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685 results for “errors”
Dataset for publication: Error in the measurement of partial discharge pulses according to the frequency response of HFCT sensors
<p>Data set belonging to the <a href="https://ieeexplore.ieee.org/xpl/conhome/9611374/proceeding">2021 IEEE Electrical Insulation Conference (EIC)</a> paper with DOI: <a href="https://doi.org/10.1109/EIC49891.2021.9612353">10.1109/EIC49891.2021.9612353</a></p> <p>The authors preprint is available from Zendodo with DOI: <a href="https://doi.org/10.5281/zenodo.5769815">10.5281/zenodo.5769815</a>. </p> <p>Kamlichi, Abderrahim, Garnancho Fernando; Álvarez Fernando</p> <p><em>Error in the measurement of partial discharge pulses according to the frequency response of HFCT sensors</em></p>
Dataset from: Phonon downconversion to suppress correlated errors in superconducting qubits
<p>Quantum error correction can preserve quantum information in the presence of local errors, but correlated errors are fatal. For superconducting qubits, high-energy particle impacts from background radioactivity produce energetic phonons that travel throughout the substrate and create excitations above the superconducting ground state, known as quasiparticles, which can poison all qubits on the chip. We use normal metal reservoirs on the chip back side to downconvert phonons to low energies where they can no longer poison qubits. We introduce a pump-probe scheme involving controlled injection of pair-breaking phonons into the qubit chips. We examine quasiparticle poisoning on chips with and without back-side metallization and demonstrate a reduction in the flux of pair-breaking phonons by over a factor of 20. We use a Ramsey interferometer scheme to simultaneously monitor quasiparticle parity on three qubits for each chip and observe a two-order of magnitude reduction in correlated poisoning due to background radiation.</p> <p>Article has been accepted for publication in Nature Communications.</p>
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>
The Tale of Errors in Microservices (Artifact part 1)
<p>This dataset provides comprehensive microservice traces (around 1.4 million) collected from Uber microservice architecture, as described in our paper <strong>The Tale of Errors in Microservices</strong>, presented at <strong>SIGMETRICS 2025</strong>. This dataset enables researchers to study microservice behaviors, optimize performance, and investigate latency reduction techniques.</p> <p>The data has been sanitized to protect proprietary information while retaining critical performance characteristics for academic research.</p> <h3>Artifact Structure and Decompression Instructions:</h3> <p>Due to Zenodo's file size constraints and upload issues, the large <code>trace1-sanitized.tar.zst</code> and <code>trace2-sanitized.tar.zst</code> files have been split into multiple pieces. The artifact is available in two parts (<a href="#" rel="noopener">10.5281/zenodo.13947828</a> and <a href="https://doi.org/10.5281/zenodo.13952897">10.5281/zenodo.13952897</a>). To access the sanitized microservice traces, download all the split parts from both artifacts. After downloading, reassemble the files using the following commands and then decompress the <code>.zst</code> files individually. Each <code>.zst</code> file will require 300-500GB of disk space to decompress.</p> <h4>Reassembling the split files:</h4> <p>For <code>trace1-sanitized.tar.zst</code> and <code>trace2-sanitized.tar.zst</code>, use the following commands to reassemble them:</p> <div> <div><code>cat trace1_* > trace1-sanitized.tar.zst</code></div> <div><code>cat trace2_* > trace2-sanitized.tar.zst</code></div> </div> <p>Once reassembled, you can decompress the files:</p> <div> <div><code>zstd -d trace1-sanitized.tar.zst </code></div> <div><code>zstd -d trace2-sanitized.tar.zst </code></div> </div> <h4>Contents of the Traces:</h4> <ul> <li><code>trace1-sanitized.tar.zst</code> and <code>trace2-sanitized.tar.zst</code> (in <a href="#" rel="noopener">10.5281/zenodo.13952897</a>) contain around 1.4 million traces that correspond to the data described in Sections 3 and 4 of the original paper. <strong>Note:</strong> The traces in this dataset were collected on different days than those used in the paper, so analysis results may vary slightly from what is reported in the publication.</li> <li><code>driver-sanitized.tar.zst</code> contains the sanitized version of the original trace and corresponds to the <strong>App-Launch Use Case</strong> discussed in Section 6.3 and Figure 17 of the original paper.</li> </ul> <p><strong>Note</strong></p> <ul> <li>Due to privacy and security concerns, most unrelated fields and tags are removed. However, <strong>error-related</strong> tags are retained.