Separate tables for the last ten years of Community Data Snapshot releases (2015-2025) are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs) There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary. Looking to match human-friendly labels to field names? Use the CDS Data Dictionary Crosswalk.When using multiple releases of the snapshots, please don’t compare overlapping ACS 5-Year Estimates. The Census Bureau provides specific guidance for when it is appropriate to compare ACS data across time. CMAP uses the most recently available 5-Year Estimates, which are usually available on a two year lag:CDS yearACS 5-Year Estimates data vintageCompare to previous CDS year20252019-20232020, 201520242018-2022201920232017-2021201820222016-2020201720212015-2019201620202014-2018201520192013-2017 20182012-2016 20172011-2015 20162010-2014 20152009-2013 NOTE: Much of the data is from five-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

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.

Separate tables are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs) There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary. Looking to match human-friendly labels to field names? Use the CDS Data Dictionary Crosswalk.NOTE: Much of the data is from 5-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

Q: Was the month cooler or warmer than usual? A: Colors show where and by how much the monthly average temperature differed from the month’s long-term average temperature from 1991-2020. Red areas were warmer than the 30-year average for the month, and blue areas were cooler. White and very light areas had temperatures close to the long-term average. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments collect the highest and lowest temperature of the day at each station over the entire month, and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly average of daily mean temperatures, then plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). To calculate the difference-from-average temperatures shown on these maps—also called temperature anomalies—NCEI scientists take the average temperature in each 5x5 km grid box for a single month and year, and subtract its 1991-2020 average for the same month. If the result is a positive number, the region was warmer than average. A negative result means the region was cooler than usual. Q: What do the colors mean? A: Shades of blue show places where average monthly temperatures were below their long-term average for the month. Areas shown in shades of pink to red had average temperatures that were warmer than usual. The darker the shade of red or blue, the larger the difference from the long-term average temperature. White and very light areas show where average monthly temperature was the same as or very close to the long-term average. Q: Why do these data matter? A: Comparing an area’s recent temperature to its long-term average can tell how warm or how cool the area is compared to usual. Temperature anomalies also give us a frame of reference to better compare locations. For example, two areas might have each had recent temperatures near 70°F, but 70°F could be above average for one location while below average for another. Knowing an area is much warmer or much cooler than usual can encourage people to pay close attention to on-the-ground conditions that affect daily life and decisions. People check maps like this to judge crop progress, estimate energy use, consider snow and lake ice melt; and to understand impacts on wildfire regimes. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Q: Data Format Description A: NetCDF (Version: 4) Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Average Temperature NClimGrid Temperature Normals References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Cl

The State Snapshots provide graphical representations of State-specific health care quality information, including strengths, weaknesses, and opportunities for improvement. The goal is to help State officials and their public- and private-sector partners better understand health care quality and disparities in their State. State-level information used to create the State Snapshots is based on data collected for the National Healthcare Quality Report (NHQR). The State Snapshots include summary measures of quality of care and States' performances relative to all States, the region, and best performing States by overall health care quality, types of care (preventive, acute, and chronic), settings of care (hospitals, ambulatory care, nursing home, and home health), and clinical conditions (cancer, diabetes, heart disease, maternal and child health, and respiratory diseases). Special focus areas on diabetes, asthma, Healthy People 2010, clinical preventive services, disparities, payer, and variation over time are also featured. The Agency for Healthcare Research and Quality (AHRQ) has released the State Snapshots each year in conjunction with the 2004 NHQR through the 2009 NHQR.

