cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3923525
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
DOI: 10.6084/m9.figshare.13488777
title: Coding common clinical language in SNOMED CT and ICD-10-CM_SIADs_Thierry Klein.xlsx

description: A homogeneous corpus with a focus on systemic inflammatory and autoimmunedisorders (SIADs) . An internist, head of the department of internal medicine in an academic hospital,has encoded on-line medical history elements (MHE) in a self-made software application for thirty years. A total of 13130 clinical MHEs has been collected. This corpus is totally homogeneous, on-line entered by a single user. We have extracted from this corpus the most frequent SIADs mentioned in the MHE.In a first step, a gold standard reference has been built. We manually coded the clinical phrases in SNOMED CT expressions following the postcoordination technique. We extracted then systematically the SNOMED CT precoordinated codes and we finally applied the SNOMED CT to ICD-10-CM mapping. In the second step, a Natural Language Processing software (NLP) annotated the clinical text phrases in SNOMED CT and ICD10-CM. <br><br>
DOI: 10.6084/m9.figshare.13488786
title: Problematic Social Media Usage and Anxiety among University Students during the COVID-19 Pandemic: The Mediating Role of Psychological Capital and the Moderating Role of Academic Burnout

description: Problematic Social Media Usage and Anxiety among University Students during the COVID-19 Pandemic: The Mediating Role of Psychological Capital and the Moderating Role of Academic Burnout
DOI: 10.6084/m9.figshare.13488786.v1
title: Problematic Social Media Usage and Anxiety among University Students during the COVID-19 Pandemic: The Mediating Role of Psychological Capital and the Moderating Role of Academic Burnout

description: Problematic Social Media Usage and Anxiety among University Students during the COVID-19 Pandemic: The Mediating Role of Psychological Capital and the Moderating Role of Academic Burnout
DOI: 10.5517/ccdc.csd.cc25njft
title: CCDC 2015726: Experimental Crystal Structure Determination

description: Related Article: Dong Tang, Zafar Iqbal, Jian Sun, Jingwen Ji, Minghua Yang, Zhixiang Yang|2020|Tetrahedron Lett.|62|152685|doi:10.1016/j.tetlet.2020.152685
description: Related Article: Dong Tang|2020|CSD Communication|||
DOI: 10.5281/zenodo.4288604
title: Digitized patient level time to event data of overall survival

description: This dataset contains digitized patient level time to event data for overall survival of patients with locally advanced and metastatic (stage IIIB/IV) Non-small cell lung cancer (NSCLC). The data can be used to recreate the original Kaplan-Meier survival curves that were published in randomized controlled trials, in order to perform secondary analysis on the survival data. In order to recreate a survival curve, you need two csv.files per trial arm that are in this <br> database: (1) starting with 'surv_', containing the individual patient level time to event data, and (2) starting with 'natrisk_', containing the corresponding numbers at risk table. For the methodology and r-code that can be used for this purpose we refer to article that is linked to this dataset.
description: The "Technology Assessment of Next Generation Sequencing in Personalized Oncology" (TANGO) project is funded by the ZonMw Personalised Medicine Programme under Dossier number: 846001002 in collaboration with KWF and Zilveren Kruis. The project period is 31 December 2016 – 31 May 2021.
DOI: 10.21227/5byd-gx52
title: Holoscopic 3D Micro-Gesture Database

description: HoMG database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes). Each subset has been devided into three partitions: training set, validation set and testing set where there is not overlap between them in term of the subjects. The database has been used for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018) that was held at IEEE Face & Gesture 2018 (FG2018 ) - Xi'an, China, 15-19th May 2018 (https://fg2018.cse.sc.edu/Challenges.html). The database is now public available for wider research community in the research areas of holoscopic 3D image processing, machine learning for gesture recognition and its application in AR and VR.
DOI: 10.21227/430t-fd03
title: Holoscopic 3D Micro-Gesture Database

description: HoMG database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes). Each subset has been devided into three partitions: training set, validation set and testing set where there is not overlap between them in term of the subjects. The database has been used for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018) that was held at IEEE Face & Gesture 2018 (FG2018 ) - Xi'an, China, 15-19th May 2018 (https://fg2018.cse.sc.edu/Challenges.html). The database is now public available for wider research community in the research areas of holoscopic 3D image processing, machine learning for gesture recognition and its application in AR and VR.
DOI: 10.21227/6x5y-w385
title: Holoscopic 3D Micro-Gesture Database

description: HoMG database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes). Each subset has been devided into three partitions: training set, validation set and testing set where there is not overlap between them in term of the subjects. The database has been used for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018) that was held at IEEE Face & Gesture 2018 (FG2018 ) - Xi'an, China, 15-19th May 2018 (https://fg2018.cse.sc.edu/Challenges.html). The database is now public available for wider research community in the research areas of holoscopic 3D image processing, machine learning for gesture recognition and its application in AR and VR.
DOI: 10.21227/7ff6-t995
title: Holoscopic 3D Micro-Gesture Database

description: HoMG database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes). Each subset has been devided into three partitions: training set, validation set and testing set where there is not overlap between them in term of the subjects. The database has been used for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018) that was held at IEEE Face & Gesture 2018 (FG2018 ) - Xi'an, China, 15-19th May 2018 (https://fg2018.cse.sc.edu/Challenges.html). The database is now public available for wider research community in the research areas of holoscopic 3D image processing, machine learning for gesture recognition and its application in AR and VR.
DOI: 10.6084/m9.figshare.13488792.v1
title: Seasonally resolved NEEM Be10 for 1887-1950

description: <strong>Supplementary dataset to Zheng et al., "Solar activity of the past 100 years inferred from 10Be in ice cores – implications for long-term solar activity reconstructions".</strong><strong>(Always quote the paper above when using data)</strong><br>
DOI: 10.5281/zenodo.4393352
title: Neutrino emission by plasmon decay in a strong magnetic field: Data material

description: This data reference paper summarizes the fundamental calculated results obtained analytically for the case of a neutron star (NS) crust: it’s cooling rate, time-scale evolution and neutrino luminosity, based on two different theories- Photo-Neutrino (PN) interaction and conventional weak interaction. Considering the NS environment as a degenerate plasmon composition the calculation tables are given.
description: {"references": ["Majumder, Brajagopal. PhD thesis: Some aspects of stellar neutrino emission in a strong magnetic field from the view point of photo-neutrino weak coupling theory. Awarded by Calcutta University, India."]}
DOI: 10.5281/zenodo.4393351
title: Neutrino emission by plasmon decay in a strong magnetic field: Data material

description: This data reference paper summarizes the fundamental calculated results obtained analytically for the case of a neutron star (NS) crust: it’s cooling rate, time-scale evolution and neutrino luminosity, based on two different theories- Photo-Neutrino (PN) interaction and conventional weak interaction. Considering the NS environment as a degenerate plasmon composition the calculation tables are given.
description: {"references": ["Majumder, Brajagopal. PhD thesis: Some aspects of stellar neutrino emission in a strong magnetic field from the view point of photo-neutrino weak coupling theory. Awarded by Calcutta University, India."]}
DOI: 10.6084/m9.figshare.13487427
title: S2 Table

description: DNA concentration after WGA for PGT-A, Outgrowth, niPGT-A
DOI: 10.6084/m9.figshare.13487427.v2
title: S2 Table

description: DNA concentration after WGA for PGT-A, Outgrowth, niPGT-A
DOI: 10.6084/m9.figshare.13487427.v1
title: S2 Table.pdf

description: DNA concentration after WGA for PGT-A, Outgrowth, niPGT-A
DOI: 10.6084/m9.figshare.13488815.v1
title: Supplementary Data from Forcing the issue: testing gecko-inspired adhesives

description: Data associated with all figures in the manuscript.
DOI: 10.6084/m9.figshare.13488815
title: Supplementary Data from Forcing the issue: testing gecko-inspired adhesives

description: Data associated with all figures in the manuscript.
DOI: 10.21227/ahmw-pm44
title: Holoscopic 3D Micro-Gesture Database

description: HoMG database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes). Each subset has been devided into three partitions: training set, validation set and testing set where there is not overlap between them in term of the subjects. The database has been used for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018) that was held at IEEE Face & Gesture 2018 (FG2018 ) - Xi'an, China, 15-19th May 2018 (https://fg2018.cse.sc.edu/Challenges.html). The database is now public available for wider research community in the research areas of holoscopic 3D image processing, machine learning for gesture recognition and its application in AR and VR.
DOI: 10.21227/kp3d-dy26
title: Holoscopic 3D Micro-Gesture Database

description: HoMG database was recorded using a holoscopic 3D camera, which have 3 conventional gestures from 40 participants under different settings and conditions. The principle of holoscopic 3D (H3D) imaging mimics fly’s eye technique that captures a true 3D optical model of the scene using a microlens array. For the purpose of H3D micro-gesture recognition. HoMG database has two subsets. The video subset has 960 videos and the image subset has 30635 images, while both have three type of microgestures (classes). Each subset has been devided into three partitions: training set, validation set and testing set where there is not overlap between them in term of the subjects. The database has been used for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018) that was held at IEEE Face & Gesture 2018 (FG2018 ) - Xi'an, China, 15-19th May 2018 (https://fg2018.cse.sc.edu/Challenges.html). The database is now public available for wider research community in the research areas of holoscopic 3D image processing, machine learning for gesture recognition and its application in AR and VR.
DOI: 10.6084/m9.figshare.13488835
title: 6lu7-dist-1.xlsx from Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease

description: The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has no publicly-available vaccine or antiviral drugs at the time of writing. An attractive coronavirus drug target is the main protease (M<sup>pro</sup>, also known as 3CL<sup>pro</sup>) because of its vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which bind and block the active site of the main protease, but little has been done to address potential non-competitive inhibition, targeting regions other than the active site, partly because the fundamental biophysics of such allosteric control is still poorly understood. In this work, we construct an Elastic Network Model (ENM) of the SARS-CoV-2 M<sup>pro</sup> homodimer protein and analyse its dynamics and thermodynamics. We found a rich and heterogeneous dynamical structure, including allosterically correlated motions between the homodimeric protease's active sites. Exhaustive 1-point and 2-point mutation scans of the ENM and their effect on fluctuation free energies confirm previously experimentally identified bioactive residues, but also suggest several new candidate regions that are distant from the active site, yet control the protease function. Our results suggest new dynamically driven control regions as possible candidates for non-competitive inhibiting binding sites in the protease, which may assist the development of current fragment-based binding screens. The results also provide new insights into the active biophysical research field of protein fluctuation allostery and its underpinning dynamical structure.
DOI: 10.6084/m9.figshare.13488835.v1
title: 6lu7-dist-1.xlsx from Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease

description: The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has no publicly-available vaccine or antiviral drugs at the time of writing. An attractive coronavirus drug target is the main protease (M<sup>pro</sup>, also known as 3CL<sup>pro</sup>) because of its vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which bind and block the active site of the main protease, but little has been done to address potential non-competitive inhibition, targeting regions other than the active site, partly because the fundamental biophysics of such allosteric control is still poorly understood. In this work, we construct an Elastic Network Model (ENM) of the SARS-CoV-2 M<sup>pro</sup> homodimer protein and analyse its dynamics and thermodynamics. We found a rich and heterogeneous dynamical structure, including allosterically correlated motions between the homodimeric protease's active sites. Exhaustive 1-point and 2-point mutation scans of the ENM and their effect on fluctuation free energies confirm previously experimentally identified bioactive residues, but also suggest several new candidate regions that are distant from the active site, yet control the protease function. Our results suggest new dynamically driven control regions as possible candidates for non-competitive inhibiting binding sites in the protease, which may assist the development of current fragment-based binding screens. The results also provide new insights into the active biophysical research field of protein fluctuation allostery and its underpinning dynamical structure.
DOI: 10.6084/m9.figshare.13488844.v1
title: 6lu7-1.pdb from Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease

description: The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has no publicly-available vaccine or antiviral drugs at the time of writing. An attractive coronavirus drug target is the main protease (M<sup>pro</sup>, also known as 3CL<sup>pro</sup>) because of its vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which bind and block the active site of the main protease, but little has been done to address potential non-competitive inhibition, targeting regions other than the active site, partly because the fundamental biophysics of such allosteric control is still poorly understood. In this work, we construct an Elastic Network Model (ENM) of the SARS-CoV-2 M<sup>pro</sup> homodimer protein and analyse its dynamics and thermodynamics. We found a rich and heterogeneous dynamical structure, including allosterically correlated motions between the homodimeric protease's active sites. Exhaustive 1-point and 2-point mutation scans of the ENM and their effect on fluctuation free energies confirm previously experimentally identified bioactive residues, but also suggest several new candidate regions that are distant from the active site, yet control the protease function. Our results suggest new dynamically driven control regions as possible candidates for non-competitive inhibiting binding sites in the protease, which may assist the development of current fragment-based binding screens. The results also provide new insights into the active biophysical research field of protein fluctuation allostery and its underpinning dynamical structure.
DOI: 10.6084/m9.figshare.13488838
title: 6lu7-2point-25-Grel-0-0.25.xlsx from Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease

description: The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has no publicly-available vaccine or antiviral drugs at the time of writing. An attractive coronavirus drug target is the main protease (M<sup>pro</sup>, also known as 3CL<sup>pro</sup>) because of its vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which bind and block the active site of the main protease, but little has been done to address potential non-competitive inhibition, targeting regions other than the active site, partly because the fundamental biophysics of such allosteric control is still poorly understood. In this work, we construct an Elastic Network Model (ENM) of the SARS-CoV-2 M<sup>pro</sup> homodimer protein and analyse its dynamics and thermodynamics. We found a rich and heterogeneous dynamical structure, including allosterically correlated motions between the homodimeric protease's active sites. Exhaustive 1-point and 2-point mutation scans of the ENM and their effect on fluctuation free energies confirm previously experimentally identified bioactive residues, but also suggest several new candidate regions that are distant from the active site, yet control the protease function. Our results suggest new dynamically driven control regions as possible candidates for non-competitive inhibiting binding sites in the protease, which may assist the development of current fragment-based binding screens. The results also provide new insights into the active biophysical research field of protein fluctuation allostery and its underpinning dynamical structure.