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685 results for “errors”
Data from: Sharing is caring? measurement error and the issues arising from combining 3D morphometric datasets
Geometric morphometrics is routinely used in ecology and evolution and morphometric datasets are increasingly shared among researchers, allowing for more comprehensive studies and higher statistical power (as a consequence of increased sample size). However, sharing of morphometric data opens up the question of how much nonbiologically relevant variation (i.e., measurement error) is introduced in the resulting datasets and how this variation affects analyses. We perform a set of analyses based on an empirical 3D geometric morphometric dataset. In particular, we quantify the amount of error associated with combining data from multiple devices and digitized by multiple operators and test for the presence of bias. We also extend these analyses to a dataset obtained with a recently developed automated method, which does not require human-digitized landmarks. Further, we analyze how measurement error affects estimates of phylogenetic signal and how its effect compares with the effect of phylogenetic uncertainty. We show that measurement error can be substantial when combining surface models produced by different devices and even more among landmarks digitized by different operators. We also document the presence of small, but significant, amounts of nonrandom error (i.e., bias). Measurement error is heavily reduced by excluding landmarks that are difficult to digitize. The automated method we tested had low levels of error, if used in combination with a procedure for dimensionality reduction. Estimates of phylogenetic signal can be more affected by measurement error than by phylogenetic uncertainty. Our results generally highlight the importance of landmark choice and the usefulness of estimating measurement error. Further, measurement error may limit comparisons of estimates of phylogenetic signal across studies if these have been performed using different devices or by different operators. Finally, we also show how widely held assumptions do not always hold true, particularly that measurement error affects inference more at a shallower phylogenetic scale and that automated methods perform worse than human digitization.
Error
<p>..</p>
A dataset to reproduce Slingshot error 'curves appear to be going in opposite directions'
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Relative xylanase activities [%] obtained from fermentation by using C. phragmiteti in LB medium containing 1 w/w% of dry matter waste paper as a function of fermentation time. Average values of relative xylanase activity are presented with error bars representing standard deviations of tripli-cates.
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Dataset for "LLM-aided explanations of EDA synthesis errors"
<div> <h1>Dataset for "LLM-aided explanations of EDA synthesis errors"</h1> </div> <div>Authors: Siyu Qiu, Benjamin Tan, Hammond Pearce</div> <div> </div> <div>This Zenodo contains the open-source data used for the ISLAD submission "LLM-aided explanations of EDA synthesis errors" which aimed to use OpenAI LLMs for generating novice-focused explanations of common synthesis errors.</div> <h2>Error explanations for RTL and HDL Code with OpenAI's LLMs</h2> <div>Welcome to our error explanation tool for RTL and HDL code in Verilog and VHDL! This repository helps you generate error explanations for your code using OpenAI's models, making it easier to find and fix bugs.<br> <h2>Directory Structure</h2> </div> <div> <div>- `new_structure/`: Contains all bugs in separate files, along with labelling CSV files that match the LLM responses for each bug.</div> <div>- `bug_id/`: Each bug has responses from gpt-3.5-turbo (40 responses), gpt-4 (4 responses), and gpt-4-turbo-preview (4 responses).</div> <br> <div>- `rtl/`: Includes RTL code used for bugs, compatible with both Quartus and Vivado.</div> <br> <div>- `Quartus/`: Contains Quartus project files (.qpf) and constraints files (.qsf).</div> <br> <div>- `Vivado/`: Holds Vivado project files (.xpr) and constraints files (.xdc).</div> <br> <div>- `llm_responses/`: Contains CSV files with records of the generated LLM responses for each bug.</div> <br> <div>- `labelling/`: Contains CSV files with manually scored metrics for evaluating the LLM responses.</div> <br> <div>- `error_list.csv`: This file lists all bugs with details like bug id, type, IDE, file name, language, and error message. main.py uses this file to process bugs.</div> <br> <div>- `main.py`: Use this script to generate error explanations for your RTL and HDL code with OpenAI's models. It reads bug information from error_list.csv, loads the buggy code, and interacts with the OpenAI API to get explanations for the bugs.</div> <br> <div>- `try.py`: This script defines dictionaries to store statistics about bug evaluations. It loads bug data from CSV files, processes each file to update the statistics, and prints a summary table using the tabulate library.</div> <br> <div>The labelling directories contains CSV files with metrics for evaluating the LLM responses. These metrics include Conceptual Accuracy, Inaccuracy, Relevance, Completeness, and Solution Provided. try.py processes these files to generate summary statistics.</div> <br> <h2>Important note:</h2> You need to create an API key for OpenAI and save it as a file called OPENAI_TOKEN in the root project directory. gitignore will ignore this file.</div>
