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.

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.

Digital light processing (DLP) vat photopolymerization (VP) additive manufacturing (AM) uses patterned UV light to selectively cure a liquid photopolymer into a solid layer. Subsequent layers are printed on to preceding layers to eventually form a desired 3 dimensional (3D) part. This data set characterizes the 3D geometry of a single layer of voxels (volume pixels) printed with photomasks assigned random intensity levels at every pixel. The masks are computer generated, then printed onto a glass cover slide.

Videos and .stl files from Synergistic Fire Resistance of Nanobrick Wall Coated 3D Printed Photopolymer Lattices.The videos are from the fire tests of 3D printed photopolymer parts, from which time series still images were acquired. In these tests, printed parts (with or without a nanocoating) are subjected to direct blowtorch exposure until part failure (defined as when the part shatters). Most videos include time after torch removal while the part continues to burn.The .stl files are the files used to 3D print the parts studied in this work.

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.

Gas speed measurements using hot-wire anemometers (HWA) on a commercial laser powder bed fusion (LBPF) machine were recorded as a function of position (X, Y, Z) and nozzle type (standard and grid).

Round 11 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of image classification AIs trained on synthetic image data build from Cityscapes. 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 288 AI models using a small set of model architectures.

This repository contains scripts and supporting information for a set of demonstrations related to the 2022 AM-Bench challenge series.In particular, the data used in this demonstration comes from the 2018 AM-Bench challenge documented at https://www.nist.gov/ambench/amb2018-02-description.The script queries the AM-Bench 2018 repository located at https://ambench.nist.gov/, then processes the returned XML-based results, does some image processing, and generates plots of melt pool dep

Macro-scale tensile data on additively manufactured (AM) IN625 in the as-built microstructural condition at room temperature and at different strain rates are provided in this data publication. The specimens are machined in accordance with ASTM-E8 subsize specimen with a gauge width of 6 mm and a gauge thickness of 4 mm. Three specimens are tested at a nominal strain rate of 0.001 1/s and 2 specimens are tested at a nominal strain rate of 0.01 1/s on a servo hydraulic material testing machine using a constant crosshead displacement rate of 0.03175 mm/s and 0.3175 mm/s, respectively.

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.