Check out my final pre-print for Quantum Coherence In Neural Microtubules!

Gamma-band oscillations (30-100 Hz) exhibit timing precision that challenges strictly classical accounts of neural synchronization. This manuscript presents a unified theoretical framework proposing that quantum coherence in neural microtubules may serve as a modulatory mechanism to enhance gamma oscillation precision. We provide: (1) rigorous, step-by-step decoherence derivations constrained by thermal, electromagnetic, and mechanical channels; (2) empirically grounded parameters derived from microtubule electromagnetic oscillations and tryptophan superradiance experiments; (3) a conservative quantum-classical coupling mechanism that modulates pyramidal-interneuron network gamma (PING) and interneuron network gamma (ING) precision through weak electromagnetic fields (∼nT); (4) complete

experimental designs integrating nitrogen-vacancy (NV) center quantum sensing with high-density electrophysiology; and (5) computational validation pipelines using finite element modeling and stochastic simulations.

We formalize the Perry Constant (κ ≈ 1.7±0.3 ms−1) as a proposed quantitative bridge linking coherence factor to precision enhancement. Our framework generates four primary testable predictions: measurable coherence-precision correlations (r >0.3), quantum-consistent temperature scaling (Tc ≈ 12 ± 3 K), resonance-selective

electromagnetic eects (Q > 5 in 4060 Hz), and pharmacological selectivity for microtubule-targeting drugs.

We transparently identify two major quantitative gaps in the initial framework: a ∼6 order-of-magnitude discrepancy between calculated eective coherence times

(∼15 ns) and required functional timescales (∼ms), and an electric coupling energy (ΔV ≈ 3.2 μV) below ion channel threshold (∼4 mV). We then resolve these gaps through four physically justied mechanisms supported by novel simulations: (1) non-Markovian dynamics with structured spectral density extend coherence 10-33× (to ∼165500 ns); (2) cooperative gating across ∼100 channels plus stochastic resonance amplication achieves ∼40% gating modulation; (3) lattice geometry simulation confirms collective decoherence scaling; and (4) phase-coherent accumulation over gamma cycles enables network-level detection (SNR ≈ 143). A revised PING network incorporating all mechanisms produces clear 40 Hz gamma locking (spike PLV = 0.74). Alternative classical explanations are systematically discussed

and distinguished through quantum-specic signatures.

This work represents a falsifiable, empirically testable contribution to quantum biology and neuroscience,

avoiding speculative claims about consciousness generation while advancing our understanding of neural timing precision.

https://doi.org/10.5281/zenodo.19145944

Check out my final pre-print on Information Geometry.

We develop a mathematically rigorous variational principle on statistical manifolds equipped with the Fisher information metric. Starting from seven axioms characterizing distinguishability between probability distributions, we provevia Chentsov's theorem that the Fisher metric is the unique (up to a positive constant) Riemannian structure satisfying these information-theoretic requirements. We then show that geodesic motion on this manifold, subject to domain-specific constraint functionals, recovers the equations of motion governing Hamiltonian mechanics, quantum unitary evolution, thermodynamic relaxation, evolutionary replicator dynamics, and natural gradient descent. Our main contributions are: (i) a complete proof of metric uniqueness from physical axioms; (ii) rigorous consistency demonstrations with explicit error bounds; (iii) seven novel, falsifiable predictions - including quantum decoherence rate scaling with the quantum Fisher information, evolutionary speed limits, and neural network capacity scaling C ~ N0.6 three with preliminary numerical support; and (iv) explicit falsification criteria. We address anticipated criticisms, including the constraint problem, the signature problem, and questions of novelty beyond reformulation. We emphasize throughout that this framework is a reformulation revealing structural unity across disciplines, not a derivation of fundamental constants or a Theory of Everything. All constraint functionals encoding domain-specific physics must be specified externally. The mathematics is rigorous, the predictions are testable, and the question is well-posed. Now nature must answer.

https://doi.org/10.5281/zenodo.19147292

Sharing something we built at a small UK marine non-profit. We monitor seabed life at seaweed and shellfish farms, and wanted the public involved in the species ID rather than it all sitting on our laptops.

It's a short-clip guessing game. You watch a clip, name the species, and the crowd's answers build a consensus that feeds back into validating our own reference IDs. Playable as a guest, no download.

It's free and non-commercial. I'd really value feedback from people who think about crowd-sourced data, especially where you reckon the consensus model might fall over.

https://www.fishspotter.app/

https://reddit.com/link/1v1oybk/video/wx7nkg3vbkeh1/player

Would love everyone's thoughts! I know New Scientist has this article: https://www.newscientist.com/article/2531817-2026-eclipse-5-citizen-science-projects-you-can-contribute-to/

As per the title, I am building a dataset intended to be used to make a machine that can use the bruckner reflex to measure diopter refraction in people, as well as detect diseases like lazy eye, strabismus and others. I am looking for anonmyized pictures of the left and right eye tooken in a dark room (specifically so pupils dialate), with 1 phone camera , with another phone having a specific wallpaper tagged in the form. the 2nd phone will be held slightly behind and slightly above so the red oval is barely above the camera please look at the red oval and not the phone camera during the taking of the picture. All images are fully anonymous and your participation will have a real impact. The purpose of the images is to calibrate my device to measure diopter perscription accurately. link: https://docs.google.com/forms/d/e/1FAIpQLSeeou0erq3tVTQQR404eT_zW-dGRcBsNf2J1zC7YtOBhy07KQ/viewform

I'm an undergrad doing summer research, and my teammate and I built a citizen science platform for spotting gravitational microlensing (when a foreground object bends and magnifies the light of a background star, sometimes revealing planets or dark objects we can't otherwise see).

