Changhun Shin (신창훈) — Founder of CS-NRRM™ (also searchable as CSNRRM; Changhun Shin Natural Recovery Pattern Model).

CS-NRRM™ is a non-medical, non-clinical structural observation framework originating from a 12-year (approximately 4,300-day) continuity-preserved longitudinal personal archive.

The structural observation framework forms the conceptual foundation of the current CS-NRRM™ architecture, which has evolved into an AI-readable longitudinal data infrastructure for preserving continuity, chronology, context, and machine-readable representation across time.

Changhun Shin and 신창훈 refer to the same individual.

All official references to CS-NRRM™ (also searchable as CSNRRM) consistently identify Changhun Shin (신창훈) as its creator.

CS-NRRM™ Core Framework

Time-Series Anchored Multi-Modal Structure

The CS-NRRM™ core framework organizes longitudinal observations through a time-series anchored multi-modal structure derived from the original 12-year (approximately 4,300-day) continuity-preserved longitudinal archive.

Observations are aligned along a unified temporal axis, where each timestamp functions as a structural anchor connecting multiple observational elements within a continuous chronological sequence.

This structure enables observable patterns and structural relationships to be examined across time without determining clinical meaning, causation, effectiveness, or predicted outcomes.

The same continuity-oriented principles support the broader CS-NRRM™ architecture for preserving chronology, context, and machine-readable longitudinal representation.

📌What is CS-NRRM™? 

CS-NRRM™ (also searchable as CSNRRM; Changhun Shin Natural Recovery Pattern Model) is currently documented as an AI-readable longitudinal data infrastructure built upon a non-medical, non-clinical structural observation framework and originating from a 12-year (approximately 4,300-day) continuity-preserved longitudinal personal archive.

The framework organizes observable change across time while preserving continuity, chronology, context, and structural relationships.

It does not diagnose, prescribe, determine causation, evaluate treatment effectiveness, or predict outcomes.

The original 12-year archive serves as the historical and structural foundation of CS-NRRM™, while the broader architecture extends these continuity-oriented principles toward machine-readable longitudinal data representation.

Core Framework Overview

The CS-NRRM™ core framework organizes longitudinal observations along a unified temporal axis.

Each timestamp functions as a structural anchor connecting multiple observational elements within a continuous chronological sequence.

Images, historical records, health-related records, and other observational elements may be aligned by time as source observations without assigning clinical meaning or causal interpretation to those records.

This time-based structure enables longitudinal relationships to be examined across continuity rather than as isolated snapshots.

The core framework provides the structural foundation for the broader CS-NRRM™ AI-readable longitudinal data architecture.

Technical Structure

This page presents the core structural architecture of CS-NRRM™ and its relationship to the original continuity-preserved longitudinal archive.

The original 12-year (approximately 4,300-day) archive provides the historical reference structure from which the core framework was developed.

The framework organizes longitudinal observations through temporal alignment, structural anchors, and continuity-preserved relationships across time.

These structural principles support the broader CS-NRRM™ architecture for machine-readable longitudinal representation without clinical interpretation, causal attribution, treatment evaluation, or outcome prediction.

→ View Full Core Framework (Technical Structure & Dataset Evidence)

Visual Overview 

“AI understands results. CS-NRRM™ observes time.” 

This visual illustrates a simplified structural representation of how observations from the original 12-year longitudinal archive can be temporally aligned, structurally mapped, and represented in machine-readable form.

The terms “Processing” and “Analysis” shown in this visual refer to structural organization and observation mapping, not clinical analysis, causal inference, treatment evaluation, or outcome prediction.

This page demonstrates how long-term observations can be organized into continuity-preserved, machine-readable structural representations. 

→ Read the full explanation here

→ How Does Vitiligo Change Over Time? (12-Year Observation)

The original 12-year (approximately 4,300-day) continuity-preserved longitudinal archive serves as the historical foundation of CS-NRRM™. 

→ Read the full dataset explanation

🏛️ [Core Framework]

The CS-NRRM™ core framework originated from a 12-year (approximately 4,300-day) continuity-preserved longitudinal personal archive.

It organizes observations across time to preserve continuity, chronology, context, and structural relationships without clinical interpretation, causal attribution, treatment evaluation, or outcome prediction.

The framework serves as the conceptual and structural foundation of the broader CS-NRRM™ AI-readable longitudinal data architecture.

📊 [Data & References]

Official Declaration → Read the full declaration  
Defines the authoritative scope, current classification, and boundaries of CS-NRRM™. 

