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334 results for “Python”
VUDENC - python corpus for word2vec
<p>Python corpus for training a word2vec model, and one trained model.</p>
Automatic display of deformations revealed by GPR study on the walls of a Turkish mosque under restoration with the Python program
Open the record for dataset details and reuse information.
Empirical analysis of Type-Related Defects in Python projects
<p>This is the replication material related to the following paper submitted for TSE.</p> <ul> <li>Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)</li> </ul>
Empirical analysis of Type-Related Defects in Python projects
<p>This is the replication material related to the following paper submitted for TSE.</p> <ul> <li>Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)</li> </ul>
Artifact for "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"
<p>This is the artifact for the paper titled "Efficient Construction of Practical Python Call Graphs with Entity Knowledge Base"</p>
[DEPRECATED] A manually-curated categorisation of Java Maven libraries along Python PyPI Topics (dataset)
<p>This dataset has been superseded by <strong><a title="https://zenodo.org/records/10480832" href="../records/10480832">https://zenodo.org/records/10480832</a></strong></p> <p> </p> <p>-- Test edit (new version?)</p>
Test dataset for python-apd-restitution-analysis
<p>The dataset consists of two files:</p> <ul> <li>APD_res_min5.5_max13_step0.25_OFF_10_54.sif: optical map of a mouse heart which was injected with di-4-AN-PQ (!check). The mouse heart was paced from 5.5 to 13 Hz at 0.25 Hz steps, according to the APD restitution protocol. The recording was made with an Andor Solis camera. Image was captured by Callum Zgierski-Johnston</li> <li>Basler_acA720-520um__40190569__20230919_114100243_vol_denoised.tif: optical map of a mouse heart which was injected with di-4-AN-PQ (!check). The recording consisted of multiple TIF files, captured by a Basler acA720-520um camera running at 478 fps. The software used for acquisition was the pylon viewer app by Basler. After collating the 2D TIF files into one large one, the volume was then denoised with <a href="https://github.com/NICALab/SUPPORT" target="_blank" rel="noopener">SUPPORT</a>. The image was acquired by Thomas Kok.</li> </ul> <p>This is the test dataset used for testing out the code from <a href="https://github.com/tk231/python-apd-restitution-analysis" target="_blank" rel="noopener">https://github.com/tk231/python-apd-restitution-analysis</a> and acquired at the Institute for Experimental Cardiovascular Medicine (IEKM), University Medical Center Freiburg.</p> <p>TODO:</p> <ul> <li>Add animal experimentation license number!</li> </ul>
Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R
<p><strong><span>The folder organization and datasets within each are as follows:</span></strong></p> <p><strong><span>Collection Folder:</span></strong><span> the original dataset that we scraped is placed. We have removed the user names and email addresses to keep the users’ privacy. </span><strong><span>RQ1 Folder</span></strong><span> has three subfolders: </span></p> <p><span><span>❖<span> </span></span></span><strong><span>Manual Training:</span></strong><span> The initial manually labeled data we used to initially train the classifiers is included. Note that columns A-O in this dataset are all extracted from GitHub’s API. Column O (heading “message”) is the commit message itself. The following columns P and Q (heading “author_a” and “author_b”) are the final classification (upon which the Cohen Kappa was calculated). Column R (heading “notes”) contains some commentaries on specific cases that may be meaningful.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Predicted:</span></strong><span> The results of the automatic classifiers (both 1st and 2nd round) are included. The additional columns are generated by the classifiers.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Verifications</span></strong><span> contain the manually labeled data that we used as 1st and 2nd verification rounds. This is a simplified dataset with the commit’s sha and the parsed message. The authors classified columns E and F independently and individually. The labels stated here are those that the authors agreed to (without having access to column D). Note that column D was added afterward by sha-matching by another author to calculate the Cohen Kappa. The yellow rows are those with disagreements.</span></p> <p><span> </span><strong><span>RQ2_RQ3 Folder</span></strong><span> contains the manually labeled dataset for RQ2 and RQ3 (SATD Types and Activities). </span></p> <p><span>NOTE: Kindly note that many messages or classifications are <em>multiline</em>. This means that the cells have to be expanded to be capable of reading all text included in a cell.</span></p>
python file for Brazilian e commerce
<p>this a python file for the grapghs and diagrams</p>
PyNose: a test smell detector for Python — sources and artifacts
<p>This archive contains:</p> <ol> <li>The source code of PyNose, a test smell detector for Python.</li> <li>The pre-built version of the tool that can be used as a plugin inside PyCharm.</li> <li>Lists of projects used in our study.</li> <li>A full list of examples of the newly introduced Suboptimal Assert test smell.</li> <li>An example of a change graph used to discover Python-specific test smells.</li> <li>The results of the small-scale mapping study: a full list of papers, a full list of test smells, and the correlation between them.</li> </ol> <p>You can find all the details in README.txt</p>
MICRE - ACP publication - co-located EAMv1 and observation data at Macquarie Island and python scripts for analysis
<p>We simulate immersion-mode INP concentrations using the Energy Exascale Earth System Model version 1 (E3SMv1) by combining simulated aerosols with recently developed deterministic INP parameterizations and the native classical nucleation theory (CNT) for mineral dust in E3SMv1. Here, we provide Python scripts, co-located model data, and MICRE INP observations. </p>
Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"
<p>Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux1 Transfer Events on the Ionosphere"</p> <p>Author: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023</p> <p><br> Figure 1 has been plotted from two data files named C0_320.vtk and c1_320.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit</p> <p>Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb"</p> <p><br> Figure 3 has been plotted using the data file named "t_4964.vtk" and the visualisation toolkit VisIt.</p> <p><br> Figure 4 has been plotted using the ipython notebook named "figure_4.ipynb"</p> <p><br> Figure 5 has been plotted using the ipython notebook named "figure_5.ipynb"</p> <p><br> Figure 6 has been plotted using the ipython notebook named "figure_6.ipynb"</p> <p><br> Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb"</p> <p><br> Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb"</p> <p><br> Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb"</p> <p><br> All the associated data files are uploaded with the ipython notebooks</p>
supplementary_data_for_python_codes
<p>The two csv files include information of isolated sequence clones of human noroviruses</p>
Dicionary dataset for experiments calling Julia from Python
<p>ERRORED, please ignore this upload</p>
ScienceDex guides
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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.