Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

The Forensic Anthropology Skeletal Trauma (FAST) database is a novel resource, funded by the National Institute of Justice, which provides trauma analysis data for education, training, and case comparisons. Students, academics, and practitioners can gain an interdisciplinary perspective of skeletal trauma through an examination of outcomes from experimental research utilizing human specimens with known loading mechanisms. The largest obstacle for the field of forensic anthropology is exposure to trauma analysis. Few researchers get quality hands-on training with trauma cases and even fewer have experience with cases involving unequivocally known loading and injury mechanisms. Improvement in skeletal trauma analyses and interpretations is dependent on dissemination in a user-friendly format that allows for training and education and supports forensic professionals in practice. FAST features pre- and post-test imaging, data collected from advanced instrumentation during the impact event, and fracture analysis data. The Forensic Anthropology Skeletal Trauma Database provides a unique opportunity to explore a large sample of skeletal trauma on various regions of the human body and gain insight into objective trauma interpretation. The ability for students and professionals, at all stages in their career, to be exposed to skeletal trauma with known parameters has the potential to be transformative for the field. This freely available resource is an innovative solution to break down pre-existing barriers students and professionals have in accessing trauma specimens. Our goal is to continue to develop FAST through inclusion of past and future experimental skeletal trauma research.

In this article, I show how to fit a generalized linear model to N observations on p variables stored in a relational database, using one sampling query and one aggregation query, as long as N12+δ observations can be stored in memory, for some δ>0. The resulting estimator is fully efficient and asymptotically equivalent to the maximum likelihood estimator, and so its variance can be estimated from the Fisher information in the usual way. A proof-of-concept implementation uses R with MonetDB and with SQLite, and could easily be adapted to other popular databases. I illustrate the approach with examples of taxi-trip data in New York City and factors related to car color in New Zealand. Supplementary materials for this article are available online.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

This database contains reactor physic data gathered from high-fidelity sodium fast reactor MCNP models. Each reactor design contains values such as k-eff, beta-eff, sodium void coefficient, Doppler coefficient, etc. The data is stored as an h5 database and can easily be converted to a Pandas dataframe for manipulation.

This Data set include the FAST data that referred to the FRB 20190520B discovery Paper. In total 79 bursts were detected in FAST observation. The first 4 bursts are from FAST drift scan survey (CRAFTS project), and the other are using tracking mode.

A new, relational database to be used for disease gene discovery, gene annotation and reporting, and searching for genes for future studies in model organisms. It incorporates 5 layers of information about the genes residing in it- the expression information from a gene (as reported in Unigene), the cytological location of the gene (if available), the ortholog of each gene in the available species within the database, the divergence information between species for each gene, and functional information as reported by OMIM and the Enzyme Commission (EC) reference number of genes. Tables have also been created to help record polymorphism data and functional information about specific changes within or between species, such as measured by Granthams distance (1) or model organism studies.

Xtract.io's Restaurant POI data delivers a comprehensive view of the brand's extensive QSR and fast food restaurant locations across the United States and Canada. Franchise investors, business analysts, and market researchers can utilize this QSR and fast food location data to understand Subway and other fast food market penetration, identify potential growth areas, and develop targeted strategic insights for quick service restaurant analysis.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive restaurant location intelligence landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail -Restaurant chain locations -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ for Fast Food POI Data? At LocationsXYZ, we: -Deliver restaurant POI data with 95% accuracy -Refresh QSR location data every 30, 60, or 90 days to ensure the most recent information -Create on-demand fast food chain datasets tailored to your specific needs -Handcraft boundaries (geofences) for restaurant locations to enhance accuracy -Provide restaurant POI data and polygon data in multiple file formats

Unlock the Power of Restaurant Location Data With our point-of-interest data for food service establishments, you can: -Perform thorough market analyses for QSR expansion -Identify the best locations for new restaurant stores -Gain insights into consumer behavior and dining patterns -Achieve an edge with competitive intelligence in the fast food industry

LocationsXYZ has empowered businesses with geospatial insights and restaurant location intelligence, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge Subway restaurant POI data.

