I am an independent researcher bridging an academic background in Law with data engineering, specializing in building reproducible open-science infrastructure. My work directly addresses the ongoing replication crisis in applied canine ethology and veterinary behavioral science by shifting the paradigm away from subjective, non-standardized narrative descriptions and moving toward deterministic, type-enforced data pipelines.

Leveraging my background in rule-governed legal systems, I design software architectures that act as strict digital gatekeepers. By replacing anthropomorphic storytelling and institutional appeals to authority with hardcoded validation models and international biodiversity informatics standards, my objective is to establish traceably verified, physical realities for animal welfare research, epidemiological modeling, and evidence-based animal legislation.

My development environments are structured to guarantee absolute isolation and eliminate environment drift between localized scripts and production-grade cloud engines:

• Core Applied Language Layers: Python (Pandas, Pydantic v2 Strict Verification Modules).
• API Delivery & Environments: FastAPI, Model Context Protocol (MCP), NoSQL Storage (Firebase/Cloud Firestore), Docker, Dev Containers, GitHub Actions automated CI/CD pipelines.
• Academic Infrastructure & Grants-in-Kind:
  • GitHub Education Program: Weaponizing advanced automation layers and automated container testing via provisioned student developer licenses.
  • Google Developers Program: Developing advanced semantic parsing tiers within Google AI Studio, forcing state-of-the-art architectures (gemini-2.5-pro) to operate with clamped temperature thresholds (0.0) and rigid JSON schemas.
  • NVIDIA Developer Ecosystem: Strategically positioning our modular architectures for post-deadline multi-modal scaling, targeting local open-weights frameworks (Gemma) and GPU compute acceleration to process markerless pose estimation alongside textual data.

EthoPipe is an open-science, CPU-driven Extract, Transform, Load (ETL) pipeline engineered to resolve the data fragmentation bottleneck currently plaguing applied animal behavior monitoring.

[Qualitative Narrative] ──> [Gemini 2.5 Pro Parser] ──> [Strict Pydantic Validation] ──> [Darwin Core (DwC) Mapping] ( messy observer logs ) ( clamped temp: 0.0 ) ( veterinary physiological bounds ) ( global data interoperability )

• The Validation Gate: The engine natively rejects malformed records or non-scientific designations. Physiological vital inputs are checked against veterinary baselines derived from published literature (e.g., locking feline and canine resting ranges natively to prevent impossible data corruption).
• Global Interoperability Matrix: Implements international Darwin Core (DwC) terminology mappings directly onto our data arrays. Subject classifications route to dwc:individualID, categorical behaviors align cleanly to dwc:measurementType (such as play_bow or licking_of_lips), and observation methods are segregated by telemetry limits using dwc:basisOfRecord (HumanObservation vs MachineObservation).

All engineering tasks are tracked dynamically on our centralized workspace kanban boards to map our development footprints toward strict publication readiness criteria:

• Pydantic Type Enforcement: Successfully mapped strict validation models using ConfigDict(strict=True) inside src/models.py.
• Open-Source Licensing: Deployed formal, plain-text LICENSE.txt guidelines natively into the root directory to satisfy JOSS and Zenodo peer parameters.

• Continuous Integration Optimization: Verify clean isolation across the distinct .github/workflows/ci.yml matrix splits (lint, tests, schema-validation).
• Deterministic Semantic Parser Layer: Complete integration of the gemini-2.5-pro API engine with clamped non-stochastic validation loops mapping text inputs to structured outputs.

Maintaining an independent, traceably verified empirical infrastructure requires consistent computational resources. Community contributions directly offset the serverless backend run costs, high-volume model API token allocations, and public NoSQL persistence storage blocks necessary to keep our data engines completely open-access for welfare epidemiologists worldwide.

Consider backing our research space across our finalized, dedicated sponsorship tiers:

• ORCID Persistent Scientific iD: 🆔 0009-0003-0048-8982
• Zenodo Software Deposit Archive (DOI): 💾 10.5281/zenodo.21211371
• Global Open Dashboard Ecosystem: thetransparencyproject.me
• Sustained Patreon Framework Hub: Support the Mission