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478 results for “artifact”
FIGURE 3 in "Endangered or an artifact of unsound taxonomy? Case of the critically endangered bush frog Philautus sanctisilvaticus Das and Chanda, 1997"
FIGURE 3. Map of eastern India showing collection localities of three species of the genus Raorchestes.
FIGURE 2 in "Endangered or an artifact of unsound taxonomy? Case of the critically endangered bush frog Philautus sanctisilvaticus Das and Chanda, 1997"
FIGURE 2. Images of Raorchestes sanctisilvaticus NCBS-BH648 male from Kanger Valley National Park, Chhattisgarh
FIGURE 5 in "Endangered or an artifact of unsound taxonomy? Case of the critically endangered bush frog Philautus sanctisilvaticus Das and Chanda, 1997"
FIGURE 5. Maximum likelihood phylogeny of members of the "Bombayensis" clade of Raorchestes base on 16S rRNA gene reconstructed through 1000 non-parametric bootstrap replicates. Numbers at nodes represent bootstrap support and Bayesian posterior probability. Inset image of Raorchestes sanctisilvaticus from Kanger Valley National Park, Chhattisgarh.
FIGURE 4 in "Endangered or an artifact of unsound taxonomy? Case of the critically endangered bush frog Philautus sanctisilvaticus Das and Chanda, 1997"
FIGURE 4. Advertisement call of Raorchestes sanctisilvaticus recorded at Amarkantak, Entire call Amplitude (a) & Spectrogram (b), Single call Amplitude (c) & Spectrogram (d). No data on temperature available.
Correcting Artifacts in Single Molecule Localization Microscopy Analysis Arising from Pixel Quantum Efficiency Differences in sCMOS Cameras
<p>Jupyter notebooks and supplementary data for the paper "Correcting Artifacts in Single Molecule Localization<br> Microscopy Analysis Arising from Pixel Quantum Efficiency Differences in sCMOS Cameras".</p>
Datsets for Publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation"
<p>This dataset contains Magnetic Particle Imaging and Intravascular optical coherence tomography data for the profiles</p> <ul> <li>Standard Profile (3x)</li> <li>Bending Profile (3x)</li> <li>Heart Beat Profile (3x)</li> </ul> <p>used in the publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation".</p>
Artifact for Paper Submission
<p>This is the artifact for ICSE submission #1431.</p> <p>It includes the source code of the modified fuzzer ("aflpp-mutated-bytes") and the raw data from the fuzzing experiments ("FUZZ RESULTS").<br><br></p>
Artifact of An LLM-based Readability Measurement for Unit Tests' Context-aware Inputs
<div> <p><strong>Artifact of An LLM-based Readability Measurement for Unit Tests' Context-aware Inputs</strong></p> <p> </p> <p>A README.md file can be found in the base folder after unzipping the artifact.</p> <p> </p> </div>
ICSE 2025 Artifact for "An Empirical Study on Package-Level Deprecation in Python Ecosystem"
<div> <h1>Artifact</h1> <div>This artifact includes the source code and data needed to reproduce the results of our paper.</div> <h2>Files</h2> <div> <ul> <li><strong>inactive_task</strong>: This folder contains the dataset we collected, including:</li> </ul> </div> <ul> <li> <ul> <li>A list of all packages in PyPI as of 2023.1.9 in <strong>all_package.json</strong></li> <li>A list of halted packages in <strong>halted_packages.pkl</strong></li> <li>A list of packages that haven't received any commit for a long time in <strong>long_time_no_commit.json</strong></li> <li>A list of inactive packages and their corresponding GitHub repositories in <strong>inactive_pkg_repo_list.json</strong></li> <li>A mapping of packages to their corresponding GitHub links in <strong>pgk2url.json</strong></li> <li>A dataset with details in <strong>deprecation_dataset/</strong>, which includes rationales, alternative solutions, and package characteristics (RQ1). Click [here](inactive_task/deprecation_dataset/README.md) for more details.</li> </ul> </li> </ul> <div> <ul> <li><strong>ghd_dataset</strong>: This folder contains the dependency information for PyPI, which we used to build the dependency network. Please unzip the file before use.