Definitions
This page defines every entity, category, scoring axis, and classification used across the DIGIT Capabilities Database. It is the reference for interpreting data on the Cohort Map, Capabilities Browser, and Query Builder. All definitions are grounded in published UK health research standards including the HRA/CAG consent framework, HDRUK Data Utility Framework, DPUK metadata standards, and NIHR clinical research infrastructure classifications.
Overview
The DIGIT Capabilities Database is a metadata catalogue of UK mental health research sites, cohorts, and trial capabilities, built for the UKRI Mental Health Goals Programme. It helps Alliance Managers match industry enquiries with UK capabilities, helps researchers discover trial sites and cohort access, and supports statisticians with trial design and feasibility planning.
The database organises information around four core entities (sites, cohorts, capabilities, and diagnosis areas) and uses a three-dimensional mapping model to visualise cohorts along three independent axes: consent level, characterisation depth, and data availability.
Core Entities
Site
A UK location where a mental health clinical trial could be designed, recruited into, or run. Includes NIHR BRCs, NHS Trusts, university departments, Clinical Trials Units, and more.
Cohort
A defined research cohort, data resource, biobank, or electronic health record system containing mental health data that could support trial recruitment, feasibility assessment, or observational research.
Capability
A piece of research infrastructure or expertise at a site relevant to planning a mental health trial. Capabilities describe what a site can do — infrastructure and capacity.
Diagnosis Area
A mental health condition or condition group that a site has experience researching, or that a cohort has participants diagnosed with. Maps to ICD-10 codes.
The 3D Mapping Axes
The Cohort Map visualises cohorts along three independent axes forming a 4x4x4 cube. Each axis uses a 4-level ordinal scale following the precedent set by the HDRUK Data Utility Framework. The axes are designed to be orthogonal — each carries genuinely independent information:
| Axis | Question it answers | How it is scored |
|---|---|---|
| Consent Level | How can participants be approached for new research? | Expert-judged per HRA/CAG consent framework |
| Characterisation Depth | How deeply is the mental health state assessed? | Expert-judged per precision psychiatry literature |
| Data Availability | How broad is the range of data modalities available? | Auto-derived from 10 boolean data type flags |
Consent Level (1-4)
Grounded in the HRA/CAG consent framework, NHS Digital Section 251 guidance, Kaye et al. 2014 dynamic consent spectrum, and the SLaM Consent for Contact (C4C) model.
Characterisation Depth (1-4)
Grounded in NIHR BRC deep phenotyping definitions, the RDoC units of analysis (NIMH), and precision psychiatry literature. Each level is strictly cumulative — higher levels include everything from the levels below.
Data Availability (1-4)
This axis is automatically derived from the 10 boolean data type flags (see below). Each level is strictly cumulative.
Cohort Data Type Flags
Each cohort has 10 boolean flags recording whether specific modalities of data exist in its archive. These are factual indicators — a flag is set to true only when there is evidence that the data type exists and is accessible to researchers.
Group A: Research Assessments
Data collected through structured research assessments administered directly to participants.
has_questionnaire_dataValidated self-report instruments, structured interviews, or patient-reported outcomes applied to participants (e.g. PHQ-9, GAD-7, SCID, WEMWBS, GHQ-12).
has_cognitive_dataComputerised cognitive testing or formal neuropsychological assessment data (e.g. CANTAB, CogState, n-back, Stroop, TMT, WAIS).
has_physical_measuresAnthropometry, vital signs, non-genomic blood biomarkers, or other objective physical measurements (e.g. height/weight/BMI, blood pressure, grip strength, blood biochemistry, ECG).
Group B: Biological
Stored biological material and molecular data derived from participant samples.
has_biosamplesStored biological specimens available for analysis: blood, plasma, serum, DNA, saliva, CSF, tissue, or urine that researchers can apply to access.
has_genomic_dataGenotyping array data, whole-exome sequencing (WES), whole-genome sequencing (WGS), or GWAS summary statistics for participants.
has_omics_dataNon-genomic molecular profiling: proteomics, metabolomics, transcriptomics, epigenomics (DNA methylation), or other -omics data.
Group C: Advanced Modalities
High-dimensional, technology-driven data modalities and administrative data linkage.
has_imaging_dataBrain or body imaging data: structural MRI, functional MRI, diffusion MRI, PET, SPECT, EEG, MEG, DEXA, or retinal imaging acquired from participants.
has_wearable_dataData from body-worn devices: accelerometry, actigraphy, continuous heart rate monitoring, electrodermal activity (EDA), or sleep sensors.
has_digital_phenotypingPassive smartphone or sensor data: GPS traces, screen time, call/text metadata, app usage patterns, voice/speech analysis, or similar behavioural data.
has_linked_health_recordsThe cohort’s research data has been linked to NHS administrative records (HES hospital episodes, GP records, prescribing data, ONS death registry).
