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685 results for “error”
Classes of errors in DOI names: evaluation dataset
<p>This dataset contains the results of the evaluation of the methodology presented in the article <em>Identifying and correcting invalid citations due to DOI errors in Crossref data</em> (<a href="https://arxiv.org/abs/2111.11263">https://arxiv.org/abs/2111.11263</a>).</p> <p>The file named 10_random_citations_per_rule.csv contains 193 randomly selected citations from the corrected citations obtained by the process described in the article (<a href="https://doi.org/10.5281/zenodo.4892551">10.5281/zenodo.4892551</a>). They were extracted using the script called evaluation.py, which can be viewed in the GitHub repository <em>open-sci/2020-2021-grasshoppers-code </em>(<a href="https://doi.org/10.5281/zenodo.4723983">10.5281/zenodo.4723983</a>).</p>
Data for "Soil CO2 efflux errors are lognormally distributed - Implications and guidance."
<p>Soil CO2 flux data at site ES-LMa of four automatic chambers in the control-openLand-subplot for the period from 2015-11-10 to 2016-11-10.</p> <p>These data were used for the publication:</p> <p>Wutzler, et al. (2020) "Soil CO2 efflux errors are lognormally distributed - Implications and guidance." Geoscientific Instrumentation, Methods, and Data Systems</p> <p>Variables, units and description are found in the ReadmeDataDescription.csv file</p> <p> </p>
Data for SciKit-SurgeryFRED publication "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research."
<p>This is data used in the publication;</p> <p><a href="https://www.spiedigitallibrary.org/profile/Steve.Thompson-90188">Stephen Thompson</a>, <a href="https://www.spiedigitallibrary.org/profile/Thomas.Dowrick-4289932">Tom Dowrick</a>, <a href="https://www.spiedigitallibrary.org/profile/Mian.Ahmad-4289934">Mian Ahmad</a>, <a href="https://www.spiedigitallibrary.org/profile/Jeremy.Opie-4314392">Jeremy Opie</a>, and <a href="https://www.spiedigitallibrary.org/profile/notfound?author=Matthew_Clarkson">Matthew J. Clarkson</a> "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980U (15 February 2021); <a href="https://doi.org/10.1117/12.2580159">https://doi.org/10.1117/12.2580159</a></p> <p>Data in summerSchoolGameLogs was collected using scikit-surgeryfred: v0.0.3 summer school 2020 (2020). DOI 10.5281/zenodo.3946090</p> <p>Data in in registration_results was collected using scikit-surgeryfred: v0.0.8 browser based user interface (2020). DOI 10.5281/ zenodo.4314971</p> <p>Each directory contains Python scripts to analyse the data as described in the above paper.</p> <p> </p>
Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'
<p><strong>Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>. </strong></p> <p>Data supporting the paper:</p> <blockquote> <p>Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. <em>The Cryosphere. </em>16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022</p> </blockquote> <p>Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley <em>et al.</em> (2022). </p> <p>NetCDF files are created by, and tested to be readable by, Python's xarray package.</p> <p>The dimensions of the netCDF file are as follows:</p> <ul> <li><strong>X</strong> - <em>x </em>coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>Y</strong> - <em>y</em> coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>time</strong> - temporal midpoint of velocity field.</li> </ul> <p>The variables of the netCDF file are as follows:</p> <ul> <li><strong>dmag</strong> - the absolute magnitude of the velocity, in metres per day.</li> <li><strong>dx</strong> - the velocity in the <em>x</em> direction, in metres per day.</li> <li><strong>dy</strong> - the velocity in the <em>y</em> direction, in metres per day.</li> <li><strong>date1</strong> - the date and time of the first scene acquisition.</li> <li><strong>date2</strong> - the date and time of the second scene acquisition.</li> <li><strong>baseline</strong> - the temporal baseline, in days, between scene acquisitions.</li> <li><strong>orbit_pair</strong> - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li><strong>mag_rmse</strong> - the root mean square error of the absolute velocity of the off-ice area. </li> <li><strong>dx_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dx_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>y</em> direction.</li> </ul>
Kinetochore life histories reveal an Aurora B dependent error correction mechanism in anaphase
