This tool is used to implement Monte Carlo analysis, which uses probabilistic sensitivity analysis to account for uncertainty. This tool is developed to follow the simulation segment of ASTM E1369. This technique involves a method of model sampling. Specification involves defining which variables are to be simulated, the distribution of each of these variables, and the number of iterations performed. The software then randomly samples from the probabilities for each input variable of interest. Three common distributions that are used include triangular, normal, and uniform.

The Manufacturing Cost Guide is a tool that estimates the costs that US manufacturers face and can be used to help gauge the potential returns on manufacturing industry research projects through cost reductions. These costs are grouped into various standardized categories such as the North American Industry Classification System (NAICS) and the Standard Occupational Classification (SOC) system along with other non-standardized costs.

The tool can be used to answer questions such as:

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

This database allows the public to browse and search recent FTZ Board manufacturing approvals.

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.

The QIF PMI Report (QPR) software generates a spreadsheet from a QIF (Quality Information Framework) file containing Product and Manufacturing Information (PMI). QIF is a unified XML framework standard for computer-aided quality QIF systems, available free to all implementers. QIF enables the capture, use, and re-use of metrology-related information throughout the Product Lifecycle Management (PLM) and Product Data Management (PDM) domains. QIF was created by the Digital Metrology Standards Consortium.

This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct 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.).

This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.

This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters.

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.