cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5101243
title: Mean and standard deviation climatologies for T & S (for use in e.g. MITprof & ECCO)

description: All variables are gridded on the 50 levels and lat-lon-cap (LLC90) grid defined in in the ECCO Version 4 paper (https://doi.org/10.5194/gmd-8-3071-2015) 1. Monthly climatologies of potential temperature and salinity, blended as described in https://doi.org/10.5194/gmd-8-3071-2015 : - T_OWPv1_M_eccollc_90x50.bin<br> - S_OWPv1_M_eccollc_90x50.bin 2. Ocean basin masks : basin_masks_eccollc_90x50.bin 3. Non-seasonal standard deviation climatologies, which is used in MITprof as an estimate of representation error for data sets that do not resolve the mess-scale and below (see https://doi.org/10.1175/JPO3072.1 ) e.g. in ECCO v4 for the MITprof insitu data constraints (see https://doi.org/10.5194/gmd-8-3071-2015). These global, time-mean, maps are updated versions of the Forget and Wunsch estimate (https://doi.org/10.1175/JPO3072.1). The latest ones are based on median absolute deviations computed at different resolutions and rescaled according to theory for normal distributions. Non-seasonal temperature standard deviation : - sigma_T_eccollc.bin<br> - sigma_T_mad_feb2013.bin<br> - sigma_T_nov2015.bin (latest, used in MITprof)) Non-seasonal salinity standard deviation : - sigma_S_eccollc.bin<br> - sigma_S_mad_feb2013.bin<br> - sigma_S_nov2015.bin (latest, used in MITprof)
DOI: 10.5281/zenodo.5101242
title: Mean and standard deviation climatologies for T & S (for use in e.g. MITprof & ECCO)

description: All variables are gridded on the 50 levels and lat-lon-cap (LLC90) grid defined in in the ECCO Version 4 paper (https://doi.org/10.5194/gmd-8-3071-2015) 1. Monthly climatologies of potential temperature and salinity, blended as described in https://doi.org/10.5194/gmd-8-3071-2015 : - T_OWPv1_M_eccollc_90x50.bin<br> - S_OWPv1_M_eccollc_90x50.bin 2. Ocean basin masks : basin_masks_eccollc_90x50.bin 3. Non-seasonal standard deviation climatologies, which is used in MITprof as an estimate of representation error for data sets that do not resolve the mess-scale and below (see https://doi.org/10.1175/JPO3072.1 ) e.g. in ECCO v4 for the MITprof insitu data constraints (see https://doi.org/10.5194/gmd-8-3071-2015). These global, time-mean, maps are updated versions of the Forget and Wunsch estimate (https://doi.org/10.1175/JPO3072.1). The latest ones are based on median absolute deviations computed at different resolutions and rescaled according to theory for normal distributions. Non-seasonal temperature standard deviation : - sigma_T_eccollc.bin<br> - sigma_T_mad_feb2013.bin<br> - sigma_T_nov2015.bin (latest, used in MITprof)) Non-seasonal salinity standard deviation : - sigma_S_eccollc.bin<br> - sigma_S_mad_feb2013.bin<br> - sigma_S_nov2015.bin (latest, used in MITprof)
DOI: 10.5281/zenodo.4877925
title: epicPCR sequencing data, May 28 2021

