cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.60508/ptfg-w336
title: Real-Time Segmentation of Burst Suppression Patterns in Critical Care EEG Monitoring

description: This dataset is intended to allow reproducibility of the results reported in  
the following manuscript: "Real-time segmentation of burst suppression
patterns in critical care EEG monitoring. J Neurosci Methods. 2013 Sep
30;219(1):131-41".

Brief abstract: Automatic  computational analysis of burst suppression
patterns in neurological critically ill patients can provide important
clinical information regarding brain state, for example during recovery from
hypoxic ischemic encephalopathy and for guiding medical treatment of
refractory status epilepticus with pharmacologically induced coma. A robust
method is presented for automated segmentation of adult burst suppression ICU
EEG patterns, and is validated against 20 manual EEG segmentations
encompassing a wide range of different burst suppression depths and patterns.
Expert-level automated real-time segmentation of burst suppression EEG data is
demonstrated.
DOI: 10.60508/yvna-zd74
title: Real-Time Segmentation of Burst Suppression Patterns in Critical Care EEG Monitoring

description: This dataset is intended to allow reproducibility of the results reported in  
the following manuscript: "Real-time segmentation of burst suppression
patterns in critical care EEG monitoring. J Neurosci Methods. 2013 Sep
30;219(1):131-41".

Brief abstract: Automatic  computational analysis of burst suppression
patterns in neurological critically ill patients can provide important
clinical information regarding brain state, for example during recovery from
hypoxic ischemic encephalopathy and for guiding medical treatment of
refractory status epilepticus with pharmacologically induced coma. A robust
method is presented for automated segmentation of adult burst suppression ICU
EEG patterns, and is validated against 20 manual EEG segmentations
encompassing a wide range of different burst suppression depths and patterns.
Expert-level automated real-time segmentation of burst suppression EEG data is
demonstrated.
DOI: 10.48366/r655962
title: Review: Detection of abnormal movements of ships based on AIS data in water transport using machine learning methods

description: The table provides an insight into the scientific papers that dealt with the problem of anomalous movements of ships based on AIS data in water transport using machine learning methods. Anomaly detection in water transport refers to the process of identifying unusual or abnormal behavior in vessels or maritime activities. This can include detecting deviations from typical sailing patterns, unexpected changes in speed or direction, irregular stops or deviations from established routes, and other behaviors that do not conform to expected or normal maritime activities. Anomalies in water transport are significant as they can indicate potential safety hazards, security threats, or non-compliance with maritime regulations. Detecting and addressing anomalies is crucial for ensuring the safety, security, and efficiency of maritime traffic and transportation. Advanced anomaly detection methods, such as kinematic interpolation, Support Vector Machines (SVM), Random Forest, Neural Networks, k-means clustering, DBSCAN,  Parallel Meta-Learning (PML)  can be trained to detect anomalies by learning patterns from historical data and identifying deviations from these patterns. The aim of anomaly detection in water transport is to identify potential safety or security threats, such as illegal activities, accidents, or navigational errors, as well as to improve maritime situational awareness and operational efficiency. By leveraging machine learning methods, anomaly detection systems can help maritime authorities and operators identify and respond to abnormal vessel behavior in a timely manner, thereby enhancing overall maritime security and safety.
DOI: 10.5281/zenodo.10292161
title: IODP Expedition 383 Magnetic susceptibility (whole round)

description: Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
DOI: 10.5281/zenodo.10292162
title: IODP Expedition 383 Magnetic susceptibility (whole round)

description: Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
DOI: 10.5517/ccdc.csd.cc2dz9l9
title: CCDC 2233661: Experimental Crystal Structure Determination

description: Related Article: Zongli Xiong, Fuxing Xu, Yuqiao Zhou, Rong Zhang, Yulong Zhang, Yushuang Chen, Weijun Yao, Zhen Wang|2023|Org.Lett.|25|8302|doi:10.1021/acs.orglett.3c03378
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.cc2d1x6l
title: CCDC 2206369: Experimental Crystal Structure Determination

description: Related Article: Minli Zhong, Siyao Xia, Sanmei Liu, Caiju Jin, Shengjun Deng, Weiming Xiao, Shunmin Ding, Chao Chen|2023|Dalton Trans.|52|17846|doi:10.1039/D3DT02606J
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.cc2d1xn1
title: CCDC 2206383: Experimental Crystal Structure Determination

