoperative liver volume. was used to screen the liver regeneration courses. The Fujifilm Synapse3D software was used to calculate volume estimates from CT images. The data is structured in a tabular separated value file of the PEtab format.

  4. d

    Per-user savings percentiles (USD, 180d)

    • dune.com
    Updated Aug 28, 2025
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    merkle_manufactory (2025). Per-user savings percentiles (USD, 180d) [Dataset]. https://dune.com/discover/content/relevant?resource-type=queries&q=code%3A%22dune.merkle_manufactory.result_fee_savings_fc_pro%22
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    Dataset updated
    Aug 28, 2025
    Dataset authored and provided by
    merkle_manufactory
    License

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

    Description

    Blockchain data query: Per-user savings percentiles (USD, 180d)

  5. e

    Identification of pesticide input pathways in tropical streams as a basis to...

    • opendata-stage.eawag.ch
    • opendata.eawag.ch
    Updated Apr 25, 2022
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    (2022). Identification of pesticide input pathways in tropical streams as a basis to propose potential mitigation options - Package - ERIC [Dataset]. https://opendata-stage.eawag.ch/dataset/identification-of-pesticide-input-pathways-in-tropical-streams
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    Dataset updated
    Apr 25, 2022
    Description

    This package contains the supplementary information (SI) of chapter 4 of the dissertation of Frederik T. Weiss with the Dissertation No. ETH 27434 (defended: 24th February, 2021), entitled: "Pesticides in a tropical Costa Rican stream catchment: from monitoring and risk assessment to the identification of possible mitigation options". Generally within this thesis the supplementary information (SI) is divided into three parts (SI A, SI B, SI C). For each chapter, SI A section contains background information/data for the reader with quick and easy access added directly after each main chapter. SI B contains raw data, further processed data for analysis, and figures of processed data presented as Excel files. SI C combines the R scripts with information and commands utilized for the statistical analysis. The abstract of chapter 4 reads as follows: "Finding targeted strategies to mitigate entry of pesticides into surface waters in areas of intense agriculture is challenging. This holds especially true in little studied areas with very distinct topographic characteristics and unconventional field cultivation practices, such as in the tropical Tapezco river catchment in Costa Rica. Within this catchment, areas with steep slopes are used for intense horticultural farming of mainly vegetables. This is exclusively done by a farming practice similar to contour farming, the practice of tilling land with furrows along parallel lines of consistent elevation in order to conserve rainwater and to prevent soil losses by erosion. At the same time, slope-directed paths are implemented to act as drainage system to avoid stagnant water on the fields during heavy rain events, though as well connecting the fields directly with the streams, which enable a fast pesticide transport. Indeed, a significant contamination of streams with pesticides and pesticide transformation products (PPTP) throughout the Tapezco river catchment has been confirmed, leading to considerable toxicological risks to aquatic communities, urgently calling for effective mitigation strategies to reduce PPTP inputs. To identify how PPTP are transported from horticultural areas into streams of the Tapezco river catchment, different PPTP transportation pathways were considered. The first investigated pathway was via handling practices of pesticides by farmers and field workers, where inappropriate handling was proposed to lead to sporadically distributed pesticide inputs unrelated to hydrology. The second studied pathway was surface run-off. Typically, heavy precipitation events are found to be important drivers for the surface-based transport of pesticides into the streams. Thus, such pesticide inputs can be assumed to correlate positively with water levels in the receiving streams. Surface run-off is additionally favored by the slope-directed paths on the fields, which directly connect fields with the streams. Therefore, the influence of prevalent topographical and hydrological variables on PPTP inputs via surface run-offs were studies within this thesis. The third potential investigated input pathway was the leaching of pesticides into the ground from where pesticides can enter streams via exfiltration through river banks. This path would be expected to lead to a constant input that is negatively correlated with water levels. To investigate the role of these pathways in transporting PPTP into the