AndreaBozzo/ceres-open-data-index huggingface dataset:
Wind speed estimates of the December 2021 Quad-State Tornado in Mayfield, KY based on treefall pattern analysis

A violent tornado outbreak occurred on December 10-11, 2021 in the Midwest US. One of the tornadoes, known as the Quad-State tornado, tracked across four states and devastated the downtown area of Mayfield, KY, producing high-end EF-4 damage. The data here provides a series of wind speed and direction time histories of the Quad-State tornado for 44 damaged residential houses in Mayfield, KY, which can be useful for detailed forensic analysis of the residential building damage. The data was generated using a software that performs a treefall pattern analysis method, developed by the first author. In addition to the many structural damage, the tornado damaged a large number of trees in the Mayfield area. The fall direction of the damaged trees displayed a converging pattern, caused by a rotational wind flow, which is a typical indicator of a tornado. The converging treefall pattern then can be analyzed to characterize the tornadic flow and estimate the wind field (i.e., treefall pattern analysis method). The treefall pattern analysis method simulates a series of tornadoes using an idealized Rankine vortex model and generates a virtual treefall pattern, which is used to compare to the treefall pattern observed in the field and iterated until the "best-matching" pattern is found. In order to reduce the uncertainty in the estimates, the translational speed of the tornado was estimated based on tracking the motion of the vortex signature from the nearest NEXRAD radar, and the Radius of Maximum Wind (RMW) and decay exponent of the Rankine vortex model were estimated based on the structural damage. Then, the software was used to estimate the rest of the vortex parameters and wind time history (e.g., wind speed and direction) at selected locations. More detailed description on the parameter estimation and software will be published later in the NIST Technical Note.

Tags: mayfield-ky, near-surface, time-history, tornado, treefall, wind-direction, wind-speed
Organization: National Institute of Standards and Technology
Predicting ABM Results with Covering Arrays and Random Forests

Our goal is to explore the feasibility and usefulness of using a combination of covering arrays and machine learning models for predicting results of an agent- based simulation model within the vast parameter value combination space. The challenge is to select parameter values that are representative of the overall behavior of the model, so that we can train the machine learning model to be able to correctly predict behavior on previously untested areas of the parameter space. We have chosen Wilensky's Heat Bugs model in NetLogo for our study. It is a simple model, amenable to quick data generation, with a limited number of outputs to predict, and with emergent behavior. This model therefore allows exploration of this new approach.We utilize covering arrays to reduce the parameter value space systematically, run the model for each parameter set in the 2-way and 3-way covering arrays, train a random forest model on the 2-way data (33, 351 parameter combinations), and test its ability to predict the outcome of the simulation on the significantly larger 3-way data that was not seen during the training of the model (3, 971, 955 parameter combinations).

Tags: agent-based-modeling-machine-learning-calibration
Organization: National Institute of Standards and Technology
`thermoextrap`: Thermodynamic Extrapolation/Interpolation Library

This is a python package to perform thermodynamic extrapolation andinterpolation of observables calculated from molecular simulations. This allowsfor more efficient use of simulation data for calculating how observables changewith simulation conditions, including temperature, density, pressure, chemicalpotential, or force field parameters.

Tags: molecular-simulation, python-package, statistical-mechanics, thermodynamics
Organization: National Institute of Standards and Technology
Database Infrastructure for Mass Spectrometry - Per- and Polyfluoroalkyl Substances

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs).Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

Tags: database, emerging-contaminants, high-resolution-mass-spectrometry, hrms, mass-spectrometry, non-targeted-analysis, nta, per-and-polyfluoroalkyl-substances, pfas, sqlite
Organization: National Institute of Standards and Technology
Data from "Full-Scale Experiments to Demonstrate Flammability Risk of Residential Upholstered Furniture and Mitigation Using Barrier Fabric"

