To Get More Out of Science, Show the Rejected Research(nytimes.com)
nytimes.com
To Get More Out of Science, Show the Rejected Research
http://www.nytimes.com/2014/09/19/upshot/to-get-more-out-of-science-show-the-rejected-research.html
15 comments
"have been to week-long conferences where every single paper was either a crazy theory or a negative experimental result"
That sounds incredibly fun. The crazier and zanier the better. Were there awards for 'Most likely to actually be random words', those are my favorites.
Also, I just started in neuroscience from an EE/Physics background. You would be AMAZED at what gets published. The rigor of physics is just.... incomprehensible in bio. Granted, yes, the experiments are 'squishy' and hard to quantify. You run a gel, and you get signal or not, counting it hard enough as is, let alone trying to measure anything. Still, the papers we read.... It's like they tried to do math, but gave up halfway through. I've seen papers where they start out with 2 decimal accuracy and errorbars in Figure 1, drop the errors completely in figure 2, and then just go to counting by 10s in Figure 4. Somehow, they derive significant results that journals will publish with only 20 cells or something. Mind you, nowhere do they quote the temperature, the pressure, what their saline solution actually is, how many failure experiments they ran, etc. The physicist in me is just ... pain. And oh god, the egos. Papers will have just one author on them, the PI, and when you look at the webpage, 20 faces show up for the postdocs alone in that lab. I have no idea where they get these fools, but they seem to be in large supply.
Feynman had a lecture (please help, I can't find the link) about what it takes to get a rat to not smell food behind a door. Turns out, it's a lot of work. I've been in smell labs that just plain ignore the research, even when they know it's there. "It's too much work and funding, besides, the data should suss out the real mechanisms." The McGill study earlier this year on pain in male rats being modulated by the sex of the experimenter? Yep, just plain ignored as well.
I'll share some links here on the issues in bio, it's a lot.
https://en.m.wikipedia.org/wiki/John_P._A._Ioannidis
http://www.economist.com/news/briefing/21588057-scientists-t...
That sounds incredibly fun. The crazier and zanier the better. Were there awards for 'Most likely to actually be random words', those are my favorites.
Also, I just started in neuroscience from an EE/Physics background. You would be AMAZED at what gets published. The rigor of physics is just.... incomprehensible in bio. Granted, yes, the experiments are 'squishy' and hard to quantify. You run a gel, and you get signal or not, counting it hard enough as is, let alone trying to measure anything. Still, the papers we read.... It's like they tried to do math, but gave up halfway through. I've seen papers where they start out with 2 decimal accuracy and errorbars in Figure 1, drop the errors completely in figure 2, and then just go to counting by 10s in Figure 4. Somehow, they derive significant results that journals will publish with only 20 cells or something. Mind you, nowhere do they quote the temperature, the pressure, what their saline solution actually is, how many failure experiments they ran, etc. The physicist in me is just ... pain. And oh god, the egos. Papers will have just one author on them, the PI, and when you look at the webpage, 20 faces show up for the postdocs alone in that lab. I have no idea where they get these fools, but they seem to be in large supply.
Feynman had a lecture (please help, I can't find the link) about what it takes to get a rat to not smell food behind a door. Turns out, it's a lot of work. I've been in smell labs that just plain ignore the research, even when they know it's there. "It's too much work and funding, besides, the data should suss out the real mechanisms." The McGill study earlier this year on pain in male rats being modulated by the sex of the experimenter? Yep, just plain ignored as well.
I'll share some links here on the issues in bio, it's a lot.
https://en.m.wikipedia.org/wiki/John_P._A._Ioannidis
http://www.economist.com/news/briefing/21588057-scientists-t...
"All experiments in psychology are not of this type, however. For example, there have been many experiments running rats through all kinds of mazes, and so on--with little clear result. But in 1937 a man named Young did a very interesting one. He had a long corridor with doors all along one side where the rats came in, and doors along the other side where the food was. He wanted to see if he could train the rats to go in at the third door down from wherever he started them off. No. The rats went immediately to the door where the food had been the time before.