<p>Dataset of kinetochore tracks in human RPE1 cells showing chromosome dynamics and segregation from prometaphase through to anaphase as described in detail in Sen, Harrison, Burroughs and McAinsh, 2021, https://doi.org/10.1101/2021.03.30.436326 Tracks correspond to 3D time-lapse movies of Ndc80-eGFP and were acquired in the 488nm channel using 1\% laser power, 50 ms exposure time/z-plane, 93 z-planes, 307 nm z-step, which results in 4.7 s/z-stack time frame. Cells are subject to nocodazole arrest-and-release or equivalent treatment with DMSO as indicated in the folder names, and some cells are subject to additional treatment with ZM to inhibit Aurora B (also indicated in folder names). Tracks were produced using kinetochore tracking software, KiT v2.3 (see Armond et al., 2016, Bioinformatics), available from https://github.com/cmcb-warwick/KiT/ </p>
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
Taming the fixed-node error in diffusion Monte Carlo via range separation
<p>Suplementary information.</p> <p>Contains the org-mode computational notebook with all the input data (geometries, basis sets, pseudo-potentials) and output data (computed energies, densities, number of determinants) related to the article.</p> <p>A csv file is created by the notebook and an HTML export of the notebook is also provided.</p>
Ionospheric Vertical Correlation Lengths Derived From IRI-2016 Model Errors
<p>Ionospheric vertical correlation lengths based on IRI-2016 model and Incoherent Scatter Radar (ISR) data.</p> <p><strong>Important! The analysis was performed in log space. </strong></p> <p>ISR used for this analysis:</p> <p>Jicamarca, Arecibo, Millstone Hill, Poker Flat ISR, and ResoluteBay North ISR.</p> <p>This metadata can be used for the construction of the covariance matrix for ionospheric data assimilation.</p> <p>Inside of the .nc file:</p> <p>lat=array of geomagnetic latitudes (degrees)<br> alt=arrays of altitudes (km)<br> vert_corr1=array of size (nalt, nlat), contains vertical correlation length above the reference point for different latitudes<br> vert_corr2=array of size (nalt, nlat), contains vertical correlation length below the reference point for different latitudes<br> </p>
Dataset: Neural correlates of error prediction in a complex motor task
<p>There are two files for each subject:</p> <p>1. errorsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. hitsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 5 cm) in the task (segment and electrode information can be found below).</p> <p>The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodes</p> <p>datapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.</p> <p>segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. </p> <p>electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]</p> <p> </p> <p> </p>
Dataset: Brain negativity as an indicator of predictive error processing: The contribution of visual action effect monitoring
<p>There are two files for each subject:</p> <p>1. sub##_error.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. sub##_hit.dat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 7 cm) in the task (segment and electrode information can be found below).</p> <p><br> The data in the *.dat-files are stored in a two dimensional matrix: n*1400 datapoints x 15 electrodes</p> <p>n represents the number of segments. 1400 datapoints per segment translate to a segment length of 2800 ms (from 600 ms before to 2200 ms after ball release). The ball´s release is located at the 301st datapoint and the feedback was presented at datapoint 726 (850 ms after ball release) in every segment.</p> <p>datapoints: The first dimension (rows) includes the measured neural activations in microvolts. The data is stored vectorized,<br> i.e. hit/error #1 -> row 1 to 1400, hit/error #2 -> row 1401 to 2800, ..., hit/error #n -> (n-1) * 1400 + 1 to n * 1400</p> <p>electrodes: The second dimension (columns) consists of the 15 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz Mastre]</p>
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
162 Human Error Descriptions and Categorizations from a User Study
<p><i><strong>Software Engineers' Human Errors</strong></i></p><p>This dataset contains descriptions of 162 human errors experienced by software engineering students during a user study described in the following publication:</p><ul><li>Benjamin S. Meyers and Andrew Meneely. Taxonomy-Based Human Error Assessment for Senior Software Engineering Students. Special Interest Group on Computer Science Education (SIGCSE) Technical Symposium. Forthcoming in 2024.</li></ul><p><i><strong>Included Files</strong></i></p><p>The "experienced_human_errors.csv" file contains a dataset of 162 human errors experienced during our user study. Participants documented their human errors in a Google Form with 8 questions.</p><p><i><strong>CSV Fields</strong></i></p><ul><li><strong>PARTICIPANT</strong>: Anonymous participant ID.</li><li><strong>INTERVIEW_DATE</strong>: Date of interview discussing human error.</li><li><strong>ID</strong>: Unique ID for experienced human error. Prefixed with "P1" for Phase 1 or "P2" for Phase 2.