VICT3R Webinar: Standardization and Reuse of Data from Academic In Vivo Studies

I recently had the opportunity to speak at the VICT3R Webinar Series Season 2 about a topic that sits at the core of my work: how to standardize and reuse data from academic in vivo studies. The session focused on why metadata is the missing infrastructure for reproducibility, and how FAIR-by-design practices can make preclinical data more reusable, more interoperable, and more impactful.

Replay and resources

Why metadata is the real reproducibility bottleneck

I opened with a simple analogy: a library without shelves. The knowledge is there, but without structure, it cannot be found or reused. In preclinical research, the same is true for experimental context. We often lose critical details between the lab bench and the final publication, and those gaps quietly break reproducibility.

Metadata is that missing context. It includes the who, what, when, and under which conditions the data were generated. Without it, published results are hard to interpret, compare, or replicate across labs.

The hidden fragmentation in the preclinical workflow

Most labs work across disconnected systems: animal management software, ELNs, behavioral acquisition tools, spreadsheets, analysis scripts, and repositories. Each step sheds context. By the time data are published, a large share of experimental detail is already gone.

The goal is not to replace these tools, but to connect them through a stable, shared metadata layer that captures context as experiments happen.

From descriptive to operational standardization

Traditional standardization is largely descriptive: checklists and free text filled in at the end of a study. What we need is operational standardization, where structured metadata is captured early and continuously using schemas, controlled vocabularies, and machine-readable formats.

This approach aligns with FAIR principles and makes reuse practical rather than aspirational.

Reuse: the case for virtual control groups

One of the most promising outcomes of well-structured metadata is the ability to build virtual control groups from historical data. With stable identifiers, temporal structure, and consistent provenance, labs can reduce animal use, increase statistical power, and improve ethical outcomes while maintaining scientific rigor.

FAIR plus the 3Rs: FAIRRR

FAIR data enables Reduction, Refinement, and Replacement. This is why I framed the idea of FAIRRR: FAIR principles that directly operationalize the 3Rs. When data are structured and reusable, they become a practical New Approach Method in their own right.

What Metadatapp is building

At Metadatapp, we are developing an API-first metadata layer that sits above existing lab tools. The platform captures, validates, and links metadata as studies progress, and exports interoperable formats for analysis and sharing. The long-term vision is a federated ecosystem where labs keep control of their data while sharing standardized metadata for cross-lab reuse.

Q&A highlights

We covered several practical points during the Q&A:

SEND compatibility is on the roadmap, but the immediate focus is on building robust metadata foundations.
FAIRification effort varies by lab; automation and better practices reduce the overhead.
Federated architectures can support global collaborations while respecting local regulatory constraints.
There is no meaningful minimal metadata set yet; meaningful reuse likely requires more than 100 metadata elements, which is why software support matters.

If you are working in preclinical research and want to improve reproducibility, reuse, or FAIR alignment, I would be happy to discuss how structured metadata can help.