description: Related Article: Minli Zhong, Siyao Xia, Sanmei Liu, Caiju Jin, Shengjun Deng, Weiming Xiao, Shunmin Ding, Chao Chen|2023|Dalton Trans.|52|17846|doi:10.1039/D3DT02606J
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: 10.5281/zenodo.10292169
title: IODP Expedition 383 Magnetic susceptibility (point or contact system)

description: Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10-5).
DOI: 10.5281/zenodo.10292170
title: IODP Expedition 383 Magnetic susceptibility (point or contact system)

description: Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10-5).
DOI: 10.5281/zenodo.10292177
title: IODP Expedition 383 Natural gamma radiation

description: Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.
DOI: 10.5281/zenodo.10292178
title: IODP Expedition 383 Natural gamma radiation

description: Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.
DOI: 10.5281/zenodo.10292189
title: IODP Expedition 383 Photomicrographs

description: Microscopic images of discrete samples were acquired using stereo and upright light microscopes and captured on digital cameras. Image files were uploaded along with a brief description and a record of the microscopic and lighting conditions when the image was taken.
DOI: 10.5281/zenodo.10292190
title: IODP Expedition 383 Photomicrographs

description: Microscopic images of discrete samples were acquired using stereo and upright light microscopes and captured on digital cameras. Image files were uploaded along with a brief description and a record of the microscopic and lighting conditions when the image was taken.
DOI: 10.5281/zenodo.10258395
title: IS-477 Fall 2023 Final Project

description: End-to-end reproducible analysis of car evaluation dataset
DOI: 10.5281/zenodo.10258396
title: IS-477 Fall 2023 Final Project

description: End-to-end reproducible analysis of car evaluation dataset
DOI: 10.5281/zenodo.10269730
title: IS-477 Fall 2023 Final Project

description: End-to-end reproducible analysis of car evaluation dataset
DOI: 10.5281/zenodo.10292310
title: IS-477 Fall 2023 Final Project

description: End-to-end reproducible analysis of car evaluation dataset
DOI: 10.5281/zenodo.10246933
title: Herwig dataset for HadML particle GAN training

description: These are the simulation data of hadronization using Herwig 7.2.1 Monte Carlo generator, which uses a cluster hadronization model.  They have been used for the HadML particle GAN training and evaluation. Two samples are prepared. One uses a nominal set of hadronization parameters, the other one uses varied parameters which affect both the hadron kinematics and the hadron type distributions. In both samples, each cluster only decays to exactly two hadrons.
The code to read the data and use them for training can be found in https://github.com/hep-lbdl/hadml/releases/tag/2.0.0.
DOI: 10.5281/zenodo.10246934
title: Herwig dataset for HadML particle GAN training

description: These are the simulation data of hadronization using Herwig 7.2.1 Monte Carlo generator, which uses a cluster hadronization model.  They have been used for the HadML particle GAN training and evaluation. Two samples are prepared. One uses a nominal set of hadronization parameters, the other one uses varied parameters which affect both the hadron kinematics and the hadron type distributions. In both samples, each cluster only decays to exactly two hadrons.
The code to read the data and use them for training can be found in https://github.com/hep-lbdl/hadml/releases/tag/2.0.0.
DOI: 10.5281/zenodo.10292492
title: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"

description: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".
 
We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.
DOI: 10.5281/zenodo.10292493
title: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"

description: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".
 
We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.
DOI: 10.5281/zenodo.10292572
title: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"

description: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".
We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.
DOI: 10.5281/zenodo.10292573
title: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"

description: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".
We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.
DOI: 10.57967/hf/1433
title: UltrachatBR
DOI: 10.5281/zenodo.10292595
title: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"

description: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".
We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.
DOI: 10.5281/zenodo.10292594
title: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"

description: Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".
We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.
DOI: 10.34740/kaggle/dsv/7150237
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.5281/zenodo.10293010
title: wer
DOI: 10.5281/zenodo.10293011
title: wer
DOI: 10.5517/ccdc.csd.cc2f9jhs
title: CCDC 2243485: Experimental Crystal Structure Determination

description: Related Article: Wei Han, Yu-Lan Chen, Xi Tang, Jie Zhou, Mengtao Ma, Zhi-Liang Shen, Xue-Qiang Chu|2023|Green Chemistry|25|9672|doi:10.1039/D3GC03717G
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.cc2fbmjy
title: CCDC 2244540: Experimental Crystal Structure Determination

