cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fnx45
title: CCDC 2254417: Experimental Crystal Structure Determination

description: Related Article: Yang-Guang Chen, Heng-Bin Yu, Yin Tian, Cheng Peng, Ming-Sheng Xie, Hai-Ming Guo|2023|Org.Lett.|25|5585|doi:10.1021/acs.orglett.3c01915
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: 10.21227/a3sp-m529
title: Odor-regulated driving excitement in the EEG data set

description: Driving excitement may lead to speeding and serious traffic accidents. Although olfactory stimulation has been proven to be effective in regulating human emotions, the mechanism driving excitement is still unclear. This study aims to explore the regulatory effects of different arousal and valence levels of odors (mint, rose, banana, and musk) on driving excitement, deepening our understanding of the relationship between drivers emotions and driving experience. By analyzing the frequency domain and time-frequency characteristics of EEG signals in alpha and gamma waves and combining them with the subjective feelings of the participants, we investigated the effect of odor on the regulation of driving excitement. The results show that, compared with odorless regulation, the banana odor significantly reduces the arousal level of driving excitement in the participants, with a reduction rate of 50.64%. This reduction leads to a moderately aroused positive emotion, which aligns with the optimal emotional state for driving and demonstrates a high level of regulation. However, the odors of mint, rose, and musk have no significant effect on driving excitement, and the participants still exhibit a highly aroused positive emotion with a low level of regulation. Therefore, this study holds significant guiding implications for the design of odor in the vehicle-mounted fragrance system to regulate driving excitement, thereby providing drivers with a safer and more comfortable driving experience.
DOI: 10.5281/zenodo.4108311
title: PTX-227: A dataset with 227 pneumothorax chest X-ray images

description: Pneumothorax is a common medical emergency defined as the abnormal collection of air in the pleural space between the lung and chest wall. Its typical symptoms include chest pain and dyspnea, leading to oxygen deficiency or even life-threatening in severe cases. Therefore, an efficient and automatic pneumothorax diagnosis algorithm would be useful in many clinical scenarios. Recently, deep learning methods have achieved impressive progress in medical image segmentation tasks. However, large-scale dataset is one of the critical components for the success of deep learning. On the other hand, there are few public chest X-ray images with pneumothorax. To stimulate the researchers' interest in pneumothorax diagnosis algorithm, <strong>we released a new data set PTX-227 here, which contains 227 chest X-ray images of pneumothorax with pixel-level annotations</strong>. All images were resized to 1024×1024. The raw image intensity was clipped within the range from 2.5th to 97.5th percentile and then normalized to 0 to 255. The contours of the pneumothorax area were labelled by two senior radiologists using ITK-SNAP. The dataset was anonymized and every record related to patients' privacy was removed. Only the image data and the corresponding labels were included in PTX-227. <strong>The paper is still under review and the citation is temporarily unavailable. The citation will be updated once the paper is published.</strong>
description: See also: https://github.com/wangyunpengbio/DeepSDM
DOI: 10.5281/zenodo.4108310
title: PTX-498: A multi-center pneumothorax segmentation chest X-ray image dataset

