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Dataset results
478 results for “artifact”
MRI raw data to publication: Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation.
<p>These Data are refering to the maunscript "Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation" authored by </p> <p>Renzo Huber1, Rüdiger Stirnberg2, A Tyler Morgan1, David A Feinberg3,4-5, Samantha J Ma6, Philipp Ehses3, Omer Faruk Gulban2,7, Kenshu Koiso2, Isabel Gephart1, Stephanie Swegle1, Susan Wardle1, Emily Ma2, Andrew Persichetti1, Alexander JS Beckett4-5, Tony Stöcker3, Nicolas Boulant8, Benedikt A Poser2, Peter Bandettini1</p> <p><strong> </strong></p> <p>1 NIMH, NIH, Bethesda, United States,</p> <p>2 German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,</p> <p>3 Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States, </p> <p>4 Advanced MRI Technologies, Sebastopol, CA, United States,</p> <p>5 CN, FPN, University of Maastricht, The Netherlands,</p> <p>6 Siemens Medical Solutions USA, Inc., Berkeley, CA, USA,</p> <p>7 Brain Innovation, Maastricht, The Netherlands,</p> <p>8 CEA, NeuroSpin, University Paris Saclay, France.</p> <p> </p> <h1>Abstract</h1> <p><strong>Purpose:</strong> High resolution fMRI is an emerging research field focused on capturing functional signal changes across cortical layers. However, the data acquisition is limited by low spatial frequency EPI artifacts; termed as Fuzzy Ripples. These artifacts limit the practical applicability of acquisition protocols with higher spatial resolution, faster acquisition speed, and they challenge imaging in lower brain areas. </p> <p><strong>Methods: </strong>We characterize Fuzzy Ripple artifacts across commonly used sequences and distinguish them from conventional EPI Nyquist ghosts, off-resonance effects, and GRAPPA artifacts. To investigate their origin, we employ dual polarity readouts.</p> <p><strong>Results: </strong>Our findings indicate that Fuzzy Ripples are primarily caused by kx-specific imperfections in gradient trajectories, which can be exacerbated by inductive coupling between third-order shims and readout gradients. We also find that these artifacts can be mitigated through complex-valued averaging of dual polarity EPI or by disconnecting the third-order shim.</p> <p><strong>Conclusion:</strong> The proposed mitigation strategies allow for overcoming current limitations in layer-fMRI protocols: </p> <p>(1) Achieving resolutions beyond 0.8mm is feasible, and even at 3T, we achieved 0.53mm voxel functional connectivity mapping. </p> <p>(2) Temporal acquisition speed can be increased to GRAPPA 8. </p> <p>(3) Sub-millimeter fMRI is achievable in lower brain areas, including the cerebellum.</p> <p> </p>
Artifacts for Characterizing Human Aspects in Reviews of COVID-19 Apps
<p>This archive contains the artifacts associated with our study titled "Characterizing Human Aspects in Reviews of COVID-19 Apps".</p> <p>Specifically, the archive contains:<br> * The dataset of reviews we downloaded from the App Store and the Google Play store (`Reviews.zip`)<br> * The keywords we used to filter the reviews (`Keywords.json`)<br> * The filtered dataset of reviews (`Filtered_Reviews.zip`)<br> * The codebook to characterize human aspects (`Human_Aspects_Codebook.pdf`)<br> * The codebook to characterize review types (`Review_Types_Codebook.pdf`)<br> * The labeled App Store reviews (`Labeled_Reviews-Apple_Store.csv`)<br> * The labeled Google Play store reviews (`Labeled_Reviews-Apple_Store.csv`)</p>
Artifacts of the ASE2022 Submission JUnitTestGen
<p>This release includes the experimental results that are used for the ASE artifact analysis. All the results are described in our paper. All required artifacts are attached to this release.</p> <p><br> The source code is made publicly available on the bitbucket page: <a href="http://bitbucket.org/se_anonymous/junittestgen/src/master/">https://bitbucket.org/se_anonymous/junittestgen/src/master/</a><br> Users can easily access and execute our tool following the steps in the Setup part.</p> <p>In addition, all of our experimental results are publicly available on this page. For simplicity, we provide the following instructions for our artifacts:</p> <p>1.<strong>Experimental_Setup.zip</strong> : Experimental Setup, including 10,000 Android apps(for each target SDK version between 21 (i.e., Android 5.0) and 30 (i.e., Android<br> 11.03) from AndroZoo).<br> 2.<strong>All_Tests.zip </strong>: All the generated test cases.<br> 3.<strong>TestName_TargetAPI_Mapping.txt</strong> : The mapping of test case name and its corresponding target API.<br> 4.<strong>JUnitTestRun_SDK*_log.txt </strong>: Tests Execution results on SDK 21 - 30(* refers to SDK version).<br> 5.<strong>EvoSuite-tests.zip</strong> : The generated test cases by using Evosuite<br> 6.<strong>Evosuite_results.zip</strong> : Execution results of tests generated by Evosuite<br> 7.<strong>CiD.zip</strong> : CiD results</p>
artifact collection, per 2019
Made with 3df zephyr aerial, using 208 photos. This is the majority of my artifact collection (a few larger hardstone artifacts were left out) as of November 2019. It is comprised of 95 flint (and clay) pieces, most of which are personal finds. I try to do a unique arrangement for the flint pieces each year; the large arrowhead in the center is the one I used in my main display-case this year. Source: Objaverse 1.0 / Sketchfab
