Nathaniel Comfort, “Genes Are Overrated”: Mukherjee gives us a Whig history of the gene, told with verve and color, if not scrupulous accuracy. The gene, he tells us, was first described by the Augustinian friar Gregor Mendel, in the mid-19th century. Tragically, no one noticed—not even the great Charles Darwin. “If Darwin had actually read” the reference […]
Continue reading →Kent clearly does not like open access. He thinks it is bad for scholarly publishing – that it undercuts publisher’s ability to make money, and, more importantly to him, it erodes the quality of the products they produce (which is why we all find it so ironic that his first job at AAAS is to […]
Continue reading →As my own review of Nicholas Wade’s book suggested, his treatment of genetics has many deep and fundamental problems. And as my blog post noted, many others have called some of those out. I want to call out here a particularly clean such account, which came from evolutionary biologist Michael Eisen a few weeks ago. He cuts […]
Continue reading →Below find #6 in my Best of Neuron Culture Moving Party — a run of 10 of my favorite posts from the blog’s tenure at WIRED, posted as I move the blog here. This piece, originally published in May 2011. looks at the attempt one scientist makes to free his father’s papers from the calcified structures […]
Continue reading →In perhaps the biggest action of the open-science movement, a list of researchers 1,600-strong and growing is petitioning against the policies of Elsevier, one of the largest science journal publishers. Neuron Culture blogger David Dobbs reports.
Continue reading →Should you be able to read research you’ve helped to fund? A few years ago, Congress decided this was a good idea — but a new bill threatens a year-long grace period of open access. Neuron Culture blogger David Dobbs explains why.
Continue reading →How is a box score like a genome sequence? This starts a wee slow, but go with it: Noted computational biologists James Fraser (UCSF) and Michael Eisen (Berkeley) explain how obsessions with baseball stats led them — and can lead others — to be data-heavy biologists. First lesson, for example: Predictions are stronger based on (open) […]
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