Dataset consists of twelve monthly images for 1991-2020, available in small, large, broadcast media, full size zip, and KML archive formats. These images were derived from NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid).Description from Climate.gov:Q:How much rain and snow usually fall this month?A:Based on daily observations from 1991-2020, colors on the map show long-term average precipitation totals in 5x5 km grid cells for the month displayed. The darker the color, the higher the total precipitation.Q:Where do these measurements come from?A:Daily totals of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gathered the data from 1991 to 2020 and submitted them to the National Centers for Environmental Information (NCEI). After scientists checked the quality of the data to omit any systematic errors, they calculated each station’s monthly total and plotted it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid).Q:What do the colors mean?A:White areas on the map received an average of zero measurable precipitation during the month from 1991-2020. Areas shown in the lightest green received a monthly average of less than one inch of water from rain or snow over the 30-year period. The darker the color on the map, the higher the average precipitation total for the month. Areas shown in dark blue received an average of eight or more inches of water that fell as either rain or snow. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation.Q:Why do these data matter?A:Understanding these values provides insight into the “normal” conditions for a month. This type of information is widely used across an array of planning activities, from designing energy distribution networks, to the timing of crop and plant emergence, to choosing the right place and time for recreational activities.Q:How did you produce these snapshots?A:Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps.Additional informationThe data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources:NClimGrid Precipitation Normals ReferencesNOAA Monthly U.S. Climate Gridded Dataset (NClimGrid)NOAA Monthly U.S. Climate Divisional Database (NClimDiv)Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions)NCEI Monthly National Analysis)Climate at a Glance - Data Information)NCEI Climate Monitoring - All Products

NSP Snapshot Reports provide a synopsis of financial performance of NSP grantees. The snapshots provide data on commitments, drawdowns, activity types, and program income. There are program-wide snapshots for NSP1, NSP2, and NSP3 as well as individual snapshots for each grantee. These snapshots are intended to increase transparency to the public about the progress of NSP. Snapshots are posted quarterly.

Historical snapshots of Vermont E911 GIS data. Snapshots are periodically taken, curated, and retained for persisting use cases.

Q: How much rain and snow fell through the month? A: Colors show monthly precipitation totals across the contiguous United States. The darker the color, the higher the total precipitation. Q: Where do these measurements come from? A: Daily measurements of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gather the data and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly total precipitation and plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Areas shown in white received little or no measurable precipitation for the month. Areas shown in the lightest green received less than one inch of water from rain or snow. The darker the color on the map, the higher the precipitation for the month. Areas shown in dark blue received eight inches or more of precipitation that fell as either rain or snow. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation. Q: Why do these data matter? A: Farmers and gardeners who depend on rain for their plants want to know if enough precipitation has fallen to support plant growth. Similarly, forest managers and ranchers check monthly precipitation to monitor the status of the environment. Water managers who work to ensure that towns and cities have enough water for drinking, washing, and industrial uses are also interested in how much precipitation falls each month. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Q: Data Format Description A: NetCDF (Version: 4) Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Total Precipitation References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Climate at a Glance - Data Information NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-total This upload includes two additional files:* Precipitation - Monthly Total _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-total )* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

The City of Philadelphia's datasets are snapshots published on a daily basis. These data sets include de-identified, aggregate datasets showing COVID-19 cases, hospitalizations, and deaths by date, zip code, race, and age/sex and are made available accumulated into historical data sets of the daily snapshots collected. The cases and deaths by date, zip code, and age/sex are accumulated since 6/4/2020. Cases and death by race and hospitalizations are accumulated since 8/15/2020. Data is collected from the City's data published at https://www.opendataphilly.org/showcase/philadelphia-covid-19-information through a daily scrape of the web site.

To provide a snapshot of citywide student enrollment and demographic information across multiple years. Data is collected using multiple data sources, including DOE's Audited Register, biographic data from Automate The Schools (ATS) system and the Location Code Generation and Management System (LCGMS). Data can be used to view citywide demographic and enrollment trends over time. Enrollment counts are based on the October 31 Audited Register for each school year. Please note that October 31 enrollment is not audited for charter schools or Pre-K Early Education Centers(NYCEECs). Charter schools are required to submit enrollment as of BEDS Day the first Wednesday in October to the New York State Education Department of Education. Enrollment counts will exceed operational enrollment counts due the fact that long term absence (LTA) students are excluded for funding purposes.

