cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6400601
title: ECG and MSNA for Baseline and LBNP Experiments from Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

description: Theoretical and practical advances in time–frequency analysis, in general, and the continuous wavelet transform (CWT), in particular, have increased over the last two decades. Although the Morlet wavelet has been the default choice for wavelet analysis, a new family of analytic wavelets, known as generalized Morse wavelets, which subsume several other analytic wavelet families, have been increasingly employed due to their time and frequency localization benefits and their utility in isolating and extracting quantifiable features in the time–frequency domain. The current paper describes two practical applications of analysing the features obtained from the generalized Morse CWT: (i) electromyography, for isolating important features in muscle bursts during skating and (ii) electrocardiography for assessing heart rate variability, which is represented as the ridge of the main transform frequency band. These features are subsequently quantified to facilitate exploration of the underlying physiological processes from which the signals were generated.This article is part of the themed issue ‘Redundancy rules: the continuous wavelet transform comes of age’.
DOI: 10.6084/m9.figshare.6400601.v1
title: ECG and MSNA for Baseline and LBNP Experiments from Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

description: Theoretical and practical advances in time–frequency analysis, in general, and the continuous wavelet transform (CWT), in particular, have increased over the last two decades. Although the Morlet wavelet has been the default choice for wavelet analysis, a new family of analytic wavelets, known as generalized Morse wavelets, which subsume several other analytic wavelet families, have been increasingly employed due to their time and frequency localization benefits and their utility in isolating and extracting quantifiable features in the time–frequency domain. The current paper describes two practical applications of analysing the features obtained from the generalized Morse CWT: (i) electromyography, for isolating important features in muscle bursts during skating and (ii) electrocardiography for assessing heart rate variability, which is represented as the ridge of the main transform frequency band. These features are subsequently quantified to facilitate exploration of the underlying physiological processes from which the signals were generated.This article is part of the themed issue ‘Redundancy rules: the continuous wavelet transform comes of age’.
DOI: 10.6084/m9.figshare.6400583
title: Probing the basis set limit for thermochemical contributions of inner-shell correlation: balance of core-core and core-valence contributions<sup>*</sup>

description: The inner-shell correlation contributions to the total atomisation energies of the W4-17 computational thermochemistry benchmark have been determined at the CCSD(<i>T</i>) level near the basis set limit using several families of core correlation basis sets, such as aug-cc-pCVnZ (<i>n</i> = 3–6), aug-cc-pwCVnZ (<i>n </i>= 3–5) and nZaPa-CV (<i>n </i>= 3–5). The three families of basis sets agree very well with each other (0.01 kcal/mol RMS) when extrapolating from the two largest available <i>n</i>: however, there are considerable differences in convergence behaviour for the smaller basis sets. nZaPa-CV is superior for the core-core term and awCVnZ for the core-valence term. While the aug-cc-pwCV(<i>T</i>+d)Z basis set of Yockel and Wilson is superior to aug-cc-pwCVTZ, further extension of this family proved unproductive. The best compromise between accuracy and computational cost, in the context of high-accuracy computational thermochemistry methods, is CCSD(<i>T</i>)/awCV{<i>T</i>,<i>Q</i>}Z, where the {<i>T</i>,<i>Q</i>} notation stands for extrapolation from the awCVTZ and awCVQZ basis set pair. For lower-cost calculations, we recommend a previously proposed combination of CCSD-F12b/cc-pCVTZ-F12 and CCSD(<i>T</i>)/pwCVTZ(no f). While in first-row molecules core-valence correlation on average accounts for over 90% of the inner-shell contribution, in second-row molecules core-core contributions may become important, particularly in systems like P<sub>4</sub> and S<sub>4</sub> with multiple adjacent second-row atoms.
DOI: 10.6084/m9.figshare.6400583.v1
title: Probing the basis set limit for thermochemical contributions of inner-shell correlation: balance of core-core and core-valence contributions<sup>*</sup>

