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685 results for “errors”
Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
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Data and code from: Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments
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Data from: RAD sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference
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Code and data from: Representational geometry explains puzzling error distributions in behavioral tasks
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Data from: Identifying error and accurately interpreting eDNA metabarcoding results: a case study to detect vertebrates at arid zone waterholes
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Error, noise and bias in de novo transcriptome assemblies
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Biodiversity Soup II: A bulk‐sample metabarcoding pipeline emphasizing error reduction
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Data from: RAC2 gain of function variants causing inborn error of immunity drive NLRP3 inflammasome activation
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Example Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) data from the mouse colon
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Data from: Assessing error upon sampling bagged animal feed
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Are geometric morphometric analyses replicable? Evaluating landmark measurement error and its impact on extant and fossil Microtus classification
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Predictive coding and internal error correction in speech production
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Ionospheric Horizontal Correlation Length Derived From IRI-2016 Model Errors
<p>Ionospheric horizontal correlation length based on IRI-2016 model and JPL Global Ionospheric Maps. This metadata can be used for the construction of the covariance matrix for ionospheric data assimilation.</p> <p>each .nc file corresponds to one bin. </p> <p>Binning in season (winter, summer, equinox):</p> <p>00=month 1,2,11,12<br> 01=month 5,6,7,8<br> 02=month 3,4,9,10<br> <br> Binning in F10.7 solar flux (low, moderate, high):</p> <p>00=0-100 s.f.u<br> 01=100-150 s.f.u.<br> 02=150-350 s.f.u. <br> <br> Binning in UT: 2-h frames. </p> <p>Example: <br> The name of the .nc file: <br> Corr_Length_IRI_seazon_01_sf_00_UT_18.nc <br> would correspond to summer season, low solar flux, 18-20 UT. </p> <p>Inside of the .nc files:</p> <p>longitudes=array of geomagnetic longitudes (degrees)<br> latitudes=array of geomagnetic latitudes (degrees)<br> zonal_corr_length=array of size (nlon, nlat), is a map of zonal correlation lengths<br> merid_corr_length=array of size (nlon, nlat), is a map of meridional correlation lengths<br> L_4d=array of size (nlon, nlat, 4), is a map of correlation lengths in 4 perpendicular azimuthal directions; 0=0 deg, 1=90 deg, 2=180 deg, 3=-90 deg<br> L_az=array of size (nlon, nlat, 36), is a map of correlation lengths in 36 azimuthal directions (0,10,20,30,...,350 deg)<br> </p>
standard with together) corner left bottom (analysis genetic the in included species among bold gene in shown oxidase-I are SE cytochrome and species the within at) % (divergence divergence sequence average The . pairwise) corner showing right upper (Matrix) %;. SE 4 ABLE (T error in Description of a new species of the Rhinolophus trifoliatus-group (Chiroptera: Rhinolophidae) from Southeast Asia
standard with together) corner left bottom (analysis genetic the in included species among bold gene in shown oxidase-I are SE cytochrome and species the within at) % (divergence divergence sequence average The . pairwise) corner showing right upper (Matrix) %;. SE 4 ABLE (T error
Data from: Predicted tracking error triggers catch-up saccades during smooth pursuit
For foveated animals, visual tracking of moving stimuli requires the synergy between saccades and smooth pursuit eye movements. Deciding to trigger a catch-up saccade during pursuit influences the quality of visual input. This decision is a trade-off between tolerating sustained position error when no saccade is triggered or a transient loss of vision during the saccade due to saccadic suppression. Although catch-up saccades have been extensively investigated, it remains unclear how the trigger decision is made by the brain. de Brouwer et al (2002) demonstrated that catch-up saccades were less likely to occur when the expected time to foveate a target using pursuit alone is between 40 and 180ms into the future, referred to as the smooth zone. However, this descriptive result lacks a mechanistic explanation for how the trigger decision is made. More recently, we proposed a decision model (Coutinho et al., 2018) that relies on a probabilistic estimation of predicted position error (PEpred) during visual tracking. To test the model predictions, we investigated how human participants combined predicted position error, retinal slip, and the uncertainty in those estimates to make trigger decisions. We found a significant effect of the pre-saccadic magnitude of PEpred on trigger time and occurrence of catch-up saccades. To test the role of uncertainty, we blurred the moving target which led to longer and more variable saccade trigger times and more smooth pursuit trials, consistent with model predictions. As predicted by our model, large PEpred (>10deg) produced early saccades regardless of the level of uncertainty while saccades preceded by small PEpred (<10deg) were significantly modulated by high uncertainty. Our model also predicted increased signal dependent noise as retinal slip increases, which resulted in longer saccade trigger times and more smooth trials. In conclusion, the data supports our hypothesized role of PEpred in deciding when to trigger a catch-up saccade during smooth pursuit while taking into account uncertainty in sensory estimates.
