Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves showing predictive performance of our proposed model for the classification of digital images of earthworms... by Saiqa Andleeb (11442771)

ThermoPred: AI-Enhanced Quantum Chemistry Data Set and ML Toolkit for Thermochemical Properties of API-Like Compounds and Their Degradants by Diullio P. Santos (22770713)

Subjects:

Data_Sheet_1_Razorback, an Open Source Python Library for Robust Processing of Magnetotelluric Data.pdf by Farid Smaï (9330911)

“…This paper presents Razorback, an open source Python library, implemented to handle, manipulate, and combine time series of synchronous data. …”

Probabilities of normal and various cyber attack types over time sequence. by Mohanad Faeq Ali (21354273)

Initial Weight Models of Predictive Coding Deep Neural Network (Chainer & Pytorch) by Eiji Watanabe (3862120)

“…Sample Initial Weight Model of Predictive Coding Deep Neural Network.<div><br></div><div><div>Use the model with predictive coding deep neural network,</div><div>https://doi.org/10.6084/m9.figshare.5483710</div><div>and with training dataset,</div><div>https://doi.org/10.6084/m9.figshare.5483668</div><div><br></div><div>Sample command.…”

Computing Mixture Adsorption in Porous Materials through Flat Histogram Monte Carlo Methods by Hsuan-Chu Chen (9038109)

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Number of tweets collected over time. by Sylvia Iasulaitis (8301189)

S1 Code - by Christina M. Homer (3561131)

BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

“…<p>Spatiotemporal point process models have a rich history of effectively modeling event data in space and time. However, they are sometimes neglected due to the difficulty of implementing them. …”

Time-lapse movie of a bacterial microcolony analyzed with DeLTA 2.0. by Owen M. O’Connor (11965460)

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Data and analysis codes for coarse-grained simulations of metal-organic cages by Emma Wolpert (21223817)

Predicting coding regions on unassembled reads, how hard can it be? - Genome Informatics 2024 by Amanda Clare (98717)

“…The locations and directions of the predictions on the reads are then combined with the information about locations and directions of the reads on the genome using Python code to produce detailed results regarding the correct, incorrect and alternative starts and stops with respect to the genome-level annotation.…”

Machine Learning Models for Predicting Infant Undernutrition in Rural Rajasthan, India Code and Dataset by Ruchit Nagar (9985781)

“…The code is extensively commented for clarity.</p><p dir="ltr">1. 1_low_birth_weight_model.py: Script for developing the low birth weight prediction model.…”

Data and Codes to Support Geospatial Constrained Optimization by Lianfa Li (1922281)

“…<div><div>The shared files provide the data and code to support the paper, titled "Geospatial constrained optimization to simulate and predict spatiotemporal trends of air pollutants" to be published in <i>Spatial Statistics</i>. …”

The archive ‘S1_Data_Code_Repository.zip’ contains all relevant data sets and program code for generating the results reported in the manuscript. by Udo A. Ernst (11690163)

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

“…Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. …”

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

“…Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. …”

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

“…Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. …”

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

“…Heavy data preprocessing is needed: gridded data must be aggregated, reshaped, or reduced in dimensionality in order to fit the strict formatting requirements of Python's data science tools. Efficiently implementing this gridpoint-wise workflow is a time-consuming logistical burden which presents a high barrier to entry to earth data science. …”

FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair by 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. …”