Data and code for "Evaluating the three-level approach of the U-smile method for imbalanced binary classification" by Katarzyna B. Kubiak (19781694)

An EEG Imagined Speech Dataset for BCI Applications with Binary and Numerical Stimuli by Diego Gabriel Hernández Solís (22253014)

Healthy and Diseased Leaves by Manan Kumar (21726473)

“…<br><br>For this version, images of healthy and diseased leaves were extracted from the original dataset and organized into two separate folders: <code>healthy/</code> and <code>diseased/</code>, to support binary classification tasks in machine learning workflows. …”

Grouping of populations of <i>J. erucifolia</i> subspecies into clusters based on Dice distances calculated from AFLP binary matrices. by Barbara Gawrońska (8173719)

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

<b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

“…</p><h2> Dataset statistics</h2><p><br></p><p dir="ltr">- Total samples: 6,000 (5,000 train / 1,000 test)<br>- Classes: 4 (balanced distribution across train/test)<br>- Planes: Axial / Coronal / Sagittal (balanced representation)<br>- Imaging modality: T1-weighted MRI<br>- Annotation quality: Reviewed and corrected by medical experts</p><h2> Citation</h2><p dir="ltr">If you use BRISC in your work, please cite:</p><p dir="ltr"><code>@article{fateh2025brisc,</code><br><code>title={Brisc: Annotated dataset for brain tumor segmentation and classification with swin-hafnet},</code><br><code>author={Fateh, Amirreza and Rezvani, Yasin and Moayedi, Sara and Rezvani, Sadjad and Fateh, Fatemeh and Fateh, Mansoor and Abolghasemi, Vahid},</code><br><code>journal={arXiv preprint arXiv:2506.14318},</code><br><code>year={2025}</code><br><code>}</code></p><h2> Acknowledgments</h2><p dir="ltr">Thanks to the collaborating radiologists and physicians for expert annotation and review.…”

Attention to women's rights in NGO press releases, 1996–2018: A curated, coded dataset of organizational attention to women and violence against women by Margarita Solntseva (22563608)

“…The classification pipeline combines LLM-based coding with deterministic fallbacks and rigorous human validation, ensuring valid measurements suitable for cross-national comparative research and temporal analysis of advocacy priorities.…”

Dataset for Electrical Line Fault Detection & Classification by Anish Biswas (22616135)

“…</li><li>A target column indicating fault detection (1 / 2) or fault classification (1 – 11).</li></ul><p dir="ltr">Together, these datasets provide a comprehensive basis for machine learning and deep learning studies on:</p><ul><li>Fault detection (binary classification).…”

Classification result on the ModelNet40 dataset. by Lei Wang (6656)

Classification results on the FG3D dataset. by Lei Wang (6656)

S1 Archive - by Hootan Kamran (21519846)

Data Sheet 1_DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding.pdf by Zhengtao Luo (4903051)

“…Feature comparison experiments confirmed the superiority of our proposed Binary-Weight encoding method over other encoding techniques. t-SNE visualization further validated the model’s effective classification capabilities. …”

Distributions of classification scores from 5-fold cross-validation experiments. by Marcos Soares Barbeitos (20416883)

Additional file 5 of Chemical classification program synthesis using generative artificial intelligence by Christopher J. Mungall (6793694)

“…Not shown is the thinking process for generating the final version of the program, repeated below: “Below is our “thinking‐out‐loud” summary before the code. In our last attempt the logic was very “binary” (simply net charge positive versus zero) so that many small, protonated species got classified as cations even though their positive charge is pH–dependent, while many zwitterionic molecules with a robust “cationic lipid” character (for example, many phosphatidylcholines) were missed. …”

FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair by Sakina Fatima (15362704)

