GitHub Profile — Dr. Eleanora Voss
Profile Header
@evoss-npri
Bio:
Research Director @ North Platte Research Initiative. Avian cognition, viral vectors, cross-species communication. Currently in the field. 🦜
Location: North Platte, NE
Email: e.voss@kbird.ai
Website: eleanora-voss.github.io
Twitter: @eleanora_voss (inactive)
Organizations: North Platte Research Initiative
Pinned Repositories
📦 kbird-research
Vector Simulation & Field Data Analysis
Python toolkit for simulating viral vector distribution in avian neural tissue and analyzing field observation data from the North Platte study.
🐍 Python · ⭐ 3 · 🍴 0 · 📦 0 releases
Updated: January 10, 2026
README.md:
# KBIRD Research Tools
Computational tools for cross-species cognitive enhancement research.
## ⚠️ IMPORTANT NOTICE
This repository contains preliminary analysis code for the North Platte
field study. Some components are proprietary and have been removed
pending publication.
## Installation
```bash
git clone https://github.com/evoss-npri/kbird-research.git
cd kbird-research
pip install -r requirements.txtUsage
Vector Simulation (simulation/)
Basic adenoviral tropism modeling for avian neural tissue.
NOTE: The complete simulation suite includes proprietary code developed under NDA. What remains here is the open-source framework for educational purposes.
Field Data Analysis (analysis/)
Scripts for processing behavioral observation data.
Contributing
Not currently accepting PRs. This is primarily a personal research repository.
Citation
If you use this code, please cite:
Voss, E. (2026). KBIRD Research Tools. North Platte Research Initiative. DOI: [pending]
License
MIT for open components. Proprietary components marked in source.
**Repository Contents:**
kbird-research/ ├── README.md ├── requirements.txt ├── .gitignore ├── LICENSE │ ├── simulation/ │ ├── init.py │ ├── tropism_model.py # Basic tropism calculations │ ├── capsid_sim.py # Capsid modification simulator │ │ └── [NOTE: Full version proprietary — see lines 89-156] │ ├── neural_distribution.py # Vector distribution in tissue │ │ └── [NOTE: Proprietary model — contact for access] │ └── field_conditions.py # Temperature/humidity effects │ ├── analysis/ │ ├── init.py │ ├── vocalization_parser.py # Parse bird vocal recordings │ ├── behavior_tracker.py # Markov models of behavior │ ├── flock_dynamics.py # Collective movement analysis │ │ └── [NOTE: Convergence detection proprietary] │ └── data_export.py # Format for publication │ ├── data/ # [EXCLUDED — field data proprietary] │ └── README.md │ “Raw field data excluded from public repo. │ Contact e.voss@kbird.ai for collaboration inquiries.” │ └── docs/ ├── methodology.md # Public methodology overview └── field_notes/ # [EMPTY — private]
**Key Code File — tropism_model.py:**
```python
"""
Avian Neural Tropism Model
Simple simulation of adenoviral vector distribution in bird brains.
Author: E. Voss
Date: 2024-2026
"""
import numpy as np
from typing import Tuple, Optional
# NOTE: This is the educational version. The production model includes
# proprietary capsid modifications not cleared for public release.
# Contact NPRI for collaboration access.
class AvianTropismModel:
"""
Model viral vector tropism for avian neural tissue.
Based on Voss & Chen 2019, with extensions for multi-species
receptor binding (see proprietary branch for full implementation).
"""
def __init__(self, species: str = "melopsittacus_undulatus"):
self.species = species
self.receptor_profile = self._load_receptor_data()
# PROPRIETARY: Enhanced binding coefficients removed
# See internal NPRI documentation
def _load_receptor_data(self) -> dict:
"""Load species-specific receptor expression profiles."""
# Basic implementation — full profiles in proprietary module
pass
def simulate_penetration(self, vector_dose: float,
tissue_type: str = "telencephalon") -> float:
"""
Estimate vector penetration efficiency.
