Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
478
datasets available to search
ShareScore release 0.9.0
Dataset results
478 results for “artifact”
Artifacts of the TOSEM Submission Mario
<p>This is the online repository of <em><strong>Mario</strong></em>, a journal-first paper under review by ACM TOSEM.</p> <p> </p> <p><strong>Dataset: </strong>The dataset used in our study are all open-sourced. We provide the links to them below.</p> <ul> <li>Empirical dataset -- collected by Liu et al. (<a href="https://github.com/TruX-DTF/debug-method-name">here</a>)</li> <li>Evaluation dataset -- collected by Alon et al. (<a href="https://github.com/tech-srl/code2seq">here</a>)</li> </ul> <p> </p> <p><strong>Source code: </strong>We release three files here.</p> <ol> <li><em>Transformer.py:</em> This is the Transformer model used in <em><strong>Mario</strong></em> which is implemented in Pytorch. </li> <li><em>evaluation.py:</em> This file shows the workflow of <em><strong>Mario</strong></em> and calculates the overall performance of it.</li> <li><em>prior_knowledge.json:</em> This file stores the prior knowledge extracted from our empirical dataset for predicting field-relevant method names to unique fields.</li> </ol> <p>We will build a homepage for <em><strong>Mario</strong> </em>on Github and release the whole project upon acceptance.</p> <p> </p> <p><strong>Explanation of Figure3b:</strong></p> <p>In our experiment, we find that <span class="math-tex">\(\overline{\mathbb{S}_{M}}\)</span> is slightly higher than <span class="math-tex">\(\overline{\mathbb{S}_{T}}\)</span>. Through our manual observation, we find that it happens because tokens composing the investigated method names are repetitive. In the following, we give a concrete example.</p> <p>For the ErrorsTag.java class of <a href="https://github.com/apache/struts">Apache struts1</a> project, it contains 12 field-relevant method names obtained by combining the Verbs = {<em>get</em>, <em>set</em>} and the Fields = {<em>bundle</em>, <em>footer</em>, <em>locale</em>, <em>name</em>, <em>property</em>, <em>header</em>} in pairs. When <span class="math-tex">\(\alpha\)</span> = 0.5, its proximate classes totally have 14 field-relevant method names composed by the above 12 ones plus with <em>prepareName</em> and <em>createLocale</em>. Under such a condition, the Jaccard similarity of the method name level is 0.857 (12/14), higher than that of the token level which is 0.8 (8/10).</p> <p> </p> <p><strong>User Study:</strong></p> <p>We release the queries from the developers and the predictions of Mario in our user study. Note that due to the confidential policy, we only show the last two words of the full qualified class name for each query.</p> <p> </p> <p>***.impl.MarketService.java: {Init, set config file name, parse config, get config, get config file name}</p> <p>***.user.UserController.java: {Login, register, logout, delete role, find roles, create admin user, is login, get user by username, find permissions expired}</p> <p>***.db.Bot.java: {Respond, create kernel handler, init}</p> <p>***.controller.LoginController.java: {On click, show login form, login, logout}</p> <p>***.controller.UserController.java: {Delete user, save user, get user by id, get customer users, serve user, send activation email, get activation link, set passward}</p> <p>***.login.LoginService.java: {Login, logout, login with scm, get authentication}</p> <p>***.dto.ClientLoginDto.java: {To string, get topology}</p> <p>***.impl.IdaasServiceImpl.java: {Get mapper id, execute, get job id}</p> <p>***.router.DBRouterJoinPoint.java: {Materialize string, to string infix, get id}</p> <p>***.aspect.LogAspect.java: {Log to db, do before, do around, do after returning, do after in service layer, web service}</p> <p>***.service.DocService.java: {Get project docs, add enum doc strings, search docs, service added, add exception doc strings, doc values string, find supported services, copy of docs}</p> <p>***.dataclean.convertMessageStructureService.java: {Start up, delete, add module, list types, get type, shut down, delete all, list modules}</p> <p>***.dataclean.messageRouteAndSendService.java: {On bind, on create, on destroy, send message, on start command, handle message, configure, get error message}</p> <p>***.addresssimilarity.addressSimilarityService.java: {Delete, get hosted connection, start limited on connection, notify, opened change, on disconnected list changed, create, on initialize, on map changed}</p>
Artifact of MADUSA: Mobile Application Demo Generation based on Usage Scenarios
<p>Artifact of MADUSA: Mobile Application Demo Generation based on Usage Scenarios</p>
Comparing the Intensity of Variability Changes in Software Product Line Evolution - Related Research Artifacts
