Table_1_XCast: A python climate forecasting toolkit.docx حسب Kyle Joseph Chen Hall (13049001)

"…These tools are useful for producing and analyzing both experimental and operational climate forecasts. In this study, we describe the development of XCast, and present in-depth technical details on how XCast brings highly parallelized gridpoint-wise versions of traditional Python data science tools into Python's gridded earth data ecosystem. …"

Table_2_XCast: A python climate forecasting toolkit.docx حسب Kyle Joseph Chen Hall (13049001)

"…These tools are useful for producing and analyzing both experimental and operational climate forecasts. In this study, we describe the development of XCast, and present in-depth technical details on how XCast brings highly parallelized gridpoint-wise versions of traditional Python data science tools into Python's gridded earth data ecosystem. …"

Table_1_XCast: A python climate forecasting toolkit.docx حسب Kyle Joseph Chen Hall (13049001)

"…These tools are useful for producing and analyzing both experimental and operational climate forecasts. In this study, we describe the development of XCast, and present in-depth technical details on how XCast brings highly parallelized gridpoint-wise versions of traditional Python data science tools into Python's gridded earth data ecosystem. …"

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

"…<p dir="ltr">This is the replication package associated with the paper: 'FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test for Code Repair'</p><p><br></p><p dir="ltr">### Requirements</p><p dir="ltr">This is a list of all required python packages:</p><p dir="ltr">-imbalanced_learn==0.8.1</p><p dir="ltr">-numpy==1.19.5</p><p dir="ltr">-pandas==1.3.3</p><p dir="ltr">-transformers==4.10.2</p><p dir="ltr">-torch==1.5.0</p><p dir="ltr">-scikit_learn==0.24.2</p><p dir="ltr">-openai==v0.28.1</p><p><br></p><p dir="ltr">#Automated tool for labelling dataset with flaky test fix categories</p><p><br></p><p dir="ltr">This is a step-by-step guideline for automatically labelling dataset with flaky test fix categories</p><p><br></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/IdoFT_dataset_filtered.csv</p><p dir="ltr">https://figshare.com/s/47f0fb6207ac3f9e2351</p><p><br></p><p dir="ltr">### Output Files:</p><p dir="ltr">* Results/IdoFT_dataset_filtered.csv</p><p><br></p><p><br></p><p dir="ltr">### Replicating the experiment</p><p><br></p><p dir="ltr">This experiment can be executed using the following commands after navigating to the `Code\` folder:</p><p><br></p><p dir="ltr">```console</p><p dir="ltr">bash Automated_labelling_tool.sh</p><p>```</p><p><br></p><p dir="ltr">It will generate the dataset required to run our prediction models to predict the category of the fix, given a flaky test code</p><p><br></p><p>---</p><p><br></p><p dir="ltr"># Prediction models for fix categories using the test case code</p><p><br></p><p dir="ltr">This is the guideline for replicating the experiments we used to evaluate our prediction models i.e. …"

PyRates—A Python framework for rate-based neural simulations حسب Richard Gast (8129451)

"…While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of rate-based neural models. …"

Settings for ABC-SMC code. حسب Román Zapién-Campos (9592478)

Connecting Metadata and Digital Assets: ArchivesSpace and Preservica حسب John Dewees (16616646)

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" حسب FirstName LastName (20554465)

"…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …"

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" حسب FirstName LastName (20554465)

"…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …"

Code for the HIVE Appendicitis prediction modelRepository with LLM_data_extractor_optuna for automated feature extraction حسب Anoeska Schipper (18513465)

"…</p><p dir="ltr"><b>LLM Data Extractor optuna repo</b> is a Python framework for generating and evaluating clinical text predictions using large language models (LLMs) like <code>qwen2.5</code>. …"

PyLandStats: An open-source Pythonic library to compute landscape metrics حسب Martí Bosch (8087852)

"…This article presents PyLandStats, an open-source Pythonic library to compute landscape metrics within the scientific Python stack. …"

Emotion diversity in translated versions. حسب Yan Li (23143)

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Emotion word types and tokens in five translations. حسب Yan Li (23143)

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Comparison of Dong’s emotion words in Martin’s system and the Chinese dictionary. حسب Yan Li (23143)

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Comparison of Xu’s emotion words in Martin’s system and the Chinese dictionary. حسب Yan Li (23143)

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Word type and tokens in different versions. حسب Yan Li (23143)

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Emotion categories and Word numbers in NRC Emotion Intensity Lexicon (NRC-EIL). حسب Yan Li (23143)

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Comparison of Zhang’s emotion words in Martin’s system and the Chinese dictionary. حسب Yan Li (23143)

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Emotion category. حسب Yan Li (23143)

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Categories of emotions in five translations. حسب Yan Li (23143)

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