Cohort Groups
The group is derived by a database rule, evaluated in order — every cohort gets exactly one group, and identical inputs always produce the same group. (The groups descend from Figure 1 of the grant application, which sketched them by hand; they are now computed.)
The rules run top to bottom; the first match wins. Because the group is computed from consent level, characterisation depth, data availability (itself derived from the data type flags), and population type, it cannot be edited directly. Rare, documented exceptions use the group_label_override column with a justification note — none are currently in use.
Population Types
Who the cohort primarily contains, from a study-planning perspective. This is deliberately separate from the 3D axes: a general-population registry and a patient cohort can carry identical consent and characterisation scores while serving completely different industry needs. Patient supply concentrates in clinical and case-enriched cohorts; general-population, occupational, and data-resource entries form the feasibility and natural-history layer.
clinicalRecruited via clinical services or on diagnosis
case_enrichedVolunteers with a condition (registry or sign-up criteria)
general_populationNo health-based entry criteria
occupationalSampling frame is an occupation, not health status
data_resourceEHR, administrative, or SDE asset — not a recruited cohort
network_umbrellaRecruitment network or umbrella programme
Industry Engagement Status
Whether the cohort's custodian has operationalised industry or trial access. This is an organisational property of the study team, not a consent property — cohorts with identical consent levels differ completely in whether an approach from industry could actually be actioned. Values are populated from cohort-owner meetings and default to unknown until a conversation settles them.
provenThe cohort has supplied participants, samples, or data to at least one commercial trial or industry study.
established_processA documented application and governance route exists that industry can use (e.g. the NIHR BioResource access process, UK Biobank AMS).
open_in_principleThe custodian has expressed willingness to consider industry access but no formal process exists yet.
never_consideredThe study team has not previously considered clinical trials or industry access — recorded directly from owner meetings, not inferred.
declinedThe custodian has decided against industry access.
unknownNot yet established with the custodian. This is the default until an owner meeting settles it.
Cohort Relationships
Cohorts can be part of a larger programme (recorded as a parent link): a sub-study recruits within its parent (STRADL within Generation Scotland), an extension adds new participants or data layers (GLAD-omics extending GLAD), a wave is a repeat collection round, and an umbrella member sits under a programme umbrella (GLAD under the NIHR Mental Health BioResource). A sub-study only gets its own database entry when it differs from its parent on something a user filters on — axis scores, population type, recontactability, or access route; otherwise it is recorded as a note on the parent.
(sub_study)Extension (extension)Wave (wave)Umbrella member (umbrella_member)Site Types
Every site is classified into one institutional type. When a site could fit multiple types, the type with higher infrastructure significance takes precedence (BRC > CRF > CTU > NHS Trust > University).
brcA site designated by NIHR as a Biomedical Research Centre with a mental health or neuroscience theme. BRC status takes precedence over other classifications.
Examples: Maudsley BRC, Oxford BRC, Cambridge BRC
nhs_trustAn NHS Trust or Foundation Trust whose primary function is providing mental health services, with research delivery capability.
Examples: Pennine Care NHS FT, Tees Esk and Wear Valleys NHS FT
universityA university department (typically psychiatry, psychology, or neuroscience) with trial delivery capability that is not an NIHR BRC.
Examples: University of Edinburgh (Psychiatry), University of Bristol
ctuA UKCRC-registered Clinical Trials Unit that designs, manages, and analyses clinical trials. Provides trial methodology, statistics, and regulatory support.
Examples: King's CTU, Priment CTU (UCL), Edinburgh Clinical Trials Unit
crfAn NIHR Clinical Research Facility — a dedicated, staffed facility for conducting clinical research visits, with beds, nursing, and pharmacy.
Examples: NIHR Wellcome King's CRF, Manchester CRF
arcAn NIHR Applied Research Collaboration conducting applied health research in partnership with NHS organisations, focusing on implementation science.
Examples: ARC South London, ARC North Thames
health_boardA Scottish NHS Health Board or Welsh Health Board providing integrated health services with research delivery capability. The devolved equivalent of an NHS Trust.
Examples: NHS Greater Glasgow and Clyde, NHS Lothian, Betsi Cadwaladr UHB
hsctA Northern Ireland Health and Social Care Trust — the integrated health and social care provider. The NI equivalent of an NHS Trust.
