Seaborn bug responsible for finding of declining disruptiveness in science
arxiv.org
arxiv.org
Then I thought that it was a paper claiming that a bug in the seaborn plotting library in python was responsible for the decline in disruptiveness in science, which is absurd!
Finally I understood, that this is a paper that is debunking another meta paper that claimed that disruptiveness in science had declined. And this new, arxiv paper is showing that a bug in the seaborn plotting library is responsible for the mistake in the analysis that led to that widely publicized conclusion about declining disruptiveness in science. oh boy so many levels...
It's arxiv, not a press release. :)
ETA: For those who don’t click through, the paper title is “Dataset Artefacts are the Hidden Drivers of the Declining Disruptiveness in Science.” The first few sentences of the abstract are:
“Park et al. [1] reported a decline in the disruptiveness of scientific and technological knowledge over time. Their main finding is based on the computation of CD indices, a measure of disruption in citation networks [2], across almost 45 million papers and 3.9 million patents. Due to a factual plotting mistake, database entries with zero references were omitted in the CD index distributions, hiding a large number of outliers with a maximum CD index of one, while keeping them in the analysis [1].”
> floating point errors could cause the largest datapoint(s) to be silently dropped
However, the paper does not contain the string “float”, instead saying only:
> A bug in the seaborn 0.11.2 plotting software [3], used by Park et al. [1], silently drops the largest data points in the histograms.
So at the very least, the paper is silent on a key aspect of the bug.
https://www.tiktok.com/t/ZT8oG7ym6/
This is one of my favorite TikToks of all time, and you’ll see why. It goes into detail about how charts killed the Challenger crew. But the storytelling is second to none.
This is going off-topic, but Tufte's attempt to cast the problem as fundamentally one of poor data presentation is rather self-servingly tendentious, IMHO, in a way that unfairly attributes a degree of culpability to the engineers who tried to stop the launch.
The excellent video you link to, taken as a whole, supports this view, I believe.
Hypothetically, what would be the most fair argument in that situation? It’s quite remarkable that a line engineer was convinced the rocket was going to explode, even to the extent of hopping in his car with his daughter and frying to stop the launch after his company gave the go-ahead. Data presentation seems like one of the few things that could have convinced upper management that there was a serious problem.
One thing I don’t understand (possibly unrelated to your point): if there were very few launches in cold temperature in general, how could he have convinced himself that there was going to be a disaster due to the weather? If I were in his shoes, I might’ve talked myself out of it by saying "well, I suppose it’s true we don’t have much data about cold temperature launches; how certain am I that the cold weather problems till now weren’t a fluke or a non-issue?"
In other words, in most mechanical systems, a certain amount of wear and tear is acceptable. It’s only at extremes (way too cold) that it becomes a disaster. Convincing yourself that you’re certain there will be a disaster this time is a level of scientific and engineering confidence that’s hard to fathom.
In that situation I would have done everything possible to alert management that there was a high chance of an issue, but would I have grabbed my daughter and drove to stop the launch because I was 100% certain it would blow? Probably not.
In Tufte's version, the meeting in question was the tipping point where it all went wrong, while the reality is that it was the last forlorn chance for NASA to to escape, by the skin of its collective teeth, from an overdue disaster that had been years in the making. As the Rogers Commission revealed, NASA had, in an environment of over-promising and political horse-trading, developed a culture in which deviance was normalized, and it was not ready to handle evidence contrary to the semi-official dogma of shuttle flights being routine and established events.
I'm not in a position to say how Boisjoly felt so sure the launch would end disastrously, but I can make a few guesses. I think it is quite possible that he gradually became aware, and then concerned, that the O-rings did not fare well in cold weather, as the data trickled in one launch at a time. I can imagine that when it became clear, a few days before the launch, that the temperature would be below freezing, his concerns sharpened into near-certainty that things would go wrong. One does not need a theory of what, precisely, was happening to the O-rings to suppose that if below-normal temperatures led to problems, then nothing good could come from an exceptionally low one. Perhaps he was too close to the data; I can also imagine that this seemed so clear to him that he never imagined his managers - who were also engineers - not also seeing it, instead clinging to older estimates of risk. I further imagine that he was completely blindsided by the somewhat rhetorical and sarcastic response, which went something like "are we supposed to wait until July?"
