cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8126289
title: SRF-Net

description: SRF-Net is a deep learning model for inversion of surface-wave dispersion and receiver function.
DOI: 10.5281/zenodo.8126288
title: SRF-Net

description: SRF-Net is a deep learning model for inversion of surface-wave dispersion and receiver function.
DOI: 10.6084/m9.figshare.22738655
title: Data for "<strong>Characterizing</strong> <strong>the</strong> <strong>flux</strong> <strong>effect</strong> <strong>on</strong> <strong>the</strong> <strong>irradiation</strong> <strong>embrittlement of</strong> <strong>reactor</strong> <strong>pressure</strong> <strong>vessel</strong> <strong>steels</strong> <strong>using</strong> <strong>machine</strong> <strong>learning</strong>"

description: This *pkl model file are supplementary files for paper entitled "<strong>Characterizing the of flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning</strong>" by Yu-chen Liu, Dane Morgan, Takuya Yamamoto and G. Robert Odette.
DOI: 10.6084/m9.figshare.22738655.v1
title: Data for "<strong>Characterizing</strong> <strong>the</strong> <strong>flux</strong> <strong>effect</strong> <strong>on</strong> <strong>the</strong> <strong>irradiation</strong> <strong>embrittlement of</strong> <strong>reactor</strong> <strong>pressure</strong> <strong>vessel</strong> <strong>steels</strong> <strong>using</strong> <strong>machine</strong> <strong>learning</strong>"

description: This *pkl model file are supplementary files for paper entitled "<strong>Characterizing the of flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning</strong>" by Yu-chen Liu, Dane Morgan, Takuya Yamamoto and G. Robert Odette.
DOI: 10.5281/zenodo.7786511
title: Transition and Drivers of Elastic to Inelastic Deformation in the Abarkuh Plain from InSAR Multi-Sensor Time Series and Hydrogeological Data

description: This repository contains the datasets used in Mirzadeh et al., 2023. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research. Dataset 1: Envisat descending track 292 Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions) Processor: ISCE/stripmapStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 Dataset 2: ALOS-1 ascending track 569 Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions) Processor: ISCE/stripmapStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 Dataset 2: Sentinel-1 ascending track 130 and descending track 137 Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions) Processor: ISCE/topsStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.
DOI: 10.5281/zenodo.5972150
title: Transition and Drivers of Elastic to Inelastic Deformation in the Abarkuh Plain from InSAR Multi-Sensor Time Series and Hydrogeological Data

description: This repository contains the datasets used in Mirzadeh et al., 2023. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research. Dataset 1: Envisat descending track 292 Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions) Processor: ISCE/stripmapStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 Dataset 2: ALOS-1 ascending track 569 Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions) Processor: ISCE/stripmapStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 Dataset 2: Sentinel-1 ascending track 130 and descending track 137 Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions) Processor: ISCE/topsStack + MintPy Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5 Mean LOS Velocity (in HDF-EOS5 format): velocity.h5 Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5 Geometry (in HDF-EOS5 format): geometryRadar.h5 The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.
DOI: 10.5281/zenodo.7626581
title: Alphafold predicted structures of VPS13 proteins from model organisms

