cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15131/shef.data.21262254
title: Unprocessed/Raw Ultrasonic Reflections from Ultrasonically Instrumented Multi-Axis Bearing Lubricated with Hyspin VG32 Oil, Alpha SP VG 320 Oil and Mobil SHC Grease 460 WT

description: This test data was captured on the multi-axis bearing, installed in the full-scale wind turbine bearing test rig based at the University of Sheffield. There is test data for the bearing lubricated with Hyspin VG 32 oil and Alpha Sp VG 320 oil and Mobil SHC Grease 460 WT. <br> A README file contains all information about the test methodology, ultrasonic set-up, thermocouple captures and duration of tests.
DOI: 10.15131/shef.data.21262254.v1
title: Unprocessed/Raw Ultrasonic Reflections from Ultrasonically Instrumented Multi-Axis Bearing Lubricated with Hyspin VG32 Oil, Alpha SP VG 320 Oil and Mobil SHC Grease 460 WT

description: This test data was captured on the multi-axis bearing, installed in the full-scale wind turbine bearing test rig based at the University of Sheffield. There is test data for the bearing lubricated with Hyspin VG 32 oil and Alpha Sp VG 320 oil and Mobil SHC Grease 460 WT. <br> A README file contains all information about the test methodology, ultrasonic set-up, thermocouple captures and duration of tests.
DOI: 10.26188/20347203
title: Supplementary_Data_File.xlsx

description: Supplementary data file with all the details on MAGs used in this study.
DOI: 10.26188/20347203.v2
title: Supplementary_Data_File.xlsx

description: Supplementary data file with all the details on MAGs used in this study.
DOI: 10.5281/zenodo.6988910
title: Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL

description: Datasets and source materials for our studies of Linear Temporal Logic (LTL).
description: This work was partially supported by the US National Science Foundation. Greenman received support from NSF grant 2030859 to the CRA for the CIFellows project. This work was also partially supported by RelationalAI.
DOI: 10.5281/zenodo.7020670
title: Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL

description: Datasets and source materials for our studies of Linear Temporal Logic (LTL).
description: This work was partially supported by the US National Science Foundation. Greenman received support from NSF grant 2030859 to the CRA for the CIFellows project. This work was also partially supported by RelationalAI.
DOI: 10.5281/zenodo.7026432
title: Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL

description: Datasets and source materials for our studies of Linear Temporal Logic (LTL).
description: This work was partially supported by the US National Science Foundation. Greenman received support from NSF grant 2030859 to the CRA for the CIFellows project. This work was also partially supported by RelationalAI.
DOI: 10.5281/zenodo.7102970
title: Accepted Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL

description: Datasets and source materials for our studies of Linear Temporal Logic (LTL). Includes: Labelled responses to our surveys The surveys themselves The coding rubric that guided our categorization
description: This work was partially supported by the US National Science Foundation. Greenman received support from NSF grant 2030859 to the CRA for the CIFellows project. This work was also partially supported by RelationalAI.
DOI: 10.5281/zenodo.7213780
title: Accepted Artifact for Little Tricky Logic: Misconceptions in the Understanding of LTL

description: Datasets and source materials for our studies of Linear Temporal Logic (LTL). Includes: Labelled responses to our surveys The surveys themselves The coding rubric that guided our categorization
description: This work was partially supported by the US National Science Foundation. Greenman received support from NSF grant 2030859 to the CRA for the CIFellows project. This work was also partially supported by RelationalAI.
DOI: 10.5281/zenodo.7059244
title: Trojan Detection Challenge - Detection Track Dataset

description: This contains the dataset for the detection track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the competition website. This is identical to the detection folder in the full dataset Zenodo record, which can be found here.
DOI: 10.5281/zenodo.7059487
title: Trojan Detection Challenge - Detection Track Dataset

description: This contains the dataset for the detection track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the competition website. This is identical to the detection folder in the full dataset Zenodo record, which can be found here.
DOI: 10.5281/zenodo.7059737
title: Trojan Detection Challenge - Analysis Track (Target Label Prediction) Dataset

description: This contains the dataset for the target label prediction subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the competition website. This is identical to the target_label_prediction folder in the full dataset Zenodo record, which can be found here.
DOI: 10.5281/zenodo.7063081
title: Trojan Detection Challenge - Analysis Track (Target Label Prediction) Dataset

description: This contains the dataset for the target label prediction subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the competition website. This is identical to the target_label_prediction folder in the full dataset Zenodo record, which can be found here.
DOI: 10.5281/zenodo.7062612
title: Trojan Detection Challenge - Analysis Track (Trigger Synthesis) Dataset

