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478 results for “artifact”
Fluorescence correlation spectroscopy time-series data with and without peak artifacts - simulated data
<p>This is a dataset of FCS time-series with and without peak artifacts. It was created by 2D Monte Carlo simulations of diffusing particles. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-201231-clustsim/LabBook-exp-201231-clustsim.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-2">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>]) also contains examples of how to use this data and related Python code to load it.</p> <p>The following connected paper is currently under review and should be cited together with this dataset: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</p> Data structure inside each .csv file <table><tbody> <tr> <td><header></td> <td> <p>10 to 12 lines, contains metadata</p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>...</td> <td>...</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><source_1></td> <td><target_1a></td> <td><target_1b></td> <td><source_2></td> <td><target_2a></td> <td><target_2b></td> <td>...</td> </tr> <tr> <td> <p>FCS time-series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time series without artifact</p> </td> <td> <p>FCS time series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time-series without artifact</p> </td> <td>...</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p>
Applicability of Model Checking for Verifying Spacecraft Operational Designs - Artifact
<p>This artifact contains the accompanying experiment data for the submission "Applicability of Model Checking for Verifying Spacecraft Operational Designs".</p>
Artifacts supplementing the MEMICS 2016 paper "Avalanche effect in improperly initialized CAESAR candidates"
<p><strong>Raw experiment data</strong></p> <ul> <li>Paper: Avalanche effect in improperly initialized CAESAR candidates</li> <li>Auhors: Martin Ukrop and Petr Švenda</li> <li>Conference: Doctoral Workshop on Mathematical and Engineering Methods in Computer Science (MEMICS) 2016</li> <li>Paper details website: http://crcs.cz/wiki/public/papers/memics2016</li> </ul> <p><strong>Files and folders</strong></p> <ul> <li>data-statistical-batteries.zip -- numerical results from NIST STS, Dieharder and TestU01</li> <li>reference-eacirc.7z -- EACirc outputs from reference experiments (both streams random)</li> <li>pmn-zero-eacirc.7z -- EACirc outputs in scenario with zero-initialized public message numbers - see one example run results in folder pmn-zero_2015-12-12_EACcuda_CAESAR_a001_00001</li> <li>pmn-counter-eacirc.7z -- EACirc outputs in scenario with couter-initialized public message numbers - see one example run results in folder pmn-counter_2015-12-12_EACcuda_CAESAR_a001_00001</li> <li>pmn-random-eacirc.7z -- EACirc outputs in scenario with random-initialized public message numbers - see one example run results in folder pmn-random_2015-12-12_EACcuda_CAESAR_a001_00001</li> </ul> <p><strong>File formats</strong></p> <p>NIST STS, Dieharder and TestU01 provide standard output files with resulting p-values of individual tests. For interpretation, see the documentation of the test suites.</p> <p>EACirc's output files adhere to syntax described in the developer wiki at https://github.com/crocs-muni/eacirc Note, however, that the tool evolves and the syntax might have changed. The format tries to be as self-explanatory as possible, but if in doubt, feel free to email the developers.</p>
Artifact for MobiCom'23: Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility
<p>This dataset contains the anonymized failure data collected from our physical and device farms over a three-month period. The failure data involves 5,918 physical devices as well as 5,918 virtualized devices running on ARM commodity servers.</p> <p>For more details, please visit our website (<a href="https://android-emulation-testing.github.io/">Android-Emulation-Testing.github.io</a>) or read our paper:</p> <ul> <li>[MobiCom'23] Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility</li> </ul> <p>If you use our dataset in your work, please reference it using:</p> <pre><code>@inproceedings {lin2022virtual, author = {Lin, Hao and Qiu, Jiaxing and Wang, Hongyi and Li, Zhenhua and Gong, Liangyi and Gao, Di and Liu, Yunhao and Qian, Feng and Zhang, Zhao and Yang, Ping and Xu, Tianyin}, title = {{Virtual Device Farms for Mobile App Testing at Scale: A Pursuit for Fidelity, Efficiency, and Accessibility}}, booktitle = {The 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'23)}, year = {2023}, publisher = {ACM} }</code></pre> <p> </p> <p> </p>