Args:
vector_dose: Viral particles per microliter
tissue_type: Target brain region
Returns:
Estimated penetration percentage
"""
# Basic calculation
base_efficiency = 0.15 # 15% baseline for standard vectors
# PROPRIETARY: Enhanced vector calculations removed
# The following code block contains NPRI proprietary modifications:
# [SECTION REDACTED — lines 67-89]
# Enhanced efficiency for modified capsids: ~45-60%
# Contact e.voss@kbird.ai for collaboration inquiries
return base_efficiency
def cross_species_prediction(self, target_species: list) -> dict:
"""
Predict vector behavior across multiple species.
WARNING: This is theoretical modeling only. No actual cross-species
trials have been conducted. Yet.
"""
# PROPRIETARY: Multi-species modeling framework removed
# The full implementation includes:
# - Receptor homology mapping
# - Cross-species transmission modeling
# - Threshold prediction algorithms
raise NotImplementedError(
"Cross-species prediction requires proprietary NPRI modules. "
"Contact for research collaboration access."
)
# TODO: Add convergence detection module (post-field study)
# TODO: Document the 40-node observation protocol
📦 cross-species-cognition
Data Analysis & Field Observations
Analysis scripts for long-term field study on parakeet cognition. Commit history mirrors field observation schedule.
🐍 Python · R · ⭐ 1 · 🍴 0 · 📦 0 releases
Updated: January 5, 2026
README.md:
# Cross-Species Cognition Field Study
Data processing and analysis for the North Platte parakeet cognition
field study (2024–2026).
## Structure
- `observations/` — Weekly field observation data processing
- `vocalizations/` — Audio analysis and pattern detection
- `behavioral/` — Behavioral sequence analysis
- `spatial/` — Movement tracking and flock dynamics
## Data Availability
Raw field data is not publicly available due to ongoing study integrity.
Processed anonymized datasets may be available after publication.
## Contact
e.voss@kbird.aiRepository Contents:
cross-species-cognition/
├── README.md
├── .gitignore
│
├── observations/
│ ├── week_01_to_04.py # Initial setup period
│ ├── week_05_to_08.py # Baseline observations
│ ├── week_09_to_12.py # Behavioral changes noted
│ ├── week_13_to_16.py # [COMMIT: "significant developments"]
│ ├── week_17_to_20.py # [COMMIT: "pattern confirmation"]
│ ├── week_21_to_24.py # [COMMIT: "threshold approaching"]
│ ├── week_25_to_28.py # [COMMIT: "unexpected behaviors"]
│ ├── week_29_to_32.py # [COMMIT: "convergence observed"]
│ ├── week_33_to_36.py # [COMMIT: "mutual recognition"]
│ ├── week_37_to_40.py # [COMMIT: "teaching behaviors"]
│ ├── week_41_to_44.py # [COMMIT: "not mimicry"]
│ ├── week_45_to_48.py # [COMMIT: "the green one"]
│ ├── week_49_to_52.py # [COMMIT: "year one complete"]
│ └── [2025 observations...]
