cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5436136.v2
title: 1000 Empirical Time series

description: A diverse selection of 1000 empirical time series, along with results of an <i>hctsa</i> feature extraction, using v0.95 of <i>hctsa</i> and Matlab 2017a, computed on a linux server at Monash University.The results of the computation are in the <i>hctsa</i> file, <b>HCTSA_Empirical1000.mat</b>The input file, <b>INP_Empirical1000.mat</b>,<b> </b>is for use with <i>hctsa</i>, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using <i>hctsa</i>, can proceed as<b>&gt;&gt; TS_init('INP_Empirical1000.mat');</b>See link in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use <i>hctsa</i>.<br>
DOI: 10.6084/m9.figshare.5442457.v1
title: Assembly in the translation milieu imposes evolutionary constraints on homomeric proteins

description: <b>Supplementary Video 1. Simulation</b> <b>of Tet@N with short linker. </b>For all movies the red segment is the Tet, and yellow is the YFP beta-barrel. Both cotranslational folding and misassembly take place once the Tet appear outside of the ribosome tunnels.<br> <b>Supplementary Video 2. Simulation</b> <b>of Tet@C with short linker. </b>There was no cotranslational assembly as the Tet of the leading ribosome leaves the ribosomal tunnel prior to the translation of the second ribosome, which allows it time to diffuse before the second Tet leaves the ribosome tunnel. <b> </b> <b>Supplementary Video 3. Simulation</b> <b>of Tet@N with long linker. </b>Cotranslational assembly takes place, but not misassembly. Similarly to movies S1-S2, the red segment is the Tet and the yellow is the YFP reporter-gene, the cyan segment is the long-linker. The less frequent misassembly events fit the observed <i>in vivo and in vitro</i> data. <b> </b>
DOI: 10.6084/m9.figshare.5436136.v3
title: 1000 Empirical Time series

description: A diverse selection of 1000 empirical time series, along with results of an <i>hctsa</i> feature extraction, using v0.96 of <i>hctsa</i> and Matlab 2017a, computed on a linux server at Monash University.The results of the computation are in the <i>hctsa</i> file, <b>HCTSA_Empirical1000.mat</b>The input file, <b>INP_Empirical1000.mat</b>,<b> </b>is for use with <i>hctsa</i>, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using <i>hctsa</i>, can proceed as<b>&gt;&gt; TS_init('INP_Empirical1000.mat');</b>See link in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use <i>hctsa</i>.<br>
DOI: 10.6084/m9.figshare.5436136.v4
title: 1000 Empirical Time series

description: A diverse selection of 1000 empirical time series, along with results of an <i>hctsa</i> feature extraction, using v0.96 of <i>hctsa</i> and Matlab 2017a, computed on a linux server at Monash University.<br>The results of the computation are in the <i>hctsa</i> file, <b>HCTSA_Empirical1000.mat</b><br>The input file, <b>INP_Empirical1000.mat</b>,<b> </b>is for use with <i>hctsa</i>, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using <i>hctsa</i>, can proceed as<b>&gt;&gt; TS_init('INP_Empirical1000.mat');</b><br>Some visualizations of the dataset are in <b>CarpetPlot.png </b>(first 1000 samples of all time series as a carpet (color) plot) and <b>150TS-250samples.png</b> (conventional time-series plots of the first 250 samples of a sample of 150 time series from the dataset). More visualizations can be performed by the user using <b>TS_plot_timeseries</b> from the <i>hctsa</i> package.<br><br>See links in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use <i>hctsa</i>.
DOI: 10.6084/m9.figshare.5436136.v6
title: 1000 Empirical Time series

