AndreaBozzo/ceres-open-data-index huggingface dataset:
TIGER/Line Shapefile, 2021, County, Marion County, MS, All Roads

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).  The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S".  This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, stairways, and winter trails.


Tags: 28, 28091, address range, county or equivalent entity, linear feature, marion, mississippi, ms, road feature, roads, state or equivalent entity, street centerline, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2021, State, Michigan, 2010 Public Use Microdata Areas (PUMA)

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).  The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

After each decennial census, the Census Bureau delineates Public Use Microdata Areas (PUMAs) for the tabulation and dissemination of decennial census Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) PUMS data, and ACS period estimates. Nesting within states, or equivalent entities, PUMAs cover the entirety of the United States, Puerto Rico, Guam, and the U.S. Virgin Islands. PUMA delineations are subject to population, building block geography, geographic nesting, and contiguity criteria. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name. 


Tags: 26, mi, michigan, polygon, public use microdata area, puma, state or equivalent entity, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2021, County, Hardin County, TX, All Roads

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).  The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S".  This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, stairways, and winter trails.


Tags: 48, 48199, address range, county or equivalent entity, hardin, linear feature, road feature, roads, state or equivalent entity, street centerline, texas, tx, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2021, State, New Mexico, 2010 Public Use Microdata Areas (PUMA)

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).  The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

After each decennial census, the Census Bureau delineates Public Use Microdata Areas (PUMAs) for the tabulation and dissemination of decennial census Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) PUMS data, and ACS period estimates. Nesting within states, or equivalent entities, PUMAs cover the entirety of the United States, Puerto Rico, Guam, and the U.S. Virgin Islands. PUMA delineations are subject to population, building block geography, geographic nesting, and contiguity criteria. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name. 


Tags: 35, new mexico, nm, polygon, public use microdata area, puma, state or equivalent entity, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2021, County, Elmore County, ID, All Roads

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).  The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S".  This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, stairways, and winter trails.


Tags: 16, 16039, address range, county or equivalent entity, elmore, id, idaho, linear feature, road feature, roads, state or equivalent entity, street centerline, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2019, Series Information for the All Lines County-based Shapefile

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected
                geographic and cartographic information from the U.S. Census Bureau's Master
                Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database
                (MTDB).  The MTDB represents a seamless national file with no overlaps or gaps between parts,
                however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they
                can be combined to cover the entire nation.
                
                
                Edge refers to the linear topological primitives that make up MTDB.  The All Lines Shapefile contains
                linear features such as roads, railroads, and hydrography.  Additional attribute data associated with
                the linear features found in the All Lines Shapefile are available in relationship (.dbf) files that
                users must download separately.  The All Lines Shapefile contains the geometry and attributes of each
                topological primitive edge.  Each edge has a unique TIGER/Line identifier (TLID) value.  
            

Tags: address range, county or equivalent entity, feature, hydrography, linear, railways, road feature, roads, street centerline, u.s, united states
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2019, Series Information for the Address Ranges County-based Relationship File

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's
                Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).  The MTDB represents a seamless national file with no
                overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire
                nation.
                
                The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship.  The purpose of this
                relationship file is to identify all street names associated with each address range.  An edge can have several feature names; an address range located on an edge
                can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names).  The address range is
                identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf).  The linear feature name is
                identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

Tags: 5-digit zip code, county fips code, county gnis code, from house number, permanent edge id, side indicator flag, state fips code, state gnis code, to house number, u.s, united states, zip +4 code
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2021, County, Monroe County, AL, All Roads

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).  The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S".  This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, stairways, and winter trails.


Tags: 01, 01099, address range, al, alabama, county or equivalent entity, linear feature, monroe, road feature, roads, state or equivalent entity, street centerline, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile,  2016, Series Information for the   Topological Faces-Area Landmark State-based Relationship File

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the
                                 U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).
                                 The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to
                                 stand alone as an independent data set, or they can be combined to cover the entire nation.

                                 The Topological Faces / Area Landmark Relationship File (FACESAL.dbf) contains a record for each face / area landmark relationship.
                                 Face refers to the areal (polygon) topological primitives that make up MTDB.  A face is bounded by one or more edges; its boundary
                                 includes only the edges that separate it from other faces, not any interior edges contained within the area of the face.  The face to
                                 which a record in the Topological Faces / Area Landmark Relationship File (FACESAL.dbf) applies can be determined by linking to the
                                 Topological Faces Shapefile (FACES.shp) on the permanent topological face identifier (TFID) attribute.  The area landmark to which a
                                 record in the Topological Faces / Area Landmark Relationship File (FACESAL.dbf) applies can be determined by linking to the Area
                                 Landmark Shapefile (AREALM.shp) on the area landmark identifier (AREAID) attribute.  A face may be part of multiple area landmarks.
                                 An area landmark may consist of multiple faces.
            

