cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bztnt
title: CCDC 2174577: Experimental Crystal Structure Determination

description: Related Article: Klaus Roths, Roland Fröhlich, Constantin G. Daniliuc, Rudolf Aumann|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bzvjq
title: CCDC 2174604: Experimental Crystal Structure Determination

description: Related Article: Klaus Roths, Roland Fröhlich, Constantin G. Daniliuc, Rudolf Aumann|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bzwfn
title: CCDC 2174632: Experimental Crystal Structure Determination

description: Related Article: Klaus Roths, Roland Fröhlich, Constantin G. Daniliuc, Rudolf Aumann|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bzyfq
title: CCDC 2174694: Experimental Crystal Structure Determination

description: Related Article: Peter G. Jones, Ali M. S. Hebishy, Galal H. Elgemeie, Lobna M. Gouda|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c00dt
title: CCDC 2174755: Experimental Crystal Structure Determination

description: Related Article: J. G. Malecki|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c00fv
title: CCDC 2174756: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.L. Gill|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c00gw
title: CCDC 2174757: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.L. Gill|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c00hx
title: CCDC 2174758: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.L. Gill|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c00jy
title: CCDC 2174759: Experimental Crystal Structure Determination

description: Related Article: Klaus Roths, Roland Fröhlich, Constantin G. Daniliuc, Rudolf Aumann|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c03gz
title: CCDC 2174850: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.20409430
title: Supplementary data for Islam et al. 2022 - Benchmark

description: All results from the benchmarking with synthetic datasets, including the appropriate input used to run each of the tools as well as the output generated by each of the tools.
DOI: 10.6084/m9.figshare.20411483
title: Supplementary data for Islam et al. 2022 - Benchmark of initialization, normalization and objective function

description: All results from the benchmarking of the different options available in SigProfilerExtractor for matrix normalization, NMF initialization, and NMF objective function.
DOI: 10.5281/zenodo.6949616
title: Tracker Analysis File

description: The csv data file contain the following properties of tracks: 'Longitude [°]', 'Latitude [°]',<br> 'Mean Precipitation [mm h$^{-1}$]',<br> 'Maximum Precipitation [mm h$^{-1}$]',<br> 'Volume [km² h]', 'Rain Volume [m³ E6]',<br> 'Area [km²]', 'Duration [h]', 'Trackers' indicates the tracker used,<br> 'OBS_ModEns' indicates whether the track originates from observations or model,<br> 'Models' indicates the model used,<br> 'Years' and 'Months' indicate the starting date,<br> 'GRIPHO_ornot' indicates whether the track centroid is within or outside of the GRIPHO-Italy domain.
DOI: 10.5281/zenodo.6949615
title: Tracker Analysis File

description: The csv data file contain the following properties of tracks: 'Longitude [°]', 'Latitude [°]',<br> 'Mean Precipitation [mm h$^{-1}$]',<br> 'Maximum Precipitation [mm h$^{-1}$]',<br> 'Volume [km² h]', 'Rain Volume [m³ E6]',<br> 'Area [km²]', 'Duration [h]', 'Trackers' indicates the tracker used,<br> 'OBS_ModEns' indicates whether the track originates from observations or model,<br> 'Models' indicates the model used,<br> 'Years' and 'Months' indicate the starting date,<br> 'GRIPHO_ornot' indicates whether the track centroid is within or outside of the GRIPHO-Italy domain.
DOI: 10.6084/m9.figshare.17138921
title: Table S1

description: <b>Table S1. </b>Echocardiogram and Pulse Wave Tissue Doppler Measurements.<b></b>
DOI: 10.6084/m9.figshare.17138921.v2
title: Table S1

description: <b>Table S1. </b>Echocardiogram and Pulse Wave Tissue Doppler Measurements.<b></b>
DOI: 10.6084/m9.figshare.17138921.v1
title: Table S1

description: <b>Table S1. </b>Echocardiogram and Pulse Wave Tissue Doppler Measurements.<b></b>
DOI: 10.5287/bodleian:2avawbyqe
title: Visualizing macroscopic inhomogeneities in perovskite solar cells
DOI: 10.3886/e176662
title: References to Evidence-Based Program Registry (EBPR) Websites for Behavioral Health in U.S. State Government Statutes and Regulations

