5 ms·

I've been lucky enough to work in outstanding labs, with people published in Nature, and other journals of that quality. I've worked in 2 countries, and for 4 d

I've been lucky enough to work in outstanding labs, with people published in Nature, and other journals of that quality. I've worked in 2 countries, and for 4 different labs. I've also talked with people from all over the world, who have worked everywhere, from Harvard to the Pasteur Institute to Cambridge University. The stories are all the same. I hoped I would find some place where people were trying to do things the right way, but what I found is that currently, you don't need to to be published in top journals, so why bother? It's really refreshing to hear you talk about trying to troubleshoot why an experiment didn't work the way you expected, I hear mostly of people retrying blindly until it "succeeds". What did you do with what you learned with the water causing the failure? Did you publish this, so that someone (or you!) could try to figure out why water was a problem, or at least so that no one would have the same issue? This is the other point: when people do bother about finding about why things fail, I've never seen any of them try to follow up on that, and figure out not only what made it fail, but why it made it fail. "Yeah, the annealing temp was not the right one". Ok, but why? Of course playing with systems we don't understand is the point, but we have to be very careful about them. We should be varying 1 parameter at a time. This is mostly impossible in biology, but right now we're not even trying to do anything about it.

nycticorax 11y ago
People don't investigate things like that, because, frankly, no one cares. Nor should they. If your computer is acting funny, and you find out a spider has made a nest inside it, and it works fine once you clear out the spider nest, would you then decide to determine exactly why that spider nest, in that place, causes the exact problems you observed? No, of course not. Because that's not an interesting question. Computers are complicated machines, and they can break in lots of different ways, most of which are not very interesting in their details. Similarly, suppose you study cultured cells (which are notoriously finicky), and you want to compare what the cells do in the presence of drug X vs control. But at first, you find that all of your control cells die. And eventually you find out that if you use brand X of bottled water vs tap water, the control cells thrive. Are you seriously proposing that you should then drop all work on drug X, and get to work determining exactly what is up with the tap water in your town? I mean, maybe that would be a fruitful research avenue, if you're worried that the tap water isn't safe for human consumption, or you think that there's something interesting about exactly how the tap water is killing your control cells. But most of the time, investigating the tap water would be an expensive distraction from the question you actually want to answer. And most scientists, I think, would (reasonably) decide to get on with investigating the effects of drug X on their cells, and not worry too much about precisely why the tap water killed the control cells. And I don't think there's anything wrong with that. Life is short, and you have to choose your questions carefully.
- TisButMe 11y ago
  So you just accept for no reason that tap water is bad somehow, and discard the result you've just gotten? I do understand that you have a limited amount of time, and can't just go after everything, but when something happens in science, it needs to be documented. Yeah, maybe someone else should investigate, but someone should. Maybe that particular phenomena that lead to the water influencing your result will give you knowledge about cell metabolism. Who knows? If it has that much of an effect on cell growth that you need to deal with it, it's already more active than a lot of compounds we try out, anyway... To go back to the computer analogy, it feels like my program is bugged, and to debug it I'm changing variables names (which as far as I know shouldn't matter), and then the code magically works again. Sure, some days, I'll go "Ok, compiler magic, got it", but most days I'd be pretty intrigued, and I'd look into it, because yeah, I might just have found a GCC bug. I agree, no one cares, but I did. I don't know what I don't know yet, and I don't want to presume anything. The tap water thing might actually lead us to solid models which would explain why tap water breaks the experiment. That's why I really think we should start a movement of publishing everything, and trying to deal with simpler models/systems we do understand before going up to models with so many unknowns that the results are basically a dice roll.