IIRC, Boisjoly anticipated that the joints would fail catastrophically immediately after the boosters were ignited, and for a minute afterwards he experienced profound relief...
Despite this all coming out in the Rodgers Commission report, NASA followed the same normalization of deviance path after it became apparent that ice was damaging the tiles, which is one reason why I doubt that better charts would have stopped the launch.
https://waynehale.wordpress.com/category/after-ten-years/
Also, I recall reading somewhere that the chairman of the Columbia Accident Investigation Board, Admiral Harold Gehman, decided to conduct a test to see if the piece of foam seen hitting the leading edge could have broken it. As, at the time, it had not been decided to end the shuttle program, this was not an easy decision, as it meant sacrificing an essentially irreplaceable spare part. What finally convinced Gehman to go ahead was the fact that a great many NASA engineers firmly believed it could not possibly have been the cause.
The bug in Seaborn simply meant that the histograms that could have alerted them that something was wrong with their analysis, didn't.
And I hope the original authors tell Nature to retract their paper. It's already highly influential unfortunately.
On mobile and can’t read the rest of the paper, the impact could be massive.
There are (at the time of posting this comment) no comments raising any substantive issue with the arxiv submission itself (which ofc has to go through the peer review process of publication, and hopefully the original authors will respond / rebut this new article) - so curious why its been flagged? It’s not dead, so cannot vouch for it.
If folks in the HN community who have flagged it have done so because there are serious issues with what the paper is asserting, please comment / critique instead of just flagging it. If it’s because of the ambiguity in the title, I hope @dang and the moderators editorialize - there are some valuable comments in this thread that helped me understand what the issue is and what the bug is!
Seaborn is a wrapper around matplotlib. It's popular because it removes a lot of the boilerplate from matplotlib and is pandas-aware
For example, you call the pairplot function with a dataframe, and you just get a matrix of correlation plots and histograms. Versus matplotlib where half the documentation/search results use imperative w/ global state and the other half is OOP, and all the extra subplots shenanigans you have to decipher to get something that looks good.
It's convenience, really. The people who use seaborn don't want to dive into matplotlib because the interface is kinda a mess with multiple incompatible ways to do things. It also documents what arguments mean instead of hiding most of them in **kwargs soup. You get plots in 1 minute of seaborn that would otherwise take 10 minutes in matplotlib to write.
kind of like the difference between "trusting science" and "trusting THE science" if I had to hazard a guess
presumably he doesn't mean as opposed to "traditional ways of knowing"
I have definitely done that with benchmarks / profiles.
It’s probably even easier when the incentives encourage “the find”.
Will they own up to it and retract their broken paper that's eroding people's confidence in funding science at the highest levels? This has been an incredibly widely read and influential paper already.
I have my doubts that the authors will accept that their paper is bogus.
Particularly because the lead author landed a faculty position at a good institution based exclusively on this junk paper.
2nd. One of the ways we discover problems with data is by plotting. When the plot library has a bug that hides a problem, well shit.
3rd. They did check their own findings multiple ways. Mistakes happen. The biggest critics of scientific mistakes are often those that have never done science themselves. Its easy, and its a cheap play.
There should be a real incentive/compensation for reviewing properly and real consequences if a paper gets retracted for reasons that should have been caught in review.
In this case it's fortunate that it did get found out in the end.
* is the treatment of existing work semi-thorough (even experts don’t know everything) and fair?
* are the claims novel w.r.t the existing work? If not, provide a reference to someone who has already done it.
* can you understand the experiments?
* do the experiments and their results lead to the conclusions claimed as novel?
* does the writing inhibit understanding of the technical content?
No peer review I have ever seen or done would catch anything but the most egregious bug of this nature.
Like others, expecting a wildy different article...
So I thought the article would be about some ocean-faring insect or microbe that somehow affected scientists' mental acuity.
...nor does it have anything to do with tech companies hoarding cash by the trillions of dollars oversees instead of spending it on R&D, and even what R&D they internally produce they have no incentive to publish or productize, because virtually no new business will be more profitable than the monopoly business they already have...
Edit: Not mentioned in the abstract but it is in the main paper. Editorialised title.
> A bug in the seaborn 0.11.2 plotting software [3], used by Park et al. [1], silently drops the largest data points in the histograms.