The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1".Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2".This dataset contains OSM and IDF zip files for every U.S. county. Each zip file contains the generated buildings from that county.The .csv input data contains the following data fields:1. ID - unique building ID2. Centroid - building center location in latitude/longitude (from Footprint2D)3. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...)4. State_abbr - state name5. Area - estimate of total conditioned floor area (ft2)6. Area2D - footprint area (ft2)7. Height - building height (ft)8. NumFloors - number of floors (above-grade)9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)10. CZ - ASHRAE Climate Zone designation11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards12. Standard - building vintageThis data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA).

Knowing who your consumers are is essential for businesses, marketers, and researchers. This detailed demographic file offers an in-depth look at American consumers, packed with insights about personal details, household information, financial status, and lifestyle choices. Let's take a closer look at the data:

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Lifestyle and Interests One of the most useful features of the dataset is its extensive lifestyle segmentation:

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Consumer Behavior and Purchase Habits The file also sheds light on how consumers behave and shop:

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Demographic Clusters and Segmentation Pre-built segments like:

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Ethnicity and Language Preferences In today's multicultural market, knowing your audience's cultural background is key. The file includes:

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Education and Occupation Data The dataset also tracks education and career info:

Education level and occupation codes Home-based business indicators This data is essential for B2B marketers, recruitment agencies, and education-focused campaigns.

Digital and Social Media Habits With everyone online, digital behavior insights are a must:

Internet, TV, radio, and magazine usage Social media platform engagement (Facebook, Instagram, LinkedIn) Streaming subscriptions (Netflix, Hulu) This data helps marketers, app developers, and social media managers connect with their audience in the digital space.

Political and Charitable Tendencies For political campaigns or non-profits, this dataset offers:

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Neighborhood Characteristics By incorporating census data, the file provides a bigger picture of the consumer's environment:

Population density, racial composition, and age distribution Housing occupancy and ownership rates This offers important context for understanding the demographic landscape.

Predictive Consumer Indexes The dataset includes forward-looking indicators in categories like:

Fashion, automotive, and beauty products Health, home decor, pet products, sports, and travel These predictive insights help businesses anticipate consumer trends and needs.

Contact Information Finally, the file includes ke...

he dataset used for this experiment consists of structured data where each row represents an individual Airbnb listing from the United States. The dataset contains approximately 50,000 rows and 15 columns, capturing detailed information about various Airbnb properties across different locations. Each row corresponds to a unique listing and includes features such as listing_id, host_id, city, property_type, room_type, price, number_of_reviews, and additional attributes that can potentially influence the listing price. The main objective of this experiment is to predict the listing price, which is a numeric and continuous variable, based on the provided input features. By utilizing various machine learning regression techniques, such as Random Forest Regressor or XGBoost, the goal is to model the relationships between the property features and the final listing price accurately. Preprocessing steps including handling missing values, encoding categorical variables, and outlier removal will be applied to ensure high data quality. The predictive models will be evaluated based on metrics such as Mean Squared Error (MSE) and R-squared (R²), ensuring robust and interpretable results.

This dataset provides annual numbers for each state in the United States for 2013-2018. Includes the following data: total population, median income, and number of people living at or below the poverty level.

Helpful information on using U.S. Census data is found at https://censusreporter.org/

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GreatShoes PII Dataset

The GreatShoes PII Dataset is a synthetic dataset created for research purposes to evaluate models for Personal Identifiable Information (PII) redaction. This dataset is generated to simulate customer support interactions for a fictional shoe store, "Great Shoes," and includes multiple types of PII such as names, order numbers, phone numbers, addresses, and emails.

  Dataset Description

The dataset consists of customer and support agent… See the full description on the dataset page: https://huggingface.co/datasets/UlrikKoren/GreatShoes_PII_Dataset.

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This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia. Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day. Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily. All identifying health information is excluded from the dataset.

This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this dataset, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We sampled airfoil designs over both parameter spaces to explore the full range of possible shape variations. The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, for or three different Reynolds' numbers (3M, 6M, and 9M), and for 25 angles of attack from -4 deg. to 20 deg. with 1 degree increments. Across all these various parameters, this dataset includes the results from over 250,000 CFD simulations. The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy, in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_2k_data.ipynb.