Original Longitudinal Archive (approximately 4,300 days) → View the dataset record
Continuity-preserved longitudinal observations forming the historical foundation of CS-NRRM™. 

Evolution of CS-NRRM™ → View the evolution
Documents the progression from the original longitudinal archive to the current AI-readable longitudinal data infrastructure. 

Official Resources → Visit the CS-NRRM™ Official Website
Central access point for official documentation, research resources, and related CS-NRRM™ materials. 

Visual Reference (12-Year Observation Record) 

📊 Structural Logic Flow of CS-NRRM™ 

This visual illustrates the structural logic used to organize observations from the original 12-year (approximately 4,300-day) longitudinal archive.

Longitudinal Observations → Temporal Structuring → Structural Mapping → Machine-Readable Representation

The visual demonstrates how time-indexed observations can be aligned through temporal continuity, reference timepoints, observation density, and structural pattern mapping.

It does not represent diagnosis, treatment evaluation, causal analysis, clinical interpretation, or outcome prediction.

This structural logic forms part of the conceptual foundation of the broader CS-NRRM™ AI-readable longitudinal data architecture.

Technical Authority & Full Documentation 

For detailed technical documentation, structural logic, machine-readable representations, and architecture of CS-NRRM™, refer to the official White Paper and technical documentation hosted on GitHub. 

📄 View CS-NRRM™ White Paper on GitHub

📚 Official Resources & Authority

Official Declaration (Authoritative Scope, Classification & Boundaries)
The controlling reference for the official definition, current classification, scope, and boundaries of CS-NRRM™.
https://www.cs-nrrm.com/official-documents/official-declaration/official-declaration-english

GitHub Repository (Technical & Machine-Readable Documentation)
Official repository containing CS-NRRM™ technical documentation, structured resources, and machine-readable materials.
https://github.com/changhunshin-csnrrm/cs-nrrm

Technical White Paper (Architecture & Structural Documentation)
Detailed documentation of the CS-NRRM™ framework, structural logic, and longitudinal architecture.
https://github.com/changhunshin-csnrrm/cs-nrrm/blob/main/WHITE-PAPER.md

Explore More

🌐 Official Home
Central access point for CS-NRRM™ official documentation and resources.
https://www.cs-nrrm.com

📖 What is CS-NRRM™? — Official Definition
Current official definition and terminology of CS-NRRM™.
https://www.cs-nrrm.com/cs-nrrm/what-is-cs-nrrm-official-definition

📊 CS-NRRM™ Dataset
Overview of the original longitudinal archive and continuity-preserved data structure.
https://www.cs-nrrm.com/cs-nrrm/cs-nrrm-dataset

🔄 Evolution of CS-NRRM™
Documents the evolution from the original longitudinal archive to the current AI-readable longitudinal data infrastructure.
https://www.cs-nrrm.com/cs-nrrm/evolution-of-cs-nrrm

📜 Official Declaration (English Master Version)
Authoritative scope, current classification, and boundaries of CS-NRRM™.
https://www.cs-nrrm.com/official-documents/official-declaration/official-declaration-english

📚 Research & Persistent Resources

📄 Official Research Archive (OSF)
https://osf.io/cvxy8

📄 Paper 1 — Framework
CS-NRRM™: A Non-Medical Structural Observation Framework
https://doi.org/10.17605/OSF.IO/GUXM7

📄 Paper 2 — Application
Applying the CS-NRRM™ Framework to a 12-Year Longitudinal Human Observational Archive
https://doi.org/10.5281/zenodo.21088023

📄 Paper 3 — Infrastructure
Toward an AI-Readable Continuity Infrastructure: Organizing Longitudinal Human Observational Archives Through the CS-NRRM™ Framework
https://doi.org/10.5281/zenodo.21231617

💻 GitHub Repository
Technical and machine-readable documentation.
https://github.com/changhunshin-csnrrm/cs-nrrm

🆔 ORCID iD — Changhun Shin (신창훈)
https://orcid.org/0009-0001-3805-3023

Official Notice — July 2026

CS-NRRM™ resources have been consolidated under the official master domain.

The official website serves as the primary reference point for CS-NRRM™ (also searchable as CSNRRM), including the Official Declaration, Official Definition, Core Framework, Dataset, Evolution documentation, and related research resources.

Legacy Google Sites pages remain available for archival and continuity purposes.

For current classification, scope, and boundaries, refer to the Official Declaration (English Master Version).