cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/xf13-b260
title: PRIDE (Personal Risk Detection) dataset

description: We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate.The PRIDE (Personal Risk Detection) dataset is built with the help of 18 test subjects and a period of data collection of one week each, 24 h per day; the normal conditions dataset (NCDS) is built in this manner. Next, to build the anomaly conditions dataset (ACDS), the same 18 test subjects collaborate in another process to gather data under specific abnormal or stressful conditions. The scenarios include activities such as running several meters, going up and down the stairs of a several-floor building as fast as possible, simulating a fight, and so on.
DOI: 10.6084/m9.figshare.24769500
title: Database

description: Database used.
DOI: 10.6084/m9.figshare.24769500.v1
title: Database

description: Database used.
DOI: 10.5517/ccdc.csd.cc2fjd1f
title: CCDC 2250074: Experimental Crystal Structure Determination

description: Related Article: Ping Wu, Liandi Wang, Kaikai Wu, Zhengkun Yu|2023|Org.Lett.|25|8434|doi:10.1021/acs.orglett.3c03302
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.cc2fjzdc
title: CCDC 2250643: 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.cc2flc1g
title: CCDC 2251965: Experimental Crystal Structure Determination

description: Related Article: Ashwini Kumar, Ayushi Chaudhary, Bhumika Agrahari, Kajal Chaudhary, Pooran Kumar, Ritika Gautam Singh|2023|Chem.Commun.|59|14305|doi:10.1039/D3CC04519F
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.cc2flc2h
title: CCDC 2251966: Experimental Crystal Structure Determination

description: Related Article: Ashwini Kumar, Ayushi Chaudhary, Bhumika Agrahari, Kajal Chaudhary, Pooran Kumar, Ritika Gautam Singh|2023|Chem.Commun.|59|14305|doi:10.1039/D3CC04519F
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.cc2flc3j
title: CCDC 2251967: Experimental Crystal Structure Determination

description: Related Article: Ashwini Kumar, Ayushi Chaudhary, Bhumika Agrahari, Kajal Chaudhary, Pooran Kumar, Ritika Gautam Singh|2023|Chem.Commun.|59|14305|doi:10.1039/D3CC04519F
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.cc2flc4k
title: CCDC 2251968: Experimental Crystal Structure Determination

description: Related Article: Ashwini Kumar, Ayushi Chaudhary, Bhumika Agrahari, Kajal Chaudhary, Pooran Kumar, Ritika Gautam Singh|2023|Chem.Commun.|59|14305|doi:10.1039/D3CC04519F
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.cc2flc5l
title: CCDC 2251969: Experimental Crystal Structure Determination

description: Related Article: Ashwini Kumar, Ayushi Chaudhary, Bhumika Agrahari, Kajal Chaudhary, Pooran Kumar, Ritika Gautam Singh|2023|Chem.Commun.|59|14305|doi:10.1039/D3CC04519F
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.cc2fp3py
title: CCDC 2254620: Experimental Crystal Structure Determination

description: Related Article: Prantika Bhattacharjee, Bipul Sarma, Utpal Bora|2023|Org.Biomol.Chem.|21|9275|doi:10.1039/D3OB01561K
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.cc2fr5cr
title: CCDC 2256594: Experimental Crystal Structure Determination

description: Related Article: Kirill Bulgarevich, Kazuo Takimiya|2023|Materials Horizons|10|5492|doi:10.1039/D3MH01055D
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.cc2fr5ds
title: CCDC 2256595: Experimental Crystal Structure Determination

description: Related Article: Kirill Bulgarevich, Kazuo Takimiya|2023|Materials Horizons|10|5492|doi:10.1039/D3MH01055D
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.cc2fr5ft
title: CCDC 2256596: Experimental Crystal Structure Determination

description: Related Article: Kirill Bulgarevich, Kazuo Takimiya|2023|Materials Horizons|10|5492|doi:10.1039/D3MH01055D
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.cc2ft2y9
title: CCDC 2258441: Experimental Crystal Structure Determination

description: Related Article: Jingfeng Ye, Yunyun Liu, Jin Luo, Jie-Ping Wan|2023|Org.Lett.|25|8451|doi:10.1021/acs.orglett.3c03353
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.24769497
title: Data Descriptor: EEG and physiological signals data from university students during interaction with the Neurohumanities Lab's partially immersive space. (Pre-processed 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. (Pre-processed EEG Data).
DOI: 10.6084/m9.figshare.24769497.v1
title: Data Descriptor: EEG and physiological signals data from university students during interaction with the Neurohumanities Lab's partially immersive space. (Pre-processed 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. (Pre-processed EEG Data).
DOI: 10.6084/m9.figshare.24769673
title: Supplementary Material 1. from The adaptive significance of large size at birth in marine snakes

