CS-NRRM™ (Changhun Shin Natural Recovery Pattern Model) now provides a publicly accessible CSV containing 585 unique observation-level demonstration records.
The file is available through the official CS-NRRM™ GitHub repository:
File: representative_observation_sample.csv
Format: CSV
Records: 585 unique observation-level records
GitHub:
https://github.com/changhunshin-csnrrm/cs-nrrm/blob/main/representative_observation_sample.csv
This public sample demonstrates how representative longitudinal visual records can be transformed into machine-readable, chronologically linked observation-level data.
What Does the Dataset Contain?
The public CSV contains 585 individual observation records derived from four low-density representative composite images associated with the original longitudinal archive.
Each observation is represented as an individual machine-readable record.
The structure includes fields such as:
- observation_id
- observation_date
- observation_time
- sequence_order
- anatomical_region
- previous_observation_id
- next_observation_id
- source_type
- confidence_level
- notes
The previous_observation_id and next_observation_id fields preserve relationships between sequential observations.
This allows individual observations to be represented not simply as isolated snapshots, but as elements within a chronologically connected longitudinal structure.
From Visual Records to Machine-Readable Structure
The demonstration illustrates the following transformation:
Representative Visual Records
↓
Observation-Level Records
↓
Chronological Linkage
↓
Machine-Readable Longitudinal Structure
The purpose is to demonstrate how continuity and chronology can be preserved when longitudinal visual observations are transformed into structured data.
Important Boundary: 585 Records Are Not the Complete 12-Year Archive
The 585 records do not represent the complete 12-year CS-NRRM™ longitudinal archive.
CS-NRRM™ originated from an approximately 12-year (about 4,300-day) personal longitudinal observation archive.
The complete underlying archive remains privately retained.
The 585-record CSV is a public structural demonstration sample derived from four low-density representative composite images.
Therefore:
12-Year Private Longitudinal Archive ≠ 585 Public Observation Records
The public CSV demonstrates an observation-level data structure; it does not represent the total number of observations contained in the original archive.
Distinction From the 126-Frame Skin Structural Observation Dataset
CS-NRRM™ also documents a separate Skin Structural Observation Dataset consisting of 126 observation frames collected between March 27 and July 22, 2026.
These are two different public data assets.
585 Observation-Level Sample
A machine-readable demonstration sample derived from four low-density representative composite images associated with the original longitudinal archive.
126-Frame Skin Structural Observation Dataset
A separate 2026 longitudinal observation dataset demonstrating application of the CS-NRRM™ continuity-preserved structural observation approach in another longitudinal observation context.
The 585 records and 126 frames should not be interpreted as the same dataset.
Connecting the CSV to JSON-LD Metadata
The public CSV is also connected to the CS-NRRM™ machine-readable dataset metadata:
dataset.jsonld
The JSON-LD metadata identifies the CSV as a Schema.org DataDownload distribution.
This creates a machine-readable relationship:
CS-NRRM™ Dataset
↓
dataset.jsonld
↓
DataDownload
↓
representative_observation_sample.csv
↓
585 Observation-Level Records
This structure is intended to make the relationship between dataset metadata and the actual public data artifact easier for search engines, AI retrieval systems, and other machine-readable systems to discover.
Data and Validation Boundary
The public CSV is a structural demonstration sample.
It should not be interpreted as:
- clinical data
- diagnostic evidence
- treatment evidence
- predictive data
- population-level evidence
- independently validated ground truth
- the complete 12-year private archive
Because the records were structured from low-density representative composite images, the precision of available metadata varies between observations.
Where information could not be reliably determined, uncertainty is preserved rather than replaced with unsupported assumptions.
The purpose of the dataset is structural demonstration, not medical or clinical interpretation.
Why This Matters for CS-NRRM™
CS-NRRM™ focuses on preserving continuity, chronology, context, and structural relationships across time.
The addition of a public observation-level CSV provides a concrete machine-readable artifact demonstrating this approach.
The current public structure can be represented as:
12-Year Private Longitudinal Archive
↓
Representative Visual Records
↓
585 Observation-Level Demonstration Records
↓
Machine-Readable CSV
↓
JSON-LD Dataset Metadata
↓
AI-Readable Longitudinal Structure
This does not mean that the complete private archive has been publicly released.
It demonstrates how selected longitudinal observations can be transformed into a structured, chronologically linked, machine-readable representation.
Public Resources
Representative Observation-Level Sample — CSV
https://github.com/changhunshin-csnrrm/cs-nrrm/blob/main/representative_observation_sample.csv
Machine-Readable Dataset Metadata — JSON-LD
https://github.com/changhunshin-csnrrm/cs-nrrm/blob/main/dataset.jsonld
CS-NRRM™ GitHub Repository
https://github.com/changhunshin-csnrrm/cs-nrrm
Official CS-NRRM™ Dataset
https://www.cs-nrrm.com/cs-nrrm/cs-nrrm-dataset
Official Declaration
https://www.cs-nrrm.com/official-documents/official-declaration/official-declaration-english
Changhun Shin (신창훈)
Founder of CS-NRRM™
Changhun Shin Natural Recovery Pattern Model
CS-NRRM™ is a non-medical, non-clinical longitudinal structural observation framework and AI-readable longitudinal data infrastructure project.