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I think there's also a lot of confusion about what the scientific literature should be.

Outsiders think of journals as pre-packaged nuggets of science fact, with conclusions that you can read and trust.

Scientists who publish in the journals view them as a way to communicate a body of work of "I did this, here is the result I saw. I think it might mean this."

The difference between these two views can not be understated. Each group wants the literature to be useful to them, so that they can use it, and that's understandable.

For most areas of science, especially in the early days of that science, it's absolutely essential that scientist's view be allowed to persist. Is it better to share early, or to wait to publish until you've tried all possible things that could potentially go wrong? I think the answer is obviously that you share the early data, and what you think it means, even if you may be wrong about it.

If the goal is to advance knowledge as quickly as possible, I think that the scientist's view is probably a better idea of what a journal needs to be than what the outsiders' view is. In some fields, like fMRI studies, the field is realizing that they may need to go about things differently. And that means that a lot of the interpretations that were published earlier are incorrect. But that process of incorrect interpretation to corrected interpretation is an essential part of science.

Probably, the real problem is that "the scientific literature should be". It's not the right question to discuss today, because scientific literature is just a brief approximation of scientific knowledge and this is not what we need. Whole modern scientific system is outdated and has to be upgraded: journals, citation indexes, degrees should go away and be replaced. Science should become more formal and more digital, so that it will be easier to find and validate the results. There should be databases, search engines and digital signatures of the raw data and chains of proofs. Counting citations should be replaced with counting chains of proof produced by the scientist and included in others' works. Reliability of proof and contribution to ratings should be based on independent confirmations, which should be counted too - replication of new results should be considered an achievement too. Journals may remain, but not as a primary way to exchange scientific information - more likely, they should become the portals to the data which will link the results in databases to their human-readable interpretations.

Respectfully, I think you're missing the complications outlined in the rest of the thread. The difficulty in the biosciences and even in chemistry and some physics is that they can't really be formalized in the way that is necessary to construct these "chains of proof".

The scientific literature is an ongoing conversation anchored by rigorous experimental facts and data. But rigorous doesn't mean it's clean like a mathematical proof. In fact, most science approaches its "proofs" in quite a different way than math. For example, as far as science would be concerned P!=NP in complexity theory. We've done the experiment many times, tried different things, it's pretty much true. But it's still not mathematically proven because there isn't a formal proof.

That's not to say it's invalid to expect more rigor, or that we wouldn't all love to have "chains of proof" and databases and signatures for data etc. It's that it's simply not practical given how noisy and complex biological systems are. In contrast to math, you pretty much never know the full complement of objects/chemicals/parameters in your experimental space. You try to do the right controls to eliminate the confounding variables, but you're still never fully in control of all the nobs and switches in your system. That's why usually you need multiple different experiments tackling a problem from multiple different approaches for a result to be convincing.

Formalized systems would be great, but I don't think we're even close to understanding how to properly formalize all of those difficulties and variables in a useful way. And it may not even be possible.

I thought that statistics is the right tool to handle uncertainity? I had the impression that all the "soft" science is based on statistics. We can't prove that smoking kills people. But, given a certain confidance interval (or whatever equivalent measure for baysian statistics?), we can state that smoking is not a good idea if you want to live longer than mean expected years... Sure, some unforseen event may interfere with your experiment. But statistics should account for that, shouldn't it? Just wondering..

Statistics is also the way to look at data in the "hard" sciences. Every measurement has error, and that's how you deal with it.

However it's not a simple thing that automatically combines different studies. It takes skilled application to understand how data connects, what's comparable, what's not, etc. Traditionally, "meta-analysis" is the sub-field of statistics that combines studies. But that only combines extremely simple studies, such as high-controlled clinical trials. It's inappropriate for the complex type of data that appears in a typical molecular biology paper is a chain of lots of different types of experimental setups.

Those who don't know the body of stats, trying to reason about application, is a lot like an MBA trying to reason about software architecture. The devil is in the details, and the details are absolutely 100% important with application of statistics to data.

