> Something is really wrong in science, and this is just the beginning. >But since no PI ,including me, wants to jeopardize their career, I fear things are going to go off a cliff.

I think part of the decline in biology specifically may be the rapid growth of NIH funded research in the 90's due to the success of molecular biology and the necessity of many warm bodies at the bench, which was followed by the NIH budget "crunch" of the last decade. This combination dramatically increased the competitive nature of getting grants and high-impact papers. In this environment, those who are careful in their claims are selected out of the career pool (not all obviously, but enough to affect the proportion).

A couple of my experiences: I had one paper in a mid-range journal where we found a very interesting biological pattern in the data, which we discussed but ultimately decided to dismiss because there were confounding factors. A very well known lab took the exact same data, did a couple minor experiments to justify dismissing our concerns, and published in a top-tier journal.

In another case, I reviewed a paper from a VERY famous lab and their own metrics proved that 95% of the data was noise. They simply removed the offending metrics and published in a different, equally high-profile journal.

> we found a very interesting biological pattern in the data

Isn't it related to p-hacking? Get lot of data, search for a pattern then publish some theory the pattern would "prove". Instead of having a theory first, setting up experiments which can disprove it then analyzing the data from those experiments.

The observed pattern exactly fit into a previously established hypothesis and was totally unexpected. I did not slice and dice the data with multiple statistical tests. The only way it could have been related to p-hacking is that I am familiar with a bunch of biological theories and could recognize data patterns that are compatible with any of them. So, maybe a Bonferroni-type correction of 30-70x (i.e., number of theories my internal neural net has been trained on)?

I'd say if they were explicit about using the data to construct a theory, it's okay, no?

> EDIT: If any rich silicon valley investor is reading this, please invest in a companies trying to automate and standardize research lab work.

This. Take gut microbiome research. Hardly any of it is reproducible. Many labs find different microbial abundance between CFS/ME/IBS and controls but every lab finds a different set of microbes to blame (in addition to the fact that the overlap between control groups is terrible). I do not understand how folks can publish this work. At the start of the research, I can understand, but we are a decade into this modality.

And then there are the consumer microbiome companies like ubiome. I am an early adopter and have spent thousands analyzing the results they sent me but i have yet to find anything repeatable or reliable. Even looking at their smartgut test, I do not understand how they can peddle this stuff. I have yet to find a doctor who is using their results. Until the reproducibility problem is solved, it seems like everyone is just playing in their sandbox and science can only go so far operating in this fashion. I wish these groups (ubiome, american gut, thryve, etc) would collaborate to resolve these descripencies.

>>Take gut microbiome research. Hardly any of it is reproducible.

Totally agree, and even one level up is unreproducible, or done in ways that are psuedoscientific - nutrition and effects of diet on humans. So many "ubiome"-like places doing blood testing and other diet-related "science" based on complete nonsense.

What do you mean by repeatable?

The gut microbiome is in constant flux; it constantly changes every day.

A sample sent to 3 different companies on 3 different days would have 3 different results, even if their analysis was perfect.

I've not had my stools analyzed. If I were to, the biggest thing I would look for would be the level diversity in my microbiome, as opposed to individual strains.

I only have minor knowledge about this, so maybe I am missing something.

> What do you mean by repeatable?

This is a complicated question but let's break it down to the simplest problem i can think of. Suppose I do a study comparing the gut microbiota of CFS patients compared to controls. Then, another group does the same study. The control group better have reasonable overlap. If they do not, then both labs should process the raw samples and see if they get reasonable overlap. If not, start here to fix.

> The gut microbiome is in constant flux; it constantly changes every day.

It depends on who you talk to which is somewhat my point. Eric Alm's lab showed that the gut microbiome is relatively stable over time (they sample daily for over a year). Other labs have shown it varies daily. My own lab has shown that it varies greatly depending on where you take the sample (stool is actually quite heterogeneous in terms of bacterial populations). You could argue how you define "same" as a function of a scale, in this case taxon level. But the problem still remains.

> A sample sent to 3 different companies on 3 different days would have 3 different results, even if their analysis was perfect.

If the analysis was "perfect" (whatever that means), then why are they different. Can we model the differences?

The real truth is that stool is quite heterogenous in terms of bacterial populations. Its quite easy to get an OOM difference between abundances in the same sample.

