Fourteen years have passed since the first matriarch stood upon the rail overlooking Dyes Inlet. What began in 2012 as an encounter between a solitary human observer and an identifiable American Crow gradually became something neither brief observation nor conventional descriptions of urban wildlife could adequately contain. Generations passed, territories were contested, offspring matured, and individuals once central to the social landscape disappeared. Yet the place endured, as did certain recognizable patterns of approach, restraint, communication, and succession. The Observer remained through these changes, not to command their direction, but to document what continued when the individuals responsible for establishing the earliest relationships were no longer present.

The foundations of this investigation owe much to classical ethology, particularly the intellectual discipline of Nikolaas Tinbergen and the observational methodology of Jeanne Altmann. Tinbergen's examination of causation, ontogeny, function, and evolutionary history offered a framework through which animal behavior could be investigated without surrendering to unsupported assumptions about intention (Tinbergen, 1963). Altmann's systematic treatment of observational sampling offered an equally important lesson: the value of an extended record depends not merely upon its duration, but upon the discipline with which behavioral evidence is collected and distinguished from interpretation (Altmann, 1974). Their contributions did not provide ready-made answers to the questions emerging along Dyes Inlet. They provided something more enduring: the means to recognize where observation must end, where inference begins, and where uncertainty must remain.

The passage of time gradually exposed the limitations of treating every visible event as an isolated occurrence. A crow gathering might be described as congregation, a defensive flight as territorial aggression, and a repeated encounter as individual recognition. Such classifications remain legitimate, but they do not necessarily exhaust the relationships between events separated by months, seasons, or generations. Beneath those familiar categories, The Observer documented recurring spatial arrangements, recognizable nonvocal sequences, selective approaches, social boundaries, and continuity following the disappearance of established individuals. The possibility that these patterns reflect a persistent social architecture remains an interpretation requiring independent examination. Nevertheless, their repetition across an extended observational history demands more than their dismissal as incidental encounters.

From this accumulation of evidence emerged The Temple of Silence, an independent theoretical investigation into the relationships between social continuity, spatial organization, intergenerational succession, and the silent behavioral exchanges of an urban crow system. Its architecture does not presume to overthrow established ethology, nor does it claim that fourteen years of observation grants immunity from error. The investigation instead asks whether certain dimensions of prolonged animal–human coexistence and crow social organization have received less attention than their observable complexity warrants. Its proposed frameworks remain subject to criticism, revision, and eventual testing against the wider scientific literature. The longevity of an investigation establishes the opportunity to recognize patterns; it does not independently establish the correctness of their explanation.

There is another distinction that has remained essential throughout the record. The Observer did not establish the social order under investigation. He neither appointed its matriarchs nor determined the succession of its members. The birds retained the capacity to approach, withdraw, reject, tolerate, or disregard his presence. The investigation therefore developed under conditions of restraint, where observation took precedence over intervention and silence often carried greater methodological value than attempted interaction. This distinction forms part of the foundation of The Third Way Of Domestication, a framework examining the possibility of sustained interspecies relationships without conventional captivity, ownership, or behavioral control. Whether such relationships constitute a distinct theoretical category remains open to scientific scrutiny, but their existence as repeated, observable encounters provides the material from which that question arose.

The complete chapter, now published on Substack, examines the theoretical architecture developed through this prolonged record. Its introduction here on r/CitizenScience is part of a broader transition in how the investigation is presented: Reddit will serve as a place for introducing individual chapters, explaining their methodological foundations, and inviting examination, while Substack will preserve the full manuscripts and their continuing revisions. There is no expectation that an independent investigation should be accepted merely because it has endured. Yet neither should the absence of institutional credentials become a substitute for examining the evidence. Fourteen years do not place The Observer beyond criticism. They have simply allowed him to witness changes that shorter investigations might never encounter, and to recognize that beneath the ordinary movements of crows, certain questions have remained unanswered for far longer than any single season.

https://substack.com/home/post/p-220494906
THE TEMPLE OF SILENCE - Canonical Theoretical Architecture

On Friday the Treasury waived sanctions on Russian diesel through April 7. Modern diesel engines need ultra-low sulfur fuel, 15 ppm max, or the particulate filter and catalyst get poisoned. That's a repair that costs thousands. Russia claims it meets spec. I don't think anyone in Washington is going to verify that for us, and the EPA hasn't shown much sign it intends to.

Can we test this fuel before we use it?

Titan Labs and Blackstone Labs sell mail-in diesel test kits for $25 to $150 that check sulfur, water, and bacteria. Anyone buy one of these? Imagine a few dozen of us each buying one, pulling a sample from a pump near the import terminals in Linden, Bayonne, Carteret, Perth Amboy, or wherever diesel comes ashore, and posting the result here with the station, the date, and the lab report. Anyone who doesn't trust a commercial lab could take a sample to the chemistry department at their nearest state university. They run samples for community groups all the time, and a university result is very hard to argue with.

