Daniel Liévano
Illustrator
From this contributor
Pooling data points to new potential treatment for spinal cord injury
By gathering raw data from multiple labs, we identified an overlooked predictor of recovery after spinal cord injury. Many more insights remain trapped in scattered data.
We found a major flaw in a scientific reagent used in thousands of neuroscience experiments — and we’re trying to fix it.
As part of that ambition, we launched a public-private partnership to systematically evaluate antibodies used to study neurological disease, and we plan to make all the data freely available.
Simply making data publicly available isn’t enough. We need to make it easy — that requires community buy-in.
I helped create a standard to make it easy to upload, analyze and compare functional MRI data. An ecosystem of tools has since grown up around it, boosting reproducibility and speeding up research.
Incentivizing data-sharing in neuroscience: How about a little customer service?
To make data truly reusable, we need to invest in data curators, who help people enter the information into repositories.
Incentivizing data-sharing in neuroscience: How about a little customer service?
Explore more from The Transmitter
Leucovorin saga, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 15 June.
Leucovorin saga, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 15 June.
Models at the speed of thought: How AI coding is reshaping theoretical neuroscience
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Six neuroscientists weigh in on what that tectonic change may bring to the field.
Models at the speed of thought: How AI coding is reshaping theoretical neuroscience
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Six neuroscientists weigh in on what that tectonic change may bring to the field.
Writing science that humans and machines can read
Large language models are now routinely used to search, summarize and synthesize the literature at scales impossible for any individual researcher—yet scientific publishing has not adapted to that reality.
Writing science that humans and machines can read
Large language models are now routinely used to search, summarize and synthesize the literature at scales impossible for any individual researcher—yet scientific publishing has not adapted to that reality.