Reproducible implementation for article: Reproducibility study of the modular and reusable model of epithelial transport in the proximal convoluted tubule by Leyla Noroozbabaee (14301173)

EthoPy: Reproducible Behavioral Neuroscience Made Simple by Alexandros Evangelou (22235917)

“…To overcome these challenges, we developed EthoPy, an open-source, Python-based behavioral control framework that integrates stimulus presentation, hardware management, and data logging. …”

Comparison of performance between our next reaction implementation and the Python library from Ref. [3]. by Samuel Cure (22250922)

“…<p>We simulate SIR epidemic processes on Watts-Strogatz networks with parameters <i>k</i>₀ = 5, <i>p</i> = 0.1 <b>(a)</b> and Barabási-Albert networks with parameter <i>m</i> = 5 <b>(b)</b> using the Python wrapper of our C++ implementation and compare its performance with the Python library from Ref. …”

Python implementation of a wildfire propagation example using m:n-CAk over Z and R. by Pau Fonseca i Casas (9507338)

Example change statistic implementations. by Alex Stivala (8356257)

“…ALAAMEE implements both the stochastic approximation and equilibrium expectation (EE) algorithms for ALAAM parameter estimation, including estimation from snowball sampled network data. …”

PYTHON by Kan Jia (21666161)

Python sript. by Kan Jia (6438599)

Overview of the implemented simulation tool and training framework in Diffrax. by Paul van Lent (16876977)

Secure Python Code Manager: A Tool for Protected Python Code Distribution and Management by Pavel Izosimov (20096259)

“…This tool allows developers worldwide to <b>share Python code securely</b>, ensuring their intellectual property is better protected from unauthorized access and misuse.…”

Parameter estimates and estimated standard errors from the EE algorithm. by Alex Stivala (8356257)

Python file of the computational framework. by Benoit Haut (9753029)

Type II error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

Type I error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

Apple point cloud generated by python script. by Xiaoyang Liu (1815211)

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

“…By lowering the barrier for the implementation of computational and inference approaches, Epydemix makes epidemic modeling more accessible to a wider range of users, from academic researchers to public health professionals.…”

Overview of the SBML models used in this study. The collection of models was retrieved from Biomodels [43] and a previously reported collection of SBML benchmark models [11]. The n... by Paul van Lent (16876977)

Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions by Zahir Barahmand (18008947)

“…</p><p dir="ltr">This Python code was optimized and debugged using ChatGPT-4o to ensure implementation efficiency, accuracy, and clarity.…”

Design and Implementation of a Browser-Based Toolfor Protecting Gaming Assets from UnauthorizedAccess by Alim Imashev (22606007)

“…<p dir="ltr">The project <b>“Design and Implementation of a Browser-Based Tool for Protecting Gaming Assets from Unauthorized Access”</b> focuses on developing a security-oriented software solution that safeguards digital game assets within browser environments.…”

Python Code for Fine-Tuned BERT Model in High School Student Advising by Suha Assayed (22454038)

“…<p dir="ltr">This repository contains the Python implementation and fine-tuned BERT model for answering high school student questions on college and career advising, based on the methodology described in Assayed et al. (2024). …”

Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. …”