> I've not had my stools analyzed. If I were to, the biggest thing I would look for would be the level diversity in my microbiome, as opposed to individual strains.

Diversity on which level though? Most consumer sequencing companies do not go the strain level. Most are genus and you get about 50-75% of the species.

> I only have minor knowledge about this, so maybe I am missing something.

Appreciate your honesty. Science has done a sub par job (in my opinion) of emphasizing to the public that we (the community) understand what is going on. All we really know now, broadly speaking, is that the gut microbiota is different when compared to healthy controls be we really have no idea why. That doesnt mean we dont understand the metabolite profiles, but the simple fact that Provetella has been implicated in both sickness and in health should indicate that reproducibility would be very insightful.

I upvoted your comment, but want to add a compliment. This kind of dialogue and knowledgeable comment are why I keep reading HN, often starting with the comments.

Thank you for addressing the previous comment in a thorough manner while not talking down to him or the rest of us who are smart and interested but not educated in the specifics of this domain.

Thanks. In my mind, what gets done by berating someone in this context. The poster said they don't really know much so I assume they are part of the general public. Its up to science to educate the public on these matters.

Also, I have run into quite a few microbiome zealots, all are what i call armchair scientists -- no college background and think they know everything from reading abstracts on pubmed. The kind of arguments they put up cannot be well addressed on the internet because they can find some study which proves their point. Which this proves my point -- the studies are inconsistant.

I welcome thoughtful and engagine conversation about the matter and have problem being told I am wrong when evidence is presented.

Thank you for the detailed response. I am part of the general public, so to speak, but am also interested in this research because I have a certain disease. I am treating this disease with diet modification, which seems to be working. (The Specific Carbohydrate Diet.) However, it seems like there are many questions as to why it works that is very much not understood.

> The control group better have reasonable overlap.

I wonder if there is enough understanding of what makes a "healthy" gut microbiome in order to properly assess what "overlap" means. Isn't a microbiome a complex system with many moving parts? For example, a study of successful businesses could include many successful businesses that are completely different from one another. Comparing these to "unhealthy" businesses would probably not be very useful.

It seems like you're answering this question by saying the different labs should process the same samples, to at least see if the same samples are returning the similar results. Is that right?

I wonder if there is a resource for further learning on this topic. Reading scientific studies, as you say, provides only limited value to a layman. The number of people with diseases affected by the gut microbiome is very, very large.

>I've already had to have a meeting with several postdocs who said something similar to I don't trust 90% of published articles.

GOOD. They should feel that way. Science is in a state of crisis and that 90% number is an accurate reflection of reality. In the study mentioned below 90% of landmark cancer research papers could not be reproduced. Someone will surely chime in that their own favorite branch of science is better, but better than 90% failure is a really low bar.

http://www.reuters.com/article/us-science-cancer/in-cancer-s...

Yes and no. In many ways the probelems we are addressing are much more complicated than were being addressed 100 years ago. Often advanced statistical methods need to be used to make inferences. We are often looking for 2% signal over noise, trying to understand complex dynamical systems. Even without anything nefarious, and careful execution of rigorous methods, unmeasured 3rd variables can be different across study samples, and you can't measure everything. Even if you could, you won't have the statistical power. Random assignment is nice, but not every field can do it ethically. Even with random assignment, there is no guarantee that the effect of unmeasured 3rd variables will average to zero. That is. Science is harder than many give credit for.

That said, the competition and incentives in science are perverse. Peer review quality has declined, if only because everyone is in a rat race to survive, and being a peer reviewer does not do much for getting you a job or tenure.

A lot of the problem is that people pay attention to the hype of news articles rather than the particulars of the papers.

In the one you link, they don't describe what they mean by "hold up." It turns out that the only definition that those authors give is in the caption of Table 1

http://www.nature.com/nature/journal/v483/n7391/full/483531a...

>The term 'non-reproduced' was assigned on the basis of findings not being sufficiently robust to drive a drug-development programme.

Now, this is a completely different thing from reproducing in terms of getting the same science. Driving a drug program is not biology, it means does this phenomena that you found in one thing apply to the vast majority, so that a pharma like Amgen can run a trial, put all stage 4 patients on it, and have it work on enough people that the trial has good enough stats.

