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.

For two defined gamma distributions with equal expected values, many two-sample random drawings can be done, sample averages calculated from them and ultimately their difference. This is one realization of this difference expression. Doing so many times (here ten thousand times for the given distributions and sample size), many realisations are available, enough to estimate properly the distribution of the sample average difference under the null hypothesis: the two gamma distributions have the same expected values. Once the distribution is available, its 2.5 % and 97.5% p percentiles are available and thus also the multiple d in the equation p = d * (square root of the true variance of the difference of the two sample averages). If d does not fluctuate much, it can be used in practice to calculate back the percentiles (with an estimate of the square root), regardless of the form of the gamma distributions. If such percentiles are at hand, they can be used for testing the null hypothesis in practice. The dataset item contains files, such as "d_sc3_30.xls". The file name means that it concerns sample sizes of 30 and various combinations of the two gamma distribution parameters (the combinations belong to scenario 3 - see below), and the file contains the d multiple estimates for each parameter combination within the scenario. There is the "d_lower", which is the multiple leading to the 2.5% percentile, and "d_upper" leading to the 97.5% percentile.The scenarios are: scenario 1 ..both shape parameters are 1..10, scenario 2..both shape parameters are 11..20, scenario 3.. both shape params are 21..30, scenario 4..shape 1 is 1..10, shape 2 is 11..20,scenario 5..shape 1 is 1..10, shape 2 is 21..30, scenario 6..shape 1 is 11..20, shape 2 is 21..30. The scale 1 parameter is always 1..30. The scale 2 parameter is such that both distributions have the same expected values. Returning to the example "d_sc3_30.xls", this file contains d's for shape 1 = 21 = shape 2, scale 1 = 1, shape1 = 21, shape2 = 22, shape1=21, ..., shape1 = shape2=30, scale1=30. Each row in the file has the d_ lower and d_upper for one of these combinations, and the sample sizes are both always 30.

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.

This indicator represents the tracts ranked by their percentile level of median household incomes per census tract, per capita income. The data source is 2017-2021 American Community Survey, 5-year estimates. The percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale. For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

Rent estimates at the 50th percentile (or median) are calculated for all Fair Market Rent areas. Fair Market Rents (FMRs) are primarily used to determine payment standard amounts for the Housing Choice Voucher program, to determine initial renewal rents for some expiring project-based Section 8 contracts, to determine initial rents for housing assistance payment (HAP) contracts in the Moderate Rehabilitation Single Room Occupancy program (Mod Rehab), and to serve as a rent ceiling in the HOME rental assistance program. FMRs are gross rent estimates. They include the shelter rent plus the cost of all tenant-paid utilities, except telephones, cable or satellite television service, and internet service. The U.S. Department of Housing and Urban Development (HUD) annually estimates FMRs for 530 metropolitan areas and 2,045 nonmetropolitan county FMR areas. Under certain conditions, as set forth in the Interim Rule (Federal Register Vol. 65, No. 191, Monday October 2, 2000, pages 58870-58875), these 50th percentile rents can be used to set success rate payment standards.

The map provides an overview of Switzerland's current groundwater temperatures. The calculation is based on percentiles of the dataset for the standard period 1991–2020 or, in the case of a shorter data series, the standard period 2001–2020 (basic data in °C). A figure significantly lower than the average conditions (low groundwater temperature) is reached if the current value is below the long-term 10th percentile, i.e. it is among the lowest 10% of all the data measured for the relevant month in the standard period. A groundwater temperature between the 10th and 90th percentiles means that conditions are normal. However, a figure significantly higher than the long-term average conditions (high groundwater temperature) is reached if the current value is above the 90th percentile.

This number is calculated from all the lead sample results taken at sites within a water system during a monitoring period. If your water supply’s 90th percentile number is over 12 ppb, your community has a lead Action Level Exceedance (ALE) and must take corrective action.

On January 1, 2025, the lead action level in Michigan was reduced from 15 ppb to 12 ppb. 90th percentiles calculated for samples collected before January 1, 2025 were subject to the 15 ppb lead action level.

This dataset contains the geographic data used to create maps for the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt

Demographic data from the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Demographics/q9ix-kfws

Filter by the Indicator column to select data for a particular indicator map.