description: This upload contains AlphaFold-predicted structures of VPS13 proteins from a variety of organisms. Given the large size of these proteins, only partial sequences were predicted with AlphaFold(1) and the resulting structures were aligned in PyMOL(2). A summary of the structures uploaded here is presented as a collection of domain cartoons in the "VPS13 domain organization across eukaryotic evolution.pdf" file. The structures were generated with AlphaFold v2.029 on the Yale High Performance Cluster. Each *.zip file contains the best ranked predictions (out of five) for each sequence (*.pdb files) and the PyMOL assembled full structure (*.pse file). In a few cases, where a good alignment was not possible due to long disordered regions in the C-terminal portions (mostly in proteins from <em>D. discoideum</em> and <em>A. thaliana</em>), the full structures were aligned manually in PyMOL based on the continuity of the lipid transfer groove. The structures in PyMOL can be colour-coded by the confidence value of AlphaFold predictions using the following prompt: set_color n0, [0.051, 0.341, 0.827]<br> set_color n1, [0.416, 0.796, 0.945]<br> set_color n2, [0.996, 0.851, 0.212]<br> set_color n3, [0.992, 0.490, 0.302]<br> color n0, b &lt; 100; color n1, b &lt; 90<br> color n2, b &lt; 70; color n3, b &lt; 50 Considering that full length structures were assembled by aligning different protein fragments and in view of the presence of flexible loops with low prediction confidence scores, the relative positions of different folded domains are not necessarily correct. <strong>References</strong> 1. J. Jumper, <em>et al.</em>, Highly accurate protein structure prediction with AlphaFold. <em>Nature</em> 596, 583–589 (2021). 2. The PyMOL Molecular Graphics System, Version 2.0. Schrödinger LLC.
description: {"references": ["J. Jumper, et al., Highly accurate protein structure prediction with AlphaFold. Nature 596, 583\u2013589 (2021).", "The PyMOL Molecular Graphics System, Version 2.0. Schr\u00f6dinger LLC."]}
DOI: 10.5281/zenodo.7626580
title: Alphafold predicted structures of VPS13 proteins from model organisms

description: This upload contains AlphaFold-predicted structures of VPS13 proteins from a variety of organisms. Given the large size of these proteins, only partial sequences were predicted with AlphaFold(1) and the resulting structures were aligned in PyMOL(2). A summary of the structures uploaded here is presented as a collection of domain cartoons in the "VPS13 domain organization across eukaryotic evolution.pdf" file. The structures were generated with AlphaFold v2.029 on the Yale High Performance Cluster. Each *.zip file contains the best ranked predictions (out of five) for each sequence (*.pdb files) and the PyMOL assembled full structure (*.pse file). In a few cases, where a good alignment was not possible due to long disordered regions in the C-terminal portions (mostly in proteins from <em>D. discoideum</em> and <em>A. thaliana</em>), the full structures were aligned manually in PyMOL based on the continuity of the lipid transfer groove. The structures in PyMOL can be colour-coded by the confidence value of AlphaFold predictions using the following prompt: set_color n0, [0.051, 0.341, 0.827]<br> set_color n1, [0.416, 0.796, 0.945]<br> set_color n2, [0.996, 0.851, 0.212]<br> set_color n3, [0.992, 0.490, 0.302]<br> color n0, b &lt; 100; color n1, b &lt; 90<br> color n2, b &lt; 70; color n3, b &lt; 50 Considering that full length structures were assembled by aligning different protein fragments and in view of the presence of flexible loops with low prediction confidence scores, the relative positions of different folded domains are not necessarily correct. <strong>References</strong> 1. J. Jumper, <em>et al.</em>, Highly accurate protein structure prediction with AlphaFold. <em>Nature</em> 596, 583–589 (2021). 2. The PyMOL Molecular Graphics System, Version 2.0. Schrödinger LLC.
description: {"references": ["J. Jumper, et al., Highly accurate protein structure prediction with AlphaFold. Nature 596, 583\u2013589 (2021).", "The PyMOL Molecular Graphics System, Version 2.0. Schr\u00f6dinger LLC."]}
DOI: 10.6073/pasta/72435e5b3863216b93c9fd1eafe57d76
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 6 meteorological station: Daily average soil volumetric water content data: 2013 - ongoing

description: Daily Average of 30-minute Soil Volumetric Water Content data at 10 cm at CSIS Block-6. Collection of soil water content data at Jornada LTER Cross-scale Interactions Study (CSIS) blocks, New Mexico, supports the environmental monitoring objectives of the Jornada LTER monitoring program that look at plant-soil water dynamics. Soil volumetric water content is measured every 30 minutes at 10 cm depth at 3 locations at each of the 4 plots per block and averaged for the daily value. The locations in each plot are under bare soil, under perennial grass, and under shrub. Volumetric water content, bulk electrical conductivity, soil temperature, and bulk dielectric permittivity are recorded at an automated meteorological station installed at Jornada LTER CSIS Block-6 site. This is an ONGOING dataset.
DOI: 10.6073/pasta/d9f230413d4d24adbfd5f13c6f55d972
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 6 meteorological station: Daily summary data: 2013 - ongoing