description: A diverse selection of 1000 empirical time series, along with results of an <i>hctsa</i> feature extraction, using v0.96 of <i>hctsa</i> and Matlab 2017a, computed on a linux server at Monash University.<br>The results of the computation are in the <i>hctsa</i> file, <b>HCTSA_Empirical1000.mat</b> for use in Matlab using v0.96 of <i>hctsa</i>.<br><b><br></b>The same data is available in <b>.csv</b> format (e.g., for use with non-Matlab computing environments) for the <b>hctsa_datamatrix.csv</b> (results of feature computation), with information about rows (time series) in <b>hctsa_timeseries-info.csv</b>, information about columns (features) in <b>hctsa_features.csv</b> and the data of individual time series (each line a time series, for time series described in <b>hctsa_timeseries-info.csv</b>) is in <b>hctsa_timeseries-data.csv</b>. Note that these files were produced by running <b>&gt;&gt;OutputToCSV(HCTSA_Empirical1000.mat,true);</b> in <i>hctsa</i>.<b><br></b>The input file, <b>INP_Empirical1000.mat</b>,<b> </b>is for use with <i>hctsa</i>, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using <i>hctsa</i>, can proceed as<b>&gt;&gt; TS_init('INP_Empirical1000.mat');</b><br>Some visualizations of the dataset are in <b>CarpetPlot.png </b>(first 1000 samples of all time series as a carpet (color) plot) and <b>150TS-250samples.png</b> (conventional time-series plots of the first 250 samples of a sample of 150 time series from the dataset). More visualizations can be performed by the user using <b>TS_plot_timeseries</b> from the <i>hctsa</i> package.<br><br>See links in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use v0.96 of <i>hctsa</i>.
DOI: 10.6084/m9.figshare.5436136.v5
title: 1000 Empirical Time series

description: A diverse selection of 1000 empirical time series, along with results of an <i>hctsa</i> feature extraction, using v0.96 of <i>hctsa</i> and Matlab 2017a, computed on a linux server at Monash University.<br>The results of the computation are in the <i>hctsa</i> file, <b>HCTSA_Empirical1000.mat</b><br>The input file, <b>INP_Empirical1000.mat</b>,<b> </b>is for use with <i>hctsa</i>, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using <i>hctsa</i>, can proceed as<b>&gt;&gt; TS_init('INP_Empirical1000.mat');</b><br>Some visualizations of the dataset are in <b>CarpetPlot.png </b>(first 1000 samples of all time series as a carpet (color) plot) and <b>150TS-250samples.png</b> (conventional time-series plots of the first 250 samples of a sample of 150 time series from the dataset). More visualizations can be performed by the user using <b>TS_plot_timeseries</b> from the <i>hctsa</i> package.<br><br>See links in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use <i>hctsa</i>.
DOI: 10.6084/m9.figshare.3761562.v478
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v477
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.5188273.v3
title: The Reconstruction of 2,631 Draft Metagenome-Assembled Genomes from the Global Oceans

description: Data for 2,631 draft genomes (TOBG-GENOMES.tar.gz) generated using the Tara Oceans microbial metagenomic data sets - additionally includes:SECONDARY_CONTIGS.province.tar.gz -- SECONDARY contigs by provinceTOBG-READCOUNT.tar.gz -- read count values of each sample against SECONDARY contigsTOBG-BINS.tar.gz -- genome bins &lt;50% complete (15,557 bins with &gt;5 contigs)PRIMARY_CONTIGS.province.tar.gz -- PRIMARY contigs by province and sample
DOI: 10.6084/m9.figshare.5488918.v1
title: alpha and flava virus data

description: Jittered (5km) gps points of cases (alpha and flavi viruses) and raw data for lasso model and table 1.
DOI: 10.6084/m9.figshare.19106819
title: Quality and accuracy of patient-oriented web-based information on tooth avulsion