This data set contains username, proper name, office location, telephone number, organizational and various account details such as group membership and profile information for DOT employees, contractors, and guests that have access to the USDOT network. The data set does not include users that are part of the FAA, OIG, or STB.

This dataset fulfills a request from multiple regional units as an operational aid that provides an authoritative companion to the Land Areas Report sliced along multiple administrative and political boundaries. This dataset powers a dynamic and interactive dashboard called Forests by the Numbers. The Forests by the Numbers series is expected to continue with other assets (roads, waterways, recreation areas, etc) to act as a quick reference for internal operations and the public.
This dataset covers National Forest System Lands including federally owned units of forest, range, and related land consisting of national forests, purchase units, national grasslands, land utilization project areas, experimental forest areas, experimental range areas, designated experimental areas, other land areas, water areas, and interests in lands that are administered by the U.S. Department of Agriculture (USDA) Forest Service or designated for administration through the Forest Service. Each polygon is attributed with ownership (USDA Forest Service or Non-FS), wilderness status, Forest Service Administrative jurisdiction and geopolitical membership.

CEQR Open Data contains information on projects that are undergoing or have completed review through the City Environmental Quality Review (CEQR) process. Project information available at the Open Data Portal includes the CEQR Number, Project Name, the Project Description, the Lead Agency, project milestones, and geographical locations. CEQR Open Data contains information on CEQR projects, which were filed with the Mayor’s Office from January 1, 2005 to the present. For associated documents, please follow the links to the CEQR Access Database.

