AndreaBozzo/ceres-open-data-index huggingface dataset:
Determining angle of arrival of radio frequency fields using subwavelength, amplitude-only measurements of standing waves in a Rydberg atom sensor

Deep subwavelength RF imaging with atomic Rydberg sensors has overcome fundamental limitations of traditionalantennas and enabled ultra-wideband detection of omni-directional time varying fields all in a compactform factor. However, in most applications, Rydberg sensors require the use of a secondary strong RF referencefield to serve as a phase reference. Here, we demonstrate a new type of Rydberg sensor for Angle-of-Arrival(AoA) sensing which utilizes subwavelength imaging of standing wave fields. By placing a metallic plate withinthe Rydberg cell, we can determine the AoA independent of the strength of incoming RF field and without requiringa secondary strong RF phase reference field. We perform precision AoA measurements with a roboticantenna positioning system for 4.2, 5.0, and 5.7 GHz signals and demonstrate a 1.7◦ polar angular resolutionfrom 0◦ to 60◦ AoA and 4.1◦ over all possible angles.

Tags: atomic-physics, electric-field, fields-strength, receivers, rydberg-atoms, volts-meter
Organization: National Institute of Standards and Technology
NIST/NIJ Characterized Authentic Drug Sample (CADS) Project

Development and validation of new analytical or data tools for seized drug analysis require demonstration of the approach's ability to accurately detect the components of complex, realistic drug mixtures. However, gaining access to these types of samples, or the data from these types of samples, can be difficult. To help address this gap, the Characterized Authentic Drug Samples (CADS) project aims to: 1) source authentic seized drug samples from collaborating forensic laboratories, 2) characterize the samples using a variety of analytical tools, and 3) make available small amounts of the samples, as well as the corresponding data, to researchers and practitioners.This dataset provides pdf reports of analysis for all available CADS samples.Disclaimer: Certain commercial products are identified in order to adequately specify the procedure; this does not imply endorsement or recommendation by NIST, nor does it imply that such products are necessarily the best available for the purpose. A portion of this work was supported by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice.

Tags: seized-drugs-validation-gc-ms-dart-ms-ftir
Organization: National Institute of Standards and Technology
NNDSS - TABLE 1DD. Rubella to Rubella, congenital syndrome

NNDSS - TABLE 1DD. Rubella to Rubella, congenital syndrome - 2021. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

Notice: Due to data processing issues at CDC, data for the following jurisdictions may be incomplete for week 7: Alaska, Arizona, California, Connecticut, Delaware, Florida, Hawaii, Louisiana, Maryland, Michigan, Missouri, North Dakota, New Hampshire, New York City, Oregon, Pennsylvania, and Rhode Island.

Note: 
This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html. 

Footnotes:
U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.
-: No reported cases — The reporting jurisdiction did not submit any cases to CDC.
N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.
NN: Not nationally notifiable — This condition was not designated as being nationally notifiable.
NP: Nationally notifiable but not published.
NC: Not calculated — There is insufficient data available to support the calculation of this statistic.
Cum: Cumulative year-to-date counts.
 Max: Maximum — Maximum case count during the previous 52 weeks.
  * Case counts for reporting years 2020 and 2021 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. 
†Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).

Tags: 2021, nedss, netss, nndss, rubella, rubella-congenital-syndrome, wonder
Organization: U.S. Department of Health & Human Services
Python Package for Generating LAMMPS Input Scripts for Alchemical Processes: generate_alchemical_lammps_inputs

The parsers featured in this module are constructed to parse LAMMPS output files, (i.e., dump files), containing data for given potential energy values (an approximation of the Hamiltonian), U_{𝞴,𝞴'}, at specified values of 𝞴 and 𝞴', where  varies from one to zero scaling the potential between solute and solvent in a reversible manner. The system is equilibrated at a 𝞴-state and energy values are evaluated at alternative 𝞴 values denotes by, 𝞴'. Because generating the input files can be cumbersome, functions have been included to generate the appropriate sections. This includes input scripts that generate formatted output files for the case where U has a separable dependence on 𝞴 or for analysis using thermodynamic integration (TI), Bennett Acceptance Ratio (BAR), and Multi-state Bennett Acceptance Ratio (MBAR). These resulting output files are generally formatted text files which can be readily analyzed using the python package, alchemlyb*. An example is available for benzene solvated in water and a solvated Lennard-Jones dimer [DOI: 10.18434/mds2-3637]. *Certain commercial equipment, instruments, or materials are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