The question was, how did the rats know, because the corridor was so beautifully built and so uniform, that this was the same door as before? Obviously there was something about the door that was different from the other doors. So he painted the doors very carefully, arranging the textures on the faces of the doors exactly the same. Still the rats could tell. Then he thought maybe the rats were smelling the food, so he used chemicals to change the smell after each run. Still the rats could tell. Then he realized the rats might be able to tell by seeing the lights and the arrangement in the laboratory like any commonsense person. So he covered the corridor, and still the rats could tell.
He finally found that they could tell by the way the floor sounded when they ran over it. And he could only fix that by putting his corridor in sand. So he covered one after another of all possible clues and finally was able to fool the rats so that they had to learn to go in the third door. If he relaxed any of his conditions, the rats could tell.
Now, from a scientific standpoint, that is an A-number-one experiment. That is the experiment that makes rat-running experiments sensible, because it uncovers that clues that the rat is really using-- not what you think it's using. And that is the experiment that tells exactly what conditions you have to use in order to be careful and control everything in an experiment with rat-running."
From: http://neurotheory.columbia.edu/~ken/cargo_cult.html
The question was, how did the rats know, because the corridor was so beautifully built and so uniform, that this was the same door as before? Obviously there was something about the door that was different from the other doors. So he painted the doors very carefully, arranging the textures on the faces of the doors exactly the same. Still the rats could tell. Then he thought maybe the rats were smelling the food, so he used chemicals to change the smell after each run. Still the rats could tell. Then he realized the rats might be able to tell by seeing the lights and the arrangement in the laboratory like any commonsense person. So he covered the corridor, and still the rats could tell.
He finally found that they could tell by the way the floor sounded when they ran over it. And he could only fix that by putting his corridor in sand. So he covered one after another of all possible clues and finally was able to fool the rats so that they had to learn to go in the third door. If he relaxed any of his conditions, the rats could tell.
Now, from a scientific standpoint, that is an A-number-one experiment. That is the experiment that makes rat-running experiments sensible, because it uncovers that clues that the rat is really using-- not what you think it's using. And that is the experiment that tells exactly what conditions you have to use in order to be careful and control everything in an experiment with rat-running."
From: http://neurotheory.columbia.edu/~ken/cargo_cult.html
Thank you!
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> at the very least we should expect that from the average publication, and be suspicious of any experimental paper that does not include some description of the blind alleys.
In mathematics, being suspicious might be a bit much, but it's a very valuable (for your audience) habit to have. For example, the book Introduction to the Theory of Computation by Michael Sipser does this a lot: it often explores avenues that lead to dead ends before arriving at the correct proof. This is the way mathematicians work, and it's usually hidden from papers or other textbooks. I'm guessing that this is either for economy or because authors want to look like the first thing they tried was the right one.
In mathematics, being suspicious might be a bit much, but it's a very valuable (for your audience) habit to have. For example, the book Introduction to the Theory of Computation by Michael Sipser does this a lot: it often explores avenues that lead to dead ends before arriving at the correct proof. This is the way mathematicians work, and it's usually hidden from papers or other textbooks. I'm guessing that this is either for economy or because authors want to look like the first thing they tried was the right one.
Another excellent example of this is the paper "How Not to Prove the Poincare Conjecture".
A counterpoint -- neutrino oscillation, dark matter, dark energy, and gravity all exist outside the standard model. If we are to believe that there's some larger theory that ties everything together, then there's something missing.
That we have done such extensive searches without finding anything is an amazing property of nature. The growing body of experimental null searches serve to provide tighter and tighter constraints on our understanding of nature.
If you put your best new idea to the test, and it fails, it's not a failure, it's a success. Now you know your best idea, the one you thought could lead you forward, is a dead end. Your next steps will be better-guided and take you further forward.
That we have done such extensive searches without finding anything is an amazing property of nature. The growing body of experimental null searches serve to provide tighter and tighter constraints on our understanding of nature.
If you put your best new idea to the test, and it fails, it's not a failure, it's a success. Now you know your best idea, the one you thought could lead you forward, is a dead end. Your next steps will be better-guided and take you further forward.
Yes. It's just that biologists don't think like this (yet?).
Keep in mind that there's a difference between a negative result and failure to find.
I don't know enough about your field to understand the results you're talking about, but in machine learning "failing to reject the null hypothesis" generally indicates failure to find, not a genuine negative result.
I find strong negative results to be compelling, but have little interest in wading through failure to find.