description: <strong>Background/Aim </strong>Tooth avulsion (TA) is a severe form of traumatic dental injury. Treatment outcome depends on the immediate measures taken. Usually, first responders are laypeople and internet has become a favored platform for them to access health-related information. The aim of this study was to evaluate the accuracy, quality, readability and popularity of patient-oriented web information on emergency management of TA. <strong>Methods </strong>Three search engines (Google, Bing and Yahoo.com) were browsed with six VPNs using ‘knocked out tooth’, ‘fallen out tooth’ and ‘tooth avulsion’ as keywords. Top 20 results for each were evaluated. Webpages were included if they had information oriented to caregivers. They were analyzed for accuracy (cut-off ≥ 95%). Quality of the web pages was assessed using DISCERN and Journal of the American Medical Association (JAMA) benchmark. Readability was evaluated using: Flesch-Kincaid Grade Level (FKGL), Gunning Fog (FOG), Simple Measure of Gobbledygook (SMOG), and Coleman-Liau Index (CLI). Popularity was assessed by Alexa Popularity Rank (APR). Accurate and inaccurate webpages were compared using Chi-square analysis and Mann-Whitney U test. Spearman's correlation between accuracy and popularity was established. p&lt;0.05 was considered significant. <strong>Results </strong>Seventy-two webpages were included. 23 (32%) were accurate (k = 0.94). Median DISCERN rating for accurate and inaccurate webpages was 3.0 [2.5–3.0] and 3.0 [2.0–3.0] respectively (p= 0.331). 10(44%) of accurate and 12(24.5%) of the inaccurate webpages had high-quality JAMA (p=0.089). Median readability scores for accurate: FKGL (8[6.75-9.2]), FOG (10.6 [9.55-12.3]), CLI (7[6-7]), SMOG (6.7 [6.15-7.5]); and inaccurate webpages: FKGL (7.85 [6.275-8.525]), FOG (10.55 [8.875-11.425]), CLI (7 [7-8.25]), SMOG (7 [6.2-7.925]). 68(94.5%) and 4(5.5%) webpages had average and difficult readability, respectively with a significantly higher CLI score in inaccurate webpages (p=.0035). Median APR was 1,02,538 [26852-1443755] for accurate and 7,63,190 [219799-2992067] for inaccurate webpages (p=0.163), with no correlation between accuracy and APR, Spearman rho = -0.241, p (2-tailed) = 0.0704. <strong>Conclusion </strong>Relevant webpages were limited and <strong>had </strong>low accuracy. <strong>This can impact the emergency management by laypeople and adversely affect the outcome of TA. </strong><strong></strong>
DOI: 10.6084/m9.figshare.5531224.v1
title: Exponential Integrators for Weather and Climate Simulations using Global Spectral Methods

description: This work studies exponential integrator (EI) methods in the context of climate and weather simulations. We put our focus on a rational approximation of exponential integrators (REXI) which can be expressed via U (∆t) ≈ n β n (∆tL + α n ) −1 U (0) where α, β ∈ C, L a linear operator (with oscillatory or diffusive stiffness) and the initial conditions, U (0) (see e.g. [Clancy and Lynch, 2011; Haut et al., 2015]). The additional degrees of freedom for parallelization are made available by the independent terms in the sum which can be solved in parallel. This formulation enables us to overcome the time step limitation due to the linear stiffness. A special focus is put on PDEs relevant for climate and weather simulations.<br>Such EI methods have a rich history (see e.g. [Moler and Van Loan, 2003; Hochbruck and Ostermann, 2010]) and are of increasing interest in the parallel-in-time (PinT) community. In the context of PinT methods, these EI formulations were first mentioned by [Gander and Guettel, 2013] as the ParaEXP method. In the context of the development of dynamical cores for climate and weather simulations, recent research [Clancy and Lynch, 2011; Garcia et al., 2014; Clancy and Pudykiewicz, 2013] exploited EI formulations and also showed promising results regarding higher accuracy. Additionally, EI are a crucial component when applying the asymptotic PinT approach [Haut and Wingate, 2014] to a more realistic problem. Parallel performance and numerical accuracy studies of the REXI method were conducted for the linear shallow-water equations (SWE) on the plane [Schreiber et al., 2016] using spectral solvers for Fourier space and finite-difference formulations. Up to two orders of magnitude of speedup for oscillatory linear stiff problems were presented in this work. The successful application of spectral solvers to solve for each REXI term motivated an investigation of EI to the linearized SWE including the effect of a rotating sphere. Spherical Harmonics are used which exploit the low-bandwidth structure of the linear rotational SWE in spectral space[Schreiber and Loft, 2017].<br>In this talk, we will present our current research on extending our previous work to the full non-linear PDEs. First, we will give a brief overview of the results to solve the linear SWE on the plane and rotating sphere with EI. Second, we will discuss time splitting approaches and study the direct application of EI formulations which can be expressed via the aforementioned sum and their extension to non-linearities (see e.g. [Cox and Matthews, 2002; Luan and Ostermann, 2016]). Finally, we will compare EI formulations with state-of-the art time stepping methods which are currently used in operational weather forecasts.<br>ERRATA:page 21: \leq =&gt; \geqpage 21: exp(i) =&gt; exp(i0)
DOI: 10.6084/m9.figshare.5027375.v2
title: Chloropicophyceae, a new class of picophytoplanktonic prasinophytes.