However, that's really not how cancer works. Cancer is extremely diverse. If somebody is publishing a "landmark" paper (whatever their definition is of that), then they are publishing something new and unexpected most likely. That means that the landmark finding is almost certainly not going to generalize to enough that a large pharma can run an uninformed clinical trial and throw it all patients without pre-selecting for patients where it will most likely work.

Further, that silly thing is just an editorial. It's a plea for science journals to only publish things when it's found in the majority of cancers, or something. If we did that, we'd know far less about cancer, because it's treating something that's actually diverse as a monolithic entity.

Are you going to get a particular cell line to have exactly the same gene expression as another lab? Probably not, if it's a tricky to grow cell line. Are you going to get cancer drug sensitivities to be all the same? Probably not, if you're testing when the cell lines are growing at different densities.

People really should distrust that any individual data point from a particular model biological model is going to generalize, especially for difficult biological models. That doesn't mean we should stop publishing, because if we do, we're not going to be able to figure out what's actually driving those differences.

Physics is doing just fine, thank you.

Edit: but I agree most medical/biology/pharmacy papers are plagued with issues, mainly because the incentives are screwed up and there's more noise overall.

Of cource, science itself isn't failing, and hopefully it isn't science itself that people are losing faith in.

Rather, performing proper science is despressingly difficult. So much that we would like to believe is unbiased can so easily be biased without us knowing it.

Actually, I'm glad for this lack of trust in ourselves. Self-doubt is the beginning of wisdom, so they say.

Just like communism, real science is infallible.

Seriously, is there a threshold? If this continues for, say, a hundred years, will science have failed then? A thousand?

What would it even mean for “science” to have failed? Science is just testing your assumptions against reality to gain knowledge.

If you mean the academic institution of, uh, Big Science or whatever you’d call it—sure, it has failed in its current incarnation to impose the rigour for reproducibility, and incentive toward reproduction. But academic institutional cultures evolve in response to noticing problems like that. Like software evolves in response to discovering vulnerabilities. A given institutional culture (like a given piece of software) won’t have the same vulnerability twice; if Big Science is still having crises 1000 years from now, they will be new and different crises.

The thing is, even fallible science is better than non-science. The only thing that has consistently put understanding of the universe forward is science. I mean, if we don't trust the scientific method, what's the alternative? Bible?

> EDIT: If any rich silicon valley investor is reading this, please invest in a companies trying to automate and standardize research lab work.

This is how one knows publicly funded research is fundamentally broken. Sloppy research is fueled by the push to publish, which is fueled by too many people climbing over each other for too few spots.

Is it a bureaucratic problem, perhaps? I don't want to recapitulate the many (and far better informed/experienced) discussions of this on HN in the past, but the economics of peer review seem really broken. There's a natural temptation for reviewers to rubber stamp things both favorably and unfavorably depending on circumstances, but it doesn't seem efficient to have experienced scientists spending valuable think time reviewing things like basic methodology - naturally they're most interested in the new claim, and secondarily interested int heir own academic reputation depending on how they're cited and so on.

One possible way out of this (which may have been tried and rejected already for all I know) could be to take up the approach of legal journals, which are usually edited by students rather than professors. There are downsides to this as well - a fetishization of formatting and a busywork demand for citations on even the most prosaic claims, not to mention a tendency to proliferation, but there's an endless supply of eager reviewers who are highly motivated to do good work to burnish their academic and subsequent practice credentials. Being an editor of the Harvard Law Review can get you into all sorts of high-powered jobs, for example - though so, depressingly, can putting your name on a ghost-writer's monument to your commercial vanity, so that shouldn't be the only reason to do it :-/

I agree with the necessity for automation, and maybe some sort of tokenization so that people can save time by being able to rely on trusted certifications. Remember when Creative Commons seemed like an idea whose time had come and a lot of art work was appearing with standardized icons that quickly summarized the scope of the legal rights being asserted over the work's copyright? After a great start that model seems to have run out of steam, perhaps because copyright as a political has been sidelined by more fundamental concerns - at any rate, I had anticipated seeing the CC 'branding' much more widely by now than I do. But similar shorthand cor certifications of statistical rigor or procedural validity might help to make scientific publications more reliable, as well as accessible to lay readers.

