A New Tool for Recording and Scaling Laboratory Expertise
How and When to Involve Crowds in Scientific Research | James Evans
The lone academic in a basement lab is an endangered species. Years ago I asked Paul Ginsparg, who founded arXiv, what features his machine-learning filter used to flag speculative submissions. The top three: single-authored, submitted on a weekend, and heavy citation of Newton and Einstein. Science is a contact sport now. Marion Poetz and Henry Sauermann's How and When to Involve Crowds in Scientific Research (https://lnkd.in/g5pwVbfD) is the field manual. What crowds can do: Volume. Zooniverse mobilizes 2.7 million people to classify galaxies and court records. Galaxy Zoo's co-founder hand-classified 50,000 galaxies in one week before concluding that isolation would break him. Reach. NASA spent years failing to predict solar flares, then broadcast the problem. The winner was a semi-retired radio engineer in rural New Hampshire who swapped satellite data for radio data. Experience. Patients and caregivers asked to generate research questions produced 826 that scientists had missed, including the link between aging and wound healing. Bench-to-bedside runs backward. One caution the book underplays: an open call is not an inclusive one. Participation costs in time, money, and access screen people out. Self-selection can narrow the very diversity that makes crowds thrive. As AI floods the labor supply of routine, homogeneous cognition, human crowds become more valuable, not less. Machines process. People notice what nobody thought to ask. Check out my review @ https://lnkd.in/gkzvWrRC and the book @ https://lnkd.in/g5pwVbfD!
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.
LabFolio
The most complete and granular data collection of scientist activities and the platform to share and collaborate with other scientists.
Ink & Switch
An independent research lab exploring the future of tools for thought.
A Research Lab for Open Source ✸ Software Stewardship Lab
Today, we're launching a world-class research lab dedicated to Open Source sustainability with a team of some of the most experienced people in the world.
Scientific sleuths come in from the cold
Research integrity investigators are starting to organize, but the field, and the people, remain idiosyncratic
Dispatch
An occasional update on the lab’s latest findings, appearances, and happenings.
Metagov x Future of Science Seminar - Discourse Graphs with Matt Akamatsu
Arduino Science Journal
Transform your mobile device into a science tool! Use sensors to record data, document your observations, and experiment like a real scientist.
Launching openRxiv Labs - openRxiv
Over the last thirteen years, bioRxiv and medRxiv have grown into widely used infrastructure for rapid research sharing in biology and medicine. The reliability and researcher-first values that have defined these platforms since their founding remain central to how we operate, and building on the trust, partnerships, and community relationships researchers worldwide depend on is…
Artificial intelligence and illusions of understanding in scientific research
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
The Robyn Dawes Institute for the Improvement of Science
Making research quality visible so everyone can act on reliable evidence.
Managing scientific knowledge for policy on the ATmosphere - Mathew's newsletter
Two types of Atmosphere toolkit are needed if ATScience is to help scientists do science and better communicate it to other audiences: one serving the researchers and their teams, the other focused on aggregation and synthesis.