streams, pesticide peaks unrelated to hydrology were identified based on measured environmental concentrations (MEC) of PPTP and compared with water level time series. Survey data about pesticide handling practices were evaluated additionally. Temporal PPTP distributions were investigated during three sampling periods (ΔT1, Δ2a, Δ2b) within 2015 and 2016 and spatial trends were studied at eight sub-catchment (SC) sites. In addition, knowledge on the topography (share of horticultural land, share of forest in the 100 m stream buffer zone, average slopes of the horticultural fields) and hydrology (median water level factors) was considered. These variables were referred to as explanatory variables while 20-, 50- and 80-percentiles of MEC were considered dependent variables. The explanatory and dependent variables were correlated via linear regression modelling for identifying the most important determinants of PPTP transport. There, 20-percentiles represent a scenario with low precipitations, no or low surface run-offs and low PPTP inputs; 50-percentiles a scenario with medium precipitations, resulting in medium surface run-offs and PPTP inputs; and 80-percentiles a scenario with high precipitations, heavy surface run-offs and high PPTP inputs into streams. With a focus on potential mitigation measures achieving the highest effectiveness for reducing risks to aquatic biota, analyses were performed on a sub-set of PPTP that dominated the risks to aquatic organisms, along with three transformation products (TP) to calculate TP/PPTP ratios as a measure of pesticide residence time. The correlation analysis of the PPTP input pathways was again based on eight SC sites. The input of three pesticides were very likely due to inappropriate handling. For five additional pesticides, the input via inappropriate handling seemed probable. Temporal exposure trends were observed by comparing the MEC during the sampling period with reduced precipitation (ΔT1, in 2015) with the MEC detected at periods with normal precipitations (Δ2a, Δ2b, in 2016). In addition, spatial trends were investigated by conducting a cluster analysis with the MEC PPTP data (20-, 50- and 80-percentiles) among the different sites. Particularly the pesticide distributions at SC2 and SC3 were different compared to other sites (SC1, SC4, SC6, SC7 and SC8). However, except for the 20-percentile scenario, the pesticide distribution at SC5 was similar compared to that at SC2 and SC3, forming one sub-cluster. Linear regression models helped to find relationships between two explanatory variables, namely, the share of forest in the buffer zone, and mean slopes of horticultural fields, and the dependent variable, MEC percentiles in streams. For five PPTP, boscalid, diazinon, diuron-desdimethyl, linuron and prometryn + terbutryn the percentile concentrations decreased significantly with increasing share of forest in 100 m river buffer zone considering all scenarios. With regard to the horticultural mean slope, for cyhalothrin and thiamethoxam, the percentile concentrations increased with increasing mean slopes of the horticultural areas for all three scenarios. A high share of forest in the buffer zone worked generally as barrier for input via surface run-off, but not for all PPTP. For the fungicide, carbendazim, increased average slopes did not favor the input into the streams and inputs were low even at sites with horticultural areas with a high mean slope (80 percentile scenario). By analyzing groundwater samples it became apparent that, especially in SC with horticultural fields with low average slopes, a leaching of PPTP into groundwater and further transport into the streams via exfiltration might be possible. Based on this assessment, three avenues for mitigating input of PPTP into the streams could be deduced: to provide training workshops for better handling as well as biobeds for proper disposal; to avoid cultivation of crops in high need insecticides on steep slopes; and to establish forested buffer zones between the fields and the streams."

  6. Average monthly pay of employees in the UK in 2025, by percentile

    • statista.com
    Updated May 14, 2025
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    Statista (2025). Average monthly pay of employees in the UK in 2025, by percentile [Dataset]. https://www.statista.com/statistics/1224844/monthly-pay-of-employees-uk/
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    Dataset updated
    May 14, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2025
    Area covered
    United Kingdom
    Description

    In March 2025, the top one percent of earners in the United Kingdom received an average pay of over 16,000 British pounds per month, compared with the bottom ten percent of earners who earned around 800 pounds a month.

  7. g

    MERRA-2 statM 2d pct Nx: 2d, Single-Level, Monthly Percentiles V1...