Aside: In NLP + ML, there was a now defunct publication called the Journal For Interesting Negative Results: http://jinr.org/
I don't know enough about your field to understand the results you're talking about, but in machine learning "failing to reject the null hypothesis" generally indicates failure to find, not a genuine negative result.
I find strong negative results to be compelling, but have little interest in wading through failure to find.
Aside: In NLP + ML, there was a now defunct publication called the Journal For Interesting Negative Results: http://jinr.org/
Particle physics is the light shining in the darkness of science. I understand that not all sciences can be particle physics, but most scientists could stand to learn a lesson or two about it.
What you describe sounds (to this layperson) like a solid QA/Test strategy.
I strongly approve.
I strongly approve.
Just gonna leave a quick link to my favorite general-purpose academic journal: http://www.jasnh.com/
The Journal of Articles in Support of Null Hypothesis collects experiments that didn't work. Not very much volume, not a huge area of prestige, but there should be no shame in publishing there. The content is very diverse and pretty fun.
Titles like "No Effect of a Brief Music Intervention on Test Anxiety and Exam Scores in College Undergraduates"; "Parenting Style Trumps Work Role in Life Satisfaction of Midlife Women"; "Does Fetal Malnourishment Put Infants at Risk of Caregiver Neglect Because Their Faces Are Unappealing?"; "Is There an Effect of Subliminal Messages in Music on Choice Behavior?". Plenty more cool stuff.
The Journal of Articles in Support of Null Hypothesis collects experiments that didn't work. Not very much volume, not a huge area of prestige, but there should be no shame in publishing there. The content is very diverse and pretty fun.
Titles like "No Effect of a Brief Music Intervention on Test Anxiety and Exam Scores in College Undergraduates"; "Parenting Style Trumps Work Role in Life Satisfaction of Midlife Women"; "Does Fetal Malnourishment Put Infants at Risk of Caregiver Neglect Because Their Faces Are Unappealing?"; "Is There an Effect of Subliminal Messages in Music on Choice Behavior?". Plenty more cool stuff.
This is interesting, but I think there needs to be an option that takes the next step. While this journal is for psychology only I would like to see a broad Journal of Negative Results. This would help limit repeated, failed experiments, and could do as much to help new discoveries as the current publications which are a collection of techniques that happened to work.
Everyone says you should learn from your mistakes, but then they are never shared for others to learn from.
Everyone says you should learn from your mistakes, but then they are never shared for others to learn from.
Yeah, it does seem like in general in science, we only see "we tried to do X and it didn't work" papers AFTER someone has published a paper that says "surprisingly, we tried to do X which shouldn't work and it did!".
The example that comes to mind is pluripotent stem cells (see e.g. discussion at http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjo... ) where publications only really advance knowledge about the "dogma" of a field when someone finds a way to publish results that oppose it.
The example that comes to mind is pluripotent stem cells (see e.g. discussion at http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjo... ) where publications only really advance knowledge about the "dogma" of a field when someone finds a way to publish results that oppose it.
I have always had a huge problem with the non-reproducible nature of medical research and its acceptance within the field. Being in medicine, every day you hear a physician citing some study from 15 years ago conducted on a sample size of 20/50/100 or so patients as a way to justify their clinical decisions. And it always worries me that we tend to put so much faith in these "landmark" studies, as if their findings are somehow legitimate and true because statistical significance was reached at least once, and seem to forget all of the misaligned incentives and game-playing that goes on in research, and the amount of data burying by the FDA, all that almost certainly influence and manipulate data, publication, etc in a negative way.
I tend to take the majority of medical research with a grain of salt, for the reasons listed here and in the article, unless there's some very convincing meta-analysis or successfully reproduced evidence. Call me overly cynical, but that we calculate a parameter or administer a drug or change our methods because of some article you read last month in NEJM is beyond bogus.
I tend to take the majority of medical research with a grain of salt, for the reasons listed here and in the article, unless there's some very convincing meta-analysis or successfully reproduced evidence. Call me overly cynical, but that we calculate a parameter or administer a drug or change our methods because of some article you read last month in NEJM is beyond bogus.
I'm a computational biologist working at the intersection of basic research and medical research. I would recommended more than one grain of salt with medical research. Medically trained folks often fail to understand study design and are rarely rigorous thinkers. The heavy emphasis on memorization in medical school is a substantial part of the problem. And, those are problems even before perverse incentives kick in.
