AndreaBozzo/ceres-open-data-index huggingface dataset:
Smart Streetlight RFI Responses

##Overview

This guide to the City of Pittsburgh Smart Streetlight Request for Information (RFI) was created to provide clarity and transparency to the ongoing Smart Streetlight project in the City of Pittsburgh. 

Pittsburgh has approximately 40,000 City-owned and operated streetlights, the vast majority of which are traditional high-pressure sodium HID luminaires controlled by simple photocell units. Due to high energy and maintenance costs, the City is developing a plan to replace the bulk of this system with remotely managed LED luminaires, with the goal being a potential energy savings of 60-80%. These savings can finance the conversion program through an Energy Savings Performance Contract. The City has previously piloted this approach in 2012, upgrading 4,500 luminaires through a State-funded energy savings program.  

The City recognizes that updated streetlights can offer a ready-made physical and communications platform for the sensors, controls and backhaul that power a Smart City. In the case of a Smart Streetlight deployment however, the problem is not pre-defined, so it is unclear what the City may actually need. This is why the City chose to release an RFI instead of the more common Request for Proposals (RFP), in order to understand the state of the industry, perform a market analysis and provide a learning experience for City personnel.  This RFI was designed to help the City understand the industry's perspective on these emerging technologies while providing a more efficient and equitable lighting system. 

The City of Pittsburgh Office of Management and Budget released the Smart Streetlights RFI on March 2nd, 2017 and closed it on April 10th, 2017.  The City received twenty-five responses from different businesses who offered a wide variety of services, products and concepts. All of the responses, as well as the RFI itself and the resulting questions and answers, are available to view.

##"Smart Cities"

The term "Smart City" refers to a range of technologies designed to sense and control various real-world phenomena, and software design and development projects to collect, present and administer those technologies' data and functions. 

##LEDs

LEDs have a higher efficiency in their conversion of electricity to light. LEDs also last much longer than sodium lamps. They also allow for fine-tuned control to maximize energy savings and performance.

Tags: led, procurement, request-for-information, rfi, smart, smart-cities, streetlight
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Allegheny County Human Services Community Profiles

The Allegheny County data warehouse was created to support integrated social services for people and to improve decision-making to create better systems to serve people in the County.  The warehouse is managed by the Allegheny County Department of Human Services (DHS).

This resource uses information from the warehouse and provides Municipality and Pittsburgh Neighborhood level information for specific community indicators and for individuals receiving specific DHS services.

Tags: _etl, human-services, programs, social-services
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Pittsburgh Police Firearm Seizures

This data includes counts of  firearms by type seized by the Pittsburgh Bureau of Police. Each row describes a seizure incident involving one or more firearms.
 
Firearm seizures can occur for several reasons. One of the Bureau of Police justifications for seizing a firearm involves a suspicion that a firearm was used in a criminal offense. Seizures may also occur to protect officer or public safety. For example, a weapon held by someone involved in a serious medical emergency, or an unattended  weapon found in a public space may be seized and held for safekeeping.
 
The Bureau’s firearms tracking unit conducts investigations to determine the rightful owner of firearms that have been seized. Where possible, the Bureau makes every effort to return firearms that have been seized to their rightful owners.
 
Firearms seized by non-City of Pittsburgh law enforcement agencies within the City of Pittsburgh may not be included in this data. Federal, State, County, and other special law enforcement agencies (e.g. campus, transit, hospital, etc.) operate within the City limits.
 
Data collected through supplemental reports filled out by Pittsburgh Bureau of Police officers upon taking possession of a firearm.
 
Locations where the seizure occurred  are anonymized to the address block or nearest intersection. It's not always possible to geocode these anonymized addresses.  In cases where geocoding fails, the latitude and longitude fields are left empty.  These will display as "None" in table views on the WPRDC site.
 
Exact date and report numbers have been removed to protect the privacy of individuals and any active investigations.

Tags: fire-arms, firearm, guns, pistol, pistols, public-safety, revolvers, rifles, seize, seizures, shotguns, weapons
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
City of Pittsburgh Traffic Count

This traffic-count data is provided by the City of Pittsburgh's Department of Mobility & Infrastructure (DOMI). Counters were deployed as part of traffic studies, including intersection studies, and studies covering where or whether to install speed humps. In some cases, data may have been collected by the Southwestern Pennsylvania Commission (SPC) or BikePGH.

