AndreaBozzo/ceres-open-data-index huggingface dataset:
NIST/NIJ DART-MS Data Interpretation Tool

Direct Analysis in Real Time Mass Spectrometry (DART-MS) is an analytical chemistry technology that is being increasingly employed in forensic applications. This form of mass spectrometry rapidly yields rich structural information about an analyte with minimal sample preparation. The challenge with DART-MS data, much like other data generated with high throughput technologies, lies in the data interpretation. This is especially true when the analyzed samples are multi-component mixtures like seized drug evidence. The NIST/NIJ DART-MS Data Interpretation Tool (DIT) is a freely available and open-source software tool developed to support the interpretation of in-source collision induced dissociation (is-CID) DART-MS data. The NIST/NIJ DART-MS DIT can be used to view reference mass spectra from DART-MS spectral libraries, search query DART-MS mass spectra of mixtures against reference libraries, using the Inverted Library Search Algorithm, and generate printable reports from search results. Several of the features, including the formatting of generated reports, were iteratively designed with input from local, state, and federal forensic practitioners, ensuring that the program is intuitive and usable for the expected users.

Tags: dart-ms, forensic-chemistry, ilsa, seized-drug-analysis, source-code
Organization: National Institute of Standards and Technology
TV_VTT (TrecVid Video-To-Text) Dataset

This dataset contains short videos (ranging from 3 seconds to 10 seconds) from TRECVID VTT task from 2016 to 2024. There are 73,893 videos with captions. Each video has between 2 and 5 captions, which have been written by dedicated annotators hired by NIST.

Tags: image-captioning, video-captioning, video-retrieval, video-to-text
Organization: National Institute of Standards and Technology
Digital control of a superconducting qubit using a Josephson pulse generator at 3 K

Included here are data used to generate figures from the paper "Digital control of a superconducting qubit using a Josephson pulse generator at 3 K".Abstract: Scaling of quantum computers to fault-tolerant levels relies critically on the integration of energy-efficient, stable, and reproducible qubit control and readout electronics. In comparison to traditional semiconductor control electronics (TSCE) located at room temperature, the signals generated by Josephson junction (JJ) based rf sources benefit from small device sizes, low power dissipation, intrinsic calibration, superior reproducibility, and insensitivity to ambient fluctuations. Previous experiments to co-locate qubits and JJ-based control electronics resulted in quasiparticle poisoning of the qubit; degrading the qubit's coherence and lifetime. In this paper, we digitally control a 0.01~K transmon qubit with pulses from a Josephson pulse generator (JPG) located at the 3~K stage of a dilution refrigerator. We directly compare the qubit lifetime $T_1$, coherence time $T_2^*$, and thermal occupation $P_{th}$ when the qubit is controlled by the JPG circuit versus the TSCE setup. We find agreement to within the daily fluctuations on $\pm 0.5~\mu$s and $\pm 2~\mu$s for $T_1$ and $T_2^*$, respectively, and agreement to within the 1\% error for $P_{th}$. Additionally, we perform randomized benchmarking to measure an average JPG gate error of $2.1 	imes 10^{-2}$. In combination with a small device size ($<25$~mm$^2$) and low on-chip power dissipation ($\ll 100~\mu$W), these results are an important step towards demonstrating the viability of using JJ-based control electronics located at temperature stages higher than the mixing chamber stage in highly-scaled superconducting quantum information systems

Tags: cryogenic-control, josephson-junction-jj, quantum-computing, scalable, single-flux-quantum-sfq, superconductor, transmon, voltage-metrology
Organization: National Institute of Standards and Technology
Simulation Files (.prj and .cvf) for Virus Particle Exposure in Residences (ViPER) Webtool

This dataset is comprised of the .prj and .cvf files used to build the database for the Virus Particle Exposure in Residences (ViPER) Webtool, a single zone indoor air quality and ventilation analysis tool developed by the National Institute of Standards and Technology (NIST).