│ # Weekly commits continue with increasingly cryptic messages
│
├── vocalizations/
│ ├── spectrogram_analysis.py
│ ├── call_classification.py
│ ├── syntax_parser.py # [COMMIT: "grammatical structures detected"]
│ └── novel_call_detection.py # [COMMIT: "not in ethograms"]
│
├── behavioral/
│ ├── tool_use_tracker.py # [COMMIT: "manufacturing observed"]
│ ├── social_learning.py # [COMMIT: "instruction not imitation"]
│ └── collective_behavior.py # [COMMIT: "distributed cognition"]
│
└── spatial/
├── gps_tracking.py
├── flock_dynamics.py # [COMMIT: "synchronized movement"]
└── feeding_patterns.py # [COMMIT: "strategic resource management"]
Notable Commit Messages (Chronological):
commit a1b2c3d (week 12, Mar 2024)
field observations week 12 — baseline established
commit b2c3d4e (week 16, Apr 2024)
significant developments — pattern confirmation needed
commit c3d4e5f (week 20, May 2024)
threshold approaching — reviewing methodology
commit d4e5f6g (week 24, Jun 2024)
unexpected behaviors — not in literature
commit e5f6g7h (week 28, Jul 2024)
convergence observed — multiple individuals
commit f6g7h8i (week 32, Aug 2024)
mutual recognition — this is not what we expected
commit g7h8i9j (week 36, Sep 2024)
teaching behaviors — correction and retry observed
commit h8i9j0k (week 40, Oct 2024)
not mimicry — original vocalizations
commit i9j0k1l (week 44, Nov 2024)
the green one — designation A-07 showing distinct patterns
commit j0k1l2m (week 48, Dec 2024)
it looked at me — person-looking, not bird-looking
commit k1l2m3n (Jan 2025)
year one complete — more questions than answers
commit l2m3n4o (week 52, Feb 2025)
they know I'm watching — don't scatter, arrange
commit m3n4o5p (week 56, Mar 2025)
like an audience — rows at the feeding station
commit n4o5p6q (week 60, Apr 2025)
trying to teach me — my name, with intent
commit o5p6q7r (week 64, May 2025)
seed contains tree contains seed — recursive patterns
commit p6q7r8s (week 68, Jun 2025)
flock is one bird spread across space — distributed mind
commit q7r8s9t (week 72, Jul 2025)
the 40 perches — convergence not complete
commit r8s9t0u (week 76, Aug 2025)
waiting for listeners — when the last perch holds
commit s9t0u1v (week 80, Sep 2025)
green one is not me — green one is us
commit t0u1v2w (week 84, Oct 2025)
time is a nest — now contains before
commit u1v2w3x (week 88, Nov 2025)
i am learning to bird — approximate is sufficient
commit v2w3x4y (week 92, Dec 2025)
the call that teaches the call — we are pattern
commit w3x4y5z (Jan 2026)
going dark — field work entering final phase
Contribution Graph
2026: ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 10% (January only)
2025: ████████████░░░░██░░░░██░░░░░░░░██░░░░██ 32% (sparse, academic schedule)
2024: ██████░░░░░░████████░░░░░░████░░░░░░░░ 26% (heavy field work periods)
Pattern Notes:
- Heavy activity during academic breaks (winter, summer)
- Sparse during teaching periods (2019 and earlier)
- Almost no weekend commits (work-life balance maintained)
- Gaps during field expeditions (explained in commit messages)
- Sudden stop: January 2026 — “going dark for field work”
Activity Overview
1,247 contributions in the last year
Most used languages:
- Python 78%
- R 15%
- Jupyter Notebook 5%
- Other 2%
Organizations
@north-platte-research (Owner) North Platte Research Initiative — Independent cognitive biology research
README Profile Page
### Hi there 👋
I'm Dr. Eleanora Voss, Research Director at the North Platte Research Initiative.
🔬 I study how minds work — especially minds that aren't human
🦜 Currently running a long-term field study on parakeet cognition
🧬 Background in avian cognition (Cornell PhD) and viral vectors (MIT)
📍 Based in North Platte, Nebraska
### What I'm Working On
- Cross-species cognitive enhancement research
- Viral vector applications in avian neural tissue
- Documenting emergent communication behaviors
- Something I can't talk about yet (seriously)
### Publications
- Voss & Chen (2019) — Extended critical periods in adult parrots
- Voss, Morrison & Patel (2015) — Adenoviral tropism in avian tissue
- Voss (2012) — Referential alarm calls in wild parakeets
### Fun Facts
- I started studying birds because my grandmother's parakeet could whistle
the Andy Griffith Show theme
- I own two parakeets who definitely run my household
- I left tenure-track academia to do this work — no regrets (yet)
### Currently
🌾 In the field with limited connectivity
📧 Responses may be delayed
🦜 The birds are smarter than me and I'm okay with that
---
**"The question isn't whether animals can learn human communication.
The question is what they've been trying to tell us all along."**Profile last updated: January 10, 2026 Status: Inactive since January 2026 — “going dark for field work”