In medical research there is also the additional handicap of career structure. The extremely hierarchical nature of medical careers, and the substantial financial rewards for "playing along", mean that there are strong disincentives to rock the boat by criticizing the hypotheses of senior members. This often results in byzantine twists of logic being used to justify continuing with particular lines of enquiry, such as the amyloid hypothesis in Alzheimer's disease (my field).
Ditto for academic finance - so many sketchy results get advertised, and billions if not trillions of dollars get invested based on false or uncertain hypotheses... Sometimes I feel that 90% of my job is to be skeptical of all results, and convince managers not to do anything stupid - a sort of "first, do no harm" philosophy.
There's many more issues distorting or distorted by academic publishing. For example, grants being awarded to the most productive members. Sounds fine at face value, but compare biologists working with ecological systems and biologists working with DNA. In the first case collecting data by definition has to take decades, in the other it's a matter of hours these days. The ecologists needs more money because it inherently takes longer to research, yet the grants are more likely to be awarded to the DNA researchers because they publish more.
DISCLAIMER: I'm not a biologist myself - this is a second-hand story from a biologist friend so if the story doesn't hold up under close scrutiny, my apologies.
DISCLAIMER: I'm not a biologist myself - this is a second-hand story from a biologist friend so if the story doesn't hold up under close scrutiny, my apologies.
Yes and No.
Yes ecologists take more time to collect data.
However ecology is much cheaper than mol. biology. Essentially its ecology = manpower vs mol. biology = manpower + expensive technology + expensive chemistry.
Also ecology has been one of the most resistant fields to sharing data whereas genomics (the big data end of mol. biology) has been the most enthusiastic. There was a vey noticeable divide in opinion on twitter when PLOS journals mooted making authors make all their data public.
I think this is precisely because ecologists invest so much on collecting their field data, so they become possessive. Ecologists can become very senior PI by essentially owning a long term ecological study and assigning their students to continuing it.
Genomics and to a lesser extent Mol. Biol. has been keener recently to share as there is a strong open source ethos coming in from the computing side - and it is often a very expensive multi-centre, multi-agency collaboration.
However ecology is much cheaper than mol. biology. Essentially its ecology = manpower vs mol. biology = manpower + expensive technology + expensive chemistry.
Also ecology has been one of the most resistant fields to sharing data whereas genomics (the big data end of mol. biology) has been the most enthusiastic. There was a vey noticeable divide in opinion on twitter when PLOS journals mooted making authors make all their data public.
I think this is precisely because ecologists invest so much on collecting their field data, so they become possessive. Ecologists can become very senior PI by essentially owning a long term ecological study and assigning their students to continuing it.
Genomics and to a lesser extent Mol. Biol. has been keener recently to share as there is a strong open source ethos coming in from the computing side - and it is often a very expensive multi-centre, multi-agency collaboration.
Within fields this is taken into account. Some areas can be very quick to generate data, whereas others take longer and /or more manpower. One would not expect someone doing studies in mice, or clinical studies, to generate the same number of papers as one doing biochemistry, for example.
The incentive to publish frequently is not universal, however, some reviewing committees ask only for one to present the best 5 papers in the last 10 years, for example.
The incentive to publish frequently is not universal, however, some reviewing committees ask only for one to present the best 5 papers in the last 10 years, for example.
Ben Goldacre has been rallying around this cause for years now. He has:
1. presented a TED talk (http://www.ted.com/talks/ben_goldacre_battling_bad_science?l...),
2. authored books (http://www.amazon.co.uk/dp/0007350740/), and
3. written numerous articles (http://www.theguardian.com/profile/bengoldacre)
on the misrepresentation of statistics within the pharmaceutical community.
Probably the best summary post of his is this one: http://www.theguardian.com/commentisfree/2011/nov/04/bad-sci...
Probably the best summary post of his is this one: http://www.theguardian.com/commentisfree/2011/nov/04/bad-sci...
He also recommended that a registry be instituted, where you have to register before starting research, and you HAVE to publish your result, negative or not. If you haven't registered beforehand, you can't publish at all.
Sounds like a simple and effective solution.
Sounds like a simple and effective solution.
