cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7896187
title: Datasets of the paper: A third species of glassfrog in the genus Chimerella (Anura: Centrolenidae) from central Peru, discovered by an integrative taxonomic approach

description: The dataset includes a soundfile (.wav) of a new species of Chimerella described in this paper, a table in Excel format with all DNA sequences used as well as their metadata and Genbank accession numbers, and the concatenated mitochondrial DNA sequences used for IQtree analyses as well as the resulting tree.
DOI: 10.5281/zenodo.7940926
title: Telethon EEG project

description: In this study, we focus on brain activation as a proxy measure of video effectiveness and we compare within a multivariate setting physiological responses induced by four fundraising campaigns realized using different communication strategies. We model electroencephalographic (EEG) signals using methodological tools belonging to graphical models to estimate partial correlation networks corresponding to different advertisements. The structure of estimated EEG networks is then compared in posthoc analysis using resampling procedures.
DOI: 10.5281/zenodo.7940925
title: Telethon EEG project

description: In this study, we focus on brain activation as a proxy measure of video effectiveness and we compare within a multivariate setting physiological responses induced by four fundraising campaigns realized using different communication strategies. We model electroencephalographic (EEG) signals using methodological tools belonging to graphical models to estimate partial correlation networks corresponding to different advertisements. The structure of estimated EEG networks is then compared in posthoc analysis using resampling procedures.
DOI: 10.25934/pr00008951
title: Data Challange: Exploring the Link Between Gram-Negative Pathogen Resistance and Omadacycline Effectiveness

description: Title: Investigating the Link Between Antibiotic Resistance and Effective Treatment.

Research Question(s): What is the relationship between the rates of antibiotic resistance in gram-negative pathogens and the effectiveness of omadacycline in treating infections caused by these pathogens? How does this relationship vary across different countries?

Background: 
Antibiotic resistance has become a growing concern in healthcare, making it crucial to explore new approaches to combat this global threat. Our research aims to understand the relationship between antibiotic resistance in gram-negative pathogens and the effectiveness of omadacycline, a promising antibiotic by combining the relevant information from the KEYSTONE dataset (omadacycline susceptibility data) with GEARS dataset (AMR rates in gram-negative pathogens) based on common identifiers such as bacterial species or geographical locations.

By investigating this link, we strive to improve patient outcomes, strengthen antimicrobial stewardship, inform public health practices, and enhance health systems.

Our research aims to understand the connection between rising antibiotic resistance rates in gram-negative pathogens and the effectiveness of omadacycline, a potential solution. By investigating this relationship, we seek to improve patient outcomes by identifying those who would benefit most from omadacycline treatment. Additionally, our research aims to strengthen antimicrobial stewardship by providing insights into the performance of omadacycline in the context of resistance rates. The findings will inform public health practices by examining resistance rates and omadacycline effectiveness across different countries, leading to evidence-based guidelines for combating antibiotic resistance and improving population health. Furthermore, our research contributes to strengthening health systems by optimizing treatment strategies, reducing the burden of resistant infections on healthcare facilities, promoting responsible antibiotic use, and empowering health systems with data-driven insights for effective resource allocation and targeted interventions. Through these efforts, we strive to make a meaningful impact in the fight against antimicrobial resistance, benefiting patients and healthcare systems worldwide.
DOI: 10.6084/m9.figshare.22887086.v1
title: <strong>Carica papaya pangenome</strong>

description: Non-reference contigs of <strong>Carica papaya.</strong>
DOI: 10.6084/m9.figshare.22194535.v3
title: FEEDS database

description: MEROPS database and DIAMOND builded file to use in the FEEDS tools.
DOI: 10.5517/ccdc.csd.cc2d7zg3
title: CCDC 2212205: Experimental Crystal Structure Determination

description: Related Article: Manjeet Singh, Manish Kumar, Sachin A. Nalawade, DRGKoppalu R. Puneeth Kumar, Hosahudya N. Gopi|2023|Org.Biomol.Chem.|21|3766|doi:10.1039/D3OB00127J
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.cc2dh828
title: CCDC 2219199: Experimental Crystal Structure Determination

description: Related Article: Qi Zhou, Rongbin Cai, Huan Li, Yunzhi Liu, Bing Li, Tianjiao Hou, Long Zhu, Guixiang Wang, Yu Zhang, Jun Luo|2023|Org.Chem.Front.|10|2551|doi:10.1039/D3QO00384A
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.cc2dhwph
title: CCDC 2219807: Experimental Crystal Structure Determination

