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

“…</li><li>The <b>CIPrediction</b> folder contains model training code.</li><li>The <b>ParallelComputation</b> folder contains geographic computation code.…”

Engineering Various Components of a Magneto-Optical Trap and Simulation of Ultracold Atoms in a Harmonic Potential for the Formation of Bose-Einstein Condensate by Rahul Das (22607879)

“…Key experimental components include a magnetic-field compensator, and RF antenna integrated with a Python-based GUI control system. Complementary simulations model atom dynamics in a harmonic potential and cloud evolution under quenches, outlining pathways toward achieving a Bose–Einstein condensate. …”

Simple implementation examples of agent AI on free energy calculation and phase-field simulation by Toshiyuki Koyama (22828581)

“…</p> <p>Using Gibbs energy calculations and diffusion simulations as examples, we demonstrated the implementation method and usefulness of simple agent AI, where sample python codes are distributed as supplemental materials.…”

Scope of our collection of pathogen models of metabolism. by Emma M. Glass (13102872)

“…The average MEMOTE score across models is 84% (d–f) Boxplots representing the spread of genes, reactions, and metabolites in each model, classified by phylum. …”

DeepB3Pred_source_code by Muhammad Arif (17012472)

Testing Code for JcvPCA and JsvCRP. by Océane Dubois (21989812)

“…<p>This file contains the code that implements both metrics in python and apply them on a simulated dataset.…”

Data and code for: Automatic fish scale analysis by Christian Vogelmann (21646472)

“…</p><h3>Includeed in this repository:</h3><ul><li><b>Raw data files:</b></li><li><code>comparison_all_scales.csv</code> – comparison_all_scales.csv - manually verified vs. automated measurements of 1095 coregonid scales</li></ul><ul><li><ul><li><code>Validation_data.csv</code> – manually measured scale data under binocular</li><li><code>Parameter_correction_numeric.csv</code> – calibration data (scale radius vs. fish length/weight)</li></ul></li><li><b>Statistical results:</b></li><li><ul><li><code>comparison_stats_core_variables.csv</code> – verification statistics (bias, relative error, limits of agreement)</li><li><code>Validation_statistics.csv</code> – summary statistics and model fits (manual vs. automated)</li></ul></li><li><b>Executable script (not GUI):</b></li><li><ul><li><code>Algorithm.py</code> – core processing module for scale feature extraction<br>→ <i>Note: The complete Coregon Analyzer application (incl. …”

Integrating Scientific GUI Applications into Autoscaled Science Gateway Environments by Julien Chastang (640650)

Workflow of a typical Epydemix run. by Nicolò Gozzi (8837522)

“…<div><p>We present Epydemix, an open-source Python package for the development and calibration of stochastic compartmental epidemic models. …”

A free tutorial book from NSF Cybertraining C2D: Cybertraining for Chemical Data scientists by Xiangliang Zhang (19460047)

“…In Chapter 2, we guide chemists through setting up a Python environment tailored for data science applications in chemistry. …”

Processing parameters. by Hosamane Guruswamy Prashantha Kumar (21433265)

“…An improvement of 33% occurred in the sliding performance after LSP treatment because the COF reduced from 0.30 to 0.20. The constructed Python-based digital twin model employed multi-variable regression analysis for 30 experimental trials yielding an R² value of 0.91 and an RMSE value of 0.026 mm³/N·m. …”

PTPC-UHT bounce by David Parry (22169299)

“…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …”

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)

“…In this work, we present an open-access quantum-chemistry database of more than 14,500 API-like molecules and their degradation products, all optimized at the M06-2<i>X</i>/6-31G(d) compound model. …”

Flowchart describing the AI-LES program. by Evan Mitchell (20995743)

“…Tools exist to automate the retrieval of relevant journal articles, but pulling data out of those articles is currently still a manual process. In this article, we present a proof-of-concept Python program that leverages artificial intelligence (AI) tools (specifically, ChatGPT) to parse a batch of journal articles and extract relevant results, greatly reducing the human time investment in this action without compromising on accuracy. …”

Topological and functional comparison of the NeKo networks created for the medulloblastoma subgroups using the HURI databases. by Marco Ruscone (11747264)

Comparison of previous predictive models. by Saeid Rasouli (20370998)

“…<div><p>Background</p><p>Optic neuritis (ON) can be an initial clinical presentation of multiple sclerosis This study aims to provide a practical predictive model for identifying at-risk ON patients in developing MS.…”

Data sets and coding scripts for research on sensory processing in ADHD and ASD by Vesko Varbanov (9687029)

“…The repository includes raw and matched datasets, analysis outputs, and the full Python code used for the matching pipeline.</p><h4>Ethics and Approval</h4><p dir="ltr">All procedures were approved by the University of Sheffield Department of Psychology Ethics Committee (Ref: 046476). …”

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

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

Dentate granule cells have polarised responses to neuromodulators switch spiking behaviour under neuromodulation. by Domenico Guarino (20428000)