description: This data package contains hourly air temperature, relative humidity, wind speed, wind direction, and precipitation summary data from Jornada Basin LTER Cross Scale Interactions Study (CSIS) Block-6 met station. Air temperature, relative humidity, wind speed, wind direction, and precipitation are measured and calculated at an automated meteorological station connected to a 900 MHz spread spectrum wireless network. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Measurements are aggregated at intervals of 5, 30, 60 minutes and daily, with precipitation additionally provided with 1 second aggregation during precipitation events. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Precipitation is not measured at Block 1 and Block 12 because of proximity to Block 2 and Block 13, respectively . This is an ONGOING dataset.
DOI: 10.6073/pasta/4086f97575b5ac48e4526ef54690a2dd
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 7 meteorological station: 30-minute summary data: 2017 - ongoing

description: This data package contains 30-minute air temperature, relative humidity, and wind summary data from Jornada Basin LTER Cross Scale Interactions Study (CSIS) Block-1 met station. Average air temperature, relative humidity, wind speed, wind direction, and precipitation are measured and calculated at an automated meteorological station connected to a 900 MHz spread spectrum wireless network. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. Measurements are aggregated at intervals of 5, 30, 60 minutes and daily, with precipitation additionally provided with 1 second aggregation during precipitation events. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Precipitation is not measured at Block 1 and Block 12 because of proximity to Block 2 and Block 13, respectively. This is an ONGOING dataset.
DOI: 10.5281/zenodo.7799972
title: Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

description: The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA – Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers, or benchmarking mutation testing frameworks, and more applications are yet to be discovered. This dataset is a supplementary material for a paper entitled <strong>Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant</strong> submitted to the <strong>MDPI Data</strong> journal.
DOI: 10.5281/zenodo.7799971
title: Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

description: The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA – Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers, or benchmarking mutation testing frameworks, and more applications are yet to be discovered. This dataset is a supplementary material for a paper entitled <strong>Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant</strong> submitted to the <strong>MDPI Data</strong> journal.
DOI: 10.6084/m9.figshare.23648484
title: Appendix II. Questionnaire

description: Manager survey questionnaire.
DOI: 10.6084/m9.figshare.23648484.v1
title: Appendix II. Questionnaire

description: Manager survey questionnaire.
DOI: 10.5281/zenodo.8126348
title: Input dataset - Comparative Study of Spatial and Non-spatial Modelling in Price Prediction

description: Raw and pre-processed dataset for case-study: Comparative Study of Spatial and Non-spatial Modelling in Price Prediction. GitHub
DOI: 10.5281/zenodo.8126347
title: Input dataset - Comparative Study of Spatial and Non-spatial Modelling in Price Prediction

description: Raw and pre-processed dataset for case-study: Comparative Study of Spatial and Non-spatial Modelling in Price Prediction. GitHub
DOI: 10.5281/zenodo.8126359
title: 3195404

description: 3195404
DOI: 10.5281/zenodo.8126358
title: 3195404

description: 3195404
DOI: 10.5281/zenodo.8119344
title: qm9datasets

description: qm9
DOI: 10.5281/zenodo.8119343
title: qm9datasets

description: qm9
DOI: 10.34943/134978bd-e69b-4008-9bdd-4ac2a7892470
title: Fortune Bay Navigation System Deployed 2021-08-18

description: The Garmin 19x HVS GPS 2J5048010 was deployed on 2021-08-18 at Fortune Bay. Fortune Bay is a large natural bay located in the Gulf of St. Lawrence, on the south coast of Newfoundland, Canada. This device is a Navigation System. Navigational instruments are used to measure and record positional information of ships, buoys, and autonomous underwater vehicles (AUVs). Typical sensors include latitude, longitude, speed, heading, pitch, roll, and depth. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.6084/m9.figshare.23643555.v1
title: Movie Clip Dataset: A Novel Dataset for Generalised Real-World Violence Detection

description: A dataset with violent and non-violent scenes was collected from movies.
DOI: 10.34943/2a53393c-581f-41d3-bc59-532b6dccf68b
title: Fortune Bay Barometric Pressure Sensor Deployed 2021-08-18

