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Transform Data Chaos into Reusable Assets
Give teams the power to define their own processes, use their own datatypes, and innovate as quickly as they want. Gain organizational-wide data findability while building reliable, trustable, version-controlled data products.
What Are Data Packages?
Data packages are intelligent manifests that combine pointers to data (like objects in Amazon S3) with rich context about that data, including lineage, metadata, and revision history.
If you can't find the data, you can't reproduce the analysis.
Data without context is not reusable. Traditional file storage separates data from its meaning, making collaboration and discovery nearly impossible.
Intelligent Manifests
Combine pointers to data with rich context and lineage
Self-Contained
Everything needed to understand and reproduce the analysis
Versioned
Track every change with cryptographic integrity
Discoverable
Find and access data through metadata and search
Packages are where AI in life sciences starts
A model is only as good as the data it can find and trust. A Quilt package bundles your data with its metadata and a versioned, cryptographic history. That gives models and agents the context they need, and lets you trace any result back to the exact data that produced it.
Packages you can verify
Data, metadata, and an immutable version history in one addressable unit. Every output traces back to an exact dataset, so it stays reproducible and audit-ready for regulated work.
Qurator: search in plain English
Ask for data the way you'd ask a colleague. Qurator searches your governed catalog and returns the right packages, scoped to what each person is allowed to see. No ontology expertise needed.
Bring your own model (MCP)
Connect Claude, ChatGPT, or any MCP-compatible agent to your Quilt data with per-user OAuth. Models can read, visualize, and build on your data, and they only ever see what that user can.
From instrument to model, your data stays in your AWS account: governed, versioned, and ready to use.
What data packages give you
Data packages pair familiar data management with cloud-native storage, so your data stays reliable, easy to find, and ready to reuse.
Data Provenance
Immutable hash-based version history — audit-ready for GxP and 21 CFR Part 11
Rich Visualizations
Document previews, dashboards, and in-browser charts anchored to package versions
Powerful Search
Curator natural-language search across metadata and file contents
Team Collaboration
Web catalog, role-scoped permissions, and shareable package URIs
Browse packages with full context
Every package combines your files, README, and metadata in one view — backed by S3 in your AWS account, never copied elsewhere.
Browse your data like objects in S3, with context
Every package is a self-contained unit: data, a README, rich metadata, and previews. Explore the tree, read the docs, and see exactly what's inside before you pull a byte.
- File tree, README, and key/value metadata in one view
- In-browser previews for images, tables, and notebooks
- Backed by your own S3, so the data never moves
Bulk RNA-seq across 1,284 hepatocyte samples. Aligned with STAR, quantified with Salmon, QC via MultiQC.
Built for Your Infrastructure
Data packages work with your existing cloud infrastructure and analysis platforms, giving your data a vendor-neutral foundation.
Amazon Web Services
Native S3 integration with advanced AWS technology partnership
Amazon Web Services
Native S3 integration with advanced AWS technology partnership
Amazon Web Services
Native S3 integration with advanced AWS technology partnership
The Data Package Lifecycle
Data packages follow a simple workflow. Start with the free Python SDK for basic packaging, or use the full platform for team collaboration.
Create
Bundle data with metadata (SDK) or use web interface (Platform)
Version
Track changes with SHA-256 checksums
Share
Collaborate across teams and platforms
Discover
Access via SDK commands or rich web search (Platform)
Real-World Applications
See how teams across biotech and life sciences use data packages to accelerate discovery and ensure reproducibility.
Genomics Research
Package sequencing data with sample metadata for reproducible analysis pipelines
Genomics Research
Package sequencing data with sample metadata for reproducible analysis pipelines
Genomics Research
Package sequencing data with sample metadata for reproducible analysis pipelines
Genomics Research
Package sequencing data with sample metadata for reproducible analysis pipelines
Outcomes teams see with Quilt
Trusted by life-sciences organizations
- Allen Institute
- Inari
- Flagship Pioneering
- Cellarity
- Resilience
- Tessera
Data lookups that used to take our scientists days now take minutes, with a single, governed source of truth the whole team can trust.
From Expendable Resource to Reusable Asset
AI and Machine Learning are creating new opportunities to answer much broader questions than lab data was originally intended for. To be competitive in biotech, using data beyond its original scope is no longer just nice to have. It's an imperative.
Beyond the lab
Extend the rigor of your data beyond instruments, spreadsheets, and scattered hard drives.
Reusable assets
Versioned packages turn one-off datasets into durable, shareable assets your team can trust.
AI-ready by default
Governed, contextual data that your models and agents can actually use.