Vermont E911 Site locations (ESITEs) including buildings, facilities, and development sites; locations are represented by points. Points are attributed with addresses--composing an address points layer. Dataset is updated weekly.Field Descriptions:OBJECTID: Internal feature number. Auto-generated by Esri software.SEGMENTID: Unique segment ID.ESITEID: Unique ESITE ID.GEONAMEID: Ties ESITE to GEONAMEID (unique ID for each road name) in VT E911 Road Centerlines.PD: Prefix Direction, previously name PRE.DIR.PT: Prefix Type.SN: Street Name. Previously named STREET.ST: Street Type.SD: Suffix Direction, i.e., W for West, E for East etc.PRIMARYNAME: A concatenation of the street-name parts (PD, PT, SN, ST, SD).ALIAS1: Alternate road name.ALIAS2: Alternate road name.ALIAS3: Alternate road name.ALIAS4: Alternate road name.ALIAS5: Alternate road name.PRIMARYADDRESS: A concatenation of house number and street-name parts (PD, PT, SN, ST, SD).SITETYPE: Type of site. Uses SiteTypes domain*.TOWNNAME: Town name.MCODE: Municpal code.ESN: Emergency Service Number. Developed for each town that indicates a unique town code for each law, fire, and EMS provider. These providers are compared against the master list to determine if they are already present. If they are, the existing state code is used. If the provider is new, they are added to the state master list with the next unique provider number.ZIP: Zip code.PARCELNUM: Parcel number.GPSX: GPS X coordinate.GPSY: GPS Y coordinate.MAPYEAR: Date added to E911 data.UPDATEDATE: Update date.STATE: US State.FIPS8: Federal information processing standards codes.SPAN: Pulled from the VCGI parcel dataset via spatial join 1-3 times per year; NOT MAINTAINED DAILY.SUBTYPE: Field not in use.GlobalID_1: System-generated ID.UNITCOUNT: For commercial and residential, number of units in the site.PRIMARYADD1: Concatenation of house number, full street name, and E911 town. E911 TOWN (AKA E911 JBOUND) IS NOT ALWAYS THE SAME AS POSTAL TOWN NOR IS IT ALWAYS THE SAME AS TOWN DEFINED BY MUNICIPAL BOUNDARY. E911 TOWN (E911 JBOUND) was originally defined for the Master Street Address Guide (MSAG) Community; E911 JBOUND contains names chosen by towns for representing town names for 911 purposes.PRIMARYADD2: Concatenation of PRIMARYADD1 plus zip code.SITETYPE_MULTI1: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI2: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI3: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI4: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.SITETYPE_MULTI5: Additional SITETYPE--if applicable. For development sites, contains the main use the site is to become. Uses SiteTypes domain*.COUNTY: County.COUNTRY: Country.SOURCEOFDATA: Source of data.DRIVEWAYID: Field not in use.ESZ: Emergency Service Zone--a defined area covered by four primary-response agencies.HOUSE_NUMBER: House number.HOUSE_NUMBERSUFFIX: For addresses not in compliance with standards (typically in urbanized areas where otherwise renumbering needs to occur). For example, a new house between 8 and 10 is built and the town calls it 8 1/2 or 8A instead of renumbering; the 1/2 or A would be in this field; there are approximately less than 300-400 of these cases.HOUSE_NUMBERPREFIX: For the three streets where alpha characters come before the house number (e.g., A20 or B12).FIPS: County FIPS number.Shape: Feature geometry.*SiteTypes Domain:ABANDONEDACCESS POINTACCESSORY BUILDINGAIR SUPPORT / MAINTENANCE FACILITYAIR TRAFFIC CONTROL CENTER / COMMAND CENTERAIRPORT TERMINALAMBULANCE SERVICEAUDITORIUM / CONCERT HALL / THEATER / OPERA HOUSEBANKBOAT RAMP / DOCKBORDER CROSSINGBORDER PATROLBUS STATION / DISPATCH FACILITYCAMPCAMPGROUNDCEMETERYCITY / TOWN HALLCOAST GUARDCOLLEGE / UNIVERSITYCOMMERCIALCOMMERCIAL CONSTRUCTION SERVICECOMMERCIAL FARMCOMMERCIAL GARAGECOMMERCIAL W/RESIDENCECOMMUNICATION BOXCOMMUNICATION TOWERCOMMUNITY / RECREATION FACILITYCOURT HOUSECULTURALCUSTOMS SERVICEDAY CARE FACILITYDEVELOPMENT SITEEBS TOWEREDUCATIONALEMERGENCY PHONE / CALLBOXFAIR / EXHIBITION/ RODEO GROUNDSFERRY TERMINAL / DISPATCH FACILITYFIRE STATIONFISH FARM / HATCHERYFITNESS FACILITYFOOD DISTRIBUTION CENTERGAS STATIONGATED W/BUILDINGGATED W/O BUILDINGGOLF COURSEGOVERNMENTGRAVEL PITGREENHOUSE / NURSERYGROCERY STOREHARBOR / MARINAHAZARDOUS MATERIALS FACILITYHAZARDOUS STORAGE FACILITYHEALTH CLINICHELIPAD / HELIPORT / HELISPOTHISTORIC SITE / POINT OF INTERESTHOSPITAL / MEDICAL CENTERHOUSE OF WORSHIPHYDROELECTRIC FACILITYICE ARENAINDUSTRIALINSTITUTIONAL RESIDENCE / DORM / BARRACKSLANDFILLLAW ENFORCEMENTLIBRARYLODGINGLOOKOUT TOWERLUMBER MILL / SAW MILLMANUFACTURING FACILITYMINEMOBILE HOMEMORGUEMULTI-FAMILY DWELLINGMUSEUMNATIONAL GUARD / ARMORYNUCLEAR FACILITYNURSING HOME / LONG TERM CAREOFFICE BUILDINGOFFICE OF EMERGENCY MANAGEMENTOIL / GAS FACILITYOTHEROTHER COMMERCIALOTHER RESIDENTIALOUTPATIENT CLINICPARK AND RIDE / COMMUTER LOTPHARMACYPICNIC AREAPOST OFFICEPRISON / CORRECTIONAL FACILITYPRIVATE AND EXPRESS SHIPPING FACILITYPSAPPUBLIC BEACHPUBLIC GATHERINGPUBLIC TELEPHONEPUBLIC WATER SUPPLY INTAKEPUBLIC WATER SUPPLY WELLPUMP STATIONRACE TRACK / DRAGSTRIPRADIO / TV BROADCAST FACILITYRAILROAD STATIONRESIDENTIAL FARMREST STOP / ROADSIDE PARKRESTAURANTRETAIL FACILITYRV HOOKUPSCHOOLSEASONAL HOMESINGLE FAMILY DWELLINGSKI AREA / ALPINE RESORTSOLAR FACILITYSPORTS ARENA / STADIUMSTATE CAPITOLSTATE GARAGESTATE GOVERNMENT FACILITYSTATE PARKSTORAGE UNITSSUBSTATIONSUGARHOUSETEMPORARY STRUCTURETOWN GARAGETOWN OFFICETRAILHEADTRANSFER STATIONUNKNOWNUS FOREST FACILITYUS GOVERNMENT FACILITYUTILITYUTILITY POLE W/PHONEVETERINARY HOSPITAL / CLINICVISITOR / INFORMATION CENTERWAREHOUSEWASTE / BIOMASS FACILITYWASTEWATER TREATMENT PLANTWATER TANKWATER TOWERWIND FACILITY / WIND TOWERYOUTH CAMP

