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685 results for “errors”
Data from: Inability of recognizing offspring underlies parental errors in the selection of offspring
<p>Inclusive fitness theory is an important theoretical framework in demonstrating the evolution of social behaviors. However, in the scenario of caring for offspring, it is difficult for the inclusive fitness theory to demonstrate the errors parents made in offspring selection, either rejecting their own offspring (type I error) or accepting the offspring of others (type II error). To address why parents make errors in caring for offspring and the relationship between making errors and parental ability of offspring recognition, we designed a series of experiments in the azure-winged magpie (<em>Cyanopica cyanus</em>). By manipulating the nest spatial position or cross-fostering chicks, we examined parental care strategies for the manipulated offspring and tested whether offspring age and parent-offspring familiarity affected the occurrence of both errors. In the nest resettlement experiment, the probability of type I error significantly increased with nest-moving distances while decreased with offspring ages. In the cross-fostering experiments, the probability of type II error significantly decreased with the age difference between cross-fostered chicks. In the experiments of reselection between familiar offspring and unfamiliar unrelated chicks or between unfamiliar offspring and familiar cross-fostered chicks, the probability of both errors was closely related to the time when parents were deprived the association with their offspring. Given offspring phenotypic traits become individualized and fixed with their growth, it imposes an aging effect on parental ability of offspring recognition. Parental errors in offspring selection depend largely on the age of offspring when the environmental or offspring signals are manipulated.</p>
Error Windows 7
Error Windows 7 YouTube : https://www.youtube.com/channel/UC_fCF2P36WoxfJVulbzXntw?view_as=subscriber twitter : https://twitter.com/Win_Nder Source: Objaverse 1.0 / Sketchfab
Data and code from: Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments
<p>Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (<em>Negaprion brevirostris</em>) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.</p>
SDSS DR8 with Petrosian Errors
<p>SELECT<br> G.ra, G.dec, S.mjd, S.plate, S.fiberID, S.class, S.z, S.zErr, S.rChi2, S.velDisp, S.velDispErr,<br> G.extinction_u, G.extinction_g, G.extinction_r, G.extinction_i, G.extinction_z,<br> G.fiberMag_u, G.fiberMag_g, G.fiberMag_r, G.fiberMag_i, G.fiberMag_z, <br> G.modelMag_u, G.modelMag_g, G.modelMag_r, G.modelMag_i, G.modelMag_z, <br> G.cmodelMag_u, G.cmodelMag_g, G.cmodelMag_r, G.cmodelMag_i, G.cmodelMag_z,<br> phioffset_r, G.fracdeV_r, <br> G.deVRad_r, G.deVRadErr_r, G.deVAB_r, G.deVABErr_r, deVPhi_r, G.lnLDeV_r, G.deVMag_r, G.deVMagErr_r, <br> G.expRad_r, G.expRadErr_r, G.expAB_r, G.expABErr_r, expPhi_r, G.lnLExp_r, G.expMag_r, G.expMagErr_r, <br> G.petroMag_u, G.petroMag_g, G.petroMag_r, G.petroMag_i, G.petroMag_z, <br>G.petroMagErr_u, G.petroMagErr_g, G.petroMagErr_r, G.petroMagErr_i, G.petroMagErr_z, <br> G.petroRad_u, G.petroRad_g, G.petroRad_r, G.petroRad_i, G.petroRad_z, G.petroR50_r, G.petroR90_r, G.petroR50Err_r, G.petroR90Err_r, <br> GSL.h_alpha_flux, GSL.h_alpha_flux_err, GSL.oiii_5007_flux, GSL.oiii_5007_flux_err,<br> GSX.d4000, GSX.d4000_err, <br> GSE.bptclass, GSE.lgm_tot_p50, GSE.sfr_tot_p50, G.objID, <br> GSI.specObjID<br>INTO mydb.sdss_specgal_dr8 FROM SpecObj S CROSS APPLY<br> dbo.fGetNearestObjEQ(S.ra, S.dec, 0.06) N, Galaxy G,<br> GalSpecInfo GSI, GalSpecLine GSL, GalSpecIndx GSX, GalSpecExtra GSE<br>WHERE N.objID = G.objID<br> AND GSI.specObjID = S.specObjID<br> AND GSL.specObjID = S.specObjID<br> AND GSX.specObjID = S.specObjID<br> AND GSE.specObjID = S.specObjID<br> AND (G.petroMag_r > 10 AND G.petroMag_r < 18)<br> AND (G.modelMag_u-G.modelMag_r) > 0<br> AND (G.modelMag_u-G.modelMag_r) < 6<br> AND (modelMag_u > 10 AND modelMag_u < 25)<br> AND (modelMag_g > 10 AND modelMag_g < 25)<br> AND (modelMag_r > 10 AND modelMag_r < 25)<br> AND (modelMag_i > 10 AND modelMag_i < 25)<br> AND (modelMag_z > 10 AND modelMag_z < 25)<br> AND S.rChi2 < 2<br> AND (S.zErr > 0 AND S.zErr < 0.01)<br> AND S.z > 0.0</p>
ND250 as a prediction error signal in orthographic processing: insights from the comparison of handwritten and printed words
