The CCQM_Retrospectoscope system combines a nominally complete database of results from Consultative Committee for the Amount of Substance: Metrology in Chemistry and Biology (CCQM) studies with a number of graphical tools for trying to make sense of the data. This system supports a diverse collection of often eye-opening appraisals of participation and measurement performance throughout the history of the CCQM activities.

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots.

Numerical values of all data points shown in figures for manuscript "Trap-Integrated Superconducting Nanowire Single-Photon Detectors with Improved RF Tolerance for Trapped-Ion Qubit State Readout", available on arXiv at https://arxiv.org/abs/2302.01462Manuscript in press at Applied Physics Letters.

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included.

Software benchmarking study of finalists in NIST's lightweight cryptography standardization process. This data set includes the results on several microcontrollers, as well as the benchmarking framework used.

Round 13 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained both on synthetic image data build from Cityscapes and the DOTA_v2 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 128 AI models using a small set of model architectures.

This collection is comprised of multiple sets of experiments involving iPSCs with varying dosages of fluorescent light excitation. Samples were imaged every 2 minutes using phase-contrast microscopy for 20+ hours with or without fluorescence excitation. Each experiment was done on different days (i.e. exp0 vs exp1) but with triplicate wells (i.e. exp1-0, exp1-1, exp1-2). Additionally, data used to train our 2D U-Net and 3D U-Net models are included. Detailed information for each dataset can be found in Dataset_Information.xlsx.

Round 12 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of pdf malware classification AIs trained Contaigio dataset feature vectors. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 120 AI models using a small set of model architectures.

This is a research data set extracted from NIST Standard Reference Database 103b and contains raw experimental data for viscosity and surface tension of binary mixtures.

Raw data used to investigate the use of Wi-Fi signals to monitor human respiratory motion. Data are from an anatomically correct breathing manikin. These data have been published in "Monitoring Respiratory Motion With Wi-Fi CSI: Characterizing Performance and the BreatheSmart Algorithm," DOI: 10.1109/ACCESS.2022.3230003Each figure from the publication is broken up into its own folder. Within each folder, the raw real/imaginary data from the Wi-Fi channel state information frames can be found.