description: The Vaisala PTB110 Barometer G4830002 was deployed on 2021-08-18 at Fortune Bay. Fortune Bay is a large natural bay located in the Gulf of St. Lawrence, on the south coast of Newfoundland, Canada. This device is a Barometric Pressure Sensor. Barometric pressure sensors are used to monitor weather fluctuations and conditions above the surface of the water. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/209cf473-ffaa-46db-83a9-7279bc8d8633
title: Fortune Bay Wind Monitoring System Deployed 2021-08-18

description: The RM Young Wind Monitor 5106 SN111534 was deployed on 2021-08-18 at Fortune Bay. Fortune Bay is a large natural bay located in the Gulf of St. Lawrence, on the south coast of Newfoundland, Canada. This device is a Wind Monitoring System. Wind monitoring systems measure the speed and direction of wind. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/7c6ddc9e-7f33-470e-b8ed-3f6d96c14282
title: Fortune Bay Wind Monitoring System Deployed 2021-08-18

description: The WindSonic SN 12460007 was deployed on 2021-08-18 at Fortune Bay. Fortune Bay is a large natural bay located in the Gulf of St. Lawrence, on the south coast of Newfoundland, Canada. This device is a Wind Monitoring System. Wind monitoring systems measure the speed and direction of wind. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/899ad463-8bb5-4c2e-aa54-2437032fec52
title: Fortune Bay Wave Buoy Deployed 2021-08-18

description: The TRIAXYS Wave Buoy TAS04651 was deployed on 2021-08-18 at Fortune Bay. Fortune Bay is a large natural bay located in the Gulf of St. Lawrence, on the south coast of Newfoundland, Canada. This device is a Wave Buoy. Wave buoys provide measurements of wave conditions. Measurements include wave height, period, direction, and spread. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/8018090f-f2d0-4000-bacd-4679ed9a1424
title: Fortune Bay Air Temperature and Humidity Sensor Deployed 2021-08-18

description: The Rotronic Meteorological Probe MP101A-T7 61062281 was deployed on 2021-08-18 at Fortune Bay. Fortune Bay is a large natural bay located in the Gulf of St. Lawrence, on the south coast of Newfoundland, Canada. This device is a Air Temperature and Humidity Sensor. Air temperature and humidity sensors are used to monitor weather conditions above the surface of the water. Air temperature affects wind speed and direction, relative humidity, the rate of evaporation and precipitation in the atmosphere. Air temperature is measured in numerous ways including thermistors, thermocouples and mercury thermometers. Humidity is the amount of water vapour in the air and can indicate precipitation, dew or fog. Humidity is typically measured as absolute humidity (the water content of air), relative humidity (measures the current absolute humidity relative to maximum for that temperature), or specific humidity (as a ratio of water vapour content to the total air content based on a mass). It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/ea309511-60fc-4e1c-b5fd-245bf0c4f923
title: Fortune Bay Datalogger Deployed 2021-08-18

description: The AXYS WatchMan Data Logger WM503470 was deployed on 2021-08-18 at Fortune Bay. Fortune Bay is a large natural bay located in the Gulf of St. Lawrence, on the south coast of Newfoundland, Canada. This device is a Datalogger. Data loggers, or recorder devices, are electronic devices that record data over time, or in relation to location, either with a built-in instrument or sensor or via external instruments and sensors, and are programmed with appropriate recording parameters. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34740/kaggle/dsv/6099655
title: Egypt Gold Prices | Daily Updated
DOI: 10.34740/kaggle/dsv/6099661
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6073/pasta/3413ddf8438a1de141f15d560c7880f0
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 7 meteorological station: 5-minute summary wind and air temperature data: 2013 - ongoing

description: This data package contains 5-minute air temperature and wind summary data from Jornada Basin LTER Cross Scale Interactions Study (CSIS) Block-7 met station. Average air temperature, wind speed, and wind direction are measured and calculated at an automated meteorological station connected to a 900 MHz spread spectrum wireless network. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This is a subset of baseline weather data collected at the 15 Jornada Basin LTER CSIS study blocks, New Mexico. Measurements are aggregated at intervals of 5, 30, 60 minutes and daily, with precipitation additionally provided with 1 second aggregation during precipitation events. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Precipitation is not measured at Block 1 and Block 12 because of proximity to Block 2 and Block 13, respectively . This is an ONGOING dataset.