description: Related Article: Qi Zhou, Rongbin Cai, Huan Li, Yunzhi Liu, Bing Li, Tianjiao Hou, Long Zhu, Guixiang Wang, Yu Zhang, Jun Luo|2023|Org.Chem.Front.|10|2551|doi:10.1039/D3QO00384A
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.cc2dy4zh
title: CCDC 2232557: Experimental Crystal Structure Determination

description: Related Article: Zhikang Chen, Wuchao Zhao, Conglei Liu, Liuying Jiang, Gang Fu, Yuetao Zhang, Hongping Zhu|2023|Polym.Chem.|14|2344|doi:10.1039/D3PY00301A
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.cc2f3rgs
title: CCDC 2237935: Experimental Crystal Structure Determination

description: Related Article: Alexander A. Lukoyanov, Andrey A. Tabolin, Yulia V. Nelyubina, Svetlana A. Aksenova, Alexey Yu. Sukhorukov|2023|Org.Biomol.Chem.|21|3871|doi:10.1039/D3OB00235G
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.cc2f3rht
title: CCDC 2237936: Experimental Crystal Structure Determination

description: Related Article: Alexander A. Lukoyanov, Andrey A. Tabolin, Yulia V. Nelyubina, Svetlana A. Aksenova, Alexey Yu. Sukhorukov|2023|Org.Biomol.Chem.|21|3871|doi:10.1039/D3OB00235G
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.cc2f3rjv
title: CCDC 2237937: Experimental Crystal Structure Determination

description: Related Article: Alexander A. Lukoyanov, Andrey A. Tabolin, Yulia V. Nelyubina, Svetlana A. Aksenova, Alexey Yu. Sukhorukov|2023|Org.Biomol.Chem.|21|3871|doi:10.1039/D3OB00235G
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.cc2f3rkw
title: CCDC 2237938: Experimental Crystal Structure Determination

description: Related Article: Alexander A. Lukoyanov, Andrey A. Tabolin, Yulia V. Nelyubina, Svetlana A. Aksenova, Alexey Yu. Sukhorukov|2023|Org.Biomol.Chem.|21|3871|doi:10.1039/D3OB00235G
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.cc2f3tgv
title: CCDC 2237997: Experimental Crystal Structure Determination

description: Related Article: Saumyaranjan Mishra, Suman Kumar Tripathy, Debasish Paul, Paltan Laha, Manas Kumar Santra, Srikanta Patra|2023|Inorg.Chem.|62|7003|doi:10.1021/acs.inorgchem.3c00272
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.cc2dz1k0
title: CCDC 2233412: Experimental Crystal Structure Determination

description: Related Article: Yu Shi, Xiang-Long Qu, Qi-Liang Lu, Jie Zhao, Qian-Cheng Ma, Wen Sun, Guang-Xiong OuYang, Wen Fu, Xinyong Tao, Dong-Sheng Huang|2023|Inorg.Chem.|62|6934|doi:10.1021/acs.inorgchem.3c00109
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.cc2dz1l1
title: CCDC 2233413: Experimental Crystal Structure Determination

description: Related Article: Yu Shi, Xiang-Long Qu, Qi-Liang Lu, Jie Zhao, Qian-Cheng Ma, Wen Sun, Guang-Xiong OuYang, Wen Fu, Xinyong Tao, Dong-Sheng Huang|2023|Inorg.Chem.|62|6934|doi:10.1021/acs.inorgchem.3c00109
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.cc2dz1m2
title: CCDC 2233414: Experimental Crystal Structure Determination

description: Related Article: Yu Shi, Xiang-Long Qu, Qi-Liang Lu, Jie Zhao, Qian-Cheng Ma, Wen Sun, Guang-Xiong OuYang, Wen Fu, Xinyong Tao, Dong-Sheng Huang|2023|Inorg.Chem.|62|6934|doi:10.1021/acs.inorgchem.3c00109
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.cc2dz1n3
title: CCDC 2233415: Experimental Crystal Structure Determination

description: Related Article: Yu Shi, Xiang-Long Qu, Qi-Liang Lu, Jie Zhao, Qian-Cheng Ma, Wen Sun, Guang-Xiong OuYang, Wen Fu, Xinyong Tao, Dong-Sheng Huang|2023|Inorg.Chem.|62|6934|doi:10.1021/acs.inorgchem.3c00109
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: 10.25345/c5gm81z9d
title: MassIVE MSV000091973 - GNPS Example Dataset_GF vs. SPF Mouse Duodenum
title: GNPS Example Dataset_GF vs. SPF Mouse Duodenum GNPS Example Dataset_GF vs. SPF Mouse Duodenum
DOI: 10.5281/zenodo.7854714
title: Dataset from VR Streaming Server (Emulated) and Radio Access Network for Streaming Traffic