description: The inner-shell correlation contributions to the total atomisation energies of the W4-17 computational thermochemistry benchmark have been determined at the CCSD(<i>T</i>) level near the basis set limit using several families of core correlation basis sets, such as aug-cc-pCVnZ (<i>n</i> = 3–6), aug-cc-pwCVnZ (<i>n </i>= 3–5) and nZaPa-CV (<i>n </i>= 3–5). The three families of basis sets agree very well with each other (0.01 kcal/mol RMS) when extrapolating from the two largest available <i>n</i>: however, there are considerable differences in convergence behaviour for the smaller basis sets. nZaPa-CV is superior for the core-core term and awCVnZ for the core-valence term. While the aug-cc-pwCV(<i>T</i>+d)Z basis set of Yockel and Wilson is superior to aug-cc-pwCVTZ, further extension of this family proved unproductive. The best compromise between accuracy and computational cost, in the context of high-accuracy computational thermochemistry methods, is CCSD(<i>T</i>)/awCV{<i>T</i>,<i>Q</i>}Z, where the {<i>T</i>,<i>Q</i>} notation stands for extrapolation from the awCVTZ and awCVQZ basis set pair. For lower-cost calculations, we recommend a previously proposed combination of CCSD-F12b/cc-pCVTZ-F12 and CCSD(<i>T</i>)/pwCVTZ(no f). While in first-row molecules core-valence correlation on average accounts for over 90% of the inner-shell contribution, in second-row molecules core-core contributions may become important, particularly in systems like P<sub>4</sub> and S<sub>4</sub> with multiple adjacent second-row atoms.
DOI: 10.6084/m9.figshare.6400541
title: Proposal

description: Project proposal for GSoC<br>
DOI: 10.6084/m9.figshare.6400541.v3
title: Proposal

description: Project proposal for GSoC<br>
DOI: 10.6084/m9.figshare.6400394
title: fern transcriptome assemblies

description: assemblies of fern transcriptomes.
DOI: 10.6084/m9.figshare.6400394.v1
title: fern transcriptome assemblies

description: assemblies of fern transcriptomes.
DOI: 10.6084/m9.figshare.6400196
title: Testing Data

description: Cell000~Cell899: Deposited energies of a CsI arraySum: summation of deposted energiesTrueEnergy: incident gamma-ray energy<br>
DOI: 10.6084/m9.figshare.6400196.v1
title: Testing Data

description: Cell000~Cell899: Deposited energies of a CsI arraySum: summation of deposted energiesTrueEnergy: incident gamma-ray energy<br>
DOI: 10.6084/m9.figshare.6400193
title: Refugee Data.zip

description: OSM, Wikipedia, Google News and Google Trends data over refugee camps
DOI: 10.6084/m9.figshare.6400193.v1
title: Refugee Data.zip

description: OSM, Wikipedia, Google News and Google Trends data over refugee camps
DOI: 10.6084/m9.figshare.6400160
title: Training data

description: Cell001~Cell899: Deposited energies of each pixelSum: Summation of energies of 900 pixelsTrueEnergy: Incident energy of gamma-ray<br>
DOI: 10.6084/m9.figshare.6400160.v1
title: Training data

description: Cell001~Cell899: Deposited energies of each pixelSum: Summation of energies of 900 pixelsTrueEnergy: Incident energy of gamma-ray<br>
DOI: 10.6084/m9.figshare.6400052.v1
title: Test for popper archive

description: Popper is a CLI tool for researchers
DOI: 10.6084/m9.figshare.6400019
title: Spatial Uncertainty in Digital Image Correlation

description: This is the original materials of my paper "spatial uncertainty of measurement errors in digital image correlation."<br>The spatial uncertainty is caused by the differences of intensity distribution between different subsets. In the paper, I try to build a theoretical model to estimate the spatial uncertainty of measurement errors in DIC for speckle patterns that are composed of randomly positioned speckles. The essenstial difference between this paper and my previous work "statistical model for speckle pattern optimization" is that this work can estimate the spatial uncertainty while the previous work can't.<br>The data set, which is the same as our previous paper, can be found in Figshare. The "SpatialUncertainty" contains the MATLAB codes.<br>The mathematical derivations are somewhat complex, so I upload my note. In the note, the derivation details can be found. A problem may be that this note is written in Chinese...<br>If you have any question or find something is wrong, you can send me an email. My email address is suyong@ustc.edu.cn.
DOI: 10.6084/m9.figshare.6399971
title: The restorative effect of the natural environment on university students' psychological health

description: An intervention aimed at improving psychological health in university students. It measured life satisfaction, stress, and burnout both before and after a natural environment intervention.
DOI: 10.6084/m9.figshare.6399965
title: obesity

description: obesity MRI data
DOI: 10.6084/m9.figshare.6399959
title: models

description: pre-trained models<br>
DOI: 10.6084/m9.figshare.6399959.v1
title: models

