Our Tools

The landscaping of mental health datasets maps and categorises available data sources across the UK, covering electronic health record-based datasets, linked datasets, and linkable administrative datasets.

A resource cataloguing genetic data from UK mental health studies, making it easier for researchers to find and access key genetic variables via the HDR UK Innovation Gateway and Catalogue of Mental Health Measures.

An online searchable discoverability platform with information about thousands of longitudinal datasets from across the world. The Atlas provides a global spread of these datasets and enables users to explore, filter for, compare, favorite and save them. Each dataset has a designated page with key information about its features, sample characteristics, data types collected, links to relevant papers and detailed data access procedures. The Atlas is continuously being reviewed, updated and expanded, as well as verified by dataset study teams.

The Innovation Gateway is a central hub for health data, enabling researchers to search, discover, and request access to diverse datasets, tools, and resources. While it does not provide direct access, it streamlines the discovery and application process. By integrating with the Catalogue of Mental Health Measures and DATAMIND’s datasets, it enhances the accessibility of mental health and broader health research data within the HDR UK ecosystem.

Harmonised Data: Linking Mental & Physical Health – Two reports demonstrating how EHR data can support research into the physical health of people with severe mental illness. They also highlight gaps in data availability and consistency in both EHRs and longitudinal studies, offering valuable insights for researchers and policymakers.

An accessible, user-friendly tool co-created with the DATAMIND PPIE team to demystify the complex language of mental health data science. It offers clear, straightforward explanations of key terms to support better understanding, communication, and collaboration across all levels of knowledge and experience.

The HDR UK Phenotype Library is a comprehensive, open access resource providing the research community with information, tools and phenotyping algorithms for UK electronic health records. It enables researchers to explore phenotypes defined by codelists and algorhythms, supporting standardised and transparent health research. The Library also includes the DATAMIND collection, which focuses on mental health data, fostering collaboration and enhancing research efficiency by streamlining access to validated code lists and algorithms.

An interactive web-based discoverability tool of UK-based cohort and longitudinal studies with detailed information about their mental health and wellbeing measures collected over time. The Catalogue provides an overview of each study, its sample characteristics, data availability and access, and what other related topics it has measured such as physical health conditions. The Catalogue also includes an inventory of commonly used mental health measures, upcoming longitudinal studies and other longitudinal data resources.

A digital tool designed to assess the representativeness of mental health clinical trial participants by comparing study samples to population data. It helps identify underserved groups and supports targeted recruitment efforts to improve trial diversity. 

A practical tool to help industry partners navigate and access mental health data that supports innovation and the development of effective health interventions. Created by DATAMIND, this guide connects industry researchers with relevant resources and expert support, ensuring they can find, access, and use the right data to make meaningful impact. For tailored guidance, industry users are encouraged to contact the DATAMIND team directly.

The DATAMIND Mental Health Measures Map has been developed to provide an overview of the measures currently used across the DATAMIND ecosystem.

PHENOMIND is a set of tools designed to support the development and use of clinical coding phenotypes for research. It includes a web-based app that allows users to explore clinical coding schemas and build code lists for these phenotypes.

A developing cross UK dashboard designed to make school-based mental health data more accessible and useful for schools. Working with networks SHRN and SHINE, the dashboard supports data discovery, harmonisation of measures, and improved understanding of student mental health trends. Pilot dashboards have been launched in Wales and Scotland, with ongoing efforts to expand and enhance accessibility and use across the UK.

A standardised tool for integrating mental health data into physical health trials, improving research on mental-physical health links.

A powerful tool for harmonising mental health questionnaire items across studies. Using transformer-based AI, it compares items across different formats and languages, and integrates with the Catalogue of Mental Health Measures to support data-driven, collaborative research.

A co-designed digital platform that helps young people engage in mental health research through Patient and Public Involvement and Engagement (PPIE). It offers interactive features like polls, feedback tools, and project updates, making it easier for young people to contribute to research in a way that fits their lives.

Uses UK primary care data to identify common mental health symptom patterns across disorders. By analysing symptom clusters with network analysis, it supports better diagnosis, treatment planning, and research into mental health conditions.

Expertise has been developed at King’s College London and the South London and Maudsley NHS Foundation Trust over 10-15 years in the development of NLP algorithms, their successful application on health records text, and support for enhanced research output (>300 publications). This experience comes particularly from the Clinical Record Interactive Search (CRIS) and CogStack platforms, and this expertise is being made more widely available for mental health research and clinical practice through DATAMIND. It is also complemented by a dedicated public involvement programme in the development and application of NLP. 

As a means of making natural language processing (NLP) functionality more widely available (see tool above), MH-TAC is a secure cloud-based platform that applies NLP to mental health electronic health records (EHRs), enabling researchers to extract a range of key characteristics from unstructured text data while maintaining strict data security within the NHS firewall.

Clinical Record Interactive Search (CRIS) data at the South London and Maudsley has been successfully linked with a range of internal and external data resources, including local neuroimaging data and national data on hospitalisations, mortality, disease registries, education, benefits and Census returns. The Maudsley’s Clinical Data Linkage Service (CDLS) provides expertise and resources for negotiating linkages, assembling, hosting and curating linked resources within an NHS trusted research environment (TRE), and supporting research using linked data, alongside a long-running public involvement programme to advise on the use of linked data.  

the Clinical Informatics Service (CIS) at the South London and Maudsley is successfully deploying research resources (e.g. natural language processing (NLP) algorithms) to enhance information on cases and caseloads made available for clinicians and clinical services from live processing of EHR information.

A healthcare software suite  with interchangeable modules for analysing clinical data using AI to draw insights from text or documents in Electronic Health Records. CogStack has a wide range of features include Generative AI, natural language processing, full search, alerting, cohort selection, population health dashboards, deep phenotyping and clinical research.