description: The dataset contains an experiment in a site where UEs attach to a gNodeB that provides access to a streaming server that is stressed with high demanding transcoding workloads to emulate VR/AR processes. The UEs are realized through the Remote UE mode enabled by Amarisoft Simbox emulator, and the gNodeB is realized through the Amarisoft Callbox, which also provides the user plane function. The emulated VR streaming server is deployed as a Nginx pod in a Kubernetes cluster. We rely on MonB5G sampling functions that feed monitoring data (CPU and RAN parameters) to the monitoring system. A streaming video server has been deployed with the help of a NGINX server. It provides video-on-demand and video streaming, which can be accessed by any user (or UE) for real-time reproduction. This VR video streaming emulation aids to assess the performance of the network and therefore the benefits that each solution has brought. The video “Big Buck Bunny” with h.264 encoding and a resolution of 1920x1080p has been used for the experiments.The description of dataset features are:<br> 1-) Index Number,<br> 2-) Time: Time of the experiment,<br> 3-) N: number of VR streaming clients,<br> 4-) C: Average CPU of VR streaming server [mc]<br> 5-) O: Outbound traffic at the server average outbound traffic (O) flowing from the data interface of the video server. <br> 6-) R: Instantaneous downlink bit rate [Mbps], The original video file information: Video codec Advanced Video Codec (AVC) Width 1920 pixels Height 1080 pixels Display aspect radio 16:9 Duration 10 min 34 s Max Bitrate 16.7 Mb/s Frame rate 30 FPS
DOI: 10.5281/zenodo.7854715
title: Dataset from VR Streaming Server (Emulated) and Radio Access Network for Streaming Traffic

description: The dataset contains an experiment in a site where UEs attach to a gNodeB that provides access to a streaming server that is stressed with high demanding transcoding workloads to emulate VR/AR processes. The UEs are realized through the Remote UE mode enabled by Amarisoft Simbox emulator, and the gNodeB is realized through the Amarisoft Callbox, which also provides the user plane function. The emulated VR streaming server is deployed as a Nginx pod in a Kubernetes cluster. We rely on MonB5G sampling functions that feed monitoring data (CPU and RAN parameters) to the monitoring system. A streaming video server has been deployed with the help of a NGINX server. It provides video-on-demand and video streaming, which can be accessed by any user (or UE) for real-time reproduction. This VR video streaming emulation aids to assess the performance of the network and therefore the benefits that each solution has brought. The video “Big Buck Bunny” with h.264 encoding and a resolution of 1920x1080p has been used for the experiments. The description of dataset features are:<br> 1-) Index Number,<br> 2-) Time: Time of the experiment,<br> 3-) N: number of VR streaming clients,<br> 4-) R: Instantaneous downlink bit rate [Mbps],<br> 5-) C: Average CPU of VR streaming server [mc] The original video file information: Video codec Advanced Video Codec (AVC) Width 1920 pixels Height 1080 pixels Display aspect radio 16:9 Duration 10 min 34 s Max Bitrate 16.7 Mb/s Frame rate 30 FPS
DOI: 10.5281/zenodo.7944285
title: Dataset from VR Streaming Server (Emulated) and Radio Access Network for Streaming Traffic

description: The dataset contains an experiment in a site where UEs attach to a gNodeB that provides access to a streaming server that is stressed with high demanding transcoding workloads to emulate VR/AR processes. The UEs are realized through the Remote UE mode enabled by Amarisoft Simbox emulator, and the gNodeB is realized through the Amarisoft Callbox, which also provides the user plane function. The emulated VR streaming server is deployed as a Nginx pod in a Kubernetes cluster. We rely on MonB5G sampling functions that feed monitoring data (CPU and RAN parameters) to the monitoring system. A streaming video server has been deployed with the help of a NGINX server. It provides video-on-demand and video streaming, which can be accessed by any user (or UE) for real-time reproduction. This VR video streaming emulation aids to assess the performance of the network and therefore the benefits that each solution has brought. The video “Big Buck Bunny” with h.264 encoding and a resolution of 1920x1080p has been used for the experiments.The description of dataset features are:<br> 1-) Index Number,<br> 2-) Time: Time of the experiment,<br> 3-) N: number of VR streaming clients,<br> 4-) C: Average CPU of VR streaming server [mc]<br> 5-) O: Outbound traffic at the server average outbound traffic (O) flowing from the data interface of the video server. <br> 6-) R: Instantaneous downlink bit rate [Mbps], The original video file information: Video codec Advanced Video Codec (AVC) Width 1920 pixels Height 1080 pixels Display aspect radio 16:9 Duration 10 min 34 s Max Bitrate 16.7 Mb/s Frame rate 30 FPS
DOI: 10.5281/zenodo.7944219
title: Complexities of Extremely Shallow Damaging Earthquakes as Evidence for Seismogenic Intersecting Faults in Low Seismicity Regions Unloaded by Erosion