<p><span>This dataset contains electroencephalography (EEG) recordings and behavioral data from a study investigating the neural mechanisms of visual word recognition in native Chinese speakers. The study used a color decision task, where participants viewed printed and handwritten Chinese single-character words varying in lexical frequency (high-frequency vs. low-frequency). The primary aim was to examine the N250 ERP component, a 250-ms difference in brain activity observed between certain word types, and determine whether it reflects activation of the orthographic lexicon or a prediction error signal during orthographic processing. The findings suggest that the N250 is related to prediction error, providing support for the Interactive Account of orthographic processing.</span></p>
Data from "Hardware-efficient quantum error correction using concatenated bosonic qubits"
<p>Includes data for characterizing the bit-flip and logical phase-flip rates of the logical memory. See the README for more details.</p>
Constructing CIs for 'the' Generalization Error
<p>This data contains information that is too large to be included in our GitHub repository: https://github.com/slds-lmu/paper_2023_ci_for_ge.<br><br>After downloading the data you need to first extract/decompress it.<br><br>The files are:<br><br>* results - Result datasets that need to be moved into the the GitHub repository in order to reproduce the analysis from the paper. Both the raw and processed datasets are contained.<br>* figures-granular - additional figures on a very granular level<br><br>The folders also contain READMEs describing the datasets and figures.<br><br>If you want to work with the datasets (all in .rds format) but don't want to work in R, you can simply load them in R (using readRDS(<path>)) and then convert them e.g. to csv using the write.csv function.</p>
Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns (repository for Genome Research paper, 2022)
<p>Simulated ONT and PacBio RNA-Seq data for "Sequencing of individual barcoded cDNAs on Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns" paper (Mikheenko et al., Genome Research, 2022). All details can be found in the Methods section of the paper.</p> <p><strong>PacBio.simulated_uniform_coverage.fasta.gz</strong> and <strong>ONT.simulated_uniform_coverage.fasta.gz files</strong> were used in Supplemental Note “Benchmarking of the read-to-isoform assignment algorithm”.</p> <p><strong>ONT.simulated_real_expression.fasta.gz</strong> file and all GTF files were used in the Section "Splice site correction improves transcript discovery precision". <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> was used as the annotation file for all tools. <strong>mouse.gencode.M26.spatial.15percent.expressed.gtf </strong>contains the set of all expressed isoforms. <strong>mouse.gencode.M26.spatial.15percent.expressed_kept.gtf</strong> contains those of the isoforms that are in presented in the annotation file ("known" transcripts), <strong>mouse.gencode.M26.spatial.15percent.reduced.gtf</strong> contains expressed isoforms that were removed from the annotation ("novel" transcripts).</p>
Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework
<p>Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework. Please see the <strong>README.pdf</strong> for step-by-step instructions for reproducing the entire analysis described in the paper.</p>
Code for APJAS publication - Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the APJAS publication "Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate"</p> <p>There are three WDM6 codes for simulations. The original WDM6 code (ORG) using parameter defined by Hong et al (2004), the revised WDM6 code (NEW) those revised by removing the numerical errors, and the additional WDM6 code (SEN) for sensitivity experiment adopting the column-shaped parameters.</p> <p>In supplement, several cloud-ice characteristics presented in the paper were induced in detail and compared with Hong et al (2004) and this study.</p>
Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
