An agentic AI measurement tool for deep research over local corpora of PDF and Markdown documents. Given a research question, it orchestrates a programmatic AI pipeline to exhaustively evaluate, synthesize, and verify information from your documents, producing a Markdown report with inline footnote citations -- then automatically measures the quality of every citation using LM-judge measurement probes.

"polysr: Symbolic Regression applied to Polymeric Use Cases" is a GitHub repository that demonstrates the utility of symbolic regression for understanding polymer data. Specifically, three distinct use cases are considered. The Robust to Noise use case demonstrates the robustness of symbolic regression to noise. Both the case where noise is added to the predicted quantity and the case where noise is added to one of the features are considered. Specifically, synthetic data is generated from the Flory Huggins spinodal equation for the tests.

Theoretical calculation, modeling and experimental measurement data for the paper "Integrated optical isolators for broadband multi-laser operation" accepted for publication in Nature Photonics (2026)

Repository for NIST-EPA Legionella study Python tools. Includes scripts for aerosol particle size distribution analysis, emission-rate calculations, data cleaning, visualization, and statistical modeling of shower-generated aerosols under varying temperature, humidity, and exhaust-fan conditions.

Python-based automation system for the NIST Building 423 Manufacturing House IAQ test facility. Provides incremental backups of instrumentation data, weather station logs, and thermostat telemetry to mission network storage with detailed error logging, retry logic, and support for scheduled execution.

With this tool, users can rapidly calculate binary classifier metrics like Matthew's Correlation Coefficient, F-Scores, and Average Precision Scores from scalar and binary predictions. It is primarily used to calculate threshold sensitivity studies, and has several routines to significantly speed up metric calculation (or approximation) when aggregate measures of performance over all thresholds are needed.

This mass spectrometry (MS) data set encompasses spectra of the extractables and leachables (E&L) of 100 polymer materials sourced from the Scientific Polymer Products Inc Polymer Samples Kit (see: SciPoly_PolymerSampleKit205_Items.xlsx). Extractions of these polymers were carried out using three HPLC-grade solvents of varying polarity (water, isopropanol, and hexane) with 10 mg polymer/mL solvent for 24 hours at 50 °C with shaking.

Electronic files containing the certified value(s) and uncertainty(ies), and the data used to assign the value(s). Data are provided for amino acid analysis measurements that were used to determine the certified value for BSA concentration. Data for density measurements needed to convert mass fraction values to mass concentrations are also included. Biuret measurements were used to assign a non-certified value for total protein concentration.

Hyperpolarized low-Field MRI data of cellphone-band microwave radiation in tissue mimics with associated electromagnetic and simulation data

Lithium ion battery (LIB) fires are a growing problem that extends across the supply chain, including mining, production, warehousing, shipping, and waste disposal, as well as the consumer side. But data on such fires is fragmented and mostly incomplete. Many are secondary data sources, and for the National Fire Incident Reporting System (NFIRS) and the Consumer Product Safety Commission data sets identification of LIB fires is difficult.