description: The Seismic data used in this paper for inversion Centroid Moment Tensor and First Motion Mechanism
DOI: 10.5281/zenodo.7944218
title: Complexities of Extremely Shallow Damaging Earthquakes as Evidence for Seismogenic Intersecting Faults in Low Seismicity Regions Unloaded by Erosion

description: The Seismic data used in this paper for inversion Centroid Moment Tensor and First Motion Mechanism
DOI: 10.34740/kaggle/dsv/5706582
title: The HELP framework
DOI: 10.17632/8ypy94frxg.1
title: Index Index dataset

description: This is the data used for the development of the Index Index model.
DOI: 10.34740/kaggle/dsv/5706930
title: YouTube Daily
DOI: 10.5281/zenodo.7944343
title: SqueezeNet_Dataset_TransferLearning

description: We have collected and used this dataset for the detection of volcanic thermal anomalies from satellite images. We developed a Deep Learning approach based on a Convolutional Neural Network (CNN) model to detect the presence or not of volcanic thermal activity, using satellite remote sensing data. In particular, we selected 200 images from ESA Sentinel-2 MSI and NASA&USGS Landsat 8 OLI satellite sensors, using the NIR and SWIR bands, and we used the Deep Squeezenet CNN model. This dataset was fed to the CNN model to retrain it, applying the Transfer Learning approach. We divided the dataset into two classes, "Yes Activity" (Class1, i.e., presence of lava flows and active vents) and "No Activity" (Class0, i.e., volcanoes at rest, in scenes with clouds, snows, etc...). To have a balanced dataset, each class contains the same number of images (100 images).
DOI: 10.5281/zenodo.7944342
title: SqueezeNet_Dataset_TransferLearning

description: We have collected and used this dataset for the detection of volcanic thermal anomalies from satellite images. We developed a Deep Learning approach based on a Convolutional Neural Network (CNN) model to detect the presence or not of volcanic thermal activity, using satellite remote sensing data. In particular, we selected 200 images from ESA Sentinel-2 MSI and NASA&USGS Landsat 8 OLI satellite sensors, using the NIR and SWIR bands, and we used the Deep Squeezenet CNN model. This dataset was fed to the CNN model to retrain it, applying the Transfer Learning approach. We divided the dataset into two classes, "Yes Activity" (Class1, i.e., presence of lava flows and active vents) and "No Activity" (Class0, i.e., volcanoes at rest, in scenes with clouds, snows, etc...). To have a balanced dataset, each class contains the same number of images (100 images).
DOI: 10.15468/dl.bmujpg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Euphorbia)",
 "HasCoordinate is true",
 "Country is Madagascar"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0413278-210914110416597/datasets/export for details.
DOI: 10.15468/dl.5xaard
title: Occurrence Download

description: A dataset containing 3394 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2024",
 "Geometry POLYGON ((-130.7383528944533 -59.46314024951374, -130.7383528944533 32.71876227804215, -26.26729810228821 32.71876227804215, -26.26729810228821 -59.46314024951374, -130.7383528944533 -59.46314024951374))",
 "OccurrenceStatus is Present",
 "TaxonKey is Peucaea humeralis (Cabanis, 1851)"
 ]
}

 The dataset includes 3394 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052398-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z84lkq
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Felis silvestris Schreber, 1777"
 ]
}

 The dataset includes 20 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007102-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.015of8
title: Occurrence Download

description: A dataset containing 2053 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Orcinus orca (Linnaeus, 1758)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2053 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002306-150912100344238/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dwyjt0
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Issue is Coordinate rounded",
 "Issue is Elevation non numeric"
 ]
 },
 "TaxonKey is Drosera androsacea Diels"
 ]
}

 The dataset includes 43 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007102-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.