Tags: alchemical, alchemistry, bennett-acceptance-ratio, lammps, molecular-dynamics, python, thermodynamic-integration
Organization: National Institute of Standards and Technology
InGaAs Trap Detector: Advancing Toward a Short-Wave Infrared Standard with 1% Uncertainty

No additional requirements: Working data, derived data (OISM-provided Level 1 storage) Individual user responsibility: Publishable results, Published results (OISM-provided, Level 2 storage) Not available to the public: Working data, derived data (experimental raw data), and publishable results. Made available to the public as described below: Published results that appear in publicly accessible reports, such as in  journals and conference proceedings.

Tags: calibration, ingaas-photodiode, metrology, trap-detector
Organization: National Institute of Standards and Technology
CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields) is a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform provides robust evaluation beyond conventional metrics such as energy, focusing on complex properties including elastic constants, phonon spectra, defect formation energies, surface energies, and interfacial and amorphous phase properties. Utilizing 16 graph-based MLFF models including ALIGNN-FF, CHGNet, MatGL, MACE, SevenNet, ORB and OMat24, the CHIPS-FF workflow integrates the Atomic Simulation Environment (ASE) with JARVIS-Tools to facilitate automated high-throughput simulations. Our framework is tested on a set of 104 materials, including metals, semiconductors and insulators representative of those used in semiconductor components, with each MLFF evaluated for convergence, accuracy, and computational cost. Additionally, we evaluate the force-prediction accuracy of these models for close to 2 million atomic structures. By offering a streamlined, flexible benchmarking infrastructure, CHIPS-FF aims to guide the development and deployment of MLFFs for real-world semiconductor applications, bridging the gap between quantum mechanical simulations and large-scale device modeling.Enter description here...

Tags: force-field, machine-learning, materials, semiconductors
Organization: National Institute of Standards and Technology
Training and Validation Datasets for Neural Network to Fill in Missing Data in EBSD Maps

This dataset consists of the synthetic electron backscatter diffraction (EBSD) maps generated for the paper, titled "Hybrid Algorithm for Filling in Missing Data in Electron Backscatter Diffraction Maps" by Emmanuel Atindama, Conor Miller-Lynch, Huston Wilhite, Cody Mattice, Günay Doğan, and Prashant Athavale. The EBSD maps were used to train, test, and validate a neural network algorithm to fill in missing data points in a given EBSD map.The dataset includes 8000 maps for training, 1000 maps for testing, 2000 maps for validation. The dataset also includes noise-added versions of the maps, namely, one more map per each clean map.

Tags: electron-backscatter-diffusion-ebsd-maps, image-inpainting, neural-networks, training-data
Organization: National Institute of Standards and Technology
Demonstration of dispersion gas barometry

Supplemental material to the 2025 article  "Demonstration of dispersion gas barometry"  by  Y. Yang, J.A. Stone, and P.F. Egan.The archive file contains two-color data for the gases helium, neon, argon, and nitrogen. Two-color data means measured quadruplets of pressure and temperature together with refractivity at two wavelengths 1542.3912 nm (194.368624 THz)  and 632.9919 nm (473.611873 THz).Two analysis scripts are included:1.  plotHelium.py:  performs the helium analysis to deduce the cavity distortion coefficient kappa and the conversion factor epsilon_p needed to realize the optical pressure scale.   The script reproduces Fig. 2 from the article.2. plotGases.py:  analyzes the gases neon, argon, and nitrogen and deduces the two parameters describing dispersion polarizability A_epsilon and A_2.  The script reproduces Fig 3 from the article.Additionally, the script "pgtProp.py" is a library function, which offers best knowledge (as of January 2025) of gas properties to be used in Polarizing Gas Thermometry.  The library functions synthesize the optical and thermophysical properties of helium, neon, argon, and nitrogen.  The synthesis combines literature sources plus the measurement results from the main text.