description: U.S. state governments have the responsibility to regulate and license behavioral health care interventions, such as for addiction and mental illness, with increasing emphasis on implementing evidence-based programs (EBPs). A serious obstacle to this is lack of clarity or agreement about what constitutes “evidence-based.” The study’s purpose was to determine the extent to which and in what contexts web-based Evidence-based Program Registries (EBPRs) are referenced in state government statutes and regulations (“mandates”) concerning behavioral health care. The study employed the Westlaw Legal Research Database to search for 30 known EBPR websites relevant to behavioral health care within the statutes and regulations of all 50 states. Questions of interest included prevalence of EBPR references in statutes and regulations, purpose of references to EBPRs, context of references to EBPRs, and service areas represented by the mandates. <br>
description: N/A<br>
description: Response Rates: Response rate not applicable. 33 U.S. state statutes and/or regulations were included in the study.<br>
description: Presence of Common Scales: N/A<br>
description: This dataset includes the universe of 33 U.S. state statutes and/or regulations that reference an evidence-based program register for behavioral health.<br>
description: Statutes and mandates of the 50 United States that referenced select evidence-based program registers for behavioral health<br>. Smallest Geographic Unit: U.S. state
description: web scraping;
DOI: 10.3886/e176662v1
title: References to Evidence-Based Program Registry (EBPR) Websites for Behavioral Health in U.S. State Government Statutes and Regulations

description: U.S. state governments have the responsibility to regulate and license behavioral health care interventions, such as for addiction and mental illness, with increasing emphasis on implementing evidence-based programs (EBPs). A serious obstacle to this is lack of clarity or agreement about what constitutes “evidence-based.” The study’s purpose was to determine the extent to which and in what contexts web-based Evidence-based Program Registries (EBPRs) are referenced in state government statutes and regulations (“mandates”) concerning behavioral health care. The study employed the Westlaw Legal Research Database to search for 30 known EBPR websites relevant to behavioral health care within the statutes and regulations of all 50 states. Questions of interest included prevalence of EBPR references in statutes and regulations, purpose of references to EBPRs, context of references to EBPRs, and service areas represented by the mandates. <br>
description: N/A<br>
description: Response Rates: Response rate not applicable. 33 U.S. state statutes and/or regulations were included in the study.<br>
description: Presence of Common Scales: N/A<br>
description: This dataset includes the universe of 33 U.S. state statutes and/or regulations that reference an evidence-based program register for behavioral health.<br>
description: Statutes and mandates of the 50 United States that referenced select evidence-based program registers for behavioral health<br>. Smallest Geographic Unit: U.S. state
description: web scraping;
DOI: 10.18112/openneuro.ds003673.v2.0.1
title: Yale Resting State fMRI/Pupillometry: Arousal Study
DOI: 10.18112/openneuro.ds004228.v1.0.0
title: Early stressful experiences are associated with reduced neural responses to naturalistic emotional and social content in children
DOI: 10.17909/99vs-b998
title: Neptune's Dark Vortex NDS-2018 ("NDS-2018")

description: A Northern Dark Spot discovered in 2018 (NDS-2018) was detected in the annual observations of Neptune from the OPAL program. Once 2019 OPAL data showed that the spot was drifting very close to the equator, where numerical simulations find that spots may perhaps cause planetary-scale disturbances as they disrupt themselves, additional observing programs were conducted to understand how the dark vortex was changing on shorter time scales, and to capture finer detail by taking a higher number of blue (F467M) images in each HST orbit. Dark spots require high spatial resolution at blue wavelengths to be observed, so no ground-based observatory has yet published a detection of one. Only the Voyager 2 spacecraft and Hubble have made published observations of dark vortices on Neptune.  Wong et al. (2022) reviews the overall collection of HST imaging data from the WFC3/UVIS instrument. These have been processed to correct for geometric distortion, cosmic ray hits, and fringing (at narrowband red wavelengths), then navigated to define latitude, longitude, emission, and incidence angles for each pixel as described in Wong et al. (2020). Frames have been corrected for loss of contrast due to scattered light from the wings of the point spread function, and limb darkening and large-scale zonal brightness variation were removed by constructing difference images. The team prepared stacks of coadded difference images to maximize PSF sampling and to reduce noise from pixel instability.
DOI: 10.5281/zenodo.6949657
title: Plasmid Sequences for Brophy et al., 2022: Synthetic genetic circuits as a means of reprogramming plant roots

