cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3902465
title: An inclination-shallowing-corrected Early Triassic paleomagnetic pole for the North China Craton: Implication for the Mesozoic Geography of Proto-Asia

description: This is a Supplementary Table for a manuscript submitted to JGR: Solid Earth entitled "An inclination-shallowing-corrected Early Triassic paleomagnetic pole for the North China Craton: Implication for the Mesozoic Geography of Proto-Asia".
DOI: 10.3886/e119547v1
title: Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure

description: Has rising import competition contributed to the polarization of U.S. politics? Analyzing multiple measures of political expression and results of congressional and presidential elections spanning the period 2000 through 2016, we find strong though not definitive evidence of an ideological realignment in trade-exposed local labor markets that commences prior to the divisive 2016 U.S. presidential election. Exploiting the exogenous component of rising import competition by China, we find that trade exposed electoral districts simultaneously exhibit growing ideological polarization in some domains—meaning expanding support for both strong-left and strong-right views—and pure rightward shifts in others. Specifically, trade-impacted commuting zones or districts saw an increasing market share for the FOX News channel (a rightward shift), stronger ideological polarization in campaign contributions (a polarized shift), and a relative rise in the likelihood of electing a Republican to Congress (a rightward shift). Trade-exposed counties with an initial majority white population became more likely to elect a GOP conservative, while trade-exposed counties with an initial majority-minority population become more likely to elect a liberal Democrat, where in both sets of counties, these gains came at the expense of moderate Democrats (a polarized shift). In presidential elections, counties with greater trade exposure shifted towards the Republican candidate (a rightward shift). These results broadly support an emerging political economy literature that connects adverse economic shocks to sharp ideological realignments that cleave along racial and ethnic lines and induce discrete shifts in political preferences and economic policy.
DOI: 10.5281/zenodo.3902507
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574820433

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902508
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574820433

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902514
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574822161

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902513
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574822161

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902516
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574823532

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902515
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574823532

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902522
title: Pore-scale 3D dynamic morphological modeling and physical characterization of hydrate-bearing sediment based on computed tomography images

description: This supporting information includes one Figure-S1-REV300, which is the representative elemental volume (REV) used in this study. And the Figure-S1-REV300 is 300*300*300 with a voxel size of 4.4 μm. There are two phases in the REV: the sand and the pore. Figure S1 is uploaded with file name Figure S1-REV300.tif, which is the representative elemental volume (REV) used in this study
DOI: 10.5281/zenodo.3902521
title: Pore-scale 3D dynamic morphological modeling and physical characterization of hydrate-bearing sediment based on computed tomography images

description: This supporting information includes one Figure-S1-REV300, which is the representative elemental volume (REV) used in this study. And the Figure-S1-REV300 is 300*300*300 with a voxel size of 4.4 μm. There are two phases in the REV: the sand and the pore. Figure S1 is uploaded with file name Figure S1-REV300.tif, which is the representative elemental volume (REV) used in this study
DOI: 10.5281/zenodo.3902518
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1592368893

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902517
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1592368893

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902527
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574822997

description: This is an automated upload from the PyMedPhys library.
DOI: 10.5281/zenodo.3902526
title: auto-segmentation-1.2.840.10008.5.1.4.1.1.481.3.1574822997

description: This is an automated upload from the PyMedPhys library.
DOI: 10.21227/mvta-1w15
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.7303/syn22176124.1
title: Dataset: Comparison of Scoring Systems for Prediction of Mortality and Sepsis in Emergency Department Patients with Suspected Sepsis
DOI: 10.7303/syn22176126
title: Dataset: Comparison of Scoring Systems for Prediction of Mortality and Sepsis in Emergency Department Patients with Suspected Sepsis
DOI: 10.21227/8bmm-1893
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3902506
title: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

description: <strong>Content</strong> This repository contains pre-trained computer vision models, data labels, and images used in the pre-print publication "A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains": <em>ADPdevkit</em>: a folder containing the 50 validation ("tuning") set and 50 evaluation ("segtest") set of images from the Atlas of Digital Pathology database formatted in the VOC2012 style--the full database of 17,668 images is available for download from the original website <em>VOCdevkit</em>: a folder containing the relevant files for the PASCAL VOC2012 Segmentation dataset, with both the trainaug and test sets <em>DGdevkit</em>: a folder containing the 803 test images of the DeepGlobe Land Cover challenge dataset formatted in the VOC2012 style <em>cues</em>: a folder containing the pre-generated weak cues for ADP, VOC2012, and DeepGlobe datasets, as required for the SEC and DSRG methods <em>models_cnn</em>: a folder containing the pre-trained CNN models <em>models_wsss</em>: a folder containing the pre-trained SEC, DSRG, and IRNet models, along with dense CRF settings <strong>More information</strong> For more information, please refer to the following article. <strong>Please cite this article when using the data set.</strong> @misc{chan2019comprehensive,<br> title={A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains},<br> author={Lyndon Chan and Mahdi S. Hosseini and Konstantinos N. Plataniotis},<br> year={2019},<br> eprint={1912.11186},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> } For the full code released on GitHub, please visit the repository at: https://github.com/lyndonchan/wsss-analysis <strong>Contact</strong> For questions, please contact:<br> Lyndon Chan<br> lyndon.chan@mail.utoronto.ca<br> http://orcid.org/0000-0002-1185-7961
DOI: 10.5281/zenodo.3902505
title: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

description: <strong>Content</strong> This repository contains pre-trained computer vision models, data labels, and images used in the pre-print publication "A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains": <em>ADPdevkit</em>: a folder containing the 50 validation ("tuning") set and 50 evaluation ("segtest") set of images from the Atlas of Digital Pathology database formatted in the VOC2012 style--the full database of 17,668 images is available for download from the original website <em>VOCdevkit</em>: a folder containing the relevant files for the PASCAL VOC2012 Segmentation dataset, with both the trainaug and test sets <em>DGdevkit</em>: a folder containing the 803 test images of the DeepGlobe Land Cover challenge dataset formatted in the VOC2012 style <em>cues</em>: a folder containing the pre-generated weak cues for ADP, VOC2012, and DeepGlobe datasets, as required for the SEC and DSRG methods <em>models_cnn</em>: a folder containing the pre-trained CNN models <em>models_wsss</em>: a folder containing the pre-trained SEC, DSRG, and IRNet models, along with dense CRF settings <strong>More information</strong> For more information, please refer to the following article. <strong>Please cite this article when using the data set.</strong> @misc{chan2019comprehensive,<br> title={A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains},<br> author={Lyndon Chan and Mahdi S. Hosseini and Konstantinos N. Plataniotis},<br> year={2019},<br> eprint={1912.11186},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> } For the full code released on GitHub, please visit the repository at: https://github.com/lyndonchan/wsss-analysis <strong>Contact</strong> For questions, please contact:<br> Lyndon Chan<br> lyndon.chan@mail.utoronto.ca<br> http://orcid.org/0000-0002-1185-7961
DOI: 10.6084/m9.figshare.12522494.v1
title: figshare.xls

description: We sequenced <i>CLU </i>rs9331896 in 353 sporadic PD patients and 329 healthy-matched controls. The result showed TT genotype in rs9331896 in a recessive model was found associated with increased risk of PD.
DOI: 10.6084/m9.figshare.12522494.v3
title: CLU rs9331896 variant and Parkinson's disease risk

description: 我们对353例散发性PD患者和329例健康匹配的对照中的<i>CLU</i> rs9331896进行了测序。结果表明，在隐性模型中，rs9331896中的TT基因型与PD风险增加有关。
DOI: 10.6084/m9.figshare.12522494.v2
title: CLU rs9331896 variant and Parkinson's disease risk

description: 我们对353例散发性PD患者和329例健康匹配的对照中的<i>CLU</i> rs9331896进行了测序。结果表明，在隐性模型中，rs9331896中的TT基因型与PD风险增加有关。
DOI: 10.6084/m9.figshare.12522857.v1
title: A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring

description: Nine datasets from the UC Irvine (UCI) machine learning repository, i.e., the Australian, Japanese, German (Asuncion & Newman, 2007), Taiwan (Yeh & Lien, 2009) and Polish credit datasets (Zięba et al., 2016) were adopted for the current study. The Polish credit datasets contain five datasets distinguished five classification cases that depend on the forecasting period (e.g., the Polish 1, the Polish 2, the Polish 3, the Polish 4 and the Polish 5). AER credit dataset (Greene, 2003), which is a credit card dataset for econometric analysis. Creator dataset, which is published in 2019 by a Chinese digital government services provider named Creator Information Technology Co., Ltd[1]. The Creator dataset contains the property rights, financial statements, and basic company information of 35960 Chinese companies.<br>[1] http://www.chinacreator.com/cn/
DOI: 10.21227/s7wn-t489
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.21227/vpmm-8f92
title: the datasets of Salient Object Detect (SED2,pascal,ECSSD,THUR,MSRA,JuddDB,DUTOMRON))

description: These File is group by 7 different datasets with the task of salient object detect.Each folder is an open data set of SOD, which is composed of multiple JPG files. Each JPG picture corresponds to a annotation picture which exists in PNG format.
DOI: 10.21227/nhw8-s353
title: the datasets of Salient Object Detect (SED2,pascal,ECSSD,THUR,MSRA,JuddDB,DUTOMRON))

description: These File is group by 7 different datasets with the task of salient object detect.Each folder is an open data set of SOD, which is composed of multiple JPG files. Each JPG picture corresponds to a annotation picture which exists in PNG format.
DOI: 10.5281/zenodo.3902232
title: LIBS laboratory spectra LANL

description: Laser Induced Breakdown Spectroscopy spectra obtained on iron oxide mixtures with basaltic material (JSC Mars-1 simulant doped with ilmenite). These samples were probed at the Los Alamos National Laboratory (United States), and used to build an iron calibration for ChemCam data, on Mars.
DOI: 10.5281/zenodo.3902231
title: LIBS laboratory spectra LANL

description: Laser Induced Breakdown Spectroscopy spectra obtained on iron oxide mixtures with basaltic material (JSC Mars-1 simulant doped with ilmenite). These samples were probed at the Los Alamos National Laboratory (United States), and used to build an iron calibration for ChemCam data, on Mars.
DOI: 10.15161/oar.it/30445
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5519/qd.p467fxno
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2469 records)
DOI: 10.5519/qd.csjljr1l
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (169 records)
DOI: 10.5281/zenodo.3902646
title: Reconfigurable radiofrequency filters based on versatile soliton microcombs

description: New version Available data for "Reconfigurable radiofrequency filters based on versatile soliton microcombs"
DOI: 10.5281/zenodo.3894728
title: Reconfigurable radiofrequency filters based on versatile soliton microcombs

description: New version Available data for "Reconfigurable radiofrequency filters based on versatile soliton microcombs"
DOI: 10.18112/openneuro.ds002598.v1.0.1
title: Audiovisual Learning MEG Dataset
DOI: 10.18112/openneuro.ds002814.v1.0.0
title: TODO: name of the dataset
DOI: 10.5281/zenodo.3902651
title: Monash, UEA & UCR Time Series Regression Datasets

description: This contains all the datasets used for time series regression. Please refer to the website http://timeseriesregression.org/ for more details