  - nycticorax 11y ago
    "So you just accept for no reason that tap water is bad somehow, and discard the result you've just gotten?" What is the "result" that you referring to here? That the tap water in town X kills cultured cells of type Y? Yeah, I guess you could try writing that up and publishing it, but that's a good way to waste a lot of time publishing results that are interesting only to a very small audience. Honestly, if it were me, I'd send an e-mail to people in the same town that might be working with cells of the same or similar type, and then move on. "but when something happens in science, it needs to be documented" No, it really doesn't. Stuff doesn't document itself. That takes time, which sometimes is better spent doing other things. Like answering more interesting questions. "Maybe that particular phenomena that lead to the water influencing your result will give you knowledge about cell metabolism. Who knows?" That's true, but my point is that it doesn't make you a bad scientist if you shrug your shoulders about why the tap water kills your cells, and get on with your original experiment. For every experiment you do, there's a million others you're not doing, and so it makes sense to focus on the one experiment you're most interested in, not chase after a bunch of side-projects that will (probably) not lead to any kind of an interesting result. And all of this is very different than a case where you compare control to treatment, find no difference, and therefore just start fiddling with other experimental parameters until you do get a difference between control and treatment. That is, I think you'll agree, a bad way to do science. "To go back to the computer analogy, it feels like my program is bugged, and to debug it I'm changing variables names (which as far as I know shouldn't matter), and then the code magically works again. Sure, some days, I'll go "Ok, compiler magic, got it", but most days I'd be pretty intrigued, and I'd look into it, because yeah, I might just have found a GCC bug." I think a better analogy would be if compiler x acts in ways you don't understand, so you switch to gcc, which (most of the time) works as you expect. Are you really required to figure out exactly why compiler x acts as it does? Or would you just get on with using a compiler that works the way you think it should? "I agree, no one cares, but I did. I don't know what I don't know yet, and I don't want to presume anything. The tap water thing might actually lead us to solid models which would explain why tap water breaks the experiment." They might, but there are a lot of experiments that have a very very small chance of an interesting outcome, and a near-one chance of a pedestrian outcome. You can do those experiments, and you might get lucky and get the interesting result, but probably you will just get the pedestrian result. And there's nothing wrong (and a lot right) with instead focusing on experiments where (for instance), either outcome would be interesting to people in the field. "That's why I really think we should start a movement of publishing everything, and trying to deal with simpler models/systems we do understand before going up to models with so many unknowns that the results are basically a dice roll." I think you're conflating a number of things here. I agree that a reductionist approach to science has bourne a lot of fruit, historically. I agree that studying systems with a lot of unknowns has risks. And it may be that "publish everything" would work better than what we have now. But even if scientists all decide to publish everything they do, they'll still have to make strategic choices about what experiment to do on a given day, and in many cases that will mean not doing a deep dive into questions like "why does the tap water kill my cells, even in control"?
mirimir 11y ago
Here's a famous counterexample: http://www.anapsid.org/cnd/hormones/sabotage3.html http://www.anapsid.org/cnd/hormones/sabotage3.html tl;dr: Sonnenschein and Soto were studying effects of estrogens on in vitro proliferation of breast cancer cells. Their assays stopped working. Eventually they figured out that Corning had added p-nonylphenol, which is estrogenic, to the plastic, to reduce brittleness.

