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685 results for “errors”
Mutations in ABHD12 cause the neurodegenerative disease PHARC: An inborn error of endocannabinoid metabolism
GEO Series GSE23151. Homo sapiens. 26 samples. Type: SNP genotyping by SNP array.
Comparison of mitochondrial transcription errors in wild type and mutant mitochondrial RNA polymerase overexpression flies
GEO Series GSE154310. Drosophila melanogaster. 4 samples. Type: Expression profiling by high throughput sequencing.
Training data for the shared task Ideology and Power Identification in Parliamentary Debates - with errors please do not use!
<p><strong>This dataset is an early release with errors. Please do not use this data set. The official dataset for the shared task will be announced on <a href="https://touche.webis.de/clef24/touche24-web/ideology-and-power-identification-in-parliamentary-debates.html">the shared task webpage</a>.</strong></p>
A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
<p>This repository makes available the audio samples related to the paper:</p> <p>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, <em>A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes</em>, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022.</p> <p>The experiment and the three case studies are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li> <p><strong>Experiment Samples</strong>: the 10 seconds long samples used in the experiment;</p> </li> <li> <p><strong>Long Samples</strong>: the original 6 minutes long samples, one for each of the identified cases.</p> </li> </ul> <p>Here is the notation of the file naming:</p> <ul> <li> <p>W: recording (writing);</p> </li> <li> <p>R: reproducing;</p> </li> <li> <p>3: 3.75 NAB;</p> </li> <li> <p>7N: 7.5 NAB;</p> </li> <li> <p>7C: 7.5 CCIR;</p> </li> <li> <p>15C: 15 CCIR.</p> </li> </ul> <p>For what concerns the <strong>Experiment Samples</strong> directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation:</p> <ul> <li> <p>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</p> </li> <li> <p>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</p> </li> <li> <p>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</p> </li> </ul> <p>Here are the variants of the samples:</p> <ul> <li> <p>REFERENCE: produced by using the correct equalization standard;</p> </li> <li> <p>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</p> </li> <li> <p>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</p> </li> <li> <p>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</p> </li> <li> <p>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</p> </li> </ul> <p>Here is the samples list:</p> <ul> <li> <p>SET A:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample1: Taylor Swift - <em>Shake It Off</em>;</p> </li> <li> <p>sample5: Queen - <em>We Will Rock You</em>;</p> </li> <li> <p>sample8: Bruno Maderna - <em>Continuo</em>;</p> </li> <li> <p>sample9: Luciano Berio - <em>Différences</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample22: CLIPS project - <em>LP4m18bZ</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample15: CLIPS project - <em>LP1f20bZ</em>;</p> </li> <li> <p>sample16: CLIPS project - <em>LP4m20bZ</em>;</p> </li> <li> <p>sample17: CLIPS project - <em>LP1f19bZ</em>;</p> </li> <li> <p>sample18: CLIPS project - <em>LP4m19bZ</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET C:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample2: The Weeknd - <em>Save Your Tears</em>;</p> </li> <li> <p>sample6: Eagles - <em>Hotel California</em>;</p> </li> <li> <p>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</p> </li> <li> <p>sample12: Bruno Maderna - <em>Syntaxis</em>.</p> </li> </ul> </li> </ul> </li> </ul>
error
<p>Data </p>
Dataset of the paper "Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models"
<p>The Excel file contains the dataset whose analysis has been presented in the manuscript entitled <em>"Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models" </em>and published in J Neuroeng Rehabil (doi: 10.1186/s12984-024-01339-4; PMID: 38566189).</p> <p>This data set cannot be publicly available because of sensitive information. Please send your request to: a.caronni@auxologico.it.</p>
Error Governor for Active Fault Tolerance in PID Control of MIMO Systems
<p>The dataset complements the article under review, "Error Governor for Active Fault Tolerance in PID Control of MIMO Systems," by reporting the simulation results obtained from testing the proposed Error Governor approach on the Zagi flying-wing UAV model.</p> <p>Run the main file "plot_images.m" to generate plots from the simulation data.</p>
A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes: audio samples
