cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3900469
title: Geopackage of data for Historical Archaeology and JOAD article

description: This file is a Geopackage database that includes the following: For raster files, *Raw* indicates that data is the same as the previously archived version and "Rendered" indicates the visualization of that data. "DigitalElevationModelRaw" and "DigitalElevationModelRendered" are based upon Digital Elevation Model for Blue Mountain Charcoal Research Project. https://zenodo.org/record/1252441 "HillshadeRaw" and "HillshadeRendered" are based upon Hillshade Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project". https://zenodo.org/record/1252520 "SlopeRaw" and "SlopeRendered" are based upon Slope Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project". https://zenodo.org/record/1252977 "Hearths" = Identified Charcoal Hearths from "Slope Analysis of 'Digital Elevation Model for Blue Mountain Charcoal Research Project'". https://zenodo.org/record/1252985 "ResearchArea" = Blue Mountain Charcoal Project Research Area. https://zenodo.org/record/1252418 The collection of this data is described here: Description of Methods for Identifying Charcoal Hearths along the Blue Mountain of Pennsylvania. https://zenodo.org/record/1255101 And is published here: Carter, Benjamin P. 2019. “Identifying Landscape Modification Using Open Data and Tools: The Charcoal Hearths of the Blue Mountain, Pennsylvania.” <em>Historical Archaeology</em> 53 (2): 432–43. https://doi.org/10.1007/s41636-019-00171-1. Carter, Benjamin. 2019. “Data for ‘Identifying Landscape Modification Using Open Data and Tools: The Charcoal Hearths of the Blue Mountain, Pennsylvania.’” <em>Journal of Open Archaeology Data</em> 7 (July). https://doi.org/10.5334/joad.53. This data can be visualized here: NEEDED
description: {"references": ["Carter, Benjamin. 2019. \"Data for 'Identifying Landscape Modification Using Open Data and Tools: The Charcoal Hearths of the Blue Mountain, Pennsylvania.'\" Journal of Open Archaeology Data 7 (July). https://doi.org/10.5334/joad.53.", "Carter, Benjamin P. 2019. \"Identifying Landscape Modification Using Open Data and Tools: The Charcoal Hearths of the Blue Mountain, Pennsylvania.\" Historical Archaeology 53 (2): 432\u201343. https://doi.org/10.1007/s41636-019-00171-1."]}
DOI: 10.5281/zenodo.3903479
title: Wasp Spider Venomics

description: Supplementary Files for the wasp spider Venomics project: S1: Docker Images combined for bioinformatic analysis S2: 2D SDS PAGE Gel used for interpretation and MALDI MS (Zoom in for Spot assignments) S3: Mascot output for MALDI MS S4: Mascot output for nanoLC ESI MS S5: Annotated Proteome Hits incl Sequences
description: Corresponding Article under Review in "Biomolecules"
DOI: 10.21227/y51r-rk07
title: IP address assignment durations

description: This dataset covers IP address assignment durations over 6 years, from 13/3/2014 to 30/4/2020. Around 600,000 assignment durations for IPv4 addresses and 190,000 durations for IPv6 addresses is covered. The dataset was processed from the archive of RIPE Atlas ftp.ripe.net/ripe/atlas/. IP addresses of the Atlas probes installed behind NAT are included.
DOI: 10.5281/zenodo.3903504
title: Data for the assement of beaver influence on fish assemblages of small steppe rivers

description: The data was collected on small steppe rivers of the Khoper River basin (tributary of the Don River) in and around of the State nature reserve “Privolzhskaya Lesostep” (Penza Region, Russia, N 52°48′58.4′′, E 44°27′40.4′′), in 2015-2016. It content abundance, density and biomass of fish, and abiotic parameters of river habitats - water area, amount of dissolved oxygen, pH, water temperature and water current velocity.
DOI: 10.5281/zenodo.3903503
title: Data for the assement of beaver influence on fish assemblages of small steppe rivers

description: The data was collected on small steppe rivers of the Khoper River basin (tributary of the Don River) in and around of the State nature reserve “Privolzhskaya Lesostep” (Penza Region, Russia, N 52°48′58.4′′, E 44°27′40.4′′), in 2015-2016. It content abundance, density and biomass of fish, and abiotic parameters of river habitats - water area, amount of dissolved oxygen, pH, water temperature and water current velocity.
DOI: 10.5255/ukda-sn-854342
title: Gentrification, displacement, and the impacts of council estate renewal in C21st London 2017-2020
DOI: 10.5258/soton/d1431
title: Light data sets for thesis 'Sustainability benefits of energy behaviour change in a NHS Trust'