description: Pneumothorax is a common medical emergency defined as the abnormal collection of air in the pleural space between the lung and chest wall. Its typical symptoms include chest pain and dyspnea, leading to oxygen deficiency or even life-threatening in severe cases. Therefore, an efficient and automatic pneumothorax diagnosis algorithm would be useful in many clinical scenarios. Recently, deep learning methods have achieved impressive progress in medical image segmentation tasks. However, a large-scale dataset is one of the critical components for the success of deep learning. On the other hand, there are few public chest X-ray images with pneumothorax. To stimulate the researchers' interest in the pneumothorax diagnosis algorithm, <strong>we released a new data set PTX-498 here. It contains 498 chest X-ray images of pneumothorax collected from three hospitals, and each image contains pixel-level annotations.</strong> All images were resized to 1024×1024. The raw image intensity was clipped according to the window width and level inside the dicom tag and then normalized to 0 to 255. The contours of the pneumothorax area were labelled by two senior radiologists using ITK-SNAP. The dataset was anonymized and every record related to patients' privacy was removed. Only the image data and the corresponding labels were included in PTX-498. <strong>Please use the latest v2-fix version which removes duplicate images and uses the window width and level from the original dicom tag for normalization.</strong> <strong>Citation: If you are interested in this dataset and applying it in your research, please cite the following article.</strong><br> Paper link: https://doi.org/10.1016/j.neucom.2021.05.029<br> Cite this article as Yunpeng Wang, Kang Wang, Xueqing Peng, Lili Shi, Jing Sun, Shibao Zheng, Fei Shan, Weiya Shi, Lei Liu*. DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images [J]. Neurocomputing, 2021, 454: 201-211.
description: See also: https://github.com/wangyunpengbio/DeepSDM
description: {"references": ["Yunpeng Wang, Kang Wang, Xueqing Peng, Lili Shi, Jing Sun, Shibao Zheng, Fei Shan, Weiya Shi, Lei Liu. DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images [J]. Neurocomputing, 2021, 454, 201-211."]}
DOI: 10.5281/zenodo.4616393
title: PTX-498: A multi-center pneumothorax segmentation chest X-ray image dataset

description: Pneumothorax is a common medical emergency defined as the abnormal collection of air in the pleural space between the lung and chest wall. Its typical symptoms include chest pain and dyspnea, leading to oxygen deficiency or even life-threatening in severe cases. Therefore, an efficient and automatic pneumothorax diagnosis algorithm would be useful in many clinical scenarios. Recently, deep learning methods have achieved impressive progress in medical image segmentation tasks. However, a large-scale dataset is one of the critical components for the success of deep learning. On the other hand, there are few public chest X-ray images with pneumothorax. To stimulate the researchers' interest in the pneumothorax diagnosis algorithm, <strong>we released a new data set PTX-498 here. It contains 498 chest X-ray images of pneumothorax collected from three hospitals, and each image contains pixel-level annotations</strong>. All images were resized to 1024×1024. The raw image intensity was clipped within the range from 2.5th to 97.5th percentile and then normalized to 0 to 255. The contours of the pneumothorax area were labelled by two senior radiologists using ITK-SNAP. The dataset was anonymized and every record related to patients' privacy was removed. Only the image data and the corresponding labels were included in PTX-498.
description: See also: https://github.com/wangyunpengbio/DeepSDM
description: {"references": ["Yunpeng Wang, Kang Wang, Xueqing Peng, Lili Shi, Jing Sun, Shibao Zheng, Fei Shan, Weiya Shi, Lei Liu*. DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images [J]. Neurocomputing, 2021, 454, 201-211."]}
DOI: 10.5281/zenodo.8266529
title: PTX-498: A multi-center pneumothorax segmentation chest X-ray image dataset

