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1,034 results for “Empirical”
An Empirical Characterization of Anycast Convergence Time
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Unveiling the Skills and Responsibilities of Serverless Practitioners: An Empirical Investigation
<p>This replication package accompanies the research paper titled "Unveiling the Skills and Responsibilities of Serverless Practitioners: An Empirical Investigation." The package contains the data and code used to perform the analyses presented in the paper.</p> <p><strong>Purpose:</strong> The primary goal of this dataset is to provide the necessary resources for replicating the results of our study on the skills and responsibilities of serverless practitioners. Researchers can use this package to verify our findings, perform additional analyses, or adapt our methodology for their own research.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Analysis Sheet.xlsx</strong>: This Excel file includes the raw data and the processed data used in our analyses. The file consists of the following sheets:</p> <ul> <li><strong>Raw Data</strong>: Contains the unprocessed data collected from various sources. </li> <li><strong>Meta Data</strong>: Provides detailed information about the data collection process.</li> <li><strong>Pilot Sample</strong>: Includes data from the pilot study conducted to test the research methodology. This sheet helps in understanding the preliminary results and adjustments made for the main study.</li> <li><strong>Selected Sample</strong>: Contains data from the sample selected for the main study. </li> <li><strong>Roles Sorting</strong>: This sheet categorizes different roles based on the data collected. </li> <li><strong>Initial Coding</strong>: Provides the initial coding of qualitative data. This includes codes assigned to various responses and observations, serving as a foundation for thematic analysis.</li> <li><strong>Responsibilities Thematic</strong>: Contains thematic analysis of the responsibilities associated with different roles. This sheet categorizes responsibilities into various themes based on the data.</li> <li><strong>Soft Skill Thematic</strong>: Includes thematic analysis of soft skills identified in the study. The sheet categorizes soft skills into different themes, helping in understanding the skill requirements across roles.</li> <li><strong>Hard Skill Thematic</strong>: Provides a thematic analysis of hard skills identified in the study. Similar to the soft skills sheet, this categorizes hard skills into different themes to analyze their distribution and importance across roles.</li> </ul> </li> <li> <p><strong>Python Script</strong>: The Python script used to automate the data collection process from Glassdoor. This script employs Selenium, Selenium Stealth, and PostgreSQL to retrieve real-time job listings, ensuring data integrity and minimizing selection bias.</p> </li> </ol> <p> </p>
Gold mining and social accountability: An empirical approach in Colombia
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Dataset of the Paper "Architecture Decisions in Quantum Software Systems: An Empirical Study on Stack Exchange and GitHub"
<p>This dataset was collected from GitHub and Stack Exchange (including Stack Overflow, Quantum Computing Stack Exchange, and Computer Science Stack Exchange) to conduct an empirical study on architecture decisions in quantum software systems. We provide below a brief description of each file:</p><p><strong>1. Dataset (GitHub).xlsx</strong></p><p>contains selected quantum software projects from GitHub with project names, issue IDs, and issue URLs and the data extracted from the GitHub issues that are related to architecture decisions in quantum software development.</p><p><strong>2. Dataset (SO).xlsx</strong></p><p>contains the IDs and URLs of Stack Overflow (SO) labeled posts and the extracted data from the Stack Overflow posts that are related to architecture decisions in quantum software development.</p><p><strong>3. Dataset (QC).xlsx</strong></p><p>contains the IDs and URLs of Quantum Computing (QC) Stack Exchange labeled posts and the extracted data from the Quantum Computing Stack Exchange posts that are related to architecture decisions in quantum software development.</p><p><strong>4. Dataset (CS).xlsx</strong></p><p>contains the IDs and URLs of Computer Science (CS) Stack Exchange labeled posts and the extracted data from the Computer Science Stack Exchange posts that are related to architecture decisions in quantum software development.</p><p><strong>5. Extracted Data (GitHub+SO+QC+CS).xlsx</strong></p><p>provides the final results of data extracted from the related GitHub issues, SO posts, QC posts, and CS posts.</p>
Artifact for "Understanding Misconfigurations in ROS: An Empirical Study and Current Approaches"
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Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems
<p>Replication package for the paper "Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems "</p>
Replication package for: Empirical Investigation of a Sufficient Statistic for Monetary Shocks
<p>This replication package codes and datasets to replicate the results in "Empirical Investigation of a Sufficient Statistic for Monetary Shocks" by Fernando Alvarez, Andrea Ferrara, Erwan Gautier, Hervé Le Bihan and Francesco Lippi when to be published at RESTUD. The readme file describes how to run the code and how to access the data underlying the results. Please note that the micro CPI and PPI data sets used to compute the price rigidity statistics are proprietary data, provided to the authors by Insee (French Statistical Office) after a bilateral agreement between Banque de France and Insee and these micro price data sets cannot be shared.</p>