description: Plasmid Sequences for Brophy et. al. 2022: Synthetic genetic circuits as a means of reprogramming plant roots
DOI: 10.5281/zenodo.6949656
title: Plasmid Sequences for Brophy et al., 2022: Synthetic genetic circuits as a means of reprogramming plant roots

description: Plasmid Sequences for Brophy et. al. 2022: Synthetic genetic circuits as a means of reprogramming plant roots
DOI: 10.6084/m9.figshare.20412156.v1
title: ZEBRA_Images.h5

description: SBF-SEM stack of the villus volume for Zebra
DOI: 10.6084/m9.figshare.20412156
title: ZEBRA_Images.h5

description: SBF-SEM stack of the villus volume for Zebra
DOI: 10.6084/m9.figshare.20412135
title: HORSE_2_Images.h5

description: SBF-SEM stack of the villus volume for Horse 2
DOI: 10.6084/m9.figshare.20412135.v1
title: HORSE_2_Images.h5

description: SBF-SEM stack of the villus volume for Horse 2
DOI: 10.6084/m9.figshare.20412138
title: Vesicle_Data.xlsx

description: Morphometric data associated with vesicles reconstructed in this study
DOI: 10.6084/m9.figshare.20412138.v1
title: Vesicle_Data.xlsx

description: Morphometric data associated with vesicles reconstructed in this study
DOI: 10.6084/m9.figshare.20412141
title: HORSE_1_Labels.model

description: Segmented labels for the Horse 1 villus volume
DOI: 10.6084/m9.figshare.20412141.v1
title: HORSE_1_Labels.model

description: Segmented labels for the Horse 1 villus volume
DOI: 10.6084/m9.figshare.20412147
title: HORSE_2_Labels.model

description: Segmented labels for the Horse 2 villus volume
DOI: 10.6084/m9.figshare.20412147.v1
title: HORSE_2_Labels.model

description: Segmented labels for the Horse 2 villus volume
DOI: 10.6084/m9.figshare.20412150
title: HORSE_1_Images.h5

description: SBF-SEM stack of the villus volume for Horse 1
DOI: 10.6084/m9.figshare.20412150.v1
title: HORSE_1_Images.h5

description: SBF-SEM stack of the villus volume for Horse 1
DOI: 10.6084/m9.figshare.20412153.v1
title: ZEBRA_Labels.model

description: Segmented labels for the Zebra villus volume
DOI: 10.6084/m9.figshare.20412153
title: ZEBRA_Labels.model

description: Segmented labels for the Zebra villus volume
DOI: 10.5066/p9i0b0x4
title: Topographic and bathymetry survey (2019) along the Chincoteague Living Shoreline, Virginia

description: This dataset contains field topographic and bathymetric data measured during February to April 2019 across oyster castles and mud flats along the Chincoteague Living Shoreline, Virginia, where constructed oyster reefs (CORs, aka oyster castles) were installed to protect the shoreline and enhance habitat for oyster and other species.
DOI: 10.5281/zenodo.4726136
title: Los Angeles County Archetypes in Weather Research and Forecasting (WRF) Region from ORNL's AutoBEM