Examples: Belfast HSCT, Southern HSCT
crn_siteAn NIHR Clinical Research Network partner site that recruits patients into trials but is not otherwise classified as a BRC, Trust, or CRF.
Examples: (Most CRN partners are better classified as another type)
otherAny site that does not fit the above categories, such as charities with research arms or independent research institutes.
Examples: Research charities, independent institutes
Capability Categories
Research capabilities at sites are grouped into eight functional domains. Capabilities describe what a site can do (infrastructure), not what data a cohort already has (that is captured by the data type flags).
Equipment and expertise for acquiring brain or body images in a research context.
Examples: MRI (structural, functional, diffusion), PET, SPECT, EEG, MEG, retinal imaging
Laboratory facilities and expertise for measuring biological markers from participant samples.
Examples: Blood biomarker assays, CSF analysis, genotyping services, proteomics/metabolomics platforms
Equipment, software, and trained staff for administering standardised cognitive assessments.
Examples: CANTAB, CogState, NIH Toolbox, paper-and-pencil neuropsychological testing (WAIS, TMT, Stroop)
Digital health technology infrastructure for research data collection.
Examples: Smartphone apps for EMA, wearable device platforms, digital phenotyping systems, remote monitoring
Physical spaces or equipment purpose-built for research that do not fit other categories.
Examples: Sleep labs, exercise physiology labs, sensory testing rooms, mother-baby units, secure/forensic research facilities
Infrastructure for collecting, processing, storing, and distributing biological samples for research.
Examples: -80°C freezers, liquid nitrogen storage, sample processing facilities, LIMS, consent management
Equipment for non-invasive or invasive brain stimulation used in research or as an intervention.
Examples: TMS (repetitive, single-pulse, theta burst), tDCS, tACS, deep brain stimulation (DBS), ECT (research protocols)
Expertise and infrastructure for advanced clinical trial designs relevant to mental health.
Examples: Adaptive trial design, basket/umbrella trials, TWiCs infrastructure, decentralised trials, N-of-1 methodology
Diagnosis Area Categories
Diagnosis areas are grouped into 15 clinical categories. Each category maps to ICD-10 code ranges and contains individual conditions relevant to mental health trial design.
Conditions characterised by delusions, hallucinations, disorganised thinking, or grossly disorganised behaviour.
Includes: Schizophrenia, schizoaffective disorder, first-episode psychosis, treatment-resistant schizophrenia
Conditions characterised by persistent disturbance in mood (depression or mania/hypomania).
Includes: Major depressive disorder, bipolar disorder, treatment-resistant depression, dysthymia
Conditions characterised by excessive fear, anxiety, or avoidance behaviour.
Includes: Generalised anxiety disorder, social anxiety, panic disorder, specific phobias, agoraphobia
Conditions characterised by obsessions (intrusive thoughts) and/or compulsions (repetitive behaviours).
Includes: OCD, body dysmorphic disorder, hoarding disorder, trichotillomania
Conditions arising from exposure to traumatic or stressful events.
Includes: PTSD, complex PTSD, acute stress disorder, adjustment disorders
Conditions characterised by persistent disturbance in eating behaviour.
Includes: Anorexia nervosa, bulimia nervosa, binge eating disorder, ARFID
Conditions with onset in the developmental period, manifesting as developmental deficits affecting functioning.
Includes: ADHD, autism spectrum disorder, Tourette syndrome, intellectual disability with co-occurring MH conditions
Conditions arising from the use of psychoactive substances, including behavioural addictions.
Includes: Alcohol use disorder, opioid use disorder, stimulant use disorder, gambling disorder
Enduring patterns of inner experience and behaviour that deviate from cultural expectations and cause distress or impairment.
Includes: Borderline personality disorder (BPD/EUPD), antisocial personality disorder
Progressive loss of neuronal structure or function, often with psychiatric manifestations.
Includes: Dementia, Alzheimer’s disease, Parkinson’s disease-related MH, frontotemporal dementia, Lewy body dementia
Conditions characterised by abnormal sleep patterns causing distress or functional impairment.
Includes: Insomnia, hypersomnia, circadian rhythm disorders, sleep apnoea (in MH context)
Mental health conditions occurring during pregnancy or in the first year postpartum.
Includes: Perinatal depression, perinatal anxiety, postpartum psychosis, tokophobia
Intentional self-injury with or without suicidal intent. A critical clinical and research domain that cross-cuts diagnostic categories.