Empirical Data from a Longitudinal Software Maintenance Experiment
<p>This dataset contains IDE interaction traces (logs) and screen videos obtained from a comprehensive study conducted to understand in detail how software professionals plan and execute maintenance activities on real-life software.</p>
Dataset of "How to Communicate when Submitting Patches: an Empirical Study of the Linux Kernel"
<p>This repository includes the data of the paper "How to Communicate when Submitting Patches: an Empirical Study of the Linux Kernel".</p> <p>It contains three parts:</p> <p>1) The related online documents.</p> <p>2) The survey results.</p> <p>3) The classified patches of LKML.</p> <p> </p> <p></p>
An Empirical Study on the Impact of Refactoring Activities on Evolving Client-Used APIs
<p>Context: Refactoring is recognized as an effective practice to maintain evolving software systems. For software libraries, we study how library developers refactor their Application Programming Interfaces (APIs), especially when it impacts client users by breaking an API of the library. Objective: Our work aims to understand how clients that use a library API are affected by refactoring activities. We target popular libraries that potentially impact more library client users. Method: We distinguish between library APIs based on their client-usage (refereed to as client-used APIs) in order to understand the extent to which API breakages relate to refactorings. Our tool-based approach allows for a large-scale study across eight libraries (i.e., totaling 183 consecutive versions) with around 900 clients projects. Results: We find that library maintainers are less likely to break client-used API classes. Quantitatively, we find that refactoring activities break less than 37% of all client-used APIs. In a more qualitative analysis, we show two documented cases of where non-refactoring API breaking changes are motivated other maintenance issues (i.e., bug fix and new features) and involve more complex refactoring operations. Conclusion: Using our automated approach, we find that library developers are less likely to break APIs and tend to break client-used APIs when addressing these maintenance issues.</p>
On the abandonment and survival of open source projects: An empirical investigation
<p>This replication package contains the results of our analysis as well as other data and scripts used in the process, which allows to reproduce and extend the results presented in the paper study.</p>
Empirical Analysis of Performance Changes at Code Level - Analysis and Measurement Data
<p>This dataset contains the information of the paper 'Empirical Analysis of Performance Changes at Code Level'. In particular, it contains 4 folders:<br> (1) the versions where performance could have changed based on regression test selection (dependencies)<br> (2) the measurement data and the derived performance changes (measurement)<br> (3) the tracediffs (tracediffs) and<br> (4) the properties of the changes and the manual classification of the changes (properties).<br> The folders contain READMEs with detailed information about each folder.</p>
Data accompanying "An empirical pipeline for choosing the optimal clustering threshold in RADseq studies"
<p>Data files accompanying "An empirical pipeline for choosing the optimal clustering threshold in RADseq studies"</p>
Dataset of "how to communicate when submitting patches: an Empirical Study of the Linux Kernel"
<p>This repository includes the data of the paper "How to Communicate when Submitting Patches: an Empirical Study of the Linux Kernel".</p> <p>It contains three parts:</p> <p>1) Related online documents.</p> <p>2) Original questionnaire of the survey.</p> <p>3) Survey results.</p>
An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps: Appendix
<p>The appendix of our "An Empirical Catalog of Code Smells for the Presentation Layer of Android Apps" paper.</p>
waveform data of the Empirical Green's Functions (EGFs) and earthquakes for the manuscript "Unraveling the Mantle Dynamics in Central Asia with Full Waveform Inversion Tomography"
<p>The dataset uploaded contains the original ASDF files and the SAC files (with '_sac') converted from ASDF</p> <p>For the ASDF files:</p> <p>The ASDF data file could be read through python module pyasdf and obspy after decompressing. </p> <p>The earthquake information could be accessed by the following command</p> <p>import pyasdf</p> <p>ds=pyasdf.ASDFDataSet(ASDFfile, mode='r')</p> <p>event=ds.events[0].preferred_origin()</p> <p>print(event)</p> <p>the waveforms coul dbe accessed through</p> <p>stream=ds.waveforms[net.station].raw_recording</p>
EduLifeDesks Archive: Honors Desert Ecology - Empire High School (647) DwCA
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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>
Dataset for ISSTA'24 An Empirical Study on Kubernetes Operator Bugs
<p>This is the dataset for the ISSTA'24 submission <strong>An Empirical Study on Kubernetes Operator Bugs</strong>. It contains 210 operator bugs collected from 36 popular Kubernetes operators.</p> <p>It can be used to replicate and reproduce the paper's study, as well as to serve as a bug benchmark for future work on combating operator bugs in Kubernetes.</p>
Simulation and empirical data for "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations"
<p>Simulation data and empirical data used to generate figures from "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations". Can be used with code provided on the associated github to regenerate the figures. </p>
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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.