description: Data sets gathered over a nine month period from the light monitors located in three older persons' acute care wards of the NHS community hospital participating in the research project entitled 'Sustainability benefits of energy behaviour change in a NHS Trust'.
DOI: 10.5258/soton/d1432
title: Noise level (dB) data sets for thesis 'Sustainability benefits of energy behaviour change in a NHS Trust'

description: Noise level (dB) data sets gathered over a nine month period from the sound monitors located in three older persons' acute care wards of the NHS community hospital participating in the research project entitled 'Sustainability benefits of energy behaviour change in a NHS Trust'.
DOI: 10.5258/soton/d1433
title: Patient bed movement data sets for thesis 'Sustainability benefits of energy behaviour change in a NHS Trust'

description: Patient bed movement data sets gathered over a nine month period from the motion loggers located on the patients' beds in three older persons' acute care wards of the NHS community hospital participating in the research project entitled 'Sustainability benefits of energy behaviour change in a NHS Trust'.
DOI: 10.5258/soton/d1434
title: Window movement data sets for thesis 'Sustainability benefits of energy behaviour change in a NHS Trust'

description: Window movement data sets gathered over a nine month period from the motion loggers located on the windows in three older persons' acute care wards of the NHS community hospital participating in the research project entitled 'Sustainability benefits of energy behaviour change in a NHS Trust'.
DOI: 10.5258/soton/d1435
title: Air temperature and relative humidity data sets for thesis 'Sustainability benefits of energy behaviour change in a NHS Trust'

description: Data sets gathered over a nine month period from the air temperature and relative humidity monitors located in three older persons' acute care wards of the NHS community hospital participating in the research project entitled 'Sustainability benefits of energy behaviour change in a NHS Trust'.
DOI: 10.5281/zenodo.3903506
title: Acceleration of relativistic beams using laser-generated terahertz pulses

description: Dataset for the figures contained in the manuscript entitled "Acceleration of relativistic beams using laser-generated terahertz pulses".
DOI: 10.5281/zenodo.3903505
title: Acceleration of relativistic beams using laser-generated terahertz pulses

description: Dataset for the figures contained in the manuscript entitled "Acceleration of relativistic beams using laser-generated terahertz pulses".
DOI: 10.5281/zenodo.3903552
title: Computer Aided Design (CAD) files for capillaric circuit with 8 retention burst valves

description: AutoCAD design file and STL file for capillaric circuit with 8 retention burst valves.
DOI: 10.5281/zenodo.3903551
title: Computer Aided Design (CAD) files for capillaric circuit with 8 retention burst valves

description: AutoCAD design file and STL file for capillaric circuit with 8 retention burst valves.
DOI: 10.21227/677y-nf61
title: Paper "Dynamic regulation in electrical networks with non-controlled sources"

description: The uploaded data file is a part of data used or generated by a real time security system for frequency control in electrical grids with variable renewable generation proposed in a paper entitled: “Dynamic regulation in electrical networks with non-controlled sources”. The proposed security system analyzes the electrical network in both steady-state and dynamic state. The test systems IEEE 39-bus were used adding wind generation models to evaluate the proposed security system.
DOI: 10.21227/d89x-wv35
title: Paper "Dynamic regulation in electrical networks with non-controlled sources"

description: The uploaded data file is a part of data used or generated by a real time security system for frequency control in electrical grids with variable renewable generation proposed in a paper entitled: “Dynamic regulation in electrical networks with non-controlled sources”. The proposed security system analyzes the electrical network in both steady-state and dynamic state. The test systems IEEE 39-bus were used adding wind generation models to evaluate the proposed security system.
DOI: 10.5281/zenodo.3736630
title: NSD_atlas_of_hypothalamic_region