description: pre-trained models<br>
DOI: 10.6084/m9.figshare.6399953
title: Full-length transcriptome of diploid loach, Misgurnus anguillicaudatus

description: This study generated 33,330 full-length transcripts of diploid M. anguillicaudatus using PacBio SMRT Sequencing.
DOI: 10.6084/m9.figshare.6399935
title: hcpLSDataANTs

description: functional and structural MRI example data
DOI: 10.6084/m9.figshare.6399935.v1
title: hcpLSDataANTs

description: functional and structural MRI example data
DOI: 10.6084/m9.figshare.6399881
title: Elephant seal outer blubber proteome

description: Proteins were extracted from the outer (closest to skin) blubber layer of 4 northern elephant seals using either a standard detergent lysis buffer (1% sodium deoxycholate, 8 M urea, 5 mM DTT in 50 mM ammonium bicarbonate; SDC method) or a phenol and guanidine thiocyanate reagent (Qiazol; QIA method). Tandem mass spectrometry analysis was performed using an Orbitrap Fusion Tribrid instrument in DDA mode. Precursor ions were fragmented using stepped collision HCD. MS/MS data was analyzed using Proteome Discoverer v2.2 and PEAKS v8.5. This dataset contains lists of proteins identified by SEQUEST (in Proteome Discoverer) and PEAKS DB searches against the UniProt SwissProt (2/13/18) database (&lt; 1% FDR) and de novo peptide sequences obtained by PEAKS (fasta files) for proteins extracted using QIA and SDC methods.<br>
DOI: 10.6084/m9.figshare.6399881.v2
title: Elephant seal outer blubber proteome

description: <b>Proteins were extracted from the outer (closest to skin) blubber layer of 4 northern elephant seals using either a standard detergent lysis buffer (1% sodium deoxycholate, 8 M urea, 5 mM DTT in 50 mM ammonium bicarbonate; SDC method) or a phenol and guanidine thiocyanate reagent (Qiazol; QIA method). Tandem mass spectrometry analysis was performed using an Orbitrap Fusion Tribrid instrument in DDA mode. Precursor ions were fragmented using stepped collision HCD. MS/MS data was analyzed using Proteome Discoverer v2.2 and PEAKS v8.5. This dataset contains lists of proteins identified by SEQUEST (in Proteome Discoverer) and PEAKS DB searches against the UniProt SwissProt (2/13/18) database (&lt; 1% FDR) and de novo peptide sequences obtained by PEAKS (fasta files) for proteins extracted using QIA and SDC methods.</b><br>
DOI: 10.6084/m9.figshare.6399881.v1
title: Elephant seal outer blubber protein sequences

description: Protein sequences identified by LC-MS/MS and PEAKS de novo sequencing from four outer blubber samples from juvenile northern elephant seals (Mirounga angustirostris). Tissue was lysed either using Qiazol (PHCL Method) or a standard detergent buffer (SDC Method; 1% w/v sodium deoxycholate, 8 M urea, 5 mM DTT in 50 mM ammonium bicarbonate). Details of protein isolation, LC-MS/MS, and PEAKS analysis will be provided in a manuscript currently in preparation.
DOI: 10.6084/m9.figshare.6399875
title: unit library performance benchmark

description: A benchmark comparing the performance of unyt 1.0.1, Pint 0.8.1, and astropy 3.0.1. The benchmarks are in json format as output by the perf benchmark tool. They can be loaded using perf or any other tool that can parser json.<br>
DOI: 10.6084/m9.figshare.6399875.v1
title: unit library performance benchmark

description: A benchmark comparing the performance of unyt 1.0.1, Pint 0.8.1, and astropy 3.0.1. The benchmarks are in json format as output by the perf benchmark tool. They can be loaded using perf or any other tool that can parser json.<br>
DOI: 10.6084/m9.figshare.6399872
title: Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models

description: Topic models, and more specifically the class of latent Dirichlet allocation (LDA), are widely used for probabilistic modeling of text. Markov chain Monte Carlo (MCMC) sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6399872.v1
title: Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models

description: Topic models, and more specifically the class of latent Dirichlet allocation (LDA), are widely used for probabilistic modeling of text. Markov chain Monte Carlo (MCMC) sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6399827
title: DNA Template Dilution Dataset

