Have you contributed to or are currently participating in a citizen science project?

This could include uploading to iNaturalist, monitoring local wildlife, taking samples or measurements of water bodies, online tasks on websites such as Zooniverse.

I am a master’s student at City St George’s University of London, currently completing my degree in Human Computer Interaction Design. I am conducting a design project for my dissertation provisionally titled: Exploring the use of a personalised user experience design to support participant retention in virtual citizen science.

I am currently recruiting participants for a short 10 min survey with questions about your citizen science participation, then a 45min user research session which would take place online. This would then lead to a design review where you give feedback on the prototype I create around 3 weeks later.

I’m looking for people at all levels of experience and contribution frequency, so if you know anyone who may be interested, please share this post with them.

The online sessions will be done through Microsoft Teams and will consist of you running through a couple of tasks on two existing Zooniverse projects, you’ll be encouraged to share your thoughts and opinions throughout this. I am hoping to get these done by September 7th.

If you’re interested, this link will take you to the survey which contains the full participant information sheet and consent form: https://cityunilondon.eu.qualtrics.com/jfe/form/SV_3pHfQy2iVcCjRye

You can also download the information sheet here: Information Sheet

Started this project for a very unglamorous reason: I couldn't find reliable info on where wild rhinoceros beetles actually show up near me, and my kid wanted to go catch some. Existing info was scattered across old forum posts and vague directions.

So I built a simple crowdsourced logging tool — you find a species, snap a photo, pin the GPS location. Everyone's pins show up on a shared map, so the dataset gets more useful as more people contribute. Each user also gets a personal "dex" — a deduplicated collection view of every species they've logged, with a counter for repeat sightings.

Right now it's beetle/insect-focused since that's where the need started, but the underlying idea — location-tagged species sightings, contributed by ordinary users, aggregated into something searchable — is the same pattern a lot of citizen science projects use (iNaturalist etc., just much smaller and more casual/gamified).

Live at https://spotthe.world if anyone wants to look at the approach. Genuinely curious how this sub thinks about data quality/verification at small scale — right now there's no expert verification layer, just crowd density (more pins in an area = more trustworthy), curious if that's a reasonable enough approach for something this informal.

4 years ago, a friend found an uncommon organism washed ashore on the Italian coast.

That encounter, and another one later on, eventually led to two scientific publications on stranded organisms in the Mediterranean Sea.

Now we're taking the next step: we built Stranding, a citizen science platform where anyone can report organisms found washed ashore and contribute data that can support marine biology research.

We're currently looking for our first 100 beta testers before the public launch.

So cool! There's a NASA-supported project called Space Cloud Watch, where volunteerssubmit photos of noctilucent clouds. Those are the eerie ones that keep glowing silver after sunset and before sunrise, because they're catching sunlight from way up high. They've been showing up more often and lower in the sky than they used to, and scientists want to know why. Problem is, they're really easy to mix up with ordinary lower clouds, so the project leaders were stuck verifying tons of submissions by hand.

Enter a volunteer named Namai Chandra. He noticed all this manual checking and thought, this is exactly the kind of repetitive task a machine learning pipeline could handle, while still leaving the tricky judgment calls to humans. So he built one. He reached out to the project scientists, trained his model on real noctilucent clouds and all the sneaky look-alikes, tested and refined it over several rounds, and released it to the project.

And now it's in use! Contributors who aren't sure what they photographed can run their image through it first, and scientists use it to flag images for review. A volunteer saw a problem, taught himself past it, and gave the whole project a better workflow. That's the dream version of what citizen science can be!

If you've ever hesitated to submit because you weren't sure you were even looking at a noctilucent cloud, now you can check before you share.

Full write-up: https://science.nasa.gov/get-involved/citizen-science/volunteer-develops-machine-learning-tool-to-identify-rare-clouds/

Join the project: https://citsci.org/projects/space-cloud-watch

Hi Reddit,
I teach 8th grade science. We had the incredible opportunity to go to a talk with a NASA instrumentation scientist who worked on the Nancy Grace Roman Telescope that’s launching this month.

He put out a call to action for citizen scientists because the data will become available around Christmas this year and there’s a massive amount of it coming out (he said they’re predicting 20k TERABYTES in five years); my students were stoked. They want to start a Citizen Scientist club at school and work on analyzing some of the research data.

I’m blown away by their passion for this, and I want do to right by them. I might be a science teacher, but citizen science of this magnitude feels out of my depth.

Where do I start? What kind of resources or tools should I be finding/compiling/learning for them to use once the club is up and running?

Thank you!!

register here! $20 for farmers to give feedback on an irrigation citizen science project: https://farmerirrigationnation.com/events/

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!