🔍️ Extract provenance information (W3C PROV) from GitLab projects.
A provenance library for bioinformatics workflows 🧬 🔀 📝
⬆️ Import W3C PROV documents to Neo4j.
A reference implementation for the BACARDI PROV data model.
PHP implementation of the W3C Provenance Data Model (PROV-DM). Build provenance graphs (entities, activities, agents), serialize to PROV-JSON / PROV-N / PROV-XML / PROV-JSONLD, and validate against PROV-CONSTRAINTS.
An institutional-grade, neuro-symbolic multi-agent AI framework designed for autonomous credit risk control, financial modeling, and deterministic workflow orchestration.
Post-hoc trace reconstruction for AI agents: turns raw runtime traces into per-property reconstructability reports naming evidenced, missing, and opaque decision facts.
Reference implementation, full provenance dataset (7,004 experiments), and revision-ready submission package for the Nature Communications study 'Same Prompt, Different Answer: Hidden Non-Determinism in LLM APIs Undermines Scientific Reproducibility' (Rover et al., 2026, under Major Revision).
Mission HUD AppSpec — open JSON Schema for AI-native app specification. CC-BY 4.0 spec + MIT reference implementation.
JSON-LD specification for the PAC-AI protocol — defining context envelope structure, artifact schemas, and W3C PROV provenance mappings for transparent, auditable AI agent communication.
PAC-AI: Protocol for Auditable Context in AI — Python SDK
PAC-AI protocol use cases — Healthcare (Art. 14 human oversight) and Education (Art. 13 negative proof) compliance scenarios with benchmarks, built on the jhcontext SDK for EU AI Act auditability.
V.E.D.A. - Provenance Framework | Status: DEPRECATED(Now Private) | PROV-based metadata tracking for GDOT traffic camera recordings.
AWS production deployment of the PAC-AI protocol (jhcontext) — Chalice REST API + MCP proxy on Lambda, DynamoDB + S3 storage, and CrewAI multi-agent scenarios demonstrating EU AI Act compliance (Articles 13 & 14) with auditable context envelopes and W3C PROV provenance.
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