The University of Hawaii Sea Level Center (UHSLC) assembles and distributes the Fast Delivery (FD) dataset of hourly- and daily-averaged tide gauge water-level observations. Tide gauge operators, or data creators, provide FD data to UHSLC after a level 1 quality assessment (see processing_level attribute). The UHSLC provides an independent quality assessment of the time series and makes FD data available within 4-6 weeks of collection. This is a "fast" turnaround time compared to Research Quality (RQ) data, which are available on an annual cycle after a level 2 quality assessment. RQ data replace FD data in the data stream as the former becomes available. This file contains hybrid time series composed of RQ data when available with FD data appended to the end of each RQ series. acknowledgement=The UHSLC Fast Delivery database is supported by the National Oceanic and Atmospheric Administration (NOAA) Office of Climate Observations (OCO). cdm_data_type=TimeSeries cdm_timeseries_variables=uhslc_id, latitude, longitude Conventions=CF-1.10, ACDD-1.3, COARDS Easternmost_Easting=358.862 featureType=TimeSeries geospatial_lat_max=82.492 geospatial_lat_min=-69.0 geospatial_lat_units=degrees_north geospatial_lon_max=358.862 geospatial_lon_min=3.412 geospatial_lon_units=degrees_east infoUrl=https://uhslc.soest.hawaii.edu/data/ institution=University of Hawaii Sea Level Center Northernmost_Northing=82.492 processing_level=Fast Delivery (FD) data undergo a level 1 quality assessment (e.g., unit and timing evaluation, outlier detection, combination of multiple channels into a primary channel, etc.). In this file, FD data are appended to Research Quality (RQ) data that have received a level 2 quality assessment (e.g., tide gauge datum evaluation, assessment of level ties to tide gauge benchmarks, comparison with nearby stations, etc.). sourceUrl=(local files) Southernmost_Northing=-69.0 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=latitude, longitude, station_name, station_country, station_country_code, record_id, uhslc_id, gloss_id, ssc_id, last_rq_date time_coverage_end=2025-10-31T12:00:00Z time_coverage_start=1846-01-04T12:00:00Z Westernmost_Easting=3.412

Get the latest USA Fast import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

The Allegheny County Health Department has generated this list of fast food restaurants by exporting all chain restaurants without an alcohol permit from the County’s Fee and Permit System. A chain restaurant defined by the County is any restaurant that has more than one location in the County. Chain restaurants capture both local and national chains (including locally owned national chains) so long as there is one or more establishments in operation within the County.

This product summarizes data used in the analysis portion of our Flood and Storm Tracker (FaST) manuscript (see larger work citation). The dataset titled HUCsppMatrices2012-2022.csv has each Hydraulic Unit Code (HUC) with an introduced taxon in each storm and the HUC it connected to by flood waters (lateral or longitudinal). The dataset titled ConnectionPoints_2012-2022.csv has each lateral (not longitudinal or downstream) connection point for each storm event. The dataset titled LongitudinalConnectionPoints_2012-2022.csv has each longitudinal or downstream connection point for each storm event.

.FAST Whois Database, discover comprehensive ownership details, registration dates, and more for .FAST TLD with Whois Data Center.

Data files for the fast reverse geocoder available at https://github.com/kno10/reversegeocode For source code to generate or use the data, see above URL.

Calibrated Fluxgate Data acquired by the Fast Auroral SnapshoT Small Explorer, FAST, Magnetometer Instrument. Data have been calibrated, despun, and detrended against the International Geomagnetic Reference Field, IGRF, using IGRF Coefficients for the Date of Acquisition. Data are provided in several Coordinate Systems. Non detrended Data in Spacecraft and Geocentric Equatorial Inertial Coordinates are provided. Ephemeris Data are also provided.

Dataset

This dataset was created by Chris Anderson

Contents

Comprehensive nutritional details (calories, total fat, protein, sodium, carbohydrates, saturated fat, trans fat, sugars, cholesterol) for over 23,771 menu items across 9 major fast food chains in 80+ countries.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

Context

This is a list of 10,000 fast-food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Inspiration

You can use this data to rank cities with the most and least fast-food restaurants across the U.S. E.g.:

1. Cities with the most and least McDonald's per capita
2. Fast food restaurants per capita for all states
3. Fast food restaurants with the most locations nationally
4. Major cities with the most and least fast food restaurants per capita
5. Small cities with the most fast-food restaurants per capita
6. States with the most and least fast food restaurants per capita
7. The number of fast-food restaurants per capita

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Comprehensive dataset containing 516,757 verified Fast food restaurant businesses in India with complete contact information, ratings, reviews, and location data.