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.

These data from the laboratory tests of a prototype residential liquid-to-air ground-source air conditioner (GSAC) using CO2 as the refrigerant. The data collection and processing methods are described in detail in this report:

Report Title: "Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner", NIST Technical Note 2068
Publication Date: October 2019
DOI: https://doi.org/10.6028/NIST.TN.2068
Authors: Harrison Skye, Wei Wu

SEDCORR is an open-source Python module designed to correct for the systematic energy deficits in atom probe mass spectra of electrically insulating samples. The assumption of the algorithm is that the mass spectrum for a dataset is conserved throughout the dataset and that any changes to the peak positions arise from an unknown slowly-fluctuating accelerating voltage. For computational speed, the unknown accelerating voltage is determined using a template matching FFT-based cross correlation method.

Data files for the publication "High Accuracy Near-infrared Carbon Dioxide Intensity Measurements to Support Remote Sensing" in Geophysical Research Letters

SCATMECH is a library of object-oriented C++ computer codes originally developed for disseminating models for polarized light scattering from surfaces and aerosols and for diffraction from gratings. The pySCATMECH package has been developed as an interface to the SCATMECH library, simplifying use of the codes and allowing for more rapid development of software for these applications.

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.

Data for mass concentration analysis and spectral analysis for the paper titled, "Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury"

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of mercury. A unit of SRM 3133 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of mercury. The solution contains nitric acid at an approximate mass fraction of 10 %. This data is public in the Certificate of Analysis for this material.

Standard Reference Material-1721: Southern Oceanic Air. Ambient Nominal Amount of Substance Fraction for Carbon Dioxide, Methane & Nitrous Oxide. This Standard Reference Material (SRM) is a primary gas mixture for which the amount-of-substance fraction, expressed as concentration, may be related to secondary working standards. This SRM is intended for the calibration of instruments used for ambient carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) determinations and for other applications. This data is public in the Certificate of Analysis for this material.

JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments.

The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials.