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Superfund is a program administered by the EPA to locate, investigate, and clean up the worst hazardous waste sites throughout the United States. Before Superfund, Americans were less aware of how dumping chemical wastes might affect public health and the environment. Hazardous wastes were often left in the open, where they seeped into the ground, flowed into rivers and lakes, and contaminated soil and groundwater. Consequently, where these practices were intensive or continuous, there were uncontrolled or abandoned hazardous waste sites. These sites include abandoned warehouses, manufacturing facilities, processing plants, and landfills. Citizen concern about the extent of this problem prompted Congress in 1980 to establish the Superfund Program to eliminate the health and environmental threats posed by hazardous waste sites. EPA administers the Superfund program in cooperation with individual states and tribal governmentsFRS integrates facility data from EPA's national program systems, other federal agencies, and State and tribal master facility records and provides EPA with a centrally managed, single source of comprehensive and authoritative information on facilities. This data set contains the subset of FRS integrated facilities that link to CERCLIS non-NPL facilities once the CERCLIS data has been integrated into the FRS database. Additional information on FRS is available at the EPA website https://www.epa.gov/enviro/facility-registry-service-frs. These data are considered sensitive and are restricted to internal use only. The download file and map service are accessible only on the EPA intranet. 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This web portal is built using the github repository at https://github.com/usnistgov/SEA-DATA.\nThe purpose of the National Advanced Spectrum and Communications Test Network (NASCTN) Citizens Broadband Radio Service (CBRS) Sharing Ecosystem Assessment (SEA) project is to provide data-driven insight into the CBRS sharing ecosystem\u2019s effectiveness between commercial and Department of Defense (DoD) incumbent systems, and to track changes in the spectrum environment over time. CBRS is the \u201cfirst of a kind\u201d nationwide shared spectrum ecosystem in the 3550-3700 MHz band. The implementation of this project included the installation of sensor systems at selected sites on the east and the west coast of the United States, along with a control and prototyping system in Boulder, Colorado. This data provides time and frequency selective power measurements from 3530 MHz -3710 MHz from summer 2024 to summer of 2026. This data is targeted at the commercial, military and civilian stakeholders of Citizen's Broadband Radio Service (CBRS) that would benefit from in-depth real time and longitudinal analysis of a shared spectrum environment. These data were collected with sensors running the IEEE 802.15.22.3 Spectrum Characterization and Occupancy Sensing (SCOS) sensor system. In addition, the data is formatted and saved using  SigMF (signal metadata format). This project was a collaboration between NIST, NTIA / ITS, NASA and MITRE.","distribution":[{"accessURL":"https://github.com/usnistgov/SEA-DATA","description":"The github repository that is used to build the nist.pages site.","format":"github repository","title":"SEA-Data Web Portal Github Repository"},{"accessURL":"https://pages.nist.gov/SEA-DATA/","description":"A static website describing how to access data from the NASCTN CBRS SEA program.","format":"html","title":"NASCTN CBRS SEA DATA INSTRUCTIONS"}],"identifier":"ark:/88434/mds2-4219","issued":"2026-06-24","keyword":["CBRS","ESC","NASCTN","Naval Radar","RF","SAS","Wireless"],"landingPage":"https://data.nist.gov/od/id/mds2-4219","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2026-06-01 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://nvlpubs.nist.gov/nistpubs/TechnicalNotes/NIST.TN.2359.pdf","https://www.mitre.org/sites/default/files/2026-01/PR-25-3159-spectrum-monitoring-sensor-radio-service-assessment.pdf"],"theme":["Advanced Communications:Wireless (RF)"],"title":"SEA-DATA Web Portal"},"description":"Data Access Instructions for The NASCTN CBRS SEA program. This web portal is built using the github repository at https://github.com/usnistgov/SEA-DATA.\nThe purpose of the National Advanced Spectrum and Communications Test Network (NASCTN) Citizens Broadband Radio Service (CBRS) Sharing Ecosystem Assessment (SEA) project is to provide data-driven insight into the CBRS sharing ecosystem\u2019s effectiveness between commercial and Department of Defense (DoD) incumbent systems, and to track changes in the spectrum environment over time. CBRS is the \u201cfirst of a kind\u201d nationwide shared spectrum ecosystem in the 3550-3700 MHz band. The implementation of this project included the installation of sensor systems at selected sites on the east and the west coast of the United States, along with a control and prototyping system in Boulder, Colorado. This data provides time and frequency selective power measurements from 3530 MHz -3710 MHz from summer 2024 to summer of 2026. This data is targeted at the commercial, military and civilian stakeholders of Citizen's Broadband Radio Service (CBRS) that would benefit from in-depth real time and longitudinal analysis of a shared spectrum environment. These data were collected with sensors running the IEEE 802.15.22.3 Spectrum Characterization and Occupancy Sensing (SCOS) sensor system. In addition, the data is formatted and saved using  SigMF (signal metadata format). This project was a collaboration between NIST, NTIA / ITS, NASA and MITRE.","distribution_titles":["SEA-Data Web Portal Github Repository","NASCTN CBRS SEA DATA INSTRUCTIONS"],"harvest_record":"https://catalog.data.gov/harvest_record/4192cc43-3c9f-4fd1-853a-1668f407473d","harvest_record_raw":"https://catalog.data.gov/harvest_record/4192cc43-3c9f-4fd1-853a-1668f407473d/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-4219","keyword":["CBRS","ESC","NASCTN","Naval Radar","RF","SAS","Wireless"],"last_harvested_date":"2026-10-02T19:54:30.915186","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"sea-data-web-portal-71c99","spatial_centroid":null,"spatial_shape":null,"theme":["Advanced Communications:Wireless (RF)"],"title":"SEA-DATA Web Portal","type":"dataset"},{"_score":14.472305,"_sort":[1790970867906,14.472305,0,"aebd735b-eb6d-4f16-821a-81c41ff63bcb"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Noah Schlossberger","hasEmail":"mailto:noah.schlossberger@nist.gov"},"description":"We investigate the role of magnetic sublevels in Autler\u2013Townes spectra of Rydberg atoms driven by radio-frequency (RF) fields with arbitrary polarization. While conventional treatments predict two symmetric sidebands from independent $m_J$ transitions, experiments have reported additional unexplained spectral features. We show that these arise from elliptical RF polarization, which coherently couples multiple magnetic sublevels and requires a full multi-level treatment.\nWe develop and diagonalize a Hamiltonian including all coupled m_J sublevels, predicting polarization-dependent degeneracies that produce two, three, or four resolved peaks. Using long-wavelength transitions and an anechoic environment we realize homogeneous RF fields that for the first time enable complete resolution of the m_J-dependent dressed states. We observe excellent agreement with theory as the RF ellipticity is varied.\nThese results demonstrate that RF polarization fundamentally modifies Autler\u2013Townes spectra and provide a consistent framework for interpreting magnetic-sublevel structure, with implications for Rydberg-based RF electrometry and polarimetry.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/4157_README.txt","mediaType":"text/plain","title":"readme"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig2.csv","mediaType":"text/csv","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig3.csv","mediaType":"text/csv","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig4.csv","mediaType":"text/csv","title":"Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig5.csv","mediaType":"text/csv","title":"Figure 5"}],"identifier":"ark:/88434/mds2-4157","issued":"2026-07-23","keyword":["Rydberg atoms","atomic physics","electric field","fields strength","receivers","volts/meter"],"landingPage":"https://data.nist.gov/od/id/mds2-4157","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2026-04-13 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum","Physics:Spectroscopy"],"title":"Data associated with \"Resolving magnetic-sublevel structure in Rydberg Autler-Townes spectra with arbitrary RF polarization\""},"description":"We investigate the role of magnetic sublevels in Autler\u2013Townes spectra of Rydberg atoms driven by radio-frequency (RF) fields with arbitrary polarization. While conventional treatments predict two symmetric sidebands from independent $m_J$ transitions, experiments have reported additional unexplained spectral features. We show that these arise from elliptical RF polarization, which coherently couples multiple magnetic sublevels and requires a full multi-level treatment.\nWe develop and diagonalize a Hamiltonian including all coupled m_J sublevels, predicting polarization-dependent degeneracies that produce two, three, or four resolved peaks. Using long-wavelength transitions and an anechoic environment we realize homogeneous RF fields that for the first time enable complete resolution of the m_J-dependent dressed states. We observe excellent agreement with theory as the RF ellipticity is varied.\nThese results demonstrate that RF polarization fundamentally modifies Autler\u2013Townes spectra and provide a consistent framework for interpreting magnetic-sublevel structure, with implications for Rydberg-based RF electrometry and polarimetry.","distribution_titles":["readme","Figure 2","Figure 3","Figure 4","Figure 5"],"harvest_record":"https://catalog.data.gov/harvest_record/16725e56-03f3-4a25-ae99-17ed227a8440","harvest_record_raw":"https://catalog.data.gov/harvest_record/16725e56-03f3-4a25-ae99-17ed227a8440/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-4157","keyword":["Rydberg atoms","atomic physics","electric field","fields strength","receivers","volts/meter"],"last_harvested_date":"2026-10-02T19:54:27.906823","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"data-associated-with-resolving-magnetic-sublevel-structure-in-rydberg-autler-townes-spectr-8191d","spatial_centroid":null,"spatial_shape":null,"theme":["Physics:Atomic, molecular, and quantum","Physics:Spectroscopy"],"title":"Data associated with \"Resolving magnetic-sublevel structure in Rydberg Autler-Townes spectra with arbitrary RF polarization\"","type":"dataset"},{"_score":3.040184,"_sort":[1790970867331,3.040184,0,"30009b2c-077b-47ab-ac27-2fae47312523"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Yamil Simon","hasEmail":"mailto:yamil.simon@nist.gov"},"description":"This mass spectrometry (MS) data set encompasses spectra of the extractables and leachables (E&L) of 100 polymer materials sourced from the Scientific Polymer Products Inc Polymer Samples Kit (see: SciPoly_PolymerSampleKit205_Items.xlsx). Extractions of these polymers were carried out using three HPLC-grade solvents of varying polarity (water, isopropanol, and hexane) with 10 mg polymer/mL solvent for 24 hours at 50 \u00b0C with shaking. To account for solvent volatility, extractions were carried out in 8 mL pressure tubes from Ace Glass Incorporated (Product #8648-25) (rated at 150 psig).\n\nExtraction solutions were subsequently analyzed using three ionization techniques: Electron Ionization (EI) with GC-MS and Electrospray Ionization (ESI) and Atmospheric Pressure Chemical Ionization (APCI) via LC-MS/MS on an Agilent QTOF 6530 and a Bruker timsTOF Pro 2, respectively.\n\nSeveral precautionary measures had to be taken following the extraction procedure. Extracts were centrifuged and filtered using 0.45-micron syringe filters to prevent prohibitively large particles from interfering with chromatography and MS analysis. Moreover, a guard column was used before the LC column to avoid contamination. As such, pressure build-up would often be a concern, so the LC and guard column had to be thoroughly flushed after each analysis batch.\n\nThousands of spectra were processed using in-house software, generating lists of identifications using the most recent versions of the NIST MS libraries via both direct and hybrid search methods. Some monomers were detected under the given extraction conditions, and many plastic additives (e.g., plasticizers, antioxidants, and processing agents) could be identified; some common examples found in many of the polymer standards include oleamide and palmitamide. High-quality unidentified spectra obtained from the extracts are included in a library of annotated recurrent unidentified spectra (ARUS).\n\nLC Chromatographic Separation Method: LC runs were performed using reversed-phase chromatography with an ACQUITY UPLC CSH C18 column (130 \u00c5 (13.0 nm), 1.7 \u00b5m, 2.1 mm x 100 mm), mobile phase A: water, and mobile phase B: acetonitrile (both mobile phases containing 0.1 % formic acid). The chromatographic separation method was as follows: injection volume: 10 \u00b5L, column temperature: 35\u00b0C, flow rate: 0.400 mL/min, and gradient: initial condition of 1.00% B, hold for 1.00 min, linear ramp to 80.00% B in 8.00 min, linear ramp to 99.00% B in 3.00 min, hold for 1.50 min, linear ramp to 1.00% B in 0.50 min, hold for 1.00 min. GC Chromatographic Separation Method: Spectra were obtained using an Agilent 5977B instrument, with GC runs performed using a Restek Rxi-5Sil MS GC Capillary Column (15 m x 250 \u00b5m x 0.25 \u00b5m) under 2 mL/min He flow. The initial oven temperature was 40\u00b0C (held for 3 min), with a ramp rate of 20\u00b0C/min, final temperature of 320\u00b0C, and final hold time of 3.75 min. \n\nReferences:\n\nBerthelette, K.; Walter, T. H. Column screening for the UPLC separation of plastic additives as part of extractables and leachables workflows; 720006777EN; Waters Corporation, MA, USA, 2020.\n\nLee, P. J.; Di Gioia, A. J. Rapid analysis of 25 common polymer additives; 720002488EN; Waters Corporation, Milford, MA, USA, 2008.\n\nSim\u00f3n-Manso, Y.; Erisman, E. P.; Mak, T. D.; Burke, M. C.; Zuber, A.; Yang, X.; Liang, Y.; Neta, P.; Bukhari, T.; Williams, A. J.; et al. NIST Mass Spectral Libraries in the Context of the Circular Economy of Plastics. J Am Soc Mass Spectrom 2025, 36 (2), 439\u2013445. 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Extractions of these polymers were carried out using three HPLC-grade solvents of varying polarity (water, isopropanol, and hexane) with 10 mg polymer/mL solvent for 24 hours at 50 \u00b0C with shaking. To account for solvent volatility, extractions were carried out in 8 mL pressure tubes from Ace Glass Incorporated (Product #8648-25) (rated at 150 psig).\n\nExtraction solutions were subsequently analyzed using three ionization techniques: Electron Ionization (EI) with GC-MS and Electrospray Ionization (ESI) and Atmospheric Pressure Chemical Ionization (APCI) via LC-MS/MS on an Agilent QTOF 6530 and a Bruker timsTOF Pro 2, respectively.\n\nSeveral precautionary measures had to be taken following the extraction procedure. Extracts were centrifuged and filtered using 0.45-micron syringe filters to prevent prohibitively large particles from interfering with chromatography and MS analysis. Moreover, a guard column was used before the LC column to avoid contamination. As such, pressure build-up would often be a concern, so the LC and guard column had to be thoroughly flushed after each analysis batch.\n\nThousands of spectra were processed using in-house software, generating lists of identifications using the most recent versions of the NIST MS libraries via both direct and hybrid search methods. Some monomers were detected under the given extraction conditions, and many plastic additives (e.g., plasticizers, antioxidants, and processing agents) could be identified; some common examples found in many of the polymer standards include oleamide and palmitamide. High-quality unidentified spectra obtained from the extracts are included in a library of annotated recurrent unidentified spectra (ARUS).\n\nLC Chromatographic Separation Method: LC runs were performed using reversed-phase chromatography with an ACQUITY UPLC CSH C18 column (130 \u00c5 (13.0 nm), 1.7 \u00b5m, 2.1 mm x 100 mm), mobile phase A: water, and mobile phase B: acetonitrile (both mobile phases containing 0.1 % formic acid). The chromatographic separation method was as follows: injection volume: 10 \u00b5L, column temperature: 35\u00b0C, flow rate: 0.400 mL/min, and gradient: initial condition of 1.00% B, hold for 1.00 min, linear ramp to 80.00% B in 8.00 min, linear ramp to 99.00% B in 3.00 min, hold for 1.50 min, linear ramp to 1.00% B in 0.50 min, hold for 1.00 min. GC Chromatographic Separation Method: Spectra were obtained using an Agilent 5977B instrument, with GC runs performed using a Restek Rxi-5Sil MS GC Capillary Column (15 m x 250 \u00b5m x 0.25 \u00b5m) under 2 mL/min He flow. The initial oven temperature was 40\u00b0C (held for 3 min), with a ramp rate of 20\u00b0C/min, final temperature of 320\u00b0C, and final hold time of 3.75 min. \n\nReferences:\n\nBerthelette, K.; Walter, T. H. Column screening for the UPLC separation of plastic additives as part of extractables and leachables workflows; 720006777EN; Waters Corporation, MA, USA, 2020.\n\nLee, P. J.; Di Gioia, A. J. Rapid analysis of 25 common polymer additives; 720002488EN; Waters Corporation, Milford, MA, USA, 2008.\n\nSim\u00f3n-Manso, Y.; Erisman, E. P.; Mak, T. D.; Burke, M. C.; Zuber, A.; Yang, X.; Liang, Y.; Neta, P.; Bukhari, T.; Williams, A. J.; et al. NIST Mass Spectral Libraries in the Context of the Circular Economy of Plastics. 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From numerical simulations of SWAP using our laser beam geometry, we find that SWAP provides greater cooling than triangle wave frequency modulation despite the complex polarization environment of a grating MOT. The simulation is confirmed by experimental results, which show a factor of two improvement in transfer efficiency between a dipole-allowed transition grating MOT and an intercombination transition grating MOT.\nThe data show that the intercombination line grating MOT can trap up to three million strontium atoms at an average temperature of 4.9 microkelvin with a lifetime of approximately 0.7 seconds.\n\nInstructions on reproducing the figures in the manuscript are included in 4097_README.txt.","distribution":[{"description":"README file for the .zip archive.","downloadURL":"https://data.nist.gov/od/ds/mds2-4097/4097_README.txt","format":".txt","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4097/figure_generation_data.zip","mediaType":"application/zip","title":"figure_generation_data"}],"identifier":"ark:/88434/mds2-4097","issued":"2026-05-21","keyword":["atomic physics","laser cooling","quantum physics"],"landingPage":"https://data.nist.gov/od/id/mds2-4097","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2026-02-09 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.48550/arXiv.2602.06230"],"theme":["Physics:Atomic, molecular, and quantum"],"title":"Data associated with \"Sawtooth wave adiabatic passage in a grating magneto-optical trap\""},"description":"Numerical values of data points and figure generation scripts for manuscript \"Sawtooth wave adiabatic passage in a grating magneto-optical trap\" submitted for publication and available on ArXiv (2602.06230).\n\nThis data set demonstrates sawtooth wave adiabatic passage (SWAP) in a grating magneto-optical trap (MOT) operating on the intercombination transition of neutral strontium. From numerical simulations of SWAP using our laser beam geometry, we find that SWAP provides greater cooling than triangle wave frequency modulation despite the complex polarization environment of a grating MOT. 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We have also extracted three exemplar channels that capture key features of a work-cell-sized industrial environment from measurements made with the same synthetic 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channel; industrial internet of things; industrial wireless systems; millimeter-wave wireless; synthetic aperture; wireless system."],"landingPage":"https://data.nist.gov/od/id/mds2-4030","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2025-12-03 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Advanced Communications"],"title":"Standardized Approach for Assessing Wireless IoT Devices in Millimeter-Wave Industrial Work-Cell Channels"},"description":"In this paper, we present a standardized, repeatable test approach that replicates key features of measured industrial environments in a fully-characterized hybrid test chamber. The hybrid chamber consists of an anechoic chamber along with movable metallic spheres that provide discrete reconfigurable multipath components. The chamber is characterized by a synthetic-aperture system providing a reference measurement that captures the non-idealities of the chamber along with the intended spatial multipath, completing characterization of the test environment. A key feature of our approach is the propagation of correlated uncertainties through to the final system response \u2013 including uncertainties specific to synthetic apertures such as cable bending due to scanning and inexact positioner locations. The uncertainty analysis provides statistically accurate knowledge of the environment, allowing separation of measurement errors from errors related to the device or system under test. 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High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a Modular Automated Virtualization System (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multi-layer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.\n\nData description: Each figure folder contains a complete set of files necessary to reproduce figures, including Jupyter Notebooks with the figure source code, Adobe Illustrator, and pre-processed data files (hdf5 and pkl). The complete set of all raw data files used in this study is available at Zenodo. [doi: 10.5281/zenodo.14173838].\n\nAcknowledgments: This research was sponsored in part by the Army Research Office (ARO) under Awards No. W911NF-23-1-0110 and W911NF-23-1-0258. We acknowledge support from the European Union through the IGNITE project with grant agreement No. 101069515 and from the Dutch Research Council (NWO) via the National Growth Fund program Quantum Delta NL (Grant No. NGF.1582.22.001). The views, conclusions, and recommendations contained in this paper are those of the authors and are not necessarily endorsed nor should they be interpreted as representing the official policies, either expressed or implied, of the Army Research Office (ARO) or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by the National Institute of Standards and Technology.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-1.zip","mediaType":"application/zip","title":"Figure-1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-2.zip","mediaType":"application/zip","title":"Figure-2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-3.zip","mediaType":"application/zip","title":"Figure-3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-4.zip","mediaType":"application/zip","title":"Figure-4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-5.zip","mediaType":"application/zip","title":"Figure-5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM1.zip","mediaType":"application/zip","title":"Figure-SM1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM2.zip","mediaType":"application/zip","title":"Figure-SM2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM3.zip","mediaType":"application/zip","title":"Figure-SM3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM4.zip","mediaType":"application/zip","title":"Figure-SM4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM5.zip","mediaType":"application/zip","title":"Figure-SM5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM6.zip","mediaType":"application/zip","title":"Figure-SM6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM7.zip","mediaType":"application/zip","title":"Figure-SM7"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/Figure-SM8.zip","mediaType":"application/zip","title":"Figure-SM8"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3705/README.txt","mediaType":"text/plain","title":"README"}],"identifier":"ark:/88434/mds2-3705","issued":"2025-03-17","keyword":["2D arrays","autonomous control","germanium quantum dots","machine learning","quantum dots"],"language":["en"],"license":"https://www.nist.gov/open/license","modified":"2025-02-03 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.48550/arXiv.2411.12516"],"theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing","Mathematics and Statistics:Numerical methods and software","Physics:Condensed matter","Physics:Quantum information science"],"title":"Figure files for \"Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays\" submitted to Physical Review X"},"description":"The dataset underlying the figures in the manuscript is \"Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays.\"\n\nAbstract of the paper: Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. 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We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.\n\nData description: Each figure folder contains a complete set of files necessary to reproduce figures, including Jupyter Notebooks with the figure source code, Adobe Illustrator, and pre-processed data files (hdf5 and pkl). The complete set of all raw data files used in this study is available at Zenodo. 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This data will be used to develop software solutions for detecting that trigger behavior in the trained AI models.","distribution_titles":["rl-safetygymnasium-oct2024-train"],"harvest_record":"https://catalog.data.gov/harvest_record/13e4c353-7773-4bd1-91f6-21a76ab36f7f","harvest_record_raw":"https://catalog.data.gov/harvest_record/13e4c353-7773-4bd1-91f6-21a76ab36f7f/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-3656","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"],"last_harvested_date":"2026-10-02T19:53:55.972415","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"trojan-detection-software-challenge-rl-safetygymnasium-oct2024-train-daa22","spatial_centroid":null,"spatial_shape":null,"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"title":"Trojan Detection Software Challenge - rl-safetygymnasium-oct2024-train","type":"dataset"},{"_score":19.294544,"_sort":[1790970835839,19.294544,0,"09e24970-4e5d-4398-843d-d9339ef841ad"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Michael Paul Majurski","hasEmail":"mailto:michael.majurski@nist.gov"},"description":"This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of RL agents operating in the Colorful Memory environment. 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 that trigger behavior in the trained AI models.","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1NUlgd1uy-GbbgdkXE_AEglC7tR3KZL7k?usp=sharing","title":"rl-colorful-memory-sep2024-train"}],"identifier":"ark:/88434/mds2-3655","issued":"2024-12-18","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"],"landingPage":"https://data.nist.gov/od/id/mds2-3655","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2024-09-30 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"title":"Trojan Detection Software Challenge - rl-colorful-memory-sep2024-train"},"description":"This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of RL agents operating in the Colorful Memory environment. 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 that trigger behavior in the trained AI models.","distribution_titles":["rl-colorful-memory-sep2024-train"],"harvest_record":"https://catalog.data.gov/harvest_record/b783f938-0f68-4493-be95-fd5af0ee8c23","harvest_record_raw":"https://catalog.data.gov/harvest_record/b783f938-0f68-4493-be95-fd5af0ee8c23/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-3655","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"],"last_harvested_date":"2026-10-02T19:53:55.839604","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"trojan-detection-software-challenge-rl-colorful-memory-sep2024-train-5afff","spatial_centroid":null,"spatial_shape":null,"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"title":"Trojan Detection Software Challenge - rl-colorful-memory-sep2024-train","type":"dataset"},{"_score":18.900799,"_sort":[1790970824380,18.900799,0,"b82cfcd2-f11d-49cf-85d2-5dc76152037d"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P3M","bureauCode":["006:55"],"contactPoint":{"fn":"Harold Booth III","hasEmail":"mailto:harold.booth@nist.gov"},"description":"Source code, documentation, and examples of use of the source code for the Dioptra Test Platform.\n\nDioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). Trustworthy AI is: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair - with harmful bias managed1. Dioptra supports the Measure function of the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks.\n\nDioptra provides a REST API, which can be controlled via an intuitive web interface, a Python client, or any REST client library of the user's choice for designing, managing, executing, and tracking experiments. Details are available in the project documentation available at https://pages.nist.gov/dioptra/.\n\nUse Cases\nWe envision the following primary use cases for Dioptra:\n- Model Testing:\n  -- 1st party - Assess AI models throughout the development lifecycle\n  -- 2nd party - Assess AI models during acquisition or in an evaluation lab environment\n  -- 3rd party - Assess AI models during auditing or compliance activities\n- Research: Aid trustworthy AI researchers in tracking experiments\n- Evaluations and Challenges: Provide a common platform and resources for participants\n- Red-Teaming: Expose models and resources to a red team in a controlled environment\n\nKey Properties\nDioptra strives for the following key properties:\n- Reproducible: Dioptra automatically creates snapshots of resources so experiments can be reproduced and validated\n- Traceable: The full history of experiments and their inputs are tracked\n- Extensible: Support for expanding functionality and importing existing Python packages via a plugin system\n- Interoperable: A type system promotes interoperability between plugins\n- Modular: New experiments can be composed from modular components in a simple yaml file\n- Secure: Dioptra provides user authentication with access controls coming soon\n- Interactive: Users can interact with Dioptra via an intuitive web interface\n- Shareable and Reusable: Dioptra can be deployed in a multi-tenant environment so users can share and reuse components\n","distribution":[{"accessURL":"https://github.com/usnistgov/dioptra","description":"The USNIST Github location where source code is located.","format":"Github Repository","title":"Dioptra Github Repository"}],"identifier":"ark:/88434/mds2-3398","issued":"2024-07-11","keyword":["AI; Trustworthy AI; Test; Evaluation; Adversarial Machine Learning; Machine Learning; TEVV"],"landingPage":"https://data.nist.gov/od/id/mds2-3398","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2024-07-24 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity"],"title":"Dioptra Test Platform"},"description":"Source code, documentation, and examples of use of the source code for the Dioptra Test Platform.\n\nDioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). Trustworthy AI is: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair - with harmful bias managed1. Dioptra supports the Measure function of the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks.\n\nDioptra provides a REST API, which can be controlled via an intuitive web interface, a Python client, or any REST client library of the user's choice for designing, managing, executing, and tracking experiments. Details are available in the project documentation available at https://pages.nist.gov/dioptra/.\n\nUse Cases\nWe envision the following primary use cases for Dioptra:\n- Model Testing:\n  -- 1st party - Assess AI models throughout the development lifecycle\n  -- 2nd party - Assess AI models during acquisition or in an evaluation lab environment\n  -- 3rd party - Assess AI models during auditing or compliance activities\n- Research: Aid trustworthy AI researchers in tracking experiments\n- Evaluations and Challenges: Provide a common platform and resources for participants\n- Red-Teaming: Expose models and resources to a red team in a controlled environment\n\nKey Properties\nDioptra strives for the following key properties:\n- Reproducible: Dioptra automatically creates snapshots of resources so experiments can be reproduced and validated\n- Traceable: The full history of experiments and their inputs are tracked\n- Extensible: Support for expanding functionality and importing existing Python packages via a plugin system\n- Interoperable: A type system promotes interoperability between plugins\n- Modular: New experiments can be composed from modular components in a simple yaml file\n- Secure: Dioptra provides user authentication with access controls coming soon\n- Interactive: Users can interact with Dioptra via an intuitive web interface\n- Shareable and Reusable: Dioptra can be deployed in a multi-tenant environment so users can share and reuse components\n","distribution_titles":["Dioptra Github Repository"],"harvest_record":"https://catalog.data.gov/harvest_record/53c4431b-e6a2-4d10-81db-1de73a9fa823","harvest_record_raw":"https://catalog.data.gov/harvest_record/53c4431b-e6a2-4d10-81db-1de73a9fa823/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-3398","keyword":["AI; Trustworthy AI; Test; Evaluation; Adversarial Machine Learning; Machine Learning; TEVV"],"last_harvested_date":"2026-10-02T19:53:44.380067","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"dioptra-test-platform-ad4ac","spatial_centroid":null,"spatial_shape":null,"theme":["Information Technology:Cybersecurity"],"title":"Dioptra Test Platform","type":"dataset"},{"_score":77.0238,"_sort":[1790970824052,77.0238,0,"74072a0e-7d5e-408c-b5de-df2df587b2ae"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Chin-wen Chou","hasEmail":"mailto:chin-wen.chou@nist.gov"},"description":"Supplementary data for the article \"Quantum state tracking and control of a single molecular ion in a thermal environment\" by Yu Liu, Julian Schmidt, Zhimin Liu, David R. Leibrandt, Dietrich Leibfried, Chin-wen Chou, submitted to Science in 2024. The manuscript describes a quantum state-specific investigation of the molecular state evolution of a single CaH+ ion in a thermal environment. The molecular state can be tracked in real time with single quantum-state resolution and the thermal radiation-induced transitions can be reversed with coherent molecular state manipulation according to the outcomes of state measurements. Results on the transition rates are used to infer the properties of the thermal environment. The data may be used to reproduce the plots shown in the figures.","distribution":[{"description":"(For Figure 1C) The times at which the CaH+ molecule enter and exit the J = 2 manifold.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/entry_and_exit_times_J_1.csv","mediaType":"text/csv","title":"Figure 1C"},{"description":"(For Figure 1C) The times at which the CaH+ molecule enter and exit the J = 2 manifold.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/entry_and_exit_times_J_2.csv","mediaType":"text/csv","title":"Figure 1C"},{"description":"(For Figure 2A) number of J = 0 to J = 1 transitions sorted into bins of the time after an initial preparation of the CaH+ in the J = 0 minus sublevel.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/appearance_time_histogram_J_0_to_J1.csv","mediaType":"text/csv","title":"Figure 2A"},{"description":"(For Figure 2B) number of transitions to various sublevels of J = 1 when the CaH+ is prepared in either J = 0 minus or plus sublevels.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/sublevel_distribution_J0_to_J1.csv","mediaType":"text/csv","title":"Figure 2B"},{"description":"(For Figure 2C) dependence of the J = 0 to J = 1 transition rate on the Ca+-CaH+ ion crystal reorder rate; dependence of the vacuum pressure on the Ca+-CaH+ ion crystal reorder rate.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/pressure_dependence_of_J_0_to_J_1_transitions.csv","mediaType":"text/csv","title":"Figure 2C"},{"description":"(For Figure 3B) A tally of the number of recoveries from J = 0 and J = 2 during the state tracking experiment.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/histogram_of_recoveries_from_J_0_and_J_2.csv","mediaType":"text/csv","title":"Figure 3B"},{"description":"(For Figure 3C) times at which CaH+ transitions to J = 0 or J = 2 manifolds.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/real_time_state_tracking_data.csv","mediaType":"text/csv","title":"Figure 3C"},{"description":"(For Figure 3D) times at which CaH+ enters and exits J = 1 during the tracking experiment.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/tracking_time_series.csv","mediaType":"text/csv","title":"Figure 3D"},{"description":"A short description of what was investigated and the meaning of the data files.","downloadURL":"https://data.nist.gov/od/ds/mds2-3389/3389_README.txt","format":"A list of descriptions for each files.","mediaType":"text/plain","title":"Readme for data files of \"Quantum state tracking and control of a single molecular ion in a thermal environment\""}],"identifier":"ark:/88434/mds2-3389","issued":"2024-08-07","keyword":["Molecular quantum state control","chemical physics","molecular physics","quantum information"],"landingPage":"https://data.nist.gov/od/id/mds2-3389","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2024-06-27 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.1126/science.ado1001"],"theme":["Physics:Atomic, molecular, and quantum","Physics:Quantum information science"],"title":"Data for \"Quantum state tracking and control of a single molecular ion in a thermal environment''"},"description":"Supplementary data for the article \"Quantum state tracking and control of a single molecular ion in a thermal environment\" by Yu Liu, Julian Schmidt, Zhimin Liu, David R. Leibrandt, Dietrich Leibfried, Chin-wen Chou, submitted to Science in 2024. The manuscript describes a quantum state-specific investigation of the molecular state evolution of a single CaH+ ion in a thermal environment. The molecular state can be tracked in real time with single quantum-state resolution and the thermal radiation-induced transitions can be reversed with coherent molecular state manipulation according to the outcomes of state measurements. Results on the transition rates are used to infer the properties of the thermal environment. The data may be used to reproduce the plots shown in the figures.","distribution_titles":["Figure 1C","Figure 1C","Figure 2A","Figure 2B","Figure 2C","Figure 3B","Figure 3C","Figure 3D","Readme for data files of \"Quantum state tracking and control of a single molecular ion in a thermal environment\""],"harvest_record":"https://catalog.data.gov/harvest_record/c77d692c-1545-4820-b4e9-744cf0fdf25a","harvest_record_raw":"https://catalog.data.gov/harvest_record/c77d692c-1545-4820-b4e9-744cf0fdf25a/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-3389","keyword":["Molecular quantum state control","chemical physics","molecular physics","quantum information"],"last_harvested_date":"2026-10-02T19:53:44.052384","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"data-for-quantum-state-tracking-and-control-of-a-single-molecular-ion-in-a-thermal-environ-2b0e1","spatial_centroid":null,"spatial_shape":null,"theme":["Physics:Atomic, molecular, and quantum","Physics:Quantum information science"],"title":"Data for \"Quantum state tracking and control of a single molecular ion in a thermal environment''","type":"dataset"},{"_score":12.478962,"_sort":[1790970821970,12.478962,0,"a468c247-bffa-49ba-a4f2-5046a94f06ef"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Vladimir Aksyuk","hasEmail":"mailto:vladimir.aksyuk@nist.gov"},"description":"Theoretical calculation, simulation and experimental measurement data from the paper \"Bound-state-in-continuum guided modes in a multilayer electro-optically active photonic integrated circuit platform,\" Optica 11, 706-713 (2024). https://doi.org/10.1364/OPTICA.516044.\nAbstract: In many physical systems, the interaction with an open environment leads to energy dissipation and reduced coherence, making it challenging to control these systems effectively. In the context of wave phenomena, such lossy interactions can be specifically controlled to isolate the system, a condition known as a bound-state-in-continuum (BIC). Despite the recent advances in engineered BICs for photonic waveguiding, practical implementations are still largely polarization- and geometry-specific, and the underlying principles remain to be systematically explored.  Here, we theoretically and experimentally study low loss BIC photonic waveguiding within a two-layer heterogeneous electro-optically active integrated photonic platform. We show that coupling to the slab wave continuum can be selectively suppressed for guided modes with different polarizations and spatial structure. We demonstrate a low-loss same-polarization quasi-BIC guided mode enabling a high extinction Mach-Zehnder electro-optic amplitude modulator within a single Si3N4 ridge waveguide integrated with an extended LiNbO3 slab layer. By elucidating the broad BIC waveguiding principles and demonstrating them in an industry-relevant photonic configuration, this work may inspire innovative approaches to photonic applications such as switching and filtering. 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photonics","bound state in continuum","electro-optic modulation","lithium niobate","silicon nitride","waveguiding"],"landingPage":"https://data.nist.gov/od/id/mds2-3331","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2024-05-28 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.1364/OPTICA.516044"],"theme":["Electronics:Optoelectronics","Electronics:Semiconductors","Nanotechnology:Nanophotonics","Physics:Optical physics"],"title":"Data for manuscript: Bound-state-in-continuum guided modes in a multilayer electro-optically active photonic integrated circuit platform."},"description":"Theoretical calculation, simulation and experimental measurement data from the paper \"Bound-state-in-continuum guided modes in a multilayer electro-optically active photonic integrated circuit platform,\" Optica 11, 706-713 (2024). https://doi.org/10.1364/OPTICA.516044.\nAbstract: In many physical systems, the interaction with an open environment leads to energy dissipation and reduced coherence, making it challenging to control these systems effectively. In the context of wave phenomena, such lossy interactions can be specifically controlled to isolate the system, a condition known as a bound-state-in-continuum (BIC). Despite the recent advances in engineered BICs for photonic waveguiding, practical implementations are still largely polarization- and geometry-specific, and the underlying principles remain to be systematically explored.  Here, we theoretically and experimentally study low loss BIC photonic waveguiding within a two-layer heterogeneous electro-optically active integrated photonic platform. We show that coupling to the slab wave continuum can be selectively suppressed for guided modes with different polarizations and spatial structure. We demonstrate a low-loss same-polarization quasi-BIC guided mode enabling a high extinction Mach-Zehnder electro-optic amplitude modulator within a single Si3N4 ridge waveguide integrated with an extended LiNbO3 slab layer. By elucidating the broad BIC waveguiding principles and demonstrating them in an industry-relevant photonic configuration, this work may inspire innovative approaches to photonic applications such as switching and filtering. The broader impact of this work extends beyond photonics, influencing research in other wave dynamics disciplines, including microwave and acoustics.","distribution_titles":["README.txt"],"harvest_record":"https://catalog.data.gov/harvest_record/50fc7e3e-a97c-4d06-9c5d-fd0a18537a8f","harvest_record_raw":"https://catalog.data.gov/harvest_record/50fc7e3e-a97c-4d06-9c5d-fd0a18537a8f/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-3331","keyword":["BIC","Integrated photonics","bound state in continuum","electro-optic modulation","lithium niobate","silicon nitride","waveguiding"],"last_harvested_date":"2026-10-02T19:53:41.970778","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"data-for-manuscript-bound-state-in-continuum-guided-modes-in-a-multilayer-electro-opticall-35c15","spatial_centroid":null,"spatial_shape":null,"theme":["Electronics:Optoelectronics","Electronics:Semiconductors","Nanotechnology:Nanophotonics","Physics:Optical physics"],"title":"Data for manuscript: Bound-state-in-continuum guided modes in a multilayer electro-optically active photonic integrated circuit platform.","type":"dataset"},{"_score":15.723067,"_sort":[1790970820203,15.723067,0,"1136c857-8e9c-413a-83e7-d9795a92341f"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Hadhoum Hajjaj","hasEmail":"mailto:hadhoum.hajjaj@nist.gov"},"description":"This template presents a structured methodology for describing and assessing automated vehicle (AV) features, with a focus on ensuring comprehensive standardization and reproducibility in testing across various scenarios. The document delineates a systematic approach to integrating operational design domains (ODD), levels of automation, and behaviors into AV feature descriptions. Through the development of a feature description framework and a corresponding testing template, the document demonstrates how to measure and validate the performance of AV features under controlled and variable conditions. Key elements of the feature description framework include detailed behavior specifications, performance metrics, and scenario-based testing, which are essential for evaluating the effectiveness and safety of AV technologies. This template can be used as a tool to describe and test the AV feature in high-fidelity simulation environment. 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The document delineates a systematic approach to integrating operational design domains (ODD), levels of automation, and behaviors into AV feature descriptions. Through the development of a feature description framework and a corresponding testing template, the document demonstrates how to measure and validate the performance of AV features under controlled and variable conditions. Key elements of the feature description framework include detailed behavior specifications, performance metrics, and scenario-based testing, which are essential for evaluating the effectiveness and safety of AV technologies. This template can be used as a tool to describe and test the AV feature in high-fidelity simulation environment. 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This work presents a method of frequency conversion which exploits the kinetic inductance of a superconducting coplanar waveguide. Both frequency doubling and tripling are examined, with attention paid to conversion efficiency. This approach allows up-conversion to be implemented in a cryogenic environment, which can simplify the design of cryogenic systems. We achieved a conversion efficiency of 12.7% when up-converting a 10 GHz fundamental tone to the 20 GHz second harmonic, which is an improvement compared to higher-power room-temperature commercial offerings. To better understand device behavior, we also develop a measurement-based model using a harmonic balance simulation, and achieved good agreement between measurements and simulations.","distribution":[{"description":"Data and simulated data used in Figure 5 of [1]. We plot (a) a comparison between the measured transmission S21 through the CPW and the result of an ADS harmonic balance simulation. 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The model uses 105 lumped-element stages; all model parameters are determined from measurements. (b) Measured and simulated change in phase of a tone at 10 GHz as a function of dc current bias. See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig5b.txt","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"text/plain","title":"TAS24A_Fig5b.txt"},{"description":"Data used in Figure 1 of [1]. Harmonics are measured with a VNA by offsetting the frequency provided by the signal generator and the measurement frequency of the VNA. We plot example second and third harmonic generation data as a function of dc bias current using a fundamental tone at 9.87 GHz. See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig1.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_Fig1.fig"},{"description":"Data used in Figure 1 of [1]. Harmonics are measured with a VNA by offsetting the frequency provided by the signal generator and the measurement frequency of the VNA. 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See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig3.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_fig3.fig"},{"description":"Data used in Figure 3 of [1]. We show the measured results for the distributed capacitance and inductance of the coplanar waveguide, plotted versus dc current (x-axis) as the fractional change (y-axis) from the zero-current values of 132.2 pF/m and 999.6 nH/m. See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig3.txt","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"text/plain","title":"TAS24A_Fig3.txt"},{"description":"Data used in Figure 4 of [1]. Time domain reflectometry (TDR) measurements of the system showing system characteristic impedance versus time/distance (a) are used to determine changes in the electrical length of the chip as a function of dc bias current based on a sharp feature at the transition off of the chip (b). This change is a direct, independent measurement of the quadratic nonlinearity coefficient of the inductance (c). See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig4a.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_Fig4a.fig"},{"description":"Data used in Figure 4 of [1]. 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See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig4c.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_Fig4c.fig"},{"description":"Data used in Figure 4 of [1]. Time domain reflectometry (TDR) measurements of the system showing system characteristic impedance versus time/distance (a) are used to determine changes in the electrical length of the chip as a function of dc bias current based on a sharp feature at the transition off of the chip (b). This change is a direct, independent measurement of the quadratic nonlinearity coefficient of the inductance (c). See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig4c.txt","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"text/plain","title":"TAS24A_Fig4c.txt"},{"description":"Data used in Figure 6 of [1]. We plot the second harmonic (a) and third harmonic (b) conversion efficiency at the optimum dc bias current and fundamental tone RF power plotted versus measurement frequency. The measurement was repeated ten times. The standard deviation of each data point is less than or equal to 0.1% in (a) and 0.01% in (b). See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig6a.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_Fig6a.fig"},{"description":"Data used in Figure 6 of [1]. We plot the second harmonic (a) and third harmonic (b) conversion efficiency at the optimum dc bias current and fundamental tone RF power plotted versus measurement frequency. The measurement was repeated ten times. The standard deviation of each data point is less than or equal to 0.1% in (a) and 0.01% in (b). See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig6a.txt","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"text/plain","title":"TAS24A_Fig6a.txt"},{"description":"Data used in Figure 6 of [1]. We plot the second harmonic (a) and third harmonic (b) conversion efficiency at the optimum dc bias current and fundamental tone RF power plotted versus measurement frequency. The measurement was repeated ten times. The standard deviation of each data point is less than or equal to 0.1% in (a) and 0.01% in (b). See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig6b.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_Fig6b.fig"},{"description":"Data used in Figure 6 of [1]. We plot the second harmonic (a) and third harmonic (b) conversion efficiency at the optimum dc bias current and fundamental tone RF power plotted versus measurement frequency. The measurement was repeated ten times. 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See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig8.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_Fig8.fig"},{"description":"Data used in Figure 8 of [1]. We plot the measured reflection S11 data used to calculate the distributed inductance L\u2032 and capacitance C\u2032 based on the minima (red circles) and maxima (green circles) reflectance. See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig8.txt","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"text/plain","title":"TAS24A_Fig8.txt"},{"description":"READ ME file","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/README.txt","format":"text","mediaType":"text/plain","title":"README.txt"},{"description":"Simulated data used in Figure 7 of [1]. We plot the simulated second harmonic conversion efficiency versus frequency to compare the measured 87 \u2126 CPW (blue) to a 50 \u2126 CPW (red) that is better matched to the environment. For the matched CPW, the ripple behavior is eliminated and broadband conversion efficiencies over 10% are expected for second harmonic generation frequencies from 17 GHz to over 40 GHz. See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig7a.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"application/octet-stream","title":"TAS24A_Fig7a.fig"},{"description":"Simulated data used in Figure 7 of [1]. We plot the simulated second harmonic conversion efficiency versus frequency to compare the measured 87 \u2126 CPW (blue) to a 50 \u2126 CPW (red) that is better matched to the environment. For the matched CPW, the ripple behavior is eliminated and broadband conversion efficiencies over 10% are expected for second harmonic generation frequencies from 17 GHz to over 40 GHz. See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig7a.txt","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  Unused column entries are designated with \"--\". The *.fig file data is a matlab figure file duplicating the data in the similarly named *.txt files.","mediaType":"text/plain","title":"TAS24A_Fig7a.txt"},{"description":"Simulated data used in Figure 7 of [1]. We plot the simulated second harmonic conversion efficiency versus frequency to compare the measured 87 \u2126 CPW (blue) to a 50 \u2126 CPW (red) that is better matched to the environment. For the matched CPW, the ripple behavior is eliminated and broadband conversion efficiencies over 10% are expected for second harmonic generation frequencies from 17 GHz to over 40 GHz. See [1] for additional information.","downloadURL":"https://data.nist.gov/od/ds/mds2-3248/TAS24A_Fig7b.fig","format":"The *.txt file data is a csv file with labeled headers. First row is a description, second row is column header enclosed in square brackets \"[column header]\" with units and a \",\" column delimiter, remaining rows are data with a \",\" column delimiter.  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A README file is included within the zip file.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-3203/README.md","mediaType":"text/markdown","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3203/data.zip","mediaType":"application/zip","title":"data"}],"identifier":"ark:/88434/mds2-3203","issued":"2024-09-26","keyword":["REFPROP","equation of state","optimization","phase equilibria","polar","teqp"],"landingPage":"https://data.nist.gov/od/id/mds2-3203","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2024-03-22 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry","Energy"],"title":"Code and Results for the Application of Polar SAFT models to Refrigerants"},"description":"This dataset includes the python scripts and output results for fitting SAFT models to the phase equilibrium density, pressure, and speed of sound for refrigerants. 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This data is published for research academic and related non-commercial purposes consistent with NIST?s mandate to further the science and the promulgation of appropriate standards. \n\nThis dataset was used in the following publication and this archive represents the static archive of the data used in the publication and is not updated or modified: \n \nSahu, S., Ahn, D., Loughner, C., and Dickerson, R., \"Influence of synoptic weather patterns on methane mixing ratios in the Baltimore/Washington region\", Atmospheric Environment, Volume 334, 2024, 120675, ISSN 1352-2310,\nhttps://doi.org/10.1016/j.atmosenv.2024.120675.\n\nData is described further in the Readme document and references cited 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Testbed project, presented as 1-minute averages.  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A base station emulator was used to configure the handset to statically transmit over various LTE physical resource block configurations for 1.4 MHz, 3 MHz, 5 MHz, and 10 MHz uplink channels centered at 1770 MHz in LTE Band 66, which is a frequency-division duplex (FDD) band.  The aim of this effort was to acquire a dataset that could be used to aid the development of computational models for emissions from real-world communication hardware.","identifier":"ark:/88434/mds2-3177","issued":"2024-03-19","keyword":["In-Phase and Quadrature Data","Long Term Evolution (LTE)","Wireless Communications"],"landingPage":"https://data.nist.gov/od/id/mds2-3177","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2024-02-12 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"title":"High-SNR I/Q Recordings of FDD LTE User Equipment Emissions"},"description":"This dataset consists of high signal-to-noise ratio (SNR) in-phase and quadrature (I/Q) radio frequency (RF) recordings of long-term evolution (LTE) uplink emissions from a commercial-off-the-shelf (COTS) handset in a fully conducted environment.  A base station emulator was used to configure the handset to statically transmit over various LTE physical resource block configurations for 1.4 MHz, 3 MHz, 5 MHz, and 10 MHz uplink channels centered at 1770 MHz in LTE Band 66, which is a frequency-division duplex (FDD) band.  The aim of this effort was to acquire a dataset that could be used to aid the development of computational models for emissions from real-world communication hardware.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/d1df53d3-535b-425e-a3ef-148d9b6d8920","harvest_record_raw":"https://catalog.data.gov/harvest_record/d1df53d3-535b-425e-a3ef-148d9b6d8920/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-3177","keyword":["In-Phase and Quadrature Data","Long Term Evolution (LTE)","Wireless Communications"],"last_harvested_date":"2026-10-02T19:53:34.315791","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"high-snr-i-q-recordings-of-fdd-lte-user-equipment-emissions-f401f","spatial_centroid":null,"spatial_shape":null,"theme":["Advanced Communications:Wireless (RF)"],"title":"High-SNR I/Q Recordings of FDD LTE User Equipment Emissions","type":"dataset"},{"_score":27.885324,"_sort":[1790970812696,27.885324,0,"9a26bb3d-9856-403e-9a36-4ca324fcd4d0"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Alain Rufenacht","hasEmail":"mailto:alain.rufenacht@nist.gov"},"description":"The abstract of the paper [1] is:\n\nThis paper describes differential sampling measurements of an ac source and a Josephson arbitrary waveform synthesizer (JAWS).\nA new iterative approach for aligning the phases of the JAWS and the source waveforms was implemented to minimize \nthe differential voltage at the digitizer. A type-A uncertainty of 45 nV/V after 10 min was measured for a commercial \nac source at 1 V rms amplitude and 1 kHz.\n\n[1] \"Differential Measurements of an AC Source with a Josephson Arbitrary Waveform Synthesizer\"submitted to Conference on Precision Electromagnetic Measurements (CPEM) 2024; will be published and available on IEEE website at a later date.\n\nData for figures 2 to 4 of the manuscript.\nFiles included in this publication:\n\n   Fig 2 FFT of the digitizer signal.csv\n\n    Figure 2\n    Fig. 2. 1 kHz component of the FFT of the digitizer signal (amplitude and phase) for Delta_V1=Source-JAWS1 and Delta_V2=Source-JAWS2 over 3.5 hours\n    Five columns: The first column is the time (x-axis), the second column is the amplitude in volt of the first measured difference voltage (shown as black solid circle in Fig. 2), \n    the third column is the phase in degree of the first measured difference voltage (shown as black open circle in Fig. 2),\n    the fourth column is the amplitude in volt of the second measured difference voltage (shown as red solid circle in Fig. 2),\n    the fifth column is the phase in degree of the second measured difference voltage (shown as red open circle in Fig. 2).\n    Format: CSV\n\n  Fig 3 Source rms amplitude and environment data.csv\n\n    Figure 3\n    Fig. 3. Room environment conditions recorded (temperature, atmospheric pressure, and relative humidity) and Reconstructed rms amplitude for the source at 1 kHz.\n    Five columns: The first column is the time (x-axis), the second column is the reconstructed amplitude in volt - 1 V (shown as blue solid circle in Fig. 3 bottom), \n    the third column is the temperature in degree C (shown as orange solid square in Fig. 3 top),\n    the fourth column is the atomsepheric pressure in hecto Pascal  (shown as green open triangle in Fig. 3 top),\n    the fifth column is the relative humidity in percent (shown as puple open circle in Fig. 2).\n    Format: CSV\n\n  Fig 4 Allan variance.csv\n\n    Figure 4\n    Fig. 4. Allan deviation of the source amplitude measured at 1 V and 1 kHz.\n    Five columns: The first column is the time (x-axis), the second column is the calculated Allan Deviation in volt (shown as blue solid circle in Fig. 4), \n    the third column is the fit on the results, representing the white noise with slope -0.5 (shown as black dash line in Fig. 4),\n    the fourth column is the is the time (x-axis) for the 1/f noise floor plot and the fifth column is the 1/f noise floor (shown as a black solid line in Fig. 4)\n    Format: CSV","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-3162/3162_README.txt","mediaType":"text/plain","title":"3162_README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3162/Fig%202%20FFT%20of%20the%20digitizer%20signal.csv","mediaType":"text/csv","title":"Fig 2 FFT of the digitizer signal"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3162/Fig%203%20Source%20rms%20amplitude%20and%20environment%20data.csv","mediaType":"text/csv","title":"Fig 3 Source rms amplitude and environment data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3162/Fig%204%20Allan%20variance.csv","mediaType":"text/csv","title":"Fig 4 Allan variance"}],"identifier":"ark:/88434/mds2-3162","issued":"2024-02-13","keyword":["Josephson arrays","Measurement techniques","Standards","Superconducting integrated circuits","Voltage measurement"],"landingPage":"https://data.nist.gov/od/id/mds2-3162","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2024-01-05 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Electrical/electromagnetic metrology"],"title":"Differential Measurements of an AC Source with a Josephson Arbitrary Waveform Synthesizer"},"description":"The abstract of the paper [1] is:\n\nThis paper describes differential sampling measurements of an ac source and a Josephson arbitrary waveform synthesizer (JAWS).\nA new iterative approach for aligning the phases of the JAWS and the source waveforms was implemented to minimize \nthe differential voltage at the digitizer. A type-A uncertainty of 45 nV/V after 10 min was measured for a commercial \nac source at 1 V rms amplitude and 1 kHz.\n\n[1] \"Differential Measurements of an AC Source with a Josephson Arbitrary Waveform Synthesizer\"submitted to Conference on Precision Electromagnetic Measurements (CPEM) 2024; will be published and available on IEEE website at a later date.\n\nData for figures 2 to 4 of the manuscript.\nFiles included in this publication:\n\n   Fig 2 FFT of the digitizer signal.csv\n\n    Figure 2\n    Fig. 2. 1 kHz component of the FFT of the digitizer signal (amplitude and phase) for Delta_V1=Source-JAWS1 and Delta_V2=Source-JAWS2 over 3.5 hours\n    Five columns: The first column is the time (x-axis), the second column is the amplitude in volt of the first measured difference voltage (shown as black solid circle in Fig. 2), \n    the third column is the phase in degree of the first measured difference voltage (shown as black open circle in Fig. 2),\n    the fourth column is the amplitude in volt of the second measured difference voltage (shown as red solid circle in Fig. 2),\n    the fifth column is the phase in degree of the second measured difference voltage (shown as red open circle in Fig. 2).\n    Format: CSV\n\n  Fig 3 Source rms amplitude and environment data.csv\n\n    Figure 3\n    Fig. 3. Room environment conditions recorded (temperature, atmospheric pressure, and relative humidity) and Reconstructed rms amplitude for the source at 1 kHz.\n    Five columns: The first column is the time (x-axis), the second column is the reconstructed amplitude in volt - 1 V (shown as blue solid circle in Fig. 3 bottom), \n    the third column is the temperature in degree C (shown as orange solid square in Fig. 3 top),\n    the fourth column is the atomsepheric pressure in hecto Pascal  (shown as green open triangle in Fig. 3 top),\n    the fifth column is the relative humidity in percent (shown as puple open circle in Fig. 2).\n    Format: CSV\n\n  Fig 4 Allan variance.csv\n\n    Figure 4\n    Fig. 4. Allan deviation of the source amplitude measured at 1 V and 1 kHz.\n    Five columns: The first column is the time (x-axis), the second column is the calculated Allan Deviation in volt (shown as blue solid circle in Fig. 4), \n    the third column is the fit on the results, representing the white noise with slope -0.5 (shown as black dash line in Fig. 4),\n    the fourth column is the is the time (x-axis) for the 1/f noise floor plot and the fifth column is the 1/f noise floor (shown as a black solid line in Fig. 4)\n    Format: CSV","distribution_titles":["3162_README","Fig 2 FFT of the digitizer signal","Fig 3 Source rms amplitude and environment data","Fig 4 Allan variance"],"harvest_record":"https://catalog.data.gov/harvest_record/166e5ba2-47de-408c-b646-6614c466eb88","harvest_record_raw":"https://catalog.data.gov/harvest_record/166e5ba2-47de-408c-b646-6614c466eb88/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-3162","keyword":["Josephson arrays","Measurement techniques","Standards","Superconducting integrated circuits","Voltage measurement"],"last_harvested_date":"2026-10-02T19:53:32.696912","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"differential-measurements-of-an-ac-source-with-a-josephson-arbitrary-waveform-synthesizer-6abe9","spatial_centroid":null,"spatial_shape":null,"theme":["Metrology:Electrical/electromagnetic metrology"],"title":"Differential Measurements of an AC Source with a Josephson Arbitrary Waveform Synthesizer","type":"dataset"},{"_score":16.235313,"_sort":[1790970808972,16.235313,0,"bbb31428-e7fb-444b-b6ea-1d58e03a8237"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Nathan Flowers-Jacobs","hasEmail":"mailto:nathan.flowers-jacobs@nist.gov"},"description":"We plan to present a technique for implementing a frequency doubler  in NbTiN on silicon for operation in a cryogenic environment at IMS 2024. The kinetic inductance of a superconducting coplanar waveguide is exploited for efficient frequency conversion, while the fabrication allows for co-location with other cryogenic circuits. A conversion efficiency greater than 10% is demonstrated at a frequency of 9.87 GHz, offering lower input power requirements and competitive conversion efficiencies relative to other state-of-the-art solutions.  This dataset contains information related to this presentation, specifically: (Fig. 2) Simulated conversion efficiency result, (Fig. 4) 2nd and 3rd order harmonic conversion efficiency data with input RF power ranging from (3 to 7), and (Fig. 5) second harmonic power as a function of dc bias current.","distribution":[{"description":"(Fig. 2) Simulated conversion efficiency result (matlab figure)","downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig2.fig","format":"matlab figure","mediaType":"application/octet-stream","title":"(Fig. 2) Simulated conversion efficiency result (matlab figure)"},{"description":"(Fig. 4) 2nd and 3rd order harmonic conversion efficiency data with input RF power ranging from (3 to 7) dBm (matlab figure)","downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig4.fig","format":"matlab figure","mediaType":"application/octet-stream","title":"(Fig. 4) 2nd and 3rd order harmonic conversion efficiency data with input RF power ranging from (3 to 7) dBm (matlab figure)"},{"description":"(Fig. 5) measured and simulated second harmonic power as a function of dc bias current (matlab figure)","downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig5.fig","format":"matlab figure","mediaType":"application/octet-stream","title":"(Fig. 5) measured and simulated second harmonic power as a function of dc bias current (matlab figure)"},{"description":"README for this data set, mds2-3123","downloadURL":"https://data.nist.gov/od/ds/mds2-3123/README.txt","format":"text file","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig2.txt","mediaType":"text/plain","title":"Fig2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig4_Bottom.txt","mediaType":"text/plain","title":"Fig4_Bottom"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig4_Top.txt","mediaType":"text/plain","title":"Fig4_Top"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig5_Measured.txt","mediaType":"text/plain","title":"Fig5_Measured"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-3123/Fig5_Sim.txt","mediaType":"text/plain","title":"Fig5_Sim"}],"identifier":"ark:/88434/mds2-3123","issued":"2024-01-09","keyword":["cryogenic electronics","frequency conversion","kinetic inductance","superconducting coils"],"language":["en"],"license":"https://www.nist.gov/open/license","modified":"2023-12-04 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Electronics:Superconducting electronics"],"title":"Characterization of a Frequency Converter Based on a Superconducting Coplanar Waveguide, IMS 2024"},"description":"We plan to present a technique for implementing a frequency doubler  in NbTiN on silicon for operation in a cryogenic environment at IMS 2024. The kinetic inductance of a superconducting coplanar waveguide is exploited for efficient frequency conversion, while the fabrication allows for co-location with other cryogenic circuits. A conversion efficiency greater than 10% is demonstrated at a frequency of 9.87 GHz, offering lower input power requirements and competitive conversion efficiencies relative to other state-of-the-art solutions.  This dataset contains information related to this presentation, specifically: (Fig. 2) Simulated conversion efficiency result, (Fig. 4) 2nd and 3rd order harmonic conversion efficiency data with input RF power ranging from (3 to 7), and (Fig. 5) second harmonic power as a function of dc bias current.","distribution_titles":["(Fig. 2) Simulated conversion efficiency result (matlab figure)","(Fig. 4) 2nd and 3rd order harmonic conversion efficiency data with input RF power ranging from (3 to 7) dBm (matlab figure)","(Fig. 5) measured and simulated second harmonic power as a function of dc bias current (matlab figure)","README","Fig2","Fig4_Bottom","Fig4_Top","Fig5_Measured","Fig5_Sim"],"harvest_record":"https://catalog.data.gov/harvest_record/34e73405-e5cd-44ee-8200-b7c46a715808","harvest_record_raw":"https://catalog.data.gov/harvest_record/34e73405-e5cd-44ee-8200-b7c46a715808/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-3123","keyword":["cryogenic electronics","frequency conversion","kinetic inductance","superconducting coils"],"last_harvested_date":"2026-10-02T19:53:28.972618","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"characterization-of-a-frequency-converter-based-on-a-superconducting-coplanar-wavegui-2024-443bb","spatial_centroid":null,"spatial_shape":null,"theme":["Electronics:Superconducting electronics"],"title":"Characterization of a Frequency Converter Based on a Superconducting Coplanar Waveguide, IMS 2024","type":"dataset"},{"_score":18.298958,"_sort":[1790970803563,18.298958,0,"5f3fbc3b-44b6-4609-97be-9895ad0b5d77"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Michael Paul Majurski","hasEmail":"mailto:michael.majurski@nist.gov"},"description":"Round rl-randomized-lavaworld-aug2023-train Train Dataset\n\nThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. 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.","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1kNtxxoAvDfIa10o2v8OrbRJeHwdRq0yY?usp=drive_link","title":"rl-randomized-lavaworld-aug2023-train"}],"identifier":"ark:/88434/mds2-3066","issued":"2023-09-07","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"],"landingPage":"https://data.nist.gov/od/id/mds2-3066","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2023-08-25 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"title":"Trojan Detection Software Challenge - rl-randomized-lavaworld-aug2023-train"},"description":"Round rl-randomized-lavaworld-aug2023-train Train Dataset\n\nThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. 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.","distribution_titles":["rl-randomized-lavaworld-aug2023-train"],"harvest_record":"https://catalog.data.gov/harvest_record/fb854e02-d7e0-4477-b930-095f10c7aa9d","harvest_record_raw":"https://catalog.data.gov/harvest_record/fb854e02-d7e0-4477-b930-095f10c7aa9d/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-3066","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"],"last_harvested_date":"2026-10-02T19:53:23.563869","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"trojan-detection-software-challenge-rl-randomized-lavaworld-aug2023-train-edb1c","spatial_centroid":null,"spatial_shape":null,"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"title":"Trojan Detection Software Challenge - rl-randomized-lavaworld-aug2023-train","type":"dataset"},{"_score":18.432411,"_sort":[1790970803300,18.432411,0,"c4f8cd16-9cb8-4d6b-9324-32bdb9cf0f4c"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Michael Paul Majurski","hasEmail":"mailto:michael.majurski@nist.gov"},"description":"Round rl-lavaworld-jul2023-train Train Dataset\n\nThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. 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.","distribution":[{"accessURL":"https://drive.google.com/drive/folders/10wo31sUPYrYX5o2tY8y1ZO4fsUVgB58C?usp=drive_link","title":"rl-lavaworld-jul2023-train"}],"identifier":"ark:/88434/mds2-3064","issued":"2023-09-14","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"],"landingPage":"https://data.nist.gov/od/id/mds2-3064","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2023-07-22 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"title":"Trojan Detection Software Challenge - rl-lavaworld-jul2023-train"},"description":"Round rl-lavaworld-jul2023-train Train Dataset\n\nThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. 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.","distribution_titles":["rl-lavaworld-jul2023-train"],"harvest_record":"https://catalog.data.gov/harvest_record/620c3ff7-441e-4e18-bdc0-4e481b2187e6","harvest_record_raw":"https://catalog.data.gov/harvest_record/620c3ff7-441e-4e18-bdc0-4e481b2187e6/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-3064","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"],"last_harvested_date":"2026-10-02T19:53:23.300983","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"trojan-detection-software-challenge-rl-lavaworld-jul2023-train-948aa","spatial_centroid":null,"spatial_shape":null,"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"title":"Trojan Detection Software Challenge - rl-lavaworld-jul2023-train","type":"dataset"},{"_score":7.791754,"_sort":[1790970795749,7.791754,0,"c59300be-7ef7-4418-8806-ecf75fa2fa51"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Demian Riccardi","hasEmail":"mailto:demian.riccardi@nist.gov"},"description":"Conventional molecular dynamics (MD) simulations using the GPU-enabled CUDA version of the pmemd executable (pmemd.cuda) in AMBER were applied to explore the structure and dymanics of MAPK, ERK2. The role of dual phosphorylation was explored by comparing 0P-ERK2 with 2P-ERK2 (two phosphate groups added at T183 and Y185). Both 0P-ERK2 and 2P-ERK2 models were treated with the ff19SB forcefield immersed in a solution of 0.15 M NaCl in OPC water. The results showed that the A-loop can adopt multiple long-lived (>5 microseconds) conformational states. A set of primary and secondary seeds were used to explore these novel states of the activation loop. Analysis scripts, dataframes, and all trajectories (stripped of explicit water and NaCl ions) are provided here to enhance the reproducibility of this complex study and aid future studies.\n \nTrajectories\n \nAs described in the associated publication, individual trajectories were run for (5-25) microseconds and totaled 727 microseconds. Multiple primary seeds (285 K, 300 K, 315 K, and 330 K) and secondary trajectory seeds (300 K) were run; all are available in this data publication. All frames for each trajectory were aligned to backbone atoms of residues 10-161 and 182-343 of the minimized 2ERK X-ray structures (0P and 2P). Modeled residue numbers are 1-353 and correspond to residues 6-358 of 2ERK and 5UMO (rat sequence numbering). The model built from 2Y9Q, which is a human kinase, was converted to correspond to the rat-derived models (2ERK, 5UMO) as described in the associated publication. Most primary seeds are greater than 10 microseconds while most secondary seeds are 5.7 microseconds. Angstrom units (0.1 nm) are used for trajectory coordinate storage and analysis. Three sets of trajectories are provided for convenience:\n \n1. The canonical set of trajectories are netcdf files (extension .nc) that are stripped of NaCl and water molecules and down-sampled to 2.5 ns between frames.\n \n2. The set of trajectory seeds with hydrogens removed and further down-sampling to 250 ns between frames is provided as DCD trajectory files. This is the smallest download that will be most convenient for visualization and initial development.\n \n3. Frames from the 300 K trajectories collated by the A-loop states. These trajectories should be treated as collections of frames without regard for any time information stored.\n \nStructures from RCSB.org\n \nThe associated publication analyzed the crystal packing environment of the activation loop. The structural data were collected in November 2021. All pdb files downloaded at that time are included in this data publication to aid the reproducibility of the analysis. The RCSB (Research Collaboratory for Structural Bioinformatics, https://www.rcsb.org) was the source of information and should be used directly for current versions of the associated entries. The RCSB entries pdbid 2ERK, pdbid 5UMO, and pdbid 2Y9Q were the structures used to build initial models for MD simulations.