description: Fungal ITS2 sequence data and metadata for manuscript: <br>Castle et al. (2018) DNA template dilution impacts amplicon sequencing-based estimates of soil fungal diversity. Phytobiomes.
DOI: 10.6084/m9.figshare.6399827.v1
title: DNA Template Dilution Dataset

description: Fungal ITS2 sequence data and metadata for manuscript: <br>Castle et al. (2018) DNA template dilution impacts amplicon sequencing-based estimates of soil fungal diversity. Phytobiomes.
DOI: 10.6084/m9.figshare.6399740
title: Combined_data_hairs_2018-05-24.csv

description: Raw data for input parameters and output results used for sensitivity analysis and plots in Figs. 3, 4, and 5.
DOI: 10.6084/m9.figshare.6399740.v1
title: Combined_data_hairs_2018-05-24.csv

description: Raw data for input parameters and output results used for sensitivity analysis and plots in Figs. 3, 4, and 5.
DOI: 10.5281/zenodo.4007699
title: Storage. Speicherung von Forschungsdaten. SUH.

description: Leitfragen zur Ermittlung des Bedarfs:<br> 1. Wie viel Speicher benötigen Sie im Durchschnitt?<br> 2. Welche Kriterien müsste eine Infrastruktur erfüllen, um Ihren<br> Anforderungen gerecht zu werden? Die Fragen an das Forschungsprojekt beziehen sich auf Ihre<br> Forschungsdaten:<br> ● Welche Datentypen und welche Datenformate fallen in Ihrem Projekt<br> an?<br> ● Bitte schätzen Sie Umfang/Volumen der Forschungsdaten ein<br> (ungefähre Größenordnung, d.h. weniger als 100 GB, 1 TB, 10 TB,<br> 100 TB)?<br> ● Können Sie Aussagen zur Datenqualität machen? (z.B. Text: Wie<br> viele Seiten? PDF, Word? Printqualität, Webqualität u.ä.;<br> Audiointerview: Wieviel Stunden? Stereo, Mono? Ist eine<br> Sendequalität erforderlich?<br> ● Sind die Daten besonders schutzwürdig oder öffentlich (Frage nach<br> der Sensibilität)?<br> ● Wo speichern Sie die Forschungsdaten, die im Rahmen Ihrer<br> Projekte anfallen und zu welchem Zweck sollen die Daten aufbewahrt<br> werden? (mit Angabe der Aufbewahrungsdauer, wenn möglich)<br> ● Werden Sie Ihre Daten lokal oder in einem fachspezifischen,<br> externen Repositorium speichern (mit Begründung, wenn möglich)?<br> ● Mit welchen Metadaten bzw. Metadatenstandards arbeiten Sie im<br> Projekt (sofern dies schon der Fall sein sollte)?<br> Metadaten sind strukturierte Informationen, die die vorliegenden<br> Daten näher beschreiben. Während formatspezifische Metadaten,<br> auch technische Metadaten genannt, Auskunft über Dateityp, -größe<br> und Erstelldatum geben, enthalten deskriptive Metadaten<br> Informationen über den Inhalt der Daten, die die Auffindbarkeit,<br> Nachnutzung und Zitierbarkeit der Daten erleichtern.<br> UNEKE zielte insbesondere in der wisseschaftlichen Begleitstudie auf die Frage, was<br> Forschende abhält bzw. ermutigt zukünftig Daten zu publizeren und zu teilen.
DOI: 10.6084/m9.figshare.6397310
title: G3/2018/200467

description: <b>Supplemental materials list:</b><b>1.</b><b>Supplemental</b><b> Table 1. Oligoes used in this article;</b><b>2.</b><b>Supplemental Table 2. The sgRNAs used in generation of ZFERV-deficient zebrafish;</b><b>3.</b><b>Supplemental Table 3. The percentages of abnormal embryos resulted from injection of different sgRNAs into Cas9 transgenic zebrafish embryos;</b><b>4.Supplemental Figures for promoter testing in this article and the Figures for ZFERV knockout detection.</b><b><br></b><b></b><b></b>
DOI: 10.6084/m9.figshare.6397310.v2
title: G3/2018/200467

description: <b>Supplemental materials list:</b><b>1.</b><b>Supplemental</b><b> Table 1. Oligoes used in this article;</b><b>2.</b><b>Supplemental Table 2. The sgRNAs used in generation of ZFERV-deficient zebrafish;</b><b>3.</b><b>Supplemental Table 3. The percentages of abnormal embryos resulted from injection of different sgRNAs into Cas9 transgenic zebrafish embryos;</b><b>4.Supplemental Figures for promoter testing in this article and the Figures for ZFERV knockout detection.</b><b><br></b><b></b><b></b>
DOI: 10.6084/m9.figshare.6397277
title: H2Db:Kb peptides used for spectral library generation