Export notes: Dataset may not automatically open correctly in Excel due to geospatial data. To export the data for geospatial analysis, select Shapefile or GEOJSON as the file type. To view the data in Excel, export as a CSV but do not open the file. Then, open a blank Excel workbook, go to the Data tab, select “From Text/CSV,” and follow the prompts to import the CSV file into Excel. Alternatively, use the exploration options in "View Data" to hide the geographic column prior to exporting the data.

USER NOTES: 4/7/2025 - The maps and data have been removed for the Health Professional Shortage Areas indicator due to inconsistencies with the data source leading to some missing health professional shortage areas. We are working to fix this issue, including exploring possible alternative data sources.

5/21/2025 - The following changes were made to the 2023 report data (Equity Report Year = 2023). Self-Sufficiency Wage - a typo in the indicator name was fixed (changed sufficienct to sufficient) and the percent for one PUMA corrected from 56.9 to 59.9 (PUMA = San Diego County (Northwest)--Oceanside City & Camp Pendleton). Notes were made consistent for all rows where geography = ZCTA. A note was added to all rows where geography = PUMA. Voter registration - label "92054, 92051" was renamed to be in numerical order and is now "92051, 92054". Removed data from the percentile column because the categories are not true percentiles. Employment - Data was corrected to show the percent of the labor force that are employed (ages 16 and older). Previously, the data was the percent of the population 16 years and older that are in the labor force. 3- and 4-Year-Olds Enrolled in School - percents are now rounded to one decimal place. Poverty - the last two categories/percentiles changed because the 80th percentile cutoff was corrected by 0.01 and one ZCTA was reassigned to a different percentile as a result. Low Birthweight - the 33th percentile label was corrected to be written as the 33rd percentile. Life Expectancy - Corrected the category and percentile assignment for SRA CENTRAL SAN DIEGO. Parks and Community Spaces - corrected the category assignment for six SRAs.

5/21/2025 - Data was uploaded for Equity Report Year 2025. The following changes were made relative to the 2023 report year. Adverse Childhood Experiences - added geographic data for 2025 report. No calculation of bins nor corresponding percentiles due to small number of geographic areas. Low Birthweight - no calculation of bins nor corresponding percentiles due to small number of geographic areas.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

Verbal and Quantitative Reasoning GRE scores and percentiles were collected by querying the student database for the appropriate information. Any student records that were missing data such as GRE scores or grade point average were removed from the study before the data were analyzed. The GRE Scores of entering doctoral students from 2007-2012 were collected and analyzed. A total of 528 student records were reviewed. Ninety-six records were removed from the data because of a lack of GRE scores. Thirty-nine of these records belonged to MD/PhD applicants who were not required to take the GRE to be reviewed for admission. Fifty-seven more records were removed because they did not have an admissions committee score in the database. After 2011, the GRE’s scoring system was changed from a scale of 200-800 points per section to 130-170 points per section. As a result, 12 more records were removed because their scores were representative of the new scoring system and therefore were not able to be compared to the older scores based on raw score. After removal of these 96 records from our analyses, a total of 420 student records remained which included students that were currently enrolled, left the doctoral program without a degree, or left the doctoral program with an MS degree. To maintain consistency in the participants, we removed 100 additional records so that our analyses only considered students that had graduated with a doctoral degree. In addition, thirty-nine admissions scores were identified as outliers by statistical analysis software and removed for a final data set of 286 (see Outliers below). Outliers We used the automated ROUT method included in the PRISM software to test the data for the presence of outliers which could skew our data. The false discovery rate for outlier detection (Q) was set to 1%. After removing the 96 students without a GRE score, 432 students were reviewed for the presence of outliers. ROUT detected 39 outliers that were removed before statistical analysis was performed. Sample See detailed description in the Participants section. Linear regression analysis was used to examine potential trends between GRE scores, GRE percentiles, normalized admissions scores or GPA and outcomes between selected student groups. The D’Agostino & Pearson omnibus and Shapiro-Wilk normality tests were used to test for normality regarding outcomes in the sample. The Pearson correlation coefficient was calculated to determine the relationship between GRE scores, GRE percentiles, admissions scores or GPA (undergraduate and graduate) and time to degree. Candidacy exam results were divided into students who either passed or failed the exam. A Mann-Whitney test was then used to test for statistically significant differences between mean GRE scores, percentiles, and undergraduate GPA and candidacy exam results. Other variables were also observed such as gender, race, ethnicity, and citizenship status within the samples. Predictive Metrics. The input variables used in this study were GPA and scores and percentiles of applicants on both the Quantitative and Verbal Reasoning GRE sections. GRE scores and percentiles were examined to normalize variances that could occur between tests. Performance Metrics. The output variables used in the statistical analyses of each data set were either the amount of time it took for each student to earn their doctoral degree, or the student’s candidacy examination result.

Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.

Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022).

At-a-station Hydraulic Geometry

We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:

\(w={aQ^b}\)

\(d=cQ^f\)

\(v=kQ^m\)

where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:

1. We considered only measurements which reported Q, v, w and A.
2. For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.
3. We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which \( Q eq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.
4. Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables.
   1. We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.
   2. Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.
   3. Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).
5. If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.

Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.

At-a-station Manning's n

We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by

\(n = {{R^{2/3}S^{1/2}} \over v}\)

where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10^-5 m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q₅₀; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10—which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.

Daily discharge percentiles

We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:

1. For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.
2. A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.

We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q₁ and Q₅, increments of 5% (e.g. Q₁₀, Q₁₅, Q₂₀, etc.) between Q₅ and Q₉₅, increments of 1% between Q₉₅ and Q₉₉, and increments of 0.1% between Q₉₉ and Q₁₀₀ in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.

HG Regionalization

We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:

\(log(Q_i) = \beta_1log(DA) + \beta_0\)

where Q_i is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\), \(\beta_0\) , and the r² value of the regression relationship for Q percentiles Q₁₀, Q₂₅, Q₅₀, Q₇₅, Q₉₀, Q₉₅, Q₉₉, and Q_99.9. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).

Dataset description

We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r² values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.

Errata

In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.

Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft).SDI (Rodriguez y Silva et al. 2020) factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI is currently classified into six categories representing low through extreme difficulty. Extreme SDI zones represented in red are “watch out” situations where engagement is likely to be very challenging given the combination of potential high intensity fire behavior and difficult suppression environment (high resistance fuel types, steep terrain, and low accessibility). Low difficulty zones represented in blue indicate areas where some combination of reduced potential for dangerous fire behavior and ideal suppression environment (low resistance fuel types, mellow terrain, and high accessibility) make suppression activities easier. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work. SDI incorporates flame length and heat per unit area from basic FlamMap runs (Finney et al. 2019). SDI is based on fire behavior modeled using regionally appropriate percentile fuel moisture conditions and uphill winds. This product uses the wind blowing uphill option to represent a consistent worst-case scenario. Input fuels data are updated to the most recent fire year using a crosswalk for surface and canopy fuel modifications for fires and fuel treatments that occurred after the most recent LANDFIRE version. For example, LANDFIRE 2016 model inputs are modified to incorporate fires (Monitoring Trends in Burn Severity (MTBS), Geospatial Multi- Agency Coordination (GeoMac), and Wildland Fire Interagency Geospatial Services (WFIGS) and fuel treatments (USFS Forest Activity Tracking System (FACTS) and DOI National Fire Plan Operations and Reporting System (NFPORS) hazardous fuels reduction treatments) from 2017-present. Road and trail inputs are developed from a combination of HERE 2020 Roads, USFS, and DOI road and trails databases. Hand crew and dozer fireline production rates are from FPA 2012 (Dillon et al. 2015). Classification of topography and accessibility thresholds are detailed in Rodriguez et al. (2020). Dillon, G.K.; Menakis, J.; Fay, F. (2015) Wildland Fire Potential: a tool for assessing wildfire risk and fuels management needs. In: Keane, R.E.; Jolly, M.; Parsons, R.; Riley, K., eds. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 345 p. Finney, M.A.; Brittain, S.; Seli, R.C.; McHugh, C.W.; Gangi, L. (2019) FlamMap:Fire Mapping and Analysis System (Version 6.0) [Software]. Available from https://www.firelab.org/document/flammap-software Rodriguez y Silva, F.; O'Connor, C.D.; Thompson, M.P.; Molina, J.R.; Calkin, D.E. (2020). Modeling Suppression Difficulty: Current and Future Applications. International Journal of Wildland Fire.

As a 90.P background value, that's 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.

