Envision Portal
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See the future with eye imaging data.

The Envision Portal is your launchpad for easily preparing, sharing, and finding eye imaging data using user friendly interfaces and automation tools.

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What is the Envision Portal?

Eye imaging data are essential to advancing research in eye health and beyond. Yet, the research community faces challenges in finding and accessing eye imaging datasets that are standardized, well-documented, and ready for reuse. To address this challenge, we are developing the Envision Portal, a cloud-based, open-source platform that provides researchers and AI developers with the tools they need to conveniently share, discover, and reuse eye imaging data.

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Easy to use, intuitive, and focused on supporting your work.

Whether you are sharing data, searching for datasets, or building AI models, the Envision Portal provides the tools you need to work efficiently and responsibly.

Easy Data Sharing

Contribute your datasets through guided submission workflows. The platform supports data standardization, data de-identification, metadata completeness, and multiple access methods making your data valuable and responsibly reusable by the community.

Convenient Data Discovery

Discover diverse eye imaging datasets using powerful search and filtering tools. The platform act as a registry for all eye imaging datasets, whether they are shared through the Envision Portal or not.

Automation for Efficiency

Automated tools for data formatting, metadata completeness, and de-identification help ensure that are AI-ready while reducing effort for contributors. The platform also includes a novel LLM-based search tool where data consumers can identify the right datasets for their use cases through a series of questions.

FAIR first

Every dataset on the Envision Portal is aligned with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Rich metadata, standardized formats, and clear documentation make data ready for reuse in research, AI development, and beyond.

Roadmap

Our roadmap is designed to align with the needs of our users and ensure a smooth transition from our development environment to something you use everyday.

Year 1

Platform foundation: architecture, standards, and first build (2024/2025)

  • Agree on the technical architecture and end-to-end user workflow so everyone is aligned on the big decisions
  • Identify data standards to make datasets FAIR and AI-ready (CDS structure, DICOM for imaging, OMOP for clinical tables)
  • Create initial wireframes and start building the core platform code
  • Set up an open-source development approach (public GitHub, contributor-friendly structure)

Year 2

First dataset and public launch (2025/2026)

  • Standardize the first new dataset and publish it through the platform
  • Build the essentials for sharing: storage, DOI minting, dataset landing pages, and an access workflow
  • Index ~10 external eye imaging datasets from other repositories into the Envision Portal database
  • Launch Envision Portal so users can access the new dataset and discover indexed external datasets
  • Publish user and developer documentation, and set up a process for ongoing updates
  • Begin community outreach through conferences and webinars

Year 3

Upload experience and early automation (2026/2027)

  • Build user-facing workflows to upload, manage, standardize, and share datasets in Envision Portal
  • Kick off automation for data standardization and preparation
  • Implement PHI detection/removal tooling, plus validation checks
  • Add dataset versioning workflows
  • Develop an automated pipeline to detect and index eye imaging datasets from other repositories
  • Define a federated learning approach with the Alzheimer's Disease Data Initiative (ADDI)
  • If needed, build a desktop upload/download app to improve the experience for large transfers

Year 4

Controlled access, better discovery, and reuse (2027/2028)

  • Continue improving data standardization tooling and automation
  • Implement advanced data access features, including controlled access request workflows
  • Add stronger dataset discovery features, including improved search and filtering
  • Deliver a robust API for integration with AI/ML pipelines
  • Add in-portal preview and visualization for common formats (CSV, XLSX, DICOM, BMP, and other common image formats) before download
  • Build initial federated learning capabilities
  • Support more datasets shared by external groups and expand the external index

Year 5

Scale, security validation, and long-term sustainability (2028/2029)

  • Complete federated learning capabilities
  • Conduct full security validation to prepare for large-scale independent submissions and sharing
  • Establish contribution guidelines and support community-driven extensions of the platform
  • Pursue outreach for wide adoption and formal recognition as a trusted domain repository
  • Execute the sustainability plan to support development and operations beyond current funding