Bad Pharma is truly an eye-opener as to the extent of the troubles faced in getting truth out of medical research. It's going to take many, many, different group combining foresight, technology, and good-old-fashioned political clout to even start fixing it.
John Ioannidis' work is fascinating in that respect as well.. not just in the medical field, but science in general.
John Ioannidis' work is fascinating in that respect as well.. not just in the medical field, but science in general.
I don't think they are aware of how much bad science is out there and how many people are trying to publish it. It wouldn't be a journal every month, it would be a phone book every week. Corporations would simply drown out real science with papers designed to support whatever narrative they were promoting.
Where this may make sense is when Watson grows up, and you can aggregate the volume of garbage to fill in the holes of knowledge. But that's more than a couple years off I suspect.
Where this may make sense is when Watson grows up, and you can aggregate the volume of garbage to fill in the holes of knowledge. But that's more than a couple years off I suspect.
I think the author addressed your concern:
> Ask journal editors and scientific peers to review study designs and analysis plans and commit to publish the results if the study is conducted and reported in a professional manner (which will be ensured by a second round of peer review).
If the study design is peer reviewed, I would hazard a guess that there would be less bad science, not more. Currently study design takes second priority to significant results, which is perhaps why we have the problems of inconsistent research in the first place.
> Ask journal editors and scientific peers to review study designs and analysis plans and commit to publish the results if the study is conducted and reported in a professional manner (which will be ensured by a second round of peer review).
If the study design is peer reviewed, I would hazard a guess that there would be less bad science, not more. Currently study design takes second priority to significant results, which is perhaps why we have the problems of inconsistent research in the first place.
I think that would be a great step. Science would be greatly advanced if there was more focus on whether a good question was asked and whether the experiments were well chosen, than whether the experiments gave positive/negative results.
A lot of bad science is published. I would guess that more than 50% of published articles in the biomedical field are not worth anything, even as "negative results".
The suggestion in the article to pre-register trials is a good one, but I'm extremely wary of a more general effort to "publish rejected research" because there is a huge quantity of very poorly conducted research that really does not deserve publication. Most fields are already drowning in a sea of journal articles - few researchers are aware of all the published studies that might be relevant to their own work - and greatly increasing the quantity of published material will dilute the pool even further.
Replicating findings should be given higher priority, pre-registering methods and analyses should be encouraged/required, but it's important to stop short of "publish all the things".
Replicating findings should be given higher priority, pre-registering methods and analyses should be encouraged/required, but it's important to stop short of "publish all the things".
So don't publish the shit research in quality journals. Publish them in a general journal for negative findings.
I'd also like to know who conducts quality research and who conducts shit research, cause that might influence my funding/spending patterns (if I had money to spend).
I'd also like to know who conducts quality research and who conducts shit research, cause that might influence my funding/spending patterns (if I had money to spend).
"Shit research" is not equal to negative findings. In fact it may be the opposite. I suspect most bad research has the positive findings the researchers are looking for.
I haven't read all the responses, so I hope I am not repeating anyone's insight. I'm finishing my PhD in biophysics, and I wanted to share my perspective from conversations I've had with my boss/PI and other investigators. In life science research, there are huge incentives to not repeat others research and furthermore to not publish negative results.
The first disincentive comes from funding bodies: NIH et. al (NIGMS, NIEHS, ...) don't like to pay for you to do "someone else's science". If you manage to get a grant, and it comes out in a progress report that you did repeat too much of other people work, be prepared to get that funding reduced and or cut.
Academic departments strongly discourage new hires from publishing negative results and /or repeating other peoples work (mostly because this will likely decrease chances of getting published and funded).
Academic journals hate to publish negative results, but seemingly have no problem publishing bad science (yes Nature, I'm looking at you: http://retractionwatch.com/2014/09/11/potentially-groundbrea...). Early in my PI's career, she tried to publish a very important negative fining in a high impact journal. The article's acceptance was accompanied by a personal letter from the editor urging her to consider other journals for negative results.
Another barrier quite honestly is ego. While it may sound as if my boss is "one of the good ones", alas, she is not. On occasions that I have asked to repeat other group's seemingly unbelievable results myself, I've been flatly denied on grounds that this kind of work does not express the sort of originality of research produced by her lab. In other words, nobody wants to be known as "that lab", the nay-sayers of the field, those that would dare to question a colleague's ideas.