</li><li><strong>FINAL_CATEGORIZATION</strong>: Agreed upon T.H.E.S.E. categorization following discussion with interview facilitator.</li><li><strong>QUESTION_1</strong>: Anonymized participant answer to Question 1.</li><li><strong>QUESTION_2</strong>: Anonymized participant answer to Question 2.</li><li><strong>QUESTION_3</strong>: Anonymized participant answer to Question 3.</li><li><strong>QUESTION_4</strong>: Anonymized participant answer to Question 4.</li><li><strong>QUESTION_5</strong>: Anonymized participant answer to Question 5.</li><li><strong>QUESTION_6</strong>: Anonymized participant answer to Question 6.</li><li><strong>QUESTION_7</strong>: Anonymized participant answer to Question 7.</li><li><strong>QUESTION_8</strong>: Anonymized participant answer to Question 8.</li></ul><p><i><strong>Interview Questions</strong></i></p><ol><li>Please briefly describe the human error that you experienced.</li><li>If the human error you experienced resulted in a defect that was committed, please provide a link (or Git commit hash) to the commit below.</li><li>Is your human error a slip, lapse, or mistake?</li><li>Now, please examine the Taxonomy of Human Errors in Software Engineering (T.H.E.S.E.) and choose the specific human error that most accurately describes the human error you experienced. If you experienced multiple human errors, please submit this form once for each human error.</li><li>If there are other categories of human error that also describe the human error that you experienced, please note them here.</li><li>If you chose a 'General' or 'Other' category in Question (4), this question is required. Do you believe there is a missing human error category that better describes the human error that you experienced? If yes, please describe it below.</li><li>On a scale of 1 (not at all confident) to 5 (completely confident), how confident are you in your classification in the previous question?</li><li>Do you have any additional comments about this human error?</li></ol><p><i><strong>Anonymity</strong></i></p><p>Institutional Review Board approval for this research involving human subjects was granted by the Human Subjects Research Office at RIT on March 18, 2022. Participants signed an informed consent form acknowledging that (1) their participation was entirely voluntary and had no impact on their grades, and (2) their survey responses would be published in an anonymized format. All data released with this publication has been anonymized by replacing any personally identifiable information with participant identifiers.</p><p><i><strong>Contact</strong></i></p><p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p><p><i><strong>Acknowledgments</strong></i></p><p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
200 Annotated Developer Human Errors from GitHub
<p><em><strong>Software Engineers' Human Errors</strong></em></p> <p>This dataset contains 200 GitHub comments with manual human error annotations, released as part of the following publication:</p> <ul> <li>Benjamin S. Meyers. <a href="https://scholarworks.rit.edu/theses/11609/">Human Error Assessment in Software Engineering.</a> Rochester Institute of Technology. 2023.</li> </ul> <p><em><strong>Included Files</strong></em></p> <p>The "developer_human_errors.csv" file contains the full dataset of 200 software defect descriptions annotated with human error types (slips, lapses, mistakes) and T.H.E.S.E. categories.</p> <p><em><strong>CSV Fields</strong></em></p> <ul> <li><strong>ID</strong>: Unique identifier for the comment.</li> <li><strong>SOURCE</strong>: Whether this comment originates from a commit, issue, or pull request.</li> <li><strong>COMMENT_URL</strong>: The URL linking to the comment.</li> <li><strong>COMMENT_TEXT</strong>: The raw comment text.</li> <li><strong>HUMAN_ERROR_TYPE</strong>: Whether the software defect described is a slip, lapse, or mistake.</li> <li><strong>THESE_V4_ID</strong>: Manually assigned T.H.E.S.E. category with labels corresponding to Version 4 of T.H.E.S.E.</li> <li><strong>THESE_NAME</strong>: Name corresponding to manually assigned T.H.E.S.E. category.</li> </ul> <p><em><strong>Annotation Details</strong></em></p> <p>Human error types span slips, lapses, and mistakes from James Reason's Generic Error Modelling System (GEMS):</p> <ul> <li><strong>Slips</strong>: Failures of attention.</li> <li><strong>Lapses</strong>: Failures of memory.</li> <li><strong>Mistakes</strong>: Failures of planning.