This is our GitHub organization of compiled resources related to the STAMPED Principles of scientific research objects.

Any scientific workflow that adheres to the following principles is guaranteed to be rigorous and reproducible!

📦 Self-contained: Everything under one boundary
🔍 Tracked: Version control & provenance
▶️ Actionable: Executable & documented workflows
🧩 Modular: Independent & composable modules
🚢 Portable: Runs anywhere with explicit specifications
💨 Ephemeral: Transient compute environment
🌐 Distributable: Shareable, persistent, and retrievable

• 🚪 This Entry Point
• 📓 Journal Article (in progress)
• 🔍 Collection of Examples
• 📋 Compliance Checklist

Materials across repositories in this organization have been prepared with the assistance of agentic AI coding tools and large language models (LLMs), alongside direct human authoring and supervision.

Our working practice is:

• 👤 Human review and accountability. We strive to have final versions of all work — prose, code, figures, and specifications — to be reviewed and/or edited by human authors, who remain accountable for the content.
• 🪪 Transparent annotation. Where AI tools materially contributed to a commit, we strive to annotate that commit adequately and trustworthily (for example, via a Co-Authored-By trailer identifying the tool and model version), so that the provenance of individual changes can be inspected in the git history.
• 🔍 Inspectable provenance. Because all work is developed in public git repositories with pull-request review, readers can trace portions of the materials which involved AI assistance.

AI tools are used as assistive instruments; they do not substitute for the scientific judgment, domain expertise, or editorial responsibility of the human authors.

• 🐍 PyMVPA
• 🐧 NeuroDebian
• 💻 DataLad and The DataLad Handbook
• 📖 BIDS Standard
• 🧲 OpenNeuro
• 🌌 YODA Principles
• 🧠 DANDI Archive
• 🔥 EMBER Archive