Open science in drug discovery is gaining momentum, and for good reason - when knowledge is shared, research accelerates. It also seems that funding bodies of international research programmes more often than not require programmes to be run as open science. In Denmark, we have seen this requirement in the Novo Nordisk Foundation-funded ODIN project, which we joined together with Aarhus University.
However, openness comes with its own operational challenges. Moving fast while sharing transparently demands a level of structure and traceability that many research environments aren’t built for. Open science succeeds when the technology supporting it is as open as the science itself.
Open science needs more structure, not less
There’s a common misconception that open science simply means “share your data.” In practice, sharing raw or loosely structured data can introduce friction and confusion.
- To participate effectively in open collaboration, teams need:
- High-quality structured data
- FAIR-aligned metadata and identifiers
- A system that can scale across labs, institutions, and countries
The more collaborators, the more important it becomes to have shared standards and transparent data models. In this way, open science increases the need for structured research data. This is very apparent in the VICT3R project, where we pool control data from more than 15 organisations. Although all data are, in fact, delivered in SEND format, we still need to curate it to make sure it's aligned with the project's SEND format and terms.
What open-science teams require from their data systems
Most legacy scientific systems were built around a proprietary mindset. Open science has different needs.
Research teams embracing open collaboration typically look for systems that offer:
- Configurable vocabularies and metadata to support FAIRness without bureaucracy
- Transparent data models that anyone can understand, inspect, and build on
- Interoperability with R, Python, downstream analysis tools, and outside collaborators
- No license friction when collaborators join or expand
- Audit-ready traceability without proprietary black-box workflows
Simply put: open-science teams need infrastructure that lowers the barriers to collaboration, not tools that create new ones. That’s what we are trying to solve with grit.
How grit enables open science collaboration
A unified foundation for all modalities
grit uses a single flexible data layer for all modalities, with batches, assays, and all results stored in the same datastore. No chemistry vs. biologics divide, no parallel registries, and no stitching data across systems to answer integrated questions. This is important in open science, where multiple labs and modalities converge on shared hypotheses and public research programs.
Configurable vocabs and FAIR-friendly metadata
Also, grit supports user-defined vocabularies and structured metadata without locking teams into rigid templates. Researchers can evolve taxonomy as the science evolves, while still maintaining consistency and traceability.
It promotes FAIR-ready practices without imposing heavy bureaucracy.
Reproducibility through transparent architecture
grit is containerised and open, which enables reproducible deployments on:
- Local academic compute platforms
- Secure lab infrastructure
- Private cloud environments
- Collaborative multi-institution settings
API-first design for computational openness
The platform integrates nicely with data-science workflows and downstream analysis tools. Computational teams can work in R, Python, or custom notebooks as they prefer.
Why this matters for open discovery
Open science can bring lots of benefits, such as faster target validation, shared assay libraries, knowledge reuse, and, ultimately, broader public benefit.
It's just important that tools actually support it, instead of adding friction by defining who can contribute, restricting access based on licenses, requiring re-platforming when new modalities emerge, etc.
Open science is a collaborative model, which grit supports by giving research teams the flexibility, transparency, and control they need to share confidently and work together at scale.
Your science. Your data. Your rules.