Alien Artifacts
could be alien artifacts or precious stones made with google sketch up Source: Objaverse 1.0 / Sketchfab
Code and Build Artifacts Dataset
<p>The code and build artifacts are a compilation of source code projects and their related build outputs. The build process, which consisted of running the make command, successfully ran on 3,049 different GitHub projects. Over 30,000 build outputs were produced from C and C++ projects. The build outputs are the results of running a particular project's make command. These derivatives can include executables, .o files, .so files, .a files, or other project-specific build artifacts. The output was accepted as long as the make command completed without error; thus, there is no guarantee that every project will contain every type of artifact. These data provide an association between source code and the build artifacts of that code. The data directory contains one directory for each project downloaded from GitHub. These project directories are named with the GraphQL ID from GitHub's GraphQL API. In each of these GraphQL-ID labeled directories, there is a license.txt, a url.txt, source directory, and a derivatives directory. The license.txt contains the license for the original project, the url.txt contains a link to the original project on GitHub, the source directory contains the original code, and the derivatives directory contains the outputs from building the project, which include the previously mentioned files.</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>
Artifact for paper: "SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications"
<p>Artifact for IEEE S&P 2022 paper: "SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications"</p>
Artifact for paper "Transforming Test Suites Into Croissants"
<p>This is the artifact for the paper "Transforming Test Suites Into Croissants": <a href="https://github.com/anocroissant/Croissant">https://github.com/anocroissant/Croissant</a></p>
Research artifacts from "Hardening and Speeding Up Zero-interaction Pairing and Authentication"
<p>This record contains <strong>research artifacts (data and results)</strong> from the paper "Hardening and Speeding Up Zero-interaction Pairing and Authentication" by Mikhail Fomichev, Timm Lippert, and Matthias Hollick in Proceedings of 2023 International Conference on Embedded Wireless Systems and Networks (EWNS' 23). </p> <p>The dataset contains raw data of illuminance, RGB, CO2, and TVOC sensors recorded in two scenarios: Home and Office. While the results include computed entropy and dynamic time warping (DTW) distances of the above data collected by colocated (in the same Home or Office) and non-colocated devices in our two scenarios. </p> <p>The results additionally include entropy of audio data recorded by the colocated and non-colocated devices as well as audio similarity of such recordings. For details about audio similarity metrics and further information, see <a href="https://arxiv.org/pdf/2306.04458.pdf">our paper</a>. </p>
FS0855 Artifact test 3 Kachina
This is a modern Kachina. It is approximatly 18 inches tall. Model based on 49 photos Source: Objaverse 1.0 / Sketchfab
Artifact 2 - Fishing Weight - Sanford Museum
This is a looted, unprovenienced, artifact donated to the Sanford Museum in Sanford, Florida. This aritfact was captured in 3D using low-cost photogrammetry using Agisoft's Metashape and my personal cellphone camera, the Samsung Galaxy Note 10+. This was the first artifact I preserved using photogrammetry and is apart of a collection of looted artifacts from the Sanford Museum. Source: Objaverse 1.0 / Sketchfab
Study to Look for Artifact on Computed Tomography (CT) Representing Kidney Stones
ClinicalTrials.gov study NCT01187745. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using Color Doppler US Artifact to Identify Tumor Markers
ClinicalTrials.gov study NCT06166173. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
CT Mar Technology and MR Mavric, Semac Sequence in Removing Artifacts of Orthopedic Metal Internal Fixation
ClinicalTrials.gov study NCT04954963. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Metal Artifacts Reduction Orotocols and Electric Potential Difference on Metal Artifacts in Cone Beam Computed Tomography
ClinicalTrials.gov study NCT03726970. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Application of Megavoltage Imaging to Reduce Artifact After Seed Implants
ClinicalTrials.gov study NCT01810627. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Influence of Patient Position on Attenuation Artifacts Observed in D-SPECT Camera Myocardial Tomoscintigraphy
ClinicalTrials.gov study NCT02872545. IPD Sharing: NO. Countries: 1. Publications: 0.
NIBP (Non-invasive Blood Pressure) Algorithm Enhancements (Characterize the Robustness of Motion Artifact Suppression)
ClinicalTrials.gov study NCT05185037. IPD Sharing: NO. Countries: 1. Publications: 0.
Characterization of Artifacts in the BIS™ EEG Signals in General Anesthesia Patients in Operating Room
ClinicalTrials.gov study NCT03553953. IPD Sharing: NO. 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.