<p>The performance of species distribution models is known to be affected by the analysis grain and the positional error of species occurrences. Coarsening of the spatial analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in predictive models developed for conservation and climate change studies it is increasingly important to test this assumption. Species distribution models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate species occurrences might easier end up in unsuitable environment, which can result in inappropriate conservation actions.</p> <p>Here, we examine the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated virtual species using tree canopy height, topography wetness index, and altitude derived from LiDAR point clouds at 5 x 5 m fine-resolution. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three common modelling techniques (MaxEnt, BRT and GLM) and four discrimination metrics to evaluate model performance (Sørensen index, overprediction and underprediction rate, AUC and TSS).</p> <p>We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance.</p> <p>We recommend fitting models with the finest possible analysis grain (i.e., depending on data availablity) even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrence data, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences.</p>
Data for "Suppressing quantum errors by scaling a surface code logical qubit"
<p>Includes the circuits that were executed, the samples that were collected, the corrections predicted by several decoders, as well as other intermediate files. See the README.txt file at the root of the ZIP archive for a more detailed overview.</p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
Detecting and reducing heterogeneity of error in acoustic classification: Data
<ol> <li>Passive acoustic monitoring can be an effective method for monitoring species, allowing the assembly of large audio datasets, removing logistical constraints in data collection, and reducing anthropogenic monitoring disturbances. However, the analysis of large acoustic datasets is challenging, and fully automated machine-learning processes are rarely developed or implemented in ecological field studies. One of the greatest uncertainties hindering the development of these methods is spatial generalisability – can an algorithm trained on data from one place be used elsewhere?</li> <li>We demonstrate that heterogeneity of error across space is a problem that could go undetected using common classification accuracy metrics. Secondly, we develop a method to assess the extent of heterogeneity of error in a random forest classification model for six Amazonian bird species. Finally, we propose two complementary ways to reduce heterogeneity of error, by (i) accounting for it in the thresholding process and (ii) using a secondary classifier that uses contextual data.</li> <li>We found that using a thresholding approach that accounted for heterogeneity of precision error reduced the coefficient of variation of the precision score from a mean of 0.61±0.17 (SD) to 0.41±0.25 in comparison to the initial classification with threshold selection based on F-score. The use of a secondary, contextual classification with thresholding selection accounting for heterogeneity of precision reduced it further still, to 0.16±0.13, and was significantly lower than the initial classification in all but one species. Mean average precision scores increased, from 0.66±0.4 for the initial classification, to 0.95±0.19, a significant improvement for all species.</li> <li>We recommend assessing - and if necessary correcting for - heterogeneity of precision error when using automated classification on acoustic data to quantify species presence as a function of an environmental, spatial or temporal predictor variable.</li> </ol>
Raw and processed data for 'Purification-based quantum error mitigation of pair-correlated electron simulations'
<p>Experimental data for 'Demonstration of purification-based error mitigation for quantum simulation'</p> <p>This directory contains two folders - 'final_data_sets' and 'plots_for_paper'.<br> 'final_data_sets' contains directories with raw experimental data, either in the form of raw shots, or accumulated expectation values. Everything is stored in either json or pickle format. This data was processed to generate the datasets in subfolders of 'plots_for_paper', which also contains notebooks for generating plots in the paper (data processing scripts to appear soon). Note that the python file to generate Fig.3 is in 'plots_for_paper/paper_plot_ring_opening_sixq_10q'.</p> <p>For any further questions, please email teobrien@google.com.</p>
East Cascades Error Adjusted Area Estimates
