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Dataset results
14 results for “time alignment”
Dataset: Analysis of timing variability in human movements by aligning parameter curves in time
<p>Supplementary Data for <em><strong>Analysis of timing variability in human movements by aligning parameter curves in time</strong></em> article</p> <p>Dataset associated with the following publication:<br> Maurer, L. K., Maurer, H., & Müller, H. (2017). Analysis of timing variability in human movements by aligning parameter curves in time.</p> <p>-------------------------------------------------------------------------------</p> <p>The data files are structured in the following way:<br> (1) Basic subject information (age, sex) can be found in the file subject_data.txt (tabulator separated text file).</p> <p>(2) The folder parameter_curves contains the angle trajectories of all trials structured in blocks of 50 trials (sometimes less than 50 because of data cleaning procedures deleating corrupted data and trials in which participants released accidentally [with zero velocity]). Each participant performed five practice days with four blocks of 50 trials, i.e. 20 blocks. File names contain subject (1,...,14), day (1,...,5), and block (1,...,4) information. Within the tabulator separated text files each column contains the angle trajectory of one trial consisting of 1000 values (sampled with 1000 Hz). Index 600 is the moment when participants released the virtual ball.</p>
Interstitial cortisol measurements aligned by wake time, healthy volunteers
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Supplementary Materials for Time-Aligned Edge Plots for Dynamic Graph Visualization
<p><strong>Abstract</strong>: We present <em>time-aligned edge plots</em>: time- and edge-scalable representations of dynamic graphs. Vertices are mapped to two vertical parallel axes. The left axis depicts the source vertices, whereas the right one depicts the destination vertices. The time axis is horizontally embedded in-between the two axes, resulting in a two-dimensional graph layout. Edges are added by drawing straight lines connecting the corresponding source and destination vertices through time, while the pixels along the lines are used to encode the time-varying information. In this way, the depiction of edges at the individual timepoints is reduced to only a few pixels, resulting in a less cluttered representation of dynamic graphs, while the alignment of edges over time reveals the temporal patterns in the data and preserves the users' mental map. We evaluate our approach by comparing it theoretically and empirically against the state-of-the-art using dynamic graphs of varying complexities.</p>
360 Recording of downtown ULM Germany, with time aligned gps data
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Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
Real-time Alignments for Predicting Decision-Making Time for Diagnostics over NGS Cycles
<p>Input datasets for an interpretable learning approach to predict decision-making time for diagnostics over NGS cycles. The dataset contains alignment files of two clinical sputum samples sequenced with an Illumina MiSeq sequencing device, and following a real-time sequencing protocol.</p> <p>The datasets consist of real-time alignment files for specific sequencing cycle intervals. The alignment has been performed to a database of respiratory microbes. Data preprocessing, including the removal of human host DNA, is integrated into the real-time alignment approach that has been applied to generate the alignments.</p>
Reproduction package for the publication "Tidal disruption event AT2020ocn: early-time X-ray flares caused by a possible disc alignment process"
<p>This package contains the data analysed in the paper "Tidal disruption event AT2020ocn: early–time X–ray flares caused by a possible disc alignment process". The software XSPEC (Arnaud 1996) is needed to perform the spectral analysis and reproduce the results shown in the paper.</p> <p>The structure is as follows:</p> <p>./reproduction_ocn/nicer: contains all processed NICER data used in the paper, grouped by their epochs. "speclist-early.dat" lists all the early-time epochs before MJD 59130. "en_range.dat" lists the selected energy range at each epoch during the early-time period for X-ray spectral analysis. Within each epoch-specific folder, "src.fits" and "bkg.fits" are the source+background and background spectra re-binned using the FTOOL "ftgrouppha"; "*.arf" and "*.rmf" are ancillary file and response file for spectral analysis; rest files are direct products of the NICER data reduction process. See the paper for details.</p> <p>./reproduction_ocn/swift: contains all Swift/UVOT data used in the paper, grouped by their observation IDs. "m2.fits", "w1.fits", and "w2.fits" contain the UV lightcurves from three UV filters, produced by Swift task "uvotproduct". "swfxraypclc.dat" is the Swift/XRT lightcurve, produced by the online Swift pipeline: https://www.swift.ac.uk/user_objects/ (Evans et al. 2009). "./reproduction_ocn/MOSFiT-products/" includes MCMC products from the MOSFiT package (Mockler et al. 2019).</p> <p>./reproduction_ocn/xmm: contains the reduced XMM-Newton/EPIC-pn spectra of three epochs used in the paper. "1and2-slim.xcm" is fitting the XMM#1 and XMM#2 spectra using the slim disc model. "3-phenmnlgcl.xcm" and "3-relxillCp.xcm", are fitting the XMM#3 spectrum with, a powerlaw+zbbody model and a slim disc+relxillCp model, respectively.</p>
Tooth Discoloration and Chairside Handling Time (Bonding-Debonding) in Flowable Versus Packable Composites for Clear Aligner Bonded Attachments: A Randomized Clinical Trial
ClinicalTrials.gov study NCT07234149. IPD Sharing: NO. Countries: 1. Publications: 1.
Predictability of Part-time Wearing of Removable Clear Aligner Therapy Versus Full Time
ClinicalTrials.gov study NCT06253286. IPD Sharing: YES. Countries: 1. Publications: 22.
Evaluation of the Perceived Experience of Patients Treated With Aligners With Two Different Timings
ClinicalTrials.gov study NCT06536335. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Data from: Aligner optimization increases accuracy and decreases compute times in multi-species sequence data
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Optimizing Aligner Wear Time and Change Frequency
ClinicalTrials.gov study NCT04260633. IPD Sharing: Not stated. Countries: 0. Publications: 0.
This Study Will Examine Shortened Treatment Wear Time for Patients With Mixed Dentition Using AirFlex Aligner, Sequential Dental Aligners.
ClinicalTrials.gov study NCT07220317. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.