Scientific Data & Code Hosting
Overview
Reproducible research depends on open, structured, and executable access to the full research stack — not just the final PDF. Scientific discoveries today are built on data, code, and models as much as text. This layer of the platform provides researchers with a robust, standards-compliant foundation to store, share, and execute their research artifacts directly within the project environment.
Core Requirements
1. Scalable Storage Engine
- Support for all major file types:
- Datasets (.csv, .tsv, .xlsx, .json, .parquet)
- Code files (.py, .R, .jl, .ipynb)
- Supplementary files (images, videos, models, figures, raw instrument output)
- Drag-and-drop uploads and folder-based organization
- Metadata-aware previews (e.g., spreadsheet previews, notebook rendering, image thumbnails)
- Upload versioning and diffing (especially for datasets)
2. Structured Metadata & Standards
- Enforced metadata schemas:
- JSON-LD for semantic structure
- DataCite metadata for DOI registration
- schema.org markup for discovery by search engines and aggregators
- FAIR Principles Compliance:
- Findable: Unique identifiers (e.g., DOI, UUID), indexed
- Accessible: Via persistent links, with access control
- Interoperable: Machine-readable formats, standardized APIs
- Reusable: Clear licensing, rich metadata, versioning
- Tagging system for scientific keywords, instruments, organisms, variables
Use cases:
- Ensure reproducibility and compliance with funder mandates
- Make research assets machine-discoverable and API-accessible
3. Executable Environments
- Container-based runtime environments using Docker or Kubernetes
- Pre-configured environments for common stacks (Python, R, Julia, TensorFlow, PyTorch, etc.)
- Custom environment definition via Dockerfile or environment.yml
- Sandboxed execution of:
- Notebooks
- Analysis scripts
- Model training workflows
- Built-in compute triggers:
- “Run analysis” or “reproduce results” buttons
- Cron-style scheduled re-runs for periodic data updates
Use cases:
- Researchers can rerun each other’s analyses with one click
- Verify reproducibility at submission, review, or publication stage
- Maintain long-term scientific memory and reduce onboarding friction for new lab members
Why This Matters
Text alone doesn’t capture the complexity of modern science. For true transparency, collaboration, and reproducibility, a research platform must offer first-class treatment of data and code. By enabling structured storage and executable environments, we ensure that every piece of a project — from raw measurements to final plots — is not only shared, but reusable, verifiable, and alive.
Scientific Data & Code Hosting
Overview
Reproducible research depends on open, structured, and executable access to the full research stack — not just the final PDF. Scientific discoveries today are built on data, code, and models as much as text. This layer of the platform provides researchers with a robust, standards-compliant foundation to store, share, and execute their research artifacts directly within the project environment.
Core Requirements
1. Scalable Storage Engine
2. Structured Metadata & Standards
Use cases:
3. Executable Environments
Use cases:
Why This Matters
Text alone doesn’t capture the complexity of modern science. For true transparency, collaboration, and reproducibility, a research platform must offer first-class treatment of data and code. By enabling structured storage and executable environments, we ensure that every piece of a project — from raw measurements to final plots — is not only shared, but reusable, verifiable, and alive.