A call for human-centred approaches to qualitative longitudinal mental-health data sharing

Qualitative longitudinal mental health (QLMH) data provides a uniquely rich resource for understanding how experiences of mental health conditions unfold over time and are shaped by context and culture. While there has been considerable investment by researchers and clinicians in collecting meaningful data and building standardised protocols and repositories for quantitative mental health data, there has been a lack of similar efforts to standardise and contextualise data sharing for the rigorous and ethical reuse of qualitative data. Researchers and data stewards rarely document the social, ethical, personal, and situational factors that shape how participants share sensitive information. In particular, while positionality and reflexivity are crucial aspects for study design and data collection, these are not explicitly addressed when openly sharing QLMH data. These gaps are a consequence of research systems that prioritise technical over relational dimensions of data. This Open Letter outlines why human-centred infrastructure for QLMH data sharing is urgently needed and proposes a co-developed toolkit – the Human-Centred Metadata Canvas – which illustrates practical steps for researchers, clinicians, data stewards, funders, and institutions to embed human-centred documentation and ethical evaluation across the QLMH data lifecycle. Addressing these gaps is essential to ensuring that QLMH data is not only collected but also ethically preserved, meaningfully shared, and responsibly reused to benefit science, clinical practice, and the communities involved.