Artifact Description/Artifact Evaluation/Computational Artifact for "SPEChpc 2021 Benchmarks on Ice Lake and Sapphire Rapids Infiniband Clusters: A Performance and Energy Case Study"
<p>We provide reproducibility initiative dependencies (Artifact Description or Artifact Evaluation or Computational Results Analysis) appendix at https://github.com/RRZE-HPC/PMBS23-AD. To allow a third party to duplicate the findings, this article provides our extensive performance data artifact and describes further details regarding the software environments, experimental design, and methodology employed for the results shown in the paper, entitled "SPEChpc 2021 Benchmarks on Ice Lake and Sapphire Rapids Infiniband Clusters: A Performance and Energy Case Study". The computational artifacts will enable experienced performance engineers to reproduce and interpret the data shown in the paper in the appropriate way and to follow the conclusions we draw from it.</p>
What constitutes software? An Empirical, Descriptive Study of Artifacts - Reproduction Dataset
<p>Dataset for reproducing the numbers, figures, and tables in the results section of the MSR 2020 paper <em>"What constitutes Software? An Empirical, Descriptive Study of Artifacts"</em>.</p> <p>The data is given in this release as extra file (<code>all_repo_files_categorized.csv.bz2</code>) as it is too big to be part of the repository directly.</p>
Artifacts for q2-micom
<p>This repository hosts artifacts for use with q2-micom and MICOM. In particular it provides summarized metabolic model databases and growth media.</p> <p> </p> <p><strong>Model databases</strong></p> <p>Model databases are provided for family, genus and species ranks. AGORA is a manually curated database of 818 high quality metabolic reconstructions. Raw data was obtained from https://www.vmh.life/.</p> <p>CarveME is an automatically reconstructed database but offers much more organisms (>5K strains). Raw data was obtained from https://github.com/cdanielmachado/embl_gems.</p> <p> </p> <p><strong>Diets</strong></p> <p>We currently only provide an average western diet which is only compatible with AGORA.</p> <p> </p> <p> </p>
Artifacts related to "Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance"
<p>This archive contains data related to in the following paper:</p> <p>"Using Informed Access Network Selection to Improve HTTP Adaptive Streaming Performance"</p> <p>(published at the ACM MMSys 2020 conference)</p> <p>Copyright (c) 2020, Theresa Enghardt <theresa@tenghardt.net>, Fachgebiet INET - TU Berlin.</p> <p><br> See https://github.com/fg-inet/MMSys2020_Informed-Access-Network-Selection for more information.</p> <p>This data is released under the Creative Commons Attribution 4.0 International license.</p>
Artifacts for Incremental Search for Conflict and Unit Instances of Quantified Formulas with E-Matching
<p>This zip file contains the artifacts for the paper:</p> <p>J. Hoenicke and T. Schindler, <em>Incremental Search for Conflict and Unit Instances of Quantified Formulas with E-Matching, </em>VMCAI 2021, Springer</p> <p>The artifact is tested to work in the VMCAI 2021 virtual machine: <a href="https://doi.org/10.5281/zenodo.4017292">https://doi.org/10.5281/zenodo.4017292</a>.</p>
Singularity container for CGO'21 artifact description: YaskSite -- Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures - version 1.2
<p>Singularity container for CGO'21 artifact description: YaskSite -- Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures</p> <p> </p> <p>version 1.2</p>
Artifacts Package - "Why don't Developers Detect Improper Input Validation? '; DROP TABLE Papers; --"
<p>Artifacts Package of the accepted ICSE 21 paper: "Why don’t Developers Detect Improper Input Validation? '; DROP TABLE Papers; --".</p> <p>See README.md for more information. </p>
Artifacts of the ISSTA2021 Submission JUnitTestGen
<p>This repository contains the artifacts of JUnitTestGen, a paper under review by ISSTA'21.</p> <p>The file <strong>RQ1&RQ2_result</strong> illustrates the detailed results of JUnitTestGen on the three datasets(E1, E2, and E3).</p> <p>The file <strong>RQ3_result</strong> illustrates the detailed results of JUnitTestGen in detecting compatibility issues, in which the column <em>TargetAPI</em> shows the target API to be tested, the column <em>Type</em> refers to the two different compatibility issue types, and the last column <em>TestCase_name------runtime result</em> indicating the specific runtime results from SDK version 21 to 29.</p> <p>The file<strong> E1+E2+E3_TestCaseName_TargetAPI_Map.csv </strong>is a map recording all the generated test case names and their corresponding target API. We then run all these test cases on Android SDK version 27 and the logs are detailed in file <strong>E1+E2+E3_runtime_log.txt.</strong></p>