Tags: area landmark identifier, landmark, permanent face id, state fips code, state gnis code, state or equivalent entity, table, topological faces, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce
Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

The original contributions presented in the study are included in the article and online through the TAME Toolkit, available at: https://uncsrp.github.io/Data-Analysis-Training-Modules/, with underlying code and datasets available in the parent UNC-SRP GitHub website (https://github.com/UNCSRP). 

This dataset is associated with the following publication:
Roell, K., L. Koval, R. Boyles, G. Patlewicz, C. Ring, C. Rider, C. Ward-Caviness, D. Reif, I. Jaspers, R. Fry, and J. Rager. Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.   Frontiers in Toxicology. Frontiers, Lausanne,  SWITZERLAND, 4: 893924, (2022).

Tags: cheminformatics, data-science, exposure-science, public-health, read-across
Organization: U.S. Environmental Protection Agency
US-PROPS: Regional Application to Great Smoky Mountains National Park (McDonnell et al. 2022)

This dataset is the data and metadata associated with the application of the US-PROPS model to the Great Smoky Mountains National Park (GSMNP). It is a geodatabase of species and vegetation classes across the GSMNP and describes the distribution of species, critical loads of different vegetation classes, and the changes in the occurrence probabilities for different scenarios explored. Users should start with the "Dataset_Descriptions.docx" file, and read McDonnell et al. (2022) for context (https://www.sciencedirect.com/science/article/pii/S2666765722001065). 

McDonnell, T.C., Clark, C.M., Reinds, G.J., Sullivan, T.J. and Knees, B., 2022. Modeled vegetation community trajectories: Effects from climate change, atmospheric nitrogen deposition, and soil acidification recovery. Environmental Advances, p.100271. 

This dataset is associated with the following publication:
McDonnell, T., C. Clark, G. Reinds, T. Sullivan, and B. Knees. Modeled vegetation community trajectories: Effects from climate change, atmospheric nitrogen deposition, and soil acidification recovery.   Environmental Advances. Elsevier B.V., Amsterdam,  NETHERLANDS, 9: 1-13, (2022).

Tags: atmospheric-deposition, biodiversity, climate-change, critical-loads, naaqs, nitrogen-and-co-pollutants
Organization: U.S. Environmental Protection Agency
Investigation of chloramines, DBPs, and nitrification in chloraminated drinking water distribution systems

The data provided was used to develop the figures presented in the manuscript. 

This dataset is associated with the following publication:
Abulikemu, G., J. Mistry, D. Wahman, M. Alexander, A. Kennicutt, J. Bollman, and J. Pressman. Investigation of Chloramines, Disinfection Byproducts, and Nitrification in Chloraminated Drinking Water Distribution Systems.   JOURNAL OF ENVIRONMENTAL ENGINEERING. American Society of Civil Engineers  (ASCE), Reston, VA, USA, 149(1): 04022081, (2023).

Tags: chloramine, disinfectant-residual, rare
Organization: U.S. Environmental Protection Agency
Shoemaker_AOF_CIC_Data_Version_12_07_21_AnalyticalMethods

Data set to support journal article published in Analytical Methods that will further the science surrounding the use of AOF-CIC for detection of PFAS as total adsorbable fluorine and support the draft method delivered to OW in September 2021. This manuscript demonstrates that this AOF technique can be a useful PFAS analytical tool in the balancing act between selectivity, sensitivity, and inclusivity. 

This dataset is associated with the following publication:
Jones, J., S. Burket, A. Hanley, and J. Shoemaker. Development of a standardized adsorbable organofluorine screening method for wastewaters with detection by combustion ion chromatography.   Analytical Methods. RSC Publishing, Cambridge,  UK, 14(36): 3501-3511, (2022).

Tags: adsorbable-organofluorine, combustion-ion-chromatography, per-and-polyfluoroalkyl-substances-pfas, wastewater
Organization: U.S. Environmental Protection Agency
UV_dataset

Additional dataset for
Buse HY, Hall JS, Hunter GL, Goodrich JA. Differences in UV-C LED Inactivation of Legionella pneumophila Serogroups in Drinking Water. Microorganisms. 2022; 10(2):352. https://doi.org/10.3390/microorganisms10020352. 

This dataset is associated with the following publication:
Buse, H., J. Hall, G. Hunter, and J. Goodrich. Differences in UV-C LED Inactivation of Legionella pneumophila Serogroups in Drinking Water.   Microorganisms. MDPI, Basel,  SWITZERLAND, 10(2): 352, (2022).

