Payload Exports

All matrices, graph bindings, and neural weights are strictly open. (CC0 Data, Apache 2.0 Models, MIT Systems).

Complete Corpus Sync

JSON Data

Architectural JSON sync of 11,361 inscriptions generated directly from the PostgreSQL cloud instance. Includes canonical matrices, ML classifications, LOD connections, and WGS84 spatial anchors.

corpus.json~6.8 MB

Graph Database (Turtle)

TTL Graph

Resource Description Framework graph compiled via LAWD, DC, and GeoSPARQL rulesets. Inscriptions modelled as `lawd:WrittenWork` nodes.

corpus.ttl~1.6 MB

CNN Topography (ONNX)

ONNX Weights

1D Convolutional Neural Network execution graph. Rapid 5ms inference resolving 7 distinct epigraphic probabilities directly within WASM constraints.

cnn.onnx111 KB

1-Layer Transformer (ONNX)

ONNX Weights

Selaldine-attention encoder mapping complex linguistic dependencies across character grids. Optimized for multi-word syntax.

transformer.onnx1.2 MB

Semantic Tables

JSON Standard

Orthographic logic arrays encompassing alphabet matrices, digraph substitutions, and strict unicode conversion rules.

languages.json~12 KB

Server APIs

Execute pipeline normalizations via standard POST protocols pointing to the Server Compute Node:

curl -X POST https://www.openetruscan.com/api/normalize \
  -H "Content-Type: application/json" \
  -d '{"text": "MI AVILES"}'

Execute internal Python frameworks locally via pip install openetruscan. Root source located on GitHub.