description: Pneumothorax is a common medical emergency defined as the abnormal collection of air in the pleural space between the lung and chest wall. Its typical symptoms include chest pain and dyspnea, leading to oxygen deficiency or even life-threatening in severe cases. Therefore, an efficient and automatic pneumothorax diagnosis algorithm would be useful in many clinical scenarios. Recently, deep learning methods have achieved impressive progress in medical image segmentation tasks. However, a large-scale dataset is one of the critical components for the success of deep learning. On the other hand, there are few public chest X-ray images with pneumothorax. To stimulate the researchers' interest in the pneumothorax diagnosis algorithm, <strong>we released a new data set PTX-498 here. It contains 498 chest X-ray images of pneumothorax collected from three hospitals, and each image contains pixel-level annotations.</strong> All images were resized to 1024×1024. The raw image intensity was clipped according to the window width and level inside the dicom tag and then normalized to 0 to 255. The contours of the pneumothorax area were labelled by two senior radiologists using ITK-SNAP. The dataset was anonymized and every record related to patients' privacy was removed. Only the image data and the corresponding labels were included in PTX-498. <strong>Please use the latest v2-fix version which removes duplicate images and uses the window width and level from the original dicom tag for normalization.</strong> <strong>Citation: If you are interested in this dataset and applying it in your research, please cite the following article.</strong><br> Paper link: https://doi.org/10.1016/j.neucom.2021.05.029<br> Cite this article as Yunpeng Wang, Kang Wang, Xueqing Peng, Lili Shi, Jing Sun, Shibao Zheng, Fei Shan, Weiya Shi, Lei Liu*. DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images [J]. Neurocomputing, 2021, 454: 201-211.
description: See also: https://github.com/wangyunpengbio/DeepSDM
description: {"references": ["Yunpeng Wang, Kang Wang, Xueqing Peng, Lili Shi, Jing Sun, Shibao Zheng, Fei Shan, Weiya Shi, Lei Liu. DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images [J]. Neurocomputing, 2021, 454, 201-211."]}
DOI: 10.5519/qd.bv1hznau
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5 records)
DOI: 10.5281/zenodo.8264225
title: Dataset D1 used to evaluate HycDemux

description: Dataset D1 used to evaluate hycdemu
DOI: 10.5281/zenodo.8256480
title: Datasets S1 ~ S6 for evaluating hybrid clustering algorithm

description: These datasets are used to evaluate our hybrid clustering algorithm. For details of our algorithm, please refer to https://github.com/junhaiqi/Hybrid_clustering.git.
DOI: 10.5281/zenodo.8256499
title: A real dataset for evaluating hybrid clustering algorithm

description: This is a real dataset for testing our hybrid clustering algorithm, and details can be found in our manuscript.
DOI: 10.5281/zenodo.8264248
title: Dataset D2 used to evaluate HycDemux

description: Dataset D2 used to evaluate hycdemu.
DOI: 10.6084/m9.figshare.23994690
title: Atomically dispersed Cu coordinated Rh metallene arrays for simultaneously electrochemical aniline synthesis and biomass upgrading

description: Data for Atomically dispersed Cu coordinated Rh metallene arrays for simultaneously electrochemical aniline synthesis and biomass upgrading
DOI: 10.5281/zenodo.8260582
title: Sequences (DB4-DB7) with low sequencing error rate for evaluating HycDemux

description: These are the datasets with low sequencing error rates used to evaluate HYCDemux.
DOI: 10.34740/kaggle/ds/3644939
title: Statistics on Passenger Traffic of Hong Kong
DOI: 10.5281/zenodo.8265367
title: A eye movement data in a virtual-reality based driving experiment

description: This is a dataset of eye movement data in a virtual reality based driving experiment. Fourteen participants were involved in the experiment. The numbers in the file names represent the order of the participants, while the remaining words indicate the experimental scenarios: 'day' represents daytime, 'night' represents nighttime, 'duskon' represents the beginning of dusk, and 'duskoff' represents the end of dusk. However, this study did not investigate the effects of different scenarios
DOI: 10.5281/zenodo.8265366
title: A eye movement data in a virtual-reality based driving experiment

description: This is a dataset of eye movement data in a virtual reality based driving experiment. Fourteen participants were involved in the experiment. The numbers in the file names represent the order of the participants, while the remaining words indicate the experimental scenarios: 'day' represents daytime, 'night' represents nighttime, 'duskon' represents the beginning of dusk, and 'duskoff' represents the end of dusk. However, this study did not investigate the effects of different scenarios
DOI: 10.34740/kaggle/ds/3472313
title: OLID I
DOI: 10.21227/pzg3-dp94
title: MKR log