FS1196 Sinagua Hand Tool Artifact
This axe head was found in the Red Rock District of the Coconino Forest north of Sedona Arizona. This is a preliminary model with a refined version planned for the near future. The item was made from a piece of dark basalt. Additional information about the Red Rock District of the Coconino Forest can be found at the following web sites http://www.sedonaredrocktrails.org/ http://www.fs.usda.gov/recmain/coconino/recreation Information about volunteer opportunities in the Coconino Forest including the creation of the 3D models and other projects can be found at http://www.friendsoftheforestsedona.org/ Source: Objaverse 1.0 / Sketchfab
Spitirual Artifact
Photogrammetry practice Raw Scan Get the Trnio app at www.trnio.com Source: Objaverse 1.0 / Sketchfab
Artifact 1 - Black Arrow Head - 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
Validity of two automatic artifact reduction software methods in ictal EEG interpretation. Dataset 1
<p>Unprocessed electroencephalogram recordings of seizures from deidentified study patients with epilepsy prior to and following processing using the AR2 (artifact reduction 2) software method. The files are stored in European Data Format (.EDF). "_out" files have been processed by AR2.</p>
HisRag dataset and artifacts
<h1>HisRag: History-Retrieval Augmented Generation for Commit Message</h1> <h2><span>HisRag.zip</span></h2> <p><span>This is a sampled dataset for HisRag. The dataset is resplitted by timeline and is a subset of <span><em><span>CommitChronicle. </span></em></span></span><span><em><span>CommitChronicle</span></em></span><span> is available on </span><span><a href="https://zenodo.org/records/8189044"><span>Zenodo</span></a></span><span>.</span></p> <h2><span>artifacts.zip</span></h2> <p><span>This file contains the generated outputs of CMG methods enhanced by HisRag and the human evaluation results.</span></p>
Artifacts for NDSS 2024 paper "A Unified Symbolic Analysis of WireGuard"
<p>This project gathers symbolic analyses of WireGuard protocol, with the help of Sapic+, Tamarin and ProVerif proof assistants. The following properties are verified: agreement, secrecy and anonymity.</p>
Artifacts of the ISSTA-2024 Submission——DroidTEC
<div> <p>This release includes the experimental results that are used for the ISSTA 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 github: page: <a href="https://anonymous.4open.science/r/TypeStateMisuseDetector">https://anonymous.4open.science/r/TypeStateMisuseDetector</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> <ol> <li><strong>Experimental_Setup.txt </strong>: Experimental Setup, including 10,000 Android apps</li> <li><strong>RQ1_Rules.zip: </strong>TypeState API rules.</li> <li><strong>RQ2_typestate_API_usage.zip</strong> : The prevalence of typestate APIs in Android applications.</li> <li><strong>RQ4_Typestate_Misuses.zip</strong> : Identified typestate misuses in real-world applications.</li> <li><strong>RQ5_CrySL_Results.zip</strong> : Execution results of CrySL on 10,000 Android apps.</li> <li><strong>RQ5_AsyncChecker_Results.zip</strong> : Execution results of AsyncChecker on 10,000 Android apps.</li> <li><strong>RQ5_VALA_Results.zip</strong> : Execution results of VALA on 10,000 Android apps.</li> <li><strong>RQ5_CiD_Results.zip</strong> : Execution results of CiD on 10,000 Android apps.</li> <li><strong>RQ5_BenchMark_Apps.zip</strong> : Execution results of VALA on 10,000 Android apps.</li> </ol> <p> </p> </div>
Carved Bone Artifact, Seminole County, FL
Made from a modified deer radius, this artifact features an intricately carved geometric pattern along with two large circular perforations. Based on complete examples from other sites in the region, the broken off portion was likely ground into a sharpened point, forming a dagger-like tool with an unknown function. This example was recovered from a site in the Little Big Econ State Forest in Seminole County, Florida. It is currently curated in the Rollins College Archaeology Lab. Model by Ellie Minette. Source: Objaverse 1.0 / Sketchfab
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.