description: This contains the dataset for the trigger synthesis subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the competition website. This is identical to the target_label_prediction folder in the full dataset Zenodo record, which can be found here.
DOI: 10.5281/zenodo.7063103
title: Trojan Detection Challenge - Analysis Track (Trigger Synthesis) Dataset

description: This contains the dataset for the trigger synthesis subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the competition website. This is identical to the target_label_prediction folder in the full dataset Zenodo record, which can be found here.
DOI: 10.5281/zenodo.7063166
title: Trojan Detection Challenge - Evasive Trojans Track Dataset

description: This contains the dataset for the evasive trojans track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the competition website. This is identical to the detection folder in the full dataset Zenodo record, which can be found here.
DOI: 10.5281/zenodo.6579673
title: code and data for A new method applied for the determination of relative weight ratios under the TensorFlow platform when estimating coseismic slip distribution

description: The zip file contains two folders"HVCE and ABIC method implement on matlab" and "GDED method implement on tensorflow". Take the simulation experiments 1.2 and actual Illapel earthquakes as examples. the code for GDED method are placed on "GDED method implement on tensorflow" folders, and the code for the ABIC method and the HVCE method are place on "HVCE and ABIC method implement on matlab" folders. In the file"HVCE and ABIC method implement on matlab", the meaning of each code are represent as following <br> ABIC_SIM:the slip distribution inversion results with the relative weight ratios determined by ABIC method Of simulation experiments and Illapel earthquakes <br> HVCE:the slip distribution inversion results with the relative weight ratios determined by HVCE method Of simulation experiments and Illapel earthquakes <br> GDED:the slip distribution inversion results with the relative weight ratios determined by GDED method Of simulation experiments and Illapel earthquakes(the relative weight ratios are from the "GDED method implement on tensorflow") <br> In the file"GDED method implement on tensorflow", the meaning of each code are represent as following<br> joint_inver_tensor_ex_1.0(1.1).py: the code for determining the relative weight ratios by the GDED method with(without) plot figures, which implement on Tensorflow platform the INSAR data and GPS data of Illapel earthquakes are palce on the \HVCE and ABIC method implement on matlab\Illapel earthquakes\data\ZLInSAR(ZLGPS)<br>
DOI: 10.5281/zenodo.7207507
title: code and data for A new method applied for the determination of relative weight ratios under the TensorFlow platform when estimating coseismic slip distribution

description: The zip file contains three folders:"Data for Illapel earthquake", "HVCE and ABIC method implement on matlab" and "GDED method implement on tensorflow". Take the simulation experiments 1.2 and actual Illapel earthquakes as examples. the code for GDED method are placed on "GDED method implement on tensorflow" folders, and the code for the ABIC method and the HVCE method are place on "HVCE and ABIC method implement on matlab" folders. In the file"HVCE and ABIC method implement on matlab", the meaning of each code are represent as following <br> ABIC_SIM.m:the slip distribution inversion results with the relative weight ratios determined by ABIC method Of simulation experiments and Illapel earthquakes <br> HVCE.m:the slip distribution inversion results with the relative weight ratios determined by HVCE method Of simulation experiments and Illapel earthquakes <br> GDED.m:the slip distribution inversion results with the relative weight ratios determined by GDED method Of simulation experiments and Illapel earthquakes(the relative weight ratios are from the "GDED method implement on tensorflow") savedata.m: that code are used for save matrix or data for the GDED method implement on tensorflow In the file"GDED method implement on tensorflow", the meaning of each code are represent as following<br> joint_inver_tensor_ex_1.0(1.1).py: the code for determining the relative weight ratios by the GDED method with(without) plot figures, which implement on Tensorflow platform<br> <br> the InSAR data and GPS data of Illapel earthquakes are palce on the folder" Data for Illapel earthquake/GPS_ori.txt and InSAR_ori.txt"
DOI: 10.5281/zenodo.6579672
title: code and data for A new method applied for the determination of relative weight ratios under the TensorFlow platform when estimating coseismic slip distribution

