Cell Signaling (and much of Biology) research is in its Alchemy phase. Now, alchemy often gets a bad rap as being completely worthless, snake-oil type stuff, but this was not the case at all. Rather, individuals were working on an area about which almost nothing was known, and (more importantly) for which the key central organizing laws were not yet revealed (for Alchemy: the atomic theory of matter and chemical bonding, for Cell Signaling: how individual proteins and small molecules interact). Furthermore, the goals were lofty and almost certainly unattainable (for Alchemy: turn base metals into gold, for Cell Signaling: cure cancer), driving people to do "rush" work. It's not that the results are completely invalid, or that the experiments are useless. It's just that everyone feels like they're so close to a solution (Alchemists were way off with Phlogiston, and I'll bet Cell Signaling researchers are similarly clueless as to what really matters) that no one takes the time to step back and synthesize the results in an attempt to understand the forest from the trees.
Cell Signaling will, eventually, have its Joseph Priestley, its Dmitri Mendeleev.
From experience, the fact that 89% of these "cancer research" papers are not reproducible almost definitely has less to do with fraudulent data and so much more to do with crazy complex experimental setups that end up probing a half-dozen experimental variables all at once (without the researchers even grasping that this is going on).
Yeah, publish or perish sucks, but what sucks more is the death of basic science research. The Alchemists eventually became Chemists because they refocused on core principles (atomic theory, bonding) and forgot about the lofty goals (turn lead into gold)...
...but try telling any politician that they should decrease cancer research funding and refocus on genetics, structural biology, and evolution research. I'd love to know how they respond.
That's very optimistic, but not necessarily true (even ignoring the question of when). Priestley succeeded so well because he developed simple, reproducible experiments (expose mercury oxide to sunlight to produce oxygen etc.) that he was happy to share for no financial gain (even traveling to others' labs to help them reproduce his work ). The current environment of paywalls and competitive grants seems hardly conducive to the rise of similar figures.
When we engineer things, it's one-part-one-function. Living systems are every-part-every-function network graphs with weighted edges that are subject to dynamic reconfiguration.
A cell isn't a computer that runs "code" in the form of DNA, nor is it a "machine" as we understand it. A "structured cloud of probabilistic quantum interactions among molecular nanomachines" is a bit more accurate.
Until we conquer these meta-problems, we won't understand the cell or the genome. I don't think they can be understood classically -- and I don't mean classically in the sense of omitting quantum mechanics. (Though that's true too.) I mean classically in the sense of linear fixed-relationship cause and effect machines.
The fact that tax-payer funded research is put behind paywalls is a travesty , but it seems wrong to claim it actually hinders researchers from doing anything given.
> What do paywalls possible have to do with the reproducibility
> of results? Anyone who has access to a lab
Paywalls for publicly funded research our wrong, but the cases when it prevents people at research institutions from doing research seem to be tiny.
Now, people trying to make policy decisions could not read or evaluate the studies. If we assume they are capable of doing so in the first place( i.e. their not Lamar smith), then we have an actual argument for getting rid of paywalls. I still think the best one is its a waste of money and a tax on universities/ grants.
In other words, the profit motive dictates that making money to enrich a few individuals is more noble than sharing knowledge for the good of society.
Science is a business in the service of global capitalism. Truth hurts.
I will give you the MacArthur foundation and certain other non-profits. But most non-profits are also operated by the corporatocratic elite, like everything else on this planet.
It would be ideal if science were "funded" (read: controlled) by the public in the interest of the public good, but in reality it is controlled by the corporatocracy.
Is Goldman Sachs "funded" by the government or is the government "funded" by Goldman Sachs? The best understanding is that the government is a corporate subsidiary of Goldman Sachs, a profit-centre that Goldman Sachs invests in and controls by means of investment, and those investments reap profits.
Technically you could say that GS is funded by taxpayers, but funding implies both investment and control. So it's more accurate to say that GS funds the government, because this makes it clear where the locus of control is. Citizens are indentured servants that pay tribute to GS via taxes. Academics are indentured servants that receive money and position in exchange for their services to the subsidiary of GS called NSF.
Having said that, I think many foundations are genuinely independent of GS control, but they still can't be said to be funded by the public for the public. They are just altruistic private parties as opposed to the megalithic non-altruistic faction that is the corporatocracy.
The thing that keeps coming to mind is that it's easy to hand-wave pseudo-understanding of systems using enough jargon and vague diagrams. And if you use enough jargon and cloak things behind complex methodologies (the mechanisms to even begin investigating how cellular machinery works are enormously complex) it's easy to hide a lack of understanding. And I can't help think a similar sort of problem exists in software. The tools and terminology we have for explaining and understanding software systems leaves a lot to be desired, and one of the ways that is revealed is in the diagrams we use for describing systems. At a fairly fine grain we can use UML diagrams, but these tell us almost nothing and in complex systems simply become a giant hairball. Typically we end up describing a system as a series of layers, or as an interconnected set of services, but I can't shake the notion that such diagrams are too much like Figure 3a in the essay above than like 3b, they are the crudest sketch of a system, they don't touch on the nature or interrelation of the components.