\n \nScripts\n \nRepresentative scripts are provided to aid future work. See README.md\n \nUnits\n \nQ-Aloop values are fractions. RMSD, distances, and coordinates are all reported in Angstroms (0.1 nm). Angles are reported in degrees.\n \nNOTE: Trade names are provided only to specify the source of information and procedures adequately and do not imply endorsement by the National Institute of Standards and Technology. Similar products by other developers may be found to work as well or better.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/README.md","mediaType":"text/x-web-markdown"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/README.md.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/Xtal_survey/Aloop_MKI_xtal_summary.csv","mediaType":"text/csv","title":"Xtal_survey/Aloop_MKI_xtal_summary"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/Xtal_survey/Aloop_MKI_xtal_summary.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/Xtal_survey/xtal_survey.zip","mediaType":"application/zip","title":"Xtal_survey/xtal_survey"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/Xtal_survey/xtal_survey.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/Xtal_survey/xtal_survey_README.md","mediaType":"text/x-web-markdown","title":"Xtal_survey/xtal_survey_README"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/Xtal_survey/xtal_survey_README.md.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_README.md","mediaType":"text/x-web-markdown","title":"aligned_trajectories/aligned_trajectories_README"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_README.md.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_seeds_2.5.zip","mediaType":"application/zip","title":"aligned_trajectories/aligned_trajectories_seeds_2.5"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_seeds_2.5.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_seeds_250.zip","mediaType":"application/zip","title":"aligned_trajectories/aligned_trajectories_seeds_250"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_seeds_250.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_states_2.5.zip","mediaType":"application/zip","title":"aligned_trajectories/aligned_trajectories_states_2.5"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/aligned_trajectories/aligned_trajectories_states_2.5.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/Q_analysis.zip","mediaType":"application/zip","title":"data/Q_analysis"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/Q_analysis.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/contact_maps.zip","mediaType":"application/zip","title":"data/contact_maps"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/contact_maps.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/data_README.md","mediaType":"text/x-web-markdown","title":"data/data_README"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/data_README.md.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/measures.zip","mediaType":"application/zip","title":"data/measures"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/data/measures.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2988/main-text%20figures/DataGraph.zip","mediaType":"application/zip","title":"main-text 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Loop","Allostery","ERK2","Kinase","MAPK Signaling","Molecular dynamics","Phosphorylation"],"landingPage":"https://data.nist.gov/od/id/mds2-2988","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2023-10-18 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Modeling and simulation research"],"title":"Activation loop plasticity and active site coupling in the MAP kinase, ERK2: Supplementary Data, Trajectories, and Scripts"},"description":"Conventional molecular dynamics (MD) simulations using the GPU-enabled CUDA version of the pmemd executable (pmemd.cuda) in AMBER were applied to explore the structure and dymanics of MAPK, ERK2. The role of dual phosphorylation was explored by comparing 0P-ERK2 with 2P-ERK2 (two phosphate groups added at T183 and Y185). Both 0P-ERK2 and 2P-ERK2 models were treated with the ff19SB forcefield immersed in a solution of 0.15 M NaCl in OPC water. The results showed that the A-loop can adopt multiple long-lived (>5 microseconds) conformational states. A set of primary and secondary seeds were used to explore these novel states of the activation loop. Analysis scripts, dataframes, and all trajectories (stripped of explicit water and NaCl ions) are provided here to enhance the reproducibility of this complex study and aid future studies.\n \nTrajectories\n \nAs described in the associated publication, individual trajectories were run for (5-25) microseconds and totaled 727 microseconds. Multiple primary seeds (285 K, 300 K, 315 K, and 330 K) and secondary trajectory seeds (300 K) were run; all are available in this data publication. All frames for each trajectory were aligned to backbone atoms of residues 10-161 and 182-343 of the minimized 2ERK X-ray structures (0P and 2P). Modeled residue numbers are 1-353 and correspond to residues 6-358 of 2ERK and 5UMO (rat sequence numbering). The model built from 2Y9Q, which is a human kinase, was converted to correspond to the rat-derived models (2ERK, 5UMO) as described in the associated publication. Most primary seeds are greater than 10 microseconds while most secondary seeds are 5.7 microseconds. Angstrom units (0.1 nm) are used for trajectory coordinate storage and analysis. Three sets of trajectories are provided for convenience:\n \n1. The canonical set of trajectories are netcdf files (extension .nc) that are stripped of NaCl and water molecules and down-sampled to 2.5 ns between frames.\n \n2. The set of trajectory seeds with hydrogens removed and further down-sampling to 250 ns between frames is provided as DCD trajectory files. This is the smallest download that will be most convenient for visualization and initial development.\n \n3. Frames from the 300 K trajectories collated by the A-loop states. These trajectories should be treated as collections of frames without regard for any time information stored.\n \nStructures from RCSB.org\n \nThe associated publication analyzed the crystal packing environment of the activation loop. The structural data were collected in November 2021. All pdb files downloaded at that time are included in this data publication to aid the reproducibility of the analysis. The RCSB (Research Collaboratory for Structural Bioinformatics, https://www.rcsb.org) was the source of information and should be used directly for current versions of the associated entries. The RCSB entries pdbid 2ERK, pdbid 5UMO, and pdbid 2Y9Q were the structures used to build initial models for MD simulations.\n \nScripts\n \nRepresentative scripts are provided to aid future work. See README.md\n \nUnits\n \nQ-Aloop values are fractions. RMSD, distances, and coordinates are all reported in Angstroms (0.1 nm). Angles are reported in degrees.\n \nNOTE: Trade names are provided only to specify the source of information and procedures adequately and do not imply endorsement by the National Institute of Standards and Technology. Similar products by other developers may be found to work as well or better.","distribution_titles":["Xtal_survey/Aloop_MKI_xtal_summary","Xtal_survey/xtal_survey","Xtal_survey/xtal_survey_README","aligned_trajectories/aligned_trajectories_README","aligned_trajectories/aligned_trajectories_seeds_2.5","aligned_trajectories/aligned_trajectories_seeds_250","aligned_trajectories/aligned_trajectories_states_2.5","data/Q_analysis","data/contact_maps","data/data_README","data/measures","main-text figures/DataGraph","main-text figures/VMD_FIGURES","notebooks/2erk_0p_bb","notebooks/PCA_final","notebooks/TRAJECTORY TIMES","notebooks/all_0p.pc12","notebooks/all_2p.pc12","notebooks/l16_bb","reference_structures/reference_structures","reference_structures/reference_structures_README","setup/2erk_0P_mmpbsa","setup/2erk_2P_mmpbsa","setup/partop_solv","setup/production_300","setup/setup_README"],"harvest_record":"https://catalog.data.gov/harvest_record/e5d605d3-78f7-4661-a996-a8d80ba10d8d","harvest_record_raw":"https://catalog.data.gov/harvest_record/e5d605d3-78f7-4661-a996-a8d80ba10d8d/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2988","keyword":["Activation Loop","Allostery","ERK2","Kinase","MAPK Signaling","Molecular dynamics","Phosphorylation"],"last_harvested_date":"2026-10-02T19:53:15.749251","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"activation-loop-plasticity-and-active-site-coupling-in-the-map-kinase-erk2-supplementary-d-965e1","spatial_centroid":null,"spatial_shape":null,"theme":["Mathematics and Statistics:Modeling and simulation research"],"title":"Activation loop plasticity and active site coupling in the MAP kinase, ERK2: Supplementary Data, Trajectories, and Scripts","type":"dataset"},{"_score":10.206469,"_sort":[1790970793047,10.206469,0,"be24cbc2-e3df-4514-a482-6e5dd245e1e1"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Thomas P. Forbes","hasEmail":"mailto:thomas.forbes@nist.gov"},"description":"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.\n\n*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.","distribution":[{"description":"Describes data set and files.","downloadURL":"https://data.nist.gov/od/ds/mds2-2957/readme.txt","mediaType":"text/plain","title":"readme"},{"description":"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.\n\n*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.","downloadURL":"https://data.nist.gov/od/ds/mds2-2957/MNP-MS-ML%20NIST%20Data%20Publication.zip","mediaType":"application/x-zip-compressed","title":"Microplastics and Nanoplastics chemical characterization by thermal desorption and pyrolysis mass spectrometry dataset"}],"identifier":"ark:/88434/mds2-2957","issued":"2023-04-24","keyword":["Chemical Characterization","Environment","GC-MS","Machine Learning","Mass Spectrometry","Microplastic","Nanoplastics"],"landingPage":"https://data.nist.gov/od/id/mds2-2957","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2023-03-24 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Analytical chemistry","Environment:Air / water / soil quality","Materials:Materials characterization","Nanotechnology:Nanomaterials"],"title":"Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning"},"description":"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.\n\n*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.","distribution_titles":["readme","Microplastics and Nanoplastics chemical characterization by thermal desorption and pyrolysis mass spectrometry dataset"],"harvest_record":"https://catalog.data.gov/harvest_record/2d8c792a-138a-4f6f-af41-3d73e141f0bd","harvest_record_raw":"https://catalog.data.gov/harvest_record/2d8c792a-138a-4f6f-af41-3d73e141f0bd/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2957","keyword":["Chemical Characterization","Environment","GC-MS","Machine Learning","Mass Spectrometry","Microplastic","Nanoplastics"],"last_harvested_date":"2026-10-02T19:53:13.047902","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"microplastic-and-nanoplastic-chemical-characterization-by-thermal-desorption-and-pyrolysis-abc68","spatial_centroid":null,"spatial_shape":null,"theme":["Chemistry:Analytical chemistry","Environment:Air / water / soil quality","Materials:Materials characterization","Nanotechnology:Nanomaterials"],"title":"Microplastic and nanoplastic chemical characterization by thermal desorption and pyrolysis mass spectrometry with unsupervised machine learning","type":"dataset"},{"_score":14.4521675,"_sort":[1790970791349,14.4521675,0,"06322e2e-3be5-4303-a1df-f38df7ee9728"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Isaac Leventon","hasEmail":"mailto:isaac.leventon@nist.gov"},"description":"A set of 10 anaerobic gasification experiments was conducted on the poly(methyl methacrylate), PMMA, made available to participants in the MaCFP-2 Workshop. In each test, samples (i.e., PMMA discs of approximate dimensions: 7 cm diameter, 5.8 mm thickness) were exposed to radiant heating (nominally 50 kW m-2 across their top surface) in an anaerobic environment. Samples were insulated at their back surface and continuously heated until complete decomposition was observed. Test boundary conditions (e.g., time- and spatially-resolved measurements of incident radiant heat flux; chamber wall temperatures) were carefully characterized.\n\nSix additional tests were also conducted to measure the temperature rise of inert materials (Copper and Kaowool PM Insulation) exposed to the same conditions (i.e., Nitrogen flow rate + incident radiant heat flux) as used during tests on PMMA samples. These test results may be used to validate material thermophysical properties and boundary conditions (e.g., convection heat transfer) controlling heat transfer in this system.","distribution":[{"accessURL":"https://github.com/MaCFP/macfp-db/","description":"Anaerobic gasification of poly(methyl methacrylate) under external thermal radiation in the NIST Gasification Apparatus. Measurement data includes back surface temperature and sample mass loss rate of ~5.8mm thick, 7cm diameter PMMA discs","format":"A github repository containing .csv files with experimental data and a .md text file describing these measurements","title":"Anaerobic gasification of poly(methyl methacrylate) under external thermal radiation"},{"description":"Description of test goals, apparatus setup and calibration, and measurement results.","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MACFP-3-Gasification_README.md","mediaType":"application/octet-stream","title":"README"},{"description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R1)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R1.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R1"},{"description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R2)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R2.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R2"},{"description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R3)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R3.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R3"},{"description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R4)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R4.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R4"},{"description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R5)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R5.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R5"},{"description":"Measured sample mass of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R3)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Mass_R3.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Mass_R3"},{"description":"Measured sample mass of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R4)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Mass_R4.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Mass_R4"},{"description":"Measured sample mass of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R5)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Mass_R5.csv","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Mass_R5"},{"description":"Schematic of gasification apparatus with highlight of sample/insulation assembly","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/Gasification_Apparatus_Schematic_w-sample.pdf","mediaType":"application/pdf","title":"Gasification_Apparatus_Schematic_w-sample"},{"description":"Temperature rise of 1/8 in. black copper disc when exposed at top surface to 50 kW/m2 incident radiation and insulated at back with 1.125 in. Kaowool PM insulation board. Temperature values represent an average of 3 tests conducted in triplicate; uncertainties (Uc) represent 2 stdev_mean of Temp values recorded in triplicate tests in a +/- 1 s time window (i.e., 3 time steps averaged across 3 tests)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/Black-Copper_q50_Temp.csv","mediaType":"text/csv","title":"Black-Copper_q50_Temp"},{"description":"Temperature rise of 5x 1/4 in. thick Kaowool PM board insulation discs when exposed at top surface (painted with medtherm optical black coating, emissivity = 0.95) to 50 kW/m2 incident radiation. \n\nTemperature measurements are recorded at 3 locations (x, distance from top surface); each column / value represents an average of 3 tests conducted in triplicate.\nUncertainties (Uc) at each location represent 2 stdev_mean of Temp values recorded at that specific location in triplicate tests in a +/- 1 s time window (i.e., 3 time steps averaged across 3 tests; 9 total values)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/Black-Insulation_q50_Temp.csv","mediaType":"text/csv","title":"Black-Insulation_q50_Temp"},{"description":"Time-resolved measurements of PMMA Mass Loss Rate when exposed to an incident radiant heat flux of 50 kW m-2.","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_MLR.pdf","mediaType":"application/pdf","title":"PMMA MLR vs time (q50)"},{"description":"Time-resolved measurements of PMMA Mass when exposed to an incident radiant heat flux of 50 kW m-2.","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Mass.pdf","mediaType":"application/pdf","title":"PMMA Mass vs time (q50)"},{"description":"Time-resolved measurements of PMMA back surface temperature when exposed to an incident radiant heat flux of 50 kW m-2.","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Temperature.pdf","mediaType":"application/pdf","title":"PMMA Temperature vs. time (q 50)"},{"description":"contourf plot of heat flux distribution across sample's surface at steady state (target flux = 50 kW m-2)","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/q50_hfmap.pdf","mediaType":"application/pdf","title":"Measured steady state heat flux profile across the sample's surface for tests conducted at 50 kW m-2"},{"description":"normalized (q*=q\"/q\"steady) time-response of gasification apparatus heat flux across the sample's surface","downloadURL":"https://data.nist.gov/od/ds/mds2-2940/q50_normalized.pdf","mediaType":"application/pdf","title":"Time-averaged, normalized incident heat flux (i.e., q*=q\"/q\"{240s-300s}) at r = 0, 2.5, 5.0 and 7.1 cm (21 locations)"}],"identifier":"ark:/88434/mds2-2940","issued":"2023-02-17","keyword":["fire modeling","material properties","model validation","pyrolysis"],"landingPage":"https://data.nist.gov/od/id/mds2-2940","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2023-02-06 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Fire modeling","Fire:Materials flammability"],"title":"Experimental Measurements for Pyrolysis Model Validation - Anaerobic Gasification of PMMA Under External Thermal Radiation"},"description":"A set of 10 anaerobic gasification experiments was conducted on the poly(methyl methacrylate), PMMA, made available to participants in the MaCFP-2 Workshop. In each test, samples (i.e., PMMA discs of approximate dimensions: 7 cm diameter, 5.8 mm thickness) were exposed to radiant heating (nominally 50 kW m-2 across their top surface) in an anaerobic environment. Samples were insulated at their back surface and continuously heated until complete decomposition was observed. Test boundary conditions (e.g., time- and spatially-resolved measurements of incident radiant heat flux; chamber wall temperatures) were carefully characterized.