description: <pre>This study describes a high-resolution in-vivo magnetic resonance imaging atlas of the human hypothalamus. We employed a minimum deformation averaging (MDA) pipeline to produce a normalized (MNI152b), high-resolution template from multimodal (T1w and T2w) magnetic resonance imaging (MRI) datasets. 990 subjects derived from the HCP1200 data release were included in this study. This template was used to delineate hypothalamic (n=13) and extrahypothalamic (n=12) gray and white matter structures. Files (in bold) of the hypothalamus atlas segmentation (tables (.csv) and images (nifti format)): -a table with the structure name, abbreviation and label number: <strong>Volumes_names-labels.csv</strong> -the full volume segmentation in 0.25 and 0.5 millimeter isotropic resolution: <strong>atlas_labels_0.25mm.nii.gz atlas_labels_0.5mm.nii.gz</strong> -an archive of binary images of each structure separately: <strong>isolated_nuclei_0.25mm_resolution.zip</strong> -an archive of the three example clinical datasets containing the three labels delineating the volume of tissue activated (VTA) of the two DBS patients (example 1 Alzheimer patient with forniceal DBS; example 2 patient receiving DBS for morbid obesity) and the label delineating the metastasis of example 3. These labels are provided in MNI152b space for two reasons: 1) to allow for visualization of the label in relation to the hypothalamic atlas 2) to protect patient confidentiality <strong>example_labels_MNI152_template_space.zip</strong> The high-contrast, high-resolution MDA template images in 0.25 and 0.5 millimeter isotropic resolution: <strong>MDA_990HCP_t1_MNI152b_0.25.nii.gz MDA_990HCP_t1_MNI152b_0.5.nii.gz MDA_990HCP_t2_MNI152b_0.25.nii.gz MDA_990HCP_t2_MNI152b_0.5.nii.gz </strong> Tables that describe the average structure volumes in the subjects used to generate the MDAs: <strong>Average_left_right_hemispheric_hypothalamic_volumes_by_Sex.csv Average_left_right_hemispheric_hypothalamic_volumes.csv Estimated_volumes_of_hypothalamus_proper_its_divisions_and_nuclei.csv</strong> Authors: Clemens Neudorfer, Jürgen Germann, Gavin J.B. Elias, Alexandre Boutet, Robert Gramer, Andres M. Lozano Affiliations: Division of Neurosurgery, Depatment of Surgery, Toronto Western Hospital, University of Toronto, Canada.</pre>
DOI: 10.5281/zenodo.3903588
title: NSD_atlas_of_hypothalamic_region

description: <pre>This study describes a high-resolution in-vivo magnetic resonance imaging atlas of the human hypothalamus. We employed a minimum deformation averaging (MDA) pipeline to produce a normalized (MNI152b), high-resolution template from multimodal (T1w and T2w) magnetic resonance imaging (MRI) datasets. 990 subjects derived from the HCP1200 data release were included in this study. This template was used to delineate hypothalamic (n=13) and extrahypothalamic (n=12) gray and white matter structures. Files (in bold) of the hypothalamus atlas segmentation (tables (.csv) and images (nifti format)): -a table with the structure name, abbreviation and label number: <strong>Volumes_names-labels.csv</strong> -the full volume segmentation in 0.25 and 0.5 millimeter isotropic resolution: <strong>atlas_labels_0.25mm.nii.gz atlas_labels_0.5mm.nii.gz</strong> -an archive of binary images of each structure separately: <strong>isolated_nuclei_0.25mm_resolution.zip</strong> -an archive of the three example clinical datasets containing the three labels delineating the volume of tissue activated (VTA) of the two DBS patients (example 1 Alzheimer patient with forniceal DBS; example 2 patient receiving DBS for morbid obesity) and the label delineating the metastasis of example 3. These labels are provided in MNI152b space for two reasons: 1) to allow for visualization of the label in relation to the hypothalamic atlas 2) to protect patient confidentiality <strong>example_labels_MNI152_template_space.zip</strong> The high-contrast, high-resolution MDA template images in 0.25 and 0.5 millimeter isotropic resolution: <strong>MDA_990HCP_t1_MNI152b_0.25.nii.gz MDA_990HCP_t1_MNI152b_0.5.nii.gz MDA_990HCP_t2_MNI152b_0.25.nii.gz MDA_990HCP_t2_MNI152b_0.5.nii.gz </strong> Tables that describe the average structure volumes in the subjects used to generate the MDAs: <strong>Average_left_right_hemispheric_hypothalamic_volumes_by_Sex.csv Average_left_right_hemispheric_hypothalamic_volumes.csv Estimated_volumes_of_hypothalamus_proper_its_divisions_and_nuclei.csv</strong> Authors: Clemens Neudorfer, Jürgen Germann, Gavin J.B. Elias, Alexandre Boutet, Robert Gramer, Andres M. Lozano Affiliations: Division of Neurosurgery, Depatment of Surgery, Toronto Western Hospital, University of Toronto, Canada.</pre>
DOI: 10.21227/tjv6-cf92
title: Ultrasound Beamforming using MobileNetV2