Especially if only 11% of them are reproducible, they're only 89% invalid.
I'd go so far to say: If the reproducibility rate is 11%, you're not doing science, you're just pursuing funding.
We could just give up, or we could try to continue study of something incredibly complex. Given that we are gaining some ground, it's clearly not a useless endeavor, it's just an extremely difficult one.
What was being tested was a different lab, with different materials, trying to get the "same" results, for some definition of same. If you give 100 programmers an algorithms book, and tell them to produce code for a binary search, and only 25% of the programmers are able to make something that works, does that mean that binary search is only 25% reproducible?
If five different companies benchmark five different web frameworks for their application, and come to 2-4 different answers about which one is the 'best,' does that mean that the benchmarks are not reproducible? Of course not.
What's being highlighted here in this study is the extreme diversity of biological models. And one doesn't necessarily expect exact reproducibility in other people's hands, because we simply don't have technology to characterize every single aspect of a biological model, and it's impossible sometimes to even recreate the exact same biological context. Is something "reproducible" if it means that it replicates in 5% of other cell lines, 25% of other cell lines?
I don't follow you here. The above does not seem to be the current meaning of "reproducible":
The same person doing the same experiment is repeatable, not reproducible. And I don't believe even the repeatable bar has been met, as very few projects have funding to do the same experiment twice.
The fact that a given investigator can "repeat" his experiment have very low weight among professional scientists, because we are all human. Irving Langmuir's famous talk about Pathological Science, and especially the sad story of N-rays, is a warning to every scientist.
Part of this is that science isn't about experimentation, it's about observation. We perform experiments when possible so that we have more stuff to observe, or more controlled events.
Much of astronomy, atmospheric physics, geology, medicine, the "soft" sciences and I'm sure plenty of other "hard" fields are at the mercy of certain phenomena having sweet FA for data points. And I'm sure they all do what astrophysicists do: make sure that what we do have plenty of data for works; make our extrapolations with as few assumptions/rounding errors as we can; and revisit existing models anytime we find a new data point.
It's as scientific as anything it's simply going to take longer to sort out in some cases.
Apparently people take that crap seriously.
Cosmology on the other hand...
Reproducibility isn't about calling out people whose work isn't reproducible, it's about identifying and promoting the most robust stuff.
The current test (if I remember my philosophy of science correctly is about falsifiability - it's not science if its claims can't be disproven. From this perspective, bad experiments are still science - someone predicted that similar experiments would behave similarly, and their prediction was falsified. This is how science is supposed to work.
It gets problematic when any failure to reproduce instinctively gets explained away as experimental error on the part of the second experimenter. Even worse is when experimenters (as in this case) work to have failures to reproduce hidden from the scientific community (the authors of this study had to sign contracts that they would not identify specific failing studies before they were given the necessary data about experimental procedure.
The problem is people are not trying to reproduce results which harms the field and slows everything down.
These Amgen researcher had to sign agreements that they would not publish the results of their attempts to reproduce these experiments before they could get enough detail to attempt to reproduce them. Clearly this is not how science is supposed to work but it is exactly how businesses operate. Very sad.
This is a problem that is getting worse as funding is getting cut more, people feel they have a need to get a paper out, regardless of the results. You get positive results, make a story up about it, then run to publish it before trying to reproduce it or look further into the data. While this doesn't happen in every lab, I'm unhappy to say that I've seen this happen in many "high impact" labs.
I don't think the problem is lack of funding but screwed up incentives. When medical reaseach became focused on funding the quality of the results suffered. And if the vast majority of landmark cancer research can't be reproduced much of that money was wasted.
The solution will require a huge cultural change which may be impossible. However step one is recognizing the problem. And some efforts are already underway such as journals like PLoS which publish negative results and more recently The Reproducibility Project and Reproducibility Initiative [2,3]. Still it will be difficult.
Already funded grants are getting cut ~20% across the board. There is a ton of cuts going on right now to the NIH budget, google it and take a look.
This has been happening for years now, trying to get an RO1 (large research grant) is becoming more and more difficult and it isn't helped by the constant changing in requirements.
Every other point I agree 100% with, the culture change has to happen. Nothing is impossible, and it just takes the right people to make the right things to happen. Everyone recognizes the problem, I can't tell you how many times I hear people complaining about the same problems over and over. The problem is that they aren't taking action, and with no action, nothing is going to take place. While there are many efforts in place (my project being one of them, http://omnisci.org), they need to be implemented properly.
The same rule of startups applies to science. The ideas/concept means nothing with proper execution.
While the culture change may be slow, the academic world is having a really hard time keeping up. NIH is also fighting to stay afloat. I have a few friends who work as program officers and they really have a negative outlook on the future of research funding.
Good luck with omnisci.org this is the sort of thing that would help: open sharing of data, techniques and negative results. If this was the norm things could be very different. But one thing I have learned is it is very hard to change and organizations culture.
Oppositely, it seems logical to expect that a parade of fake and often impressive results would act as a damper to real, modest gains getting attention and funding.