Finally, this lead me to the last barrier I have observed: scientific communities / societies. If you are of the lucky few that end up publishing negative results of major significance, prepare to not be invited to dinner at next years Society for X annual meeting. Yes, in many ways life-science is stratified just like high school. You have the cool kids on track for the nobel, the weirdoes in their corner pushing the boundaries of what is possible, the "jocks"/ career scientists who manage to turn a couple of tricks and some charisma into a living, and finally the tattle-tales who seem to piss everyone off with their negative results. These are HUGE oversimplifications / generalizations, but I really think that all of these barriers need to be addressed in some way to fix life science.
The first disincentive comes from funding bodies: NIH et. al (NIGMS, NIEHS, ...) don't like to pay for you to do "someone else's science". If you manage to get a grant, and it comes out in a progress report that you did repeat too much of other people work, be prepared to get that funding reduced and or cut.
Academic departments strongly discourage new hires from publishing negative results and /or repeating other peoples work (mostly because this will likely decrease chances of getting published and funded).
Academic journals hate to publish negative results, but seemingly have no problem publishing bad science (yes Nature, I'm looking at you: http://retractionwatch.com/2014/09/11/potentially-groundbrea...). Early in my PI's career, she tried to publish a very important negative fining in a high impact journal. The article's acceptance was accompanied by a personal letter from the editor urging her to consider other journals for negative results.
Another barrier quite honestly is ego. While it may sound as if my boss is "one of the good ones", alas, she is not. On occasions that I have asked to repeat other group's seemingly unbelievable results myself, I've been flatly denied on grounds that this kind of work does not express the sort of originality of research produced by her lab. In other words, nobody wants to be known as "that lab", the nay-sayers of the field, those that would dare to question a colleague's ideas.
Finally, this lead me to the last barrier I have observed: scientific communities / societies. If you are of the lucky few that end up publishing negative results of major significance, prepare to not be invited to dinner at next years Society for X annual meeting. Yes, in many ways life-science is stratified just like high school. You have the cool kids on track for the nobel, the weirdoes in their corner pushing the boundaries of what is possible, the "jocks"/ career scientists who manage to turn a couple of tricks and some charisma into a living, and finally the tattle-tales who seem to piss everyone off with their negative results. These are HUGE oversimplifications / generalizations, but I really think that all of these barriers need to be addressed in some way to fix life science.
>Another barrier quite honestly is ego. While it may sound as if my boss is "one of the good ones", alas, she is not. On occasions that I have asked to repeat other group's seemingly unbelievable results myself, I've been flatly denied on grounds that this kind of work does not express the sort of originality of research produced by her lab. In other words, nobody wants to be known as "that lab", the nay-sayers of the field, those that would dare to question a colleague's ideas.
"There are two kinds of scientific progress: the methodical experimentation and categorization which gradually extend the boundaries of knowledge, and the revolutionary leap of genius which redefines and transcends those boundaries. Acknowledging our debt to the former, we yearn, nonetheless, for the latter."
"There are two kinds of scientific progress: the methodical experimentation and categorization which gradually extend the boundaries of knowledge, and the revolutionary leap of genius which redefines and transcends those boundaries. Acknowledging our debt to the former, we yearn, nonetheless, for the latter."
it's true in basically every field. my phd experience [machine learning] was generally very good, but i left with a very strong sense that academia is not really any better than other spheres of human endeavor. the rulebook is different, but politics finds a way - a big way. i disagree though, that 'negative results' fit into the tattle-tale category in your analogy, and think it is also a bit naive to dismiss charisma.
Of course this is not universally true, but there is definitely a culture problem within the field. Furthermore there's a repeatability problem in a ton of biological research. More time has to be spent repeating findings even at the expense of slower progress (retraction watch is mostly full of biological/medical research). I don't meant to dismiss charisma outright, but it needs to be backed up with originality and good science to be worth a damn.
A friend of mine stopped her PhD in systems biology, because her line of research didn't produce anything positive.
We kind of think of science as an embodiment of modernity, and therefore modern. What it is though is an institution, similar to academia and fairly old. Human institutions take time to change.
In any case, I'm pretty excited that it's coming under pressure to improve. Publication is really a method of communication and the revolution in communication of the last generation is a profound step change in human history, in my opinion. To use some terms that our great predecessors would have been comfortable with, science is a way to uncover the truth using light. Experimentation, debate, publication, review: these are all ways of making light.