This study looks at fire growth in a furnished compartment intended to mimic a living room where a couch, either with or without a barrier fabric, served as the primary fuel load. The experiments were designed to quantify the effect of barrier fabrics on the fire hazard (e.g., tenability conditions, heat release rate, time to flashover and smoke generation) of residential upholstered furniture (RUF) ignited by a flaming-ignition source and to validate the failure mechanisms of barrier fabrics previously observed in chair mock-up experiments.Three commercially available couches compliant with California Technical Bulletin TB-117 were procured. The couches were identical except for the type of cover fabric: two couches used a cotton blend cover and one used a thermoplastic polyester cover. One of the two couches with the cotton blend cover fabric was modified by adding a barrier fabric as a liner between the cover fabric and the couch padding material. The barrier contained only intrinsically fire-resistant fibers without additional, chemically active fire retardants. Three fire tests, one per couch type, were conducted. A throw pillow on the arm of the couch, ignited with a propane flame, acted as an ignition source. Measurements included heat release rate, gas species (O2,CO, CO2), video, heat flux and temperature.

Tags: barrier-fabric, compartment-fire, flashover, flexible-polyurethane-foam, residential-upholstered-furniture
Organization: National Institute of Standards and Technology
2022 Cartographic Boundary File (KML), Current Block Group for Connecticut, 1:500,000

The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas.

The generalized BG boundaries in this release are based on those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Tags: 2022, block group, cartographic boundary, census tract, connecticut, county, ct, generalized, kml
Organization: U.S. Census Bureau, Department of Commerce
2022 Cartographic Boundary File (SHP), Current Unified School District for Kansas, 1:500,000

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

School Districts are single-purpose administrative units within which local officials provide public educational services for the area's residents. The Census Bureau obtains the boundaries, names, local education agency codes, grade ranges, and school district levels for school districts from state officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children in poverty within each school district. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districts.

The cartographic boundary files include separate files for elementary, secondary and unified school districts. The generalized school district boundaries in this file are based on those in effect for the 2021-2022 school year, i.e., in operation as of January 1, 2022.

Tags: 2022, cartographic boundary, generalized, kansas, ks, school district, shp, unified school district
Organization: U.S. Census Bureau, Department of Commerce
NOAA/WDS Paleoclimatology - Cook - Reviere du Moulin - TSCA - ITRDB CANA035

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Quebec, Canada. The time period coverage is from 426 to -32 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Tags: biological records, canada, climate indicators, continent, doc/noaa/nesdis/ncei, earth science, eastern hemlock, land records, latitude 46.63, longitude -71.88, material: null, national centers for environmental information, nesdis, noaa, north america, paleoclimate, paleoclimate indicators, quebec, reviere du moulin, tree ring, tree rings, tree-ring, tsca, tsuga canadensis (l.) carr., u.s. department of commerce, what: age, what: number of samples, what: total ring width, what: tree ring standardized growth index, width
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
2022 Cartographic Boundary File (KML), Current Place for Idaho, 1:500,000

The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

The cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population.

The generalized boundaries of most incorporated places in this file are based on those as of January 1, 2022, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CDPs are based on those delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Tags: 2022, cartographic boundary, generalized, id, idaho, kml, place
Organization: U.S. Census Bureau, Department of Commerce
2022 Cartographic Boundary File (SHP), Current Tribal Census Tract for United States, 1:500,000

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

A tribal census tract is a relatively permanent statistical subdivision of a federally recognized American Indian reservation and/or off-reservation trust land, delineated by the American Indian tribal government and/or the Census Bureau for the purpose of presenting demographic data. For the 2010 Census and beyond, tribal census tracts are defined independently of the standard county-based census tract delineation. For federally recognized American Indian Tribes with reservations and/or off-reservation trust lands with a population less than 2,400, a single tribal census tract is defined. Qualifying areas with a population greater than 2,400 could define additional tribal census tracts within their area. The tribal census tract codes for the 2020 Census are six characters long with a leading "T" alphabetic character followed by a five-digit numeric code, for example, T01000, which translates as tribal census tract 10. Tribal block groups nest within tribal census tracts. Since individual tabulation blocks are defined within the standard State-county-census tract geographic hierarchy, a tribal census tract can contain seemingly duplicate block numbers, thus tribal census tracts cannot be used to uniquely identify census tabulation blocks for the 2020 Census. 