This file contains additional resolutions of the same images as in https://www.datalumos.org/datalumos/project/233461/version/V2/view. Q: Where was the monthly temperature warmer or cooler than usual? A: Colors show where average monthly temperature was above or below its 1991-2020 average. Blue areas experienced cooler-than-usual temperatures while areas shown in red were warmer than usual. The darker the color, the larger the difference from the long-term average temperature. Q: Where do these measurements come from? A: Weather stations on every continent record temperatures over land, and ocean surface temperatures come from measurements made by ships and buoys. NOAA scientists merge the readings from land and ocean into a single dataset. To calculate difference-from-average temperatures—also called temperature anomalies—scientists calculate the average monthly temperature across hundreds of small regions, and then subtract each region’s 1991-2020 average for the same month. If the result is a positive number, the region was warmer than the long-term average. A negative result from the subtraction means the region was cooler than usual. To generate the source images, visualizers apply a mathematical filter to the results to produce a map that has smooth color transitions and no gaps. Q: What do the colors mean? A: Shades of red show where average monthly temperature was warmer than the 1991-2020 average for the same month. Shades of blue show where the monthly average was cooler than the long-term average. The darker the color, the larger the difference from average temperature. White and very light areas were close to their long-term average temperature. Gray areas near the North and South Poles show where no data are available. Q: Why do these data matter? A: Over time, these data give us a planet-wide picture of how climate varies over months and years and changes over decades. Each month, some areas are cooler than the long-term average and some areas are warmer. Though we don’t see an increase in temperature at every location every month, the long-term trend shows a growing portion of Earth’s surface is warmer than it was during the base period. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Environmental Visualization Laboratory (NNVL) produces the source images for the Difference from Average Temperature – Monthly maps. To produce our images, we run a set of scripts that access the source images, re-project them into desired projections at various sizes, and output them with a custom color bar. Additional information Source images available through NOAA's Environmental Visualization Lab (NNVL) are interpolated from data originally provided by the National Center for Environmental Information (NCEI) - Weather and Climate. NNVL images are based on NOAA Merged Land Ocean Global Surface Temperature Analysis data (NOAAGlobalTemp, formerly known as MLOST). References NCEI Monthly Global Analysis NOAA View Temperature Anomaly Merged Land Ocean Global Surface Temperature Analysis Global Surface Temperature Anomalies Climate at a Glance - Data Information Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a... This upload includes two additional files: * Temperature - Global Monthly, Difference from Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...) * Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

UI Snapshots through the iterative cycles of design, development and evaluation - assorted visualisation modules exploring skills analysis in the EDSA project.

Lots of brief GNSS signal snapshots to develop and test snapshot GNSS satellite acquisition and positioning algorithms.

Overview

This repository contains two new dynamic GNSS satellite signal snapshot datasets. These were created by carrying SnapperGPS receivers (https://snappergps.info/) while walking or cycling in Oxfordshire, UK.

• Walking: 15 individual recordings/tracks with 26,423 datapoints in total captured in one-second intervals totalling 7:20 hours. Motion during these recordings was slower on average, but acceleration, deceleration, and directional changes were more frequent and usually sudden.
• Cycling: Twelve individual recordings/tracks with 27,237 datapoints in total captured in one-second intervals totalling 7:34 hours. Motion during these recordings was on average faster, but acceleration, deceleration, and directional changes were less frequent and usually smooth.

SnapperGPS V1.0.0 boards (https://github.com/SnapperGPS/snappergps-pcb) were used for 18 recordings and SnapperGPS V2.0.0 boards (https://github.com/SnapperGPS/snappergps-pcb-2) for nine recordings. The more expensive Siretta Echo 27 active patch antenna that was already used for a previous data collection (https://doi.org/10.5287/bodleian:eXrp1xydM) was only used for six of these recordings. In addition, either the low-cost Abracon APAM2764YK0175 active patch antenna (twelve recordings) or low-cost Taoglas GP.1575.25.2.A.02 (five recordings) or GP.1575.25.4.A.02 (four recordings) passive patch antennas were used.