Tags: airflow, contam, contaminants, occupant-exposure, residential, simulation, viper, virus-particle
Organization: National Institute of Standards and Technology
Python tools for measuring filament defects in embedded 3D printing

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J. Seppala, Suppression of filament defects in embedded 3D printing, 2022, submitted for publication

Tags: 3d-printing, additive-manufacturing, computer-vision, digital-image-analysis, opencv, python
Organization: National Institute of Standards and Technology
High Energy Arcing Fault (HEAF) Experiments - Medium Voltage Electrical Enclosures - Video

This dataset includes high definition video, thermal imaging video, and still images captured from the video.  The video was recorded during full-scale High Energy Arcing Fault experiments in medium voltage electrical enclosures.

Tags: arc-flash, electric-arc, electrical-enclosure, fire-probabilistic-risk-assessment, high-energy-arcing-fault
Organization: National Institute of Standards and Technology
Python tools for OpenFOAM simulations of filament shapes in embedded 3D printing, Version 1.1.0

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. OpenFOAM is an open source computational fluid dynamics solver. This repository contains the following Python tools: - Tools for generating input files for OpenFOAM v1912 or OpenFOAM v8 tailored to a conical or cylindrical nozzle extruding a filament into a static support bath. - Tools for monitoring the status of OpenFOAM simulations and aborting them if they are too slow. - Tools for moving output files between storage locations. (For example, it can automatically move all files to a server, but only necessary files to your hard drive) - Tools for generating images and tables from the 3D time series. - Tools for compiling images into videos. - Tools for analyzing, summarizing, and plotting data.This version is associated with the paper:Friedrich, L.M., Gunther, R.T. & Seppala, J.E. (2022) Simulated stress mitigation strategies in embedded 3D bioprinting, submitted for publication

Tags: 3d-printing, additive-manufacturing, bioprinting, computational-fluid-dynamics, embedded-3d-printing, embedded-ink-writing, openfoam, polymer, rheology
Organization: National Institute of Standards and Technology
OpenFOAM simulations of stress mitigation strategies in embedded 3D bioprinting

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Embedded 3D printing is particularly useful for bioprinting, wherein a cell-laden ink is extruded through the nozzle. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. This dataset focuses on the effect of common cell protection strategies on the extrusion of single lines and stresses imposed on cells. OpenFOAM is an open source computational fluid dynamics solver. This work used OpenFOAM 8 on a computing cluster. OpenFOAM input files were generated using Python 3.7. Output files were analyzed using Paraview 5.8.0 and Python 3.7. Associated code can be found on Github: https://github.com/usnistgov/openfoamEmbedded3DP, doi:10.18434/mds2-2602 This data is described in: Friedrich, L. M., Gunther, R. T., & Seppala, J. E. (2022). Simulated stress mitigation strategies in embedded 3D bioprinting, submitted for publication.

Tags: 3d-printing, extrusion, herschel-bulkley, openfoam, rheology, support-bath, surface-tension
Organization: National Institute of Standards and Technology
A Materials Properties Dataset for Elastomeric Foam Impact Mitigating Materials

The database includes mechanical data for structure-properties relationships and mechanical modeling of elastic impact protection foams from a variety of imaging (micro-computed tomography, digital image correlation) and force-sensing instruments (dynamic mechanical analysis, universal test system) under a wide range of experimental conditions and modes. The data repository includes directories for: dynamic mechanical analysis raw data, results, and analysis tools; intermediate rate (servo-hydraulic UTS based) raw data including 2D digital image correlation (DIC) images, results, and analysis tools; quasi-static rate (electro-mechanical UTS based) raw data including 2D digital image correlation (DIC), results, and analysis tools; micro-computed tomography data including raw volume images, filtered images, binarized images, other results, and analysis tools; and, instrumented drop tower data including backface force, high speed video, and results and analyzed data, Fourier Transform Infrared (FTIR) spectra, and differential scanning calorimetry (DSC) data.For more information see the readme and data documentation in each respective directory. A paper describing data collection, analysis, and database documentation is available here: https://doi.org/10.1038/s41597-023-02092-4. A repository containing example usage code is available at: https://github.com/materials-data-facility/foam_db. File formats for data include .txt, .xls, .tri, .tprc, .rcp, .py, .m, .csv, .mat, .vtk, .spa, .exp, .stl, and .tif.

