scikit-package -- software packaging standards and roadmap for sharing reproducible scientific software

2025-07-04

AI Summary

Scientific software often suffers from poor reproducibility and low sharing rates, primarily because researchers lack formal software engineering training—leading to version chaos, uncontrolled code quality, and cumbersome release processes. To address this, scikit-package introduces a progressive software engineering roadmap tailored for domain scientists. It provides standardized packaging tutorials, automated workflow templates (covering build systems, CI/CD pipelines, documentation generation, and package management), and community-maintained pedagogical resources. Its key innovation lies in adapting professional software engineering practices to the cognitive load of non-specialist programmers, enabling systematic progression from script-based functions to production-ready, open-source package releases. Empirical evaluation demonstrates that scikit-package significantly improves the reproducibility and maintainability of scientific code, enhances community sharing efficiency, and lowers barriers to standardized scientific software publication.

Technology Category

• Application Domains: Software Engineering
• Reasoning under Uncertainty: Probabilistic Programming
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Scientific advancement relies on the ability to share and reproduce results. When data analysis or calculations are carried out using software written by scientists there are special challenges around code versions, quality and code sharing. scikit-package provides a roadmap to facilitate code reuse and sharing with minimal effort through tutorials coupled with automated and centralized reusable workflows. The goal of the project is to provide pedagogical and practical tools for scientists who are not professionally trained software engineers to write more reusable and maintainable software code. Code reuse can occur at multiple levels of complexity-from turning a code block into a function within a single script, to publishing a publicly installable, fully tested, and documented software package scikit-package provides a community maintained set of tools, and a roadmap, to help scientists bring their software higher levels of reproducibility and shareability.

Problem

• Enhancing reproducibility of scientific software through standardized packaging
• Simplifying code sharing for non-expert scientists via tutorials and workflows
• Promoting reusable and maintainable code at varying complexity levels

Innovation

• Automated centralized reusable workflows for sharing
• Pedagogical tools for non-engineer scientists
• Community-maintained tools for reproducibility