How to read this roadmap
This roadmap is organised by time horizon rather than fixed delivery dates. Each horizon describes the likely focus, expected outcomes, current confidence level, and known dependencies.
Reliable before flashy
We prioritise maintainable, dependable improvements over adding features that cannot be supported well.
User needs shape priority
Feedback from researchers, facility teams, developers, and adopters can move work forward, backward, or out of scope.
Open development
Community discussion, issues, merge requests, and documentation contributions can all influence the roadmap.
Roadmap template
Released
Security & Dependency Updates
Migrate LabID infrastructure and dependencies to recent versions to maintain security and support.
Expected outcomes
- Update Django to 5.2 LTS.
- Update Vue to 3.5.
- Update of many dependencies to latest version.
Dependencies
- Team availability for implementation
Near term
Custom Assay Types & Ontology support
Enable administrators to define custom assay types and enhance ontology support through integration with the EBI OLS.
Expected outcomes
- New Assay Types can easily be deployed by admins without code changes using the Admin UI.
- Admin can import Ontology from EBI OLS and associate them with controlled vocabulary categories and item properties.
- Users can add new controlled vocabulary from authorized ontologies when creating or editing items.
Dependencies
- Team availability for implementation
Next
Completion of OSCARS LabID-PROV project and New Assay Type Support
Wrap up the funded OSCARS LabID-PROV project, and extend LabID assay support to include metabolomics and X-ray assays using the new custom assay type framework. Redesign the data submission workflow (e.g., to ENA/EGA) to take advantage of the new ontology framework.
Expected outcomes
- Organization of the first community workshop to share LabID-PROV outcomes and gather feedback from community: 28-29 Sept 2026 at EMBL Heidelberg.
- LabID-PROV scientific publication describing the OSCARS project outcomes and future directions.
- Documentation and training materials to support LabID-PROV adoption by the community.
- Expanded LabID support for metabolomics and X-Ray assays, including refactored assay class hierarchy, data models, and documentation to support adoption.
Dependencies
- New custom assay type framework available
- Availability of EMBL-Hamburg partners for X-Ray assay design and support
- Availability of EMBL-Heidelberg partners for Metabolomics assay design and support
Christmas Release
OME-ZARR, Templated Data Registration & ELN Versioning
This release enhances LabID and ELN workflows with improved OME-ZARR support, including automatic metadata extraction and integrated data visualization. Assay registration is simplified through UI-based sniffer selection and auto-populated registration forms. ELN Experiment records now support versioning and autosave, helping users track changes and restore previous versions.
Expected outcomes
- Support for dataset of type OME-ZARR will be improved to automatically extract metadata and use third party viewer(s) to easily visualize data from LabID.
- Enable users to select sniffers from the UI to easily register new assay in LabID. Selecting a sniffer automatically pre-fills the registration wizard.
- Bring versioning to ELN Experiment records to allow users to track changes and revert to previous versions if needed.
- New autosave functionality for the ELN Experiment records to prevent data loss and improve user experience.
Dependencies
- None
Later
Smarter ELN, Spatial Omics, Workflow execution from LabID & Remote Dataset
The ELN will be enhanced with smarter features to assist users in their data annotation tasks. Specialized support for spatial omics will be explored, including adapting data submission workflow. Additionally, we will explore options for executing Galaxy workflows directly from LabID, allowing to automate data processing upon registration. Finally, we will bring support for remote datasets that cannot be accessed by LabID.
Expected outcomes
- Enable study validation in the UI to guide users in their annotation duties according to templates defined at the study & project levels.
- Automated suggestion of Annotation (based on project settings) when users edit or create items.
- Integration of AI driven chatbox in LabID (contextual help)
- Ability to stage data hosted on S3 to Galaxy and execute a pre-configured Galaxy workflow.
- Better support for Spatial Omics data that combine both sequencing and imaging data, including new assay types as necessary, and data submission workflow update.
- Support for remote datasets i.e. that cannot be accessed by LabID, with spreadsheet based registration support.
Dependencies
- Team availability for implementation
- EBI team(s) availability for Spatial Omics support specifications
Longer term
Sensitive Data, Protocol Parameters, Extended Versioning & Extended WMS integration
LabID will be enhanced with features to improve sensitive data management, including the ability to flag sensitive projects and apply additional security measures such as storage on protected volumes. The ELN will be improved to support protocol parameters defined at the protocol level, and item versioning will be gradually extended to additional item types. Finally, we will explore deeper integration with workflow management systems such as Nextflow and Snakemake, enabling their execution to be wrapped as managed tasks.
Expected outcomes
- Integration with other Workflow Management System such as Nextflow & Snakemake, including Workflow execution monitored in a Task.
- Item versioning support extended to Consumables, (Storage) Equipment, Specimen, Sample and Protocol items.
- Ability to define Protocol Parameter on Protocol, and have them automatically added to the items upon associated with the protocol.
- Ability to flag sensitive projects and have additional security measures in place to protect associated data and control access.
Dependencies
- Team availability for implementation
- Availability of Core Facility team(s) to specify requirements for sensitive data management
- Integration with Galaxy for Workflow Execution
- Successfully implemented item versioning for ELN Experiment records
- Successfully implemented automated suggestion of Annotation based on study/project settings
Longer term
Pluggable Dataset Type Support, Flexible Data Storage & AI-driven capabilities
Generalize dataset type support by introducing format validation, automated metadata extraction, and improved visualization capabilities. Leverage AI integration to assist users and enhance platform adoption. Explore more flexible storage options to enable customizable data storage structures.
Expected outcomes
- Automated metadata extraction and format validation upon data ingestion, and on-demand.
- Plugin framework to add/customize metadata extraction and format validation for dataset type.
- Pluggable framework to integrate with third party viewers based on dataset type and metadata.
- AI integration to assist users in data annotation, experiment design, and other tasks based on context and user needs.
- Support for more flexible data storage options, allowing teams to customize their storage structure according to their needs and preferences; including cloud-only storage.
Dependencies
- Team availability for implementation
- Input from the community to define the most useful dataset types to support and associated metadata extraction and visualization needs
- Input from the community to define the most useful AI-driven capabilities to support in LabID
Caveats
Open-source roadmaps need room for reality. These caveats are part of the roadmap, not fine print.
- This roadmap is a statement of intent, not a release commitment, contract, or guarantee.
- LabID is developed as an open-source project with limited resources, so maintainer availability and funding can affect timing.
- Security work, critical bugs, infrastructure changes, partner needs, and support requests may take priority over planned roadmap items.
- Items may move between horizons, change scope, merge with other work, or be replaced when technical discovery or community feedback points to a better path.
- We prefer a smaller, reliable release over meeting an arbitrary date with work that is not ready to maintain.