The core component of a low-cost SnapperGPS receiver is an SE4150L (https://www.skyworksinc.com/Products/Amplifiers/SE4150L) integrated GPS receiver circuit. Like most civilian low-cost GPS receivers, SnapperGPS operates in the L1 band with a centre frequency of 1.57542 GHz. However, Galileo's E1 signal, BeiDou's B1C signal, GPS' novel L1C signal, and SBAS' L1 signal have the identical centre frequency. So, a SnapperGPS receiver captures those signals, too. A SnapperGPS receiver down-mixes the incoming signal to a nominal intermediate frequency of 4.092 MHz, samples the resulting near-baseband signal at 4.092 MHz and digitises it with an amplitude resolution of one bit per sample. It considers only the in-phase component and discards the quadrature component.

The datasets are expected to be more challenging than the previous data collection (https://doi.org/10.5287/bodleian:eXrp1xydM). Firstly, due to the use of lower-cost antennas and, secondly, due to more track sections in forests and along tree-lined avenues. The latter is likely to reduce the number of visible satellites and the signal strengths of visible ones.

Ground truth was collected with real-time kinematic (RTK) receivers (u-blox ZED-F9P) with persistent cellular connections to a base station to receive assistance data and perform differential GNSS for improved accuracy.

Data

The data is provided in JSON format.

• /snapshots: Raw twelve-millisecond GNSS signal snapshots from the SnapperGPS receivers stored in the same data format that is used for data transfers from SnapperGPS boards to the SnapperGPS app/website. The field snapshots is an array each of which elements has the fields timestamp, temperature, and data. data is a Base64 encoded byte array. One byte of the decoded array holds the amplitude values of eight signal samples, i.e., the first byte holds the first eight samples. A zero bit represents a signal amplitude of +1 and a one bit a signal amplitude of -1. The order of the bits is 'little', i.e., reversed. For example, the byte 0b01100000 corresponds to the signal chunk [1 1 1 1 1 -1 -1 1].
• /fixes: Fixes calculated with the SnapperGPS app/website (without smoothing) stored in the same data format that is used for data downloads from the SnapperGPS app/website. The data comes in array each of which elements has the fields datetime, latitude, longitude, confidence, temperature, and id, the first four of which are estimated. datetime is in UTC, latitude and longitude are in decimal degrees, and confidence is a confidence radius (50%) in metres.
• /ground-truth: Ground truth fixes from an RTK receiver stored in array each of which elements has the fields time, latitude, and longitude. time is in UTC, latitude and longitude are in decimal degrees.
• /smoothed: Plots of non-smoothed and smoothed tracks (see below). Non-smoothed fixes (pink), 2D RTS smoothing (dark gray), 2D GPR (purple), 2D loosely-coupled FGO (light gray), and tightly-coupled FGO (white).

Table 1: Overview of recordings.

dataset	type	duration [min]	environment	SnapperGPS board	antenna
w00	walking	5	park	V1.0.0
w01	walking	9	football pitch	V1.0.0
w02	walking	8	American football pitch	V1.0.0
w03	walking	20	park	V1.0.0
w04	walking	20	park	V1.0.0
w05	walking	20	park	V1.0.0
w06	walking	10	urban	V1.0.0
w07	walking	30	meadow	V1.0.0
w08	walking	30	meadow	V1.0.0
w09	walking	45	meadow	V1.0.0
w10	walking	45	meadow	V1.0.0
w11	walking	50	park	V2.0.0
w12	walking	50	park	V2.0.0
w13	walking	50	park	V2.0.0
w14	walking	50	park	V2.0.0
c00	cycling	60	urban+rural	V1.0.0
c01	cycling	60	urban+rural	V1.0.0
c02	cycling	60	urban+rural	V1.0.0
c03	cycling	35	urban+rural	V1.0.0
c04	cycling	35	urban+rural	V1.0.0
c05	cycling	35	urban+rural	V2.0.0
c06	cycling	40	urban+rural	V1.0.0
c07	cycling	40	urban+rural	V2.0.0
c08	cycling	40	urban+rural	V1.0.0
c09	cycling	16	urban+rural	V2.0.0
c10	cycling	16	urban+rural	V2.0.0
c11	cycling	16	urban+rural	V2.0.0

