DataCare is Animal Welfare: why FAIR metadata is the next 3R infrastructure

Animal welfare does not stop when the experiment ends.

In preclinical research, animals are used to produce knowledge. But if the data produced from those animals are incomplete, poorly described, impossible to compare, or lost in local folders, then part of the animal use has been wasted.

That is the central idea behind DataCare: caring for data is not an administrative detail. It is part of caring for the animals that generated those data.

The missing layer in the 3Rs

The 3Rs — Replacement, Reduction, and Refinement — remain the ethical backbone of animal research. They have shaped experimental design, welfare monitoring, endpoint definition, and the search for alternative methods.

But one layer is still underdeveloped: the data layer.

A well-designed experiment can still fail its ethical promise if the resulting data cannot be reused, interpreted, audited, or integrated with other datasets. In that case, animals contributed to a result that remains locked inside a narrow context.

This is not only a technical problem. It is an ethical problem.

FAIR data as a practical 3R tool

FAIR data — Findable, Accessible, Interoperable, and Reusable — is often discussed as a data management principle. In preclinical research, it should also be seen as a 3R strategy.

FAIR metadata can support Reduction by making existing datasets easier to discover and reuse. It can support Refinement by enabling better interpretation of experimental context, welfare indicators, environmental conditions, and behavioral phenotypes. It can support Replacement indirectly by making high-quality historical and multimodal datasets available for modelling, simulation, and AI-assisted inference.

But FAIR is not achieved by simply uploading a spreadsheet somewhere.

FAIR requires structure.

It requires metadata that describe the experiment, the animals, the housing, the protocols, the devices, the environment, the data processing pipeline, and the behavioral outputs. Without this context, data are not really reusable. They are only stored.

Home cage monitoring shows the problem clearly

Home cage monitoring systems are a good example.

They generate rich longitudinal data: activity, sleep/rest patterns, feeding, drinking, social proximity, circadian rhythms, and sometimes multimodal physiological or environmental measures.

This is exactly the kind of data that could transform preclinical research. It could improve welfare monitoring. It could help detect subtle phenotypes. It could reduce reliance on short, stressful, human-observed behavioral tests. It could also support historical controls and, eventually, virtual control groups.

But only if datasets are comparable.

A movement trace without context is not enough. We need to know the strain, sex, age, cage composition, light cycle, device, protocol, batch, enrichment, room conditions, preprocessing steps, and behavioral definitions.

The future value of home cage monitoring will depend less on generating more data and more on making existing data interpretable across systems.

From metadata to virtual control groups

Virtual control groups are often presented as an AI problem.

They are not only an AI problem.

Before we can model reliable historical controls, we need datasets that are standardized enough to compare. That means shared metadata structures, common vocabularies, ontologies, transparent provenance, and clear uncertainty.

The chain is simple:

FAIR metadata makes datasets readable.

Ontologies make datasets comparable.

Comparable datasets make modelling possible.

Only then can virtual control groups become scientifically credible.

Without this foundation, AI will only amplify noise, batch effects, and hidden experimental differences.

DataCare: a cultural shift

DataCare means treating data stewardship as part of experimental responsibility.

It means designing metadata capture before the experiment starts, not after the paper is written. It means making tools usable for researchers, technicians, facility managers, and data scientists. It means building systems that are FAIR by design rather than asking people to repair unstructured data afterwards.

This is why I believe the next frontier in animal welfare is not only better cages, better endpoints, or better behavioral tests.

It is also better data infrastructure.

Because when data are well described, reusable, and connected, each animal contributes more knowledge.

And when each animal contributes more knowledge, we move closer to the real spirit of the 3Rs.