Data & Infrastructure

Building a shared schema for modelling atypical human experiences.

Underneath these projects is a shared schema for friction records, tasks, notes, metrics and tags, designed to support longitudinal ND research and tooling. Current data sources include an enterprise NPS study of after‑sales service, community‑run friction protocols, and early app / plugin prototypes, all feeding into one unified pipeline.

Data Processing Pipeline
friction_record
→
task_entity
→
metric_entity
→
c_note
→
tag_entity

Core Entity Schema

Formal definition of the five core entities in the cognitive friction research pipeline.

friction_record entry_point

Captures granular moments of stuckness with intensity (1-10), brief description, and environmental context.

Identifier record_id (PK)
Core fields intensity, log_type, env_tag
Links task_entity, tag_entity
task_entity operational

Represents outward tasks and physical actions as atomic units of work originating from friction protocols.

Identifier task_id (PK)
Core fields action_type, priority, status
Links friction_record (FK)
metric_entity quantitative

Stores quantitative indicators of system load, self‑rated energy, focus, and operational timing data.

Identifier metric_id (PK)
Core fields energy_val, mood_state, latency
Usage Longitudinal Analysis
c_note qualitative

Holds narrative notes, protocol transcripts and reflections that bridge raw events and higher‑level themes.

Identifier note_id (PK)
Core fields narrative_raw, theme_id, parts
Storage Markdown / Obsidian
tag_entity metadata

Cross‑cutting taxonomy for neurophysiological, environmental, and organisational attributes.

Identifier tag_id (PK)
Core fields namespace, tag_value, weight
Scope Cross-project Querying

How the schema connects to projects

The shared schema is the backbone of all three live projects, ensuring data interoperability across domains.

NPS Service Experience Study

KPI decomposition maps onto friction_record and metric_entity. Connections: Survey data → Friction Taxonomy → Operational Dashboards.

Friction WishPool & Protocol Library

Submissions convert to friction_record with intensity scores and tags feeding the evaluation layer.

Inner Kingdom System

Internal parts, daily tasks, and state metrics all write to the same entities for longitudinal pattern analysis.

For direct integration cases and technical specifications, see:

[Case Study: NPS Integration] [Data Flow: Wish-Pool]
View Raw JSON Schema Details →