<p>This archived open repository contains open science material related to the following submission to the <a href="https://www.journals.elsevier.com/journal-of-systems-and-software/call-for-papers/software-reuse-for-the-next-generation">Journal of Systems and Software (JSS) special issue <em>Software Reuse for the Next Generation</em></a>:</p> <p>C. Kröher, L. Gerling, K. Schmid, <em>Comparing the intensity of variability changes in software product line evolution</em>, Journal of Systems and Software. Submitted November 2022.</p> <p>The paper presents the application of a fine-grained, variability-centric analysis approach to four different software product lines: <a href="https://github.com/torvalds/linux">Linux kernel</a>, <a href="https://github.com/coreboot/coreboot">coreboot firmware</a>, <a href="https://github.com/mirror/busybox">BusyBox UNIX utilities</a>, and <a href="https://sourceforge.net/projects/axtls/">axTLS embedded SSL</a>. The approach is based on the differentiation between artifact-specific and variability information in code, build, and variability model artifacts to identify the intensity (the frequency and the amount) with which developers change variability information in practice.</p> <p>In order to complement the results presented in the submission and support reproducibility as well as reuse, the following artifacts are available:</p> <ul> <li><strong>JSS-VM_2022-11-01.zip</strong>: a compressed archive containing the virtual machine in which the analysis was executed. This virtual machine was created with <a href="https://www.vmware.com/content/vmware/vmware-published-sites/us/products/workstation-player/workstation-player-evaluation.html.html">VMware Workstation 16 Player</a> based on <a href="https://ubuntu.com/download/desktop">Ubuntu Desktop 22.04.1</a> (username and password: jss). It provides all installed software, configuration files, data sets, and results as described in the submission. Download, extract, and start the virtual machine to access the detailed description of its content and usage on the desktop.</li> <li><strong>ComAnI-Applications.zip</strong>: a compressed archive containing the technical realization of the analysis approach and the configuration files used to apply it to the individual software product lines. This includes: <ul> <li><em>ComAnI_Guide.pdf</em>: the guide explaining the application and its usage in general</li> <li><em>ComAnI.jar</em>: the Java executable archive file representing the main application for starting an analysis</li> <li><em>ComAnI-PS.jar</em>: a modified version of the previous Java executable archive file, which provides the number of code, build, and variability model artifacts as specified by the regular expressions of a given configuration file as well as their total number of lines based on the current state of a repository only (no history)</li> <li><em>DeadCodeChangeAnalyzer.jar</em>: a commit analyzer plug-in for detecting changes relevant to dead code detection (not used in the submission)</li> <li><em>GitCommitExtractor.jar</em>: a commit extraction plug-in for extracting commits from Git repositories</li> <li><em>SvnCommitExtractor.jar</em>: a commit extraction plug-in for extracting commits from Subversion (SVN) repositories</li> <li><em>VariabilityChangeAnalyzer.jar</em>: a commit analyzer plug-in for detecting changes to artifact-specific and variability information in code, build, and variability model artifacts (realization of the commit analysis process described in the submission)</li> <li><em>busybox-commit-list</em>: a plain text file containing a subset of the BusyBox commits as required by the application to extract and analyze only those parts of its entire history relevant for the submission (quote from the submission: <em>"For BusyBox, we had to further exclude commits before the complete migration to Kbuild and after the introduction of a script for extracting variability model information from code artifacts, which initiated defining variability information of the variability model as part of comments in code artifacts. This mixing of information and artifact types is not supported by our tooling"</em>)</li> <li><em>axtls.properties</em>: the configuration (file) defining the required properties for extracting and analyzing commits of the axTLS history</li> <li><em>busybox.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the BusyBox history</li> <li><em>busybox-subset.properties</em>: the configuration (file) defining the required properties for extracting and analyzing the commits specified in the busybox-commit-list file only, resulting in the respective subset of the BusyBox history</li> <li><em>coreboot.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the coreboot history</li> <li><em>linux.