description: Prasinophytes are a paraphyletic group of nine lineages of green microalgae that are currently classified either at the class or order level or as clades without formal taxonomic description. Prasinophyte clade VII comprises picoplanktonic algae that are important components of marine phytoplankton communities, particularly in moderately oligotrophic waters. Despite first being cultured in the 1960s, this clade has yet to be formally described. Previous phylogenetic analyses using the 18S rRNA gene divided prasinophyte clade VII into three lineages, termed A, B and C, the latter formed by a single species, <i>Picocystis salinarum</i>, that to date has only been found in saline lakes. Strains from lineages A and B cannot be distinguished by light microscopy and have very similar photosynthetic pigment profiles corresponding to the prasino-2A pigment group. We obtained phenotypic and genetic data on a large set of prasinophyte clade VII culture strains that allowed us to clarify the taxonomy of this important marine group. We describe two novel classes, the Picocystophyceae and the Chloropicophyceae, the latter containing two novel genera, <i>Chloropicon</i> and <i>Chloroparvula</i>, and eight new species of marine picoplanktonic green algae.
DOI: 10.6084/m9.figshare.5547847.v1
title: LUSC.mRNAseq.txt

description: mRNA expression of LUSC patients
DOI: 10.6084/m9.figshare.5549299.v1
title: TW_1185_g_egg_60x

description: Original image stacks of the holotype of the type series for Acari: Trombidiformes: Eleutherengona: Myobiidae: Prohylomysobia erinaceophilus Sidorchuk and Bochkov in Sidorchuk et al. 2017 Journal of Systematic Paleontology. In Baltic amber piece, originally from the private collection of Thomas Weiterchan, coll. # 1185 (TW 1185), housed by GPIH, collection number 4597. Specimen number, age and microscopy magnification indicated in file name.<br><br>Images are obtained with Nikon Eclipse 800 compound microscope, mostly with 60x water immersion lens,Stacks of images were obtained with a Nikon DS-Fi1 digital camera, color-, noise- and sharpness-enhanced with Adobe Photoshop Lightroom 5.7.1. Combined images made with the aid of Helicon Focus Pro 6.5.2, algorithm A, with subsequent manual transfer of significant details taken from the individual images, as necessary (_comb in file names).
DOI: 10.6084/m9.figshare.5549329.v1
title: Dive Summary Data

description: HarborPorpoiseData_JEB includes the heart rate, respiration rate, dive behavior for each dive from 3 captive porpoise that was in used in McDonald et al. (Journal of Experimental Biology). Porpoise Spreadsheet Column explanations describes the variables.
DOI: 10.6084/m9.figshare.4986143.v2
title: Acquired melanocytic naevi in Han Chinese volunteers from Henan, China.

description: Clinical and dermoscopic images of acquired melanocytic naevi in Han Chinese volunteers from Henan, China.<br>
DOI: 10.6084/m9.figshare.4986143.v1
title: Supplemental file S4 lesion images.zip

description: Clinical and dermoscopic images of acquired melanocytic naevi in Han Chinese volunteers from Henan, China.<br>
DOI: 10.6084/m9.figshare.5558653.v1
title: Better Science through Better Data 2017 (#scidata17) scibe images

description: View all the digital scribe images drawn during the #scidata17 conference
DOI: 10.6084/m9.figshare.5562298.v1
title: Vital signs of 15 Volunteers

description: Vital signs such as blood pressure and heart rate were recorded before and after sauna<br>
DOI: 10.6084/m9.figshare.5572681.v1
title: TITCO.POMR.csv

description: Database of 16,047 trauma patients from trauma clinics in Mumbai, Delhi and Kolkata. Data was gathered between 2013-2015.
DOI: 10.6084/m9.figshare.5562166.v1
title: TraRECo: Assembled data

description: Transcriptome data assembled using TraRECo, a de novo transcriptome assembler with read error correction, developed by S. Yoon. Also include aata assembled with other benchmark assemblers, such as Trinity, Trans-ABySS, multi-k OASES, Bridger and BinPacker.<br>
DOI: 10.6084/m9.figshare.4210863.v197
title: Modality switch effects emerge early and increase throughout conceptual processing: Evidence from ERPs