\n\nSix additional tests were also conducted to measure the temperature rise of inert materials (Copper and Kaowool PM Insulation) exposed to the same conditions (i.e., Nitrogen flow rate + incident radiant heat flux) as used during tests on PMMA samples. These test results may be used to validate material thermophysical properties and boundary conditions (e.g., convection heat transfer) controlling heat transfer in this system.","distribution_titles":["Anaerobic gasification of poly(methyl methacrylate) under external thermal radiation","README","MaCFP-PMMA_Gasification_q50_Temp_R1","MaCFP-PMMA_Gasification_q50_Temp_R2","MaCFP-PMMA_Gasification_q50_Temp_R3","MaCFP-PMMA_Gasification_q50_Temp_R4","MaCFP-PMMA_Gasification_q50_Temp_R5","MaCFP-PMMA_Gasification_q50_Mass_R3","MaCFP-PMMA_Gasification_q50_Mass_R4","MaCFP-PMMA_Gasification_q50_Mass_R5","Gasification_Apparatus_Schematic_w-sample","Black-Copper_q50_Temp","Black-Insulation_q50_Temp","PMMA MLR vs time (q50)","PMMA Mass vs time (q50)","PMMA Temperature vs. time (q 50)","Measured steady state heat flux profile across the sample's surface for tests conducted at 50 kW m-2","Time-averaged, normalized incident heat flux (i.e., q*=q\"/q\"{240s-300s}) at r = 0, 2.5, 5.0 and 7.1 cm (21 locations)"],"harvest_record":"https://catalog.data.gov/harvest_record/1fe9148e-bf37-4ada-8521-3a878eb0c722","harvest_record_raw":"https://catalog.data.gov/harvest_record/1fe9148e-bf37-4ada-8521-3a878eb0c722/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2940","keyword":["fire modeling","material properties","model validation","pyrolysis"],"last_harvested_date":"2026-10-02T19:53:11.349447","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"experimental-measurements-for-pyrolysis-model-validation-anaerobic-gasification-of-pmma-un-40478","spatial_centroid":null,"spatial_shape":null,"theme":["Fire:Fire modeling","Fire:Materials flammability"],"title":"Experimental Measurements for Pyrolysis Model Validation - Anaerobic Gasification of PMMA Under External Thermal Radiation","type":"dataset"},{"_score":86.28752,"_sort":[1790970789232,86.28752,0,"f02cf5a7-52de-4119-b287-e1b3fc9ba5fd"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Angela Stelson","hasEmail":"mailto:angela.stelson@nist.gov"},"description":"S-parameter,  Matlab Analysis files for \"Quantifying the Effect of Guest Binding on Host Environment Associated Data\"\nAbstract:\nThe environment around a host-guest complex is defined by of intermolecular interactions between solvent molecules and counter ions. These interactions govern both the solubility of these complexes and the rates of reactions confined within them11. Such noncovalent2 interactions between solvent molecules and ions are challenging to detect by standard analytical chemistry techniques. We use microwave microfluidic spectroscopy to quantify the hydration and ion pairing of a FeII4L4 coordination cage with a set of guest molecules having widely varying physicochemical properties3,4. Significantly, we observed that the environment around a host-guest complex changes depending on the identity of the encapsulated guest. The impact of different guest properties on host ion pairing and hydration was determined through microwave microfluidic measurements paired with principal component analysis. This analysis showed that introducing guest molecules into solution displaced counterions that were bound to the cage, and the water solubility of the guest has the greatest impact on the solvent and ion-pairing dynamics surrounding the host. We also observed that cage-counterion pairing is well-described by a single ion-pairing type, with a one-step reaction model independent of the type of cargo, and that the ion-pairing association constant is reduced for cargo with higher water solubility. Looking beyond this study, microwave microfluidics elucidates structure-property relationships that connect the host interior cavity to its external environment, and these measurements enable improved design of host-guest systems for chemical separations, catalysis, and more.","distribution":[{"description":"Figure ad Data files and scripts","downloadURL":"https://data.nist.gov/od/ds/mds2-2919/Figs_data_plots.zip","mediaType":"application/x-zip-compressed","title":"Figures and Associated Data for Quantifying the Effect of Guest Binding on Host Environment"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2919/2919_README%20v2.txt","mediaType":"text/plain","title":"README file"}],"identifier":"ark:/88434/mds2-2919","issued":"2023-02-02","keyword":["Microwave Microfluidics","ion pairing","ionic conductivity","loss","permittivity"],"landingPage":"https://data.nist.gov/od/id/mds2-2919","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2022-12-12 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"title":"Quantifying the Effect of Guest Binding on Host Environment Associated Data"},"description":"S-parameter,  Matlab Analysis files for \"Quantifying the Effect of Guest Binding on Host Environment Associated Data\"\nAbstract:\nThe environment around a host-guest complex is defined by of intermolecular interactions between solvent molecules and counter ions. These interactions govern both the solubility of these complexes and the rates of reactions confined within them11. Such noncovalent2 interactions between solvent molecules and ions are challenging to detect by standard analytical chemistry techniques. We use microwave microfluidic spectroscopy to quantify the hydration and ion pairing of a FeII4L4 coordination cage with a set of guest molecules having widely varying physicochemical properties3,4. Significantly, we observed that the environment around a host-guest complex changes depending on the identity of the encapsulated guest. The impact of different guest properties on host ion pairing and hydration was determined through microwave microfluidic measurements paired with principal component analysis. This analysis showed that introducing guest molecules into solution displaced counterions that were bound to the cage, and the water solubility of the guest has the greatest impact on the solvent and ion-pairing dynamics surrounding the host. We also observed that cage-counterion pairing is well-described by a single ion-pairing type, with a one-step reaction model independent of the type of cargo, and that the ion-pairing association constant is reduced for cargo with higher water solubility. Looking beyond this study, microwave microfluidics elucidates structure-property relationships that connect the host interior cavity to its external environment, and these measurements enable improved design of host-guest systems for chemical separations, catalysis, and more.","distribution_titles":["Figures and Associated Data for Quantifying the Effect of Guest Binding on Host Environment","README file"],"harvest_record":"https://catalog.data.gov/harvest_record/2f596a5f-218e-40d2-9314-685ce60c19dc","harvest_record_raw":"https://catalog.data.gov/harvest_record/2f596a5f-218e-40d2-9314-685ce60c19dc/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2919","keyword":["Microwave Microfluidics","ion pairing","ionic conductivity","loss","permittivity"],"last_harvested_date":"2026-10-02T19:53:09.232561","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"quantifying-the-effect-of-guest-binding-on-host-environment-associated-data-ed3b1","spatial_centroid":null,"spatial_shape":null,"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"title":"Quantifying the Effect of Guest Binding on Host Environment Associated Data","type":"dataset"},{"_score":15.275305,"_sort":[1790970782370,15.275305,0,"9af44822-110f-4b22-bf92-05c9d755296d"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Jacob Pawlik","hasEmail":"mailto:jacob.pawlik@nist.gov"},"description":"We obtained stylus profilometry measurements of Parylene C devices after soaking in different fluid conditions in a microfluidic environment. The Parylene C devices consist of platinum coplanar waveguides (400 nm Pt, 50 um center conductor, 5 um gaps, 200 um ground planes) with 6.5 um of Parylene C deposited on top. A PDMS microfluidic layer was aligned on top of the 10.00 mm CPW such that the 4.00 mm channel was centered on the CPW. The Parylene C device was subjected to one of three fluid conditions over a 2 month soaking period: H2O at 20 degrees Celsius, 1xPBS (phosphate-buffered saline) at 20 degrees Celsius, or 1xPBS at 37 degrees Celsius. The cross-sectional profile of the CPW was measured in three locations along the channel before and after soaking in each fluid. Here we give these topographical profiles for each Parylene C device we measured.","distribution":[{"description":"Cross-sectional profiles of Pt CPWs coated in Parylene C and soaked in different microfluidic fluid conditions.","downloadURL":"https://data.nist.gov/od/ds/mds2-2809/Profilometry.zip","mediaType":"application/x-zip-compressed","title":"KLA profilometry of Parylene C devices"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2809/2809_README_edit.txt","mediaType":"text/plain","title":"README"}],"identifier":"ark:/88434/mds2-2809","issued":"2023-01-04","keyword":["Microwave microfluidic spectroscopy","Parylene C","S-parameters","biomedical devices","dielectric spectroscopy","implantable devices","profilometry"],"landingPage":"https://data.nist.gov/od/id/mds2-2809","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2022-07-22 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Bioscience:Biomaterials","Electronics:Electromagnetics","Materials:Materials characterization","Metrology:Electrical/electromagnetic metrology","Physics:Spectroscopy"],"title":"KLA stylus profilometry of Parylene C devices"},"description":"We obtained stylus profilometry measurements of Parylene C devices after soaking in different fluid conditions in a microfluidic environment. The Parylene C devices consist of platinum coplanar waveguides (400 nm Pt, 50 um center conductor, 5 um gaps, 200 um ground planes) with 6.5 um of Parylene C deposited on top. A PDMS microfluidic layer was aligned on top of the 10.00 mm CPW such that the 4.00 mm channel was centered on the CPW. The Parylene C device was subjected to one of three fluid conditions over a 2 month soaking period: H2O at 20 degrees Celsius, 1xPBS (phosphate-buffered saline) at 20 degrees Celsius, or 1xPBS at 37 degrees Celsius. The cross-sectional profile of the CPW was measured in three locations along the channel before and after soaking in each fluid. Here we give these topographical profiles for each Parylene C device we measured.","distribution_titles":["KLA profilometry of Parylene C devices","README"],"harvest_record":"https://catalog.data.gov/harvest_record/789a45de-d81c-4e36-81ac-f7bad861b40d","harvest_record_raw":"https://catalog.data.gov/harvest_record/789a45de-d81c-4e36-81ac-f7bad861b40d/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2809","keyword":["Microwave microfluidic spectroscopy","Parylene C","S-parameters","biomedical devices","dielectric spectroscopy","implantable devices","profilometry"],"last_harvested_date":"2026-10-02T19:53:02.370183","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"kla-stylus-profilometry-of-parylene-c-devices-8858d","spatial_centroid":null,"spatial_shape":null,"theme":["Bioscience:Biomaterials","Electronics:Electromagnetics","Materials:Materials characterization","Metrology:Electrical/electromagnetic metrology","Physics:Spectroscopy"],"title":"KLA stylus profilometry of Parylene C devices","type":"dataset"},{"_score":10.376617,"_sort":[1790970781691,10.376617,0,"a90682d4-8e79-4a04-a87c-03fc83666753"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Aaron Rowane","hasEmail":"mailto:aaron.rowane@nist.gov"},"description":"Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","distribution":[{"description":"Unaveraged speed of sound data for R-32/1234yf and R-32/1234ze(E). Each state point contains up to four speed of sound values derived from triplicated, back-to-back, speed of sound measurements. Included are the REFPROP fluid names, composition, temperature, pressure, and  speed of sound. Additionally included are the standard deviations and uncertainties in temperature, pressure, and speed of sound measurements.","downloadURL":"https://data.nist.gov/od/ds/mds2-2804/R32_R1234yf_R1234ze%28E%29_SOSData.csv","format":"The data are in .csv format for easy export into data analysis tools. Opening data in excel allow for the data to be viewed in human readable format.","mediaType":"text/csv","title":"Unaveraged speed of sound data for R-32/1234yf and R-32/1234ze(E) binary mixtures"}],"identifier":"ark:/88434/mds2-2804","issued":"2022-10-03","keyword":["Advanced Materials","Energy","Environment and Climate","Physical Infrastructure","Safety","Security and Forensics"],"language":["en"],"license":"https://www.nist.gov/open/license","modified":"2021-11-28 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"title":"Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants"},"description":"Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","distribution_titles":["Unaveraged speed of sound data for R-32/1234yf and R-32/1234ze(E) binary mixtures"],"harvest_record":"https://catalog.data.gov/harvest_record/2bc69ab3-1326-4abd-aa13-6c5ab88c5961","harvest_record_raw":"https://catalog.data.gov/harvest_record/2bc69ab3-1326-4abd-aa13-6c5ab88c5961/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2804","keyword":["Advanced Materials","Energy","Environment and Climate","Physical Infrastructure","Safety","Security and Forensics"],"last_harvested_date":"2026-10-02T19:53:01.691525","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"speed-of-sound-measurements-of-binary-mixtures-of-difluoromethane-r-32-with-2333-tetrafluo-f4efe","spatial_centroid":null,"spatial_shape":null,"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"title":"Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","type":"dataset"},{"_score":10.367353,"_sort":[1790970781542,10.367353,0,"faa594b6-b93d-4d2f-94f1-8cb05fa430ba"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Aaron Rowane","hasEmail":"mailto:aaron.rowane@nist.gov"},"description":"Workflow: The data that falls into this category are gathered from a measurement program and are published in the archival literature.","distribution":[{"description":"A readme describing the thermal conductivity data supplied on this page","downloadURL":"https://data.nist.gov/od/ds/mds2-2803/2803_README.txt","mediaType":"text/plain","title":"Readme for thermal conductivity data of R-1234yf/134a, R-1234yf/1234ze(E), and R-134a/1234ze(E) mixtures"},{"description":"Data included are the components in the binary mixture and their composition listed with the measured temperature, pressure, and thermal conductivity values. Additionally included in the data set are densities calculated to determine the thermal conductivity.","downloadURL":"https://data.nist.gov/od/ds/mds2-2803/AllTCXData.csv","format":".csv file which is a machine readable format. Opening the file in excel provides a human readable format.","mediaType":"text/csv","title":"Thermal conductivity data for R-1234yf/134a, R-1234yf/1234ze(E), and R-134a/1234ze(E) mixture"},{"description":"Thermal conductivity data for binary mixtures of R-134a, R-1234yf, and R-1234ze(E)","downloadURL":"https://data.nist.gov/od/ds/mds2-2803/SupplementalFiles.zip","mediaType":"application/x-zip-compressed","title":"Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants"}],"identifier":"ark:/88434/mds2-2803","issued":"2022-10-03","keyword":["Advanced Materials","Energy","Environment and Climate","Physical Infrastructure","Safety","Security and Forensics"],"landingPage":"https://data.nist.gov/od/id/mds2-2803","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2022-07-28 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.1021/acs.iecr.2c01924"],"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"title":"Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants"},"description":"Workflow: The data that falls into this category are gathered from a measurement program and are published in the archival literature.","distribution_titles":["Readme for thermal conductivity data of R-1234yf/134a, R-1234yf/1234ze(E), and R-134a/1234ze(E) mixtures","Thermal conductivity data for R-1234yf/134a, R-1234yf/1234ze(E), and R-134a/1234ze(E) mixture","Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants"],"harvest_record":"https://catalog.data.gov/harvest_record/4beb3357-1da1-45ef-b1d7-cfee836c99a3","harvest_record_raw":"https://catalog.data.gov/harvest_record/4beb3357-1da1-45ef-b1d7-cfee836c99a3/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2803","keyword":["Advanced Materials","Energy","Environment and Climate","Physical Infrastructure","Safety","Security and Forensics"],"last_harvested_date":"2026-10-02T19:53:01.542324","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"thermal-conductivity-of-binary-mixtures-of-1112-tetrafluoroethaner-134a-2333-tetrafluoropr-aeb81","spatial_centroid":null,"spatial_shape":null,"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"title":"Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","type":"dataset"},{"_score":71.89212,"_sort":[1790970770741,71.89212,0,"09912a18-b346-4002-b6f3-119665009436"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1Y","bureauCode":["006:55"],"contactPoint":{"fn":"Thomas Roth","hasEmail":"mailto:thomas.roth@nist.gov"},"description":"Internet of Things (IoT) is comprised of interacting networks of physical, computational, and human components that coordinate to fulfill time-sensitive functions in their environment. The development of these systems spans all industrial sectors and demands collaborative effort between research and development teams from multiple institutions. Realizing the full potential of IoT requires interoperability between heterogeneous systems and development processes supported by robust platforms for experimentation and testing across domains. Meanwhile, current design and management approaches for these systems are domain-specific and would benefit from a more universally applicable approach.\n\nThe National Institute of Standards and Technology (NIST) and its partner, the Institute for Software Integrated Systems at Vanderbilt University, have developed a collaborative experiment development environment that integrates best-of-breed tools including programming languages, network simulators, simulation platforms, hardware in the loop, and others. This environment integrates these tools into a standardized communications protocol, IEEE Standard 1516 High Level Architecture (HLA), and provides a graphical modeling language where simulators can easily be configured into different experimental configurations. Its code generation capabilities transform these simple models into executable simulations and code pre-configured to communicate using the standardized HLA services. This environment is called the Universal CPS Environment for Federation (UCEF). UCEF is distributed as a portable, self-contained Ubuntu Virtual Machine which allows it to run on any computational platform.","distribution":[{"accessURL":"https://github.com/usnistgov/ucef","description":"A GitHub repository that contains the latest UCEF source code including documentation on how to compile the source code into a working virtual machine.","format":"plain text files stored on a public GitHub repository","title":"UCEF Source Code"}],"identifier":"ark:/88434/mds2-2638","keyword":["co-simulation","cyber-physical systems","high level architecture","internet of things","modeling and simulation","tool integration"],"language":["en"],"license":"https://www.nist.gov/open/license","modified":"2022-02-05 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.1109/DESTION50928.2020.00008","https://doi.org/10.6028/NIST.SP.1900-101"],"theme":["Information Technology:Cyber-physical systems","Information Technology:Internet of Things"],"title":"Universal Cyber-Physical Systems Environment for Federation (UCEF)"},"description":"Internet of Things (IoT) is comprised of interacting networks of physical, computational, and human components that coordinate to fulfill time-sensitive functions in their environment. The development of these systems spans all industrial sectors and demands collaborative effort between research and development teams from multiple institutions. Realizing the full potential of IoT requires interoperability between heterogeneous systems and development processes supported by robust platforms for experimentation and testing across domains. Meanwhile, current design and management approaches for these systems are domain-specific and would benefit from a more universally applicable approach.