<p>This repository makes available the audio samples related to the experiments described in the paper: </p> <p><em>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza. 2021. A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes. In Proceedings of the 16th International Conference on Audio Mostly (AM '21). Association for Computing Machinery, New York, NY, USA</em></p> <p>The experiment and the three case studies are described in the publication below. Here is the summary of their recording/reproducing standards and their notation.</p> <ul> <li>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</li> <li>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</li> <li>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</li> </ul> <p>Here is the notation of the file naming: </p> <ul> <li>W: recording (writing);</li> <li>R: reproducing;</li> <li>3: 3.75 NAB;</li> <li>7N: 7.5 NAB;</li> <li>7C: 7.5 CCIR;</li> <li>15C: 15 CCIR.</li> </ul> <p>Here are the variants of the samples:</p> <ul> <li>REFERENCE: produced by using the correct equalization standard;</li> <li>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</li> <li>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</li> <li>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</li> <li>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</li> </ul> <p>Here is the samples list:</p> <p>SET A:</p> <ul> <li>Training: <ul> <li>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</li> </ul> </li> <li>Test: <ul> <li>sample1: Taylor Swift - <em>Shake It Off</em>;</li> <li>sample5: Queen - <em>We Will Rock You</em>;</li> <li>sample8: Bruno Maderna - <em>Continuo</em>;</li> <li>sample9: Luciano Berio - <em>Différences</em>.</li> </ul> </li> </ul> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li>Training: <ul> <li>sample22: CLIPS project - <em>LP4m18bZ</em>;</li> </ul> </li> <li>Test: <ul> <li>sample15: CLIPS project - <em>LP1f20bZ</em>;</li> <li>sample16: CLIPS project - <em>LP4m20bZ</em>;</li> <li>sample17: CLIPS project - <em>LP1f19bZ</em>;</li> <li>sample18: CLIPS project - <em>LP4m19bZ</em>.</li> </ul> </li> </ul> <p>SET C:</p> <ul> <li>Training: <ul> <li>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</li> </ul> </li> <li>Test: <ul> <li>sample2: The Weeknd - <em>Save Your Tears</em>;</li> <li>sample6: Eagles - <em>Hotel California</em>;</li> <li>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</li> <li>sample12: Bruno Maderna - <em>Syntaxis</em>.</li> </ul> </li> </ul> <p>Supplementary material can be found at the following DOI: 10.5281/zenodo.5118708</p> <p> </p>
Units of Analysis Error in Orthodontic Meta-Analyses
<p>Data extracted from orthodontic systematic reviews to evaluate the unit of analysis error in meta-analyses.</p>
ConSeqUMI, an error-free nanopore sequencing pipeline to identify and extract individual nucleic acid molecules from heterogenous samples.
GEO Series GSE288938. Homo sapiens; mixed DNA library; Severe acute respiratory syndrome coronavirus 2. 42 samples. Type: Other.
A robust machine learning approach for missing persons cases with high genotyping errors
GEO Series GSE209804. Homo sapiens. 24 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
CT Network error reports
Open the record for dataset details and reuse information.
Trichostatin A does not correct specific errors of somatic cell nuclear transfer on the transcriptomic level; highlighing the non-random nature of oocyte-mediated reprogramming errors.
GEO Series GSE57247. Bos taurus. 16 samples. Type: Expression profiling by array.
Proteolytic processing of LRP2 on RPE cells regulates BMP activity to control eye size and refractive error
GEO Series GSE97125. Danio rerio. 36 samples. Type: Expression profiling by high throughput sequencing.
Error Distribution of Shallow Networks
Open the record for dataset details and reuse information.
Dataset from: "Articles Examining Medical Errors in Emergency Medicine Journals: A Comprehensive Visualization and Bibliometric Analysis"
Open the record for dataset details and reuse information.
Tracking the spelling performance of disadvantaged primary- and middle-school students enrolled on an innovative cross-grade program: Variations according to the nature of spelling errors considered
<p>Data set about the number of spelling errors (phonological, orthographical and morphological) produced in both 2020-2021 and 2021-2020 by students who enrolled on an innovative cross-grade program in a disadvantaged neighborhood. The second year their spelling performance have been compared to those of control's students from the same disadvantaged neighborhood but who not enrolled on the innovative program.</p>
ATC Taskload Inherent to the Geometry of Stochastic 4-D Trajectory Flows with Flight Technical Errors
A method to quantify the probabilistic controller taskload inherent to maintaining aircraft adherence to 4-D trajectories within flow corridors is presented. An Ornstein- Uhlenbeck model of the aircraft motion and a Poisson model of the flow scheduling are introduced along with reasonable numerical values of the model parameters. Analytic expressions are derived for the taskload probability density functions for basic functional elements of the flow structure. Monte Carlo simulations are performed for these basic functional elements and the controller taskload probabilities are exhibited.
Representation error in global model CO2 simulations over India
<p>The data provides the possible representation error in the model simulations of CO<sub>2 </sub>concentrations over India, with a spatial resolution of one degree. Representation error is is estimated following Pillai et al. (2010) using WRF-Chem-GHG simulations at 10 km x 10 km spatial resolution.</p> <p>Reference.</p> <p>Pillai, D., Gerbig, C., Marshall, J., Ahmadov, R., Kretschmer, R., Koch, T., and Karstens, U.: High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals, Atmospheric Chemistry and Physics, 10, 83–94, https://doi.org/10.5194/acp-10-83-2010, 2010.</p>
Error
<p>Data set from laboratorial tests with mites in termite nests. </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.