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the Linux kernel history</li> <li><em>template.properties</em>: the configuration (file) template including descriptions of each property and its valid values</li> </ul> </li> <li><strong>ComAnI-Results.zip</strong>: a compressed archive containing the (raw) analysis results from applying the approach via its realization as provided by the previous archive to each of the software product lines. Hence, for each subject, a directory with the respective name exist, which in turn contains the following artifacts: <ul> <li><em>VariabilityChangeAnalyzer-Results_2022-09-[…]</em>: the directory containing the respective analysis results and some automated visualizations. Please note that some files are not correctly labeled, .e.g. some files for axTLS and BusyBox are prefixed with "coreboot", while containing the correct data for the respective subjects. Further, for BusyBox, two directories exist, which the additional readme-file explains</li> <li><em>project-size-trace.txt</em>: the complete trace from starting ComAnI-PS.jar (see description above) with a specific configuration file to its final output</li> </ul> </li> <li><strong>JSS23_Extended-Evolution-Analysis_Statistics.ods</strong>: a <a href="https://www.libreoffice.org/discover/calc/">LibreOffice Calc</a> spreadsheet containing data derived from the raw ones of the ComAnI-Results.zip and the respective visualizations as presented in the submission. Further, some sheets include additional data preparations used to write certain parts of the result and discussion sections.</li> </ul> <p>The additional <strong>LICENSE</strong> file defines <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache License Version 2.0, January 2004</a> to apply for all artifacts in this repository.</p> <p>This work is partially supported by the Evoline project, funded by the DFG (German Research Foundation) under Priority Programme SPP 1593 and by the ITEA3 project REVaMP², funded by the BMBF (German Ministry of Research and Education) under grant 01IS16042H. Any opinions expressed herein are solely by the authors and not of the DFG or BMBF.</p>
Sampling time-dependent artifacts in single-cell genomics studies: scRNA-seq data
<p>Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.</p> <p>This repository contains the expression matrices and Seurat objects associated with the scRNA-seq data of the manuscript: "Sampling time-dependent artifacts in single-cell genomics studies" published in Genome Biology in 2020. The purpose of this repo is to share processed files and metadata for immediate access and reproducibility. The code to analyze it is thoroughly documented at the associated Github repository (https://github.com/massonix/sampling_artifacts).</p>
A comparison of minimally-invasive sampling techniques for ZooMS analysis of bone artifacts: MALDI-TOF mass spectra
<p><span></span></p> <p>Bone and antler are important raw materials for tool manufacture in many cultures, past and present. The modification of osseous features which take place during artifact manufacture frequently makes it difficult to identify either the bone element or the host animal, which can limit our understanding of the cultural, economic, and/or symbolic factors which influence raw material acquisition and use. While biomolecular approaches can provide taxonomic identifications of bone or antler artifacts, these methods are frequently destructive, raising concerns about invasive sampling of culturally-important artifacts or belongings. Collagen peptide mass fingerprinting (Zooarchaeology by Mass Spectrometry or ZooMS) can provide robust taxonomic identifications of bone and antler artifacts. While the ZooMS method commonly involves destructive subsampling, minimally-invasive sampling techniques based on the triboelectric effect have also been proposed. In this paper, we compare three previously proposed minimally-invasive sampling methods (forced bag, eraser, and polishing film) on an assemblage of 15 bone artifacts from the pre-contact site EjTa-4, a large midden complex located on Calvert Island, British Columbia, Canada. We compare the results of the minimally-invasive methods to 10 fragmentary remains sampled using the conventional destructive ZooMS method. We assess the reliability and effectiveness of these methods by comparing MALDI-TOF spectral quality, the number of diagnostic and high molecular weight peaks as well as the taxonomic resolution reached after identification. We find that coarse fiber-optic polishing films are the most effective of the minimally-invasive techniques compared in this study, and that the spectral quality produced by this minimally-invasive method was not significantly different from the conventional destructive method. Our results suggest that this minimally-invasive sampling technique for ZooMS can be successfully applied to culturally significant artifacts, providing comparable taxonomic identifications to the conventional, destructive ZooMS method.</p>
Monitoring the Internet Computer (Artifact)