nycticorax 11y ago

People don't investigate things like that, because, frankly, no one cares. Nor should they. If your computer is acting funny, and you find out a spider has made a nest inside it, and it works fine once you clear out the spider nest, would you then decide to determine exactly why that spider nest, in that place, causes the exact problems you observed? No, of course not. Because that's not an interesting question. Computers are complicated machines, and they can break in lots of different ways, most of which are not very interesting in their details. Similarly, suppose you study cultured cells (which are notoriously finicky), and you want to compare what the cells do in the presence of drug X vs control. But at first, you find that all of your control cells die. And eventually you find out that if you use brand X of bottled water vs tap water, the control cells thrive. Are you seriously proposing that you should then drop all work on drug X, and get to work determining exactly what is up with the tap water in your town? I mean, maybe that would be a fruitful research avenue, if you're worried that the tap water isn't safe for human consumption, or you think that there's something interesting about exactly how the tap water is killing your control cells. But most of the time, investigating the tap water would be an expensive distraction from the question you actually want to answer. And most scientists, I think, would (reasonably) decide to get on with investigating the effects of drug X on their cells, and not worry too much about precisely why the tap water killed the control cells. And I don't think there's anything wrong with that. Life is short, and you have to choose your questions carefully.

TisButMe 11y ago
So you just accept for no reason that tap water is bad somehow, and discard the result you've just gotten? I do understand that you have a limited amount of time, and can't just go after everything, but when something happens in science, it needs to be documented. Yeah, maybe someone else should investigate, but someone should. Maybe that particular phenomena that lead to the water influencing your result will give you knowledge about cell metabolism. Who knows? If it has that much of an effect on cell growth that you need to deal with it, it's already more active than a lot of compounds we try out, anyway... To go back to the computer analogy, it feels like my program is bugged, and to debug it I'm changing variables names (which as far as I know shouldn't matter), and then the code magically works again. Sure, some days, I'll go "Ok, compiler magic, got it", but most days I'd be pretty intrigued, and I'd look into it, because yeah, I might just have found a GCC bug. I agree, no one cares, but I did. I don't know what I don't know yet, and I don't want to presume anything. The tap water thing might actually lead us to solid models which would explain why tap water breaks the experiment. That's why I really think we should start a movement of publishing everything, and trying to deal with simpler models/systems we do understand before going up to models with so many unknowns that the results are basically a dice roll.
- nycticorax 11y ago
  "So you just accept for no reason that tap water is bad somehow, and discard the result you've just gotten?" What is the "result" that you referring to here? That the tap water in town X kills cultured cells of type Y? Yeah, I guess you could try writing that up and publishing it, but that's a good way to waste a lot of time publishing results that are interesting only to a very small audience. Honestly, if it were me, I'd send an e-mail to people in the same town that might be working with cells of the same or similar type, and then move on. "but when something happens in science, it needs to be documented" No, it really doesn't. Stuff doesn't document itself. That takes time, which sometimes is better spent doing other things. Like answering more interesting questions. "Maybe that particular phenomena that lead to the water influencing your result will give you knowledge about cell metabolism. Who knows?" That's true, but my point is that it doesn't make you a bad scientist if you shrug your shoulders about why the tap water kills your cells, and get on with your original experiment. For every experiment you do, there's a million others you're not doing, and so it makes sense to focus on the one experiment you're most interested in, not chase after a bunch of side-projects that will (probably) not lead to any kind of an interesting result. And all of this is very different than a case where you compare control to treatment, find no difference, and therefore just start fiddling with other experimental parameters until you do get a difference between control and treatment. That is, I think you'll agree, a bad way to do science. "To go back to the computer analogy, it feels like my program is bugged, and to debug it I'm changing variables names (which as far as I know shouldn't matter), and then the code magically works again. Sure, some days, I'll go "Ok, compiler magic, got it", but most days I'd be pretty intrigued, and I'd look into it, because yeah, I might just have found a GCC bug." I think a better analogy would be if compiler x acts in ways you don't understand, so you switch to gcc, which (most of the time) works as you expect. Are you really required to figure out exactly why compiler x acts as it does? Or would you just get on with using a compiler that works the way you think it should? "I agree, no one cares, but I did. I don't know what I don't know yet, and I don't want to presume anything. The tap water thing might actually lead us to solid models which would explain why tap water breaks the experiment." They might, but there are a lot of experiments that have a very very small chance of an interesting outcome, and a near-one chance of a pedestrian outcome. You can do those experiments, and you might get lucky and get the interesting result, but probably you will just get the pedestrian result. And there's nothing wrong (and a lot right) with instead focusing on experiments where (for instance), either outcome would be interesting to people in the field. "That's why I really think we should start a movement of publishing everything, and trying to deal with simpler models/systems we do understand before going up to models with so many unknowns that the results are basically a dice roll." I think you're conflating a number of things here. I agree that a reductionist approach to science has bourne a lot of fruit, historically. I agree that studying systems with a lot of unknowns has risks. And it may be that "publish everything" would work better than what we have now. But even if scientists all decide to publish everything they do, they'll still have to make strategic choices about what experiment to do on a given day, and in many cases that will mean not doing a deep dive into questions like "why does the tap water kill my cells, even in control"?

mirimir 11y ago

Here's a famous counterexample: http://www.anapsid.org/cnd/hormones/sabotage3.html http://www.anapsid.org/cnd/hormones/sabotage3.html tl;dr: Sonnenschein and Soto were studying effects of estrogens on in vitro proliferation of breast cancer cells. Their assays stopped working. Eventually they figured out that Corning had added p-nonylphenol, which is estrogenic, to the plastic, to reduce brittleness.