Includes: Non-suicidal self-injury (NSSI), suicidal ideation, suicide attempts
Approaches that span traditional diagnostic boundaries, studying shared mechanisms or comorbidity patterns.
Includes: Anxiety-depression comorbidity, emotional dysregulation, dimensional approaches (p-factor), common mental disorders
Conditions or research areas that do not fit the above categories.
Includes: Long COVID neuropsychiatric symptoms, chronic fatigue / ME, medically unexplained symptoms
Experience Levels
A site's experience in a specific diagnosis area is classified using four levels, based on completed and active trial counts from registries (ClinicalTrials.gov, ISRCTN, NIHR CRN portfolio).
| Level | Definition | Threshold |
|---|---|---|
| Extensive | A recognised centre of excellence with sustained, high-volume trial activity. | 5+ completed MH trials in 10 years, or 3+ active trials, or specialist programme grant |
| Moderate | Meaningful trial experience with multiple completed or active studies. | 2-4 completed MH trials in 10 years, or 1-2 active trials |
| Some | At least one trial or specialist clinical expertise that could support delivery. | 1 completed or active trial, or specialist clinical service in the area |
| Emerging | Developing capability; clinical contact with patients but no completed trials. | No completed trials, but sees patients with this condition clinically |
Data Access Mechanisms
How researchers access a cohort's data. This affects feasibility timelines and cost.
| Mechanism | Definition | Examples |
|---|---|---|
| Trusted Research Environment | Data accessed within a secure, accredited TRE/SDE. Researchers submit code; results are reviewed before export. | NHS SDE, OpenSAFELY, SAIL, UK Biobank RAP |
| Direct Application | Researchers apply to the data custodian and, if approved, receive a dataset extract or portal access. | ALSPAC, GLAD, CPRD licence |
| Collaboration | Access requires a formal research collaboration with the cohort team, typically involving co-PI arrangements. | Smaller cohorts, early-stage studies |
| Open Access | Data or summary statistics freely available without formal application. | Published GWAS summary stats, public-use files |
| Commercial Licence | Industry or commercial access requires a paid licence or commercial data sharing agreement. | CPRD commercial, IQVIA datasets |
| Other | Access mechanism does not fit the above (e.g. federated analysis where no data leaves the source). | Federated platforms, bespoke arrangements |
Verification Statuses
Every site and cohort record carries a verification status indicating confidence in its data quality. Records progress from AI-inferred to verified as human review is completed.
| Status | Definition |
|---|---|
| Verified | All key fields confirmed by a human with direct knowledge (site PI, cohort lead, or programme team member) against primary sources. |
| Partially Verified | Some fields confirmed by a human, but others remain unverified. Common when a PI confirms infrastructure but not trial counts. |
| Unverified | Data collected from published sources (websites, papers, registries) by a human researcher, but not confirmed by someone at the site or cohort. |
| AI-Inferred | Data initially generated or populated with AI assistance. Requires human review before use in any formal output. |
Reading the source indicators
The help icon beside each value opens its provenance, which is one of five kinds. Field-specific source: a citation pins this exact value, usually with a quote. Searched — not published: we looked for the value and could not find it published; the record lists where we checked, so a data owner can confirm or correct it (a linked document here is the place searched, not support for the value). Whole-record source: no citation pins this exact value, but documents describing the whole record are listed. Computed value: calculated from other fields (for example, the derived cohort group). AI-inferred, unverified: no provenance recorded yet — these need review first.
Conceptual Boundaries
Several concepts in the database appear similar but carry distinct meanings. Understanding these boundaries is essential for interpreting the data correctly.
Cohort Data Type Flags vs Site Capabilities
| Cohort Data Type Flag | Site Capability | |
|---|---|---|
| Question | Does this cohort's existing archive contain this type of data? | Can this site generate new data of this type for a trial? |
| About | Archived data (what already exists) | Infrastructure (what can be generated) |
| Example | “ALSPAC has brain MRI data you can request” | “Maudsley BRC has a 3T scanner available for trials” |
Characterisation Depth vs Data Availability
These two axes are fully independent. Characterisation measures how deeply the mental health state is assessed (process quality). Data availability measures how broad the range of data modalities is (archive breadth). A cohort can have high characterisation but low data availability (e.g. detailed SCID interviews but no biosamples or imaging), or vice versa.
These definitions are maintained as part of the DIGIT Capabilities Database and are derived from the project's data dictionary (docs/data-dictionary.md). For the full technical schema, refer to that document.
Data and Digital Industry Alliance Team (DIGIT) • King's College London • IoPPN