cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e129862v1
title: Ideologia e Discurso: o posicionamento do Partido dos Trabalhadores na oposição e no governo

description: Dados para replicação do artigo: https://www.scielo.br/pdf/rbcpol/n28/2178-4884-rbcpol-28-161.pdf
DOI: 10.21258/1731243
title: Swiss national forest inventory - Result table No. 897368
DOI: 10.21258/1739551
title: Swiss national forest inventory - Result table No. 898903
DOI: 10.21258/1741154
title: Swiss national forest inventory - Result table No. 898043
DOI: 10.21258/1768223
title: Swiss national forest inventory - Result table No. 898839
DOI: 10.21258/1732639
title: Swiss national forest inventory - Result table No. 897165
DOI: 10.21258/1767940
title: Swiss national forest inventory - Result table No. 891898
DOI: 10.21258/1734909
title: Swiss national forest inventory - Result table No. 897576
DOI: 10.21258/1729708
title: Swiss national forest inventory - Result table No. 891929
DOI: 10.21258/1738635
title: Swiss national forest inventory - Result table No. 897588
DOI: 10.21258/1743332
title: Swiss national forest inventory - Result table No. 898905
DOI: 10.21258/1738634
title: Swiss national forest inventory - Result table No. 899465
DOI: 10.21258/1768620
title: Swiss national forest inventory - Result table No. 897644
DOI: 10.21258/1723330
title: Swiss national forest inventory - Result table No. 897808
DOI: 10.21258/1745575
title: Swiss national forest inventory - Result table No. 896940
DOI: 10.21258/1764342
title: Swiss national forest inventory - Result table No. 899607
DOI: 10.21258/1756113
title: Swiss national forest inventory - Result table No. 897260
DOI: 10.21258/1723982
title: Swiss national forest inventory - Result table No. 893069
DOI: 10.21258/1761717
title: Swiss national forest inventory - Result table No. 897940
DOI: 10.21258/1740720
title: Swiss national forest inventory - Result table No. 892009
DOI: 10.21258/1729585
title: Swiss national forest inventory - Result table No. 898098
DOI: 10.21258/1738428
title: Swiss national forest inventory - Result table No. 897502
DOI: 10.21258/1768242
title: Swiss national forest inventory - Result table No. 892003
DOI: 10.21258/1745604
title: Swiss national forest inventory - Result table No. 902082
DOI: 10.21258/1765529
title: Swiss national forest inventory - Result table No. 902280
DOI: 10.21258/1761194
title: Swiss national forest inventory - Result table No. 891978
DOI: 10.6084/m9.figshare.13488144
title: 20191105_noredundancy_ref_db_with_speciename_CjepI

description: CJEPI
DOI: 10.6084/m9.figshare.13488144.v2
title: 20191105_noredundancy_ref_db_with_speciename_CjepI

description: CJEPI
DOI: 10.6084/m9.figshare.13488144.v1
title: 20191105_noredundancy_ref_db_with_speciename_CjepI

description: CJEPI
DOI: 10.21258/1754542
title: Swiss national forest inventory - Result table No. 891704
DOI: 10.21258/1757264
title: Swiss national forest inventory - Result table No. 898248
DOI: 10.21258/1762412
title: Swiss national forest inventory - Result table No. 896928
DOI: 10.21258/1728551
title: Swiss national forest inventory - Result table No. 897668
DOI: 10.21258/1726959
title: Swiss national forest inventory - Result table No. 898341
DOI: 10.21258/1733042
title: Swiss national forest inventory - Result table No. 891775
DOI: 10.21258/1739345
title: Swiss national forest inventory - Result table No. 891757
DOI: 10.21258/1744614
title: Swiss national forest inventory - Result table No. 896907
DOI: 10.21258/1762042
title: Swiss national forest inventory - Result table No. 892002
DOI: 10.21258/1741942
title: Swiss national forest inventory - Result table No. 898904
DOI: 10.21258/1748585
title: Swiss national forest inventory - Result table No. 897205
DOI: 10.21258/1738423
title: Swiss national forest inventory - Result table No. 891806
DOI: 10.21258/1759650
title: Swiss national forest inventory - Result table No. 899664
DOI: 10.21258/1733680
title: Swiss national forest inventory - Result table No. 897645
DOI: 10.21258/1762384
title: Swiss national forest inventory - Result table No. 891811
DOI: 10.21258/1766063
title: Swiss national forest inventory - Result table No. 898032
DOI: 10.21258/1725635
title: Swiss national forest inventory - Result table No. 899495
DOI: 10.21258/1755149
title: Swiss national forest inventory - Result table No. 897370
DOI: 10.21258/1748463
title: Swiss national forest inventory - Result table No. 902285
DOI: 10.21258/1746202
title: Swiss national forest inventory - Result table No. 899224
DOI: 10.21258/1758246
title: Swiss national forest inventory - Result table No. 892989
DOI: 10.21258/1743249
title: Swiss national forest inventory - Result table No. 893063
DOI: 10.21258/1744321
title: Swiss national forest inventory - Result table No. 898976
DOI: 10.21258/1768022
title: Swiss national forest inventory - Result table No. 897566
DOI: 10.21258/1755706
title: Swiss national forest inventory - Result table No. 898555
DOI: 10.21258/1742794
title: Swiss national forest inventory - Result table No. 898837
DOI: 10.21258/1726096
title: Swiss national forest inventory - Result table No. 898549
DOI: 10.21258/1744382
title: Swiss national forest inventory - Result table No. 897738
DOI: 10.21258/1758654
title: Swiss national forest inventory - Result table No. 902179
DOI: 10.17632/zg67nkypyn.1
title: Data for: Identifying the essential nodes in network pharmacology based on multilayer network combined with random walk algorithm
DOI: 10.21227/4jpb-sp36
title: Food/Non-food Image Classification

description: Recently food recognition is a vital topic among researchers, as it automates the food logging process besides having applications in Food Safety, Food Quality Detection, Inspection, etc. The advancement in deep learning technology on edge computing and cloud computing leads to the development of hybrid solutions by considering the advantages of these computing platforms. For large scale, experiments, we gathered and prepared a Food/Non-Food dataset created from the existing image datasets of UECFOOD256, Caltech 256, Instagram Images, Flickr Image Dataset, Food101, Malaysian Food Dataset(gathered and crawled by us), Indoor Scene recognition Dataset, 15 scene dataset. The dataset is divided into two classes and is currently the largest Food/NonFood dataset available to the best of our knowledge. Our main contribution for this repository is the Food/Non-Food dataset, using foods of different cultures, existing food datasets, and the images of the users on the popular social media platforms. We manually cleaned the datasets for this purpose.
DOI: 10.5281/zenodo.3266172
title: Brugada clinical database

description: Clinical database containing anonymized data of 119 patients Disclaimer: These datasets were generated during the course of academic research conducted at the Faculty of Medicine, The Chinese University of Hong Kong, which received ethics approval by The Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee of the Hospital Authority. They are deposited for further advancement of medical research in full compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit The use of these datasets should provide acknowledgements of such efforts by citing this DOI.
DOI: 10.5281/zenodo.3351892
title: Brugada ECG analysis

description: Automated analysis of ECGs taken from 102 Brugada syndrome patients
DOI: 10.5281/zenodo.4382471
title: Long QT syndrome ECG analysis

description: Automated analysis of ECGs obtained from patients with long QT syndrome (generated from arrhythmia study, Faculty of Medicine, The Chinese University of Hong Kong Project)
DOI: 10.5281/zenodo.3465850
title: Long QT syndrome ECG analysis

description: Automated analysis of ECGs obtained from patients with long QT syndrome
DOI: 10.5281/zenodo.3351908
title: Long QT syndrome ECG analysis

description: Automated analysis of ECGs obtained from patients with long QT syndrome
DOI: 10.5281/zenodo.3405607
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.5281/zenodo.3405606
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.5281/zenodo.3406867
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.5281/zenodo.3406645
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.17632/2zbyxvp7nz.1
title: Data for: Multi-decadal Assessment of Water Resources in the Tigris-Euphrates Basin using Satellites, Hydrological Modeling, and In-situ Data

description: Supplementary materials for "Multi-decadal Assessment of Water Resources in the Tigris-Euphrates Basin using Satellites, Hydrological Modeling, and In-situ Data"
DOI: 10.5281/zenodo.3406169
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.5281/zenodo.3405943
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.5281/zenodo.3405632
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.5281/zenodo.3266164
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.5281/zenodo.3405612
title: Heart failure multi-modality data

description: Data set on heart failure due to different causes
DOI: 10.6084/m9.figshare.13204646.v1
title: Additional file 4 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 4. ICEHo locations in draft genomes. Positions and gene presence patterns of ICEHo-I and -II in draft de novo assemblies of 12 M. hominis strains sequenced only with Nanopore. ICEHo positions in the draft assemblies were determined by aligning the sequences of ICEHo-I of strain FBG and of ICEHo-II of strain SP3615 to the draft assemblies. The additional columns show the homology (nucleotide identity %) between the genes present in the draft assembly ICEHo elements and the genes present in ICEHo-I of FBG and the genes present in ICEHo-II of SP3615 (gene order and names correspond to Fig. 3).
DOI: 10.6084/m9.figshare.13204649.v1
title: Additional file 5 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 5. qPCR data of ICEHo and cICE. Ct and ∆Ct values for ICEHo-I, −II and cICE detection, as well as ICEHo-I and -II status based on the assembled genomes, and confirmatory detection of cICE by Sanger sequencing.
DOI: 10.6084/m9.figshare.13204652.v1
title: Additional file 6 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 6. ICEHo-I and ICEHo-II putative gene functions. ICEHo-I and -II putative gene functions, based on bioinformatics analyses (see main text) and literature review.
DOI: 10.6084/m9.figshare.13204655.v1
title: Additional file 7 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 7. M. hominis genomes in GB.
DOI: 10.6084/m9.figshare.13204646
title: Additional file 4 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 4. ICEHo locations in draft genomes. Positions and gene presence patterns of ICEHo-I and -II in draft de novo assemblies of 12 M. hominis strains sequenced only with Nanopore. ICEHo positions in the draft assemblies were determined by aligning the sequences of ICEHo-I of strain FBG and of ICEHo-II of strain SP3615 to the draft assemblies. The additional columns show the homology (nucleotide identity %) between the genes present in the draft assembly ICEHo elements and the genes present in ICEHo-I of FBG and the genes present in ICEHo-II of SP3615 (gene order and names correspond to Fig. 3).
DOI: 10.6084/m9.figshare.13204649
title: Additional file 5 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 5. qPCR data of ICEHo and cICE. Ct and ∆Ct values for ICEHo-I, −II and cICE detection, as well as ICEHo-I and -II status based on the assembled genomes, and confirmatory detection of cICE by Sanger sequencing.
DOI: 10.6084/m9.figshare.13204652
title: Additional file 6 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 6. ICEHo-I and ICEHo-II putative gene functions. ICEHo-I and -II putative gene functions, based on bioinformatics analyses (see main text) and literature review.
DOI: 10.6084/m9.figshare.13204655
title: Additional file 7 of Characterisation of mobile genetic elements in Mycoplasma hominis with the description of ICEHo-II, a variant mycoplasma integrative and conjugative element

description: Additional file 7. M. hominis genomes in GB.
DOI: 10.6084/m9.figshare.13488694
title: Additional file 1 of Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline

description: Additional file 1: Supplementary Table 1. Cox proportional hazard regression models including other forms of dementia.
DOI: 10.6084/m9.figshare.13488697
title: Additional file 2 of Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline

description: Additional file 2: Supplementary Table 2. Risks and events by year with amide I maximum ≥1644 cm− 1,
DOI: 10.6084/m9.figshare.13488694.v1
title: Additional file 1 of Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline

description: Additional file 1: Supplementary Table 1. Cox proportional hazard regression models including other forms of dementia.
DOI: 10.17632/pjh6ymdf5f.1
title: Data for: Secondary electron emission characteristics of Al2O3 /MgO double-layer structure prepared by atomic layer deposition
DOI: 10.17632/69dhf5gyxc.1
title: Data for: The Impact of COVID-19 on Therapeutic Service Delivery for Children with Disabilities

description: This dataset provides results of 207 parents of children with disabilities ages 0-21 who receive therapies for their disability-related needs. Parents were asked about their child's services, how satisfied they were with these services during COVID-19, and how satisfied they were with the family-provider partnership during COVID-19.
DOI: 10.17632/yj82fvw4nk.1
title: Data for: Flow stratification behavior of binary particles in inclined moving bed

description: Fig.6
DOI: 10.17632/f7kh5v3y76.1
title: Data for: Training Fuzzy Inference System-Based Classifiers with Krill Herd Algorithm

description: In this study, a new approach for modification of membership function parameters in a fuzzy inference system (FIS) is demonstrated. Here, the main intention is to compare Krill Herd Arlgorithm (KHA) as a mean to train a FIS, with other heuristic algorithms, as well as more conventional classification procedures. The proposed FIS training method has been designed to serve as a fuzzy classifier.
DOI: 10.21227/tav4-ty44
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. 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 coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. 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.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 286,413 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.6084/m9.figshare.13488697.v1
title: Additional file 2 of Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer’s disease in individuals with subjective cognitive decline

description: Additional file 2: Supplementary Table 2. Risks and events by year with amide I maximum ≥1644 cm− 1,
DOI: 10.21227/3c5r-h276
title: "Tennessee-Eastman-Process" Alarm Management Case Study

description: This is an alarm management case study based on the “Tennessee-Eastman-Process” (TEP). The presented case study aims to provide a suitable benchmark dataset for the development and validation of alarm management methods in complex industrial processes using both quantitative data and qualitative information from different sources. Unlike real industrial processes, simulation of the TEP allows to design and generate abnormal sequences, which can be repeated and varied without risking the loss of equipment or harming the environment. In addition, as the simulation is supervised, all induced faults and process normalizations are explicitly known and therefore act as a ground truth, facilitating the utilization of external evaluation metrics (e.g. when using cluster analysis). Further details are described in a supplementary technical report.
DOI: 10.17632/njhbctm74v.1
title: Data for: Experimental design of a soft X-ray tomography system on Keda Torus eXperiment

description: This is the data for result of Bayesian experimental design and error analysis of tomographic reconstruction.
DOI: 10.5517/ccdc.csd.cc24yk1q
title: CCDC 1994603: Experimental Crystal Structure Determination

description: Related Article: Piotr Kaszynski, Rafal Jakubowski, Anna Pietrzak, Andrienne C. Friedli|2020|Chem.-Eur.J.|26|17481|doi:10.1002/chem.202002997
DOI: 10.5517/ccdc.csd.cc24yk2r
title: CCDC 1994604: Experimental Crystal Structure Determination

description: Related Article: Piotr Kaszynski, Rafal Jakubowski, Anna Pietrzak, Andrienne C. Friedli|2020|Chem.-Eur.J.|26|17481|doi:10.1002/chem.202002997
DOI: 10.5517/ccdc.csd.cc24yk3s
title: CCDC 1994605: Experimental Crystal Structure Determination

description: Related Article: Piotr Kaszynski, Rafal Jakubowski, Anna Pietrzak, Andrienne C. Friedli|2020|Chem.-Eur.J.|26|17481|doi:10.1002/chem.202002997
DOI: 10.5517/ccdc.csd.cc24yk4t
title: CCDC 1994606: Experimental Crystal Structure Determination

description: Related Article: Piotr Kaszynski, Rafal Jakubowski, Anna Pietrzak, Andrienne C. Friedli|2020|Chem.-Eur.J.|26|17481|doi:10.1002/chem.202002997
DOI: 10.5517/ccdc.csd.cc24yk5v
title: CCDC 1994607: Experimental Crystal Structure Determination

description: Related Article: Piotr Kaszynski, Rafal Jakubowski, Anna Pietrzak, Andrienne C. Friedli|2020|Chem.-Eur.J.|26|17481|doi:10.1002/chem.202002997
DOI: 10.6084/m9.figshare.13488762
title: S1 Table

description: Results obtained from all samples
DOI: 10.5281/zenodo.4393310
title: Aftershock data for the 2017 MW5.5 Pohang earthquake

description: <strong># written by J.-U. Woo</strong><br> Data includes [1] initial earthquake catalog and [2] relocated earthquake catalog <strong>Suggested citations:</strong><br> (For the uploaded catalogs) <strong>1.</strong> J.-U. Woo, M. Kim, J. Rhie, and T.-S. Kang, 2020, Aftershock sequence and statistics of the 2017 MW 5.5 Pohang earthquake, South Korea: implications of fault heterogeneity and post-seismic relaxation, Bulletin of Seismological Society of America. Detailed information will be posted on the zenodo website. <strong>Further suggested citations:</strong><br> (For 1-D velocity model at EGS site and catalogs at Pohang EGS site) <strong>2.</strong> J.-U. Woo, M. Kim, D.-H. Sheen, T.-S. Kang, J. Rhie, F. Grigoli, W.L. Ellsworth, D. Giardini, 2019, An In-Depth Seismological Analysis Revealing a Causal Link Between the 2017 MW 5.5 Pohang Earthquake and EGS Project, JGR solid earth, doi:10.1029/2019JB018368.<br> <strong>3.</strong> W.L. Ellsworth, D. Giardini, J. Townend, S. Ge, and T. Shimamoto, 2019, Triggering of the Pohang, Korea, Earthquake (Mw 5.5) by Enhanced Geothermal System Stimulation. Seismological Research Letters, 90(5), 1844-1858.<br> <strong>4.</strong> K.-K. Lee, W.L. Ellsworth, D. Giardini, J. Townend, S. Ge, T. Shimamoto, I.-W. Yeo, T.-S. Kang, J. Rhie, D.-H. Sheen, C. Chang, J.-U. Woo, C. Langenbruch, 2019, Managing injection-induced seismic risks. Science, 364(6442), 730-732.<br> <strong>5.</strong> C. Langenbruch, W. L. Ellsworth, J.-U. Woo and D. J. Wald, 2020, Value at Induced Risk: Injection-induced seismic risk from low-probability, high-impact events. Geophysical Research Letters, 47, e2019GL085878. http://doi.org/10.1029/2019GL085878. <strong>Descriptions:</strong><br> [1] Initial earthquake catalog (cat1)<br> - First column: event index (sequentially increasing number)<br> - Second column: event ID<br> - Third column: event origin date as year/month/day_of_month<br> - Fourth column: event origin time as hour:minute:second<br> - Fifth and sixth columns: latitude and longitude in degree<br> [2] Relocated earthquake catalog (cat2)<br> - First column: event index (linked with event index of cat1)<br> - Second column: event ID (linked with event ID of cat2)<br> - Third column: event origin date as year/month/day_of_month<br> - Fourth column: event origin time as hour:minute:second<br> - Fifth and sixth columns: latitude and longitude in degree<br> - Seventh column: depth in km<br> - Eighth column: 1.96*(standard deviation of W-E relative locations obtained from 200 times of bootstrap resampling) in meter<br> - Ninth column: 1.96*(standard deviation of S-N relative locations obtained from 200 times of bootstrap resampling) in meter<br> - Tenth column: 1.96*(standard deviation of relative depths obtained from 200 times of bootstrap resampling) in meter
description: Pohang aftershock catalog