Sharing and comparing data in mental health research works better when everyone uses the same language. Today, research on digital markers (data collected from smartphones, wearables, and other sensors) lacks clear and consistent terminology. This makes studies harder to reproduce, results harder to compare, and findings harder to trust.

As digital mental health research has grown, so has the variety of data being collected, from location patterns and typing behaviour to movement and speech traits. However, there is still no common structure that connects these data to mental health concepts. As a result, similar ideas are often described in different ways, datasets are difficult to combine, and valuable evidence is fragmented.

The ontology for digital markers in mental health (ODIM-MH) addresses this gap by creating a shared, evolving framework for describing digital markers in mental health. It provides a place where digital markers can be clearly defined and linked to relevant resources, helping researchers work more consistently, transparently, and collaboratively.