OPT does not operate any buses nor does it directly employ any bus drivers or attendants. Drivers and attendants (sometimes referred to as ‘matrons’ or ‘escorts’) and employed by bus vendors themselves. OPT manages systems and processes to ensure that drivers and attendants have all requisite background checks and certifications. The Driver and Attendant summary data is available for each bus vendor and describes the number of active employees by job type.

Our lobbying firm dataset drills-down on more than 13,000 lobbying firms and other entities that have used in-house lobbyists from 1999-present.

Our lobbying data is collected and aggregated from the U.S. Senate Office of Public Records from 1999-present and is updated on a regular basis. We utilize advanced data science techniques to ensure accurate data points are collected and ingested, match similar entities across time, and tickerize publicly traded companies that lobby.

Our comprehensive and advanced lobbying database is completed with all the information you need, with more than 1.6 million lobbying contracts ready-for-analysis. We include detailed information on all aspects of federal lobbying, including the following fascinating attributes, among much more:

1. Clients: The publicly traded company, privately owned company, interest group, NGO, or state or local government that employs or retains a lobbyist or lobbying firm.

2. Registrants (Lobbying Firms): Either the name of the lobbying firm hired by the client, or the name of the client if the client employs in-house lobbyists.

3. Lobbyists: The names and past government work experience of the individual lobbyists working on a lobbying contract.

4. General Issues: The general issues for which clients lobby on (ex: ENV - Environment, TOB - Tobacco, FAM - Family Issues/Abortion).

5. Specific Issues: A long text description of the exact bills and specific issues for which clients lobby on.

6. Bills Lobbied On: A parsed version of Specific Issues that catches specific HR, PL, and ACTS lobbied on (ex: H.R. 2347, S. 1117, Tax Cuts and Jobs Act).

7. Agencies Lobbied: The names of one or more of 250+ government agencies lobbied on in the contract (ex: White House, FDA, DOD).

8. Foreign Entities: The names and origin countries of entities affiliated with the client (ex: BNP Paribas: France).

Using our intelligently designed & curated data quality, researchers can easily perform analysis by company, lobbyist, lobbying firm, government agency, or issue. We earnestly work with our customers to deliver this database in the methods or formats of their choosing, from: CSV, JSON, DTA, PKL, to other formats and methods. We're flexible.

Gain access to our highly unique and actionable U.S. lobbying database. Further information on LobbyingData.com and our alternative datasets and database can be found on our website, or by contacting us through Datarade.

We at Digital Science have been looking at the Data Citation Corpus, to dig deeper into data citation counts.The first release is based on a seed file that includes data citations from the following sources:Data citations from DataCite and Crossref DOI metadata, via Event Data.Data citations from the CZI Science Knowledge Graph, identified via a Named Entity Recognition model algorithm that searches for mentions to datasets in the full text of journal articles and preprints in Europe PMC.So we are basically looking at papers that have a link to a DataCite DOI or accession number.By combining this dataset with Dimensions.ai data in Google Big Query, we we're able to add more dimensions to the dataset (pardon the pun), such as funder or institution. The Data Citation Corpus only gave us about 70% of the paper links that were resolvable DOIs. This should improve over time.This allows us to track how well things like the NIH open data policy is encouraging linking to datasets from papers.

The data shared here are presented in: Knight, K.L.; Hou, G.; Bhaskar, A.S.; Chen, S. Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance. Water 2021, 13, 1563. https://doi.org/10.3390/w13111563

Summary:

I. INPUT FILES

Input data including: stormwater data, DEM, study area outline, service requests, recurring flood locations, precipitation data, and streamflow data. Project files including Pre GSI model, 4 GSI scenario models, and validation model. Pre- and post-processing scripts including: LID application spreadsheet, stormwater data correction, 1D and 2D output data processing. Includes description of labeling method for output data files. The coordinate system of all project files and output data: NAD83 Colorado Central State Plane (US feet)

Stormwater network data (storm manholes, storm inlets, storm sewer mains, streams, and storm water detention and water quality areas) was acquired from the City and County of Denver Open Data catalog (https://www.denvergov.org/opendata)

DEM data (1-meter and 3-meter resolution) was acquired from the National Elevation Dataset (NED) using the United States Geologic Survey (USGS) The National Map (TNM) Download Client (https://apps.nationalmap.gov/downloader/#/)

Study area outline and the bounding layer that delineates roadways from surrounding area are in NAD83 Colorado Central State Plane (US Feet).