description: Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). To reduce computational demand for representing energy-related performance of a specific portion of the building sector, AutoBEM dynamically generated archetype buildings along with multipliers based on number of buildings and conditioned floor area. Data provided include the geographical region around Los Angeles (LA) County in the state of California, data for each archetype building, and 84 building energy models for every relevant combination of building type and standard (i.e. building code vintage): <strong>Critical note: Building multipliers and models will be updated soon.</strong> <strong>Region (2KB Region.zip) - shapefile of geographical area considered</strong> <strong>Data (12KB Multipliers.csv) - minimalist list of each archetype building (rows) for the following fields (columns)</strong> ID - unique identifier County - county name State - state name CZ - ASHRAE Climate Zone designation Height - building height (meters) NumFloors - number of floors (above-grade) Area - estimate of total conditioned floor area (ft<sup>2</sup>) BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards Standard - building vintage for building code requirements. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, average 40% for commercial buildings varying by building type) Num_Building_Mult - number of buildings of each Building Type and Standard in this area. Area_Multiplier - amount simulation outputs should be multiplied by to represent metrics for all buildings in the region. <strong>Models (1.8MB Models.zip) - EnergyPlus building energy models named according to ID</strong>
description: Integrated Multisector Multiscale Modeling (IM3) project - https://im3.pnnl.gov/
DOI: 10.5281/zenodo.4725725
title: Los Angeles County Archetypes in Weather Research and Forecasting (WRF) Region from ORNL's AutoBEM

description: Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). To reduce computational demand for representing energy-related performance of a specific portion of the building sector, AutoBEM dynamically generated archetype buildings along with multipliers based on number of buildings and conditioned floor area. Data provided include the geographical region around Los Angeles (LA) County in the state of California, data for each archetype building, and 84 building energy models for every relevant combination of building type and standard (i.e. building code vintage): <strong>Critical note: Building multipliers and models will be updated soon.</strong> <strong>Region (2KB Region.zip) - shapefile of geographical area considered</strong> <strong>Data (12KB Multipliers.csv) - minimalist list of each archetype building (rows) for the following fields (columns)</strong> ID - unique identifier County - county name State - state name CZ - ASHRAE Climate Zone designation Height - building height (meters) NumFloors - number of floors (above-grade) Area - estimate of total conditioned floor area (ft<sup>2</sup>) BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards Standard - building vintage for building code requirements. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, average 40% for commercial buildings varying by building type) Num_Building_Mult - number of buildings of each Building Type and Standard in this area. Area_Multiplier - amount simulation outputs should be multiplied by to represent metrics for all buildings in the region. <strong>Models (1.8MB Models.zip) - EnergyPlus building energy models named according to ID</strong>
description: Integrated Multisector Multiscale Modeling (IM3) project - https://im3.pnnl.gov/
DOI: 10.5281/zenodo.4552901
title: Model America - Clark County (Vegas) extract from ORNL's AutoBEM

description: Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). <strong>Critical note: Building multipliers and models will be updated soon.</strong> Two sets of data are provided for 589,586 buildings located within the boundary of Clark County (Las Vegas) in the United States: <strong>Data (185MB *.csv) - minimalist list of each building (rows) for the following fields (columns)</strong> County - county name State - state name CZ - ASHRAE Climate Zone designation Clim_Zone - text label of climate zone est_year - estimated year of construction est_commercial - estimated building type (0=residential, 1=commercial) Centroid - building center location in latitude/longitude (from Footprint2D) Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) Height - building height (meters) ID - unique building ID Area2D - footprint area (ft<sup>2</sup>) BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) NumFloors - number of floors (above-grade) Area - estimate of total conditioned floor area (ft<sup>2</sup>) Standard - building vintage <strong>Models (9.2GB *.tar.gz) - EnergyPlus building energy models named according to ID</strong> Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus (v9.4) 2,784-page Input/Output Reference Guide for everything that can be retrieved or simulated from these models.
description: Integrated Multisector Multiscale Modeling (IM3) project - https://im3.pnnl.gov/
DOI: 10.5281/zenodo.6949784
title: Noise2Fast, Figure 5 Source Data

description: ND2 files represent a 20x FOV wide scan followed by a 60x zoom in on each detected mitosis.
DOI: 10.5281/zenodo.6949783
title: Noise2Fast, Figure 5 Source Data

description: ND2 files represent a 20x FOV wide scan followed by a 60x zoom in on each detected mitosis.