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685 results for “errors”
EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems
<p>Dataset of firmwares used for the experiments of the paper <em>EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems</em></p>
Fig. 7. The relative errors between the experimental and calculated 13C in Highly oxygenated isoryanodane diterpenoids from the leaves of Cinnamomum cassia and their immunomodulatory activities
Fig. 7. The relative errors between the experimental and calculated 13C NMR chemical shifts of 5 and 6.
Experimental Results for the PRICAI 2023 Paper ``Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains''
<p>This repository contains data related to the following paper:</p> <p>An overview of which domains have been tested on which software, refer to the following paper:<br> @InProceedings{Sleath2023PossibleModelingErrors,<br> author = {Kayleigh Sleath and Pascal Bercher},<br> title = {Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains},<br> booktitle = {Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023)},<br> year = {2023},<br> publisher = {Springer}<br> }</p> <p>Specifically, it contains:</p> <p>- the benchmarks used in the paper.<br> - screenshots of the output of call commands to the respective software artifacts<br> (the actual call commands are only contained in some of them)<br> - excel tables collecting the results</p> <p>Please note:</p> <p>(1) These benchmark problems are just copied in here for the sake of completeness and transparency. But if you are actually interested in using them, please use the newest version, which might have corrections and additional test cases. You find it here: https://github.com/ProfDrChaos/flawedPlanningModels</p> <p>(2) Please also note that the screenshots of our tests might not perfectly fit the folder structure of our benchmarks because we made some minor restructurings after the paper submission. Specifically, some test cases that we classified as syntactical in the submission were then changed into a semantic ones for the camera-ready version. (Hence the paths in the screenshots or tables might be slightly different.)</p> <p>(3) Also note that by the time you read this, several errors undetected by these systems at the time of the evaluation might be resolved by now. (We included the version numbers of the tested software.)</p>
Statistical error estimation from residual statistics of multiple collocated datasets: Data from synthetic experiments
<p>This archive contains the data used in the paper "How far can the statistical error estimation problem be closed by collocated data?" by A.Vogel and R.Menard (preprint available at: https://doi.org/10.5194/egusphere-2022-996) accepted for publication in Nonlinear Processes in Geophysics (NPG). </p> <p>The data refers to the synthetic experiments in Sect.5 of the paper which demonstrate the general ability to estimate statistical error covariances and cross-statistics from residual covariances, as well as the effects of inaccurate assumptions with respect to different setups.</p> <p>Further information on the data can be found in the README.txt file.</p>
Accompanying dataset: Binding of precursors to replicator assemblies can improve replication fidelity and mediate error correction
<p>Accompnaying dataset containing all of the simulated fibers. Full details are given in the aformentioned manuscript.<br> <br>The authors are grateful for support from the ERC (AdG 741774), the Dutch Ministry of Education, Culture and Science (Gravitation program 024.001.035), and the Center for Information Technology of the University of Groningen for providing access to the Peregrine high performance computing cluster. JW is supported by the China Scholarship Council. OM is partially supported by the John Templeton Foundation.</p>
Errors from linear performance estimation for CAIRT
<p>Errors from linear performance estimation for CAIRT</p>
Guangdong Lightning Mapping Array: Errors Evaluation and Preliminary Results
<p>The attached is the dataset assoicated with the paper titled "Guangdong Lightning Mapping Array: Errors Evaluation and Preliminary Results" which was submitted to Journal of Earth and Space Science.</p> <p>The Guangdong Lightning Mapping Array (GDLMA), as the first LMA in China, was deployed in Guangzhou, Guangdong province, China, in November 2018 by the Chinese Academy of Meteorological Sciences (CAMS) and New Mexico Institute of Mining and Technology (NMT). An evaluation was conducted using Monte Carlo and an aircraft track. The average timing uncertainty of GDLMA is 35 ns based on the distributions of reduced chi-square values. The detection efficiency of radiation sources within a 100 km range of the center of GDLMA exceeds 90%. Based on the aircraft track, at an altitude of 4-5 km within the network, the average horizontal error was 13 m and the average horizontal error was 41 m, consistent with the Monte Carlo results. Location errors outside the network exhibit noticeable directionality. The ability to characterize lightning channels varies with different location errors. In locations that are far from the network center, only the basic structure of lightning flash can be presented, while closer to the network, the flash channel structure can be mapped well. Compared with Low-to-Mid Frequency E-field Detection Array (MLFEDA), they were generally similar in overall structure, and some lightning flash characteristics such as flash duration and convex hull area exhibited consistency. However, GDLMA demonstrated better channel characterization capability, while MFLEDA performed better in processes such as leader/return strokes.</p>
The Effects of Differential Tropospheric Error on the Measurement of Wide-swath Interferometric Altimetry
<p>Related dataset and codes for the paper "The Effects of Differential Tropospheric Error on the Measurement of Wide-swath Interferometric Altimetry" submitted to IEEE TGRS.</p>
Trial to Reduce Antimicrobial Prophylaxis Errors (TRAPE)
ClinicalTrials.gov study NCT00114036. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Incremental Velocity Error as a New Treatment in Vestibular Rehabilitation
ClinicalTrials.gov study NCT03846830. IPD Sharing: NO. Countries: 1. Publications: 3.
Evaluation Of Non Infectious Presentation of Inborn Errors Of Immunity
ClinicalTrials.gov study NCT05870410. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Reducing Medication Errors and Time to Drugs Delivery During Pediatric Cardiopulmonary Resuscitation: a Multicenter RCT
ClinicalTrials.gov study NCT03021122. IPD Sharing: YES. Countries: 1. Publications: 5.
Effects of Power Ball on Proximal Muscle and Refractive Errors in Developmental Delay.
ClinicalTrials.gov study NCT06461572. IPD Sharing: NO. Countries: 1. Publications: 1.
Video Training to Reduce Insulin Administration Errors
ClinicalTrials.gov study NCT05658744. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Estimating Error in Locating Touch Sensation on Forearm in Healthy Middle School Going Children
ClinicalTrials.gov study NCT03546712. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
To Study Device Attributes by Investigating Errors Made in Use, Ease of Use and Preference Among Different Inhalers in Subjects With Asthma
ClinicalTrials.gov study NCT02195284. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Piggyback Sulcoflex Toric IOL for Correcting Refractive Error Following Corneal Transplantation
ClinicalTrials.gov study NCT01718184. IPD Sharing: Not stated. Countries: 1. Publications: 17.
DECIDE Just Culture: Conceptualization of Clinical Error
ClinicalTrials.gov study NCT06835517. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence: Wave 2
ClinicalTrials.gov study NCT06795477. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Maternal Inborn Errors of Metabolism in Pregnancy: A Pregnancy Registry Protocol
ClinicalTrials.gov study NCT02322177. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.