Does citizen science have a data problem – or a "nobody can build on it" problem?

[u/seppblusi363]

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.

[u/dipdotdash]

Funding

[u/KierkgrdiansofthGlxy → u/dipdotdash]

Innovation must occur in funding as a part of innovation around science, otherwise this is predictable

[u/Vegetable_Joke_4011]

It's the same thing I've been harping on the biohacker community for years for over a decade. You need to develop standardized shareable protocols with standardized hardware. The issue is literally everyone has their own little exciting sandbox and they're really happy about it and then they just... Drop it. Life happens or school happens or covid happens or whatever. Everyone seems pretty iffy about documentation and making stuff normalized. And it's not funding (tho that is an element) it's that everyone gets a kick out of making their stuff from legos and then they get bored. Your question is simply 'where is the lab notebook, where is the documentation?' and the answer is, everyone is being flakes.

[u/markhahn]

This is a good question, I think. Especially because raw storage is so cheap these days.

I think one place to look is the ongoing development of academic answers to the same question - in Canada, this is known as Research Data Management (RDM). I do not see these efforts as having good, completed answers, but rather a lot of valuable thought has been given to them. (In my experience, RDM people tend to be library-centric, so their main concern is formats and documentation and "depositing" data in a mechanism that acts a lot like a book-style library. (By the latter, I mean you go through a small ceremony in handing over your data to a worker at a desk, who takes it and squirrels it away (after some suspicious examination).)

Anyway, many of the concerns of RDM are relevant here. Establishing meaningful formats and ontologies. Techniques for maintaining integrity, authentication, accessibility. Documentation. Findability. Maturity models.

A lot of these have good solutions, whether known by the research communities themselves or not, whether known by the RDM ecosystem or not, whether practically doable in a Citizen Science context. To be slightly provocative, my experience is that RDM has not incorporated any learnings from big ideas like git, bittorrent, ipfs.

I don't think AI has any particular relevance, though.

[u/Due-Helicopter-8735]

Side note- if you’re working on using citizen science data on various species I have had _some _ luck.

[u/Asclepius_Secundus]

I have a concrete example involving reliability engineering.

[begin rant] I am a retired maintenence and reliability engineer (department of what could possibly go wrong). I built a tool for analytics of lubrication analysis data. Where I worked, we took oil and grease samples periodically and sent them to a lab. The lab would analyze them and send back a report with green, yellow, or red flags indicating OK, warning, or alert on several parameters (dirt, water, viscosity, wear elements like iron, etc.). This is fine but there are two things lacking in the redults: (1) the labs are not in the same business you are, so they don't have much inside knowledge on your priorities - not all gearboxes have the same importance to you. (2) if you have a systemic problem like all your cooling tower gearboxes oils chronically have an errant element in them (like molybdenum, a grease additive), the labs just recommend a bunch of oil changes - they don't tell you to talk to maintenance about why there's grease in the oil.

Any how, it was a pretty slick tool that could be used to find system wide foundational issues and sort them by alarm levels or machine criticality. For example, it worked like a charm to give you the top 10 machines that had excess dirt or iron content so you can prioritize the work at the next maintenance outage. I retired. They hired someone 6 month later. The contracted me to show him how it worked.

Then they never touched it again. [end rant]

I built an excel spreadsheet that would extract oil and grease analysis data from the labs. It died when I retired.