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.…”

Code and data for evaluating oil spill amount from text-form incident information by Yiming Liu (18823387)

“…The code is written in Python and operated using Jupyter Lab and Anaconda. …”

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

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

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

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

Evaluation and Statistical Analysis Code for "Multi-Task Learning for Joint Fisheye Compression and Perception for Autonomous Driving" by Basem Ahmed (18127861)

“…</li></ul><p dir="ltr">These scripts are implemented in Python using the PyTorch framework and are provided to ensure the reproducibility of the experimental results presented in the manuscript.…”

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

“…<p dir="ltr">This repository contains the code and trained model for developing the HIVE (History, Intake, Vitals, Examination) machine learning model for predicting appendicitis in patients presenting with acute abdominal pain at the Emergency Department (ED).…”

Presentation 1_De-embedding a pair of dipoles to calibrate the mutual impedance plasma density diagnostic.pdf by M. C. Paliwoda (21024197)

“…The measured S-parameters are simulated in the python package scikit-rf to de-embed the dipoles. …”

Excerpt from a provenance graph depicting the refinement process of a simulation model (SM1) during a simulation study. by Andreas Ruscheinski (7880759)

How neuromodulators control the excitability of neurons. by Domenico Guarino (20428000)

Representative examples of scanned intake forms. by Yojiro Umezaki (5916593)

Pyramidal neuron spiking behaviour is either switched or scaled. by Domenico Guarino (20428000)

Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds by Gloria Castañeda-Valencia (20758502)

“…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …”

Anisotropic Skyrmion and Multi-<i>q </i>Spin Dynamics in Centrosymmetric Gd₂PdSi₃: Data and Code by Matjaž Gomilšek (14278739)

“…Python script file<br>*.dat, [no extension] ... ASCII data file. …”

Data and analysis code for manuscript "Preparations for ultra-high dose rate 25-90 MeV electron irradiation experiments with a compact, high-peak-current, X-band linear accelerator... by Haytham Effarah (11979509)

“…</p><p dir="ltr">The environment.yml file can create a conda virtual environment named "prep4vhee" with the required Python version and dependencies using the following conda command:</p><pre>conda env create -f environment.yml<br></pre><p dir="ltr">TOPAS input decks are also included in some folders with seeds set to reproduce Monte Carlo simulation results presented in the manuscript. …”

Example provenance graph of exploring the difference between two simulation models by visualizing simulation data generated from two independent simulation experiments. by Andreas Ruscheinski (7880759)

Workflow of T cell activation analysis. by Yitong Ma (8144697)

MCCN Case Study 1 - Evaluate impact from environmental events/pressures by Donald Hobern (21435904)

“…</p><p dir="ltr">The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 1.ipynb)</p><h4><b>Research Activity Identifier (RAiD)</b></h4><p dir="ltr">RAiD: https://doi.org/10.26292/8679d473</p><h4><b>Case Studies</b></h4><p dir="ltr">This repository contains code and sample data for the following case studies. …”

Various depictions of figures can be created using different software using the same data. by Hector Torres (11708207)

A depiction of the distribution of information gathered for visualization along with representations of the distribution of Z positional information across all mRNA locations acros... by Hector Torres (11708207)

Core-Based Smart Sampling Framework: A Theoretical and Experimental Study on Randomized Partitioning for SAT Problems by DURGHAM QARALLEH (21904172)