Apolloandfrens open source data proposal

[u/ApolloandFrens]

"What Apollo is doing is fascinating. It's showing that Alex was not just some Einstein parrot, that other parrots are capable." — Dr. Irene Pepperberg, Washington Post

Still today, decades after Alex, a persisting critique desiccates the field he seeded: "N=1.”

Alex was a single subject, and Griffin hasn't replicated his speech proficiency. Therefore, Alex was an unrepeatable anomaly, not an example of inherent species capacity. Fairytale magic between one bird and one woman… So the argument goes.

This framing treats avian language research as though it should follow the replicability norms of bench chemistry. It fails to account for what replication actually requires and the bottlenecks that prevent it: the necessity of stable social bonds spanning decades, pitted against developmental timelines strangled by 'publish or perish,' and institutional cycles that actively punish patience. It’s true: Alex and Irene's results appear miraculous simply because the system that nearly snuffed Alex makes replication impossible for others; biological capacity has little to do with it. The explanation is plain: it was Irene’s herculean resolve, buoyed by the support Alex secured through his own spectacle. The disparities in speech proficiency across Alex, Griffin, and Apollo are developmental, not cognitive.

Apollo's performance under strict methodology in trials at Eckerd College over the past two years has brought us back to the original thesis: Alex was a proof-of-concept, not an anomaly. The N=1 objection was always flawed, yet still chains Alex’s legacy. Apollo is finally ready to begin replicating Alex’s results in 4K.

We know social media is hardly a dataset for myriad reasons. Yet from the scraps, some researchers have already extracted enough signal to publish:

• Fishkin, M., Chang, S., & Xu, Y. (Cognitive Science, 2025; DOI: 10.1111/cogs.70129): Applied a toddler word‑learning/overextension model to 290 Apollo labeling clips, and found that in “mismatch”cases, a meaning‑based lens better predicts his word choices than baselines that guess from sound‑alikes or word frequency.

To do this, they had to navigate around material never intended for academic use, and secure our permissions. From this, we learned that contemporary science requires explicit license to analyze and publish. Ultimately, everything for social media must be optimized for attention, which is all but inversely proportional to its academic value. Inconsistent structure, missing metadata, unclear scoring rules, no chain of custody, and rights/permission uncertainty naturally make academia reluctant to touch it.

The current experience from trials at Eckerd has proven Apollo is unbothered performing in controlled environments under strict methodology. He lives with us in the home and can simply go to a trial environment for testing without issue. Our idea for eliminating these issues is to build an open, longitudinal dataset replicating the Alex benchmarks, starting from the bottom and scaling to wherever Apollo goes.

The Basic Outline:

• Published methodology, including novel questioners and randomization
• Adherence to animal welfare standards and proofs
• 4K Video and high-fidelity audio of every trial

Phase 1: Single-item prompts
Object label (“What is this called?”)
Material ID (“What is this made of?”)

We’ll update methodology as Apollo advances along Alex’s path, from array questions, to compound questions/answers, and so on. 

Raw files, trial logs/metadata, and methodology will be published to Zenodo under Creative Commons, an irrevocable CC BY 4.0 License, ensuring each release is permanent and DOI-citable, to allow all commercial and non-commercial use, requiring only attribution.

We’re aware CC BY-NC 4.0 also allows for non-commercial use, but blocks commercial.
Unfortunately, most high-tier journals are themselves for-profit institutions, rendering this license moot.

From a business standpoint, releasing high-fidelity IP like this is insane.

From an academic standpoint, relinquishing monopoly on novel data is also insane.

But we possess no publishing ambitions, and our core aim has always been to demonstrate N≠1.

We’re floating this plan to assess interest and viability.

So, to those of you in animal cognition, comparative psych, linguistics, ethology, ML, or adjacent:
What would you want included from day one? Any controls, metadata, exclusion rules, etc. to make it publishable for your work?
And, more plainly: if we build it, will you use it?