    • gimi9.com
    Updated Nov 20, 2020
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    (2020). MERRA-2 statM 2d pct Nx: 2d, Single-Level, Monthly Percentiles V1 (M2SMNXPCT) at GES DISC | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_merra-2-statm-2d-pct-nx-2d-single-level-monthly-percentiles-v1-m2smnxpct-at-ges-disc
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    Dataset updated
    Nov 20, 2020
    Description

    The Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) contains a wealth of information that can be used for weather and climate studies. By combining the assimilation of observations with a frozen version of the Goddard Earth Observing System (GEOS), a global analysis is produced at an hourly temporal resolution spanning from January 1980 through present (Gelaro et al., 2017). It can be difficult to parse through a multidecadal dataset such as MERRA-2 to evaluate the interannual variability of weather that occurs on a daily timescale, let alone determine the occurrence of an extreme weather event. This data collection provides climate statistics compute using MERRA-2 to assist in the analysis of extreme temperature and precipitation events and the accompanying the large scale meteorological patterns across a time period of over four decades. Find the product File Specific, Readme, References, and data tools under "Documentation" tab. Sign up for the MERRA-2 mailing list to receive announcements on the latest data information, tools and services that become available, data announcements from GMAO MERRA-2 project and more! Contact the GES DISC User Services (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.

  8. EJSCREEN National Percentiles Lookup Table--2015 Public Release

    • data.wu.ac.at
    Updated Oct 6, 2017
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    U.S. Environmental Protection Agency (2017). EJSCREEN National Percentiles Lookup Table--2015 Public Release [Dataset]. https://data.wu.ac.at/schema/data_gov/YTlhODAxMjYtY2EyMi00NDFmLTlhMDQtY2NkMzgyMjdkODZk
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    Dataset updated
    Oct 6, 2017
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    7074125c21675ec6f28e34cfc260f4587e6698f2
    Description

    The USA table provides percentile breaks of important EJSCREEN elements (demographic indicators and indexes, environmental indicators and indexes) at the national summary level. This table provides support to the EJSCREEN reporting tools. EJSCREEN is an environmental justice (EJ) screening and mapping tool that provides EPA with a nationally consistent dataset and methodology for calculating "EJ indexes," which can be used for highlighting places that may be candidates for further review, analysis, or outreach as the agency develops programs, policies and other activities. The National-scale Air Toxics Assessment (NATA) environmental indicators and EJ indexes, which include cancer risk, respiratory hazard, neurodevelopment hazard, and diesel particulate matter will be added into EJSCREEN during the first full public update after the soon-to-be-released 2011 dataset is made available. All NATA associated indicator and index elements are currently set to "Null".

  9. Demographic and baseline clinical characteristics and percentiles of APTW...

    • plos.figshare.com
    xls
    Updated Jun 6, 2023
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    Daichi Momosaka; Osamu Togao; Kazufumi Kikuchi; Yoshitomo Kikuchi; Yoshinobu Wakisaka; Akio Hiwatashi (2023). Demographic and baseline clinical characteristics and percentiles of APTW signal of the poor prognosis group with mRS score ≥2 (n = 21) and good prognosis group with mRS score [Dataset]. http://doi.org/10.1371/journal.pone.0237358.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Daichi Momosaka; Osamu Togao; Kazufumi Kikuchi; Yoshitomo Kikuchi; Yoshinobu Wakisaka; Akio Hiwatashi
    License

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

    Description

    Demographic and baseline clinical characteristics and percentiles of APTW signal of the poor prognosis group with mRS score ≥2 (n = 21) and good prognosis group with mRS score

  10. d

    Replication Data for: Is the United States Still a Land of Opportunity?...

    • dataone.org
    • dataverse.harvard.edu
    Updated Nov 12, 2023
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    Chetty, Raj; Hendren, Nathaniel; Kline, Patrick; Saez, Emmanuel; Turner, Nicholas (2023). Replication Data for: Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility [Dataset]. http://doi.org/10.7910/DVN/HM91JN
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    Dataset updated
    Nov 12, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Chetty, Raj; Hendren, Nathaniel; Kline, Patrick; Saez, Emmanuel; Turner, Nicholas
    Area covered
    United States
    Description

    This dataset contains replication files for "Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility" by Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/recentintergenerationalmobility/. A summary of the related publication follows. We present new evidence on trends in intergenerational mobility in the U.S. using administrative earnings records. We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971-1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child’s probability of attending college and her parents’ income rank. We also calculate transition probabilities, such as a child’s chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the “birth lottery” – the parents to whom a child is born – are larger today than in the past. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the US Treasury Department or the Internal Revenue Service or the National Bureau of Economic Research.

  11. f

    Table1_Body mass index percentiles versus body composition assessments:...