Tags: constitutive-data, digital-image-correlation, dynamic-mechanical-analysis, elastomer, elastomers, foam, force-displacement-data, impact-mitigating-materials, impact-protection, material-data, material-modeling, materials-data, materials-databases, materials-datasets, micro-computed-tomography, strain-fields, stress-strain-data, structure-property, time-temperature-superposition, universal-test-machine, vinyl-nitrile, volumetric-image-analysis
Organization: National Institute of Standards and Technology
NIST Fingerprint Registration and Comparison Tool (NFRaCT)

The NIST Fingerprint Registration and Comparison Tool (NFRaCT) is a cross-platform GUI application which allows a user to load a pair of fingerprint images, find corresponding points in both images, register and crop the images, and finally compute a series of measurements on the registered images as described in NIST Special Publication 500-336

Tags: biometrics, fingerprint, image-analysis
Organization: National Institute of Standards and Technology
Data for "A distributed theory for contactless interconnects at terahertz frequencies"

Included here are figures and other relevant data from the paper "A distributed theory for contactless interconnects at terahertz frequencies".  Abstract: Here we test a multimodal model for distributed contactless interconnects by comparing it to 3D full-wave simulations. In comparison to 3D simulations, the model offers insight into how the interconnect works and reduces the computational cost of estimating the interconnect?s performance. We predict the performance of four distributed contactless interconnects and find good agreement between our multimodal model and 3D simulations up to 1 THz. All the interconnects have less than 1 dB insertion loss in their first pass bands, highlighting the opportunity offered by contactless interconnects.

Tags: amplifier, electronics, frequency-comb, frequency-combiner, microwave, on-wafer, photomixers, terahertz
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-summary-jan2022-holdout

Round 9 Holdout DatasetThis is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. 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 410 Sentiment Classification, Named Entity Recognition, and Extractive Question Answering AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - object-detection-jul2022-train

Round 10 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 on the COCO 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 144 AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - image-classification-sep2022-train

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. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
Data Publication: Metallurgical Analysis and Conservation of Turbine Blades from Recovered Apollo F-1 Engines

This data set includes data and metadata about a metallurgical analysis and conservation of turbine blades from a recovered F-1 engine from the Apollo 16 Saturn V rocket. This data set includes:  images of the as recovered turbine blades, cross sections of the recovered turbine blades after vacuum impregnation, x-ray fluorescence (XRF) and energy dispersive spectroscopy (EDS) measurements of the recovered turbine blades.

Tags: space-program-superalloy-turbine-marine-corrosion-metallography
Organization: National Institute of Standards and Technology
NIST Excerpts Benchmark Data

The NIST Excerpts Benchmark Data are a set of target data for deidentification algorithms. The data are configured to work with "SDNist: Synthetic Data Report Tool", a package for evaluating synthetic data generators: https://github.com/usnistgov/SDNist. An installation of SDNist will download the data resources automatically. Jan 2025 -- Benhcmark Excerpts: - NIST American Community Survey (ACS) Data Excerpts, 24 demographic features over 40k records,- NIST Survey of Business Owners (SBO) Data Excerpts, 130 demographic and financial features over 161k recordsThe data are curated subsets of U.S. Census Bureau products.

Tags: american-community-survey, demographic-data, privacy, sdnist, synthetic-data
Organization: National Institute of Standards and Technology
Single-track laser scan cross-sectional micrographs on IN625 and IN718 bare plates with melt pool depth and width measurements

Single-track laser scans were produced with Yb-fiber lasers on bare plates of IN625 and IN718 using three different laser powder bed fusion machines. The laser power, scan speed, and laser spot  diameter varied. Tracks were cross-sectioned and metallographically prepared. Optical micrographs were taken on etched samples. Melt pool depth and width measurements were made on optical micrographs. The dataset includes optical micrographs and melt pool width and depth measurements. These are supplemental experiments to the single-track laser scans for Additive Manufacturing Benchmark 2018 and 2022  challenges (https://www.nist.gov/ambench/am-bench-data-and-challenge-problems-0). Some of the data is associated with publications (1) https://doi.org/10.1016/j.jmapro.2021.10.053  and (2) https://doi.org/10.1007/s40192-022-00289-w.Users are strongly encouraged to first review the ?Master_TrackList_Measuremetns.xlsx? file for description of each image file.