</li> <li>All trace is sanitized consistently. The same service or endpoint will have identical mapping across three directories. (i.e., service 1 represents the same service in all traces). However, the mapping is <strong>inconsistent</strong> with <a href="https://zenodo.org/records/13956078">https://zenodo.org/records/13956078</a>, so please do not mix the traces between the two artifacts.</li> <li>To preserve privacy, the start time of each trace has been randomly shifted. As a result, the start and end times in the traces do not reflect the actual collection times, and users should not attempt to infer when the traces were gathered.</li> <li>Within each trace, the relative durations and timestamps of all spans remained consistent, as the shift was applied uniformly across the entire trace.</li> </ul> <p>If you use the traces in your research, please cite our paper</p> <blockquote> <p><strong>The Tale of Errors in Microservices</strong><br><em>I-Ting Angelina Lee (Washington University in St. Louis); Zhizhou Zhang, Abhishek Parwal (Uber Technologies Inc.); Milind Chabbi (Uber Technologies)<br>SIGMETRICS 2025 <a href="https://doi.org/10.1145/3700436">https://doi.org/10.1145/3700436</a></em></p> </blockquote> <p>If you have more questions, you can reach out to <a href="https://lollllcat.github.io/">Chris(Zhizhou) Zhang</a>.</p>
Data from: Prediction error and repetition suppression have distinct effects on neural representations of visual information
Predictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.
Data used in the publication: Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor
<p>These data support the results presented in the manuscript titled "Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor". They consist of Observation minus Background data and rainrate data that are used to build the symmetric error model of reflectivity. We uploaded two data sets that support both original and revised manuscripts.</p>
Reward is not Reward: Differential Impacts of Primary and Secondary Rewards on Expectation, Outcome, and Prediction Error in the Human Brain's Reward Processing Regions
<p>Raw data accompanying the following article:</p><p>Ulrich, M., Rüger, A., Durner, V., Grön, G., & Graf, H. (2023). Reward is not reward: Differential impacts of primary and secondary rewards on expectation, outcome, and prediction error in the human brain's reward processing regions. <i>Neuroimage</i>, 283, 120440. doi:10.1016/j.neuroimage.2023.120440</p>
Error Metrics from ML models trained in Desai et al.
<p>Error metrics generated from training different machine learning models with varying levels of added gaussian noise.</p>
Effects of Moderate-Intensity Interval Training (MIIT) on Mental Toughness, Sports Motivation, Athlete Burnout, Perceived Stress, Unforced Errors and Performance of Competitive Badminton Players in Ch
ClinicalTrials.gov study NCT06496737. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Ocular Development and Refractive Error Changes Among Children and Adolescent - A Longitudinal Study
ClinicalTrials.gov study NCT03589937. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Proficiency Based Training to Investigate Blood Sampling Errors Including WBIT
ClinicalTrials.gov study NCT03577561. IPD Sharing: YES. Countries: 1. Publications: 0.
Influence of Diagnostic Errors on the Prognosis of Acute Pulmonary Embolism
ClinicalTrials.gov study NCT03101384. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
A Prospective Comparison of Alcon LADARVision Wavefront-guided LASIK Enhancement and Conventional LASIK Enhancement for the Correction of Residual Refractive Errors Following LASIK Procedures
ClinicalTrials.gov study NCT00413881. IPD Sharing: Not stated. Countries: 1. Publications: 0.
First-Sight Refractive Error Correction: Direct Comparison to Autorefraction Results in Children 7 to 18 Years of Age
ClinicalTrials.gov study NCT01365884. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Visual Feedback on Angular Error
ClinicalTrials.gov study NCT07386977. IPD Sharing: NO. Countries: 1. Publications: 0.
Predicting Future Errors During Skill Performance
ClinicalTrials.gov study NCT06707207. IPD Sharing: YES. Countries: 1. Publications: 0.
Changes in Refractive Error in Patients With Both Partially Refractive Esotropia and Amblyopia
ClinicalTrials.gov study NCT04429659. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Academic Performance and Refractive Error
ClinicalTrials.gov study NCT04338880. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Reducing Medication Errors on Basis of an Individual Risk Assessment
ClinicalTrials.gov study NCT01814280. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Attention Training to Modify Error-related Negativity and Risk for Anxiety in Adolescence
ClinicalTrials.gov study NCT03176004. IPD Sharing: YES. Countries: 1. Publications: 0.
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.