Amen. You can google the controversy around the StatCheck program to really dive into why stats and their applications are beyond lies and damned-lies in their falsehoods (because you can prove the lie is right). Doing something as simple as smoking causes cancer is a very very simple experiment to interpret (hard to fund/preform though). It's the pernicious little studies on 8 cells total that get messy. Preforming a lot of experiments is hard.

I knew of a student that graduated with only 8 cells of data out of his 7 years in grad school. That may sound like a small amount, but to his committee, it was a very impressive number. He (very very) basically sliced up adult rodent brains and then used super tiny glass pipettes to poke the insides of certain cells. He chemically altered these cells' insides in a hopefully intact network of neurons, shocked the cells, and then recorded the activity of other cells in the network using the same techniques. Then he preserved and stained the little brain slice so he could confirm his results anatomically. From start to finish, it took him 13 hours total, no lunch or restroom breaks, every day, for 7 years. He got 8 confirmable cells worth of recordings total.

That is a hard experiment. But due to his efforts in adding evidence we now suspect that most adult hearing loss is not due to loss of cells in the ear, but in the coordination of signals to the brain and their timing mis-match. It is not much, bu it adds to the evidence and will for sure help out people someday.

To add, he is now a beer brewer in Bavaria and quit science. This shit takes sacrifice man.

What a crazy story. I want a beer from that dude.

> I had the impression that all the "soft" science is based on statistics.

In medicine, case studies are often used for low n issues. There are too many variables for meaningful statistics to be pulled out, but a "this patient had X, we did Y, Z happened" is still a way to pass on observational information. It's recognised that case studies aren't ideal, but it's still better than not passing on information at all.

Everything can be formalized, even uncertainties in knowledge about experimental environment (e.g. by making a statement that unaccounted parameters do not influence the result - indeed, this will be challenged and proof requested and things like fire alarm affecting mice behavior should be reliably excluded). Something that cannot be formalized, cannot be proven or falsified and thus is not a science. The only difficulty may be to build the necessary apparatus, but that's doable and that's the way to fix the science.

Umm, no. Look at Godel's Incompleteness Theorem. You can prove that you will always be able to make a paradox in any formalized system of logic; at least, under our current understanding of logic. Expanding that (Godel, Escher, Bach by Hoffseader goes into it well) you then can say that any theory of the universe must have holes in it and any machine or system that attempts to formalize the observations will always come up with paradoxes. You are right, you can formalize everything (maybe, jury is still out on that, but I think so), but at the risk of then making paradoxes in the system.

I'm aware of this theorem and it has nothing to do with scientific method, it only gives us an idea of possible results of research and it does not tell us that you cannot formalize life sciences or chemistry. Indeed, there are theories that cannot be proven, but they are itself subject to formalization and research.

Whilst the experimental subjects and data collection are inherently fraught with difficulty, there's still a LOT of low-hanging fruit regarding things like automation. Many scientists use computers to write up results, to store data and perform calculations, but there's often a lot of manual, undocumented work which could easily be scripted to help those re-running the experiment. For example, running some program to produce a figure, without documenting what options were used; providing a CSV of data, without the formulas used for the aggregate statistics; relying on a human to know that the data for "fig1.png" comes from "out.old-Restored_Backup_2015~"; etc.

Such scripting is a step on the path to formalising methods. They'd help those who just want to see the same results; those who want to perform the same analysis using some different data; those who want to investigate the methods used, looking for errors or maybe doing a survey of the use of statistics in the field; those who want a baseline from which to more thoroughly reproduce the experiment/effect; etc.

