Extreme Citizen Science

Nova Scotia’s Experiment in Research That Solves Real Problems

Locally focused research investments enable coalitions of scientists and citizens to serve community needs.

Brandon Yates on Twitter / X

I wrote a cosmology paper that's under peer review.I can't post it to arXiv because I don't have a university affiliation.If any of you happen to have published in astro-ph. CO, gr-qc, or hep-ph and can endorse me, I'd owe you one.#AcademicTwitter #PhysicsTwitter #Cosmology…— Brandon Yates (@bmichaelyates) March 13, 2026

Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Request PDF

Request PDF | On Jan 1, 2013, Jessica McCallum Breen and others published Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Find, read and cite all the research you need on ResearchGate

TreeKIT: Measuring, Mapping, and Collaboratively Managing Urban Forests

As cities across the United States expand their stock of street trees to address a range of environmental sustainability goals, municipal foresters are increasingly turning to volunteers to supplement their tree care efforts. TreeKIT is a small non-profit organization that helps city dwellers collaboratively measure, map, and manage urban forests. Using TreeKIT’s mapping methods, volunteers are able to create spatially accurate inventories of street trees. In the process, volunteers learn to identify street trees and develop an effective appreciation for their local urban forest. The following article describes the TreeKIT mapping methods and ongoing efforts to scale these techniques for large, synchronous street tree census initiatives.

Individual Experience vs. The Cochrane Review

On my decade-long exploration seeking a scientific language for singular evidence.

Science-based Shredding

Why don’t people tout RCTs of piano lessons?

GainForest — Biodiversity Observations & Nature Projects

Explore field observations, biodiversity records, and nature projects from communities and organizations using GainForest.

Want signal? We need more noise (looking at the quiet bottleneck)

We need more signal, which means we want more noise. A lot of current scientific infrastructure is designed to minimize messiness: define a narrow question, collect the minimum data required to answer it, standardize the dataset, exclude complicating variables, finish the analysis, publish the result. That approach is understandable. It is also one reason we…

Paul Novosad on Twitter / X

I made an AI running/training coach last fall.With its guidance, I smoked my 5km PR.It's 100x better than the stuff on offer from Garmin/Strava and it was the easiest thing in the world. The first pass was just a bunch of markdown files and a Codex instance.Some notes 1/ pic.twitter.com/yE360Mk8Pg— Paul Novosad (@paulnovosad) June 24, 2026

What could a human right to participate in science be?

Abstract. At first sight, the idea of a human right to participate in science may seem absurd. Many assume science must be the preserve only of those with

Job Posting: Reddit Research Czar

Job postings are a kinda weird phenomenon. For one thing, they’re very modern. It used to be that most people either inherited a job (I’m a baker because my pa was a baker and our tiny hamlet needs…

Citizen Science

Earn $100 in ETH if your essay addressing a Collison question is selected.

Improving scientific mentorship with "Open Labs" — science better

Open Labs would be a place for experienced scientists to post their open questions and curiosities — a list of the ideas they don't have time to pursue personally but wish someone would.

How public involvement can improve the science of AI

As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls have included demands from affected parties to be given a seat at the table of AI evaluation. What, if anything, can public involvement add to the science of AI? In this perspective, we summarize the sociotechnical challenge of evaluating AI systems, which often adapt to multiple layers of social context that shape their outcomes. We then offer guidance for improving the science of AI by engaging lived-experience experts in the design, data collection, and interpretation of scientific evaluations. This article reviews common models of public engagement in AI research alongside common concerns about participatory methods, including questions about generalizable knowledge, subjectivity, reliability, and practical logistics. To address these questions, we summarize the literature on participatory science, discuss case studies from AI in healthcare, and share our own experience evaluating AI in areas from policing systems to social media algorithms. Overall, we describe five parts of any quantitative evaluation where public participation can improve the science of AI: equipoise, explanation, measurement, inference, and interpretation. We conclude with reflections on the role that participatory science can play in trustworthy AI by supporting trustworthy science.

Data Everyday: Data Literacy Practices in a Division I College Sports Context

From novices to co-pilots: Fixing the limits on scientific knowledge production by accessing or building expertise

As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.