    • frontiersin.figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated Jun 19, 2023
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    Jody L. Clasey; Elizabeth A. Easley; Margaret O. Murphy; Stefan G. Kiessling; Arnold Stromberg; Aric Schadler; Hong Huang; John A. Bauer (2023). Table1_Body mass index percentiles versus body composition assessments: Challenges for disease risk classifications in children.docx [Dataset]. http://doi.org/10.3389/fped.2023.1112920.s001
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    docxAvailable download formats
    Dataset updated
    Jun 19, 2023
    Dataset provided by
    Frontiers
    Authors
    Jody L. Clasey; Elizabeth A. Easley; Margaret O. Murphy; Stefan G. Kiessling; Arnold Stromberg; Aric Schadler; Hong Huang; John A. Bauer
    License

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

    Description

    BackgroundIdentifying at-risk children with optimal specificity and sensitivity to allow for the appropriate intervention strategies to be implemented is crucial to improving the health and well-being of children. We determined relationships of body mass indexes for age and sex percentile (BMI%) classifications to actual body composition using validated and convenient methodologies and compared fat and non-fat mass estimates to normative cut-off reference values to determine guideline reliability. We hypothesized that we would achieve an improved ability to identify at-risk children using simple, non-invasive body composition and index measures.MethodsCross-sectional study of a volunteer convenience sample of 1,064 (537 boys) young children comparing Body Fat Percentage (BF%), Fat Mass Index (FMI), Fat-Free Mass Index (FFMI), determined via rapid bioimpedance methods vs. BMI% in children. Comparisons determined among weight classifications and boys vs. girls.ResultsAmongst all subjects BMI% was generally correlated to body composition measures and indexes but nearly one quarter of children in the low-risk classifications (healthy weight or overweight BMI%) had higher BF% and/or lower FFMI than recommended standards. Substantial evidence of higher than expected fatness and or sarcopenia was found relative to risk status. Inaccuracies were more common in girls than boys and girls were found to have consistently higher BF% at any BMI%.ConclusionsThe population studied raises concerns regarding actual risks for children of healthy or overweight categorized BMI% since many had higher than expected BF% and potential sarcopenia. When body composition and FMI and FFMI are used in conjunction with BMI% improved sensitivity, and accuracy of identifying children who may benefit from appropriate interventions results. These additional measures could help guide clinical decision making in settings of disease-risks stratifications and interventions.

  12. EJSCREEN Regions Percentiles Lookup Table--2015 Public Release

    • data.wu.ac.at
    Updated Oct 6, 2017
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    U.S. Environmental Protection Agency (2017). EJSCREEN Regions Percentiles Lookup Table--2015 Public Release [Dataset]. https://data.wu.ac.at/schema/data_gov/OTFmODIwMTMtODAyNy00MDk1LWEzNTktMDU4M2QxNjc5NDQz
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    Dataset updated
    Oct 6, 2017
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    63f3e66331da4b9039ee6cab08740f8a61b1e8f3
    Description

    The Regions table provides percentile breaks of important EJSCREEN elements (demographic indicators and indexes, environmental indicators and indexes) at the EPA region summary level. This table provides support to the EJSCREEN reporting tools. EJSCREEN is an environmental justice (EJ) screening and mapping tool that provides EPA with a nationally consistent dataset and methodology for calculating "EJ indexes," which can be used for highlighting places that may be candidates for further review, analysis, or outreach as the agency develops programs, policies and other activities. The National-scale Air Toxics Assessment (NATA) environmental indicators and EJ indexes, which include cancer risk, respiratory hazard, neurodevelopment hazard, and diesel particulate matter will be added into EJSCREEN during the first full public update after the soon-to-be-released 2011 dataset is made available. All NATA associated indicator and index elements are currently set to "Null".