“…</p><p><br></p><p dir="ltr">### Input Files:</p><p dir="ltr">This is a list of input files that are required to perform the binary classification for each fix category:</p><p dir="ltr">* Data/change_assertion.csv</p><p dir="ltr">*Data/change_condition.csv</p><p dir="ltr">*Data/change_data_format.csv</p><p dir="ltr">*Data/change_data_structure.csv</p><p dir="ltr">*Data/handle_exception.csv</p><p dir="ltr">*Data/reorder_data.csv</p><p dir="ltr">*Data/reset_variable.csv</p><p dir="ltr">*Data/call_static_method.csv</p><p dir="ltr">*Data/reorder_parameters.csv</p><p dir="ltr">*Data/misc.csv</p><p><br></p><p dir="ltr">### Replicating the experiment</p><p><br></p><p dir="ltr">To run experiment with our prediction model, navigate to the `Code\` folder and run the following commands:</p><p><br></p><p dir="ltr">```console</p><p dir="ltr">bash codemodel_with_fnn.sh</p><p dir="ltr">bash codemodel_with_fsl.sh</p><p>```</p><p><br></p><p dir="ltr"># Generate the repaired flaky tests using GPT 3.5 Turbo:</p><p><br></p><p dir="ltr">Input Files:</p><p dir="ltr">This is a an input file that is required to accomplish this step:</p><p><br></p><p dir="ltr">### Experiments on the 181 tests using prompts with and without labels:</p><p dir="ltr">* Data/Dataset_for_GPT.csv</p><p><br></p><p><br></p><p dir="ltr">### Experiments on the tests using prompts with, without labels and in-context learning:</p><p><br></p><p dir="ltr">###For Change Assertion:</p><p dir="ltr">* Data/change_assertion_input_FSP.csv</p><p dir="ltr">###For Change Condition:</p><p dir="ltr">* Data/change_condition_input_FSP.csv</p><p dir="ltr">###For Change DataStructure:</p><p dir="ltr">* Data/change_dataStructure_input_FSP.csv</p><p><br></p><p dir="ltr">To run this experiment, navigate to the `Code\` folder and run the following commands:</p><p><br></p><p dir="ltr">```console</p><p dir="ltr">bash gpt3.5_experiments.sh</p><p>```</p><p dir="ltr"># Execute a sample of GPT-reapired flaky tests:</p><p><br></p><p dir="ltr">Input Files:</p><p dir="ltr">This is a an input file that is required to accomplish this step:</p><p dir="ltr">* Data/sampleTests_For_Execution.csv</p><p><br></p><p dir="ltr">To execute the 35 GPT-repaired flaky tests:</p><p><br></p><p dir="ltr">-First Clone the Github project (From the 'PR Link' column in the sampleTests_For_Execution.csv file)</p><p dir="ltr">-Checkout on the the commit of the given PR link (if merged, checkout on the master branch)</p><p dir="ltr">-Navigate to the project folder and run command: </p><p dir="ltr">```console</p><p dir="ltr">*mvn clean test -Dtest=[Test Class Name]#[Test Method Name] -DfailIfNoTests=false</p><p>```</p>…”

Data Sheet 3_EPheClass: ensemble-based phenotype classifier from 16S rRNA gene sequences.csv by Lara Vázquez-González (12061883)

“…In this paper, we propose a curated pipeline for binary phenotype classification based on a count table of 16S rRNA gene amplicons, which can be applied to any microbiome. …”

Data Sheet 2_EPheClass: ensemble-based phenotype classifier from 16S rRNA gene sequences.csv by Lara Vázquez-González (12061883)

“…In this paper, we propose a curated pipeline for binary phenotype classification based on a count table of 16S rRNA gene amplicons, which can be applied to any microbiome. …”

Data Sheet 1_EPheClass: ensemble-based phenotype classifier from 16S rRNA gene sequences.pdf by Lara Vázquez-González (12061883)

“…In this paper, we propose a curated pipeline for binary phenotype classification based on a count table of 16S rRNA gene amplicons, which can be applied to any microbiome. …”

Data Sheet 7_EPheClass: ensemble-based phenotype classifier from 16S rRNA gene sequences.csv by Lara Vázquez-González (12061883)

“…In this paper, we propose a curated pipeline for binary phenotype classification based on a count table of 16S rRNA gene amplicons, which can be applied to any microbiome. …”