Is Collaborative Open Science Possible With Speech Data in Psychiatric Disorders?

Abstract

Provision of mental health care is almost entirely built on a singular medium—naturally occurring spoken language conversations. However, datasets of spoken language from patients experiencing mental health issues are surprisingly difficult to obtain. In this commentary, we discuss some of the reasons behind this, and highlight successful approaches adopted in other areas of clinical linguistics and pose some ways forward, especially for the study of psychosis. Across disciplines, researchers are rapidly adopting Open Science principles for data sharing. This movement encourages researchers, clinicians, and institutions to provide fully open access to research data, programs, and publications. For example, the National Institutes of Health’s Strategic Plan for Data Science requires that newly funded research projects share data in accord with the FAIR principles1 for open access and that they include in their budget requests for the resources necessary to complete open access. Although many disciplines, funding agencies, researchers, journals, libraries, and institutions have adopted this new model, the movement has also encountered significant resistance, particularly for open sharing of spoken language data, including spoken language data from clinical populations (SLDCP). We can identify at least 6 barriers to open sharing of SLDCP.2 Some of these barriers come from the interpretation of regulations by various institutions, while others pertain to the prevailing public perception regarding SLDCP. Here we consider each of these barriers and the ways in which systems, such as TalkBank3 or Databrary4 manage to overcome them. With emerging collaborative efforts to study language in psychosis (eg, https://discourseinpsychosis.org/), we anticipate the commentary here to eventually inform “speech bank” infrastructures for psychiatric disorders.