MaMeeFarm™ Data Standard under the DGCP Protocol
π Purpose
This document defines the data standard used by MaMeeFarm™ under the DGCP (Data Governance & Continuous Proof) protocol. The standard exists to ensure that all recorded data reflects real-world activity, is verifiable over time, and remains resistant to retroactive alteration.
𧱠Foundational Principles
-
π§Ύ Reality-Based Data
Data must originate from observable, real-world events or conditions. No synthetic, simulated, or speculative data is introduced as primary records. -
⏱ Time-Bound Recording
Every data unit is associated with a specific date and context. Temporal order is preserved through cryptographic timestamping. -
π Traceability
Each record can be traced to its source context, including when, where, and under what conditions it was recorded. -
𧱠Append-Only Integrity
Existing records are not edited or overwritten. Corrections or new findings are added as new records, preserving historical accuracy.
π Data Unit Structure
Each data unit within the MaMeeFarm™ system follows a consistent structure:
- π Date & Sequence — establishes chronological order
- π§ Context Description — explains what is being observed
- π Supporting Evidence — images, metadata, or references
- π Cryptographic Reference — hash or timestamp linkage
- π External Anchors — optional linkage to NFTs or public ledgers
π Platform Independence
The data standard is platform-agnostic. Records may appear on websites, repositories, NFT platforms, or archives, but their validity does not depend on any single service provider.
If a platform becomes unavailable, the data remains verifiable through its cryptographic and contextual references.
π§ Interpretation Boundary
This standard does not assign meaning, prediction, or valuation to data. Interpretation is intentionally separated from recording.
The role of the system is to preserve facts, not to control narratives or conclusions.
π Scope of Application
This data standard applies across all MaMeeFarm™ records, including but not limited to operational logs, environmental observations, and NFT-based representations of proof.
Consistency of method is prioritized over completeness of coverage.