description: In this paper, we propose a modular security approach using a positioning security engine featuring GPS location features that can uniquely identify the IoT user device. We propose the modular security scheme to reinforce the security and viability of IoT-centric solutions for various innovative applications, including IoT Mobile payment, Smart city heterogeneous networks, communication services, safety, and locationbased services integration. To achieve our goal of securitization and viability we target consumer IoT devices equipped with built-in locationbased GPS chips, which are vulnerable to hackers where the existing cryptographic authentication-based protocols demand power and computation resources required for authentication protocols is not sufficient to carry end to end secure transaction in an IoT environment. Therefore, to compensate this lack of environment capability to carry the end-to-end secure transaction on IoT devices, we implement a modular security approach method to offer the security environment the consumer IoT devices require when emitting various radio signals. Moreover, the implementation of our security engine has a purpose to offer optimal security facilitated by Simple Public Key Infrastructure (SPKI) following the Pretty Good Privacy (PGP) Web of Trust (WoT) approach. Our implementation of the security engine on the development board Arduino succeeded in providing and extended secure capable environment for carrying secure transactions. Our results show 70, 80 and 90 percent communication success between Security Engine modules with 70 percent of successful SSL secure key exchange by every identified user in best 15 seconds simulation time for two by third round of simulation.
DOI: 10.15468/dl.bmfnjd
title: Occurrence Download

description: A dataset containing 4909 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sapindus saponaria L.",
 "Year 1981-2022"
 ]
}

 The dataset includes 4909 records from 314 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041149-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3m3t9m
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leobordea stipulosa (Baker f.) B.-E.van Wyk & Boatwr."
 ]
}

 The dataset includes 32 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041154-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8cptu6
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lasiosiphon esterhuyseniae Magee & J.C.Manning"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041155-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4b9rev
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jurinea nivea C.Winkl."
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041156-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tdamg5
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lactuca rostrata (Blume) Kuntze"
 ]
}

 The dataset includes 50 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041157-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bacgvn
title: Occurrence Download

description: A dataset containing 246 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Albizia malacophylla (A.Rich.) Walp."
 ]
}

 The dataset includes 246 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041158-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w2bqra
title: Occurrence Download

description: A dataset containing 5004 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stigmochelys pardalis (Bell, 1828)"
 ]
}

 The dataset includes 5004 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041159-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u8mr92
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Marmota himalayana (Hodgson, 1841)",
 "Year 1000-2024"
 ]
}

 The dataset includes 127 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041160-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ffpqn
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leobordea stolzii (Harms) B.-E.van Wyk & Boatwr."
 ]
}

 The dataset includes 68 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041161-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vb4skg
title: Occurrence Download

description: A dataset containing 2105 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tantilla melanocephala (Linnaeus, 1758)"
 ]
}

 The dataset includes 2105 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041163-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5amcsz
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lasiosiphon polyanthus Gilg, 1894"
 ]
}

 The dataset includes 24 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041164-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t6xtdx
title: Occurrence Download

description: A dataset containing 34159 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Storeria dekayi (Holbrook, 1839)"
 ]
}

 The dataset includes 34159 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041162-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nnystg
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jurinea olgae Regel & Schmalh. ex Regel"
 ]
}

 The dataset includes 59 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041165-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fe46ze
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lactuca rosularis Boiss."
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041166-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9c44pc
title: Occurrence Download

description: A dataset containing 5334 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Albizia niopoides (Spruce ex Benth.) Burkart"
 ]
}

 The dataset includes 5334 records from 265 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041167-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xwngbs
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leobordea sutherlandii (Dümmer) B.-E.van Wyk & Boatwr."
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041169-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kjkhpg
title: Occurrence Download

description: A dataset containing 272 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lasiospermum bipinnatum (Thunb.) Druce"
 ]
}

 The dataset includes 272 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041171-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.npg4z7
title: Occurrence Download

description: A dataset containing 1409 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tarentola annularis (Geoffroy Saint-Hilaire, 1827)"
 ]
}

 The dataset includes 1409 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041170-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.