<p>This artifact accompanies the paper <em>Monitoring the Internet Computer</em>, which will be presented at the 25th International Symposium on Formal Methods (FM 2023). It provides the policy formulas described in Section 3.2 of the paper, the raw log files that were used in the evaluation, and all tools necessary to reproduce the experimental results, specifically those reported in Table 2 and Figure 5 in the paper.</p> <p>See README.md for additional information and instructions.</p> <p><strong>Erratum</strong> (August 14, 2023): Unlike stated in Table 1 of the paper, the block-validation-latency policy included in this artifact does not contain a future operator. The operator was present in an earlier version of the policy. It was removed because it was not required given the structure of the log data. The future operator can be added by inserting</p> <pre>EVENTUALLY [0,0]</pre> <p>at the end of line 50 of the file policy-monitoring/mfotl-policies/block_validation_latency/formula.mfotl. We did not observe a meaningful change in the results when we repeated the experiments using both the included and the modified policy.</p>
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies - Reproduction artifacts
<p># Reproducibility instructions</p> <p>This document details the steps to reproduce the results presented in the paper 'Chaining of Numerical Black-box Algorithms: Investigating the Impact of Warm-Starting and the Switching Point'</p> <p>## Algorithm code<br> The code for the individual algorithms and the switching routines is included in the 'dynas.zip' folder. </p> <p>## Data collection<br> To collect the performance data for the static algorithms, the file 'data_collection.py' can be used. This file runs all 5 static algorithm on the function suite used in the paper, using the 'ioh' package. To run it, please modify the 'ioh_dir' variable to a place where the data should be stored. <br> The resulting data is made available under the 'static.zip' file.</p> <p>## Processing code<br> The code which processes the performance data into the list of usecases is part of the 'Processing_R' notebook. <br> This results in a set of rds-files with the data in IOHanalyzer's internal format, and in a csv file ('Split_performances_relative.csv') with the selected usecases. </p> <p>This csv file can then be read in using the 'visualization' notebook, which can generate Figure 1 from the paper. This notebook can then be used to count the specific usecases, which is the data shown in Table 1. The csv-file containing the selected usecases ('usecases.csv') is also generated here.</p> <p>The 'usecases' csv file can be used in the 'Data_collection_switch' notebook to run the dynamic configurations. This results in the 'Dynamic.zip' data, which can again be read using IOHanalyzer in the 'Processing_R' notebook. This then generates several csv-files, for both the static and dynamic case, containing the per-run hitting times ('RT_samples.zip'). These are then used in the 'Visualization' notebook to generate figure 3 and 4, as well as table 2. </p> <p>## Countour plots<br> The code for generating Figure 5 is included in the end of the 'Visualization' notebook.</p> <p>## Performance comparison plot<br> Figure 6 has been generated using IOHanalyzer web-interface (iohanalyzer.liacs.nl), by uploading the files in 'cma_bfgs.zip'. </p> <p>## Switch point impact plot<br> Figure 7 has been generated using the 'split_experiment' notebook</p>
Artifacts for the IEEE Internet of Things Journal Publication: Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT
<p>Artifacts for the evaluation of the publication <em>Specification-based Symbolic Execution for Stateful Network Protocol Implementations in the IoT </em>which will be published in the IEEE Internet of Things journal. More information is available in the provided README.md file.</p>
The Artifact of the ESEC/FSE 2023 Paper Titled "Natural Language to Code: How Far are We?"
<p>In this online repository, we release the source code of each of the selected techniques as well as the experiment results from each technique (which are stored in the <em>Results.zip</em> file). For each technique, we also provide our scripts to fine this approach on the CodeSearchNet-Python dataset. For example, <em>finetune.sh/inference.sh</em> are used to finetune/evaluate CodeBERT and they are under "CodeBERT/CodeBERT".</p> <p> </p> <p>Our evaluation dataset CodeSearchNet is a well-known benchmark and it can be downloaded on its <a href="http://github.com/github/CodeSearchNet">official webpage</a>.</p> <p> </p> <p>The code to calculate the evaluation metrics are reused from <a href="https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans/evaluator/CodeBLEU">CodeBLEU</a>.</p> <p> </p> <p>Below is a piece of code generated by CodeT5. In this case, CodeT5 generates a statement recurrently, which leads to the syntactic error. Despite that, the code itself fulfills certain functionalities, and that is why it can achieve a CodeBLEU of 24.9%.</p> <pre><code class="language-python">def makeMimiLocal(filename): try: with open(filename, 'rb') as f: data = f.read() except IOError: data = b'' data = data.decode('utf-8') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\x00', b'\x00') data = data.replace(b'\ </code></pre> <p> </p> <p>We also release the 100 randomly-selected queries as well as the code generated by ChatGPT in the <em>chatGPT.jsonl</em>.</p>
