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3 results for “Quality Smells”

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zenodo40/100

QScored: A Large Dataset of Code Smells and Quality Metrics

<p>The dataset contains code quality information of more than 86 thousand GitHub repositories containing more than 1.1 billion lines of code mainly written in C# and Java. The code quality information contains detected 7 kinds of architecture smells, 19 kinds of design smells, and 11 kinds of implementation smells, and 27 commonly used code quality metrics computed at project, package, class, and method levels.</p>

opencc-by-4.0Jan 2021View details →
zenodo32/100

Smell Pittsburgh: Engaging Community Citizen Science for Air Quality

<p>Link to the files and description of the Smell Pittsburgh Dataset &ndash;<br> <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FCMU-CREATE-Lab%2Fsmell-pittsburgh-prediction%2Ftree%2Fmaster%2Fdataset%2Fv2&amp;data=05%7C01%7Cy.c.hsu%40uva.nl%7C89562067341d40d0bad308da2c475652%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637870982141190827%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=gWn0nGRUl5EAHvDfJwTlTNJN%2BWFH62NX6Mw%2B2web6XE%3D&amp;reserved=0">https://github.com/CMU-CREATE-Lab/smell-pittsburgh-prediction/tree/master/dataset/v2</a></p> <p>Smell Pittsburgh (<a href="https://smellpgh.org">https://smellpgh.org</a>) is a mobile application for crowdsourcing reports of bad odors, such as those generated from air pollution. The data is used to train a machine learning model to predict the presence of bad smell and create push notifications to inform citizens about the bad smell. The motivation, background, and design of the Smell Pittsburgh application is described in the following paper.</p> <ul> <li>Yen-Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. 2020. Smell Pittsburgh: Engaging Community Citizen Science for Air Quality. ACM Transactions on Interactive Intelligent Systems. 10, 4, Article 32. DOI:<a href="https://doi.org/10.1145/3369397">https://doi.org/10.1145/3369397</a>. Preprint:<a href="https://arxiv.org/pdf/1912.11936.pdf">https://arxiv.org/pdf/1912.11936.pdf</a>.</li> </ul>

opencc-by-4.0May 2022View details →
zenodo24/100

On The Relation of Test Smells to Software Code Quality. Appendix.

<p>Dataset of &quot;On The Relation of Test Smells to Software Code Quality&quot;</p> <p>This is the replication package of the paper &quot;On The Relation of Test Smells to Software Code Quality&quot;, accepted at the 34th IEEE International Conference on Software Maintenance and Evolution (ICSME 18).</p>

openother-openOct 2020View details →

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Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

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openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record