List of Subdatasets: Long-term data: 2000-2021 5th percentile (p05) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 50th percentile (p50) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 95th percentile (p95) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1 Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

List of Subdatasets: Long-term data: 2000-2021 5th percentile (p05) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 50th percentile (p50) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 95th percentile (p95) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1 Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

List of Subdatasets: Long-term data: 2000-2021 5th percentile (p05) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 50th percentile (p50) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 95th percentile (p95) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1 Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

This indicator represents the tracts ranked by their percentile level of percentage of sum of all single race/ethnicity categories. The data source is 2017-2021 American Community Survey, 5-year estimates. The percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale. For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

This open-access geospatial dataset (downloadable in csv or shapefile format) contains a total of 11 environmental indicators calculated for 1865 U.S. prisons. This consists of all active state- and federally-operated prisons according to the Homeland Infrastructure Foundation-Level Data (HIFLD), last updated June 2022. This dataset includes both raw values and percentiles for each indicator. Percentiles denote a way to rank prisons among each other, where the number represents the percentage of prisons that are equal to or have a lower ranking than that prison. Higher percentile values indicate higher vulnerability to that specific environmental burden compared to all the other prisons. Full descriptions of how each indicator was calculated and the datasets used can be found here: https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.

From these raw indicator values and percentiles, we also developed three individual component scores to summarize similar indicators, and to then create a single vulnerability index (methods based on other EJ screening tools such as Colorado Enviroscreen, CalEnviroScreen and EPA’s EJ Screen). The three component scores include climate vulnerability, environmental exposures and environmental effects. Climate vulnerability factors reflect climate change risks that have been associated with health impacts and includes flood risk, wildfire risk, heat exposure and canopy cover indicators. Environmental exposures reflect variables of different types of pollution people may come into contact with (but not a real-time exposure to pollution) and includes ozone, particulate matter (PM 2.5), traffic proximity and pesticide use. Environmental effects indicators are based on the proximity of toxic chemical facilities and includes proximity to risk management plan (RMP) facilities, National Priority List (NPL)/Superfund facilities, and hazardous waste facilities. Component scores were calculated by taking the geometric mean of the indicator percentiles. Using the geometric mean was most appropriate for our dataset since many values may be related (e.g., canopy cover and temperature are known to be correlated).

To calculate a final, standardized vulnerability score to compare overall environmental burdens at prisons across the U.S., we took the average of each component score and then converted those values to a percentile rank. While this index only compares environmental burdens among prisons and is not comparable to non-prison sites/communities, it will be able to heighten awareness of prisons most vulnerable to negative environmental impacts at county, state and national scales. As an open-access dataset it also provides new opportunities for other researchers, journalists, activists, government officials and others to further analyze the data for their needs and make comparisons between prisons and other communities. This is made even easier as we produced the methodology for this project as an open-source code base so that others can apply the code to calculate individual indicators for any spatial boundaries of interest. The codebase can be found on GitHub (https://github.com/GeospatialCentroid/NASA-prison-EJ) and is also published via Zenodo (https://zenodo.org/record/8306856).

Data for Figure 3.21 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.21 shows the seasonal evolution of observed and simulated Arctic and Antarctic sea ice area (SIA) over 1979-2017. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has several subplots, but they are unidentified, so the data is stored in the parent directory. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains Sea Ice Area anomalies over 1979-2017 relative to the 1979-2000 means from: - Observations (OSISAF, NASA Team, and Bootstrap) - Historical simulations from CMIP5 and CMIP6 multi-model means - Natural only simulations from CMIP5 and CMIP6 multi-model means --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - arctic files are used for the plots on the left side of the figure - antarctic files are used for the plots on the right side of the figure - _OBS_NASATeam files are used for the first row of the plot - _OBS_Bootstrap are used for the second row of the plot - _OBS_OSISAF are used for the third row of the plot - _ALL_CMIP5 are used in the fourth row of the plot - _ALL_CMIP6 are used in the fifth row of the plot - _NAT_CMIP5 are used in the sixth row of the plot - _NAT_CMIP6 are used in the seventh row of the plot --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The significance are for the grey dots, it's nan or 1 values. The data has to be overplotted to colored squares. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation). The coordinates of the data are indices, but in global attributes 'comments' of each file there are relations of indices to months, since months are the y coordinate. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.