Data is currently available for only the most-recent count at each location. 

Traffic count data is important to the process for deciding where to install speed humps. According to DOMI, they may only be legally installed on streets where traffic counts fall below a minimum threshhold. Residents can [request an evaluation](https://apps.pittsburghpa.gov/redtail/images/6554_application.pdf) of their street as part of DOMI's [Neighborhood Traffic Calming Program](https://pittsburghpa.gov/domi/traffic-calming). The City has also shared [data on the impact of the Neighborhood Traffic Calming Program](https://pittsburghpa.gov/domi/traffic-calming-data) in reducing speeds. 

Different studies may collect different data. Speed hump studies capture counts and speeds.  SPC and BikePGH conduct  counts of cyclists. Intersection studies included in this dataset may not include traffic counts, but reports of individual studies may be requested from the City. Despite the lack of count data, intersection studies are included to facilitate data requests. 

Data captured by different types of counting devices are included in this data.  StatTrak counters are  in use by the City, and capture data on counts and speeds. More information about these devices may be found on the [company's website](https://www.alltrafficsolutions.com/stattrakvehiclecounterclassifier/).  Data includes traffic counts and average speeds, and may also include separate counts of bicycles.

Tubes are deployed by both SPC and BikePGH and used to count cyclists. SPC may also deploy video counters to collect data. 

**NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.**

Tags: _etl, bike, car, city-property, count, counter, cyclist, mobility, pittsburgh-open-data, speed-limit, traffic, traffic-count
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
City of Pittsburgh Intersection Markings

Intersection markings including crosswalk and stop line locations and information.

**NOTE: The data in this dataset has not updated since 2021 (or 2018, in the case of the crosswalks data) because of a broken data feed. We're working to fix it.**

Tags: _etl, crossings, crosswalks, intersection, markings, pedestrians, transportation, travel
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Waste Recovery Locations

Locations where City residents are encouraged to drop off and dispose or recycle of unwanted materials. Information provided is subject to change. Please call ahead to confirm hours and that your specific item is still currently accepted. Non-City owned/operated locations listed are not an endorsement by the City of Pittsburgh.

**NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.**

Tags: _etl, dispose, garbage, recycling, trash, waste
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
City of Pittsburgh Courts and Rinks

Courts and rinks maintained by the City of Pittsburgh's Department of Public Works.

**NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.**

Tags: _etl, badminton, basketball, curling, dodgeball, figure-skating, foursquare, hockey, ice-skating, skating, sports, tennis, volleyball
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
City Traffic Signs

City of Pittsburgh Traffic signs such as; Stop, Stop Ahead, and Yield Signs, and Wayfinder signs.

This is an on-going inventory of Signs, as the Department of Public Works is able to update its inventory new sign types will be added.

**NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.**

Tags: _etl, signs, stop, traffic, transit, yield
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
City of Pittsburgh Trees

Trees cared for and managed by the City of Pittsburgh Department of Public Works Forestry Division.

Tree Benefits are calculated using the [National Tree Benefit Calculator Web Service](http://www.treebenefits.com/calculator/webservicedescription.cfm).

**NOTE: The data in this dataset has not updated since 2020 because of a broken data feed. We're working to fix it.**

Tags: _etl, department-of-public-works, dpw, environment, forestry, trees
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
311 Data

This data set shows 311 service requests in the City of Pittsburgh. This data is collected from the request intake software used by the 311 Response Center in the Department of Innovation & Performance. Requests are collected from phone calls, tweets, emails, a form on the City website, and through the 311 mobile application. 

For more information, see the 311 Data User Guide.

**If you are unable to download the 311 Data table due to a 504 Gateway Timeout error, use this link instead:** https://tools.wprdc.org/downstream/76fda9d0-69be-4dd5-8108-0de7907fc5a4

**NOTE:  The data feed for this dataset is broken as of December 21st, 2022. We're working on restoring it.**

Tags: 3-1-1, 311, _etl, paving, permits, potholes, service-requests
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
City of Pittsburgh Playing Fields

City of Pittsburgh playing fields.

**NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.**

Tags: _etl, baseball, capture-the-flag, cricket, dodgeball, field-hockey, flag-football, football, frisbee, hurling, quadball, recreation, soccer, softball, sports, teeball, track, ultimate
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Allegheny County Employee Salaries

This dataset includes annual salary, regular pay, incentive pay, and gross pay for employees under the County Executive and independently elected County officials for the years 2016 to the present, and is updated twice per year.

For December files, Annual Salary is the employee's annual salary or annualized wage as of the last pay period for the year, and the pay data fields (Regular Pay, Incentive Pay and Gross Pay) are payments made to the employee through the last pay period of the year. The June file contains the Annual Salary, and the pay data as of the last pay period in June. *Note that the June file is replaced by the December file each year.*

Union contracted salaries and wages which were not settled during the calendar year reflect the wage as of the end of the year. Regular Pay for these positions includes salaries and wages for the year it was paid, not the year it was earned. 

In addition to salary or wages for days worked and retroactively settled contract payments, Regular Pay also includes pay for days such as holidays, sick days, and vacation days. Overtime Pay includes pay for extra work typically at a wage rate different from regular wages as set forth in a collective bargaining agreement. Incentive Pay includes such things as a wellness incentive and longevity pay.

Employee names are included in the dataset with the following exceptions permitted by the Pennsylvania Right to Know Law:

* The names of individuals who were active sworn law enforcement officers during the year; and
* Information that would disclose individually identifiable health information.

Additionally, records related to Court of Common Pleas employees would need to be requested from the [Courts](https://www.alleghenycourts.us/).

*In March 2022, the salary data files prior to 2021 were updated so that all columns matched for consistent presentation.*

Tags: local-government
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target

Data files for "Eccles KM, Karmaus AL, Kleinstreuer NC, Parham F, Rider CV, Wambaugh JF, Messier KP. A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target. Sci Total Environ. 2023 Jan 10;855:158905. doi: 10.1016/j.scitotenv.2022.158905. Epub 2022 Sep 21. PMID: 36152849"

Tags: environmental-justice, expocast, geospatial, httk, toxcast
Organization: U.S. Environmental Protection Agency
Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods

Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747" 


Tags: computational-toxicology, high-throughput-risk-prioritization, in-silico, margin-of-exposure, threshold-of-toxicological-concern-ttc
Organization: U.S. Environmental Protection Agency
Bayesian inference of chemical exposures from NHANES urine biomonitoring data

Data and files for "Stanfield, Z., Setzer, R.W., Hull, V. et al. Bayesian inference of chemical exposures from NHANES urine biomonitoring data. J Expo Sci Environ Epidemiol 32, 833–846 (2022). https://doi.org/10.1038/s41370-022-00459-0"

Tags: biomonitoring, child-exposure-health, exposure-modeling, new-approach-methods, toxicokinetics
Organization: U.S. Environmental Protection Agency
Data and code for Quantifying coastal ecosystem condition and trophic state index

This data file contains the data used to produce the manuscript "Quantifying coastal ecosystem condition and a trophic state index with a Bayesian analytical framework"  The data were obtained from the National Coastal Condition Assessment (EPA) and from monitoring programs working near Boston, MA (Massachusetts Water Resource Authority - http://mwra.com).  The file also includes Rmd and JAGS code used to produce the manuscript. 

This dataset is associated with the following publication:
Hagy III, J.D., B.J. Kreakie, M.C. Pelletier, F. Nojavan, J.A. Kiddon, and A.J. Oczkowski. Quantifying coastal ecosystem trophic state at a macroscale using a Bayesian analytical framework.   ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 142: 109267, (2022).