The generalized boundaries of tribal census tracts are based on those delineated through the Participant Statistical Areas Program (PSAP) for the 2020 Census.

Tags: 2022, cartographic boundary, generalized, shp, united states of america (the), us
Organization: U.S. Census Bureau, Department of Commerce
2022 Cartographic Boundary File (SHP), Current State Legislative District-Upper Chamber for South Dakota, 1:500,000

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

State Legislative Districts (SLDs) are the areas from which members are elected to state legislatures. The SLDs embody the upper (senate) and lower (house) chambers of the state legislature. Nebraska has a unicameral legislature and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for the purpose of data presentation; there are no data by SLDL for either Nebraska or the District of Columbia. A unique three-character census code, identified by state participants, is assigned to each SLD within a state. In Connecticut, Illinois, Louisiana, New Hampshire, Wisconsin, and Puerto Rico, the Redistricting Data Program (RDP) participant did not define the SLDs to cover all of the state or state equivalent area. In these areas with no SLDs defined, the code "ZZZ" has been assigned, which is treated as a single SLD for purposes of data presentation.

The generarlized boundaries in this file are based on the most recent state legislative district boundaries collected by the Census Bureau for the 2022 election year and provided by state-level participants through the RDP.

Tags: 2022, cartographic boundary, generalized, sd, shp, south dakota, state legislative district, upper chamber
Organization: U.S. Census Bureau, Department of Commerce
2020 Cartographic Boundary File (KML), Current State Legislative District-Lower Chamber for New Jersey, 1:500,000

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

State Legislative Districts (SLDs) are the areas from which members are elected to State legislatures. The SLDs embody the upper (senate) and lower (house) chambers of the state legislature. Nebraska has a unicameral legislature and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for the purpose of data presentation; there are no data by SLDL for either Nebraska or the District of Columbia. A unique three-character census code, identified by State participants, is assigned to each SLD within a state. In Connecticut, Illinois, Louisiana, Maine, Maryland, Massachusetts, Michigan, Ohio, and Puerto Rico, the Redistricting Data Program (RDP) participant did not define the SLDs to cover all of the state or state equivalent area. In these areas with no SLDs defined, the code "ZZZ" has been assigned, which is treated as a single SLD for purposes of data presentation. 

The generalized boundaries in this file are based on the most recent state legislative district boundaries collected by the Census Bureau for the 2018 election year and provided by state-level participants through the RDP.

Tags: 2020, cartographic boundary, generalized, kml, lower chamber, lower district, new jersey, nj, state legislative district
Organization: U.S. Census Bureau, Department of Commerce
2020 Cartographic Boundary File (KML), 2020 Public Use Microdata Areas for Idaho, 1:500,000

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

Public Use Microdata Areas (PUMAs) are decennial census areas that permit the tabulation and dissemination of Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) data, and data from other census and surveys. For the 2020 Census, the State Data Centers (SDCs) in each state, the District of Columbia, and the Commonwealth of Puerto Rico had the opportunity to delineate PUMAS within their state or statistically equivalent entity. All PUMAs must nest within states and have a minimum population threshold of 100,000 persons. 2020 PUMAs consist of census tracts and cover the entirety of the United States, Puerto Rico and Guam. American Samoa, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands do not contain any 2020 PUMAs because the population is less than the minimum population requirement. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name.

Tags: 2020, cartographic boundary, generalized, id, idaho, kml, public use microdata area
Organization: U.S. Census Bureau, Department of Commerce