Multiple systems in macaques for tracking prediction errors and other types of surprise
<p>Data and code to reproduce the figures and major analyses in</p> <p>Grohn J, Schüffelgen U, Neubert F-X, Verhagen L, Sallet J, Kolling N, Rushworth MFS. Multiple systems in macaques for tracking prediction errors and other types of surprise. PLOS Biology. 2020.</p>
A Statistical Analysis of Error in MPI Reduction Operations Dataset
<p>This is the dataset used to generate the figures and information contained in the paper <em>A Statistical Analysis of Error in MPI Reduction Operations </em>by Samuel D. Pollard and Boyana Norris, to appear in IEEE's<em> Fourth International Workshop on Software Correctness for HPC Applications</em>, 2020.<br> <br> A description is provided in the README.md as well as the software dependencies required to re-generate these data. The log files and tab-separated-values files (tsv) allows a user to analyze the same data we used for the paper. The file datasets-pollard-correctness2020.tar.bz2 decompresses to about 5.7GB.</p> <p>The source code used to generate these data is available <a href="https://github.com/sampollard/reduce-error">on Github</a>.</p>
A heterometallic [LnLn'Ln] lanthanide complex as a qubit with embedded quantum error correction. Open data set
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1021/acsnano.0c03167">10.1039/d0sc03107k</a> plus a table with information on how the data were obtained and processed.</p>
The perfect storm: Gene tree estimation error, incomplete lineage sorting, and ancient gene flow explain the most recalcitrant ancient angiosperm clade, Malpighiales
<p>The genomic revolution offers renewed hope of resolving rapid radiations in the Tree of Life. The development of the multispecies coalescent (MSC) model and improved gene tree estimation methods can better accommodate gene tree heterogeneity caused by incomplete lineage sorting (ILS) and gene tree estimation error stemming from the short internal branches. However, the relative influence of these factors in species tree inference is not well understood. Using anchored hybrid enrichment, we generated a data set including 423 single-copy loci from 64 taxa representing 39 families to infer the species tree of the flowering plant order Malpighiales. This order includes nine of the top ten most unstable nodes in angiosperms, which have been hypothesized to arise from the rapid radiation during the Cretaceous. Here, we show that coalescent-based methods do not resolve the backbone of Malpighiales and concatenation methods yield inconsistent estimations, providing evidence that gene tree heterogeneity is high in this clade. Despite high levels of ILS and gene tree estimation error, our simulations demonstrate that these two factors alone are insufficient to explain the lack of resolution in this order. To explore this further, we examined triplet frequencies among empirical gene trees and discovered some of them deviated significantly from those attributed to ILS and estimation error, suggesting gene flow as an additional and previously unappreciated phenomenon promoting gene tree variation in Malpighiales. Finally, we applied a novel method to quantify the relative contribution of these three primary sources of gene tree heterogeneity and demonstrated that ILS, gene tree estimation error, and gene flow contributed to 15%, 52%, and 32% of the variation, respectively. Together, our results suggest that a perfect storm of factors likely influence this lack of resolution, and further indicate that recalcitrant phylogenetic relationships like the backbone of Malpighiales may be better represented as phylogenetic networks. Thus, reducing such groups solely to existing models that adhere strictly to bifurcating trees greatly oversimplifies reality, and obscures our ability to more clearly discern the process of evolution.</p>
Data for: Gene tree estimation error with ultraconserved elements: An empirical study on Pseudapis bees
<p>Summarizing individual gene trees to species phylogenies using two-step coalescent methods is now a standard strategy in the field of phylogenomics. However, practical implementations of summary methods suffer from gene tree estimation error, which is caused by various biological and analytical factors. Greatly understudied is the choice of gene tree inference method and downstream effects on species tree estimation for empirical data sets. To better understand the impact of this method choice on gene and species tree accuracy, we compare gene trees estimated through four widely used programs under different model-selection criteria: PhyloBayes, MrBayes, IQ-Tree and RAxML. We study their performance in the phylogenomic framework of > 800 ultraconserved elements from the bee subfamily Nomiinae (Halictidae). Our taxon sampling focuses on the genus <i>Pseudapis</i>, a distinct lineage with diverse morphological features, but contentious morphology-based taxonomic classifications and no molecular phylogenetic guidance. We approximate topological accuracy of gene trees by assessing their ability to recover two uncontroversial, monophyletic groups, and compare branch lengths of individual trees using the stemminess metric (the relative length of internal branches). We further examine different strategies of removing uninformative loci and the collapsing of weakly supported nodes into polytomies. We then summarize gene trees with ASTRAL and compare resulting species phylogenies, including comparisons to concatenation-based estimates. Gene trees obtained with the reversible jump model search in MrBayes were most concordant on average and all Bayesian methods yielded gene trees with better stemminess values. The only gene tree estimation approach whose ASTRAL summary trees consistently produced the most likely correct topology, however, was IQ-Tree with automated model designation (MFP). We discuss these findings and provide practical advice on gene tree estimation for summary methods. Lastly, we establish the first phylogeny-informed classification for <i>Pseudapis</i> s. l. and map the distribution of distinct morphological features of the group.</p>
ScienceDex guides
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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.