description: Samples: 1.Biol standard-Fluorinated oil, 3e6-16S<br> 2.Biol standard-Fluorinated oil, 3e5-16S<br> 3.Biol standard-Mineral oil, 3e6-16S<br> 4.Biol standard-Mineral oil, 3e5-16S<br> 5.Biol standard-Fluorinated oil, 3e6-18S<br> 6.Biol standard-Fluorinated oil, 3e5-18S<br> 7.Biol standard-Mineral oil, 3e6-18S<br> 8.Biol standard-Mineral oil, 3e5-18S<br> 9.WW + Biol standard-Fluorinated oil, 3e6-16S<br> 10.WW + Biol standard-Fluorinated oil, 3e5-16S<br> 11.WW + Biol standard-Mineral oil, 3e6-16S<br> 12.WW + Biol standard-Mineral oil, 3e5-16S<br> 13.WW + Biol standard-Fluorinated oil, 3e6-18S<br> 14.WW + Biol standard-Fluorinated oil, 3e5-18S<br> 15.WW + Biol standard-Mineral oil, 3e6-18S<br> 16.WW + Biol standard-Mineral oil, 3e5-18S<br> 17WW-Fluorinated oil, 3e6-16S<br> 18.WW-Fluorinated oil, 3e5-16S<br> 19.WW-Mineral oil, 3e6-16S<br> 20.WW-Mineral oil, 3e5-16S<br> 21.WW-Fluorinated oil, 3e6-18S<br> 22.WW-Fluorinated oil, 3e5-18S<br> 23.WW-Mineral oil, 3e6-18S<br> 24.WW-Mineral oil, 3e5-18S
DOI: 10.5281/zenodo.4877924
title: epicPCR sequencing data, May 28 2021

description: Samples: 1.Biol standard-Fluorinated oil, 3e6-16S<br> 2.Biol standard-Fluorinated oil, 3e5-16S<br> 3.Biol standard-Mineral oil, 3e6-16S<br> 4.Biol standard-Mineral oil, 3e5-16S<br> 5.Biol standard-Fluorinated oil, 3e6-18S<br> 6.Biol standard-Fluorinated oil, 3e5-18S<br> 7.Biol standard-Mineral oil, 3e6-18S<br> 8.Biol standard-Mineral oil, 3e5-18S<br> 9.WW + Biol standard-Fluorinated oil, 3e6-16S<br> 10.WW + Biol standard-Fluorinated oil, 3e5-16S<br> 11.WW + Biol standard-Mineral oil, 3e6-16S<br> 12.WW + Biol standard-Mineral oil, 3e5-16S<br> 13.WW + Biol standard-Fluorinated oil, 3e6-18S<br> 14.WW + Biol standard-Fluorinated oil, 3e5-18S<br> 15.WW + Biol standard-Mineral oil, 3e6-18S<br> 16.WW + Biol standard-Mineral oil, 3e5-18S<br> 17WW-Fluorinated oil, 3e6-16S<br> 18.WW-Fluorinated oil, 3e5-16S<br> 19.WW-Mineral oil, 3e6-16S<br> 20.WW-Mineral oil, 3e5-16S<br> 21.WW-Fluorinated oil, 3e6-18S<br> 22.WW-Fluorinated oil, 3e5-18S<br> 23.WW-Mineral oil, 3e6-18S<br> 24.WW-Mineral oil, 3e5-18S
DOI: 10.6084/m9.figshare.14979879
title: K-Means Cluster Analysis Syntax.R

description: K-Means Cluster Analysis Syntax RStudio
DOI: 10.5281/zenodo.322747
title: Microphone Brightness Modelling Dataset

description: Data relating to the development and testing of a model predicting the perception of the degree of timbral brightness imparted by to a recording by the microphone used to make that recording. Archive comprises audio files, listening test instructions and MaxMSP interfaces, MATLAB code and listening test results. <strong>References</strong> A.Pearce, "Perceived differences between microphones", PhD Thesis, Institute of Sound Recording, University of Surrey, UK. A.Pearce, T.Brookes, R.Mason, "Modelling the Microphone-Related Timbral Brightness of Recorded Signals", Applied Sciences Special Issue on Applications of Machine Learning in Audio Classification and Acoustic Scene Characterization, vol.11, iss.14, article number 6461.
description: Attribution: if publishing results generated using all or part of this dataset, please cite the most appropriate of the references listed on this page; if incorporating all or part of this dataset into a new dataset, please attribute the incorporated elements to the authors listed on this page and cite the doi. Thanks.
DOI: 10.5281/zenodo.601413
title: Microphone Brightness Modelling Dataset