The parent's list of mouse-frighteners reminds me of the push for checklists in surgery, to prevent things like equipment being left inside patients. Whilst such lists are too verbose for a methods section (it would suffice to say e.g. "Care was taken to ensure the animals were relaxed."), there's no reason the analysis scripts can't prompt the user for such ad hoc conditions, e.g. "Measurements should be taken from relaxed animals. Did any alarms sound in the previous 24 hours? y/N/?", "Were the enclosures relatively clean? Y/n/?", "Were the enclosures cleaned out in the previous 24 hours? y/N/?", etc. with output messages like "Warning: Your answers indicate that the animals may not have been relaxed during measurements. If the following results aren't satisfactory, consider ..." or "Based on your answers, the animals appear to be in a relaxed state. If you discover this was not the case, we would appreciate if you update the file 'checklist.json' and send your changes to 'experimentABC@some-curator.org'. More detailed instructions can be found in the file 'CONTRIBUTING.txt'"

I imagine it would be easy to have a git-like storage system for this information, where reproduction experiments would be a branch without the actual measurement data.

While this is interesting to speculate about, perhaps it would be best to start with something like the machine learning literature, where everything is already run computationally, and those in the field have the skills to easily scratch their own itch to improve the system so that it works for them.

Even in machine learning, how difficult would it be to get that field to adopt a unified experiment running system? It sounds like a huge engineering project that would have to adapt to all sorts of computational systems. All sorts of batch systems, all sorts of hadoop or hadoop like systems. And that's going to be far easier than handling wet lab stuff.

I think that the lack of something like this in ML shows that there's enough overhead that it would impede day-to-day working conditions. Or maybe it just hasn't been invented yet in the right form. There are loads and loads of workflow systems for batch computation, but I've never encountered one that I like.

In genomics, one of the more popular tools for that is called Galaxy. But even here, I would argue that the ML community is much better situated to develop and enforce use of such a system than genomics.

I agree that computational fields are more well-suited to spearhead such approaches, but I don't think machine learning is a good example. ML researchers are constantly pushing at the frontiers of what our current technology can do; consider that a big factor in neural networks coming back into fashion was the ability to throw GPUs at them. The choice of hardware can make a huge difference in outcomes, and some researchers are even using their own hardware (the work being done on half-precision floats comes to mind); any slight overhead will get amplified due to the massive amount of work to be computed; and so on.

Maybe a field that's less dependent on resources would be a better fit. An example I'm familiar with is work on programming languages: typechecking a new logic on some tricky examples is something that should work on basically any machine; bechmarking a compiler optimisation may be trickier to reproduce in a portable way, but as long as it's spitting out comparison charts it doesn't really matter if the speedups differ across different hardware architectures.

When the use of computers is purely an administrative thing, e.g. filling out spreadsheets, drawing figures and rendering LaTeX (e.g. for some medical study), there's no compelling reason to avoid scripting the whole thing and keeping it in git.

Check out Common Workflow Language (CWL),

> a specification for describing analysis workflows and tools in a way that makes them portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. CWL is designed to meet the needs of data-intensive science, such as Bioinformatics, Medical Imaging, Astronomy, Physics, and Chemistry.

http://www.commonwl.org

I like the idea, but who the hell is ever going to go through all of that? Yes, you made some checklist, great. But no other lab is going to go through all of that. And in your field, if you are very lucky, you may have just 1 other lab doing anything like what you are doing. It would be a checklist just for yourself/lab, so why bother recording any of it? Yes, do it, fine, but how long should you store those records that will never be seen, even by yourself? Why in god's name would you waste those hours/days just going over recordings of you watching a mouse/cell/thingy to make sure of some uncountable number of little things did/did not happen? If you need that level of detail, then you designed your experiment wrong and the results are just going to swamped in noise anyway. You are trying, then, to fish out significant results from your data, the exact wrong way to run an experiment. Just design a better trial, there is no need to generate even more confusing data that has a 1/20 chance of being significant.

The checklist is not required to be on such level of detail. It just has to exist and it has to be generic enough. It's interesting to see here example with fire alarm: to me existence of such factors is the smoking gun of potential improvements to the experimental environment. Why not excluding ALL stress factors by designing something like sound-proof cage? Needs extra budget? Probably, but how about some another unaccounted noise that will ruin the experiment? This gives us an idea of better checklist: ensure that experiment provides stressless environment by eliminating sound, vibration, smells etc.