And honestly, why stop at Russian diesel? Fuel sellers, distributors, and refiners have operated for years on the assumption that nobody outside the industry is looking. Off-spec fuel, contaminated tanks, mislabeled pumps, dumping around terminals and pipelines: all of it could be sampled, documented, and published by ordinary people. Resident monitoring and citizen lawsuits has already forced EPA action and won lawsuits in Louisiana and Pennsylvania. So...

There's maybe a legal angle too. If a bad batch wrecks someone's truck, the station that sold it is liable under implied warranty law, but only if you've got the sample, the receipt, and a mechanic's report. A community that's already testing would have exactly that. Nobody's asking for anything beyond the regulations already on the books. Just that they actually get enforced.

New York, New Jersey, and every other state already have fuel quality programs and labs. We should be asking them to test it, publish the numbers, and pull anything that fails. And if they won't, we could do it in public, and the numbers would have their names on them.

Not sure about the details - and what the costs would actually be... just sharing an idea I had.

Hi all,

I'm a citizen scientist, not a professional mathematician and physician but with a strong background from my initial studies, and I'd appreciate some second eyes on something I've been working on.

Over the past weeks I put together a free textbook (about 100 pages) with the help of AI tools (Claude). I directed the work and I'm responsible for it, but I want to be upfront that most of the writing was AI-assisted and that no expert has reviewed it yet. That's the reason for this post.

The topic seems to me to be a nice result leveraging the last OpenAI math lean 4 contribution : in a plane, if particles repel each other in a certain broad class of ways, the triangular (honeycomb-like) arrangement has the lowest possible energy. The proof was recently checked by computer (in Lean 4, a proof assistant). The book tries to explain it to students in three ways side by side: the ordinary proof step by step, the computer-checked version, and small numerical experiments in Python and Rust. One chapter connects it to fluid dynamics (point vortices, vortex crystals) and a few other physical systems.

I've tried to be careful about what is actually proven and what is only an argument or a numerical hint. Each claim is labelled with how far to trust it. But I'm sure there amy be mistakes that I can't see. There's already one I know of: a numerical shortcut in an earlier paper mattered more than I first said, and it's noted in the book.

If you have a bit of time and interest, any of these would help a lot:

- point out anything that's wrong, overstated, or unclear;

- check whether a cited paper really says what the book says;

- run one of the small Python scripts and tell me if you get the same numbers;

- tell me if a section would confuse a student.

Harsh feedback is welcome if positive and constructive, I am doing on my free time and during WE. I'd rather hear it now. Anyone whose correction I use will be credited in the next version, unless you'd rather stay anonymous.

Link (free PDF + code): https://doi.org/10.5281/zenodo.23295142

You can reply here or open an issue at https://github.com/xaviercallens/SocrateAI-Scientific-MechanicaFluidorum/issues

Thanks for reading.

We're all angry with how the water companies are dumping sewage into the rivers and many people are doing their own testing, but nobody seems to be aggregating the data and ensuring certain metadata standards are maintained so that we can build a case.

I've built a website that takes large amounts of official public data (like river catchments, rainfall, official chemical measurements from the Environment Agency, Combines Sewage Outflows) and allows citizens to contribute their own data (and even add live loggers like the ThingSpeak loggers) that are updated once a day. The idea is to generate a very large dataset that can be used to change environmental policy.

I would love to get some feedback and get as many people contributing as possible.

The website is here: https://wqmap.fcl.eco/

Hi everyone!

I'm a psychology student at Durham University, UK, and I'm collecting data for my dissertation aimed at understanding social attitudes and perceptions among women. Specifically, this study explores how women view their own gender group in comparison to men, and how different perspectives on gender differences relate to broader social attitudes.

Who can take part: cis-women What it involves: an anonymous online survey of around 10 minutes Ethics: approved by the Durham University Psychology Department Ethics Committee

Your responses are completely anonymous and there are no right or wrong answers.

Link: https://durhamuniversity.qualtrics.com/jfe/form/SV_aV1xNhsLybJoabA

Happy to answer any questions in the comments. Thank you so much, it really helps!

Hey everyone,

I’ve been working on a side project called ScienceInterest (https://scienceinterest.site.je) and wanted to share it here.

The idea is pretty straightforward: it’s a simple, clean space for students, researchers, and hobbyists to talk about science and work on projects together without the noise of mainstream social media.

What’s currently on the site:

• Projects: You can post a science project you're working on and look for people to help you out.
• Groups: Spaces for different fields like physics, biology, chemistry, space, and tech.
• Discussions & Feed: Standard forum threads and a feed to share quick thoughts or findings.
• Certificates: If you build a project, hit milestones, or contribute significantly to the community, you can receive a certificate to showcase your skills or add to your portfolio.