Smoothing

Table 2: Median horizontal localisation errors of different smoothing algorithms considering different sampling intervals and travel modes. (RTS smoother: Rauch-Tung-Striebel smoother. GPR: Gaussian process regression. FGO: factor graph optimisation.) Two-dimensional (2D) smoothers and tight FGO use a constant-position-random-velocity model, except for GPR, which uses adaptive models for the two spatial dimensions. The 4D smoother uses a constant-velocity-random-acceleration model. All estimations are loosely-coupled (snapshot positioning followed by smoothing) except for tight FGO. Localisation errors without smoothing are provided for comparison.

Algorithm	Walking			Cycling
	1 s	10 s	60 s	1 s	10 s	60 s
No smoothing	15.4 m	15.4 m	14.5 m	14.5 m	14.5 m	14.6 m
2D RTS smoother	8.2 m	10.5 m	13.5 m	7.7 m	11.8 m	14.5 m
4D RTS smoother	8.1 m	10.2 m	13.7 m	7.4 m	11.3 m	14.6 m
2D GPR	7.9 m	11.0 m	14.7 m	7.5 m	12.0 m	19.8 m
2D FGO	8.1 m	10.4 m	13.6 m	7.5 m	11.6 m	14.6 m
Tight FGO	7.9 m	10.0 m	13.5 m	7.3 m	11.3 m	14.6 m

Enrollment counts are based on the October 31st Audited Register for each school year. * Please note that October 31st enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education.

Users can access data related to quality of health care for each state and the District of Columbia. Background 2010 State Snapshots is a database maintained by the Agency for Healthcare Research and Quality (AHRQ). Data is based on information collected from the National Healthcare Quality Report (NHQR). 2010 State Snapshots database offers an in-depth analysis of the quality of care – by type of condition, level of care, treatment setting, race and income, and insurance status. User functionality Users can search for state data by using the interactive map and cl icking on the state. Users are given data for the most recent year (2010) and baseline data year which varies by state. Each state has information on the state dashboard which includes information on types of care, settings of care, and care by clinical area. Users also have the option to focus on diabetes care, asthma care, healthy people 2010 goals, clinical preventative services, disparities in care, payer, and variation in overtime. Data is presented in tables or visual charts. Data is available for download using excel and XML format. Data Notes Detailed information about the data is available under the “Methods” section. The website does not indicate when new data will become available.

Here are four archives with raw data extracted from the log-files of PE-EFF-B3LYP/pcseg-2 calculations.m1: (+)-isoterreinm2: (-)-terreinm3: (+)-isoterreinm4: (-)-terreinThe snapshots are numbered according to the following (not so obvious scheme):Files within the archives that are:ending in *0.dat are extracted at 500nsending in *9.dat are extracted at 1000 nsending in *8.dat are extracted at 1500 nsending in *7.dat are extracted at 2000 nsending in *6.dat are extracted at 1250 nsending in *5.dat are extracted at 1750 ns

The Company Snapshot is a concise electronic record of company identification, size, commodity information, and safety record, including the safety rating (if any), a roadside out-of-service inspection summary, and crash information. The Company Snapshot is available via an ad-hoc query (one carrier at a time) free of charge.

This file is a spreadsheet that contains data used to produce the Travel Trends snapshot. Data sources include the U.S. Census Bureau, the Illinois Department of Transportation, and CMAP’s 2008 regional travel survey, Travel Tracker.

Find information on population, income, jobs, wages, graduation rates, highways, water and healthcare for the Comptroller's 12 Economic Regions. See https://comptroller.texas.gov/about/policies/privacy.php for more information on our agency’s privacy and security policies.