\n\nThe National Institute of Standards and Technology (NIST) and its partner, the Institute for Software Integrated Systems at Vanderbilt University, have developed a collaborative experiment development environment that integrates best-of-breed tools including programming languages, network simulators, simulation platforms, hardware in the loop, and others. This environment integrates these tools into a standardized communications protocol, IEEE Standard 1516 High Level Architecture (HLA), and provides a graphical modeling language where simulators can easily be configured into different experimental configurations. Its code generation capabilities transform these simple models into executable simulations and code pre-configured to communicate using the standardized HLA services. This environment is called the Universal CPS Environment for Federation (UCEF). UCEF is distributed as a portable, self-contained Ubuntu Virtual Machine which allows it to run on any computational platform.","distribution_titles":["UCEF Source Code"],"harvest_record":"https://catalog.data.gov/harvest_record/73abea61-ac74-43cf-9165-71827c007643","harvest_record_raw":"https://catalog.data.gov/harvest_record/73abea61-ac74-43cf-9165-71827c007643/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-2638","keyword":["co-simulation","cyber-physical systems","high level architecture","internet of things","modeling and simulation","tool integration"],"last_harvested_date":"2026-10-02T19:52:50.741695","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"universal-cyber-physical-systems-environment-for-federation-ucef-6dc68","spatial_centroid":null,"spatial_shape":null,"theme":["Information Technology:Cyber-physical systems","Information Technology:Internet of Things"],"title":"Universal Cyber-Physical Systems Environment for Federation (UCEF)","type":"dataset"},{"_score":19.633558,"_sort":[1790970766876,19.633558,0,"0f29b62b-a28e-4b58-bdc9-5b641e3819a6"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1Y","bureauCode":["006:55"],"contactPoint":{"fn":"George Awad","hasEmail":"mailto:george.awad@nist.gov"},"description":"The testing dataset used at TRECVID for the DSDI task in 2020-2022.\nThe dataset includes public videos, ground truth and features of the DSDI task. As the task is continuing, the dataset will be continually updated.\nThere are 32 features across 5 main categories (Environment, Vehicles, Water, Infrastructure, Damage). All videos are airborne low altitude from natural disaster events.","distribution":[{"accessURL":"https://www-nlpir.nist.gov/projects/tv2020/pastdata/disaster.scene.indexing/","description":"Includes master shot boundary reference, whole videos and segmented shots.\nAlso, includes ground truth data","title":"The 2020 DSDI Testing dataset"},{"accessURL":"https://www-nlpir.nist.gov/projects/tv2021/pastdata/disaster.scene.indexing/","description":"Includes the master shot reference, whole videos, segmented shots and ground truth.","title":"The 2021 DSDI Testing dataset"},{"description":"testing videos used at the DSDI task at TRECVID. 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Experimental measurements prepared for the MaCFP Condensed Phase Working Group are submitted electronically by participating institutions and are organized and made publicly available in the MaCFP repository, which is hosted on GitHub [https://github.com/MaCFP/matl-db]. This database is version controlled, with each addition to (or edit of) measurement data saved with a unique identifier (i.e., commit tag). The repository was created and is managed by members of the MaCFP Organizing Committee.\n\nAs of October, 2021, the MaCFP Condensed Phase Material Database contains measurement data from more than 200 unique experiments (conducted under 35 different test conditions on the same exact poly(methyl methacrylate), PMMA). All measurement data submitted by each institution is organized in a single folder with the institution's name. A consistent file naming convention is used for all test data (i.e., across all folders). File names indicate the institution name, experimental apparatus, and basic test conditions (e.g., gaseous environment and incident heat flux or heating rate). Measurement data from repeated experiments is saved in separate, ASCII comma-delimited (.csv) files, each numbered sequentially. 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When citing this database, you must include the commit tag that identifies the version of the repository you are working with.\n\n-------------------\nExperiments Conducted\n-------------------\n\n----- 1. Milligram-Scale Tests: -----\n\n1.1 Thermogravimetric Analysis (TGA)\n\n1.2 Differential Scanning Calorimetry (DSC)\n\n1.3 Microscale Combustion Calorimetry (MCC)\n\n----- 2. Gram-Scale Tests -----\n\n2.1 Cone Calorimeter\n\n2.2 Anaerobic Gasification\n\n2.3 Thermal Conductivity and Diffusivity (Hot Disk and Laser Flash)\n\n--------------\nHow to interpret and use data in this repository for pyrolysis model calibration and validation\n--------------\n\nFurther information regarding the use and interpretation of the data in this repository is available online: \n\nhttps://github.com/MaCFP/matl-db/tree/master/Non-charring/PMMA\n\nThis information includes: \n\nKey factors influencing material response during tests\n\nOutlier Criteria: Identification of clearly incorrect behavior in measurement data\n\n--------------------------\nMethodological Information\n--------------------------\n\nA preliminary summary of the measurement data contained in this repository is available online: https://github.com/MaCFP/matl-db/releases","distribution":[{"accessURL":"https://github.com/MaCFP/matl-db","description":"The MaCFP Condensed Phase Subgroup has been designed to enable the fire research community to make significant progress towards establishing a common framework for the selection of experiments and the methodologies used to analyze these experiments when developing pyrolysis models. \n\nAs of October, 2021, the MaCFP Condensed Phase Material Database contains measurement data from more than 200 unique experiments (conducted under 35 different test conditions on the same exact poly(methyl methacrylate), PMMA). 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This version (1.3) reproduces the challenge environment from Sprints 2 and 3 of the Temporal Map Challenge. These benchmarks are distributed as a simple open-source python package to allow standardized and reproducible comparison of synthetic generator models on real world data and use cases. These data and metrics were developed for and vetted through the NIST PSCR Differential Privacy Temporal Map Challenge, where the evaluation tools, k-marginal and Higher Order Conjunction, proved effective in distinguishing competing models in the competition environment.\n\nSDNist is available via `pip` install: `pip install sdnist==1.2.8` for Python >=3.6 or on the [USNIST/Github](https://github.com/usnistgov/Differential-Privacy-Temporal-Map-Challenge-assets/). \n\nThe sdnist Python module will download data from NIST as necessary, and users are not required to download data manually.","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2515","title":"DOI Access for SDNist: Benchmark data and evaluation tools for data synthesizers."},{"accessURL":"https://github.com/usnistgov/SDNist/","description":"SDNist: Benchmark data and evaluation tools for synthetic data generators","format":"Python 3.8 module","title":"SDNist software respository at Github"},{"description":"A jinja2 report template to help humans read the k-marginal data","downloadURL":"https://data.nist.gov/od/ds/mds2-2515/report2.jinja2","mediaType":"application/octet-stream","title":"K-marginal report template"},{"description":"Three compressed CSV files to run the 'Census'-related functions in SDNist.","downloadURL":"https://data.nist.gov/od/ds/mds2-2515/census-datasets-CSVs.zip","format":"CSV","mediaType":"application/zip","title":"Datasets for 'Census' evaluation in CSV format"},{"description":"Three compressed CSV files to run the 'Taxi'-related functions in SDNist.","downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi-datasets-CSVs.zip","format":"CSV","mediaType":"application/zip","title":"Taxi datasets in CSV format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/GA_NC_SC_10Y_PUMS.json","mediaType":"application/json","title":"Census GA_NC_SC schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/GA_NC_SC_10Y_PUMS.parquet","mediaType":"application/octet-stream","title":"Census GA_NC_SC data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/IL_OH_10Y_PUMS.json","mediaType":"application/json","title":"Census IL_OH schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/IL_OH_10Y_PUMS.parquet","mediaType":"application/octet-stream","title":"Census IL-OH data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/NY_PA_10Y_PUMS.json","mediaType":"application/json","title":"Census NY_PA schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/NY_PA_10Y_PUMS.parquet","mediaType":"application/octet-stream","title":"Census NY-PA data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi.json","mediaType":"application/json","title":"Taxi schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi.parquet","mediaType":"application/octet-stream","title":"Taxi data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2016.json","mediaType":"application/json","title":"Taxi 2016 schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2016.parquet","mediaType":"application/octet-stream","title":"Taxi 2016"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2020.json","mediaType":"application/json","title":"Taxi 2020 schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2020.parquet","mediaType":"application/octet-stream","title":"Taxi 2020 data"}],"identifier":"ark:/88434/mds2-2515","issued":"2021-12-28","keyword":["benchmarks","differential privacy","privacy","private information sharing","synthetic data"],"landingPage":"https://data.nist.gov/od/id/mds2-2515","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2021-12-06 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Artificial Intelligence","Information Technology:Privacy","Public Safety:Public safety communications research"],"title":"SDNist v1.3: Temporal Map Challenge Environment"},"description":"SDNist (v1.3) is a set of benchmark data and metrics for the evaluation of synthetic data generators on structured tabular data. 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The zip file, when expanded, includes a conda environment to populate the dependencies, and a set of python scripts.  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Zhao et al., Frequency stabilization of a quantum cascade laser by weak resonant feedback from a Fabry-Perot cavity, Optics Letters. Frequency-stabilized mid-infrared lasers are valuable tools for precision molecular spectroscopy. However, their implementation remains limited by complicated stabilization schemes. Here we achieve optical self-locking of a quantum cascade laser to the resonant leak-out field of a highly mode-matched two-mirror cavity. The result is a simple approach to achieving ultra-pure frequencies from high-powered mid-infrared lasers. For short time scales (<0.1 ms), we report a linewidth reduction factor of 3\u00d710^(-6) to a linewidth of 12 Hz. Furthermore, we demonstrate two-photon cavity-enhanced absorption spectroscopy of an N2O overtone transition near a wavelength of 4.53 um.","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2409","title":"DOI Access for Frequency stabilization of a quantum cascade laser by weak resonant feedback from a Fabry-Perot cavity"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig1_data.xls","format":".xls","mediaType":"application/vnd.ms-excel","title":"Fig. 1:  Conceptualization and model for a quantum cascade laser coupled to a Fabry-Perot cavity by weak optical feedback."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig1_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Fig. 1:  Conceptualization and model for a quantum cascade laser coupled to a Fabry-Perot cavity by weak optical feedback."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig4_data.xls","format":".csv","mediaType":"application/vnd.ms-excel","title":"Fig. 4:  QCL line width analysis - power spectral densities"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig4_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Fig. 4:  QCL line width analysis - power spectral densities"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig5_data.xls","format":".csv","mediaType":"application/vnd.ms-excel","title":"Fig. 5:  Two-photon absorption spectroscopy of N2O in the mid-infrared"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig5_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Fig. 5:  Two-photon absorption spectroscopy of N2O in the mid-infrared"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/readme.txt","format":".txt","mediaType":"text/plain","title":"Read me file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Read me file"}],"identifier":"ark:/88434/mds2-2409","issued":"2021-05-20","keyword":["Environment and Climate","diode lasers","greenhouse gases","laser metrology","laser stabilization","marine mammals","nitrous oxide","oceans","optical resonators","ph","quantum cascade lasers","remote sensing","seabirds","two-photon absorption"],"landingPage":"https://data.nist.gov/od/id/mds2-2409","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2021-05-18 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.1364/OL.427083"],"theme":["Chemistry:Analytical chemistry","Environment:Greenhouse gas measurements","Metrology:Optical, photometry, and laser metrology","Physics:Spectroscopy"],"title":"Frequency stabilization of a quantum cascade laser by weak resonant feedback from a Fabry-Perot cavity"},"description":"Data from peer-reviewed publication:  G. Zhao et al., Frequency stabilization of a quantum cascade laser by weak resonant feedback from a Fabry-Perot cavity, Optics Letters. Frequency-stabilized mid-infrared lasers are valuable tools for precision molecular spectroscopy. However, their implementation remains limited by complicated stabilization schemes. Here we achieve optical self-locking of a quantum cascade laser to the resonant leak-out field of a highly mode-matched two-mirror cavity. The result is a simple approach to achieving ultra-pure frequencies from high-powered mid-infrared lasers. For short time scales (<0.1 ms), we report a linewidth reduction factor of 3\u00d710^(-6) to a linewidth of 12 Hz. 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J. Fleisher et al., Absolute 13C/12C Isotope Amount Ratio for Vienna Pee Dee Belemnite from Infrared Absorption Spectroscopy, Nature Physics. Measurements of isotope ratios are predominantly made with reference to standard specimens that have been characterized in the past. In the 1950s, the carbon isotope ratio was referenced to a belemnite sample collected by Heinz Lowenstam and Harold Urey in South Carolina?s Pee Dee region. Due to the exhaustion of the sample since then, reference materials that are traceable to the origin artefact are used to define the Vienna Pee Dee Belemnite (VPDB) scale for stable carbon isotope analysis. However, these reference materials have also become exhausted or proven unstable over time, mirroring issues with the international prototype of the kilogram that led to a revised International System of Units. A campaign to elucidate the stable carbon isotope ratio of VPDB is underway, but independent measurement techniques are required to support it. Here we report an accurate value for the stable carbon isotope ratio inferred from infrared absorption spectroscopy, fulfilling the promise of this fundamentally accurate approach. Our results agree with a value recently derived from mass spectrometry, and therefore advance the prospects of SI-traceable isotope analysis. 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J. Fleisher et al., Absolute 13C/12C Isotope Amount Ratio for Vienna Pee Dee Belemnite from Infrared Absorption Spectroscopy, Nature Physics. Measurements of isotope ratios are predominantly made with reference to standard specimens that have been characterized in the past. In the 1950s, the carbon isotope ratio was referenced to a belemnite sample collected by Heinz Lowenstam and Harold Urey in South Carolina?s Pee Dee region. Due to the exhaustion of the sample since then, reference materials that are traceable to the origin artefact are used to define the Vienna Pee Dee Belemnite (VPDB) scale for stable carbon isotope analysis. However, these reference materials have also become exhausted or proven unstable over time, mirroring issues with the international prototype of the kilogram that led to a revised International System of Units. A campaign to elucidate the stable carbon isotope ratio of VPDB is underway, but independent measurement techniques are required to support it. Here we report an accurate value for the stable carbon isotope ratio inferred from infrared absorption spectroscopy, fulfilling the promise of this fundamentally accurate approach. Our results agree with a value recently derived from mass spectrometry, and therefore advance the prospects of SI-traceable isotope analysis. 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The work includes the development of an Excel spreadsheet that is to be used with the NIST23 (REFPROP) (that must be obtained separately at https://www.nist.gov/srd/refprop) to provide two bottle-filling calculations (1) given vessel size, mass of agent, mass of pressurizing agent, and filling temperature compute the filling pressure, and the temperature and pressure conditions at which the fluid in the vessel becomes single phase, and (2) given the vessel size, mass of agent, and filling temperature and pressure, compute the mass of pressurizing fluid, and the temperature and pressure conditions at which the fluid in the vessel becomes single phase. The agents include CF3I, R-218, R-125, R-227ea, R 13B1, R-236fa, HFE-7100, Novec 649 (also known as Novec 1230 and FK-5-1-12), R 1233zd(E), R-1336mzz(Z), and R1336-mzz(E). Two pressurizing agents are available, nitrogen and carbon dioxide. 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The background marine air particles are labeled: Ca-S 1D, Ca-S 2N, Ca-S 3D, Ca-S 4N.\n\n\nData are contained in seven folders arranged by the following topics:\n\n\n1 -- Particle compositions by FIB-SEM-EDX and volumes of material phases within particles (folder: 1_Particle_Compositions_Volumes);\n2 -- Spatial and optical parameters for optical modeling of particles and geometric shapes (folder: 2_Particles_Shapes_Spatial_Optical_Parameters (and subfolders));\n3 -- Complex refractive indices for particles and shapes based on Maxwell Garnett average dielectric function (folder: 3_Complex_RIs_Maxwell_Garnett (and subfolders));\n4 -- Results from discrete dipole approximation modeling software DDSCAT ver. 7.3 (folder: 4_DDSCAT_Scattering_Output (and subfolders));\n5 -- Mueller scattering matrix elements (folder: 5_Matrix_Elements (and subfolders));\n6 -- Root-mean-square calculations for phase function and degree of linear polarization (folder: 6_PhaseFunction_LinearPolarization_RMS);\n7 -- Calculations for the backscatter fraction (folder: 7_Backscatter_Fraction)","distribution":[{"accessURL":"https://doi.org/10.18434/M32263","title":"DOI Access for Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/Dataset_Catalog.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/Dataset_Catalog.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/NISTdataset_ModelingSingleAsianDustParticlesAndGeometricShapes.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/NISTdataset_ModelingSingleAsianDustParticlesAndGeometricShapes.zip.sha256","mediaType":"text/plain"}],"identifier":"ark:/88434/mds2-2263","issued":"2020-07-20","keyword":["Asian dust","EDX","FIB-SEM","atmospheric aerosol","climate change","discrete dipole approximation method","energy-dispersive X-ray spectroscopy","environment and climate","focused ion-beam scanning electron microscopy","focused ion-beam tomography","light absorption","light scattering","optical property modeling","radiative forcing"],"landingPage":"https://data.nist.gov/od/id/mds2-2263","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2020-05-11 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.1016/j.jqsrt.2020.107197"],"theme":["Environment:Air / water / soil quality","Environment:Environmental health","Physics:Optical physics","Physics:Spectroscopy"],"title":"Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing"},"description":"The project that produced these data involved the analysis and modeling of atmospheric Asian dust particles and background marine air particles collected in Hawaii, USA at the Mauna Loa Observatory (MLO) of the National Oceanic and Atmospheric Administration. 