I’ve seen both sides: labs that make real efforts to share reusable code/data, and others where openness was discouraged by financial interests or competitive advantage.
Industrial work is valuable and gives us many tools we use, but it shouldn’t come at the expense of scientific transparency.
Want to explore connectivity & projection patterns yourself, like we do here? We released brain_street_view to let you pick any injection site in the Allen Connectivity Atlas and visualize where it projects in your favorite region of interest: github.com/Julie-Fabre/brain_street_view
1/8. New preprint!
Using fUSi in head-fixed mice🐭, we found that arousal events trigger a brain-wide wave of activity 🌊🧠.
Surprisingly, this pattern was preserved during opto manipulations of the locus coeruleus, pointing to a minor role for noradrenergic tone.
www.biorxiv.org/content/10.6...
⚠️ One week left to apply to our postdoc opening at the Paris Brain Institute ! If you have a background in neuroscience 🧠 , looking for a vibrant interdisciplinary environment in the heart of Paris, come join us !!
All details below ⬇️ ⬇️.
How does blood flow relate to brain activity? We discovered that it reflects two neural populations affected oppositely by arousal. Together, they explain neurovascular coupling in all brain regions and brain states!
Out today in Nature: rdcu.be/fdC2A
@uclbrainscience.bsky.social
This reflects the importance of transparency IMHO. If data, code, parameters, preprocessing choices, etc. are shared, researchers can understand where differences come from, test robustness, and gradually converge toward better practices.
Without that, disagreements stay hidden inside labs.
Interesting article on something I think is very relevant to open science: several teams analyzed the same ephys dataset and got quite different answers.
Not because the analyses were bad, but because this kind of work involves many analysis choices, and those choices can change the result.
