Last week protesters gathered in a number of Canadian cities to draw attention to the science policies of the current government. Their concerns are, according to the press, that the government is keeping scientists from communicating to the public, and also that it's defunding important scientific projects.
Here's my problem with this: the media reports are making two things on very different scales of problematic seem equivalent.
First, the money issue. If you read the second article linked to above, it's the main reason for the protests. Scientists aren't happy with the funding for science under the Harper government, particularly with regard to basic research. This is a legitimate complaint for scientists to make; they want to see Canada reap the benefits that come from having a strong research community and they see these cuts as threatening that.
We do live in a democracy, though, and the people of this country elected the Conservatives on a platform to cut government spending. So while it makes sense to argue that the cuts are ultimately going to hurt the country (an argument I am on board with, as it turns out), it's also important to realize that in this regard the government is, in fact, doing what they said they would do during the election campaign.
The muzzling issue (which is the main reason for the protests according to the first article linked to above) is an order of magnitude more serious. This isn't about saving government money. Public money was spent on research, then once the results were in the government demanded that they and they alone see the them, before deciding what to pass on to the public after suitable editing.
Selectively releasing results is a form of dishonesty; it's no different than when pharmaceutical companies release studies that show their products in a good light and bury ones that point to potential risks. When certain research outcomes are suppressed the government is, as a whole, giving the public a misleading picture.
This is an issue that should transcend political affiliation. Whether you believe in big government or small, decision makers need the clearest picture they can get from the people the public is paying to investigate some of the most pressing issues facing the country.
It's no secret that the Conservative government and the science community have been at odds. By both occupation and political leaning I am on the side of the science community, but this is a bigger issue than just some professional researchers wanting job security. Ultimately the question is this: Is the government is interested in getting the best answer to the questions that matter to policy, whether or not those answers line up with political ideology? The alternative is a government which cares about protecting their image, even if it means wilfully distorting the research the public paid for.
I'm not saying that cutting the funds to science is a good thing, or that scientists are wrong to go out and engage the public in the need for science funding. That is how you build democratic support for your position. But by conflating the funding and the muzzling, these latest protests and the media reporting them have watered down an important message about the way this government treats public research like the property of the Conservative Party. That's unacceptable, and it should have been the focus last week.
Monday, 23 September 2013
Wednesday, 11 September 2013
More on individuals and averages
I seem to keep hitting on this idea: the individual may not be well described by the average. In fact, it's possible for no individual to be well described by an average. It's an important point because it really strikes at the heart of where a lot of science reporting goes wrong.
I'm not the only one saying this: Jamil Zaki, a psychologist at Stanford, has a great post on a Scientific American blog going into detail about exactly this idea. His point is that psychology deals with averages, and sometimes there's a lot of variation around those averages that isn't often reported.
Zaki only discusses psychology in his article, but of course the idea extends beyond that. Any science that deals with populations and tries to extract generalized information from them carries the same caveat. "Populations" as I'm using it don't even have to be people; they could be animals or even stars, or events, or days. In other words, most of science, including all of economics and medicine, is covered here.
So the weather in one season in one part of the world may not be well described by a global average temperature (It was minus 20 here yesterday! What happened to global warming, eh?) Your risk of cardiac disease may not be well described by the average for other people with similar habits and backgrounds to you. It's even true that, if you smoke, the decrease in your lifespan may not be well described by the average decrease in life expectancy for smokers.
The average is simply one measure of a population. It might be a good way of describing things; it might not be. As an example, I could take the average height of my family. Adding myself, my spouse, and our toddler, and dividing by three gives me something around four and a half feet. That's not anywhere near any of our heights; in this case the average is simply an irrelevant measure.
More commonly, the average isn't a bad measure per se, it's just incomplete. What you usually need is the average, plus some indication of how spread out the population is around that average. The standard deviation is one such measure.
Any reputable scientific paper will have calculated many measures for the population it's looking at. Here's a list of, among other things, various measures that can be applied to a population. It's a little bewildering, which is likely why most media reports focus on one number and strip away the complicating details.
What to make of all this? Well, don't start smoking. Even if there is a certain amount of variance in the data, it's foolish to assume that you'll be an outlier. For well-established health issues, the average is, more often then not, a good guide.