description: Ultrasound Beamforming using MobileNetV2Sobhan Goudarzi1, Amir Asif 1, Hassan Rivaz1, 1Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada Background, Motivation and Objective In the past few years, the success of deep learning has led to a transformation in several high-level tasks in computer vision and medical image analysis such as classification and segmentation. Deep learning has also caused positive disruptions in several low-level tasks such as CT and MR image reconstruction. In this work, we are proposing a novel deep learning based approach for the low-level task of ultrasound image reconstruction from the pre-beamformed channel data. More specifically, we adapt MobileNetV2 to train a model that mimics Minimum Variance Beamforming (MVB). Statement of Contribution/Methods Herein, we consider the fact that all mathematical transformations can only represent the underlying information existing in input domain, and none of them, including deep learning approach, can generate new information. Therefore, all necessary preprocessing steps are applied to raw RF channel data before feeding to the network, and the network input contains all required information for estimating the result of MVB. More specifically, first, IQ demodulation is applied on the RF channel data since MVB requires complex signals to compute complex weights allowing for beampatterns that are asymmetrical around the center of the beam. Second, time delays are compensated to reduce the load on the network. Finally, the F-number is fixed for all image depths in order to make the image quality uniform. It has to be mentioned that the input to the network is supposed to be in range otherwise RF channel data has to be scaled proportionally. Each pixel of the image is reconstructed separately as the case for MVB. Network’s input is a matrix in which first two channels are real and imaginary parts of IQ data, is the number of channels and is the length of the window considered for temporal averaging to preserve the speckle statistics. The network output is a two dimensional vector containing real and imaginary parts of the beamformed data. As mentioned before, MobileNetV2 is used as the network structure since it is a leading architecture for networks with low computational complexity and memory requirement. This is of critical importance for commercial success of deep learning beamforming given the ultrahigh very large ultrasound frame-rate and very limited computational resources, especially in mobile ultrasound devices. As for training details, the network’s output for each images is reconstructed using MVB code provided by UltraSound ToolBox (USTB). The model is implemented using PyTorch library, and AdamW with L1-loss is used for training. The network is trained with a variety of imaging settings such as the acquisition center frequency, the sampling frequency, the transducer shape, and the number of transducer elements. Results/Discussion The results will be investigated on the test data of CUBDL.
DOI: 10.5281/zenodo.3873422
title: A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement

description: This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.
DOI: 10.5281/zenodo.3873421
title: A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement

description: This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.
DOI: 10.5066/p9x5bn1l
title: GeoNatShapes: a natural feature reference dataset for mapping and AI training

description: These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA. Figure 1 shows the areal boundary (cyan) of Bachelor Canyon, a GNIS valley feature, and a bounding box for machine learning training (black) relative to the data sources used in its delineation: A) the historical topographic map, B) respective NAIP imagery and C) 3DEP elevation data stretched and shaded.
DOI: 10.5281/zenodo.3902867
title: Monolithic piezoelectric control of soliton microcombs

description: Available data for "Monolithic piezoelectric control of soliton microcombs" This version (V3) is an updated version with more data, based on the previous version: V2: 10.5281/zenodo.3741411 V1: 10.5281/zenodo.3731488
DOI: 10.5281/zenodo.3903724
title: Monolithic piezoelectric control of soliton microcombs

description: Available data for "Monolithic piezoelectric control of soliton microcombs" This version (V4) is an updated version based on the previous version: V3: 10.5281/zenodo.3902867 V2: 10.5281/zenodo.3741411 V1: 10.5281/zenodo.3731488
DOI: 10.5281/zenodo.3731487
title: Monolithic piezoelectric control of soliton microcombs

