Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.

Measures data in specific areas related to the employment of persons with disabilities. Gives labor force participation rates, work history, barriers to employment, and types of workplace accommodations for persons with disabilities.

Sortable List of Recipient Organizations receiving EDA Revolving Loan Funds

Measures data in specific areas related to the employment of persons with disabilities. Gives labor force participation rates, work history, barriers to employment, and types of workplace accommodations for persons with disabilities.

Post-Secondary Employment Outcomes (PSEO) are experimental tabulations developed by researchers at the U.S. Census Bureau. PSEO data provide earnings and employment outcomes for college and university graduates by degree level, degree major, and post-secondary institution. These statistics are generated by matching university transcript data with a national database of jobs, using state-of-the-art confidentiality protection mechanisms to protect the underlying data.

Post-Secondary Employment Outcomes (PSEO) are experimental tabulations developed by researchers at the U.S. Census Bureau. PSEO data provide earnings and employment outcomes for college and university graduates by degree level, degree major, and post-secondary institution. These statistics are generated by matching university transcript data with a national database of jobs, using state-of-the-art confidentiality protection mechanisms to protect the underlying data.

This data set provides heat detector temperatures in a single story three-compartment structure. 1000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various t-squared fires. The peak heat release rate and time to peak range from approximately 50 kW to 2200 kW and from 50 s to 1400 s, respectively. A detailed description of this work can be found in Ref. [2]. [1] Tam, W.C., Fu, E.Y., Peacock, R., Reneke, P., Wang, J., Li, J. and Cleary, T., 2020.

This dataset presents statistics on: the number and total revenue of establishments with the product line; product line revenue; product line revenue as a percent of total revenue of establishments with the product line and of all establishments; and quantity produced and shipped for the U.S. and states. Includes only establishments of firms with payroll. Product lines are referenced by NAPCS collection codes in the table.

This dataset presents statistics on: the number and total revenue of establishments with the product line; product line revenue; and industry contribution to total product line revenue for all sectors for the U.S only. Includes only establishments of firms with payroll. Product lines are referenced by NAPCS collection codes in the table.

The goal of the MUD (Manufacturer Usage Description) specification is to provide a means for manufacturers of Things to indicate what sort of access and network functionality they require for the Thing to properly function. A manufacturer associates a MUD file with a device which specifies an ACL for the device to within deployment specific parameters. The MUD standard is defined in RFC 8520 This repository publishes a public domain scalable implementation of the IETF MUD standard. MUD is implemented on SDN capable switches using OpenDaylight as the SDN controller.