An incomplete collection of open data domains throughout the U.S. (intended for comparison with King County open data)

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

This asset is a derived view based on the system dataset 'Site Analytics: Asset Inventory' which is automatically generated by the data management platform and provides a comprehensive inventory of all assets on this site. This asset has been filtered to present an overview of the various types of data that are classified as public and have been published on the City of Austin Open Data Portal (data.austintexas.gov) by departmental data owners.

The columns of the Asset Inventory dataset contain information about every asset. These include metadata fields (e.g., Name, Description, and Category), as well as statistics, such as the number of visits, row count, column count, and downloads. This asset is updated at least once per day to sync any changes, additional assets, or removed assets.

City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

Chesapeake Bay Watershed Outline. Defines extent of the Chesapeake Bay watershed within the state of Maryland.

Link to the Open Data site for the United States Census Bureau.

DO NOT DELETE OR MODIFY THIS ITEM. This application helps run the ArcGIS Hub Page that the Open Data Portal is on. This item is managed by the Open Data application. To make changes to this site, please visit https://data-syr.opendata.arcgis.com/

Services and products

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

support associations

This page provides starting points for any user to learn more about what open data is, why it matters and how to use it.

A Polling District is a geographical subdivision of an electoral area such as an electoral Ward within which a polling place is designated.

Added new dataset OpenDataLog. The dataset stores detailed information regarding issues with the open data portal, new or changes to datasets on the portal as well as other information related to the City's Open Data Portal

Displacement risk indicator showing the distribution of renter households and renter units between different income brackets, covering the entire city from 2006 to the most recent year of data available.

This is a graphical point dataset depicting Addresses of San Antonio, Texas.

Energy Policy in Miyahuna

Fire, its components and extinguishing

2018-2023 Statistics on the work of the Legal Department

List and description of datasets available on Open Data for Fairfax County, Virginia

Statistics in the buildings of the Ministry of Justice

The dataset consists of particle jets extracted from simulated proton-proton collision events at a center-of-mass energy of 13 TeV generated with Pythia 8. The particles emerging from the collisions traverse through a simulation of the CMS detector. The particles were reconstructed from the simulated detector signals using the particle-flow (PF) algorithm. The reconstructed particles are also called PF candidates. The jets in this dataset were clustered from the PF candidates of each collision event using the anti-$k_t$ algorithm with distance parameter $R = 0.4$. The standard L1+L2+L3+residual jet energy corrections are applied to the jets and pileup contamination is mitigated using the charged hadron subtraction (CHS) algorithm.

From each collision event, only those jets with transverse momentum exceeding 30 GeV were saved to file. The jets were also required to have pseudorapidity of less than 2.5 (this indicates the jet's position in the detector). For each jet, there are variables describing the jet on a high-level, particle-level and generator-level. There are also some variables describing the collision event and the conditions of its simulation. All of the variables are saved on a jet-by-jet basis, which means that one row of data corresponds to one jet.

The origin of a jet is particularly interesting. This so-called flavor of the jet is obtained from the generator-level particles by a jet flavor algorithm, which attempts to match a reconstructed jet to a single initiating particle. As a consequence, the jet flavor definition depends on the chosen algorithm. Here three different flavor definitions are available. The ‘hadron’ definition identifies b- and c-hadrons from the jet’s constituents, so it is only useful for b-tagging studies. The ‘parton’ definition extends this to include the light jet flavors (u, d, s and gluon). Finally there is the ‘physics’ definition, which looks at the quarks and gluons of the initial collision. The ‘parton’ and ‘physics’ definitions both identify all jet flavors, but the former is more biased towards b- and c-quarks. If in doubt, it is recommended to use the ‘physics’ definition.