Don Kane just had to drop this earworm on me. <shakes fist in the direction of Michigan>

Dinosaurs are big. Really big. So it’s news when they find the biggest dinosaur footprint ever, together with a diverse population of other footprints.

“The tracks provide a snapshot, a census if you will, of an extremely diverse dinosaur fauna,” Steve Salisbury, the study’s lead author, told Gizmodo. “Twenty-one different types of dinosaurs all living together at the same time in the same area. We have never seen this level of diversity before, anywhere in the world. It’s the Cretaceous equivalent of the Serengeti! And it’s written in stone.”

It was found in Australia, so pardon the cheap Crocodile Dundee joke in the title.

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On a completely different note…do not read the youtube comments. I was surprised. This is a simple science report, and the comments are a horrible worthless cesspool of idiots ranting about “fake news” and, for some reason, bringing up Trump. Really, Google, get it together. The quality of youtube commentary has almost hit bottom (and I only say “almost” because I know they can get worse.)

Not because we can colonize it, or escape Earth.

Here we go again. The Science March is facing conflicts for familiar reasons: reasons that I’ve heard applied against science communication (“shut up, Myers, my way is the only way to explain science”) and against a better atheism (“shut up, Myers, atheism only means there is no god and including values is mission creep”). I always seem to end up on the side that is repeatedly told to shut up.

There are two paragraphs, one after the other, that very nicely encompass the problem with the march in just a few key words. Here’s the side that resents the inclusion of diversity as one of the goals:

At the heart of the disagreements are conflicting philosophies over the march’s purpose. In one corner are those who assert that the event should solely promote science itself: funding, evidence-based policies, and international partnerships.

Notice the word I emphasized. This is what’s familiar. There’s always a side that wants to limit what’s allowed and control what topics are appropriate, usually because they’re uncomfortable with new ideas and new approaches. This is the regressive side.

Now look at the characterization of the other side:

In another are those who argue that the march should also bring attention to broader challenges scientists face, including issues of racial diversity in science, women’s equality, and immigration policy.

Again, I emphasized an important word. This group agrees that “funding, evidence-based policies, and international partnerships” are appropriate and should be included, but also considers other topics are essential. They are happy to do the heavy lifting of representing their interests, and are not demanding that everyone explicitly follow their lead. One side is dictating what others are allowed to stand up for; the other side wants to stand up for their place in science, and are being told, in essence, to shut up.

Regressive authoritarians ruin everything.

I can sympathize with this comment from Stephani Page (who is now on the steering committee for the march).

“I wasn’t about to join something just to be a face or a Band-Aid,” Page said. She joined the committee in large part because she wanted to change the culture of science — “I was not going to carry the banner of an institution [of science] that continues to treat me as if I don’t belong there.”

Exactly. She is not telling others what should be excluded from the march, she is making an effort to include what is important to her. I agree. That’s a point of view that must be represented, and it does not detract from the message that we need more funding and more evidence-based decision-making, it strengthens it, because it brings the breadth of the culture of science forward, and increases the reach of science by representing more Americans.

Why would anyone oppose that?

For the answer to that question, let’s turn to that bastion of regressive orthodoxy, Reddit. They’re a reliable source, right? Especially when they’re condemning Social Justice Warriors.

In early February, an unofficial poll posted by one Reddit user in the site’s March on Science forum found that a majority of respondents said they wouldn’t participate in the march if organizers emphasized social justice issues. Several threads on the march’s Reddit community explicitly criticize the march for what they call “scope creep.”

Yes. We should trust the opinion of the users of a site where the most popular subreddit right now is r/theDonald. Has anyone polled Breitbart or /pol/ on this issue?

As for “scope creep”, I’ve always wondered who gets to define the scope. It always seems to be some loud, prominent, hostile jerk who demands that people don’t bring up subjects they don’t like. Speaking of which…

Others in the scientific community have expressed concerns about the march’s message becoming watered down. When, for instance, the diversity page was briefly removed from the march’s website in January, prominent Harvard cognitive scientist Steven Pinker tweeted that he was “glad to see that the March for Science Web site has removed the distractions.” Pinker had previously described the march as “anti-science” for its left-wing political tone.

And yet no one ever tells Steven Pinker to shut the fuck up, even as he arrogantly decides what is a distraction and what is not.

