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74 results for “error correction”
FIGURE 3 in Correction of a typographical error in Bignonia 'ghorta' (Bignoniaceae)
FIGURE 3. Lectotype of Bignonia ghonta (G00133401 image!).
FIGURE 2. Page 149 in Correction of a typographical error in Bignonia 'ghorta' (Bignoniaceae)
FIGURE 2. Page 149 of Buchanan-Hamilton's manuscript.
FIGURE 1 in Correction of a typographical error in Bignonia 'ghorta' (Bignoniaceae)
FIGURE 1. Part of Wallich's Numeric List showing 'Bignonia ghonta B. Ham.' (No. 6510).
ERROR ANALYSIS AND CORRECTION IN EFL LINGUISTICS DEVELOPMENT IN FORMAL WRITING
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A Streamlined and High-Throughput Error-Corrected Next-Generation Sequencing Method for Low Variant Allele Frequency Quantitation
<p></p><p>Quantifying mutant or variable allele frequencies (VAFs) of ≤10−3 using next-generation sequencing (NGS) has utility in both clinical and nonclinical settings. Two common approaches for quantifying VAFs using NGS are tagged single-strand sequencing and duplex sequencing. While duplex sequencing is reported to have sensitivity up to 10−8 VAF, it is not a quick, easy, or inexpensive method. We report a method for quantifying VAFs that are ≥10−4 that is as easy and quick for processing samples as standard sequencing kits, yet less expensive than the kits. The method was developed using PCR fragment-based VAFs of Kras codon 12 in log10 increments from 10−5 to 10−1, then applied and tested on native genomic DNA. For both sources of DNA, there is a proportional increase in the observed VAF to input VAF from 10−4 to 100% mutant samples. Variability of quantitation was evaluated within experimental replicates and shown to be consistent across sample preparations. The error at each successive base read was evaluated to determine if there is a limit of read length for quantitation of ≥10−4, and it was determined that read lengths up to 70 bases are reliable for quantitation. The method described here is adaptable to various oncogene or tumor suppressor gene targets, with the potential to implement multiplexing at the initial tagging step. While easy to perform manually, it is also suited for robotic handling and batch processing of samples, facilitating detection and quantitation of genetic carcinogenic biomarkers before tumor formation or in normal-appearing tissue.</p><p></p>
Benchmarking datasets used in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs"
<p>This is the raw long-read sequencing data used for benchmarking experiments in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs". The name of the dataset is labeled as : simulated/real; ecoli/metagenome; ploidy/name; pacbio/ont; sequencing error rate; average sequencing coverage per haplotype.</p>
Code and data for manuscript: Does ankle push-off correct for errors in anterior-posterior foot placement relative to center-of-mass states?
<p>Data and code documentation for paper: “Does ankle push-off correct for errors in anterior-posterior foot placement relative to center-of-mass states?” All code in the current study can be accessed via the link (https://drive.google.com/drive/folders/1AnL9ajMk_Q3Gwzv_OrOp7A8DNwS8lniC?usp=sharing). The code was written by Jian Jin, Sjoerd Bruijn and Moira van Leeuwen. The data was from (van Leeuwen et al., 2020)and can be accessed through the link (https://doi.org/https://doi.org/10.5281/zenodo.4229851).</p>
Early Feasibility Study to Evaluate the AccuraSee in Correcting Residual Refractive Errors After Cataract Surgery
ClinicalTrials.gov study NCT05113979. IPD Sharing: NO. Countries: 1. Publications: 0.
Correction of Refractive Error Surprises After Cataract Surgery in Adults
ClinicalTrials.gov study NCT06379477. IPD Sharing: Not stated. Countries: 0. Publications: 7.