Artifact for "MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software"
<p>This artifact is for the paper entitled “MAAT: A Novel Ensemble Approach to Addressing Fairness and Performance Bugs for Machine Learning Software”, which is accepted by ESEC/FSE 2022. MAAT is a novel ensemble approach to improving the fairness-performance trade-off for ML software. It outperforms state-of-the-art bias mitigation methods. The artifact has also been placed on GitHub (https://github.com/chenzhenpeng18/FSE22-MAAT) under the Apache License, publicly accessible to other researchers. In this artifact, we provide the source code of MAAT and other existing bias mitigation methods that we use in our study, as well as the intermediate results, the installation instructions, and a replication guideline (included in the README). The replication guideline provides detailed steps to replicate all the results for all the research questions.</p>
Artifact for ICSE'23 paper: Using Reactive Synthesis: An End-to-End Exploratory Case Study
<p>Artifact for ICSE'23 paper: Using Reactive Synthesis: An End-to-End Exploratory Case Study </p>
Artifact: Incorporating Failure Knowledge into Design Decisions for IoT Systems: A Controlled Experiment on Novices
<p>Artifact for "Incorporating Failure Knowledge into Design Decisions for IoT Systems: A Controlled Experiment on Novices"</p>
Artifact for Paper Search-Space Pruning with Int-Splits for Faster QBF Solving
<p>This is the artifact for the paper "Search-Space Pruning with Int-Splits for Faster QBF Solving" submitted to the <a href="http://satisfiability.org/SAT23/">SAT2023</a> conference.</p>
CoCon: A Data Set on Combined Contextualized Research Artifact Use
<p>CoCon is a large graph data set reflecting the combined use of research artifacts, contextualized in academic publications’ full-text. It comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications.</p> <p>The data set is generated from <a href="https://github.com/paperswithcode/paperswithcode-data">Papers With Code</a> and <a href="https://github.com/IllDepence/unarXive">unarXive</a>.</p> <p>You can find a Python package for loading the data as a NetworkX or Pytorch Geometric graph <a href="https://github.com/IllDepence/contextgraph">in this GitHub repository</a><br> </p>
[PLDI'23] Compound Memory Models: Artifact
<p>This is the artifact for the PLDI'23 Artifact Evaluation for the submission "Compound Memory Models". See README.md for more details.</p>
Artifacts for the 2023 Trends in Functional Programming Publication: Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture
<p>This dataset contains the artifacts for the performance evaluation conducted in the publication <em>Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture</em> which will be published in the proceedings of the 2023 <em>Trends in Functional Programming</em> conference. The provided artifacts contain a <a href="https://docker.io">Docker</a> container for executing <a href="https://embench.org">Embench</a> benchmarks with LibRISCV, RISC-V VP, Grift and Forvis. A pre-built version of the container is included.</p>
Artifact supplement for 'Search and Explore: Symbiotic Policy Synthesis in POMDPs'
<p>Artifact supplement for 'Search and Explore: Symbiotic Policy Synthesis in POMDPs' (CAV 2023).<br> <br> This repository includes:<br> - the Docker image containing the software used in the experiments along with the investigated benchmarks and original log files<br> - a guide for replicating the experiments presented in the publication<br> - two versions of the paper: the initial submission and the final version with the revised experiments</p>
GPUHarbor: Testing GPU Memory Consistency At Large (Experience Paper): Artifact
<p>Artifact for the ISSTA 2023 paper "GPUHarbor: Testing GPU Memory Consistency At Large (Experience Paper)"</p>
Raman spectra and SEM EDX analyses of artifacts resembling Ediacaran / Cambrian fossils
<p>This data is related to the paper: "Artifacts resembling Ediacaran / Cambrian fossils: how to identify them and avoid their generation" submitted for publication in the "Journal of Micropaleontology" on March 24, 2023.</p> <p>The reaction between hydrogen peroxide and pyrite can lead to the generation of objects very similar to Ediacaran and Cambrian fossils, such as <em>Cloudina</em>. This dataset and related paper provides criteria to distinguish artifacts from fossils based on their composition and structure. This dataset cotains all the Raman spectra and SEM/EDX analyses of artifacts that characterize them and are used for discussion in the related paper.</p>
Release and related artifacts
<p>Recovering Traceability Links between Release Notes and Related Software Artifacts</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.