The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications.

SolidWorks Part file (CAD file) for a CoCr "lattice" structure additively manufactured in a laser bed powder fusion (LPBF) system at NIST.

The CYCLE_D-HX software package simulates the performance of single-component refrigerants and refrigerant blends in subcritical vapor-compression refrigeration cycles. The basic system simulated by CYCLE_D-HX consists of a compressor, discharge line, condenser, expansion device, evaporator, compressor suction line, and an optional liquid-line/suction-line heat exchanger. The other cycles may contain a second compressor, one or two economizers, or an intercooler.

This data set provides heat detector temperatures in a single-story ranch structure with a living room, kitchen, dining room, and three bedrooms. 20000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various fire conditions and door opening conditions. The fire is described based on t-squared law. The peak heat release rate and time to peak range from approximately 1667 kW to 4620 kW and from 210 s to 1540 s, respectively. A detailed description of this work can be found in Ref.

The use of the microscope to assess cell viability is an inherently qualitative and subjective process. This project demonstrates a new absorbance microscopy modality for quantitative single-cell assessment of cell viability via trypan blue staining. This dataset consists of .tif images of live and dead stained and unstained Jurkat cells as well as the numerical data related to the corresponding journal article. The images were collected via brightfield microscopy through a 610nm bandpass filter.

Round 4 Test DatasetThe data being generated and disseminated is the test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. 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.

Round 4 Holdout DatasetThe data being generated and disseminated is the holdout data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. 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.

Transmission electron microscope tomographic data of aligned carbon nanotubes in epoxy at volume fractions of 0.44%, 2.6%, 4%, and 6.9%. Reduced data and analysis are available at https://doi.org/10.1021/acsnano.5b01044 .

Data files in this repository contain a static archive of all the observational and model data required to construct fossil-fuel enhancements at tower sites in Los Angeles (LA_data.csv) and DC-Baltimore (DCBalt_data.csv). These were used in the analyses detailed and presented in the publication "The impact of COVID-19 on CO2 emissions in the Los Angeles and Washington DC/Baltimore metropolitan areas" by Yadav et al.

Round 4 Train DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. 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.