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ShareScore release 0.9.0
Dataset results
6 results for “Stackoverflow”
Anonymized monthly StackOverflow activity for the Python tag
<p>CSV files containing metadata for each question, answer and comment posted on StackOverflow during 2019. Each file contains one month of activity with the body text, titles and usernames removed. User and post ids have been obscured so that they are not traceable, but are consistent throughout the dataset.</p>
Reddit and StackOverflow dataset (Programming languages)
<p>This data set contains anonymized data collected from Reddit (via the <a href="https://api.pushshift.io/redoc">Pushshift API</a>) and StackOverflow (from <a href="https://www.kaggle.com/datasets/stackoverflow/stackoverflow">Kaggle's dataset</a>).</p> <p>Each folder includes the data split by trimester. The schema of StackOverflow and Reddit-related files follows:</p> <ul> <li>Fields from StackOverflow <ul> <li>question_id</li> <li>answer_id</li> <li>creation_date - answer creation_date</li> <li>score - score of the question/answer</li> <li>tags - all tags flagged for a question</li> <li>answer_count - number of answers for a question</li> <li>start_question - question's time of creation</li> <li>last_activity_date - last update on the question</li> <li>new_id - hashed id of the answerer</li> <li>q_new_id - hashed id of the questioner</li> </ul> </li> <li>Fields from Reddit <ul> <li>comment_id</li> <li>submission_id</li> <li>score - score of the question/submission</li> <li>subreddit</li> <li>created_utc - time of creation (unrelated to last modified comments)</li> <li>new_id - hashed id</li> </ul> </li> </ul> <p>The .txt files represent the structure of the corresponding hypergraphs.</p>
Replication package for the paper: "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions"
<p>This is the replication package for the article "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions". The article was published in the Research Track of the third Brazilian Workshop on Intelligent Software Engineering (ISE'23).</p> <p>The replication package consists of 8 files:<br> 1) dataset.csv, 2) code_anayses.ipynb and 3) example_test_balanced.csv and the others are results of word cloud generation.</p> <p>In dataset.csv, we provide the data for future replications.</p> <p>In code_anayses.ipynb, we provide the code we use to arrive at the results.</p> <p>In example_test_balanced.csv, we provide an example input dataset for training the models.</p> <p>For future references in this article, please contact lead author Eliakim Gama, or one of the co-authors.</p>
Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers
<div>Supplemental material for the paper "Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers" published in DLSP 2024.</div> <div> <div> </div> </div>
Robotics on StackOverflow
<p>The shared files contained information about the initial codes, major themes, and labels of how, why, and what questions. </p> <p><strong>Random500Coded-Major-noFP.csv </strong> file contains the major themes of the 500 selected questions. </p> <p><strong>Random500Coded-noFP.csv:</strong> Contains the sub-themes of the 500 selected questions. </p> <p><strong>Random500HWWO-noFP.csv:</strong> Contains the type (how, what, why) of the 500 selected questions. </p>
APIzation: Generating Reusable APIs from StackOverflow Code Snippets - Replication Package
<p>This repository represents the replication package for the paper <em>APIzation: Generating Reusable APIs from StackOverflow Code Snippets</em>.</p> <p>The paper is published in the proceeding of the <em>36th IEEE/ACM International Conference on Automated Software Engineering (ASE)</em>.</p> <p>In this replication package, we provide all the <em>APIzations</em> we produced with our tool. Also, we include the data we used for our evaluation.</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
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DANDI Archive for NWB datasets
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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.