Here’s the bottom line: some people feel that they get to silence others in the name of keeping the “mission” pure and undiluted by subjects they don’t like. Typically, these are people who are already privileged and benefit from the status quo, and are trying to exclude people to whom these subjects are vital to their involvement in science. We have people who are arguing that diversity and inclusiveness are “distractions”, and they get far too much respect and attention. They get to claim that justice and equality and diversity are “anti-science”, and damn few people point out that that is a repulsive attitude.

No one has tried to kick Pinker out of any march — he’s free to join in with a great big sign that says nothing but “$” if he’d like. But who is giving him, and the mob of alt-righties on Reddit, the right to insist that a woman or a black man or a transgender person who wants to promote their contributions to science is diluting his sign, and must stay out of his parade?

And if they’re made uncomfortable by diversity or new perspectives, they’ve lost sight of the science. Science always pushes cultural boundaries with new ideas. If you don’t have the courage to face this novel idea that non-white, non-male people have a stake in science, then you don’t have the courage to challenge the public with ideas like climate change, or evolution, or vaccination, or any of the thousand other difficult concepts science has the duty to bring to the table.

It’s official: the Morris March for Science is officially on the list of satellite marches.

If you’re in the West Central Minnesota neighborhood, join us! There are activities planned for the day: we have our Undergraduate Research Symposium in the morning, then the march, and we end up at the Morris Theater, where CURE will be hosting a free documentary on the environment.

Ugh. I got up at 5am and tried to read a statistics paper to put myself back to sleep, and it didn’t work. Dang numbers, stop being interesting! Anyway, this paper was a meta-meta-analysis to try and dig up the causes of bias that might be causal to the reproducibility crisis in the scientific literature. Here’s the abstract from the Fanelli, Costas, and Ioannidis (2017) paper; my emphasis on some of the key points.

Numerous biases are believed to affect the scientific literature, but their actual prevalence across disciplines is unknown. To gain a comprehensive picture of the potential imprint of bias in science, we probed for the most commonly postulated bias-related patterns and risk factors, in a large random sample of meta-analyses taken from all disciplines. The magnitude of these biases varied widely across fields and was overall relatively small. However, we consistently observed a significant risk of small, early, and highly cited studies to overestimate effects and of studies not published in peer-reviewed journals to underestimate them. We also found at least partial confirmation of previous evidence suggesting that US studies and early studies might report more extreme effects, although these effects were smaller and more heterogeneously distributed across meta-analyses and disciplines. Authors publishing at high rates and receiving many citations were, overall, not at greater risk of bias. However, effect sizes were likely to be overestimated by early-career researchers, those working in small or long-distance collaborations, and those responsible for scientific misconduct, supporting hypotheses that connect bias to situational factors, lack of mutual control, and individual integrity. Some of these patterns and risk factors might have modestly increased in intensity over time, particularly in the social sciences. Our findings suggest that, besides one being routinely cautious that published small, highly-cited, and earlier studies may yield inflated results, the feasibility and costs of interventions to attenuate biases in the literature might need to be discussed on a discipline-specific and topic-specific basis.

So, in part, the reproducibility problem is cause by new researchers scrambling to get a flashy result that will get them some attention, it’s worsened if they’re working in isolation rather than as part of a team, and there are a few scientists who are ethically compromised who have been spoiling the whole barrel of apples. That all makes sense to me.

It’s hard to police against individuals with little scientific integrity — rascals are present in every field. Catching them after the fact doesn’t necessarily help, because they’ve already tainted the literature with a flash-in-the-pan compromised paper.

Scientists who had one or more papers retracted were significantly more likely to report overestimated effect sizes, albeit solely in the case of first authors. This result, consistently observed across most robustness tests, offers partial support to the individual integrity hypothesis.

Catching a scientist who publishes bad data is already severely punished, so I don’t think that one is an avenue for improving the reliability of papers. It shouldn’t be ignored, obviously, but the other observations might lead to more improvement.

The mutual control hypothesis was supported overall, suggesting a negative association of bias with team size and a positive one with country-to-author ratio. Geographic distance exhibited a negative association, against predictions, but this result was not observed in any robustness test, unlike the other two.

