Roadmap

This roadmap reflects research priorities, not a generic product backlog.

Research Direction

The main question is how to make product data collection more scalable, collaborative, and reusable.

A second question is where AI methods can help with collection, validation, normalization, and linking.

Interoperability is part of the roadmap too: keep identifiers, APIs, and dataset exports simple enough to support later reuse and linking.

Short Term

• improve the user-facing documentation of the data collection workflow
• keep the docs site aligned with the codebase as the platform changes
• continue strengthening unit and integration test coverage in areas that already exist
• make operational procedures such as backup, restore, and deployment more explicit
• make the path from live records to dataset release clearer

Medium Term

• stabilize a dataset publication workflow separate from the live application database
• make that publication workflow suitable for stable public release
• support later reuse through stable identifiers, APIs, and dataset exports
• improve admin and background-data maintenance workflows
• make camera-assisted capture easier to operate in repeated lab workflows
• improve API guidance for external analysis scripts and reproducible exports
• explore assistance features for AI-supported collection and validation workflows

Longer Term

• support more formal dataset versioning and release documentation
• add better support for downstream computational analysis and model-building workflows
• evaluate whether more automation is useful for annotation, quality control, or media processing
• refine the public-facing presentation of the project as the research output matures

Constraints That Shape The Roadmap

• the platform is developed in the context of a PhD project, so maintainability matters more than rapid feature expansion
• research needs may change as data collection progresses
• infrastructure and operations should stay proportional to the size of the project
• some roadmap items depend on decisions about publication, collaboration, and dataset governance that are partly non-technical