> who the hell is ever going to go through all of that?

It's not particularly onerous, considering the sorts of things many scientists already go through, e.g. regarding contamination, safety, reducing error, etc.

> Yes, you made some checklist, great. But no other lab is going to go through all of that. And in your field, if you are very lucky, you may have just 1 other lab doing anything like what you are doing. It would be a checklist just for yourself/lab, so why bother recording any of it?

Why bother writing any methods section? Why bother writing in lab books? I wasn't suggesting "do all of these things"; rather "these are factors which could influence the result; try controlling them if possible".

> Yes, do it, fine, but how long should you store those records that will never be seen, even by yourself?

They would be part of the published scientific record, with a DOI cited by the subsequent papers; presumably stored in the same archive as the data, and hence subject to the same storage practices. That's assuming your data is already being published to repositories for long-term archive; if not, that's a more glaring problem to fix first, not least because some funding agencies are starting to require it.

> Why in god's name would you waste those hours/days just going over recordings of you watching a mouse/cell/thingy to make sure of some uncountable number of little things did/did not happen?

I don't know what you mean by this. A checklist is something to follow as you're performing the steps. If it's being filled in afterwards, there should be a "don't know" option (which I indicated with "?") for when the answers aren't to hand.

Scientists and academic institutions add to the confusion with the way they promote their work. Nobody puts language like "I did this, here is the result I saw. I think it might mean this" in a press release. It's always "Breakthrough research! Is the end of cancer in sight??"

Absolutely true, though I know of very few scientists that have been able to tamp down the game of telephone that the university's press office starts.

http://www.phdcomics.com/comics.php?n=1174

A lot of this is because of the incentives that result from grant-based funding of science. Your ability to continue to do science is contingent on your ability to find someone to pay for it; it's much easier to convince someone to pay for it if you can point to headlines that say "Breakthrough research! Is the end of cancer in sight?"

The old system of "gentleman science" (where only independently-wealthy heirs did science, as a hobby) had a lot of problems and wouldn't really be workable today, but one thing it had going for it was that the scientist could count on the funding being there tomorrow, and so had much less of an incentive to overstate their work.

> Guys, the bio side of things is incredibly complicated

I always find it amusing when physicists talk about how mind-blowing it is that at the quantum level, things aren't entirely predictable. Over in biology, that's the starting point for everything rather than the final frontier - you don't need ridiculously expensive tools to get to the point where you're finding unpredictable stuff.

You don't need a ridiculously expensive rig to find that in physics, either: https://en.wikipedia.org/wiki/Double-slit_experiment

laser pointer plus a sharp enough carbide wheel

(trained as a chemist, grad school in biostats & genetics, now mostly design experiments & clinical trials... I have physics envy, except I don't envy their funding models!)

The double slit experiment is purely random, not unknowable. There is a large difference. In Bio, we will some day conceivably know enough to get to the point where we can build up stat models like the double slit experiment. I'd say that the double slit experiment is still leaps and bounds a better starting point than anything we have in bio and only took ~2 decades to finally parse out. Bio has been chugging along since Watson for ~8 decades before we finally got CRISPR and could really do anything about DNA.

Cre-Lox systems predate CRISPR/Cas9 or CRISPR/Cfp1 approaches by quite some time. If one exists for the system you want to study, they also tend to work better. (floxing the original mouse is, however, substantially harder)

I don't think it's entirely accurate to say that conditional editing of DNA is a new thing. The ready accessibility and combinatorial possibilities, yes, but for targeted conditional knockouts, floxing mice has been a thing for about 20 years now.

Crispr is definitely not the first technology for manipulating the genome.

While we're sharing confusing pathways, here's a visualisation of _some_ of the known metabolic pathways:

http://biochemical-pathways.com/#/map/1

It is awesome to get a visual, visceral reminder occasionally about how much we know these days.