Other landuse data (building outlines, impervious area, street centerlines) was acquired from the City and County of Denver Open Data catalog (https://www.denvergov.org/opendata).

Street polygons were produced from the street centerlines data and a buffer representing 1/2 the street width determined from the street centerline attributes of lane numbers and roadway type.

Citizen service requests and known areas of recurring flooding datasets are not publically available, for more information contact Dr. Aditi Bhaskar

Precipitation data was downloaded from USGS at 5 raingages. data files include date, time, and 5-minute precipitation data in inches.

Streamflow data was downloaded from USGS 06711575. Data files include date, time, and 5-minute streamflow data in cubic feet per second.

The LID inputs for each subcatchment utilized a single representative 'GSI unit' based on the design of a street planter bioswale from the City and County of Denver Ultra Urban Report. The LID input for each subcatchment for 1%, 2.5%, 3.5%, and 5% GSI scenarios are included in the table. There are no LIDs applied to the Pre GSI or Validation scenarios.

II. PCSWMM FILES

PCSWMM project files include the '.inp' file and the relevant project file folder that contains the input layers for each PCSWMM project. The name of the project file folder and the '.inp' file are the same and need to be located in the same folder to run simulations. Input layers in the project file folders can be edited and viewed in ArcMap as well, but it is not recommended to directly edit PCSWMM input layers in ArcMap. Rather, create a copy of the desired layer, edit in ArcMap, open the copy in PCSWMM, and update the PCSWMM input layer using the 'import GIS/CAD' tool.

III. MATLAB FILES

The raw stormwater network data from the City and County of Denver was filled and corrected using the methods summarized in Appendix A of the Thesis document. The purpose of this data pre-processing was to fill and correct the missing stormwater network data and convert all known data into the proper formatting for input into PCSWMM. All data is projected into NAD83 Colorado Central State Plane (US feet) coordinate system and clipped to the study boundary.

The hydrograph outputs from the above scenarios were processed using MATLAB. The output streamflow data for each scenario was compared to the observed hydrograph at USGS streamgage 06711575. Additionally, the calibration and validation model outputs were analyzed compared to the observed streamflow data including statistical analysis. All precipitation data is in inches; all streamflow data is in cubic feet per second.

IV. ROAD NETWORK

These are data used for the GIS road network in the traffic modeling by Guangyang Hou (guangyanghou1986@gmail.com).

As of 09/24/24, this dataset is being retired and will no longer be updated.

On 10/1/2021, VDH adjusted the Vaccine Age Group categories to better serve the response's needs. This resulted in a decrease in cases, hospitalizations, and deaths among the 16-17 Year age group and an addition of cases, hospitalizations, and deaths to the 18-24 Years age group.

This dataset includes the cumulative (total) number of COVID-19 cases, hospitalizations, and deaths for each health district in Virginia by report date and by age group. This dataset was first published on March 29, 2020. The data set increases in size daily and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon. When you download the data set, the dates will be sorted in ascending order, meaning that the earliest date will be at the top. To see data for the most recent date, please scroll down to the bottom of the data set. The Virginia Department of Health’s Thomas Jefferson Health District (TJHD) will be renamed to Blue Ridge Health District (BRHD), effective January 2021. More information about this change can be found here: https://www.vdh.virginia.gov/blue-ridge/name-change/

Eateries in in New York City Department of Parks & Recreation properties including snack bars, food carts, mobile food trucks, and restaurants.

This is a version of the 311 dataset used in the following paper:Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets, F. Chirigati, H. Doraiswamy, T. Damoulas, and J. Freire. In Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD), 2016The dataset includes records from 311, a telephone number that provides non-emergency services to New York City, from 2003 to 2014.The original data is available at the NYC Open Data portal: https://data.cityofnewyork.us/d/erm2-nwe9?category=Social-Services&view_name=311-Service-Requests-from-2010-to-Present