description: Data relating to the development and testing of a model predicting the perception of the degree of timbral brightness imparted by to a recording by the microphone used to make that recording. Archive comprises audio files, listening test instructions and MaxMSP interfaces, MATLAB code and listening test results. <strong>References</strong> A.Pearce, "Perceived differences between microphones", PhD Thesis, Institute of Sound Recording, University of Surrey, UK. A.Pearce, T.Brookes, R.Mason, "Modelling the Microphone-Related Timbral Brightness of Recorded Signals", Applied Sciences Special Issue on Applications of Machine Learning in Audio Classification and Acoustic Scene Characterization, vol.11, iss.14, article number 6461.
description: Attribution: if publishing results generated using all or part of this dataset, please cite the most appropriate of the references listed on this page; if incorporating all or part of this dataset into a new dataset, please attribute the incorporated elements to the authors listed on this page and cite the doi. Thanks.
DOI: 10.5281/zenodo.5101329
title: Functional and morphological adaptation in DNA protocells via signal processing prompted by artificial metalloenzymes

description: Data underlying the figures in the publication “Functional and morphological adaptation in DNA protocells via signal processing prompted by artificial metalloenzymes”, published in<em> Nat. Nanotechnol., </em><strong>2020</strong>, 15, 914–921. https://doi.org/10.1038/s41565-020-0761-y Table of contents: <strong>1. Dataset</strong>; Excel file containing the numerical data for <em>Figure 3</em>: Metathesis kinetics, mutant screenings and crowding. <strong>2. Experimental Information</strong>; Word file containing the experimental protocols for synthesis and analysis.
DOI: 10.5281/zenodo.5101328
title: Functional and morphological adaptation in DNA protocells via signal processing prompted by artificial metalloenzymes

description: Data underlying the figures in the publication “Functional and morphological adaptation in DNA protocells via signal processing prompted by artificial metalloenzymes”, published in<em> Nat. Nanotechnol., </em><strong>2020</strong>, 15, 914–921. https://doi.org/10.1038/s41565-020-0761-y Table of contents: <strong>1. Dataset</strong>; Excel file containing the numerical data for <em>Figure 3</em>: Metathesis kinetics, mutant screenings and crowding. <strong>2. Experimental Information</strong>; Word file containing the experimental protocols for synthesis and analysis.
DOI: 10.5281/zenodo.5031556
title: Bait sets for Atlantic salmon, Atlantic cod and Tiger shark

description: Bait sets for Atlantic salmon, Atlantic cod and Tiger Shark, in FASTA file format. Each line string starting with "&gt;" corresponds to the name of the contig/chromosome/linkage group where the bait sequence is designed from, followed by the base pairs where the bait sequence starts and ends. For salmon/cod, we include 20 extra sequences for the Random regions.
DOI: 10.5281/zenodo.5031555
title: Bait sets for Atlantic salmon, Atlantic cod and Tiger shark

description: Bait sets for Atlantic salmon, Atlantic cod and Tiger Shark, in FASTA file format. Each line string starting with "&gt;" corresponds to the name of the contig/chromosome/linkage group where the bait sequence is designed from, followed by the base pairs where the bait sequence starts and ends. For salmon/cod, we include 20 extra sequences for the Random regions.
DOI: 10.15161/oar.it/73928
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.6084/m9.figshare.14979822
title: Supplementary Material for: Validation of the Finnish Version of the Unified Dyskinesia Rating Scale

