Project Repositories & Version Control
Overview
At the heart of the platform lies the concept of the scientific “project repository” — the atomic unit of collaboration, publication, and reproducibility. Each project repository acts like a hybrid of a GitHub repo, Jupyter workspace, and scientific preprint — a structured container for everything related to a research effort: manuscripts, data, code, models, protocols, results, and metadata.
Core Requirements
1. Repository Structure & Components
Each project repository should support the following core elements:
manuscript/ — Structured text in Markdown, LaTeX, or WYSIWYGdata/ — Uploaded datasets, structured tables, or linked APIscode/ — Analysis scripts, notebooks, packagesnotebooks/ — Jupyter-style interactive documentsresults/ — Plots, figures, models, or trained weightsprotocols/ — Editable experiment plans and lab proceduresmetadata.json — DOI, authors, affiliations, funding, tags, and schema.org markup
2. File & Metadata Versioning
- Full version control for documents, datasets, and code (commit history, rollback, tagging)
- Git-native or Git-compatible backend (supporting Git LFS for large files)
- Semantic versioning (e.g., v1.0, preprint-v2.1)
- Hash-based integrity for content-tracking and reproducibility
3. Collaboration & Forking
- Forking system with attribution for downstream derivations
- Merge Requests (MRs) with discussion, review, and merge functionality
- Branching for parallel experiments or hypotheses
- Provenance tracking (who contributed what, and when)
4. In-Browser Editors & Diffs
- Markdown, LaTeX, CSV, JSON, and Jupyter-friendly inline editors
- Code-aware diffing for Python, R, Julia, etc.
- Rich data diffs for tables and structured datasets
- Visual revision timeline for rolling back and comparing versions
5. Computation-Aware Reproducibility
- Auto-executed reproducibility pipelines (e.g., run_analysis.ipynb)
- Container support (e.g., Dockerfile, Conda) for controlled environments
- Results reproducibility from raw data → code → outputs
- Execution sandboxes for secure runtime validation
6. Repository Identifiers & Citation
- DOI assignment per repository and per tagged version (via Crossref or DataCite)
- Auto-generated citations (APA, MLA, BibTeX)
- “Cite this project” badge with dynamic metadata & usage metrics
7. Programmatic Access & Export
- Public REST API for project and data access (GET/POST/PUT)
- Export bundles (zipped package with manifest, code, and metadata)
- Git-compatible CLI for advanced contributors and labs
Optional Advanced Features (Post-MVP)
- Provenance tree visualization (forks, merges, citations)
- Immutable snapshots on IPFS or blockchain
- Notebook diff viewer with output/version playback
Why This Matters
In a world where reproducibility, transparency, and collaboration are mission-critical, a robust project repository system with integrated version control is non-negotiable. This functionality underpins the platform’s credibility, researcher trust, and long-term archival integrity.
Project Repositories & Version Control
Overview
At the heart of the platform lies the concept of the scientific “project repository” — the atomic unit of collaboration, publication, and reproducibility. Each project repository acts like a hybrid of a GitHub repo, Jupyter workspace, and scientific preprint — a structured container for everything related to a research effort: manuscripts, data, code, models, protocols, results, and metadata.
Core Requirements
1. Repository Structure & Components
Each project repository should support the following core elements:
manuscript/— Structured text in Markdown, LaTeX, or WYSIWYGdata/— Uploaded datasets, structured tables, or linked APIscode/— Analysis scripts, notebooks, packagesnotebooks/— Jupyter-style interactive documentsresults/— Plots, figures, models, or trained weightsprotocols/— Editable experiment plans and lab proceduresmetadata.json— DOI, authors, affiliations, funding, tags, and schema.org markup2. File & Metadata Versioning
3. Collaboration & Forking
4. In-Browser Editors & Diffs
5. Computation-Aware Reproducibility
6. Repository Identifiers & Citation
7. Programmatic Access & Export
Optional Advanced Features (Post-MVP)
Why This Matters
In a world where reproducibility, transparency, and collaboration are mission-critical, a robust project repository system with integrated version control is non-negotiable. This functionality underpins the platform’s credibility, researcher trust, and long-term archival integrity.