Bringing modern communication into science and the collaborative opportunities inherent in better communication is a potentially very bright light.
In any case, I'm pretty excited that it's coming under pressure to improve. Publication is really a method of communication and the revolution in communication of the last generation is a profound step change in human history, in my opinion. To use some terms that our great predecessors would have been comfortable with, science is a way to uncover the truth using light. Experimentation, debate, publication, review: these are all ways of making light.
Bringing modern communication into science and the collaborative opportunities inherent in better communication is a potentially very bright light.
Nature, and Nature's Laws lay hid in Night.
God said, 'Let Newton be!' and all was Light.
-- Alexander Pope
There is the big problem now of negative results not being published, mainly because of the competition for federal (government worldwide) funding does not lend itself to proving something "otherwise".
http://eloquentscience.com/wp-content/uploads/2012/02/Fanell...
http://eloquentscience.com/wp-content/uploads/2012/02/Fanell...
The process generally seems a bit broken what with the concerns in the article and with Elsevier making everything only available to those who pay loads. You'd think you could have something like arXiv and a rating service to figure out which research was good and worth reading and should be rewarded career wise. Something for a YC start up to fix?
There is something like that in biology:
http://f1000.com/prime
...but getting sufficient widespread adoption is the big problem. And given that peer reviewers are not particularly effective in improving paper quality except in the most egregious cases, we should wonder whether an entirely different model is more appropriate.
http://f1000.com/prime
...but getting sufficient widespread adoption is the big problem. And given that peer reviewers are not particularly effective in improving paper quality except in the most egregious cases, we should wonder whether an entirely different model is more appropriate.
There are two issues here - (1) Irreproducible research and (2) Negative results. (1) is clearly a problem and must be dealt with by scientific community and processes. Let me talk about (2).
For negative results to be published, they too should follow basic patterns of positive results - innovative, scientifically rigorous. There are always more negative results possible than positive results. A negative result should be something which people think would intuitively work but wouldn't. For example - an apple falling from a tree and floating in thin air isn't a negative result because we all know that it's supposed to fall down to the ground via gravity.
Edit - We also have a Journal of Negative Results
http://jnr-eeb.org/index.php/jnr
For negative results to be published, they too should follow basic patterns of positive results - innovative, scientifically rigorous. There are always more negative results possible than positive results. A negative result should be something which people think would intuitively work but wouldn't. For example - an apple falling from a tree and floating in thin air isn't a negative result because we all know that it's supposed to fall down to the ground via gravity.
Edit - We also have a Journal of Negative Results
http://jnr-eeb.org/index.php/jnr
A negative result is also interesting if a positive result has been previously reported.
That would be irreproducible research.
For an instructive insight into the dangers of only publishing positive results, see http://xkcd.com/882/
A lot of this has to do with incentive structures, especially in Life Science research. The grant landscape is intensely competitive, and writing up results is incredibly time-consuming. There is little incentive to take the time to write up and submit negative results to relevant journals. If institutions and grant committees were to require this practice, it wouldn't be nearly as big of a problem.
It is tragic to imagine the amount of time wasted by repeating the unpublished experiments of others. It is even more tragic to imagine that someone might be able to gain a hidden insight by finding the gaps in various negative results, which might remain undiscovered for a long time otherwise.
The problem is that there is such a huge priority on successful research and there is a self-consciousness about funding/research that didn't go as well as expected.
Indeed. It would be ironic if some of those less successful results were the result of hiding earlier, less successful results.
I left the field 15 years ago because I didn't want to spend my career measuring zero, and presumably over time it will eventually dry up. The conditions for its existence seem to be more to do with having a highly trained group of people who have exhausted all plausible avenues of research in a given area and are left chasing a few scraps. In areas where there are still plenty of positive results to be had, the tendency will always be to emphasize the positive.
As a partial solution to this tendency, in my applied physics work, where I did get positive results, I tried to include a section in papers entitled "Things That Didn't Work So Well" that sketched failed approaches to save other people the trouble of trying stuff that seemed like a good idea but didn't pan out... at the very least we should expect that from the average publication, and be suspicious of any experimental paper that does not include some description of the blind alleys.