Tags: laser-powder-bed-fusion, melt-pool, microstructure, process-maps
Organization: National Institute of Standards and Technology
Additive Manufacturing Benchmark 2022 Schema

This resource is the implementation in XML Schema [1] of a data model that describes the Additive Manufacturing Benchmark 2022 series data. It provides a robust set of metadata for the build processes and their resulting specimens and for measurements made on these in the context of the AM Bench 2022 project.The schema was designed to support typical science questions which users of a database with metadata about the AM Bench results might wish to pose. The metadata include identifiers assigned to build products, derived specimens, and measurements; links to relevant journal publications, documents, and illustrations; provenance of specimens such as source materials and details of the build process; measurement geometry, instruments and other configurations used in measurements; and access information to raw and processed data as well as analysis descriptions of these datasets.This data model is an abstraction of these metadata, designed using the concepts of inheritance, normalization, and reusability of an object oriented language for ease of extensibility and maintenance. It is simple to incorporate new metadata as needed.A CDCS [2] database at NIST was filled with metadata provided by the contributors to the AM Bench project. They entered values for the metadata fields for an AM Bench measurement, specimen or build process in tabular spreadsheets. These entries were translated to XML documents compliant with the schema using a set of python scripts. The generated XML documents were loaded into the database with a persistent identifier (PID) assigned by the database.[1] https://www.w3.org/XML/Schema[2] https://www.nist.gov/itl/ssd/information-systems-group/configurable-data-curation-system-cdcs/about-cdcs

Tags: additive-manufacturing, build-plate, data-model, laser-absorptivity, material-composition, mechanical-testing, radiography
Organization: National Institute of Standards and Technology
NDN-DPDK: High-Speed Named Data Networking Forwarder

NDN-DPDK is a set of high-performance Named Data Networking (NDN) programs developed with Data Plane Development Kit (DPDK). It includes a network forwarder and a traffic generator. https://github.com/usnistgov/ndn-dpdk

Tags: information-centric-networking, named-data-networking
Organization: National Institute of Standards and Technology
Variability and Bias in Microbiome Metagenomic Sequencing: an Interlaboratory Study Comparing Experimental Protocols

This repository provides analysis code and results produced during evaluation of metagenomic sequencing (MGS) data collected through the Mosaic Standards Challenge. The Mosaic Standards Challenge asked participating laboratories analyze the same set of 7 samples using their own favored MGS laboratory methods. Each lab submitted their raw sequencing results and protocol information. The resulting MGS data was analyzed through a common bioinformatic pipeline and then evaluated to determine the effects of methodological choices. 

Tags: microbiome-methodology-metagenomic-sequencing
Organization: National Institute of Standards and Technology
Cure Kinetics of Advanced Epoxy Molding Compound Using Dynamic Heating Scan

The data are associated with figures in R. Tao, S. P. Phansalkar, A. M. Forster, B. Han, Investigation of Cure Kinetics of Advanced Epoxy Molding Compound Using Dynamic Heating Scan: An Overlooked Second Reaction, 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), Orlando, Florida, May 30 - June 2, 2023. https://doi.org/10.1109/ECTC51909.2023.00225

Tags: advanced-packaging, differential-scanning-calorimetry, filled-polymers, glass-transition, heat-of-reaction, semiconductors, thermal-gravimetric-analysis, thermosets
Organization: National Institute of Standards and Technology
Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning

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. The code employs principal component analysis for dimensionality reduction, learns the resulting datasets' latent dimensionality, and completes Gaussian mixture modeling and fuzzy c-means clustering.*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

Tags: chemical-characterization, environment, gc-ms, machine-learning, mass-spectrometry, microplastic, nanoplastics
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - object-detection-feb2023-train

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. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology