cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5715448
title: Supplementary Data for: Human pathogenic RNA viruses establish non-competing lineages by occupying independent niches

description: <strong>Supplementary Data</strong> <strong>Tables</strong> <strong>Table S1:</strong> Virus characteristics including abbreviation, family, order, tax id, sequence length threshold (for nearly complete genomes), vaccination status, mode of transmission, circulation (human or human/zoonotic), available treatment, and disease progression (acute or chronic). <strong>Table S2:</strong> Sequence IDs of outgroup constituents. <strong>Table S3:</strong> GISAID acknowledgements for the SARS-CoV-2 sequences used in this study. <strong>Table S4:</strong> Summary of manual sequence curation indicating lab- or vaccine-related keywords used to prune sequences. <strong>Table S5:</strong> Sequence IDs for EVA and H3N2 e10, e100, d10, and d100 subtrees. <strong>Table S6:</strong> Sequence IDs for ML and GL lineages. <strong>Table S7:</strong> Sequence IDs for the H3N2 “new” and “old” subtrees. <strong>Table S8:</strong> Mutation rates (in units of nucleotide substitutions per site per year) and estimated dates for the last common ancestor of all tree/subtrees. Negative dates represent years prior to 0 CE. <strong>Table S9:</strong> Mean dN/dS values. <strong>Table S10:</strong> Yearly viral cases and generation time estimations (with references). <strong>Table S11:</strong> Metadata including the sequence ID, host, date of isolation, location of isolation, and subtype. <strong>Directories</strong> <strong>Alignments</strong> ORFeome alignments (excluding stop codons) with the exception of SARS-CoV-2. <strong>Correlated Subtrees</strong> Subtrees representing all correlated-clades (genealogical lineages; GL) in Newick format. <strong>Diversity TMRCA and Skyline Plots</strong> .png files. <strong>e/d/n/o Subtrees</strong> EVA and H3N2 subtrees, evenly and diversely sampled as well as “early” and “late” subtrees for H3N2. <strong>Genealogical Trees</strong> Including global topologies (.main), subtrees, and grafted trees (.grafted). <strong>ORF References</strong> The first and last nucleotide of each ORF in the reference sequence. <strong>Redundancy Tables</strong> List of all redundant isolates and the corresponding representative in the ORFeome-unique alignment. <strong>Reference Sequences</strong> Genbank page for all reference sequences. <strong>Rooted Trees</strong> Global topologies for each virus. <strong>Ultrametric Trees </strong>For each global topology.
DOI: 10.5281/zenodo.5533869
title: Experimental source data for "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"

description: Experimental data for the manuscript "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"
description: Funding statement: U.S.S., S.R., B.D. and C.S. gratefully acknowledge the Deutsche Forschungsgemeinschaft DFG for financial support through the TRR234 "CataLight (project no: 364549901, projects A1, A4, A5, B1, B6 and Z2). Funding by the Federal State of Baden-Württemberg and Ulm University for a PhD fellowship (LGFG, S.K.) and a Margarete von Wrangell fellowship (M.A.) is acknowledged.
DOI: 10.5281/zenodo.5533868
title: Experimental source data for "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"

description: Experimental data for the manuscript "A photosensitiser-polyoxometalate dyad that enables the decoupling of light- and dark-reactions for delayed on-demand solar hydrogen production"
description: Funding statement: U.S.S., S.R., B.D. and C.S. gratefully acknowledge the Deutsche Forschungsgemeinschaft DFG for financial support through the TRR234 "CataLight (project no: 364549901, projects A1, A4, A5, B1, B6 and Z2). Funding by the Federal State of Baden-Württemberg and Ulm University for a PhD fellowship (LGFG, S.K.) and a Margarete von Wrangell fellowship (M.A.) is acknowledged.
DOI: 10.5281/zenodo.5715462
title: E3SM/EAM Simulation Data Outputs

description: Simulation data provided from the Energy Exascale Earth System Model (E3SM) simulation code in association to a paper to be published, <em>Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks.</em> E3SM is a fully coupled Earth model system that combines independent components for atmosphere, ocean, land surface, sea ice and land ice. It includes a driver and flux coupler to exchange data across components, resulting in an integrated modeling system. Through the simulations are fully coupled, we focus our analyses on the atmospheric component, the E3SM Atmosphere Model (EAM) to generate the data. EAM data is discretized using a variable resolution spectral element method, allowing for a regionally refined mesh that can be tessellated at higher resolutions in particular regions of interest. EAM consists of the High-Order Methods Modeling Environment Spectral Element dynamical core and the EAM physics and chemistry parameterizations.
DOI: 10.5281/zenodo.5715461
title: E3SM/EAM Simulation Data Outputs

description: Simulation data provided from the Energy Exascale Earth System Model (E3SM) simulation code in association to a paper to be published, <em>Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks.</em> E3SM is a fully coupled Earth model system that combines independent components for atmosphere, ocean, land surface, sea ice and land ice. It includes a driver and flux coupler to exchange data across components, resulting in an integrated modeling system. Through the simulations are fully coupled, we focus our analyses on the atmospheric component, the E3SM Atmosphere Model (EAM) to generate the data. EAM data is discretized using a variable resolution spectral element method, allowing for a regionally refined mesh that can be tessellated at higher resolutions in particular regions of interest. EAM consists of the High-Order Methods Modeling Environment Spectral Element dynamical core and the EAM physics and chemistry parameterizations.
DOI: 10.6084/m9.figshare.12986111
title: Magnetoencephalography-detected phase-amplitude coupling in Parkinson's disease

description: All the files in this dataset have both data for 23 patients with Parkinson’s disease (PD) and those for 23 healthy study participants (HSP). All data are calculated from resting-state MEG signals. Here, a synchronization index (SI) is used to quantify phase–amplitude coupling for 236-s resting-state signals.<br>comodulogram_resting_data.mat contains data of SI values for various combinations of frequencies calculated on representative cortical areas. Six cortical areas on Human Connectome Project (HCP) atlas selected for the analysis are in HCP_ROI. Cortical vertices of FreeSurfer atlas belonging to the areas are in FSA_vertex. Frequencies for phase are in phs_windows. Frequencies for amplitude are in amp_windows. PD_data_FSA contains SI values (PD_data_FSA.si) and mean phase-shuffled SI values (PD_data_FSA.shuffle) calculated on selected vertices for the patients with Parkinson’s disease. PD_data_HCP contains those averaged on selected HCP areas. <br>PSD_resting_state.mat contains power spectrum density data calculated on 15,002 vertices of FreeSurfer atlas. PD_data.freq have a list of frequencies. PD_data.psd contains power spectrum density values on each vertex for each frequency for each patient. <br>power_resting_state.mat contains data of Z-scored power values on cortical areas of HCP atlas. Three frequency bands are indicated in PD_data.freq.<br>PAC_resting_state.mat contains data of SI values for alpha–gamma and beta–gamma PAC across the whole brain. PD_data.hcp_si and PD_data.hcp_shuffle contain SI values and mean phase-shuffled SI values averaged over HCP atlas, respectively. PD_data.fsa_si and PD_data.fsa_shuffle contain values on each cortical vertex of FreeSurfer atlas.<br>
DOI: 10.6084/m9.figshare.12986111.v2
title: Magnetoencephalography-detected phase-amplitude coupling in Parkinson disease

description: This data set contains power and synchronization index (SI) of 23 patients with Parkinson's disease and 23 healthy controls detected using magnetoencephalography during the resting state. These data were calculated for each of 360 cortical areas of the Human Connectome Project brain atlas. Resting-state power was calculated for alpha (8-13 Hz), beta (13-25 Hz) and gamma (50-100 Hz) band. Synchronization index was calculated for alpha-gamma coupling and beta-gamma coupling.
DOI: 10.5517/ccdc.csd.cc22zl9z
title: CCDC 1936021: Experimental Crystal Structure Determination

description: Related Article: Jayanta Dowarah, Devanshi Patel, Brilliant N. Marak, Umesh Chand Singh Yadav, Pramod Kumar Shah, Pradeep Kumar Shukla, Ved Prakash Singh|2021|RSC Advances|11|35737|doi:10.1039/D1RA03969E
DOI: 10.5517/ccdc.csd.cc22zlb0
title: CCDC 1936022: Experimental Crystal Structure Determination

description: Related Article: Jayanta Dowarah, Devanshi Patel, Brilliant N. Marak, Umesh Chand Singh Yadav, Pramod Kumar Shah, Pradeep Kumar Shukla, Ved Prakash Singh|2021|RSC Advances|11|35737|doi:10.1039/D1RA03969E
DOI: 10.5517/ccdc.csd.cc22zlc1
title: CCDC 1936023: Experimental Crystal Structure Determination

description: Related Article: Jayanta Dowarah, Devanshi Patel, Brilliant N. Marak, Umesh Chand Singh Yadav, Pramod Kumar Shah, Pradeep Kumar Shukla, Ved Prakash Singh|2021|RSC Advances|11|35737|doi:10.1039/D1RA03969E
DOI: 10.5517/ccdc.csd.cc274mtv
title: CCDC 2060037: Experimental Crystal Structure Determination

description: Related Article: Liang-Liang Yan, Liao-Yuan Yao, Maggie Ng, Vivian Wing-Wah Yam|2021|J.Am.Chem.Soc.|143|19008|doi:10.1021/jacs.1c07971
DOI: 10.5517/ccdc.csd.cc274mwx
title: CCDC 2060039: Experimental Crystal Structure Determination

description: Related Article: Liang-Liang Yan, Liao-Yuan Yao, Maggie Ng, Vivian Wing-Wah Yam|2021|J.Am.Chem.Soc.|143|19008|doi:10.1021/jacs.1c07971
DOI: 10.5517/ccdc.csd.cc27q74c
title: CCDC 2076942: Experimental Crystal Structure Determination

description: Related Article: Yiming Zhou, Ning Li, Wei Cai, You Huang|2021|Org.Lett.|23|8755|doi:10.1021/acs.orglett.1c03225
DOI: 10.6084/m9.figshare.17054186
title: AppendixS3 Data used in our meta-analysis.xlsx

description: meta-analysis data
DOI: 10.6084/m9.figshare.17054186.v1
title: AppendixS3 Data used in our meta-analysis.xlsx

description: meta-analysis data
DOI: 10.25934/pr00007320.0
title: Available IPD datapackage for study 'Will Hydroxychloroquine Impede or Prevent COVID-19: WHIP COVID-19 Study'

description: This is a prospective, multi-site study designed to evaluate whether the use of hydroxychloroquine in healthcare workers (HCW), Nursing Home Workers (NHW), first responders (FR), and Detroit Department of Transportation bus drivers (DDOT) in SE, Michigan, can prevent the acquisition, symptoms and clinical COVID-19 infection The primary objective of this study is to determine whether the use of daily or weekly oral hydroxychloroquine (HCQ) therapy will prevent SARS-CoV-2 infection and COVID-19 viremia and clinical COVID-19 infection healthcare workers (HCW) and first responders (FR) (EMS, Fire, Police, bus drivers) in Southeast Michigan. Preventing COVID-19 transmission to HCW, FR, and Detroit Department of Transportation (DDOT) bus drivers is a critical step in preserving the health care and first responder force, the prevention of COVID-19 transmission in health care facilities, with the potential to preserve thousands of lives in addition to sustaining health care systems and civil services both nationally and globally. If efficacious, further studies on the use of hydroxychloroquine to prevent COVID-19 in the general population could be undertaken, with a potential impact on hundreds of thousands of lives.
DOI: 10.6084/m9.figshare.16621888
title: Epidemics - Early phase Lombardy

description: Data and R script for the estimation of SARS-CoV-2 serial interval
DOI: 10.5281/zenodo.5420023
title: Structured Decomposition

description: Replication Package
DOI: 10.5281/zenodo.5715509
title: Model-Based Testing for Performance Requirements Dataset

description: Ths is a dataset for the paper Model-Based Testing for Performance Requirements.
DOI: 10.5281/zenodo.4027894
title: Model-Based Testing for Performance Requirements Dataset

description: Ths is a dataset for the paper Model-Based Testing for Performance Requirements.
DOI: 10.5281/zenodo.5715521
title: LUAD TCGA

description: LUAD TCGA
DOI: 10.5281/zenodo.5715520
title: LUAD TCGA

description: LUAD TCGA
DOI: 10.5281/zenodo.5715483
title: Trichoglossum hirsutum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5715482
title: Trichoglossum hirsutum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5715524
title: Structured Decomposition

description: Replication Package
DOI: 10.5281/zenodo.5420022
title: Structured Decomposition

description: Replication Package
DOI: 10.5517/ccdc.csd.cc27zv8c
title: CCDC 2085223: Experimental Crystal Structure Determination

description: Related Article: Haruyasu Asahara, Yusuke Mukaijo, Kengo Muragishi, Kento Iwai, Akitaka Ito, Nagatoshi Nishiwaki|2021|Eur.J.Org.Chem.|2021|5747|doi:10.1002/ejoc.202101134
DOI: 10.5517/ccdc.csd.cc2800bn
title: CCDC 2085380: Experimental Crystal Structure Determination

description: Related Article: Shiyao Liu, Yoshiaki Tanabe, Shogo Kuriyama, Ken Sakata, Yoshiaki Nishibayashi|2021|Chem.-Eur.J.|27|15650|doi:10.1002/chem.202103287
DOI: 10.5517/ccdc.csd.cc2800cp
title: CCDC 2085381: Experimental Crystal Structure Determination

description: Related Article: Shiyao Liu, Yoshiaki Tanabe, Shogo Kuriyama, Ken Sakata, Yoshiaki Nishibayashi|2021|Chem.-Eur.J.|27|15650|doi:10.1002/chem.202103287
DOI: 10.5517/ccdc.csd.cc2800dq
title: CCDC 2085382: Experimental Crystal Structure Determination

description: Related Article: Shiyao Liu, Yoshiaki Tanabe, Shogo Kuriyama, Ken Sakata, Yoshiaki Nishibayashi|2021|Chem.-Eur.J.|27|15650|doi:10.1002/chem.202103287
DOI: 10.5517/ccdc.csd.cc2833vc
title: CCDC 2088372: Experimental Crystal Structure Determination

description: Related Article: Yiming Zhou, Ning Li, Wei Cai, You Huang|2021|Org.Lett.|23|8755|doi:10.1021/acs.orglett.1c03225
DOI: 10.5517/ccdc.csd.cc28494w
title: CCDC 2089497: Experimental Crystal Structure Determination

description: Related Article: Xing-Qi Yao, Wen-Yan Tong, Kang Wang, Shuanglin Qu, Jianbo Wang|2021|Organometallics|40|3526|doi:10.1021/acs.organomet.1c00352
DOI: 10.5517/ccdc.csd.cc277xhx
title: CCDC 2063189: Experimental Crystal Structure Determination

description: Related Article: Nitish Kumar Sinha, Natesan Thirupathi|2021|Organometallics|40|3535|doi:10.1021/acs.organomet.1c00414
DOI: 10.5517/ccdc.csd.cc277xkz
title: CCDC 2063191: Experimental Crystal Structure Determination

description: Related Article: Nitish Kumar Sinha, Natesan Thirupathi|2021|Organometallics|40|3535|doi:10.1021/acs.organomet.1c00414
DOI: 10.5517/ccdc.csd.cc277xl0
title: CCDC 2063192: Experimental Crystal Structure Determination

description: Related Article: Nitish Kumar Sinha, Natesan Thirupathi|2021|Organometallics|40|3535|doi:10.1021/acs.organomet.1c00414
DOI: 10.5517/ccdc.csd.cc277xm1
title: CCDC 2063193: Experimental Crystal Structure Determination

description: Related Article: Nitish Kumar Sinha, Natesan Thirupathi|2021|Organometallics|40|3535|doi:10.1021/acs.organomet.1c00414
DOI: 10.5517/ccdc.csd.cc28gz80
title: CCDC 2099762: Experimental Crystal Structure Determination

description: Related Article: Alexander V. Aksenov, Nicolai A. Aksenov, Nikita K. Kirilov, Anton A. Skomorokhov, Dmitrii A. Aksenov, Igor A. Kurenkov, Elena A. Sorokina, Mezvah A. Nobi, Michael Rubin|2021|RSC Advances|11|35937|doi:10.1039/D1RA06503C
DOI: 10.5517/ccdc.csd.cc28kw91
title: CCDC 2102553: Experimental Crystal Structure Determination

description: Related Article: Shin Ando, Akio Ohara, Tomohiko Ohwada, Tadao Ishizuka|2021|Organometallics|40|3668|doi:10.1021/acs.organomet.1c00503
DOI: 10.5281/zenodo.5715608
title: Structured Decomposition

description: Package
DOI: 10.5281/zenodo.5715607
title: Structured Decomposition

description: Package
DOI: 10.6084/m9.figshare.17054270
title: Survey responses

description: Survey responses to reproducibility questions, redux of Baker et al and other surveys
DOI: 10.6084/m9.figshare.17054270.v1
title: Survey responses

description: Survey responses to reproducibility questions, redux of Baker et al and other surveys
DOI: 10.6073/pasta/06e149ccece9015a23fc332d8f7de47c
title: STEPPS 8000 Year Forest Composition Estimates, Northeastern US, Level 2

description: These reconstructions of forest composition in the northeastern US for the last 8,000 years help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. These reconstructions are based on 1) the pollen–vegetation model (PVM) STEPPS, 2) a network of fossil and modern pollen data mostly drawn from the Neotoma Paleoecology Database (www.neotomadb.org), and 3) a vegetation calibration dataset based on a spatial statistical model of relative tree abundances from the Township Proprietor Survey (TPS) from the early EuroAmerican settlement period. STEPPS is a process-based Bayesian Hierarchical Model that is run in two stages: a parameterization stage based upon spatial data layers of forest composition and pollen assemblages, and a prediction stage based on fossil pollen assemblages. The statistical modeling of the TPS forest data and relative abundances is described by Paciorek et al. (2016, PLoS One, doi: 10.1371/journal.pone.0150087) and is available at EDI as PalEON Products msb.paleon.1 (doi: 10.6073/pasta/8544e091b64db26fdbbbafd0699fa4f9) and msb.paleon.4 (doi: 10.6073/pasta/3c4844cfe8beff6b173b62034d1cb5d8). The parameterization of STEPPS and comparison to REVEALS, a different widely used PVM, is described by Trachsel et al. (2020, Quaternary Research, doi:10.1017/qua.2019.81). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales. The parameterized STEPPS model was then run for a network of fossil pollen records from the Neotoma Paleoecology Database to produce posterior estimates of forest composition at 250-year intervals for the last 8,000 years. The work from this prediction stage was not published at the time of deposition of these records with EDI. Hence, at this time, this EDI data deposition is the primary citation endpoint. The prediction methods generally follow those described by Dawson et al. (2019, Ecology, doi: 10.1002/ecy.2856). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.
DOI: 10.6084/m9.figshare.17054276.v1
title: Subset Multivariate Collective and Point Anomaly Detection

description: In the recent years, there has been a growing interest in identifying anomalous structure within multivariate data sequences. We consider the problem of detecting collective anomalies, corresponding to intervals where one, or more, of the data sequences behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data sequences, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach is shown to consistently estimate the number and location of the collective anomalies—a property that has not previously been shown for competing methods. Our approach can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation. Supplemental files for this article are available online.
DOI: 10.6084/m9.figshare.17054276
title: Subset Multivariate Collective and Point Anomaly Detection

description: In the recent years, there has been a growing interest in identifying anomalous structure within multivariate data sequences. We consider the problem of detecting collective anomalies, corresponding to intervals where one, or more, of the data sequences behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data sequences, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach is shown to consistently estimate the number and location of the collective anomalies—a property that has not previously been shown for competing methods. Our approach can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation. Supplemental files for this article are available online.
DOI: 10.15468/bfjv9y
title: Micoteca ARAN-fungi

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/udfspw
title: Ornitho.eus

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/gzkh48
title: Base de datos de plantas vasculares del País Vasco: ARAN-EH

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/5xzjqa
title: Fauna marina de la costa vasca

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/ex4xha
title: Anillamiento SEO

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/6fdjx6
title: iNaturalist

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/7x7qjx
title: Herbario ARAN

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/c3aeba
title: Herbario VIT - Plantas Vasculares (Museo de Historia Natural de Álava)

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.15468/wzccwn
title: Herbario BIO

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.5281/zenodo.5701995
title: TNCD (Twitter News Cascade Dataset)

description: The TNCD (Twitter News Cascade Dataset) 1,200 news items posted on Twitter. It contains post URLs retrieved from sportsmen, politicians and news channels accounts, most from September 2020. For more information<br> please see https://www.sciencedirect.com/science/article/pii/S1568494621003367
DOI: 10.5281/zenodo.5701994
title: TNCD (Twitter News Cascade Dataset)

description: The TNCD (Twitter News Cascade Dataset) 1,200 news items posted on Twitter. It contains post URLs retrieved from sportsmen, politicians and news channels accounts, most from September 2020. For more information<br> please see https://www.sciencedirect.com/science/article/pii/S1568494621003367