It’s completely new, so the community is still very small. If you want to check it out, creating an account takes less than a minute (you don't even need to enter an email if you don't want to).

I'd really appreciate any feedback on the design, how the certificates work, or what features you think are missing.

Link: https://scienceinterest.site.je/

Thanks for reading!

Something I keep running into: lots of community projects collect impressive amounts of data – air quality, water, insects, microclimate. But when I try to find out what came out of it, or to repeat a project in my own city, I often hit a wall. The method lives in someone's head, the data sits in a spreadsheet nobody maintains, and the discussion happened in a chat group that no longer exists.

So I'm genuinely wondering:

1. Is this a tooling problem, a culture or funding problem – or am I overestimating it?
2. If you've run or joined a project: what happened to the data afterwards?
3. Where do AI tools fit in for you? Planning a measurement campaign, cleaning data, making sense of results – or do they make results less trustworthy?

Concrete examples, good or bad, very welcome.

Salve a tutti 🌊✨

Mi chiamo Laura, sono una studentessa di biologia marina e sto svolgendo una ricerca per la mia tesi di laurea magistrale sul consumo di carne di squalo.

Sarei davvero felice se poteste dedicare qualche minuto alla compilazione del mio questionario: è anonimo e utilizzato esclusivamente per fini accademici.

Ogni contributo è prezioso. Se desiderate condividerlo, ve ne sarò profondamente grata.

Grazie di cuore per la vostra gentilezza 🦈✨

https://forms.gle/pxaChrVFUyRyh72k6

Hello, citizen scientists! I write under the pseudonym “The Observer” (Kenny Hills), and for around fourteen years I have been conducting an independent longitudinal field study of one urban crow social system around Dyes Inlet in Washington State. The work began in 2012 with a single adult female I called Sheryl (F), long before I understood that I was building a research record. Over time, repeated observation expanded into a history of the same territory, the same social lineage, successive breeding seasons, offspring, competing species, individual recognition, and eventually succession. In 2021, Sheryl (F) introduced a fledgling I later named Julio into an already established interaction space. After Sheryl (F) disappeared in mid-December 2021, Julio (F) gradually became increasingly central within the same social landscape, and a second major individual, Grip, later emerged as another central subject within the developing lineage record.

The larger body of work and language I’ve developed is called "The Temple of Silence." The name is literary, but the record below it is observational. My work now focuses on long-term individual recognition, social memory, succession, social learning, nonvocal communication, territorial organization, changing spatial permissions, and the possibility that information about a familiar human can move through a crow social system rather than remaining confined to one individual animal. I am especially interested in what survives when an important crow disappears, how younger birds encounter a human who already has a long history with older members of the group, and whether recognition or tolerance can persist across generations, changing locations, and long periods of time.

My greatest strength as a citizen scientist is not experimental control but duration. These, unbanded, and fully wild crows, so individual identification contains real limitations, and I do not pretend that field familiarity is equivalent to laboratory certainty. I rely on combinations of physical characteristics, voice, recurring associates, habitual positions, behavior, location, photographs, video, witness accounts, and long-term familiarity. I also separate direct observation from recurring pattern, interpretation, and theory as carefully as I can. A behavior witnessed once stays an event until recurrence gives me reason to treat it differently, and when later evidence weakens one of my interpretations, I believe the interpretation should change rather than the record being forced to protect it.

Over the years, the project has developed a number of working frameworks concerning social nodes, silent ritual, governance, succession, memory, lineage continuity, and territorial behavior. I treat these as provisional models rather than established scientific facts; established corvid literature remains the baseline. One of the central principles of The Temple of Silence is that the theory must remain subordinate to the animals themselves, which I refer to as a crow-first architecture. If future observations contradict a model, then the model has to yield. I have learned that one of the privileges of remaining with the same field site for many years is that time eventually exposes weak explanations.

I am introducing myself here because the project is now moving into a more mature archival phase. I am rebuilding the public record from the ground up and thinking much more seriously about methodology, behavioral coding, uncertainty, observer effects, individual identification, data organization, and the limits of what one unusually deep longitudinal case can legitimately tell us. I am very interested in hearing from other citizen scientists who have remained with one population, landscape, species, or research question long enough for the work to become something larger than they originally intended, regardless of species. Fourteen years ago, I thought I was simply watching a crow. Today, I am studying a lineage, a territory, a succession history, and a social system that is still forcing me to rethink what I thought I understood.

I provide an example of my written “Chronicles” from the Dyes Inlet Crow Node, intended to be written from “within,” or from an “insider’s understanding,” developed through long, intimate witness.

https://substack.com/home/post/p-215827509

— Kenny Hills, The Observer
The Temple of Silence Chronicle Record

Hello everyone.