description: <b><i>Introduction:</i></b> The Unified Dyskinesia Rating Scale (UDysRS) was developed to provide a comprehensive rating tool of dyskinesia in Parkinson’s disease (PD). Because dyskinesia therapy trials involve multicenter studies, having a scale that is validated in multiple non-English languages is pivotal to international efforts to treat dyskinesia. The aim of the present study was to organize and perform an independent validation of the UDysRS Finnish version. <b><i>Methods:</i></b> The UDysRS was translated into Finnish and then back-translated into English using 2 independent teams. Cognitive pretesting was conducted on the Finnish version and required modifications to the structure or wording of the translation. The final Finnish version was administered to 250 PD patients whose native language is Finnish. The data were analyzed to assess the confirmatory factor structure to the Spanish UDysRS (the reference standard). Secondary analyses included an exploratory factor analysis (EFA), independent of the reference standard. <b><i>Results:</i></b> The comparative fit index (CFI), in comparison with the reference standard factor structure, was 0.963 for Finnish. In the EFA, where variability from sample to sample is expected, isolated item differences of factor structure were found between the Finnish and Reference Standard versions of the UDysRS. These subtle differences may relate to differences in sample composition or variations in disease status. <b><i>Conclusion:</i></b> The overall factor structure of the Finnish version was consistent with that of the reference standard, and it can be designated as the official version of the UDysRS for Finnish speaking populations.
DOI: 10.6084/m9.figshare.14979822.v1
title: Supplementary Material for: Validation of the Finnish Version of the Unified Dyskinesia Rating Scale

description: <b><i>Introduction:</i></b> The Unified Dyskinesia Rating Scale (UDysRS) was developed to provide a comprehensive rating tool of dyskinesia in Parkinson’s disease (PD). Because dyskinesia therapy trials involve multicenter studies, having a scale that is validated in multiple non-English languages is pivotal to international efforts to treat dyskinesia. The aim of the present study was to organize and perform an independent validation of the UDysRS Finnish version. <b><i>Methods:</i></b> The UDysRS was translated into Finnish and then back-translated into English using 2 independent teams. Cognitive pretesting was conducted on the Finnish version and required modifications to the structure or wording of the translation. The final Finnish version was administered to 250 PD patients whose native language is Finnish. The data were analyzed to assess the confirmatory factor structure to the Spanish UDysRS (the reference standard). Secondary analyses included an exploratory factor analysis (EFA), independent of the reference standard. <b><i>Results:</i></b> The comparative fit index (CFI), in comparison with the reference standard factor structure, was 0.963 for Finnish. In the EFA, where variability from sample to sample is expected, isolated item differences of factor structure were found between the Finnish and Reference Standard versions of the UDysRS. These subtle differences may relate to differences in sample composition or variations in disease status. <b><i>Conclusion:</i></b> The overall factor structure of the Finnish version was consistent with that of the reference standard, and it can be designated as the official version of the UDysRS for Finnish speaking populations.
DOI: 10.6084/m9.figshare.14980017
title: Supplementary Material for: Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

description: <b><i>Introduction:</i></b> Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI. <b><i>Methods:</i></b> A literature search was performed in PubMed, Cochrane, OVID, and Google Scholar. We included studies that used AI (e.g., machine learning (ML) and deep learning) to formulate prediction models for neonatal death. We excluded small studies (<i>n</i> &lt; 500 individuals) and studies using only antenatal factors to predict mortality. Two independent investigators screened all articles for inclusion. The data collection consisted of study design, number of models, features used per model, feature importance, internal and/or external validation, and calibration analysis. Our primary outcome was the average area under the receiving characteristic curve (AUC) or sensitivity and specificity for all models included in each study. <b><i>Results:</i></b> Of 434 articles, 11 studies were included. The total number of participants was 1.26 M with gestational ages ranging from 22 weeks to term. Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age. The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%). Five studies (45.5%) reported calibration plots and 2 (18.2%) conducted external validation. Eight studies reported results by AUC and 5 studies reported the sensitivity and specificity. The AUC varied from 58.3% to 97.0%. The mean sensitivities ranged from 63% to 80% and specificities from 78% to 99%. The best overall model was linear discriminant analysis, but it also had a high number of features (<i>n</i> = 17). <b><i>Discussion/Conclusion:</i></b> ML models can accurately predict death in neonates. This analysis demonstrates the most commonly used predictors and metrics for AI prediction models for neonatal mortality. Future studies should focus on external validation, calibration, as well as deployment of applications that can be readily accessible to health-care providers.
DOI: 10.13140/rg.2.2.14702.54085
title: ‬How to reopen america economy b/14-july-2021
DOI: 10.34808/8kfb-zz31
title: Nonlinear impedance as a function of A.C. voltage for  glass 40Bi2VO5.5-60SrB4O7 annealed at 473 K for 3h and next fully crystallized measured with impedance spectroscopy method at high temperature region