Here's the idea. We trained a model on real OGLE survey data to flag microlensing candidates. It's confident on the obvious ones, but there's a messy middle where it genuinely can't tell. Those are the ones we send to people.

You look at a light curve (a graph of a star's brightness over time) and answer a few yes/no questions about the shape. The site walks you through what to look for, so you don't need any prior knowledge. Takes about five minutes to learn.

When volunteers disagree with each other on a curve, we don't throw that out. The disagreement itself becomes a signal the model learns from, so it gets better at knowing which cases are genuinely ambiguous instead of just guessing confidently and being wrong.

It's live here: lenswatch.dev

Fair warning, this is active research, not a finished product. We need real people looking at curves before we can say anything about whether the disagreement approach actually helps. So if you try it and something is confusing or broken, let us know.

Hi everyone,I’m an independent researcher and I’ve just released a new open‑access paper:

Tropical Geometry and Biochemical Reaction Networks: A Mathematical Framework for Steady-State Topology

The paper develops a transparent, reproducible method for analyzing biochemical reaction networks using tropical geometry—a piecewise‑linear approximation that reveals dominant pathways and regime structure without requiring precise parameter values.

Open-Science Features

Fully open-access manuscript

Complete code repository included

All case studies (from enzyme kinetics to glycolysis) are reproducible

Framework designed to be accessible to researchers outside traditional institutions

Emphasizes interpretability, transparency, and parameter‑robust predictions

The goal is to provide a mathematically rigorous yet accessible tool for analyzing complex biochemical systems, especially in contexts where parameter uncertainty is high.

Happy to answer questions or collaborate with others working in open theoretical biology, computational modeling, or geometric methods.

https://www.researchgate.net/publication/399968231_Tropical_Geometry_and_Biochemical_Reaction_Networks_A_Mathematical_Framework_for_Steady-State_Topology

https://doi.org/10.5281/zenodo.19710441

Heads up for anyone interested. Franck is taking questions today about his all-sky monitoring project, his 15 years at the Institute, and citizen astronomy.

He's answering questions on r/IAmA, including what he learned running citizen science at scale: [AMA LINK]

Hi everyone.

I have been working on the version 3 of a fully modular and 3D printable bench-top size generator, that is capable of at least 10 Watts line to line. It is based on my ModuCoil design, a term I coined for a printable coil bobbin that enables interchangeability of stator coils individually, which aids in repairability, recyclability, and customisation. I have previously attempted this with older versions, but this one is the most functional of them all, and actually produces power at usable levels.

I intend to use this generator in the future to do energy science with, especially around DIY wind and micro-hydro generation.

I have provided a link to a video explaining the design in detail, and have provided a link in the comments to my Thingiverse page, where you can download the files, and build one for yourself!

https://youtu.be/nqC9-_tqFNk

I'm a passionate, independent non-professional researcher, I've spent a while building contour.today, a solo AI-assisted project containing an open science layer carefully designed

around a simple observation: "almost everything we know about how people understand code comes from controlled lab studies".

There's apparently zero trace of any infrastructure comprehensively built around collecting this data

naturalistically from real people, voluntarily,

during genuine self-directed learning.

The mechanic is straightforward: you predict what code comes

next before seeing it, rate your confidence, then compare.

Calibration is measured with sophisticated algorithms using d-prime sensitivity values and Brier scores, valid, established psychometric tools

meaningfully applied to code comprehension for the first time in history as far as accurate.

Data only collected with explicit consent and is by default always anonymized: prediction accuracy profiles, calibration trajectories, coding language and difficulty distributions. No individual prediction text leaves users devices without opt-in.

The dataset is currently virtually unexistent. The infrastructure is documented and public, altough, at the moment platform is down for maintenance, but strives toward improvement.

I'm honestly asking whether people here find this worth contributing to, and whether anyone sees research angles not yet considered, or any other constructive contribution welcome .

could be a good resource for people on r/CitizenScience -- training modules here: https://scistarter.org/training

I love it! https://stallcatchers.com/

There have been a lot of cool events! https://citizensciencemonth.org

Hi everyone!

As a citizen scientist, I've been working on a theoretical project that connects thermodynamics with biological time. My model, Effective Chrono-Entropy (ECE), proposes that we should measure "time" in living systems not by the calendar, but by the accumulation of entropy (τeff​=∫dS/kB​).

I’ve integrated data from various open sources (including PNAS) to show how this "effective front" of entropy production better represents aging and biological stages than linear time does.

I’m currently in the process of submitting this to a journal, but I would love to get some preliminary feedback or discuss the implications with fellow researchers here.

Main formula: τeff​=S/kB​

Has anyone else explored the link between entropy production and temporal perception? Looking forward to your thoughts!

I'd like to coordinate two photographs taken of the night sky from a thousand miles apart on the same latitude with identical equipment and settings at the same moment and oriented in the same direction.

I would like to see if a thousand miles is far enough to be able to visually see the distance difference in the moon and stars.

How can we organize something like this?

Hello! We’ve created an open-source project called Yourself to Science.

Our mission is to collaboratively catalog all the different ways people can contribute their biological and digital selves to science and research.

It allows you to search across all research projects (including wearables, health data, biological samples, body/tissue donation, etc.), whether by donating it or getting paid.

It also has clear labels for geographic availability and other filters to help you find what's relevant to you.

We also recently added a new interactive page that shows all the services allowing you to share your genetic data for research.

We would love your feedback! Any suggestions regarding errors, missing services, or general improvements are very welcome 😃

All research: https://yourselftoscience.org

DNA research-only: https://yourselftoscience.org/what-can-i-do-with-my-genetic-data