Tags: boston-harbor, national-coastal-condition-assessment, nutrients
Organization: U.S. Environmental Protection Agency
FY23 OMB Recommended Expenditure

This dataset includes the Fiscal Year* 2023 County Executive Recommended operating budgets for Montgomery County.  The data can be sorted, filtered, and exported by the following elements: Function (grouping, such as “Public Safety”), Department, Program, Cost Center (activity within a program), Fund Type , Subfund Name, Expense Type, Recommended Amount, and Account Name.The Montgomery County fiscal year starts on July 1st and ends on June 30th. 
Update Frequency: Annually

Organization: Montgomery County of Maryland
EM712 Water Column Sonar Data Collected During RR2103

Data Assimilation

Tags: acoustic scattering, acoustic sounders, aquatic ecosystems, aquatic sciences, bathymetry, bathymetry/seafloor topography, benthic, biosphere, doc/noaa/nesdis/ncei, doc/noaa/nesdis/ngdc, earth science, ecosystems, fisheries, in situ/laboratory instruments, marine ecosystems, national centers for environmental information, national geophysical data center, nesdis, noaa, ocean acoustics, oceans, pelagic, profilers/sounders, seafloor topography, u.s. department of commerce, unknown, vertical location, water column, water column mapping system, wcms
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
EK60-ek5 Water Column Sonar Data Collected During SH1904

Acoustic-Trawl Surveys of Walleye Pollock (Gadus chalcogrammus) in the Gulf of Alaska (SH2019-04). The Midwater Assessment and Conservation Engineering (MACE) program of NOAA Fisheries, Alaska Fisheries Science Center (AFSC) conducted acoustic-trawl (AT) stock assessment surveys in the Shelikof Strait, Chirikof shelf break,and Marmot Bay areas of the Gulf of Alaska (GOA) in the late winter/early spring of 2019 to estimate the distribution and abundance of walleye pollock (Gadus chalcogrammus). This survey was conducted aboard NOAA Ship Bell M. Shimada, a 64 meter stern trawler equipped with acoustic and oceanographic instrumentation. The primary acoustic instrument for the survey was a Simrad EK60 split-beam echosounder system utilizing five frequencies (18, 38, 70, 120, and 200 kHz). The vessel departed Kodiak, Alaska on 6 March and conducted an acoustic system calibration in Kalsin Bay, Kodiak Island 7 March. The Shelikof Strait was conducted 7-16 March, the Chirikof shelf break sure was conducted 16-18 March, and the Marmot Bay survey was conducted 19-20 March. A post-cruise acoustic system calibration was conducted 20-21 March in Kalsin Bay and the vessel arrived in Kodiak, Alaska on 21 March.

Tags: acoustic scattering, acoustic sounders, aquatic ecosystems, aquatic sciences, bathymetry, bathymetry/seafloor topography, benthic, biosphere, doc/noaa/nesdis/ncei, doc/noaa/nesdis/ngdc, earth science, ecosystems, fisheries, gulf of alaska, in situ/laboratory instruments, marine ecosystems, national centers for environmental information, national geophysical data center, nesdis, noaa, ocean acoustics, oceans, pelagic, profilers/sounders, seafloor topography, u.s. department of commerce, vertical location, water column, water column mapping system, wcms
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
EM712 Water Column Sonar Data Collected During RR2101

Conditioning SAMW

Tags: acoustic scattering, acoustic sounders, aquatic ecosystems, aquatic sciences, bathymetry, bathymetry/seafloor topography, benthic, biosphere, doc/noaa/nesdis/ncei, doc/noaa/nesdis/ngdc, earth science, ecosystems, fisheries, in situ/laboratory instruments, marine ecosystems, national centers for environmental information, national geophysical data center, nesdis, noaa, ocean acoustics, oceans, pelagic, profilers/sounders, seafloor topography, u.s. department of commerce, unknown, vertical location, water column, water column mapping system, wcms
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
TIGER/Line Shapefile, 2022, Nation, U.S., 2010 Census Urban Area

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 urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses.  In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint."  There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000).  Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.


Tags: nation, polygon, u.s., ua, uc, united states, urban area, urban cluster, urbanized areas
Organization: U.S. Census Bureau, Department of Commerce
TIGER/Line Shapefile, 2022, Nation, U.S., 116th Congressional District

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.
 Congressional Districts are the 435 areas from which people are elected to the U.S. House of Representatives.  After the apportionment of congressional seats among the States based on census population counts, each State is responsible for establishing congressional districts for the purpose of electing representatives.  Each congressional district is to be as equal in population to all other congressional districts in a State as practicable.  The 116th Congress is seated from January 2019 to 2021. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas.  The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 1, 2018.

Tags: congressional districts, nation, polygon, u.s., united states
Organization: U.S. Census Bureau, Department of Commerce