  13. a

    Time-mean Sea Level Projections to 2100 (cm)

    • space-geoportal-queensub.hub.arcgis.com
    • keep-cool-global-community.hub.arcgis.com
    Updated Apr 7, 2022
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    Met Office (2022). Time-mean Sea Level Projections to 2100 (cm) [Dataset]. https://space-geoportal-queensub.hub.arcgis.com/datasets/TheMetOffice::time-mean-sea-level-projections-to-2100-cm
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    Dataset updated
    Apr 7, 2022
    Dataset authored and provided by
    Met Office
    Area covered
    Description

    Please note this dataset supersedes previous versions on the Climate Data Portal. It has been uploaded following an update to the dataset in March 2023. This means sea level rise is approximately 1cm higher (larger) compared to the original data release (i.e. the previous version available on this portal) for all UKCP18 site-specific sea level projections at all timescales. For more details please refer to the technical note.What does the data show?The time-mean sea-level projections to 2100 show the amount of sea-level change (in cm) for each coastal location (grid-box) around the British Isles for several emission scenarios. Sea-level rise is the primary mechanism by which we expect coastal flood hazard to change in the UK in the future. The amount of sea-level rise depends on the location around the British Isles and increases with higher emission scenarios. Here, we provide the relative time-mean sea-level projections to 2100, i.e. the local sea-level change experienced at a particular location compared to the 1981-2000 average, produced as part of UKCP18.For each grid box the time-mean sea-level change projections are provided for the end of each decade (e.g. 2010, 2020, 2030 etc) for three emission scenarios known as Representative Concentration Pathways (RCP) and for three percentiles.The emission scenarios are:RCP2.6RCP4.5RCP8.5The percentiles are:5th percentile50th percentile95th percentileImportant limitations of the dataWe cannot rule out substantial additional sea-level rise associated with ice sheet instability processes that are not represented in the UKCP18 projections, as discussed in the recent IPCC Sixth Assessment Report (AR6). Although the time-mean sea-level projections presented here are to 2100, past greenhouse gas emissions have already committed us to substantial additional sea level rise beyond 2100. This is because the ocean and cryosphere (i.e. the frozen parts of our planet) are very slow to respond to global warming. So, even if global average air temperature stops rising, as global emissions are reduced, sea level will continue to rise well beyond the time changes in global average air temperature level off or decline. This is illustrated by the extended exploratory time-mean sea level projections and discussed further in AR6 (Fox-Kemper et al, 2021).What are the naming conventions and how do I explore the data?The data is supplied so that each row corresponds to the combination of a RCP emissions scenario and percentile value e.g. 'RCP45_50' is the RCP4.5 scenario and the 50th percentile. This can be viewed and filtered by the field 'RCP and Percentile'. The columns (fields) correspond to the end of each decade and the fields are named by sea level anomaly at year x, e.g. '2050 seaLevelAnom' is the sea level anomaly at 2050 compared to the 1981-2000 average.Please note that the styling and filtering options are independent of each other and the attribute you wish to style the data by can be set differently to the one you filter by. Please ensure that you have selected the RCP/percentile and decade you want to both filter and style the data by. Select the cell you are interested in to view all values. To understand how to explore the data please refer to the New Users ESRI Storymap.What are the emission scenarios?The 21st Century time-mean sea level projections were produced using some of the future emission scenarios used in the IPCC Fifth Assessment Report (AR5). These are RCP2.6, RCP4.5 and RCP8.5, which are based on the concentration of greenhouse gases and aerosols in the atmosphere. RCP2.6 is an aggressive mitigation pathway, where greenhouse gas emissions are strongly reduced. RCP4.5 is an intermediate ‘stabilisation’ pathway, where greenhouse gas emissions are reduced by varying levels. RCP8.5 is a high emission pathway, where greenhouse gas emissions continue to grow unmitigated. Further information is available in the Understanding Climate Data ESRI Storymap and the RCP Guidance on the UKCP18 website.What are the percentiles?The UKCP18 sea-level projections are based on a large Monte Carlo simulation that represents 450,000 possible outcomes in terms of global mean sea-level change. The Monte Carlo simulation is designed to sample the uncertainties across the different components of sea-level rise, and the amount of warming we see for a given emissions scenario across CMIP5 climate models. The percentiles are used to characterise the uncertainty in the Monte Carlo projections based on the statistical distribution of the 450,000 individual simulation members. For example, the 50th percentile represents the central estimate (median) amongst the model projections. Whilst the 95th percentile value means 95% of the model distribution is below that value and similarly the 5th percentile value means 5% of the model distribution is below that value. The range between the 5th to 95th percentiles represent the projection range amongst models and corresponds to the IPCC AR5 “likely range”. It should be noted that, there may be a greater than 10% chance that the real-world sea level rise lies outside this range. Data sourceThis data is an extract of a larger dataset (every year and more percentiles) which is available on CEDA at https://catalogue.ceda.ac.uk/uuid/0f8d27b1192f41088cd6983e98faa46eData has been extracted from the v20221219 version (downloaded 17/04/2023) of three files:seaLevelAnom_marine-sim_rcp26_ann_2007-2100.ncseaLevelAnom_marine-sim_rcp45_ann_2007-2100.ncseaLevelAnom_marine-sim_rcp85_ann_2007-2100.ncUseful links to find out moreFor a comprehensive description of the underpinning science, evaluation and results see the UKCP18 Marine Projections Report (Palmer et al, 2018).For a discussion on ice sheet instability processes in the latest IPCC assessment report, see Fox-Kemper et al (2021). Technical note for the update to the underpinning data: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp_tech_note_sea_level_mar23.pdfFurther information in the Met Office Climate Data Portal Understanding Climate Data ESRI Storymap.

