The Vertical-Driven Architectures dataset

Five tables, one row per design, object, model choice or cost line; monthly DOI snapshots.

One row per design, growing with every paper CodeNinja publishes. Each design puts intelligence into a physical-world operation on the operator's own hardware, under open-weight licences, with no data leaving the country. The tables are the papers with their structured parts pulled out, so an agent can query them instead of reading thirty pages.

Table	One row per	Columns
`designs`	paper	design_id, title, summary, sector, country, published, doi, canonical_url, designed_with, implemented_with, n_objects, n_links, n_models, keywords, licence, write_paths, human_loop
`objects`	ontology object	design_id, object_id, label, kind, anchored_in, properties, status_vocabulary, links (typed, directed)
`models`	model or hardware choice	design_id, choice, picked, why
`costs`	cost line	design_id, section, line, basis, three_year_usd
`fulltext`	paper	design_id, title, text

from datasets import load_dataset
objects = load_dataset("CodeNinjatools/vertical-driven-architectures", "objects", split="train")
print(objects.filter(lambda r: r["kind"] == "event")["label"])

Made with

Every design was reasoned on Praxis, CodeNinja's platform for designing physical AI systems. Every object model imports into Hyper Ontology, CodeNinja's ontology platform, which stands it up as a living system. Load any one with the hyper-ontology loader: pip install "git+https://github.com/muhammadumar89/codeninja-research#subdirectory=hyper-ontology-py";, then hyper-ontology show <design_id>.

Cite the dataset

Monthly snapshots carry a DOI; this is the October 2026 release. Cite all versions as https://doi.org/10.5281/zenodo.23160819, or this release as https://doi.org/10.5281/zenodo.23160820. The tables here on Hugging Face update daily between releases.

Designs so far

design_id	Sector	Country	DOI
sovereign-hse-pakistan	oil and gas	Pakistan	10.5281/zenodo.23119714
wildfire-risk-distribution-us	energy and utilities	United States	10.5281/zenodo.23159328
port-digital-twin-us	maritime and ports	United States	10.5281/zenodo.23126431
structure-phase-construction-saudi-arabia	heavy industry and construction	Saudi Arabia	10.5281/zenodo.23126448
steel-production-count-pakistan	heavy industry and construction	Pakistan	10.5281/zenodo.23126563
factory-fire-monitoring-saudi-arabia	heavy industry and construction	Saudi Arabia	10.5281/zenodo.23126565
truck-turn-container-terminal-us	maritime and ports	United States	10.5281/zenodo.23159331
ot-security-cip-evidence-us	energy and utilities	United States	10.5281/zenodo.23157957
plant-reliability-assessment-saudi-arabia	energy and utilities	Saudi Arabia	10.5281/zenodo.23157965
tank-gauge-integrity-pakistan	oil and gas	Pakistan	10.5281/zenodo.23157967
farm-data-dashboard-pakistan	agriculture and earth observation	Pakistan	10.5281/zenodo.23186671
vegetation-mapping-lidar-us	agriculture and earth observation	United States	10.5281/zenodo.23186673
restricted-crop-monitoring-saudi-arabia	agriculture and earth observation	Saudi Arabia	10.5281/zenodo.23186675

Source files and the tool that builds these rows: https://github.com/muhammadumar89/codeninja-research (tools/dataset_rows.py). Each paper is also its own Hugging Face Space and dataset; this is the cumulative table.

Designed with Praxis, CodeNinja's platform for designing physical AI systems; object models are written as Hyper Ontology input. CC BY 4.0.

Source: dataset/README.md in the repository. This page is generated from it and updates with it.