description: The nonlinear electrcial properties as a function of A.C. voltage for annealed at 473 K for 3h and next fully crystallized 40Bi2VO5.5-60SrB4O7 glass was measured by impedance spectroscopy method. 
In order to prepare glasses two compounds: Bi2VO5.5 and SrB4O7 were synthesized via a solid state reaction route. The Bi2VO5.5 was synthesized from the stoichiometric mixture of initial powders of Bi2O3 and V2O5 which were ball-milled in pure acetone for 6 h at room temperature. The milling was performed in steps of 1 h with rest intervals of 10min. The mixture was heated up to 1020K in air and kept at this temperature for 24 h. The heating and cooling rate was of 50K h-1. The obtained powder was checked by X-ray diffraction method. The SrB4O7 was synthesized from the stoichiometric mixture of analytical grade SrCO3 and H3BO3 which were mashed in a mortar. Next the mixture was heated up to 1073K and kept at this temperature for 12 h. Samples of a composition of 40Bi2VO5.5–60SrB4O7 (in mol%) were prepared by the conventional melt quenching technique. The melting was conducted in alumina crucibles at 1373K for few min. The melts were poured onto a preheated (573 K) brass plate and pressed by another plate to obtain flat circular disks of 1–2mm thickness and 20–30mm diameter. Glass was annealed at 473 K for 3h and next heat treated up to 813 K to obtain glass-ceramic.
For the electrical measurements gold electrodes were evaporated at the preheated samples. Impedance measurements were carried out in the temperature range from 573 K to 813 K, with the ac voltage varied from 0.1 to 3 Vrms with Concept 40 broadband dielectric spectrometer. The higher harmonic components (harmonic 0, harmonic 1 and harmonic 2) were measured for frequencies 0.1 and 1 Hz. Measurements were conducted during heating and cooling. Here the impedance for harmonic components was defined as the ratio of the voltage base wave to the n-th harmonic current component: Zn∗= U0∗/In∗, where Zn⁎ including the base wave generally depend on the sample voltage U1⁎ base wave amplitude. From Zn⁎ allother independent variables are calculated. The dependence of current density on the cosinusoidal electric field E(t)= E0cos(ωt) leads to the following expression:
j´ = σ´0hE0 cos (ωt) + σ´1hE0 cos (2ωt) + σ´2hE0 cos (3ωt) + …Where σ´0h denotes base conductivity, while σ´1h, σ´2h etc. are higher harmonics conductivity. The admittivity for harmonic components with n ≥1, is calculated from relation σ⁎n = i2πfε0ε⁎n.
DOI: 10.5281/zenodo.5101358
title: Database of Citizen Science Projects

description: A database of citizen science projects identified from Wikipedia's List of Citizen Science Projects, SciStarter and contributions from the ACTION consortium members. Updated to include
DOI: 10.5281/zenodo.4948051
title: Database of Citizen Science Projects

description: A database of citizen science projects identified from Wikipedia's List of Citizen Science Projects, SciStarter and contributions from the ACTION consortium members. Updated to include
DOI: 10.5281/zenodo.5101368
title: Cryo-EM structure and dynamics of the green-light absorbing proteorhodopsin