Moving outside of that, if the study is new it's always worth asking, what's the variation around the average they're reporting? We looked at a study a while back in which the a connection between autism and induced labour was reported; the actual research paper showed that the variance around their average results was so large that it threatened to undermine the conclusions.
Knowing when an average is a bad measure is a little harder. Often when this happens the person reporting the average is deliberately using a poor measure to make themselves look better. Economic data is a prime example. Whenever you see the GDP per person, unadjusted for inflation, you can safely discount that number as worthless. The average simply isn't a good measure for the typical person's income. Politicians report it, though, because it's a quantity that governments can reliably increase through monetary policy, even if life for the typical person hasn't changed.
The key point here is that populations are complex. Any time you see them reduced to a single number, it's worth asking, "What am I missing here?" And, as Zaki points out, it is not always about you.
I'm not the only one saying this: Jamil Zaki, a psychologist at Stanford, has a great post on a Scientific American blog going into detail about exactly this idea. His point is that psychology deals with averages, and sometimes there's a lot of variation around those averages that isn't often reported.
Zaki only discusses psychology in his article, but of course the idea extends beyond that. Any science that deals with populations and tries to extract generalized information from them carries the same caveat. "Populations" as I'm using it don't even have to be people; they could be animals or even stars, or events, or days. In other words, most of science, including all of economics and medicine, is covered here.
So the weather in one season in one part of the world may not be well described by a global average temperature (It was minus 20 here yesterday! What happened to global warming, eh?) Your risk of cardiac disease may not be well described by the average for other people with similar habits and backgrounds to you. It's even true that, if you smoke, the decrease in your lifespan may not be well described by the average decrease in life expectancy for smokers.
The average is simply one measure of a population. It might be a good way of describing things; it might not be. As an example, I could take the average height of my family. Adding myself, my spouse, and our toddler, and dividing by three gives me something around four and a half feet. That's not anywhere near any of our heights; in this case the average is simply an irrelevant measure.
More commonly, the average isn't a bad measure per se, it's just incomplete. What you usually need is the average, plus some indication of how spread out the population is around that average. The standard deviation is one such measure.
Any reputable scientific paper will have calculated many measures for the population it's looking at. Here's a list of, among other things, various measures that can be applied to a population. It's a little bewildering, which is likely why most media reports focus on one number and strip away the complicating details.
What to make of all this? Well, don't start smoking. Even if there is a certain amount of variance in the data, it's foolish to assume that you'll be an outlier. For well-established health issues, the average is, more often then not, a good guide.
Moving outside of that, if the study is new it's always worth asking, what's the variation around the average they're reporting? We looked at a study a while back in which the a connection between autism and induced labour was reported; the actual research paper showed that the variance around their average results was so large that it threatened to undermine the conclusions.
Knowing when an average is a bad measure is a little harder. Often when this happens the person reporting the average is deliberately using a poor measure to make themselves look better. Economic data is a prime example. Whenever you see the GDP per person, unadjusted for inflation, you can safely discount that number as worthless. The average simply isn't a good measure for the typical person's income. Politicians report it, though, because it's a quantity that governments can reliably increase through monetary policy, even if life for the typical person hasn't changed.
The key point here is that populations are complex. Any time you see them reduced to a single number, it's worth asking, "What am I missing here?" And, as Zaki points out, it is not always about you.
Wednesday, 4 September 2013
Due to genetics, or, simplicity and determinism
I typed "due to genetics" (complete with the quotation marks) into google news, and this is what I learned:
Due to genetics, you might have a higher risk of heart attacks.
Due to genetics, you can get dark circles under your eyes.
"Happiness is 50% due to genetics"
Due to genetics, some athletes are simply better at doping without getting caught. (This article was amusing for all the pictures of athletes who did get caught doping, as if somehow this proves their point)
Your enlarged pores may be due to genetics.
Other articles that came up on this search discussed autism, breastfeeding, stretch marks, and Larry Summers' views on women in science (if you're not familiar with them and you just have too much good feeling for humanity, google it). Clearly a lot of our health, in every sense of the term, is presented as being "due to genetics".