Collaboration is good. In the days when I was in a large lab, it was always a little suspicious when someone suddenly plopped a whole, completed paper down in the lab meeting and announced that they’d finished the experiment, and by the way, would you like to be an author on the paper? I always turned those offers down, because a co-authorship ought to be the product of ongoing involvement in the work, not some attempt at fishing for external approval. But more cooperation and vetting of each other’s work ought to be a general hallmark of good science.

I’m not in a big research lab anymore, but I still try to get that across in student labs. There’s always someone who objects to having to work with those other students and wants to do their lab projects all by themselves, and I have to turn them down and tell them they have to work in teams. They probably think it’s so I’ll have fewer lab reports to grade (OK, maybe that’s part of it…), but it’s mainly because teamwork is an essential part of the toolkit of science.

And now I’m getting confirmation that it also helps reduce spurious results.

The biggest effect, though, is associated with small study size.

Our study asked the following question: “If we draw at random from the literature a scientific topic that has been summarized by a meta-analysis, how likely are we to encounter the bias patterns and postulated risk factors most commonly discussed, and how strong are their effects likely to be?” Our results consistently suggest that small-study effects, gray literature bias, and citation bias might be the most common and influential issues. Small-study effects, in particular, had by far the largest magnitude, suggesting that these are the most important source of bias in meta-analysis, which may be the consequence either of selective reporting of results or of genuine differences in study design between small and large studies. Furthermore, we found consistent support for common speculations that, independent of small-study effects, bias is more likely among early-career researchers, those working in small or long-distance collaborations, and those that might be involved with scientific misconduct.

More data! This is also helpful information for my undergraduate labs, since I’m currently in the process of cracking the whip over my genetics students and telling them to count more flies. Only a thousand? Count more. MORE!

The paper does end on a positive note. They’ve identified some potential sources of bias, but overall, science is in fairly good shape.

In conclusion, our analysis offered a “bird’s-eye view” of bias in science. It is likely that more complex, fine-grained analyses targeted to specific research fields will be able to detect stronger signals of bias and its causes. However, such results would be hard to generalize and compare across disciplines, which was the main objective of this study. Our results should reassure scientists that the scientific enterprise is not in jeopardy, that our understanding of bias in science is improving and that efforts to improve scientific reliability are addressing the right priorities. However, our results also suggest that feasibility and costs of interventions to attenuate distortions in the literature might need to be discussed on a discipline- and topic-specific basis and adapted to the specific conditions of individual fields. Besides a general recommendation to interpret with caution results of small, highly cited, and early studies, there may be no one-size fits-all solution that can rid science efficiently of even the most common forms of bias.

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Fanelli D, Costas R, Ioannidis JPA (2017) Meta-assessment of bias in science. Proc.Nat.Acad.Sci USA doi: 10.1073/pnas.1618569114.

He’s still battling dualism, as seen in this New Yorker profile. He’s still arguing with Chalmers, and he’s still going strong…with some exasperation.

Despite his affability, Dennett sometimes expresses a weary frustration with the immovable intuitions of the people he is trying to convince. “You shouldn’t trust your intuitions,” he told the philosophers on the Rembrandt. “Conceivability or inconceivability is a life’s work—it’s not something where you just screw up your head for a second!” He feels that Darwin’s central lesson—that everything in biology is gradual; that it arrives “not in a miraculous, instantaneous whoosh, but slowly, slowly”—is too easily swept aside by our categorical habits of mind. It could be that he is struggling with the nature of language, which imposes a hierarchical clarity upon the world that’s powerful but sometimes false. It could also be that he is wrong. For him, the struggle—a Darwinian struggle, at the level of ideas—continues. “I have devoted half a century, my entire academic life, to the project, in a dozen books and hundreds of articles tackling various pieces of the puzzle, without managing to move all that many readers from wary agnosticism to calm conviction,” he writes, in “From Bacteria to Bach and Back.” “Undaunted, I am trying once again.”

There’s something about this concept, that the mind is a product of the physics, chemistry, and biology of the brain, that some people cannot accept. But then I have an equally strong intuition that it is, so it’s hard to fault people for wanting to disbelieve it; I can still fault them for ignoring the growing evidence for the purely material basis of the mind, the absurdity and poor quality of the evidence for dualism, and the inability to come up with a mechanism, even an outline of an idea, for how dualism would work.