description: Conference paper in <i>Proceedings of the 39th Annual Conference of the Cognitive Science Society.</i>Files: paper, poster, design overview, stimuli, EEG montage used, waveforms, window 1 close-ups, mean differences with 95% CIs, subtraction topographies, detailed waveforms, statistical results, fixed effects of final models, entire modeling, raw data. For best resolution, download file(s). Web app for plotting waveforms in each data section (link). <b>OSF archive with all data and code (link).</b><b>Abstract </b>We tested whether conceptual processing is modality-specific by tracking the time course of the Conceptual Modality Switch effect. Forty-six participants verified the relation between property words and concept words. The conceptual modality of consecutive trials was manipulated in order to produce an Auditory-to-visual switch condition, a Haptic-to-visual switch condition, and a Visual-to-visual, no-switch condition. Event-Related Potentials (ERPs) were time-locked to the onset of the first word (property) in the target trials so as to measure the effect online and to avoid a within-trial confound. A switch effect was found, characterized by more negative ERP amplitudes for modality switches than no-switches. It proved significant in three typical time windows from 270 to 750 milliseconds post word onset, with greater strength in the Slow group, in posterior brain regions, and in the N400 window. These results suggest that conceptual processing may be modality-specific in certain tasks, but also that the early stage of processing is relatively amodal.<b></b><b></b><b></b><b></b><b></b><b></b>ReferencesCollins, J., Pecher, D., Zeelenberg, R., & Coulson, S. (2011). Modality switching in a property verification task: an ERP study of what happens when candles flicker after high heels click. <i>Frontiers in Psychology, 2.</i>Hald, L. A., Marshall, J.-A., Janssen, D. P., & Garnham, A. (2011). Switching modalities in a sentence verification task: ERP evidence for embodied language processing. <i>Frontiers in Psychology, 2.</i> Hauk, O. (2016). Only time will tell—Why temporal information is essential for our neuroscientific understanding of semantics. <i>Psychonomic Bulletin & Review, 23</i>.<i></i>Louwerse, M., & Connell, L. (2011). A taste of words: linguistic context and perceptual simulation predict the modality of words. <i>Cognitive Science, 35, </i>2, 381-98.Mahon, B.Z., & Hickok, G. (2016). Arguments about the nature of concepts: Symbols, embodiment, and beyond. <em>Psychonomic Bulletin & Review, 23, </em>941-958.
DOI: 10.6084/m9.figshare.4210863.v196
title: Modality switch effects emerge early and increase throughout conceptual processing: Evidence from ERPs

description: Conference paper in <i>Proceedings of the 39th Annual Conference of the Cognitive Science Society.</i>Files: paper, poster, design overview, stimuli, EEG montage used, waveforms, window 1 close-ups, mean differences with 95% CIs, subtraction topographies, detailed waveforms, statistical results, fixed effects of final models, entire modeling, raw data. For best resolution, download file(s). Waveforms per participant and electrode (link). <b>OSF archive with all data and code (link).</b><b>Abstract </b>We tested whether conceptual processing is modality-specific by tracking the time course of the Conceptual Modality Switch effect. Forty-six participants verified the relation between property words and concept words. The conceptual modality of consecutive trials was manipulated in order to produce an Auditory-to-visual switch condition, a Haptic-to-visual switch condition, and a Visual-to-visual, no-switch condition. Event-Related Potentials (ERPs) were time-locked to the onset of the first word (property) in the target trials so as to measure the effect online and to avoid a within-trial confound. A switch effect was found, characterized by more negative ERP amplitudes for modality switches than no-switches. It proved significant in three typical time windows from 270 to 750 milliseconds post word onset, with greater strength in the Slow group, in posterior brain regions, and in the N400 window. These results suggest that conceptual processing may be modality-specific in certain tasks, but also that the early stage of processing is relatively amodal.<b></b><b></b><b></b><b></b><b></b><b></b>ReferencesCollins, J., Pecher, D., Zeelenberg, R., & Coulson, S. (2011). Modality switching in a property verification task: an ERP study of what happens when candles flicker after high heels click. <i>Frontiers in Psychology, 2.</i>Hald, L. A., Marshall, J.-A., Janssen, D. P., & Garnham, A. (2011). Switching modalities in a sentence verification task: ERP evidence for embodied language processing. <i>Frontiers in Psychology, 2.</i> Hauk, O. (2016). Only time will tell—Why temporal information is essential for our neuroscientific understanding of semantics. <i>Psychonomic Bulletin & Review, 23</i>.<i></i>Louwerse, M., & Connell, L. (2011). A taste of words: linguistic context and perceptual simulation predict the modality of words. <i>Cognitive Science, 35, </i>2, 381-98.Mahon, B.Z., & Hickok, G. (2016). Arguments about the nature of concepts: Symbols, embodiment, and beyond. <em>Psychonomic Bulletin & Review, 23, </em>941-958.