> Is it a bureaucratic problem

No, in my opinion the problem is that all the low hanging fruits in science have been collected and our old way of doing science does not work anymore. 20 or 30 years ago it was "easy" for a single PhD student to discover something completely new and groundbreaking. Of course it still happens today, but 20-30 years ago it was way easier to get truly new results as a small group of researcher with a low budget.

In the very beginning of science you did not even need anything. Just looking art the stars and thinking about it was enough.

But nowadays? We have to build a LHC with several 1000 people to do basic science! As a PhD student you have to spend 3 years just reading and learning "old science" in order to reach the current boundaries of scientific area.

The current system of "we give this topic to a PhD student and he will figure out a solution in exactly 3 years" just does not work anymore. In order to advance science we need much larger and much more connected groups who grind away on a single problem together for 5-10 years to make truly groundbraking scientific advancements.

Thanks for this succinct explanation. It bears many similarities to problems of politics and governance that I wrestle with.

I wonder to what extent crowd work can be effective for making real progress; I've been impressed by things like FoldIt and astronomical classifiers (as opposed to the BOINC distributed processing approach) but I don't know to what extent those result in real progress vs useful-but-limited stamp collection. I am very interested in the possibilities for gamification but it seems like there's a big gap between the empirical data collection/space mapping aspect and actual theoretical development of the kind done at the Santa Fe institute or the IAS.

While peer review has its problems, it's hardly the biggest problem and there are systemic issues that peer reviewers can't see. E.g. one of the biggest issues in science is publication bias and the lack of publishing "negative" findings. You don't see as a reviewer if the person submitting the study has three other studies laying on his desk that he'll never publish.

Reproducibility is a major problem and certainly has levels to it such as starting materials [1], details within protocols and methods section of papers, unaccounted variables such as room temp., technique and data interpretation as often raw data is inaccessible.

[1] https://www.linkedin.com/pulse/scientific-reproducibility-re...

Would a 3rd party observersing company would help? For example a company that's like the UN and visits the lab during the experiments and says: this lab follows the protocols.

What's your opinion on https://www.transcriptic.com/ (not affiliated)

not OP, but can give you an industry opinion (I design laboratory automation work-cells like theirs for a living :).

They're one of many core labs that offer similar "assay as a services". I like their vibe and attempt and a differentiated offering.

Transcriptic's data capabilities and web experience may be enhanced compared to industry standard, but overall their offering it's a pretty common tech to the industry. They make a big deal of their "cloud" capabilities. (personally: It seems like a nice attempt but doesn't move the needle much). The trouble with running core labs is every user wants something different. It's pretty rare to be able to put together a set of experiments that different customers across the industry will want to us... and the optimum protocols/libraries themselves are constantly changing. Regarding Transcriptic in particular, it's unclear how much tweaking users can do to their protocols, which many rule out it being acceptable to many researchers.

Many larger companies, universities and hospitals get similar robotic work-cells custom designed for their research centers (and have been for 20+ years).

That all said, there are real barriers to automation in this space, most of which is the researchers don't know it, don't trust it, or can't afford it. It is remarkable to me the number of labs that run on sneaker-net.

Thank you for the insight!

> But since no PI ,including me, wants to jeopardize their career, I fear things are going to go off a cliff.

Would speaking about this hurt your career? How so?

It’s mostly just branches of biology that have the worst problems. Medical research, specifically.

And the bloated costs mostly originate from the preservation of baseline ethical standards and safety practices applied to operating procedures.

Trying to squeeze bogus statistics out of in vivo blinded placebo trials has always been this brute force hack of russian roulette with terminal diseases, and an effort to guesstimate someone’s best hunch, without knowing what’s really happening, and praying for something obvious to jump out of the woodwork. What a clueless hack. But then again, how else to approach mysterious phenomena when left with nothing to go on but instinct and intuition?

Even more bogus is how we pin all our hopes on pills. How many pills can you really take? And with the entire world bathed in a bizarre cocktail of industrial chemicals, why is the only solution to medicate after the fact?

Wouldn’t it make sense to solve the modern problems we see, by controlling for the miasma of industrial chemical pollutants adulterating our bodies from cradle to grave? Or is that what’s wrong with science? That we pretend that everything we study is a spherical cow in a vacuum?