Tags: optical-pressure-scale, polarizing-gas-thermometry, refractometry, thermodynamic-metrology
Organization: National Institute of Standards and Technology
A mathematical framework to correct for compositionality in microbiome datasets

This repository provides the raw data and analysis code used to demonstrate the implementation of a mathematical framework that uses internal standards to correct for compositionally in MGS datasets. Some of the data contained herein was previously published and is reanalyzed in the context of this new framework, while other datasets represent newly analyzed samples. Please refer to the associated publication for further discussion and a full exploration of the results.

Tags: compositionality, experimental-design, internal-standard, metagenomic-sequencing, scaled-abundance
Organization: National Institute of Standards and Technology
Data for Advanced Metrology Suite for Linking Residual Stress to FundamentalProperties of Thermoset Packaging Materials

This repository contains data generated to support and ECTC 2025 conference manuscript titled "Advanced Metrology Suite for Linking Residual Stress to Fundamental Properties of Thermoset Packaging Materials". It contains differential scanning calorimetry (DSC), rheology, digital image correlation (DIC), vapor uptake, and stress bench data on and example commercially available encapsulant epoxy material. The goal of the study and data is to (a) demonstrate these techniques, and (b) better understand the cure kinetics, liquid-to-solid transition, and residual stresses in for this material system. More complete descriptions of the methods and finding are available in the associated manuscript.The dataset contain about 2.5 GB of data, most of which by size is DIC images in .7z compressed folders. Most data is provided in .csv, .xslx, and .mat files. Resultant figures are included for reference in .png, .tiff or similar formats.

Tags: cure-kinetics, cure-shrinkage, differential-scanning-calorimetry, digital-image-correlation, encapsulant, epoxy, residual-stress, rheology, thin-film-stress, vapor-saturation
Organization: National Institute of Standards and Technology
Advanced Characterization of the Cure Kinetics of a Liquid Encapsulant

The data are associated with figures in A. P. Kotula, R. Tao, J. Tu, Y. Lee, G. A. Holmes, Advanced Characterization of the Cure Kinetics of a Liquid Encapsulant, 2025 IEEE 75th Electronic Components and Technology Conference (ECTC), Dallas, Texas, May 27 - 30, 2025.

Tags: advanced-packaging, chips-metrology, cure-kinetics, microelectronics-encapsulation, optoelectronics, rheology
Organization: National Institute of Standards and Technology
Reference functional model for production in a circular economy

Reference functional model for the activities and processes involved in production (e.g., designing, manufacturing, and recovering products/materials) in a circular economy. The model includes inputs and outputs of each activity, as well as factors that constrain and enable the activities (controls and mechanisms), using the IDEF0 methodology. It provides a baseline scenario that is relevant for stakeholders across  product development and supply chain, including business strategists, material suppliers, designers, manufacturers, consumers, and recovers/recyclers. The model is available as downloadable files and hosted on a website to view and explore directly.

Tags: circular-economy, design, end-of-life-treatment, functional-model, manufacturing, production, system-model
Organization: National Institute of Standards and Technology
Methane emissions estimates and related data sets for Washington DC and Baltimore, MD urban areas

This dataset accompanies the publication below. Here we provide the results of the analysis, including gridded estimates of methane emissions for the Washington/Baltimore area at 0.01 degrees resolution and monthly from 2017 through 2021.  All files available for unrestricted public use with citation of this data set *except* the Observations_XX files.  Observations are for non-commercial use only, as they are purchased from a private data provider (AEM, Inc. via Earth Networks, Inc.).Methane Emissions Show Recent Decline but Strong Seasonality in Two US Northeastern CitiesAnna Karion, Subhomoy Ghosh, Israel Lopez-Coto, Kimberly Mueller, Sharon Gourdji, Joseph Pitt, and James WhetstoneEnvironmental Science & Technology 2023 57 (48), 19565-19574 DOI: 10.1021/acs.est.3c05050

Tags: emission-inventories, ghg-concentration-measurements, methane-measurements-and-models, urban-greenhouse-gas-domes
Organization: National Institute of Standards and Technology
C-V2X Interoperability Analyzer

This software is a cellular vehicle-to-everything (C-V2X) interoperability analyzer tool developed at the National Institute of Standards and Technology (NIST) Internet of Thing (IoT) Devices Interoperability Testbed. It can be used to automatically analyze and assess C-V2X protocol compatibility and interoperability among commercial on-board units (OBUs) and road-side units (RSUs) based on the IEEE 1609.2, IEEE 1609.3, and SAE J2735 standards to achieve and assure their interoperability.