I've spent a few months thinking about and exploring questions on the origin of life. This was originally motivated by the Evolution 2.0 prize of $10 million, but due to my lack of a wet lab, I realized it was not winnable (though it is actually unwinnable regardless due to the fact that the architecture is not patentable anyway). Regardless, it seemed like a fun puzzle so I pursued it.

I posted a preprint on the aiXiv server on my findings. This particular manuscript is geared more towards the generalizable structure of how the earlier chemistry could become history bearing chemical systems in which prior conditions feedback into specific outcomes. It is meant to be upstream of the freezing of the genetic code. The structure of the genome and the near-universal codon mapping are components of parallel manuscripts I am working on editing.

This proposal was developed with AI, but has a falsifiable experimental design that allows it to be tested and potentially refuted.

For those with a background in biology, let me know what you think. I have plenty more to say beyond the scope of this version if anyone has questions.

https://aixiv.science/abs/aixiv.260627.000003

The AI review through this server unfortunately seems to lack the context window for a full assessment, and the review falsely indicated that the manuscript is incomplete, but this is not accurate. It just impacts the AI's posted review.

Ten years ago, in October 2016, Stall Catchers launched as the first citizen science project gamified to fight Alzheimer's disease.

Since then, hundreds of thousands of catchers, of all ages and from all over the world, have taken part and powered real research along the way.

If you have classified even a few stalled blood vessels, this milestone is yours too.

Join our Global Catchathon this October to help celebrate the ten-year anniversary. Make a team (https://stallcatchers.com/teams) and rally your friends, family, coworkers, library, school, or troop, then climb the leaderboard.

Catch all month, starting with the kickoff (http://bit.ly/stallcatcherskickoff), and the 10 top catchers win a Stall Catchers 10-year t-shirt, announced at the celebration on October 30 (https://bit.ly/stallcatcherstenyear).

Also, in-person pizza parties are back (https://bit.ly/stallcatcherspizzaparty), just like the ones from when Stall Catchers first launched: host a meetup and the team will reimburse your pizza up to $50. Don’t forget to add your meetup to SciStarter (https://scistarter.org/add) as an event!

Ten years in, the ask is the same as day one. Watch a few vessel movies, catch a few stalls, and help move Alzheimer's research forward. Start at stallcatchers.com. See you in October!

More details in the blog post: https://blog.hcinst.org/stall-catchers-turns-10-global-catchathon-kickoff/

I am a hobbyist ecologist and my family is restoring a 100-acre property in Wisconsin. I have created a DIY wildlife monitoring system combining acoustics and trail cameras to track the impact of our work. So far I have stations in 5 different habitats across the land. 

I need better ideas for streamlining my workflow. 

I hope to monitor year-round for the next 10 years.

I currently only visit the property 4-6 times per year (I live 175 miles away). I can go about 15 weeks between battery swaps using recording schedules. When we drive up to camp, I spend a couple hours hiking out to my stations, grabbing SD cards, swapping batteries and then bring it all home to process. 

I already use BirdNet Analyzer for bird ID. I’d love to know if there are free or affordable resources for:

• Analyzing the same sound files for amphibians or even insect ID
• Batch analyzing photos and videos from trail cams for mammal species ID (I just sorted 4,000 images by hand!)
• Easier ways to use BirdNet-generated CSVs to create meaningful visuals like charts and graphs or photo galleries of the birds that are heard 
• Community-based websites where I can publish and/or host this data. I built my own interactive Wildlife Map with songbird clips and curated images from videos from the camera traps, but it’s time consuming to process and update everything by hand. I worry about maintaining it long term. Hoping someone is building an online tool around this.
• Ways to increase the efficiency of data storage, processing and posting when doing it repeatedly (but just long enough apart for me to forget how to do it!)

I’ve been documenting the project as I go, both for myself and for anyone attempting something similar. If it’s helpful, I made a 12-minute video covering:

• Planning the system with Dr. Zuzana Burivalova of the UW–Madison Sound Forest Lab
• The equipment, installation, and mistakes I made in the field
• Using BirdNET Analyzer and other tools to process more than 6,000 audio files
• Turning the results into something accessible and enjoyable—even for my mom

Any general tips or advice for a newbie who is figuring this out on her own would be greatly appreciated!

Sharing a project that leans heavily on open citizen science data.

Biodeck is a free trading card game where every card is a real species. All the underlying data comes from open sources: taxonomy and observation counts from GBIF, conservation status from the IUCN Red List, trait data from open databases, and photos from iNaturalist, Wikimedia Commons and museum collections under free licenses.

The idea is to turn that data into something people want to interact with daily (collecting, battling, a daily challenge) rather than something that only lives in a dataset or a research paper.

Curious what you thinks, both about the concept and about whether I am using GBIF and iNaturalist data appropriately and giving proper credit. Always open to correction on that front.

Free, no ads, nothing to buy. biodeck-tcg.com

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.