  14. e

    Background values - HGW: Chrome, 90th percentile (top)

    • data.europa.eu
    wms
    Updated Apr 12, 2025
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    Landesamt für Geologie und Bergbau, Rheinland-Pfalz (2025). Background values - HGW: Chrome, 90th percentile (top) [Dataset]. https://data.europa.eu/data/datasets/97ae081e-f3ee-c0de-68ce-654ad944b78e?locale=en
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    wmsAvailable download formats
    Dataset updated
    Apr 12, 2025
    Dataset authored and provided by
    Landesamt für Geologie und Bergbau, Rheinland-Pfalz
    Description

    Soils were created in millennia by the interaction of diverse natural processes. For centuries, however, they have increasingly been shaped by human activities such as land use and material inputs. For most inorganic substances, the starting substrate of soil formation determines the natural (geogenic) basic content of a soil. In addition, there is an anthropogenic component, whereby the ratio of geogenous and anthropogenic proportion varies greatly in element-specific terms. Background values characterize the typical background contents of a substance or group of substances in the soil. In accordance with the procedure of the Federal-State Working Group on Soil Protection (LABO), the 50. percentile (median) and the 90th percentile (median). The percentile is used. The median represents the median background content, which is 90. Percentile is the upper limit of the typical background content. For the purposes of determining background levels, the anthropogenic fraction shall not come from an identifiable individual source or source of pollution. Rather, they must be diffuse, i.e. they must be the result of general large-scale (ubiquitous) distributions of substances over longer periods of time. Specifically polluted soils must therefore be removed from the data before background values are calculated. The substrate is the most important differentiation criterion for natural contents of inorganic substances. The next deeper level of division are horizon groups such as topsoil, subsoil and subsoil. If there is still a sufficient number of cases, the group of topsoil horizons is further subdivided into use classes. The State Office for Geology and Mining Rhineland-Palatinate has been dealing with physical and chemical investigations of soils for many years within the framework of the land survey as well as through cooperation with other state authorities. The information bases could be significantly improved with data collected during the project Bodenbelastungskataster Rheinland-Pfalz. Since this project, comprehensive soil investigations have been successively continued with the Rhineland-Palatinate Soil Status Report. This long-term project of the Ministry of the Environment, Forests and Consumer Protection is carried out on behalf of the State Office for the Environment, Water Management and Trade Inspectorate. In the meantime, about 18 percent of the country's area with at least one investigated site per km2 has been recorded. The data basis for the spatial distribution of the substrate groups comes from the soil specialist information system (FISBO) of the LGB. This system manages data from boch heterogeneous soil mapping (scale 1:25,000 to 1:200,000). Although this leads to recognizably different resolutions and differentiation problems, these data are suitable for nationwide surveys in small-scale applications above the scale of 1:50,000, despite their inhomogeneity. Further information on this topic can be found in the loose-leaf collection background values of the soils of Rhineland-Palatinate.:As a 90.P background value, this is 90. Percentile of a Data Collective. It is the value at which 90% of the cases observed so far have lower levels. The calculation is made after the data group of outliers has been cleaned up. The 90. The percentile often serves as the upper limit of the background range to delineate unusually high levels. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 30 mg/kg for sand, 60 mg/kg for clay, silt and very silty sand and 100 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