description: Related Article: Alexander P. Lakeev, Taisiya S. Sukhikh, Irina A. Kurzina, Natalia M. Korotchenko, Dmitriy S. Nikitin|2023|New J.Chem.|47|21790|doi:10.1039/D3NJ04516A
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.cc2fbmkz
title: CCDC 2244541: Experimental Crystal Structure Determination

description: Related Article: Alexander P. Lakeev, Taisiya S. Sukhikh, Irina A. Kurzina, Natalia M. Korotchenko, Dmitriy S. Nikitin|2023|New J.Chem.|47|21790|doi:10.1039/D3NJ04516A
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.cc2fbml0
title: CCDC 2244542: Experimental Crystal Structure Determination

description: Related Article: Alexander P. Lakeev, Taisiya S. Sukhikh, Irina A. Kurzina, Natalia M. Korotchenko, Dmitriy S. Nikitin|2023|New J.Chem.|47|21790|doi:10.1039/D3NJ04516A
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.cc2ff8lr
title: CCDC 2247084: Experimental Crystal Structure Determination

description: Related Article: Suparnak Midya, Durga Prasad Hari|2023|Chemical Science|14|13560|doi:10.1039/D3SC04749K
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.cc2fhk7r
title: CCDC 2249274: Experimental Crystal Structure Determination

description: Related Article: Takahiro Watanabe, Kyohei Oga, Hiroaki Matoba, Masanori Nagatomo, Masayuki Inoue|2023|J.Am.Chem.Soc.|145|25894|doi:10.1021/jacs.3c10658
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.24769470
title: Conversational Skills in Language Learning Games: A Speech Recognition Technology Dataset

description: The "SpeechRec_LanguageLearning_ConversationalSkills" dataset is a collection of data generated in a game-based language learning environment, aiming to explore the impact of Speech Recognition Technology (SRT) on the development of conversational skills. The dataset encompasses speaking test results conducted within the context of language learning games utilizing SRT.
DOI: 10.6084/m9.figshare.24762153
title: Data Descriptor: EEG and physiological signals data from university students during interaction with the Neurohumanities Lab's partially immersive space. (Raw EEG Data)

description: This collection includes the data recordings from EEG signal of subjects interacting with the Neurohumanities Lab partial-inmersive space at Monterrey, MX, from September to October 2023 from "Neurohumanties Lab" project. Raw Data. <br>
DOI: 10.6084/m9.figshare.24762153.v1
title: Data Descriptor: EEG and physiological signals data from university students during interaction with the Neurohumanities Lab's partially immersive space. (Raw EEG Data)

description: This collection includes the data recordings from EEG signal of subjects interacting with the Neurohumanities Lab partial-inmersive space at Monterrey, MX, from September to October 2023 from "Neurohumanties Lab" project. Raw Data. <br>
DOI: 10.34740/kaggle/dsv/7150439
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.6084/m9.figshare.24763851
title: <b>Genome assembly and annotation of the rice variety Huizhan</b>

description: We successfully generated a high-quality, chromosome-level genome assembly of the rice variety Huizhan using Oxford nanopore ultra-long sequencing and next-generation sequencing technologies.
DOI: 10.6084/m9.figshare.24763851.v1
title: <b>Genome assembly and annotation of the rice variety Huizhan</b>

description: We successfully generated a high-quality, chromosome-level genome assembly of the rice variety Huizhan using Oxford nanopore ultra-long sequencing and next-generation sequencing technologies.
DOI: 10.17909/8mkr-xr82
title: JWST NGC3256 Outflow Dataset

description: JWST NIRCam, MIRI/MRS, and NIRSpec data used in Bohn et al. 2024
DOI: 10.6084/m9.figshare.24638682
title: Anisotropic fracture generation, Data points in 2D phase diagram for fractured media, and Capillary-force-dominated displacement processes in anisotropic fractures

description: The above data describes the spatial point coordinate sets for generating anisotropic aperture fields (At=0.414 for transversely-correlated aperture field; At=1.021 for isotropic aperture field; At=2.566 for longitudinally-correlated aperture field), the data points in 2D phase diagrams from other references, and the displacement processes in different anisotropic fractures under capillary fingering regime.
DOI: 10.34740/kaggle/dsv/7150459
title: BBC News