description: H2Db:Kb-peptide spectral library generation. Final list of high-confidence H2Db peptides (9-11aa, NetMHC&lt;500nM, 1% FDR) from 19 mouse tissues and 4 cell lines. Peptide sequence, source protein, source cell line/tissue, spectral counts (SC), length and predicted H2Db binding affinity (NetMHC) is indicated.
DOI: 10.6084/m9.figshare.6397274
title: List of filtered high-confidence H2Kb peptides

description: Filtered list of high-confidence H2Kb peptides (8-9aa; NetMHC&lt;500nM; 1% FDR) from 19 mouse tissues and 4 mouse cell lines. Peptide sequence, source protein, source tissue/cell line, spectral count (SC), length, predicted H2Kb binding affinity (NetMHC) is indicated.
DOI: 10.6084/m9.figshare.6397271
title: List of filtered high-confidence H2Db peptides

description: Filtered list of high-confidence H2Db peptides (9-11aa; NetMHC&lt;500nM; 1% FDR) from 19 mouse tissues and 4 mouse cell lines. Peptide sequence, source protein, source tissue/cell line, spectral count (SC), length, predicted H2Db binding affinity (NetMHC) is indicated.
DOI: 10.6084/m9.figshare.6397268
title: List of unfiltered H2Kb peptides (7-14 mers)

description: Unfiltered list of all MS-detectable 7-14 mers identified (1% FDR) from 19 mouse tissues and 4 mouse cell lines following immunoaffinity purification of H2Kb-peptide complexes. Peptide sequence, source protein, source tissue, spectral counts (SC), length, predicted H2Db/Kb binding affinity (NetMHC), and annotation factors generated by the computational pipeline described in Caron et al. (eLife, 2015) are indicated.
DOI: 10.6084/m9.figshare.6397265
title: List of unfiltered H2Db peptides (7-14 mers)

description: Unfiltered list of all MS-detectable 7-14 mers identified (1% FDR) from 19 mouse tissues and 4 mouse cell lines following immunoaffinity purification of H2Db-peptide complexes. Peptide sequence, source protein, source tissue, spectral counts (SC), length, predicted H2Db/Kb binding affinity (NetMHC), and annotation factors generated by the computational pipeline described in Caron et al. (eLife, 2015) are indicated.
DOI: 10.6084/m9.figshare.6397262
title: Annotation Table

description: Annotation of individual MS output files.
DOI: 10.6084/m9.figshare.6397259
title: Order of injection for H2Kb peptides

description: Order of injections for Kb peptides. Related to Figure 3b.
DOI: 10.6084/m9.figshare.6397256
title: Order of injection for H2Db peptides

description: Order of injections for Db peptides. Related to Figure 3b.
DOI: 10.6084/m9.figshare.6397190
title: Supporting Dataset from Thrifty phenotype versus cold adaptation: trade-offs in upper limb proportions of Himalayan populations of Nepal

description: Raw data values from data collection from both highland and lowland samples
DOI: 10.6084/m9.figshare.6397190.v1
title: Supporting Dataset from Thrifty phenotype versus cold adaptation: trade-offs in upper limb proportions of Himalayan populations of Nepal

description: Raw data values from data collection from both highland and lowland samples
DOI: 10.6084/m9.figshare.6397133
title: Mission Handbook Evangelism and Relief & Development Survival Data

description: Survival Data for U.S. Protestant Mission Agencies Engagement in Evangelism and Relief & Development respectively from 1969 to 2008 based on data provided in the publicly available hard copies of the Mission Handbook. These datasets have been formatted for survival analysis specific to activities associated with both activity sectors. Datasets are in Stata's .dta format.
DOI: 10.6084/m9.figshare.6397133.v1
title: Bok_2018_Evangelism and Relief & Development (.dta)

description: Survival Data for U.S. Protestant Mission Agencies Engagement in Evangelism and Relief & Development respectively from 1969 to 2008 based on data provided in the publicly available hard copies of the Mission Handbook. These datasets have been formatted for survival analysis specific to activities associated with both activity sectors. Datasets are in Stata's .dta format.