</li> </ul> <p>T.H.E.S.E. categories are summarized below:</p> <ul> <li>S01: Typos & Misspellings</li> <li>S02: Syntax Errors</li> <li>S03: Overlooking documented Information</li> <li>S04: Multitasking Errors</li> <li>S05: Hardware Interaction Errors</li> <li>S06: Overlooking Proposed Code Changes</li> <li>S07: Overlooking Existing Functionality</li> <li>S08: General Attentional Failure</li> <li>L01: Forgetting to Finish a Development Task</li> <li>L02: Forgetting to Fix a Defect</li> <li>L03: Forgetting to Remove Development Artifacts</li> <li>L04: Working with Outdated Source Code</li> <li>L05: Forgetting an Import Statement</li> <li>L06: Forgetting to Save Work</li> <li>L07: Forgetting Previous Development Discussion</li> <li>L08: General Memory Failure</li> <li>M01: Code Logic Errors</li> <li>M02: Incomplete Domain Knowledge</li> <li>M03: Wrong Assumption Errors</li> <li>M04: Internal Communication Errors</li> <li>M05: External Communication Errors</li> <li>M06: Solution Choice Errors</li> <li>M07: Time Management Errors</li> <li>M08: Inadequate Testing</li> <li>M09: Incorrect/Insufficient Configuration</li> <li>M10: Code Complexity Errors</li> <li>M11: Internationalization/String Encoding Errors</li> <li>M12: Inadequate Experience Errors</li> <li>M13: Insufficient Tooling Access Errors</li> <li>M14: Workflow Order Errors</li> <li>M15: General Planning Failure</li> </ul> <p><em><strong>Contact</strong></em></p> <p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p> <p><em><strong>Acknowledgments</strong></em></p> <p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion 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 Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion 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 an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale
<p>This artifact packages the data for the paper: <em>Privacy-Respecting Type Error Telemetry at Scale</em></p> <p>There are two files on Zenodo:</p> <ul> <li>data.tar.gz has the original Luau telemetry data</li> <li>artifact.tar.gz has a result PDF, intermediate data, and scripts for processing the data</li> </ul> <p>The artifact code and the source for the paper are also on GitHub:</p> <ul> <li><a href="https://github.com/bennn/luau-telemetry">https://github.com/bennn/luau-telemetry</a></li> </ul> <p>This artifact is primarily a **dataset**. It shows how we reached the conclusions in the paper.</p> <p>The scripts in this artifact are provided as-is for completeness. They may have bugs. They may not work as advertised.</p>
USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors
<p>This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.</p>
Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples
<p>Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are<br> widely used in industry for process monitoring and control. One factor that limits<br> their accuracy is the dramatic decrease in the insulation resistance at temperatures<br> above about 600 °C which results in temperature measurement errors due to electrical<br> shunting. In this work the insulation resistance of a cohort of representative MI<br> thermocouples was characterised at temperatures up to 1160 °C, with simultaneous<br> measurements of the error in indicated temperature by in situ comparison with a reference<br> Type R thermocouple. Intriguingly, there appears to be a systematic relationship<br> between the insulation resistance and the error in the indicated temperature. At<br> a given temperature, as the insulation resistance decreases, there is a corresponding<br> increasingly negative error in the temperature measurement. Although the measurements<br> have a relatively large uncertainty (up to about 1 °C in temperature error and<br> up to about 10 % in insulation resistance measurement), the trend is apparent at all<br> temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation<br> disappears at temperatures below about 600 °C, which is consistent with the<br> well-established diminution of insulation resistance breakdown effects below that<br> temperature. This raises the intriguing possibility of using the as-new MI thermocouple<br> calibration as an indicator of insulation resistance breakdown: large deviations<br> of the electromotive force (emf) in the negative direction could indicate a correspondingly<br> low insulation resistance.</p>
Data of Relative errors in derived multi-wavelength intensive aerosol optical proberties
<p>Measurement Data of "Relative errors in derived multi-wavelength intensive aerosol optical<br> properties using cavity attenuated phase shift single-scattering<br> albedo monitors, a nephelometer, and tricolour<br> absorption photometer measurements"</p> <p> </p>
Machine learning classifiers for species classification of fungi using error-prone long-reads on extended metabarcodes
<p>Machine learning models used in the decision tree of linked machine learning models (<a href="https://github.com/teenjes/fungal_ML">https://github.com/teenjes/fungal_ML</a>)</p>
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.