  15. Intergenerational Economic Mobility and the Racial Wealth Gap

    • openicpsr.org
    Updated Jan 6, 2021
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    Jermaine Toney; Cassandra Robertson (2021). Intergenerational Economic Mobility and the Racial Wealth Gap [Dataset]. http://doi.org/10.3886/E130341V1
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    Dataset updated
    Jan 6, 2021
    Dataset provided by
    American Economic Associationhttp://www.aeaweb.org/
    Authors
    Jermaine Toney; Cassandra Robertson
    License

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

    Description

    A growing body of research documents the importance of wealth and the racial wealth gap in perpetuating inequality across generations. We add to this literature by examining the impact of wealth on child income by race, while also extending our analysis to three generations. Our two stage least squares regressions reveal that grandparental and parental wealth and the younger generation’s household income is strongly positively correlated. We further explore the relationship between income and wealth by decomposing the child’s income by race. We find that the disparity in income between black and white respondents is mainly attributable to differences in family background. In context, differences in family background are stronger than differences in educational attainment. When we examine different income percentiles, however, we find that the effect of grandparental and parental wealth endowment is much stronger at the top of the income distribution. These findings indicate that wealth is an important source of income inequality.

  16. g

    Replication data for: The Research Productivity of New PhDs in Economics:...

    • search.gesis.org
    Updated Nov 13, 2019
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    ICPSR - Interuniversity Consortium for Political and Social Research (2019). Replication data for: The Research Productivity of New PhDs in Economics: The Surprisingly High Non-success of the Successful [Dataset]. http://doi.org/10.3886/E113931
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    Dataset updated
    Nov 13, 2019
    Dataset provided by
    GESIS search
    ICPSR - Interuniversity Consortium for Political and Social Research
    License

    https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de702483https://search.gesis.org/research_data/datasearch-httpwww-da-ra-deoaip--oaioai-da-ra-de702483

    Description

    Abstract (en): We study the research productivity of new graduates from North American PhD programs in economics from 1986 to 2000. We find that research productivity drops off very quickly with class rank at all departments, and that the rank of the graduate departments themselves provides a surprisingly poor prediction of future research success. For example, at the top ten departments as a group, the median graduate has fewer than 0.03 American Economic Review (AER)-equivalent publications at year six after graduation, an untenurable record almost anywhere. We also find that PhD graduates of equal percentile rank from certain lower-ranked departments have stronger publication records than their counterparts at higher-ranked departments. In our data, for example, Carnegie Mellon's graduates at the 85th percentile of year-six research productivity outperform 85th percentile graduates of the University of Chicago, the University of Pennsylvania, Stanford, and Berkeley. These results suggest that even the top departments are not doing a very good job of training the great majority of their students to be successful research economists. Hiring committees may find these results helpful when trying to balance class rank and place of graduate in evaluating job candidates, and current graduate students may wish to re-evaluate their academic strategies in light of these findings.

  17. f

    ARLs along with their respective standard deviations for α = 0.01.

    • plos.figshare.com
    xls
    Updated Feb 6, 2025
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    Muthanna Ali Hussein Al-Lami; Hossein Jabbari Khamnei; Ali Akbar Heydari (2025). ARLs along with their respective standard deviations for α = 0.01. [Dataset]. http://doi.org/10.1371/journal.pone.0316449.t007
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    xlsAvailable download formats
    Dataset updated
    Feb 6, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Muthanna Ali Hussein Al-Lami; Hossein Jabbari Khamnei; Ali Akbar Heydari
    License

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

    Description

    ARLs along with their respective standard deviations for α = 0.01.

  18. p

    Mediterranean Sea Surface Temperature extreme from Reanalysis

    • pigma.org
    • fedeo.ceos.org
    • +1more
    ogc:wmts, www:stac
    Updated Mar 30, 2023
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    IBI-PUERTOS-MADRID-ES (2023). Mediterranean Sea Surface Temperature extreme from Reanalysis [Dataset]. https://www.pigma.org/geonetwork/BORDEAUX_METROPOLE_DIR_INFO_GEO/api/records/dc1552b5-04cb-4fb9-a85b-bfc72f65ec96
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    ogc:wmts, www:stacAvailable download formats
    Dataset updated
    Mar 30, 2023
    Dataset provided by
    CMEMS
    IBI-PUERTOS-MADRID-ES
    Time period covered
    Jan 1, 1993 - Dec 31, 2020
    Area covered
    Description

    '''DEFINITION'''

    The CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019).

