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126526 results found

Economic Census: Summary Statistics for the U.S., States, and Selected Geographies: 2017

Data provided by  United States Census Bureau

This dataset provides industry statistics on number of firms; number of establishments; number of employees; payroll; and sales, value of shipments, or revenue by geographic area for establishments and firms with paid employees. Data are shown on a 2017 NAICS basis.  The statistics, NAICS levels, and the geographic areas covered vary by sector.

Tags: census

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Source: http://api.census.gov/data/2017/ecnbasic

SelectUSA Stats

Data provided by  International Trade Administration

11 Dynamic Data Visualization Dashboards designed for Investor and Economic Developer Organization (EDO) audiences to answer basic FDI questions

Tags: FDI Data,SelectUSA Stats,SelectUSA Data,State FDI Data,Country FDI Data

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Source: https://www.selectusa.gov/susa-stats

Diffraction Data for SRM 660c

Data provided by  National Institute of Standards and Technology

The data from this instrument consists of sets of measurements of Xray intensity as a function of diffraction angle. Almost all of it is collected using scintillator point detector with a graphite analyzer, and is stored in CIF format.

Tags: Xray Powder Diffraction; Diverging Beam Diffractometer; Powder Diffraction SRM

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2020 Demographic Profile

Data provided by  United States Census Bureau

This product will include topics such as age, sex, race, Hispanic or Latino origin, household type, relationship to householder, group quarters population, housing occupancy and housing tenure.

Tags: census

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Source: http://api.census.gov/data/2020/dec/dp

Annual Business Survey: Characteristics of Business Owners: 2018

Data provided by  United States Census Bureau

The Annual Business Survey (ABS) provides information on selected economic and demographic characteristics for businesses and business owners by sex, ethnicity, race, and veteran status. Further, the survey measures research and development (for microbusinesses), new business topics such as innovation and technology, as well as other business characteristics. The U.S. Census Bureau and the National Center conduct the ABS jointly for Science and Engineering Statistics within the National Science Foundation.

Tags: census

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Source: http://api.census.gov/data/2018/abscbo

Annual Business Survey: Characteristics of Businesses: 2018

Data provided by  United States Census Bureau

The Annual Business Survey (ABS) provides information on selected economic and demographic characteristics for businesses and business owners by sex, ethnicity, race, and veteran status. Further, the survey measures research and development (for microbusinesses), new business topics such as innovation and technology, as well as other business characteristics. The U.S. Census Bureau and the National Center conduct the ABS jointly for Science and Engineering Statistics within the National Science Foundation.

Tags: census

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Source: http://api.census.gov/data/2018/abscb

2018 Annual Business Survey: Company Summary

Data provided by  United States Census Bureau

The Annual Business Survey (ABS) provides information on selected economic and demographic characteristics for businesses and business owners by sex, ethnicity, race, and veteran status. Further, the survey measures research and development (for microbusinesses), new business topics such as innovation and technology, as well as other business characteristics. The U.S. Census Bureau and the National Center conduct the ABS jointly for Science and Engineering Statistics within the National Science Foundation.

Tags: census

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Source: http://api.census.gov/data/2018/abscs

Trojan Detection Software Challenge - image-classification-dec2020-train

Data provided by  National Institute of Standards and Technology

Round 3 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;

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Source: https://drive.google.com/drive/folders/1jKq-BWGZwSa_Zp73aiDqsJxqFaJa6jwJ?usp=drive_link

Trojan Detection Software Challenge - image-classification-aug2020-test

Data provided by  National Institute of Standards and Technology

Round 2 Test DatasetThe data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;

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Source: https://drive.google.com/drive/folders/1p2NVLddjLdrFfaoewe70t1iysC4LChrh?usp=drive_link

Trojan Detection Software Challenge - image-classification-aug2020-holdout

Data provided by  National Institute of Standards and Technology

Round 2 Holdout DatasetThe data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Tags: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;

Modified:

Source: https://doi.org/10.18434/mds2-2322