description: The zip file contains three folders:"Data for Illapel earthquake", "HVCE and ABIC method implement on matlab" and "GDED method implement on tensorflow". Take the simulation experiments 1.2 and actual Illapel earthquakes as examples. the code for GDED method are placed on "GDED method implement on tensorflow" folders, and the code for the ABIC method and the HVCE method are place on "HVCE and ABIC method implement on matlab" folders. In the file"HVCE and ABIC method implement on matlab", the meaning of each code are represent as following <br> ABIC_SIM.m:the slip distribution inversion results with the relative weight ratios determined by ABIC method Of simulation experiments and Illapel earthquakes <br> HVCE.m:the slip distribution inversion results with the relative weight ratios determined by HVCE method Of simulation experiments and Illapel earthquakes <br> GDED.m:the slip distribution inversion results with the relative weight ratios determined by GDED method Of simulation experiments and Illapel earthquakes(the relative weight ratios are from the "GDED method implement on tensorflow") savedata.m: that code are used for save matrix or data for the GDED method implement on tensorflow In the file"GDED method implement on tensorflow", the meaning of each code are represent as following<br> joint_inver_tensor_ex_1.0(1.1).py: the code for determining the relative weight ratios by the GDED method with(without) plot figures, which implement on Tensorflow platform<br> <br> the InSAR data and GPS data of Illapel earthquakes are palce on the folder" Data for Illapel earthquake/GPS_ori.txt and InSAR_ori.txt"
DOI: 10.34740/kaggle/dsv/4338526
title: OntoUML/UFO Catalog
DOI: 10.6084/m9.figshare.21342936
title: Dry storage alters intraspecific variation in phenotypic traits at early life stages-evidence from a dominant alpine meadow species

description: Here, we collected seeds of 10 <em>Elymus nutans</em> populations along an altitude gradient from the eastern of Qinghai-Tibet Plateau. Fresh seeds and those stored dry for 6 months were used to quantify the temperature thresholds for germination, determine seedling emergence and survival in two common gardens, and examine the correlation between these traits and environmental conditions of population provenance. <br>
DOI: 10.6084/m9.figshare.21342936.v1
title: Dry storage alters intraspecific variation in phenotypic traits at early life stages-evidence from a dominant alpine meadow species

description: Here, we collected seeds of 10 <em>Elymus nutans</em> populations along an altitude gradient from the eastern of Qinghai-Tibet Plateau. Fresh seeds and those stored dry for 6 months were used to quantify the temperature thresholds for germination, determine seedling emergence and survival in two common gardens, and examine the correlation between these traits and environmental conditions of population provenance. <br>
DOI: 10.34740/kaggle/dsv/4338564
title: BBC News
DOI: 10.34740/kaggle/dsv/4338598
title: Polish-Ukrainian translations from Tatoeba
DOI: 10.25958/7cv3-qq74
title: Emotion regulation strategies, co-rumination, and emotional wellbeing

description: The dataset represents answers to an online questionnaire that was completed by 305 participants. The dataset captures participants' levels of emotional wellbeing, cognitive reappraisal, expressive suppression, and co-rumination. The data also captures participants demographics (i.e., age in year, ethnicity, relationship status, and gender).
DOI: 10.6084/m9.figshare.21342957.v1
title: Supplementary files: The digital ischemia in patient with systemic sclerosis: a case report

description: Supplementary files: The digital ischemia in patient with systemic sclerosis: a case report
DOI: 10.5281/zenodo.7008205
title: Dataset for "Towards Better Evaluation for Dynamic Link Prediction"

description: These are the datasets used in <em>Towards Better Evaluation for Dynamic Link Prediction</em> For preparing the datasets, we closely follow the baseline methods' data preparation strategy.<br> The original networks are saved as &lt;network&gt;.csv. The networks are formatted as follows:<br> * Each edge is denoted in one line.<br> * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features.<br> * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task.<br> * The first line denotes the network format.<br> * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges. The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline).<br> Ater preprocessing the network edge-list, there are three files that are used by the models:<br> * &lt;ml_network&gt;.csv: this file contains the timestamp edge-list.<br> * &lt;ml_network&gt;.npy: this file contains the edge features in the dense `npy` format that has the features in binary format.<br> * &lt;ml_network_node&gt;.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format.<br> Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods.
DOI: 10.5281/zenodo.7008204
title: Dataset for "Towards Better Evaluation for Dynamic Link Prediction"

description: These are the datasets used in <em>Towards Better Evaluation for Dynamic Link Prediction</em> For preparing the datasets, we closely follow the baseline methods' data preparation strategy.<br> The original networks are saved as &lt;network&gt;.csv. The networks are formatted as follows:<br> * Each edge is denoted in one line.<br> * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features.<br> * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task.<br> * The first line denotes the network format.<br> * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges. The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline).<br> Ater preprocessing the network edge-list, there are three files that are used by the models:<br> * &lt;ml_network&gt;.csv: this file contains the timestamp edge-list.<br> * &lt;ml_network&gt;.npy: this file contains the edge features in the dense `npy` format that has the features in binary format.<br> * &lt;ml_network_node&gt;.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format.<br> Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods.
DOI: 10.5281/zenodo.7213796
title: Dataset for "Towards Better Evaluation for Dynamic Link Prediction"

description: These are the datasets used in <em>Towards Better Evaluation for Dynamic Link Prediction</em> For preparing the datasets, we closely follow the baseline methods' data preparation strategy.<br> The original networks are saved as &lt;network&gt;.csv. The networks are formatted as follows:<br> * Each edge is denoted in one line.<br> * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features.<br> * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task.<br> * The first line denotes the network format.<br> * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges. The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline).<br> Ater preprocessing the network edge-list, there are three files that are used by the models:<br> * &lt;ml_network&gt;.csv: this file contains the timestamp edge-list.<br> * &lt;ml_network&gt;.npy: this file contains the edge features in the dense `npy` format that has the features in binary format.<br> * &lt;ml_network_node&gt;.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format.<br> Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods.
DOI: 10.34740/kaggle/dsv/4338670
title: Solar Energy Northeast Brazil
DOI: 10.25919/3djv-yy09
title: Parkes observations for project P1054 semester 2022OCTS_03

description: With this proposal we ask time to continue our timing follow-up campaign of pulsars discovered with the MeerKAT telescope. Eighteen sources have been discovered in targeted observations of Fermi unidentified point sources, six in the Small Magellanic Cloud, 54 in Globular Clusters and 66 in a survey of the Galactic plane. A large fraction of the new discoveries are recycled pulsars (including at least one double neutron star system and several 'spider' binaries), or young pulsars. Timing observations have an essential role in exploiting the full potential of any pulsar discovery, allowing the precise measurement of rotational, astrometric and orbital parameters which, in turn, give us powerful tools to improve our understanding of the physics in extreme environments as well as of the population of neutron stars as a whole. The UWL receiver of the Parkes telescope is a sensitive, versatile instrument that is allowing us to successfully time these new sources, in the bright-end of TRAPUM discoveries. The UWL has also the unique advantage of allowing detailed studies of the eclipses of the so-called 'spider' pulsars, which make up about 1/3rd of the sources we aim at following-up with this project.
DOI: 10.25919/5dea-0291
title: Parkes observations for project PX500 semester 2022OCTS_01

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.6084/m9.figshare.21342957
title: Supplementary files: Digital ischemia in patient with systemic sclerosis: a case report

description: Supplementary files: The digital ischemia in patient with systemic sclerosis: a case report
DOI: 10.6084/m9.figshare.21342957.v2
title: Supplementary files: Digital ischemia in patient with systemic sclerosis: a case report

description: Supplementary files: The digital ischemia in patient with systemic sclerosis: a case report
DOI: 10.6084/m9.figshare.21342048
title: Clinical Risk Factors Related to Post Stroke Epilepsy Patients in Indonesia: A Hospital-Based Study

description: Stroke had been accounted to cause almost 50% of epilepsy in the elderly and may lead to poor functional outcomes. Many studies examining the risk factors have been conducted despite showing inconsistent results and currently still difficult to predict the occurrence of post-stroke epilepsy. The study aimed to determine risk factors related to post-stroke epilepsy that influence clinical seizure, electroencephalography, and functional outcome of patients in Saiful Anwar General Hospital
DOI: 10.6084/m9.figshare.21343019
title: Data and code from Disentangling the developmental origins of a novel phenotype: enhancement versus reversal of environmentally induced gene expression

description: This folder contains R code, morphological data, and analysis input data files.
DOI: 10.6084/m9.figshare.21343019.v1
title: Data and code from Disentangling the developmental origins of a novel phenotype: enhancement versus reversal of environmentally induced gene expression

description: This folder contains R code, morphological data, and analysis input data files.
DOI: 10.6084/m9.figshare.21299809
title: Supplementary Tables from Ancient DNA evidence for the ecological globalization of cod fishing in medieval and post-medieval Europe

description: Supplementary Table S1, S2, S3 and S4 containing in-detail information and genomic assignments of the ancient samples of Atlantic cod from England (n = 32) and Spain (n=5) used in this study.
DOI: 10.6084/m9.figshare.21299809.v2
title: Supplementary Tables from Ancient DNA evidence for the ecological globalization of cod fishing in medieval and post-medieval Europe

description: Supplementary Table S1, S2, S3 and S4 containing in-detail information and genomic assignments of the ancient samples of Atlantic cod from England (n = 32) and Spain (n=5) used in this study.