    '''CONTEXT'''

    The Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid ‘80s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend. The Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14°C in the Northwestern part of the basin to ~23°C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80’s as shown in Marbà et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018).

    '''CMEMS KEY FINDINGS'''

    The Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 °C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23–25 °C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016. The 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3°C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing).

    Note: The key findings will be updated annually in November, in line with OMI evolutions.

    '''DOI (product):''' https://doi.org/10.48670/moi-00266

  19. F

    Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles)

    • fred.stlouisfed.org
    json
    Updated Sep 19, 2025
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    (2025). Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) [Dataset]. https://fred.stlouisfed.org/series/WFRBLTP1246
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    jsonAvailable download formats
    Dataset updated
    Sep 19, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1246) from Q3 1989 to Q2 2025 about net worth, wealth, percentile, Net, and USA.

  20. i

    Black Sea Significant Wave Height extreme from Reanalysis

    • sextant.ifremer.fr
    • cmems-catalog-ro.cls.fr
    www:stac
    Updated Jun 18, 2020
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    CMEMS (2020). Black Sea Significant Wave Height extreme from Reanalysis [Dataset]. https://sextant.ifremer.fr/record/43e464f4-78ba-481c-b03c-0823924989e1/
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    www:stacAvailable download formats
    Dataset updated
    Jun 18, 2020
    Dataset provided by
    IBI-PUERTOS-MADRID-ES
    CMEMS
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Time period covered
    Jan 1, 1993 - Dec 31, 2023
    Area covered
    Description

    '''DEFINITION'''

    The CMEMS BLKSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_WAV_007_006) and the Analysis product (BLKSEA_ANALYSISFORECAST_WAV_007_003). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1979-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019).

    '''CONTEXT'''

    The sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, including biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015, IPCC, 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (IPCC, 2021). Significant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence demonstrating that the highest values of the average annual 99th percentiles are in the areas where high winds and long fetch are simultaneously present. The largest values of the mean 99th percentile in the Black Sea in the southewestern Black Sea are around 3.5 m, while in the eastern part of the basin are around 2.5 m (Staneva et al., 2019a and 2019b).

    '''CMEMS KEY FINDINGS'''

    Significant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence with largest values in the southwestern Black Sea, with values as high as 3.5 m, while the 99th percentile values in the eastern part of the basin are around 2.5 m. The Black Sea, the 99th mean percentile for 2002-2019 shows a similar pattern demonstrating that the highest values of the mean annual 99th percentile are in the western Black Sea. This pattern is consistent with the previous studies, e.g. of (Akpınar and Kömürcü, 2012; and Akpinar et al., 2016). The anomaly of the 99th percentile in 2020 is mostly negative with values down to ~-45 cm. The highest negative anomalies for 2020 are observed in the southeastern area where the multi-year mean 99th percentile is the lowest. The highest positive anomalies of the 99th percentile in 2020 are located in the southwestern Black Sea and along the eastern coast. The map of anomalies for 2020, presenting alternate bands of positive and negative values depending on latitude, is consistent with the yearly west-east displacement of the tracks of the largest storms.

    Note: The key findings will be updated annually in November, in line with OMI evolutions.

    '''DOI (product):''' https://doi.org/10.48670/moi-00214

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Rong Wei; Cynthia L. Ogden; Van L. Parsons; David S. Freedman; Craig M. Hales (2023). A method for calculating BMI z-scores and percentiles above the 95th percentile of the CDC growth charts [Dataset]. http://doi.org/10.6084/m9.figshare.12932858.v1

Data from: A method for calculating BMI z-scores and percentiles above the 95th percentile of the CDC growth charts

Related Article
Explore at:
pdfAvailable download formats
Dataset updated
Jun 5, 2023
Dataset provided by
Taylor & Francis
Authors
Rong Wei; Cynthia L. Ogden; Van L. Parsons; David S. Freedman; Craig M. Hales
License

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

Description

The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values. This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population. Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

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