The National Institute of Standards and Technology, Intelligent Systems Division has collected data measuring human subjects, while performing common, simulated industrial manufacturing tasks with and without wearing an exoskeleton. Five tests were completed as part of a research study to develop measurement science towards standard test methods. For simulated industrial manufacturing tasks were performed using a novel, now standardized apparatus, called the Position and Load Test Apparatus for Exoskeletons (PoLoTAE).

The NIST scan framework is a framework that greatly simplifies the process of writing and maintaining scans of experimental parameters using the ARTIQ control system and language. The framework adopts the philosophy of convention over configuration where datasets are stored for analysis and plotting in a standard directory structure. The framework provides a number of useful features such as automatic calculation of statistics, fitting, validation of fits, and plotting that do not need to be performed by the user.

The library multicomplex is an implementation of multicomplex algebra in C++ to allow for higher-order derivatives of numerical functions. Many (though not all) mathematical functions are implemented, allowing for calculation of derivatives (straight and mixed) to approximately numerical precision, which is difficult or impossible to achieve in conventional double precision

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.

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.

Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose.

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.

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.

This tool calculates metrics for investment analysis documented in NIST Advanced manufacturing Series 200-5. It calculates net present value, internal rate of return, and payback period along with executing sensitivity analysis using Monte Carlo techniques. The tool helps to identify the most economical projects/investments. For instance, it could be used to identify the most economical heating and cooling system or it might be used to rank a set of potential investments.