<p>The files in this folder include the error-adjusted area estimates of seven structural classes for five strata, as well as the annual area burned for each class at different levels of fire severity. Files with “_entire_” include the error-adjusted area estimates of each class for the complete extent of each strata, while files with “_unburned_” include the error-adjusted area estimates of each class for only the portion of each strata that was unburned during the study period. All area estimates are in hectares.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation with Dual-Weighted Residual Error Measures".</p> <p>The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.</p>
The School Leavers' Study Data Verification: A Cross Check for Typographical Errors, Omissions, and Dubious Word Interpretations
<p>This data set reflects a verification procedure to check for typographical errors, data omissions, and dubious word interpretations made by UK data curators. During the course of verification, the XML files contained in the UK Data Service repository were cross checked against the RTF files. In the course of verifying the documents, some changes were adopted to wording, as with essay 110, where the word "regretted" makes sense in the sentence as opposed to the suggested term "{regarted?}" noted in the XML file. Any changes during verification were performed by the close reading method, whereby contextual clues in the sentence guided the investigator to make a guess on the term intended by the original essay writer. When contexts were not sufficient to resolve meanings and spellings, original words and spellings were kept. Examples of former omissions within documents include essay number 148. The following statement was omitted from the XML file: "I retired not long ago after forty years with the same company. Life is very tedious now and I spend most of my time just sitting around thinking of the past." Essay 150 suffered the same problem: "life my first interview was nerve wracking.", exists in the RTF file, but was missing from the XML file. In cases where the RTF and XML files are in dispute over omissions, I have included the missing information from the RTF file into the data set. Place names have been edited so they read with standard British spellings. The data have been cleaned and standardized to American spellings, and this reflects the investigator's framework over the data. English turns of phrases were retained as much as possible. The investigator is aware that any transcription of historical data (e.g. be it from the curators' translations of writings on original printed pages to digital objects) includes adding personal perspective into the documents themselves. This is a limitation to the data cleaning process, and is here noted. Readers are invited to compare and contrast this verified data set with the original XML and RTF files.The second edition of the data deposited in the UK repository is cited as: </p> <p>Pahl, R.E. (2012). <em>School Leavers Study, 1978</em>. [data collection]. <em>2nd Edition. </em>UK Data Service. SN: 4867, <a href="http://doi.org/10.5255/UKDA-SN-4867-1">http://doi.org/10.5255/UKDA-SN-4867-1</a></p> <p>The original data are available under a Creative Commons-NonCommercial-ShareAlike 4.0 International license. The data may be accessed through the UK Data Service Qualibank.</p>
A method for correcting staggered pulse repetition time (PRT) and dual Pulse Repetition Frequency (PRF) processor errors
<p>A method for correcting dual-PRF and staggered-PRT induced radial velocity errors. This method is being developed by the authors at the University of Oklahoma and is considered to be a work in progress. v2 of the algorithm will be made available in the coming months (late 2019 or early 2020).</p>
SPAligner: alignment of long error-prone reads to assembly graphs
<p>This repository contains benchmarking datasets and scripts for the manuscript "SPAligner: alignment of long error-prone reads to assembly graphs". </p> <p>Graph representation of genome assemblies has been recently used in different applications — from gene finding to haplotype separation. While many of these applications are based on aligning DNA and protein sequences to assembly graphs, existing software tools for finding such alignments have important limitations. We present a novel SPAligner (Saint Petersburg Aligner) tool for aligning long reads to assembly graphs and demonstrate that it generates accurate alignments.</p> <p> </p> <p> </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.