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685 results for “errors”
Webis-Web-Errors-19
<p>The Webis-Web-Errors-19 comprises various annotations for the 10,000 web page archives of the <a href="https://webis.de/data.html?q=tags:webis-web-archive-17#webis-web-archive-17">Webis-Web-Archive-17</a>. The annotations are whether the page is (1) mostly advertisement, (2) cut off, (3) still loading, (4) pornographic; and whether it shows (not/a bit/ very) (5) pop-ups, (6) CAPTCHAs, or (7) error messages. If you use this dataset in your research, please cite it using <a href="https://webis.de/publications.html#filter:A+Dataset+for+Content+Error+Detection+in+Web+Archives">this paper</a>.</p>
MetaLink - Closure and Error Degree of 556M owl:sameAs statements
<p>MetaLink is a dataset that contains metadata for a very large set of owl:sameAs links that are crawled from the LOD Cloud. MetaLink encodes a previously published error metric for each of these links <a href="https://www.cs.vu.nl/~frankh/postscript/ISWC2018.pdf">[Raad et al., 2018]</a>. This error degree ranges from 0.0 (most likely correct) till 1.0 (most likely incorrect). The idea is that the more an owl:sameAs link is isolated in the network (of all owl:sameAs links), the higher error degree this link will have. Experiments shows that discarding the 1M owl:sameAs links with an error degree >0.99 can significantly increase the quality of the transitive closure. Also by keeping only the 400M owl:sameAs links with error degree <= 0.4, the resulting closure is 100% precise in several manually evaluated cases. The resulted equivalence classes from these different closures are <a href="https://zenodo.org/record/3345674">publicly available online.</a></p> <p>MetaLink is published in combination with LOD-a-lot, a dataset that is based on a very large crawl of a subset of the LOD Cloud. By combining MetaLink and LOD-a-lot, applications are able to make informed decisions about whether or not to follow specific links on the LOD Cloud. This dataset contains 4,352,602,452 unique triples, and is available in HDT (Header Dictionary Triples) format. It can be navigated online using the TriplyDB Linked Data hosting platform: <a href="https://krr.triply.cc/krr/metalink">https://krr.triply.cc/krr/metalink</a>. </p> <p>A figure describing the vocabulary of the MetaLink dataset can be found <a href="https://krr.triply.cc/krr/metalink/assets/5ced8dbfaea75b02bc98784f">here</a>. Classes are displayed by circles and properties are displayed by arcs. The MetaLink-specific classes and properties are displayed in red, the blue classes and properties are reused from existing vocabularies.</p>
error-proof_indexes v1.0
<p>This corresponds to the v1.0 of the github repository incorporating error-proof indexes for high throughput sequencing as well as the R script used to generate them. It can be cited as Jean-Francois Martin, 2019, Creating error-proof_indexes for high throughput sequencing. <a href="https://doi.org/10.5281/zenodo.3350207">DOI : 10.5281/zenodo.3350207</a></p>
Probing result for robotic assisted machining form error paper
<p>This is the probing result during our maching trials for form error prediction for robotic assisted machining.</p>
Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)
The use of microsatellite markers provides a window into the evolutionary processes of a given species. As such, these markers are widely used in scientific and applied research and are praised for their practicality and ease of use, however, the unavoidable incidence of genotyping deviations has been broadly neglected in the literature. Therefore, the present study aimed to estimate the rate of null alleles, mutations and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.
Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)
the rate of null alleles, mutations and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.
Reasearch data for publication "On optimizing metaheuristics performance with different error normalization approaches"
<p>These are the results of experiments performed for publication "On optimizing metaheuristics performance with different error normalization approaches" using authors own software -- EvANN -- which is available on github.</p>
Results for datasets with simulated error (derived from Datasets 1-5)
<p>Datasets w/ simulated error derived from Datasets 1-5.</p> <p>Version 2: Datasets were calculated with an Rstandard (R_std) value of 0.0036765 (<span>Skrzypek, G. and Dunn, P. J. H., Rapid Commun. Mass Spectrom., 34, e8890, <a href="https://doi.org/10.1002/rcm.8890">https://doi.org/10.1002/rcm.8890</a>, 2020</span>), which is more accurate than the atom percent value used in Version 1. Updating the R_std value had essentially no effect on the average values calculated using these datasets.</p> <p>There are 1000 datasets per combination of error level and skewness type.</p> <ul> <li>e1: Low error, no skew</li> <li>e2: Medium error, no skew</li> <li>e3: High error, no skew</li> <li>e4: Low error, left skew</li> <li>e5: Medium error, left skew</li> <li>e6: High error, left skew</li> <li>e7: Low error, right skew</li> <li>e8: Medium error, right skew</li> <li>e9: High error, right skew</li> </ul>
Dataset from Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.
<p>Dataset from the paper:</p> <p>Makino H. and Suhaimi A. Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.</p> <p>Each variable is described in Description.pdf.</p> <p>Analysis code is available at <a href="https://github.com/HiroshiMakinoLaboratory/RPEAccumulation" target="_blank" rel="noopener">https://github.com/HiroshiMakinoLaboratory/RewardPredictionErrorAccumulation</a>.</p>
Activation and connectivity maps - A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network
<p>Activation and connectivity maps of the study "A chronometric relationship between circuits underlying learning and error monitoring in the basal ganglia and salience network".</p> <ul> <li>Error-correct.nii corresponds to the statistical map of group-level differences in BOLD signal between correct and erroneous responses shown in figure 4;</li> <li>Late-initial.nii corresponds to the statistical map of group-level differences in BOLD signal between the initial and late learning periods shown in figure 5;</li> <li>Conn_error-correct_dACC.nii corresponds to the results from the seed-to-voxel gPPI analysis, using the dACC as seed region, showing areas of higher functional connectivity in erroneous compared to correct responses, shown in figure 8.</li> </ul>
Data from: RAD sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference
Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for non-model organisms, potentially revolutionising the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (1) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci, and; (2) quantify error rates for loci, alleles and SNPs. As an empirical example we use a double digest RAD dataset of a non-model plant species, Berberis alpina, collected from high altitude mountains in Mexico.