Tags: decontamination, drinking-water-treatment, microbial-contaminants, uvc-led-disinfection
Organization: U.S. Environmental Protection Agency
PFAS and multimorbidity among a random sample of patients from the University of North Carolina Healthcare System

This dataset contains electronic health records used to study associations between PFAS occurrence and multimorbidity in a random sample of UNC Healthcare system patients. The dataset contains the medical record number to uniquely identify each individual as well as information on PFAS occurrence at the zip code level, the zip code of residence for each individual, chronic disease diagnoses, patient demographics, and neighborhood socioeconomic information from the 2010 US Census. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Because this data has PII from electronic health records the data can only be accessed with an approved IRB application. Project analytic code is available at L:/PRIV/EPHD_CRB/Cavin/CARES/Project Analytic Code/Cavin Ward/PFAS Chronic Disease and Multimorbidity. Format: This data is formatted as a R dataframe and associated comma-delimited flat text file. The data has the medical record number to uniquely identify each individual (which also serves as the primary key for the dataset), as well as information on the occurrence of PFAS contamination at the zip code level, socioeconomic data at the census tract level from the 2010 US Census, demographics, and the presence of chronic disease as well as multimorbidity (the presence of two or more chronic diseases). 

This dataset is associated with the following publication:
Ward-Caviness, C., J. Moyer, A. Weaver, R. Devlin, and D. Diazsanchez. Associations between PFAS occurrence and multimorbidity as observed in an electronic health record cohort.   Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn,  NETHERLANDS, 6(4): p e217, (2022).

Tags: chronic-disease, electronic-health-records, multimorbidity, per-and-polyfluoroalkyl-substances-pfas
Organization: U.S. Environmental Protection Agency
Synthetic Temperature Data for Predicting Flashover Occurrence Using Surrogate Temperature Data

This data set provides heat detector temperatures in a single-story ranch structure with a living room, kitchen, dining room, and three bedrooms. 20000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various fire conditions and door opening conditions. The fire is described based on t-squared law. The peak heat release rate and time to peak range from approximately 1667 kW to 4620 kW and from 210 s to 1540 s, respectively.  A detailed description of this work can be found in Ref. [2].[1] Reneke, P.A., Peacock, R.D., Gilbert, S.W. and Cleary, T.G., 2021. CFAST Consolidated Fire and Smoke Transport (Version 7) Volume 5: CFAST Fire Data Generator (CData). NIST Technical Note 1889v5. Gaithersburg, MD.[2] Fu, E.Y., Tam, W.C., Wang, J., Peacock, R., Reneke, P., Ngai, G., Leong, H.V. and Cleary, T., 2021, May. Predicting Flashover Occurrence using Surrogate Temperature Data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14785-14794).

Tags: flashover-prediction-recurrent-neural-network-validation-with-experimental-data-smart-fire-figh
Organization: National Institute of Standards and Technology
USFS Dinkey Creek Lidar, CA 2012

Lidar was collected over Dinkey Creek from November 21st - 27th 2012 for the USFS Pacific Region. Data were collected to aid in identifying ecological restoration opportunities by assessing forest structural complexity, mapping geomorphic features, and quantifying acreages of highly suitable wildlife habitat. This dataset covers over 61,000 acres ( over 200 km2)

Organization: OpenTopography
H12049: NOS Hydrographic Survey , Louisiana Coast, Louisiana, 2009-08-23

The National Oceanic and Atmospheric Administration (NOAA) has the statutory mandate to collect hydrographic data in support of nautical chart compilation for safe navigation and to provide background data for engineers, scientific, and other commercial and industrial activities. Hydrographic survey data primarily consist of water depths, but may also include features (e.g. rocks, wrecks), navigation aids, shoreline identification, and bottom type information.
							NOAA is responsible for archiving and distributing the source data as described in this metadata record.

Tags: acoustic sounders, bathymetry, bathymetry/seafloor topography, continent, doc/noaa/nesdis/ncei, doc/noaa/nesdis/ngdc, earth remote sensing instruments, earth science, entrance to timbalier bay, global positioning system/inertial measurement unit, gps, gps receivers, gps/imu, gulf of mexico, hydrographic surveys for selected locations within the united states (hydro_bathy_2006), in situ ocean-based platforms, in situ/laboratory instruments, louisiana, marine sediments, mbes, msbs, multibeam mapping system, multibeam swath bathymetry system, national centers for environmental information, national geophysical data center, nesdis, noaa, north america, ocean, oceans, passive remote sensing, positioning/navigation, profilers/sounders, seafloor topography, sediment composition, ships, sonar, sound navigation and ranging, u.s. department of commerce, united states of america, water depth
Organization: National Oceanic and Atmospheric Administration, Department of Commerce