🔗 Project Pipeline: The Living Research Web¶
1. Concept Foundation¶
- Vision: Research as living, executable apps instead of static PDFs.
- Principles: reproducibility, openness, permission control, collaboration.
- Stakeholders: scientists, institutions, AI agents, industry partners.
2. Core Infrastructure¶
Research-as-Apps Framework¶
- Containerized code (e.g., Docker, Streamlit, Jupyter).
- Metadata standards (author, DOI, license, dependencies).
- APIs for app discovery and integration.
Permission & Licensing Layer¶
- Open / Restricted / Commercial modes.
- Usage tracking dashboards.
- Attribution & automatic citation mechanisms.
3. Agent Integration¶
AI agents as collaborators:
- Search for relevant research-apps.
- Request access to run methods.
- Explore parameter spaces, generate variations.
- Auto-acknowledge contributions.
4. Researcher Workflow¶
- Upload project → becomes an interactive app.
- Other scientists or AI agents discover it.
- With permission, they run or extend it.
- Results are shared, with attribution.
- Iterative cycle → Living Research ecosystem.
5. Network Effect & Scaling¶
- Global research landscape maps built by AI.
- Interdisciplinary pipelines assembled in real time.
- Metrics: number of runs, extensions, collaborations formed.
6. Impact & Culture Change¶
- Faster science → no reimplementation bottleneck.
- More collaboration → across labs and disciplines.
- Measurable impact → transparent usage metrics.
- Cultural shift → science becomes alive, networked, and interactive.