Error, noise and bias in de novo transcriptome assemblies
<p><i>De novo</i> transcriptome assembly is a powerful tool, widely used over the last decade for making evolutionary inferences. However, it relies on two implicit assumptions: that the assembled transcriptome is an unbiased representation of the underlying expressed transcriptome, and that expression estimates from the assembly are good, if noisy approximations of the relative abundance of expressed transcripts. Using publicly available data for model organisms, we demonstrate that, across assembly algorithms and data sets, these assumptions are consistently violated. Bias exists at the nucleotide level, with genotyping error rates ranging from 30-83%. As a result, diversity is underestimated in transcriptome assemblies, with consistent under-estimation of heterozygosity in all but the most inbred samples. Even at the gene level, expression estimates show wide deviations from map-to-reference estimates, and positive bias at lower expression levels. Standard filtering of transcriptome assemblies improves the robustness of gene expression estimates but leads to the loss of a meaningful number of protein-coding genes, including many that are highly expressed. We demonstrate a computational method, length-rescaled CPM, to partly alleviate noise and bias in expression estimates. Researchers should consider ways to minimize the impact of bias in transcriptome assemblies.</p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data (repository for simulated ONT RNA-seq data)
<p>Simulated ONT direct RNA and 1D cDNA sequencing data of varying sequencing depths (0.5 million, 1 million, 3 million, and 5 million simulated reads) used for benchmark evaluations of transcript discovery and quantification in our paper "ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data". All details can be found in the <strong>Materials and Methods</strong> section of the paper. </p> <p><em>HEK293T_DirectRNA.transcriptome_quantification.tsv</em> and <em>HEK293T_DirectRNA.transcriptome_quantification.tsv </em>are tab-separated files containing estimated raw read counts and normalized abundance values (in TPM) of transcripts annotated in GENCODE v34lift37. Transcript quantification was done using NanoSim (version 3.1.0). </p> <p><em>HEK293T_DirectRNA.NanoSim_500k.fastq.gz</em>,<em> </em><em>HEK293T_DirectRNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_DirectRNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_DirectRNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT direct RNA sequencing reads respectively. </p> <p><em>HEK293T_1DcDNA.NanoSim_500k.fastq.gz</em>,<em> HEK293T_1DcDNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_1DcDNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_1DcDNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT 1D cDNA sequencing reads respectively. </p>
Action Prediction Error: a value-free dopaminergic teaching signal that drives stable learning - Behavioral dataset
<p>Behavioral data to reproduce figures of this paper: https://doi.org/10.1101/2022.09.12.507572</p> <p>See Github repository: https://github.com/HernandoMV/APE_paper</p>
Quantification of Error Sources with Inertial Measurement Units in Sports - Data and Matlab Scripts
<p>Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filer. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to quantify each of the sources of error of an IMU separately. An optoelectronic system was used as a golden standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different trials, varying both in type of movement and in movement intensity. The error of the definition of the body frames (10.9-18.1 deg RMSD), the STA (3.6-9.4 deg RMSD) and the error of the orientation filter (2.8- 13.1 deg RMSD) were all quantified separately. The data and code to process the data can be found in this publication.</p> <p> </p>
NegativeArray Error using QuPath
<p>QuPath keeps crashing when I upload certain ome.tiff images. I attached an example of one of the images that is crashing the system to this forum. The error says NegativeArray ... If you can please help with this issue, I'd greatly appreciate it.</p>
Observing system simulation experiments to evaluate transport model error on CO2 flux estimates
<p>Atmospheric CO2 inversion using coarse-resolution transport model can cause large errors on surface carbon flux estimates. The transport model errors on flux estimates are isolated using observing system simulation experiments presented here.</p>
Global trade data of highly hazardous chemicals after data treatment and Python codes used for error analysis and mirror analysis
<p>This database includes all global trade data of the three types of highly hazaroudous chemicals listed under the Rotterdam Convention after error analysis and mirror analysis. Python codes used for error analysis and mirror analysis are also available.</p> <p> </p> <p> Corresponding authors: <a href="mailto:hongyan.zou@tjnu.edu.cn">hongyan.zou@tjnu.edu.cn</a>; <a href="mailto:Zhanyun.wang@empa.ch">Zhanyun.wang@empa.ch</a></p>
Calibration Dataset - HPOSS: A hierarchical portfolio optimization stacking strategy to reduce the generalization error of ensembles of models
<p>Calibration dataset for the study case presented in the paper "HPOSS: A hierarchical portfolio optimization stacking strategy<br> to reduce the generalization error of ensembles of models".</p> <p>It encompasses a .h5 file with a dataset called "Calibrations_LHS", which consists of a 80x5 numpy array of float numbers corresponding to (d1(m),d2(m),d3(m),d4(m),zeta_max(Pa)), where d_i, i = 1,...,4 are dimensions (in meters) of the I-beam and zeta_max is the maximum bending stress (in Pascals) developed in a simply such supported beam with 1 m of length after a point load of 1000N is applied at its center.</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.