msd-errors - LLZO simulation trajectories
<p>Simulation trajectories at a range of temperatures of LLZO, produced using MetalWalls.</p>
Numerical data for Figures in Radio Science paper entitled "Flatness Error Compensation of Large Near-Field Scanning Frame Based on Cubic Spline Interpolation"
<p>Data format is EXCEL</p>
Error Identification Strategies for Python Jupyter Notebooks
<p>Replication package for the paper "<strong>Error Identification Strategies for Python Jupyter Notebooks</strong>".</p>
Table X: Population-wise trait data (mean, standard error and ranges) of A) 1st year plants, B) 2nd year spring phase plants and C) 2nd year monsoon phase plants
<p>Plants of <em>Anisomeles indica</em> are perennial woody shrubs that initiate their life cycle from seeds. The young plants emerging from seeds enter sexual phase immediately thereafter. After setting seeds, the plants do not die. Instead, following winters, their root stock perennates where after these plants follow spring and monsoon phases for the rest of their lives. Plants follow vegetative to blooming phenology step by step without showing any functional overlap in their reproductive phase. The spring phase of perennating plants on account of its brevity is unable to ensure sustainable reproductive output necessitating the monsoon phase. Parallelly, the seeds dispersed through these phases grow and follow the same strategy of sexual to spring to monsoon phases. This modified variant of iteroparity coupled with the advantages accrued to particular morphological characters such as increased plant height, numbers of tillers and inflorescences per plant, fruits per infructescence and seed weight per unit leaf area enhance its survival and reproductive fitness.</p>
Volumetric Benchmarking of Error Mitigation with Qermit: Experimental Data
<p>Here we give results and experiment definitions which correspond to the experiments shown in the paper "Volumetric Benchmarking of Error Mitigation with Qermit". Key files and folders are the following:</p> <ul> <li>experiments.csv - Each experiment, assigned a unique ID as given in experiments.csv, is defined by a circuit, observable pair. Each circuit and observable is also given a unique ID. The pairs that define the experiments conducted in the paper "Volumetric Benchmarking of Error Mitigation with Qermit" are given in experiments.csv.</li> <li>device_experiments.csv - Contains the subset of those experiments listed in experiments.csv which were run on real devices in the paper "Volumetric Benchmarking of Error Mitigation with Qermit".</li> <li>Observables are accessible in observables.zip and the circuits in circuits.zip. In both cases the file names match their ID.</li> <li>circuits.csv - Circuits are specified by a unique ID. circuits.csv gives additional details about the properties of the circuit, including their size and type. </li> <li>results.csv - Contains experiment results. Each result is specified by a unique ID, with results.csv containing information about the device used, the error mitigation strategy used, and the time at which the experiment took place.</li> <li>gen_experiments.py - A command line tool used to generate the experiments performed.</li> <li>example_experiment.ipynb - An example of how one may access and use the experiment specifications given here. This matches roughly the process taken in the experiments of "Volumetric Benchmarking of Error Mitigation with Qermit".</li> <li>{device}_{date}.pkl - Device characteristics on the day the experiments were conducted.</li> </ul>
Astra Memory Error and System Monitoring Data Sets
<p>Memory error events and system monitoring information gathered on Astra while it was operating from January 20, 2019 to September 14, 2019. See the dataset description files in the archive for additional details.</p>
An Improved Method Based on Deep Learning for Refined Bathymetry from Satellite Altimetry: Reducing the Errors Effectively
<p><strong>The source dataset and code for this research.</strong></p>
Data accompanying the publication "Quantum error correction with silicon spin qubits"
<p>Data accompanying the publication "Quantum error correction with silicon spin qubits"</p>
Output data and namelist - README file of "Evolution of squall line variability and error growth in an ensemble of LES'
<p>See README.txt</p>
Quantum error correction with molecular spin qudits. Open dataset
<p>Data supporting the original figures 1, 3, 4, 5 of the related publication.</p>
Supplementary material 1 from: Jacobs LEO, Richardson DM, Lepschi BJ, Wilson JRU (2017) Quantifying errors and omissions in alien species lists: The introduction status of Melaleuca species in South Africa as a case study. NeoBiota 32: 89-105. https://doi.org/10.3897/neobiota.32.9842
Appendix and Supplementary Figures : Explanation note:
Estimating the redshift error in supernova data analysis_Code supplement
<p>Supplement for 'Estimating the redshift error in supernova data analysis'</p>
Statistical Errors in Experimental Software Engineering: Possible Causes and Recommended Solutions
<p>to define</p>
Datasets used in "Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat"
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Data for Streaming prediction-error filters
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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.