</li> </ul> </div> <div> <ul> <li><strong>down_deps</strong>: The folder contains scripts to process our data, including</li> </ul> </div> <ul> <li> <ul> <li><strong>similar_brothers</strong>: A script to find similar brother packages of deprecated packages.</li> <li><strong>delta_of_downdeps.py</strong>: A script that calculates the gain of downstream dependencies.</li> </ul> </li> <li><strong>regression</strong>: This folder contains scripts for the models that estimate the effect of deprecation announcements, which can be used to reproduce the results of RQ2.</li> <li><strong>questionnaire_data</strong>: The folder includes the questionnaire prototype and responses (RQ3, 4). Click [here](./questionnaire_data/README.md) for more details.</li> </ul> </div>
Verus: A Practical Foundation for Systems Verification -- Artifact -- Set 2 Screen Capture
<p>A screen capture of the experiment in Set 2 of the artifact for the SOSP24 conditionally accepted paper "Verus: A Practical Foundation for Systems Verification": the experiment was performed on an Amazon AWS c5.4xlarge machine running Windows.</p>
Artifact for Reducing Write Barrier Overheads for Orthogonal Persistence
<p>This is the artifact for the paper "Reducing Write Barrier Overheads for Orthogonal Persistence" accepted by the ACM SIGPLAN International Conference on Software Language Engineering (SLE) 2024.</p> <p> </p> <p>For a quick trial, download the following files:</p> <ul> <li>ccjava-image.tar.gz (Docker image)</li> <li>README.md</li> </ul>
Artifacts for "On Hardware Security Bug Code Fixes By Querying Large Language Models""
<p>This repository contains the benchmarks and results obtained for the work "On Hardware Security Bug Code Fixes<br> By Querying Large Language Models".<br> Follow the README.md file for more information on how to use the tools yourself.</p>
Computing Expected Visiting Times and Stationary Distributions in Markov Chains: Fast and Accurate (Artifact)
<p>This artifact contains the raw data of our experiments as well as scripts and benchmarks to reproduce the experiments.<br>Furthermore, the considered version of [Storm](http://stormchecker.org) is included, which contains our implementation.</p> <p>Please also consider the artifact of the conference paper available at [zenodo](https://zenodo.org/records/10438916) which has been accepted by the TACAS Artifact evaluation committee.</p> <p><br>This artifact contains: <br>`LICENSE`: The license document.<br>`README.md`: The instructions.<br>`raw_data.zip`: The raw data obtained during our experiments<br>`raw_data_with_results.zip`: The raw data, also including the resulting stationary distributions and evts in an explicit format. (84 GB!)<br>`reproduce.zip` contains benchmarks and scripts for reproducing the experiments<br>`storm-0b1cae2a94f06984f3cf4cecf5a5090e9bc71a56.zip` is the exact Storm version we considered.</p>
CRISP: Critical Path Analysis of Large-Scale Microservice Architectures (Artifact)
<p>This zip includes the original artifact used in the <a href="https://www.usenix.org/system/files/atc22-zhang-zhizhou.pdf">CRISP</a> paper.</p> <p>The appendix in the original paper contains the details on the setup and execution of the artifact. There are three components in this artifact: top-down analysis, bottom-up analysis, and anomaly detection.</p> <p><strong>Note</strong></p> <ul> <li>Due to privacy and security concerns, most unrelated fields and tags are removed.</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/13947828">https://zenodo.org/records/13947828</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> <h3>Top-down analysis</h3> <p>Simply run <code>bash top-down.sh</code> should be sufficient. Note the result looks slightly different since this artifact contains a subset of traces from the original paper.</p> <h3>bottom-up analysis</h3> <p><code>bash bottom-up.sh</code> should generate the figure 13-20 from the original paper. Note instead of having 1m+ traces in the original paper, this artifact contains a subset of 100k traces. So generated figure may look slightly different.</p> <h3>Anomaly detection</h3> <p>This part contains the original repo of TraceAnomaly. The dataset contains the original trace of <strong>Service 3</strong> in <strong>Table 2</strong> from the original paper.