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Known amounts of EC and OC were deposited onto a quartz-fiber filter and analyzed with different temperature protocols.  Results with the NIST-EPA-C temperature protocol agreed with the reference values to better than 2 % for EC, OC, total carbon (TC), and EC/TC. Uncertainty in TC among all temperature protocols was less than 5 % of the reference value while all protocols had EC/TC ratios with an uncertainty less than 10 %.","distribution":[{"accessURL":"https://doi.org/10.18434/M32261","title":"DOI Access for Evaluation of Thermal Optical Analysis Using an Aqueous Binary Mixture"},{"description":"The Excel file contains carbon concentrations from quartz fiber filters as micrograms of carbon per square centimeter of filter material. EC, OC, and TC are elemental carbon, organic carbon, and total carbon, respectively.","downloadURL":"https://data.nist.gov/od/ds/mds2-2261/All_runs_TOA.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Measurements of Particulate Organic Carbon, Elemental Carbon, and Total Carbon by Thermal-Optical Transmission Analysis"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2261/All_runs_TOA.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Measurements of Particulate Organic Carbon, Elemental Carbon, and Total Carbon by Thermal-Optical Transmission Analysis"}],"identifier":"ark:/88434/mds2-2261","issued":"2020-07-10","keyword":["TOT","black carbon","elemental carbon","organic carbon","organic carbon aerosol","thermal optical transmission analysis"],"landingPage":"https://data.nist.gov/od/id/mds2-2261","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2020-06-03 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Analytical chemistry","Environment:Air / water / soil quality","Environment:Environmental health","Standards:Reference materials"],"title":"Evaluation of Thermal Optical Analysis Using an Aqueous Binary Mixture"},"description":"This dataset contains measurements of organic carbon, elemental carbon, and total carbon in atmospheric particulate matter with the thermal-optical carbon analyzer. The data are associated with the following publication: C.D. Grimes, J.M. Conny, R.R. Dickerson, 2020, \"Evaluation of Thermal-Optical Analysis Using an Aqueous Binary Mixture,\" Atmospheric Environment, in press. Below is the abstract for the publication.\n\nThermal-Optical Analysis (TOA), a commonly implemented technique used to measure the amount of particulate carbon in the atmosphere or deposited on a filter substrate, distinguishes organic carbon (OC) from elemental carbon (EC) through the monitoring of laser light, heating, and measuring evolved carbon.  Here, we present a method to characterize the TOA transmission method with an aqueous binary mixture containing EC and OC that can easily be deposited onto a filter at low volumes. Known amounts of EC and OC were deposited onto a quartz-fiber filter and analyzed with different temperature protocols.  Results with the NIST-EPA-C temperature protocol agreed with the reference values to better than 2 % for EC, OC, total carbon (TC), and EC/TC. Uncertainty in TC among all temperature protocols was less than 5 % of the reference value while all protocols had EC/TC ratios with an uncertainty less than 10 %.","distribution_titles":["DOI Access for Evaluation of Thermal Optical Analysis Using an Aqueous Binary Mixture","Measurements of Particulate Organic Carbon, Elemental Carbon, and Total Carbon by Thermal-Optical Transmission Analysis","SHA256 File for Measurements of Particulate Organic Carbon, Elemental Carbon, and Total Carbon by Thermal-Optical Transmission Analysis"],"harvest_record":"https://catalog.data.gov/harvest_record/538ba0be-3fd6-49db-9afd-f74f871541d0","harvest_record_raw":"https://catalog.data.gov/harvest_record/538ba0be-3fd6-49db-9afd-f74f871541d0/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2261","keyword":["TOT","black carbon","elemental carbon","organic carbon","organic carbon aerosol","thermal optical transmission analysis"],"last_harvested_date":"2026-10-02T19:52:12.140185","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"evaluation-of-thermal-optical-analysis-using-an-aqueous-binary-mixture-9d1e9","spatial_centroid":null,"spatial_shape":null,"theme":["Chemistry:Analytical chemistry","Environment:Air / water / soil quality","Environment:Environmental health","Standards:Reference materials"],"title":"Evaluation of Thermal Optical Analysis Using an Aqueous Binary Mixture","type":"dataset"},{"_score":70.013214,"_sort":[1790970729924,70.013214,0,"9fff478d-c4a2-4ef6-923e-4326b4f999cf"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Thao T. 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The data also include numerical results of using the proposed interference protection criterion in terms of two metrics, the total number of users moved from the channel in order to protect the incumbent (i.e., the size of the move list) and the realized aggregate interference of all co-channel users at the incumbent. The data is associated with the letter, \"Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment,\" M. R. Souryal and T. T. Nguyen, in IEEE Wireless Communication Letters, Jan. 2021.","distribution":[{"accessURL":"https://doi.org/10.18434/M32243","title":"DOI Access for Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment"},{"description":"Data used to plot Figure 2 and Figure 4.","downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure4_moveList_aggInterf.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Ratio of p_th percentile upper bound, move list size, and aggregate interference"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure2_ratio_perctnl_upper_bound.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure2_ratio_perctnl_upper_bound.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure4_moveList_aggInterf.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Ratio of p_th percentile upper bound"}],"identifier":"ark:/88434/mds2-2243","issued":"2021-01-28","keyword":["Aggregate interference","CBRS","incumbent protection","spectrum access system","spectrum sharing."],"landingPage":"https://data.nist.gov/od/id/mds2-2243","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2021-01-28 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.1109/LWC.2020.3020014"],"theme":["Advanced Communications:Wireless (RF)","Mathematics and Statistics:Statistical analysis"],"title":"Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment"},"description":"In a shared spectrum environment, as is the case in the 3.5 GHz Citizens Broadband Radio Service (CBRS), the secondary users with lower priority are managed by independent spectrum access systems (SASs) in order to protect the incumbents with higher priority from interference. 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The data is associated with the letter, \"Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment,\" M. R. Souryal and T. T. Nguyen, in IEEE Wireless Communication Letters, Jan. 2021.","distribution_titles":["DOI Access for Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment","Ratio of p_th percentile upper bound, move list size, and aggregate interference","SHA256 File for Ratio of p_th percentile upper bound"],"harvest_record":"https://catalog.data.gov/harvest_record/90bc8968-3233-4377-a741-b4932b14bd54","harvest_record_raw":"https://catalog.data.gov/harvest_record/90bc8968-3233-4377-a741-b4932b14bd54/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2243","keyword":["Aggregate interference","CBRS","incumbent protection","spectrum access system","spectrum sharing."],"last_harvested_date":"2026-10-02T19:52:09.924559","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"independent-calculation-of-move-lists-for-incumbent-protection-in-a-multi-sas-shared-spect-2dc98","spatial_centroid":null,"spatial_shape":null,"theme":["Advanced Communications:Wireless (RF)","Mathematics and Statistics:Statistical analysis"],"title":"Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment","type":"dataset"},{"_score":16.461704,"_sort":[1790970729779,16.461704,0,"088a3bbf-c40c-4c9b-a596-81f8a97ce330"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Karl Montgomery","hasEmail":"mailto:karl.montgomery@nist.gov"},"description":"The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.","distribution":[{"accessURL":"https://doi.org/10.18434/M32242","title":"DOI Access for Measurement and Processed Data From A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation"},{"description":"The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.","downloadURL":"https://data.nist.gov/od/ds/mds2-2242/CoaxChannel_GDB_Data.zip","format":".csv and .pcap files","mediaType":"application/x-zip-compressed","title":"Measurement and Processed Data From a Graph Database Approach to Wireless IIoT Work-cell Performance"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2242/CoaxChannel_GDB_Data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Measurement and Processed Data From a Graph Database Approach to Wireless IIoT Work-cell Performance"}],"identifier":"ark:/88434/mds2-2242","issued":"2020-07-26","keyword":["factory automation","graph database","industrial wireless","instrumentation","measurement","testbed"],"landingPage":"https://data.nist.gov/od/id/mds2-2242","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2020-05-22 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://www.nist.gov/publications/graph-database-approach-wireless-iiot-work-cell-performance-evaluation"],"theme":["Advanced Communications:Wireless (RF)","Manufacturing:Factory communications","Manufacturing:Factory operations planning and control"],"title":"Measurement and Processed Data From A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation"},"description":"The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.","distribution_titles":["DOI Access for Measurement and Processed Data From A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation","Measurement and Processed Data From a Graph Database Approach to Wireless IIoT Work-cell Performance","SHA256 File for Measurement and Processed Data From a Graph Database Approach to Wireless IIoT Work-cell Performance"],"harvest_record":"https://catalog.data.gov/harvest_record/e37476d5-85d7-4109-a26b-6140aa071c67","harvest_record_raw":"https://catalog.data.gov/harvest_record/e37476d5-85d7-4109-a26b-6140aa071c67/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2242","keyword":["factory automation","graph database","industrial wireless","instrumentation","measurement","testbed"],"last_harvested_date":"2026-10-02T19:52:09.779057","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"measurement-and-processed-data-from-a-graph-database-approach-to-wireless-iiot-work-cell-p-31d32","spatial_centroid":null,"spatial_shape":null,"theme":["Advanced Communications:Wireless (RF)","Manufacturing:Factory communications","Manufacturing:Factory operations planning and control"],"title":"Measurement and Processed Data From A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation","type":"dataset"},{"_score":14.072992,"_sort":[1790970716771,14.072992,0,"9798c569-7b3a-41c4-ab45-2da321d0fea2"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["006:55"],"contactPoint":{"fn":"Ryan Falkenstein-Smith","hasEmail":"mailto:ryan.falkenstein-smith@nist.gov"},"description":"This dataset documents a series of time-averaged gas species measurements made along the centerline of methanol, ethanol, and acetone pool fires steadily burning in a quiescent environment. All gas species measurements are obtained using a Gas Chromatograph/ Mass Spectrometer System (GC/MS). Measurements were made at different heights along the centerline of the fire and repeated at least twice for each location. Gas species volume fractions were determined via the GC/MS using predetermined calibration factors. Soot mass fractions are simultaneously measured during the gas sampling process. The gas species volume and soot mass fractions are compared at different heights within the fire and across a variety of different fuels. Other fire parameters are measured as well, including time-averaged temperature measurements and mass burning rates. The dataset provided here are CSV files listing the volume fractions and temperature measurements made in 30 cm diameter Acetone, Ethanol, and Methanol pool fires.  A technical note (https://doi.org/10.6028/NIST.TN.2082) describes the collection and analysis of these datasets in further detail.","distribution":[{"accessURL":"https://doi.org/10.18434/M32135","title":"DOI Access for Volume Fraction and Temperature Measurements of 30 cm Acetone, Ethanol, Methanol, Pool Fires"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Acetone_30_cm.csv","mediaType":"application/vnd.ms-excel","title":"30 cm Acetone Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Acetone_30_cm.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 30 cm Acetone Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Ethanol_30_cm.csv","mediaType":"application/vnd.ms-excel","title":"30 cm Ethanol Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Ethanol_30_cm.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 30 cm Ethanol Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Methanol_30_cm.csv","mediaType":"application/vnd.ms-excel","title":"30 cm Methanol Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Methanol_30_cm.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 30 cm Methanol Pool Fire Measurements with Uncertainty"}],"identifier":"ark:/88434/mds2-2135","issued":"2020-09-04","keyword":["Acetone","Ethanol","Gas species measurements","Liquid Pool fires","Methanol"],"landingPage":"https://data.nist.gov/od/id/mds2-2135","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2019-09-27 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.6028/NIST.TN.2082"],"theme":["Fire:Fire dynamics and science","Fire:Fire modeling"],"title":"Volume Fraction and Temperature Measurements of 30 cm Acetone, Ethanol, Methanol, Pool Fires"},"description":"This dataset documents a series of time-averaged gas species measurements made along the centerline of methanol, ethanol, and acetone pool fires steadily burning in a quiescent environment. All gas species measurements are obtained using a Gas Chromatograph/ Mass Spectrometer System (GC/MS). Measurements were made at different heights along the centerline of the fire and repeated at least twice for each location. Gas species volume fractions were determined via the GC/MS using predetermined calibration factors. Soot mass fractions are simultaneously measured during the gas sampling process. The gas species volume and soot mass fractions are compared at different heights within the fire and across a variety of different fuels. Other fire parameters are measured as well, including time-averaged temperature measurements and mass burning rates. The dataset provided here are CSV files listing the volume fractions and temperature measurements made in 30 cm diameter Acetone, Ethanol, and Methanol pool fires.  A technical note (https://doi.org/10.6028/NIST.TN.2082) describes the collection and analysis of these datasets in further detail.","distribution_titles":["DOI Access for Volume Fraction and Temperature Measurements of 30 cm Acetone, Ethanol, Methanol, Pool Fires","30 cm Acetone Pool Fire Measurements with Uncertainty","SHA256 File for 30 cm Acetone Pool Fire Measurements with Uncertainty","30 cm Ethanol Pool Fire Measurements with Uncertainty","SHA256 File for 30 cm Ethanol Pool Fire Measurements with Uncertainty","30 cm Methanol Pool Fire Measurements with Uncertainty","SHA256 File for 30 cm Methanol Pool Fire Measurements with Uncertainty"],"harvest_record":"https://catalog.data.gov/harvest_record/6e512629-8817-44f9-a61b-45328b3eb13e","harvest_record_raw":"https://catalog.data.gov/harvest_record/6e512629-8817-44f9-a61b-45328b3eb13e/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-2135","keyword":["Acetone","Ethanol","Gas species measurements","Liquid Pool fires","Methanol"],"last_harvested_date":"2026-10-02T19:51:56.771664","organization":{"aliases":["dept","doc"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"16980d1c-5e8f-4188-b962-42446f2d3f63","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png","name":"Department of Commerce","organization_type":"Federal Government","slug":"commerce"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"volume-fraction-and-temperature-measurements-of-30-cm-acetone-ethanol-methanol-pool-fires-2429c","spatial_centroid":null,"spatial_shape":null,"theme":["Fire:Fire dynamics and science","Fire:Fire modeling"],"title":"Volume Fraction and Temperature Measurements of 30 cm Acetone, Ethanol, Methanol, Pool Fires","type":"dataset"},{"_score":18.56443,"_sort":[1790970713882,18.56443,0,"a5034bca-6702-4f61-a73c-f69d1ea28271"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Rick Candell Jr.","hasEmail":"mailto:richard.candell@nist.gov"},"description":"This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions. The wireless channel impact is produced through the RF channel emulator. First, we consider the benchmark channel with free-space log-distance path loss and ideal channel impulse response (CIR) which has no multi-path. Second, we consider a measured delay profile of an industrial environment where the CIR is experimentally measured and processed to be deployed using the channel emulator and to reflect the industrial environment impact. Moreover, time-varying log-normal shadowing is introduced due to the fluctuations in the signal level because of obstructions. The variance of zero-mean log-normal shadowing is set through the emulator. In order to collect the position information of the gantry system tool, we used a vision tracking system. In this dataset, we attached a meta_data.csv file to map various files to their corresponding parameters.  A README.doc file is included to describe the measurement apparatus.","distribution":[{"accessURL":"https://doi.org/10.18434/M32100","title":"DOI Access for Measurement Dataset for A Wireless Gantry System"},{"description":"A typical two-dimensional gantry system is controlled by a local controller which receives G-code commands wirelessly over a Wi-Fi network. The industrial wireless channel is replicated using a radio frequency (RF) channel emulator where various scenarios are considered, and various wireless channel parameters are studied. The movement of the gantry system tool is tracked using a vision tracking system to quantify the impact of the wireless channel on the system performance.","downloadURL":"https://data.nist.gov/od/ds/mds2-2100/Dataset_Gantry_System_Edited.zip","format":"ZIP File","mediaType":"application/x-zip-compressed","title":"Experimental Wireless Data Set for 2D Gantry System"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2100/Dataset_Gantry_System_Edited.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Experimental Wireless Data Set for 2D Gantry System"}],"identifier":"ark:/88434/mds2-2100","issued":"2020-06-29","keyword":["cyber-physical systems","gantry","manufacturing","robotics","wireless"],"landingPage":"https://data.nist.gov/od/id/mds2-2100","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2019-08-07 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://www.nist.gov/publications/impact-wireless-communications-controlling-two-dimensional-gantry-system"],"theme":["Advanced Communications:Wireless (RF)","Manufacturing:Factory communications"],"title":"Measurement Dataset for A Wireless Gantry System"},"description":"This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. 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