description: Data underlying the figures in the publication “Cryo-EM structure and dynamics of the green-light absorbing proteorhodopsin”, published in <em>Nat Commun,</em> <strong>2021</strong>, 12, 4107. https://doi.org/10.1038/s41467-021-24429-6 Table of contents: <strong>1. Dataset</strong>; Excel file containing the numerical data for <em>Figures S1A</em> and <em>4C.</em> <strong>2. Methods</strong>; Word file containing the description of the methods. The cryo-EM map was deposited in the <strong>Electron Microscopy Data Bank (EMDB)</strong> under accession code EMD-11955 and the protein coordinates were deposited in the <strong>Protein Data Bank (PDB)</strong> with the accession code 7B03. EMDB: https://www.emdataresource.org/EMD-11955 PDB: https://www.rcsb.org/structure/7B03 (DOI: 10.2210/pdb7B03/pdb)
DOI: 10.5281/zenodo.5101367
title: Cryo-EM structure and dynamics of the green-light absorbing proteorhodopsin

description: Data underlying the figures in the publication “Cryo-EM structure and dynamics of the green-light absorbing proteorhodopsin”, published in <em>Nat Commun,</em> <strong>2021</strong>, 12, 4107. https://doi.org/10.1038/s41467-021-24429-6 Table of contents: <strong>1. Dataset</strong>; Excel file containing the numerical data for <em>Figures S1A</em> and <em>4C.</em> <strong>2. Methods</strong>; Word file containing the description of the methods. The cryo-EM map was deposited in the <strong>Electron Microscopy Data Bank (EMDB)</strong> under accession code EMD-11955 and the protein coordinates were deposited in the <strong>Protein Data Bank (PDB)</strong> with the accession code 7B03. EMDB: https://www.emdataresource.org/EMD-11955 PDB: https://www.rcsb.org/structure/7B03 (DOI: 10.2210/pdb7B03/pdb)
DOI: 10.48370/ofd/kosgzf/pjnkaj
title: HMW_Carex_gracilis_Curtis_H_7235.jpg

description: :unav
DOI: 10.48370/ofd/kosgzf
title: Turzyca zaostrzona (Carex gracilis Curtis), 7235

description: Herbarium
DOI: 10.5281/zenodo.5101294
title: Dataset for the article 'Reflecting on the quantitative turn in linguistics' (Linguistics 59.5)

description: The TSV-file contains the data underlying the quantitative analysis presented in the article entitled 'Reflecting on the quantitative turn in linguistics' by Bernd Kortmann, published in Linguistics 59.5; see the article for details.
DOI: 10.5281/zenodo.5101293
title: Dataset for the article 'Reflecting on the quantitative turn in linguistics' (Linguistics 59.5)

description: The TSV-file contains the data underlying the quantitative analysis presented in the article entitled 'Reflecting on the quantitative turn in linguistics' by Bernd Kortmann, published in Linguistics 59.5; see the article for details.
DOI: 10.5281/zenodo.5101414
title: Decorating Nanostructured Surfaces with Antimicrobial Peptides to Efficiently Fight Bacteria

description: Data underlying the figures in the publication “Decorating Nanostructured Surfaces with Antimicrobial Peptides to Efficiently Fight Bacteria”, published in <em>ACS Appl. Bio Mater</em>., <strong>2020</strong>, 3, 3, 1533–1543. https://pubs.acs.org/doi/10.1021/acsabm.9b01154 Table of contents: <strong>1. Figure 1</strong>; Zip file containing the numerical data for the FCS studies of <em>Figure 1</em>. <strong>2. Figure 2</strong>; Zip file containing the data underlying <em>Figure 2</em>. <strong>3. Figure 3</strong>; Zip file containing the numerical data for the XPS studies of <em>Figure 3</em>. <strong>4. Figure 4</strong>; Zip file containing the numerical data for the QCM-D studies of <em>Figure 4</em>. <strong>5. Figure 5</strong>; Zip file containing the data underlying <em>Figure 5</em>. <strong>6. Table 1</strong>; Zip file containing the numerical data for the FCS Fitting Parameters in <em>Table 1</em>. <strong>5. Table 2</strong>; Zip file containing the numerical data for the Atomic concentrations calculated in <em>Table 1</em>.