The data used to generate the graphs in figures 1-9 of the paper "Evaluating Uncertainty of Microwave Calibrations from Regression Residuals".

The full citation is D. F. Williams, B. F. Jamroz, J. D. Rezac and R. D. Jones, "Evaluating Uncertainty of Microwave Calibration Models With Regression Residuals," in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 6, pp. 2454-2467, June 2020, doi: 10.1109/TMTT.2020.2983358.

These software are used to control an instrument that makes measurements and calculates calibration coefficients for NIST nanocalorimeters. Please cite the related paper "Practical Guide to the Design, Fabrication and Calibration of NIST Nanocalorimeters" by Feng Yi, Michael D. Grapes, and David A. LaVan in the Journal of Research of the National Institute of Standards and Technology, Volume 124 (in press).

Nanocalorimeter calibration data. The file format is Origin Pro* project files, which include multiple data worksheets and derived graphs.

*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

Please cite the related paper "Practical Guide to the Design, Fabrication and Calibration of NIST Nanocalorimeters" by Feng Yi, Michael D. Grapes, and David A. LaVan in the Journal of Research of the National Institute of Standards and Technology, Volume 124 (in press).

The RF dataset can be used to develop and test detection algorithms for the 3.5 GHz CBRS or similar bands where the primary users of the band are federal incumbent radar systems. The dataset consists of synthetically generated radar waveforms with added white Gaussian noise. The RF dataset is suitable for development and testing of machine/deep learning detection algorithms. A large number of parameters of the waveforms are randomized across the dataset. Due to its large size, the dataset is divided into groups, and each group consists of multiple files.

Four files are included in this data set for mask generation that can be used to produce photolithography contact masks to create NIST nanocalorimeters. Two different designs are included, each in AutoCAD *.dwg format and a universal *.dxf format. The masks are intended to be printed on 5 inch masks blanks used to pattern 100 mm (4 inch) wafers.

3D Solid Model of NIST Nanocalorimeter created in Solidworks 2019. The files include an assembly of three layers represented as part files - the silicon die layer, the silicon nitride membrane layer and the platinum metal layer. Please cite the related paper "Practical Guide to the Design, Fabrication and Calibration of NIST Nanocalorimeters" by Feng Yi, Michael D. Grapes, and David A. LaVan in the Journal of Research of the National Institute of Standards and Technology, Volume 124 (in press).

NDN-DPDK is a set of high-performance Named Data Networking (NDN) programs developed with Data Plane Development Kit (DPDK). It includes a network forwarder and a traffic generator. https://github.com/usnistgov/ndn-dpdk

A bilateral study to compare guarded-hot-plate measurements at extended temperatures between laboratories at the National Institute of Standards and Technology (NIST) and the National Physical Laboratory (NPL) is presented. Measurements were conducted in accordance with standardized test methods (ISO 8302 or ASTM C177) over a temperature range from 20 °C to 160 °C (293 K to 433 K). Following a blind round-robin format, specimens of non-woven fibrous glass mat, approximately 22 mm thick and having a nominal bulk density of 200 kg/m3, were prepared and studied.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions.

Python module "optbayesexpt" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given a 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.