Safety and Effectiveness of Wavefront-Guided LASIK Correction of Hyperopic Refractive Errors
ClinicalTrials.gov study NCT01675479. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: TreeFix: statistically informed gene tree error correction using species trees
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Data from: Automated size selection for short cell-free DNA fragments enriches for circulating tumor DNA and improves error correction during next generation sequencing
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A Streamlined and High-Throughput Error-Corrected Next-Generation Sequencing Method for Low Variant Allele Frequency Quantitation
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Data from: Improving transcriptome assembly through error correction of high-throughput sequence reads
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Data from: Inconsistent use of multiple comparison corrections in studies of population genetic structure: are some type I errors more tolerable than others?
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High throughput error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing
GEO Series GSE162053. Mus musculus; Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [RNA-seq]
GEO Series GSE218899. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
A cell type specific error correction signal in posterior parietal cortex
GEO Series GSE232200. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
High-throughput error corrected Nanopore single cell transcriptome sequencing
GEO Series GSE130708. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Data and software for "Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument"
<p>Supplementary material to the DESI publication "Correcting Turbulence-induced Errors in Fiber Positioning for the Dark Energy Spectroscopic Instrument".</p> <p>The main "turbfigures.py" script generates the figures in the paper from the included data files.</p> <p>Contents:</p> <p><strong>Software files</strong></p> <ul> <li>turbfigures.py: Turbulence plotting and analysis plotting routines.</li> <li>turbulence.py: Analysis routines called by turbfigures.py. The live version of this code in production for desi is in the desimeter package (https://github.com/desihub/desimeter/blob/main/py/desimeter/turbulence.py)</li> </ul> <p><strong>Data files</strong></p> <ul> <li>coord-summary-20240124-pm.fits <ul> <li>Measured centroids of fibers in for 400 consecutive images of the focal plane while at zenith. File contains the following columns: <ul> <li>expid - exposure ID</li> <li>location - "location" of fiber in focal plane; ranges from 0 - 10000. Fibers on petal 0 have numbers between 0 and 1000, etc. There are only 500 positioners per petal, so most locations are not populated.</li> <li>move - in real data, this indexes over the moves in a DESI positioning loop; garbage information here.</li> <li>fpa_{x,y} - measured position of the fiber in this exposure</li> <li>req_{x,y} - requested position of the fiber in this exposure; not used for this data set where positioners are fixed in location</li> <li>{x,y}turb - empty in this file; gets filled in by turbfigures.py with measured turbulence</li> <li>flags_cor - flags indicating that a positioner or fiber may be problematic</li> <li>postype - flags indicating that a location corresponds to a real positioner vs. a fiducial</li> <li>expected_{x,y} - empty in this file; gets filled in by turbfigures.py with "expected" positions of each fiber, so that fpa_{x,y} - expected_{x,y} is a noisy estimate of the turbulence seen by the fiber.</li> </ul> </li> <li>Note that this file has an awkward structure. It has 10000 rows, one for each possible location. Most fields are then 400 element arrays that gives the corresponding values, corresponding to the 400 exposures present in the file.</li> </ul> </li> <li>coordinates-stats.ecsv <ul> <li>statistics of positioning accuracy and turbulence amplitude in DESI positioning loops. Measured RMSes are the 5-sigma clipped root-mean-square positioning offset in 2D: sqrt(mean(dx^2 + dy^2)). Measured medians are median(sqrt(dx^2 + dy^2)).</li> <li>Contents: <ul> <li>coord_filename - file name of DESI coordinates file statistics were drawn from</li> <li>rms_turbulence - RMS for the turbulent contribution to the positioning error</li> <li>rms_positioning - RMS for positioning after removing turbulence</li> <li>rms_total - Total RMS</li> <li>med_turbulence - median turbulence in exposure</li> <li>med_positioning - median positioning error in exposure</li> <li>med_total - median total turbulence + positioning error in exposure</li> <li>expid - exposure id number</li> </ul> </li> </ul> </li> </ul> <p>Dependencies: The included software uses the DESI software stack and otherwise the usual python astronomy packages: numpy scipy matplotlib astropy. Alternatively, people with access to NERSC can load the default DESI environment and pull in all needed dependencies.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.