</p> <p>To run the experiment, first please refer to the appendix for the setup.</p> <p>Then please run the following:</p> <ul> <li><code>bash preprocess.sh</code> to generate the matrices that are used in the training</li> <li>within <code>TraceAnomaly</code> repo <ul> <li>run <code>bash run-training-WG.sh</code> to run the training using the whole graph. Correspond to <strong>Service 3</strong> row, column <strong>TA</strong> in <strong>Table 2</strong>.</li> <li>run <code>bash run-training-CS.sh</code> to run the training using the critical path only. Correspond to <strong>Service 3</strong> row, column <strong>CRISP</strong> in <strong>Table 2</strong>.</li> </ul> </li> <li>run <code>python3.6 parse-rnvp.py -i path_to_rnvp_file</code> to check the result</li> </ul> <p>Note</p> <ul> <li>The traces among <code>train</code>, <code>normal</code>, and <code>abnormal</code> are split randomly from normal traces. The error generation part can be found in lines 137-144 in <code>preprocess.py</code>. It is also described in Section 7.3 Methodology in the original paper. For real error traces, please refer to <a href="https://zenodo.org/records/13947828">https://zenodo.org/records/13947828</a>.</li> </ul> <p>If you use the traces in your research, please cite our paper</p> <blockquote> <pre>@inproceedings{zhang2022crisp, title={$\{$CRISP$\}$: Critical path analysis of $\{$Large-Scale$\}$ microservice architectures}, author={Zhang, Zhizhou and Ramanathan, Murali Krishna and Raj, Prithvi and Parwal, Abhishek and Sherwood, Timothy and Chabbi, Milind}, booktitle={2022 USENIX Annual Technical Conference (USENIX ATC 22)}, pages={655--672}, year={2022} }</pre> </blockquote> <p>If you have more questions, you can reach out to <a href="https://lollllcat.github.io/">Chris(Zhizhou) Zhang</a>.</p>
Artifacts for paper "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge"</p>
Artifact for "Modular criticality analysis for dynamic fault trees"
<p>This artifact contains the fault trees and log files of the experimental evaluation from the paper <em>"Modular criticality analysis for dynamic fault trees"</em>. The implementation is available in the <a href="https://www.safest.dgbtek.com/">SAFEST tool</a>.</p>
Anonymous Test Artifacts
<p>This is some anonymous test artifact.</p>
Artifacts of the paper under review by TSE
<p>This is the online repository of <strong>*Predictive Comment Updating with Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach</strong>*, a research paper under review by TSE. We release the source code and relevant data of <strong>Toper</strong>, the data used in our evaluation, as well as the experiment results.</p> <ul> <li><strong>Dataset</strong></li> </ul> <p>Basically, the dataset is from Liu et al.'s ASE20 paper (i.e., <a href="conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">Automating Just-In-Time Comment Updating</a>), and then cleaned by Lin et al.'s ICPC21 paper (i.e., <a href="https://conf.researchr.org/details/icpc-2021/icpc-2021-research/20/Automated-Comment-Update-How-Far-are-We-">Automated Comment Update: How Far are We?</a>). We classify the dataset into code-indicative and non-code-indicative items and store them in <code>Data</code> directory, which is named by the format of <em>[data catagory]_Items_[Dataset].jsonl</em>. For example, <strong>All_Item_Test.jsonl</strong> means this file includes all (i.e., including code-indicative and non-code-indicative) items in the test set. Similarly, <strong>NCIU_Items_Test.json</strong> means this file only covers non-code-indicative items in the test set.</p> <ul> <li><strong>The Code-Indicative Update Classifier</strong></li> </ul> <p>We design a classifier to differentiate the Code-Indicative and Non-Code-Indicative updates. The replication package is available at <strong>Code/TypeClassifier.py</strong>. To obtain the result of the classifier, please run the following command:</p> <pre><code class="language-python">python3 TypeClassifier.py -training/FilePath FeaturesForClassifier/featuresForTrain.csv -testFilePath FeaturesForClassifier/featuresForTest.csv</code></pre> <ul> <li><strong>Operation Path Extractor</strong></li> </ul> <p>The customized tool for extracting operation path from the dataset is provided by previous studies. To obtain the preprocessed data, run the following command:</p> <pre><code class="language-java">java -cp OperationPathExtractor.jar Extractor.App --data_dir path/to/data --input_name semi-finished/data/path --output_name path/to/store/data --num_threads 1 The preprocessed data are stored in Data/Preprocessed.</code></pre> <ul> <li><strong>The Non-Code-Indicative Comment Updater</strong></li> </ul> <p>Our replication code is available at <code>Code</code>, and the detail instructions of command are at <code>comment_update.py</code>. Or you can simply execute the following command:</p> <pre><code class="language-python">python3 comment_update.py -data_path path/to/data -gpu -use_features</code></pre> <p> </p>
Artifacts for ICSE 2022 Paper Submission #1221
<p><strong>This data set is for ICSE 2022 Paper Submission #1221</strong></p>
Artifacts for Fuzzing SMT Solvers with Diversified Sub-formulas
<p><strong>Fuzzing SMT solvers with Diversified Sub-formulas</strong></p> <p>Table of Contents</p> <ul> <li>Background</li> <li>Install</li> <li>Usage</li> <li>Bugs</li> </ul> <p> </p> <p><strong>Background</strong></p> <p><strong>Octopus </strong>is the tool for detecting soundness bugs in SMT solvers. <br> We have submitted 10 valid bug reports for Z3 so far. 7 bugs are confirmed/fixed by developers among these reports.</p> <p> </p> <p><strong>Install</strong></p> <p>Octopus itself has few dependencies. It uses Python3 and Python-virtualenv.</p> <p>You can install Python-virtualenv using <code>pip install virtualenv</code></p> <p>Then install Octopus.</p> <p><code>virtualenv --python=/usr/bin/python3.6 virenv</code></p> <p><code>source virenv/bin/activate</code></p> <p><code>cd octopus</code></p> <p><code>python3 setup.py install</code> </p> <p> </p> <p><strong>Usage</strong></p> <p>You can download SMT instances in SMT COMP 2021 as benchmarks.</p> <p><a href="https://www.starexec.org/starexec/secure/explore/spaces.jsp?id=1">2021-05-26 - StarExec</a></p> <p>Then install and build the SMT solver you want to test.</p> <p>For example:</p> <p><code>git clone https://github.com/Z3Prover/z3.git</code></p> <p><code>python scripts/mk_make.py </code></p> <p><code>cd build; make</code></p> <p>Then Octopus can be used to validate it, for example:</p> <p><code>octopus --benchmark=/home/SMT2021 --solver=z3 --solverbin=../z3/build/z3 --theory=LIA</code></p> <p>To run Octopus in multiple cores:</p> <p><code>octopus --benchmark=/home/SMT2021 --solver=z3 --solverbin=../z3/build/z3 --theory=LIA --cores=20</code></p> <p> </p> <p><strong>Bugs</strong></p> <p>Octopus has detected many new refutational soundness bugs in Z3.</p> <p>Here is a list of issues we reported.</p> <p><a href="https://github.com/Z3Prover/z3/issues/5373">https://github.com/Z3Prover/z3/issues/5373</a> [confirmed]<br> <a href="https://github.com/Z3Prover/z3/issues/5443">https://github.com/Z3Prover/z3/issues/5443</a> [reported]<br> <a href="https://github.com/Z3Prover/z3/issues/5447">https://github.com/Z3Prover/z3/issues/5447</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5456">https://github.com/Z3Prover/z3/issues/5456</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5457">https://github.com/Z3Prover/z3/issues/5457</a> [fixed] <br> <a href="https://github.com/Z3Prover/z3/issues/5460">https://github.com/Z3Prover/z3/issues/5460</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5468">https://github.com/Z3Prover/z3/issues/5468</a> [fixed] <br> <a href="https://github.com/Z3Prover/z3/issues/5488">https://github.com/Z3Prover/z3/issues/5488</a> [fixed]<br> <a href="https://github.com/Z3Prover/z3/issues/5502">https://github.com/Z3Prover/z3/issues/5502</a> [duplicate]<br> <a href="https://github.com/Z3Prover/z3/issues/5508">https://github.com/Z3Prover/z3/issues/5508</a> [reported]<br> <a href="https://github.com/Z3Prover/z3/issues/5423">https://github.com/Z3Prover/z3/issues/5423</a> [invalid] </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.