description: Available data for "Monolithic piezoelectric control of soliton microcombs" This version (V4) is an updated version based on the previous version: V3: 10.5281/zenodo.3902867 V2: 10.5281/zenodo.3741411 V1: 10.5281/zenodo.3731488
DOI: 10.17632/gks43g58mw.1
title: PDPTWSLT instances

description: The instances at hand constitute problem instances for the Pickup and Delivery Problem with time windows, split loads and transshipments. They have been used for the computational experiments in "Wolfinger, D., 2020. A Large Neighborhood Search for the Pickup and Delivery Problem with Time windows, Split loads and Transshipments. Submitted."
DOI: 10.17632/x9kh4m22gy.1
title: BCG vaccinations upregulate Myc, a central switch for improved glucose metabolism in diabetes. Kuhtreiber et al.

description: Normalized Raw RNAseq reads of the genes described in this paper
DOI: 10.5066/p9yased1
title: North American Breeding Bird Survey Dataset - Archival Releases of Datasets Ending With Years 2000 Through 2015

description: This page provides access to antiquated versions of the North American Breeding Bird Survey (BBS) dataset that are in a much older, legacy format. These have been superseded by more recent releases. Unless visitors have a specific need for these archived data, they should return to the Main BBS Dataset Page (https://www.sciencebase.gov/catalog/item/52b1dfa8e4b0d9b325230cd9) and choose the most recent data release, as that one will include all BBS data released to date. These data were released prior to the October 1, 2016 effective date for the USGS's policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release.
DOI: 10.34683/sr1846
title: StoneRounds Case: SR1846
title: AR Retinitis Pigmentosa

description: StoneRounds Case SR1846 was diagnosed with AR Retinitis Pigmentosa likely caused by mutations in the gene: REEP6.
DOI: 10.15468/tp3d2j
title: Checklist dos percevejos-do-mato (Hemiptera: Heteroptera: Pentatomoidea) do Estado de São Paulo, Brasil

description: This dataset contains the digitized treatments in Plazi based on the original journal article Grazia, Jocélia, Schwertner, Cristiano Feldens (2011): Checklist dos percevejos-do-mato (Hemiptera: Heteroptera: Pentatomoidea) do Estado de São Paulo, Brasil. Biota Neotropica 11: 705-716, DOI: 10.1590/S1676-06032011000500034
DOI: 10.5281/zenodo.3903770
title: A New Frontier in Ionospheric Observations: GPS Total Electron Content Measurements from Ocean Buoys

description: Ionospheric Total Electron Content (TEC) data from a pair of RIO GPS receivers that are deployed on National Oceanic and Atmospheric Administration (NOAA) Tropical Atmosphere Ocean (TAO) buoys in the Pacific Ocean. TEC measurements collected between September 1, 2018 and December 31, 2019 are included in these datasets.
description: This study was supported by the Air Force Small Business Innovation Research (SBIR) contract FA9453-13-C-0035 to ASTRA.
DOI: 10.5281/zenodo.3903769
title: A New Frontier in Ionospheric Observations: GPS Total Electron Content Measurements from Ocean Buoys

description: Ionospheric Total Electron Content (TEC) data from a pair of RIO GPS receivers that are deployed on National Oceanic and Atmospheric Administration (NOAA) Tropical Atmosphere Ocean (TAO) buoys in the Pacific Ocean. TEC measurements collected between September 1, 2018 and December 31, 2019 are included in these datasets.
description: This study was supported by the Air Force Small Business Innovation Research (SBIR) contract FA9453-13-C-0035 to ASTRA.
DOI: 10.15468/zxu37s
title: Pentatomidae Cyrtocoridae

description: This dataset contains the digitized treatments in Plazi based on the original book chapter Grazia, Jocelia, Schwertner, Cristiano Feldens (2008): Pentatomidae Cyrtocoridae. In: Claps, Lucía E., Roig-Juñent, Sergio (Eds): Biodiversidad de Artrópodos Argentinos, vol. 2. Sociedad Entomológica Argentina: 223-234, ISBN: 9789871231920, DOI: http://doi.org/10.5281/zenodo.3903611
DOI: 10.18112/openneuro.ds001353.v1.0.0
title: Information Filtering in Electronic Networks of Practice: An fMRI Investigation of Expectation [Dis]confirmation