Tags: c-v2i, c-v2v, c-v2x, communication, compatibility, connectivity, interoperability, interoperability-analyzer, obu, rsu, software-tool
Organization: National Institute of Standards and Technology
Radio Frequency Interference Measurements of Industrial Machinery

The 2.4 GHz ISM band is shared by Wi-Fi, Bluetooth, Wireless HART, ISA100.11a, and several other industrial wireless systems. Our dataset contains comprehensive electromagnetic interference (EMI) measurements from machinery taken in various industrial environments. The measurements were taken at two frequencies: 900 MHz, 2.4 GHz. This dataset may be useful for understanding EMI emitters in factories and can be instrumental in developing interference mitigation strategies, aiding in RF band selection and enterprise frequency planning, improving wireless technology, and informing communications standardization activities such as the IEEE 3388 industrial wireless performance evaluation standard.The interference measurements were taken in the following types of industrial environments:1) Infrared Curing Machine: Curing process using infrared radiation producing EMI across the 2.4 GHz band, 2) Crane with an Unshielded VFD: Overhead gantry crane operating at 900 MHz with an unshielded variable frequency drive (VFD) causing broadband interference, 3) Microwave Dryer: Two independent sets of measurements of a microwave oven baking machines used for a ceramic drying process. Multiple magnetrons are used with a power output of 1100 Watts each, 4) Unidentified Interference: General recording of the 2400 MHz band capturing both wireless network traffic and an unidentified broadband RFI emitter possibly caused by an unshielded VFD.NIST Disclaimer:  Certain commercial equipment, instruments, or materials are identified in this publication in order to describe the experimental procedures and data adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Tags: 5g, electromagnetic-interference-emi, ieee-802-11, industrial-control, industrial-wireless, radio-frequency-interference-rfi, wi-fi, wireless
Organization: National Institute of Standards and Technology
NNDSS - TABLE 1T. Invasive pneumococcal disease, age<5 , Confirmed to Invasive pneumococcal disease, age <5, Probable

NNDSS - TABLE 1T. Invasive pneumococcal disease, age<5 , Confirmed to Invasive pneumococcal disease, age <5, Probable  - 2019. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents. 

Note: 
This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html. 

Footnotes:
U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.
-: No reported cases — The reporting jurisdiction did not submit any cases to CDC.
N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.
NN: Not nationally notifiable — This condition was not designated as being nationally notifiable.
NP: Nationally notifiable but not published — CDC does not have data because of changes in how conditions are categorized.
Cum: Cumulative year-to-date counts.
 Max: Maximum — Maximum case count during the previous 52 weeks.
* Case counts for reporting years 2018 and 2019 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the US, a US territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-US Residents' category. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. 
† Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). 
§ Includes drug resistant and susceptible cases of Invasive Pneumococcal Disease.

Tags: 2019, age5, confirmed, invasive-pneumococcal-disease, nedss, netss, nndss, probable, wonder
Organization: U.S. Department of Health & Human Services
Gateway for Co-Simulation using ns-3

The Internet of Things (IoT) is comprised of networks of physical, computational, and human components that coordinate to fulfill time-sensitive functions in a shared operating environment. Development and testing of IoT systems often utilizes modeling and simulation, whether to analyze potential performance gains of new technologies or develop robust digital twins to support future operations and maintenance. However, the complexity and scale of IoT means that individual simulators are often inadequate to simulate the real-world dynamics of such systems, and simulators must be combined with other software or hardware.The National Institute of Standards and Technology (NIST) has developed a software module that extends the ns-3 network simulator with a new capability to communicate with external software and hardware at runtime. This software facilitates the development of co-simulations where ns-3 models can synchronize and exchange data with external processes to develop higher-fidelity simulations. The software is open-source and available on the NIST GitHub.

Tags: automated-vehicles, co-simulation, cyber-physical-systems, internet-of-things, network-simulation, ns-3, software
Organization: National Institute of Standards and Technology