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About 7,302 papers for “Codebase”

Saturn Codebase (as used in paper)

Jeff Guo · Figshare · 2025

Saturn codebase archive. It can also be found here: https://github.com/schwallergroup/saturn/tree/b0163fbd4790be2fbb8e617357c6baa07e42912d

Cited by 1Open access

Dataset for REdiREKT Codebase and Dataset

Burgess, Jonah · Research Portal (Queen's University Belfast) · 2019

Code for the REdiREKT project and test-cases, results etc. Note, PCAPs were not included due to size but the malicious captures were mostly collected from malware-traffic-analysis.com and broad-analysis.com and benign PCAPs were generated using custom Windows 10 honeypot (along with some code provided in the codebase). Also note that extracted feature sets were not included as they are intended for use in future ML-based detection work which has not yet been published.

Cited by 0Open access

nishunathan/wings-public: WiNGS Public codebase

geertvandeweyer, wings-public · Zenodo (CERN European Organization for Nuclear Research) · 2026

This release contains the centralized and federated repositories associated with the WiNGS API project, providing the source code and supporting materials necessary to deploy and evaluate both architectures.

Cited by 0Open access

Notebook-Driven Development: The Scientific Codebase

Andrew Hinton · Zenodo (CERN European Organization for Nuclear Research) · 2026

This paper introduces The Scientific Codebase, a framework designed to bridge the gap between messy, exploratory data science and stable, production-ready software. It addresses the fact that while AI now makes code generation easy, organizing and verifying that code remains a significant challenge. The Four Phases of Development The methodology breaks down the evolution of scientific code into four distinct stages: Lab Bench: Initial exploratory analysis and experimentation. Crystallization: Organizing and refining the initial logic. Manuscript: Documenting and stabilizing code for formal presentation or review. Graduation: Transitioning the code into a fully operational production environment. Key Innovations and Goals Managing AI-Generated Code: Since AI now accelerates code production, the framework focuses on organizing and stabilizing those artifacts rather than just writing them. Separation of Concerns: It promotes separating core scientific logic from the technical "orchestration" code (like cloud infrastructure or data pipelines). Reproducibility: The framework uses extraction and snapshot testing to ensure that scientific results remain consistent as the code moves through different stages. MLOps Alignment: It provides a structured path for modern machine learning operations while maintaining the rigor required for scientific research. Core Takeaway The Scientific Codebase moves beyond simple notebook-driven workflows. It provides a disciplined engineering path that allows researchers to use AI tools effectively without sacrificing the reliability or traceability of their final production models.

Cited by 0Open access

bschiffthaler/aspen-v2: Initial codebase freeze

Schiffthaler, Bastian, NicolasDelhomme · Zenodo (CERN European Organization for Nuclear Research) · 2021

Public scripts for the second version of the European aspen assembly

Cited by 0Open access

Generic reinforcement learning codebase in TensorFlow

Li, Bryan, Cowen-Rivers, Alexander, Kozakowski, Piotr et al. · Zenodo (CERN European Organization for Nuclear Research) · 2019

Vast reinforcement learning (RL) research groups, such as DeepMind and OpenAI, have their internal (private) reinforcement learning codebases, which enable quick prototyping and comparing of ideas to many SOTA methods. We argue the five fundamental properties of a sophisticated research codebase are; modularity, reproducibility, many RL algorithms pre-implemented, speed and ease of running on different hardware/ integration with visualization packages. Currently, there does not exist any RL codebase, to the author's knowledge, which contains all the five properties, particularly with TensorBoard logging and abstracting away cloud hardware such as TPU's from the user. The codebase aims to help distil the best research practices into the community as well as ease the entry access and accelerate the pace of the field. More detailed documentation can be found here.

Cited by 0Open access

Static analysis on a large codebase

Ella Eriksson, Jashar Vahid · 2017

Static analysis is a way to ensure high software quality by using automatic tools to find flaws. This master thesis investigates the possibilities of integrating static analysis into the development of production code. The evaluated tools are tested against a large codebase of more than 500 000 lines of code. The result on the large codebase, as well as the presentation of the error report is evaluated. The selected tools are also evaluated against fabricated, synthetic, tests from previous research.

Cited by 0

Mol*, MolViewSpec, and MolViewStories codebase snapshot

Terézia Slanináková, Zachary Charlop-Powers, Viktoriia Doshchenko et al. · Figshare · 2026

Effectively communicating knowledge related to molecular structures and their associated data remains a challenge, as traditional static figures limit interactivity and professional visualization tools often require substantial expertise. MolViewStories addresses these limitations by providing an open-source, web-based platform for creating and sharing interactive, narrative-driven molecular visualizations.The platform uses the MolViewSpec standard for reproducible scene specification, extended to support animations, interactive descriptions, and synchronized audio commentary. Visualization is powered by the Mol* Viewer, which leverages Web Graphics Library (WebGL) for efficient 3D rendering and WebXR for immersive virtual and augmented reality experiences. Users can construct molecular narratives through an intuitive graphical interface or a command-line workflow, enabling both exploratory and automated use. Completed stories can be shared online, exported locally, or distributed as self-contained packages that remain functional indefinitely. Each story is assigned a persistent uniform resource locator (URL) and can be modified or reused as a template, promoting collaboration and community-driven content creation.We demonstrate the capabilities of MolViewStories through a diverse set of narratives illustrating its broad applicability to research communication, education, and public outreach. Together, these examples highlight how interactive, web-based storytelling can make molecular data more accessible, reproducible, and engaging.MolViewStories is freely available at https://molstar.org/mol-view-stories with open-source code accessible at https://github.com/molstar/mol-view-stories.

Cited by 0Open access

Codebase for reproducing FOCS and figures

Tom Aharon Hait · Figshare · 2018

FOCS is a novel method for inferring enhancer-promoter interactions from a single omic dataset. For further information, please refer to our website: http://acgt.cs.tau.ac.il/focs/index.html FOCS.v1.zip contains the source code for reproducing FOCS analyses and the figures created in the paper tmp.zip contains intermediate files for ENCODE dataset in order to draw only the figures. data.zip contains the ENCODE RData files for running FOCS. For other datasets (Roadmap epigenomics, FANTOM5, and GROseq) please download from: http://acgt.cs.tau.ac.il/focs/download.html

Cited by 0Open access