description: Adult and offspring sizes of snakes.
DOI: 10.6084/m9.figshare.24769673.v1
title: Supplementary Material 1. from The adaptive significance of large size at birth in marine snakes

description: Adult and offspring sizes of snakes.
DOI: 10.6084/m9.figshare.24769676
title: Supplementary Material 2. from The adaptive significance of large size at birth in marine snakes

description: Lure trials.
DOI: 10.6084/m9.figshare.24769676.v1
title: Supplementary Material 2. from The adaptive significance of large size at birth in marine snakes

description: Lure trials.
DOI: 10.5281/zenodo.10276148
title: UAV Spraying Parameters-Coverage in Vineyards

description: A set of UAV spraying data collected using WSPs, across different parameter configurations. All tests were conducted in an experimental vineyard, under field conditions. All WSP samples were analysed using the DepositScan software developed by USDA (doi: 10.1016/j.compag.2011.01.003), and all coverage and VMD values are estimated by this software.
DOI: 10.6084/m9.figshare.24769692
title: Untitled Item

description: retrieve data of bibliometic analysis
DOI: 10.6084/m9.figshare.24769692.v1
title: Untitled Item

description: retrieve data of bibliometic analysis
DOI: 10.6084/m9.figshare.23938464.v1
title: <b>A theropod tooth from the Middle Jurassic of Tibetan Plateau reveals convergent evolution of dromaeosaurid dental morphology</b>

description: <b>The Tibetan Plateau is among the least explored areas in terms of dinosaur paleontology in the world. Here we report a dromaeosaurid-like tooth from the Middle Jurassic Dongdaqiao Formation at the eastern part of the plateau. The tooth shows some dromaeosaurid dental features, including the absence of constriction at the cervix, a relatively small crown base ratio, a strongly recurved apex and lack of large transverse, and marginal undulations. Parsimony phylogenetic analyses using both dentition-based and complete morphological matrices support a dromaeosaurid affinity for this tooth, and place this Tibetan theropod as the sister taxon to </b><i><b>Velociraptor</b></i><b>. However, Bayesian tip dating analyses based on the velociraptorine affinity of the Tibetan tooth place the majority of the diversifications of early-diverging Paravian, particularly of dromaeosaurids, within or earlier than the Middle Jurassic, which is in conflict with the known paravian fossil records. Furthermore, our discriminate analysis based on morphometric data place the Tibetan theropod within Metriacanthosauridae. These results may suggest that the broad resemblances between the Tibetan specimen and velociraptorine teeth is due to convergent evolution. Other dromaeosaurid-like teeth reported from the Middle and Late Jurassic deposits of several sites in Asia and Europe are thus probably nondromaeosaurids as the Tibetan tooth is. Collectively, these suggest the presence of an early diverged theropod lineage with a wide geographical distribution and probably a dromaeosaurid-like dietary niche. This study highlights the importance of using multiple lines of data to identify fragmentary fossils.</b>
DOI: 10.5519/qd.siuclaxm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.7845450
title: Twitter Dataset

description: The dataset is preprocessed based on the dataset provided by KPGNN (Cao et al., 2021) for social event detection task in HCRC accepted by Knowledge-Based System (https://www.sciencedirect.com/science/article/pii/S0950705123009759).
DOI: 10.5281/zenodo.7845451
title: Twitter Dataset

description: The dataset is preprocessed based on the dataset provided by KPGNN (Cao et al., 2021) for social event detection task in HCRC accepted by Knowledge-Based System (https://www.sciencedirect.com/science/article/pii/S0950705123009759).
DOI: 10.34740/kaggle/dsv/7150702
title: WallStreetBets 2022
DOI: 10.5517/ccdc.csd.cc28840q
title: CCDC 2093182: Experimental Crystal Structure Determination

description: Related Article: Marina A. Kiseleva, Andrei V. Churakov, Ilya V. Taydakov, Mikhail T. Metlin, Sergey A. Kozyukhin, Stanislav I. Bezzubov|2023|Dalton Trans.|52|17861|doi:10.1039/D3DT02651E
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.24616491.v1
title: Inland water bodies of 2020 over Tibetan Plateau (WBTP)

description: This is a high-resolution (2m) map of water bodies on the Tibetan Plateau. The dataset is produced by visual transformer model (Swin-UNet) based on Gaofen-1 data with overall accuracy at 98%. In addition, Water area, abundance, size and landscape indices of WBs are included in the dataset.
DOI: 10.5281/zenodo.10294588
title: IICONGRAPH

description: IICONGRAPH
The IICONGRAPH dataset is a comprehensive knowledge graph created by re-engineering the iconographical and iconological statements taken from ArCo and Wikidata. Following the structured framework of the ICON ontology, this dataset provides a unified and enriched representation of artistic interpretations and meanings associated with cultural heritage artifacts.
Dataset Components:

IICONGRAPHglobal: This subset combines both ArCo and Wikidata, offering a consolidated view of the merged iconographical and iconological statements from these sources.
IICONGRAPHarco: Focused solely on ArCo, this subset provides a detailed exploration of iconographical and iconological data originating from ArCo's rich collection.
IICONGRAPHwikidata: Dedicated to Wikidata, this subset delves into the iconographical and iconological statements specific to Wikidata.
DOI: 10.5281/zenodo.10294589
title: IICONGRAPH

description: IICONGRAPH
The IICONGRAPH dataset is a comprehensive knowledge graph created by re-engineering the iconographical and iconological statements taken from ArCo and Wikidata. Following the structured framework of the ICON ontology, this dataset provides a unified and enriched representation of artistic interpretations and meanings associated with cultural heritage artifacts.
Dataset Components:

IICONGRAPHglobal: This subset combines both ArCo and Wikidata, offering a consolidated view of the merged iconographical and iconological statements from these sources.
IICONGRAPHarco: Focused solely on ArCo, this subset provides a detailed exploration of iconographical and iconological data originating from ArCo's rich collection.
IICONGRAPHwikidata: Dedicated to Wikidata, this subset delves into the iconographical and iconological statements specific to Wikidata.
DOI: 10.5281/zenodo.10294577
title: Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha'apai volcano tsunami

description: This archive contains time series of mean sea level pressure and sea level in Australia before and after the 2022-01-15 Hunga-Tonga Hunga Hapai volcanic tsunami. An associated journal publication has been submitted to the journal Scientific Data, with title "Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha'apai volcano tsunami"
DOI: 10.5281/zenodo.8219621
title: Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha'apai volcano tsunami

description: This archive contains time series of mean sea level pressure and sea level in Australia before and after the 2022-01-15 Hunga-Tonga Hunga Hapai volcanic tsunami. An associated journal publication has been submitted to the journal Scientific Data, with title "Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha'apai volcano tsunami"
DOI: 10.5281/zenodo.8219622
title: Australian atmospheric pressure and sea level data during the 2022 Hunga-Tonga Hunga-Ha'apai volcano tsunami

description: This archive contains time series of mean sea level pressure and sea level in Australia before and after the 2022-01-15 Hunga-Tonga Hunga Hapai volcanic tsunami. An associated journal publication has been submitted.
DOI: 10.6084/m9.figshare.24711320
title: Supplementary Figures and Tables from Single-cell genomics reveals new rozellid lineages and supports their sister relationship to Microsporidia

description: Supplementary Figs. S1–S4; Supplementary Table S1
DOI: 10.6084/m9.figshare.24711320.v2
title: Supplementary Figures and Tables from Single-cell genomics reveals new rozellid lineages and supports their sister relationship to Microsporidia

description: Supplementary Figs. S1–S4; Supplementary Table S1
DOI: 10.6084/m9.figshare.24711323
title: Supplementary data from Single-cell genomics reveals new rozellid lineages and supports their sister relationship to Microsporidia

description: Draft genomes including the nucleotide transporter sequence, single-protein sequences and corresponding ML trees, matrix, list of proteins included in the protein-reduced dataset
DOI: 10.6084/m9.figshare.24711320.v1
title: Supplementary Figures and Tables from Single-cell genomics reveals new Rozellid lineages and supports their sister relationship to Microsporidia

description: Supplementary Figs. S1–S4; Supplementary Table S1
DOI: 10.6084/m9.figshare.24711323.v2
title: Supplementary data from Single-cell genomics reveals new rozellid lineages and supports their sister relationship to Microsporidia

description: Draft genomes including the nucleotide transporter sequence, single-protein sequences and corresponding ML trees, matrix, list of proteins included in the protein-reduced dataset
DOI: 10.6084/m9.figshare.24711323.v1
title: Supplementary data from Single-cell genomics reveals new Rozellid lineages and supports their sister relationship to Microsporidia

description: Draft genomes including the nucleotide transporter sequence, single-protein sequences and corresponding ML trees, matrix, list of proteins included in the protein-reduced dataset
DOI: 10.6084/m9.figshare.23550075.v2
title: CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection

description: In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20958 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.If you intend to utilize our dataset, kindly acknowledge our research by citing our work in Scientific Data.
DOI: 10.6084/m9.figshare.23550075.v3
title: CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection

description: In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.When using this work, users are required to comply with the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).If you intend to utilize our dataset, kindly acknowledge our research by citing our work in Scientific Data.