Genetics is certainly important, and an understanding of it should be a priority for science educators. It's one of the areas of science that directly impacts people's understanding of themselves and their health. I worry, though, that the dominant perception of genetics is one that is far simpler and more deterministic than the reality. To quote the heart attack risk article linked to above, "When it comes to certain diseases, preventing the onset might be almost impossible. Due to genetics and other factors involved, certain people have a greater risk of developing certain health conditions." Genetics here is simple: genes=heart attack. It's also deterministic: preventing heart attack "might be almost impossible". Genetics, though, is neither simple nor deterministic.
First, simplicity: genetics is complicated. As genetics prof John H. McDonald points out, a large number of "canonical" examples from high school biology, from eye colour to earlobes to hitchhiker's thumbs are simply wrong. Two parents with blue eyes can, in fact, have a brown eyed child.
Why is genetics so complicated? It's a question that is still a subject of academic research. One answer, though, comes from looking at what DNA actually does in a cell. What DNA does is surprisingly simple: DNA codes for proteins. That's it. That's the only thing it does. Each three base pairs in your DNA code for one amino acid, and a string of amino acids forms a protein. Everything else that DNA is responsible for is the result of those proteins interacting with other parts of the cell, such as other proteins, lipids, DNA itself, etc.
In order to go from a relatively simple mechanism (DNA-->proteins) to extremely complicated outcomes (the huge variety of cells in your body, which work together to form your nervous system, your immune system, and you) requires a lot of feedback loops. What I mean by that is that DNA codes for proteins, which then interact with the DNA to affect the way it codes for other proteins.
The interaction between your DNA and your environment, in the broad sense, is composed of many, many of these feedback loops. DNA interacts with proteins inside a cell, which interact with proteins embedded in the cell wall, which interact with proteins outside the cell (and also with the lipids that make up the cell wall), which interact with proteins on other cell walls, in a chain of microscopic links that stretches from deep inside you to the surface of your lungs, skin, or eyes. Is it any wonder this chain of interactions is hard to understand in simple terms?
What these complicated interactions mean, among other things, is that you can't in general apply average results to an individual. So if gene X causes people on average to be 50% more likely to die of a heart attack than people without the gene, that won't hold true across all sub-groups. More concretely, a vegetarian who is also an avid jogger with gene X isn't necessarily 50% more likely to die of a heart attack than a vegetarian jogger who lacks gene X. It could be larger or smaller; the 50% on its own doesn't tell us. The complicated interactions of DNA with the environment--keeping in mind that what you eat and what you do are part of "the environment"--means average results may not apply to a particular group, or worse: they may not apply to any group, and only be relevant when everyone is added together. Still useful, perhaps, but not to you as an individual.
Genetics is also less deterministic than it's made out to be. Part of this is because it's complicated. Another part is because you inherit more than DNA from your parents.
I don't just mean that metaphorically; I'm not saying here that you also pick up a variety of behaviours and ideas from them, though that's obviously true as well. No, I mean that you inherit, in a biological sense, more than just the information encoded in the base pairs of your DNA.
People have known that this is technically true for a long time. The building block of a human isn't just a piece of DNA; it's a cell, with a cell wall and organelles and structural elements and an environment. This much has been known for a while. What's changing is our appreciation for how that early environment can actually reverberate through a child's development to affect their life as an adult. (The grammarians out there should note that a child's development also effects the life of an adult.)
The interactions DNA has with the cell environment not only change the way it behaves in the short term, they can also change the DNA in ways that are passed on to daughter cells. DNA methylation is one such way. Here a particular piece of a molecule called a methyl group is attached to part of the DNA. Not only does it change the way the DNA codes for proteins, it's a modification that gets passed on when the cell divides--even, potentially, when the cells in the reproductive system divide, combine, and go on to form a new organism.
What this means is that environmental factors can alter genes for several generations before dying out. Since both the alteration and the time it takes for it to go away depends on the environment, trying to describe a deterministic role to genes is mistaken at best.
The field that looks at stuff is epigenetics. It's the field that studies the way the environment influences genetic expression, and it's currently a buzzword in science reporting. In many ways it is still a controversial field. What's clear, though, is that the environment does influence expression, even if we're still not sure exactly how it all pans out.
The amount of data available about both individual and group genomes is huge, and only growing larger. Soon people will be asked to make health decisions not only on current diagnosis but also on what their genes say about likely future scenarios. We need to keep this in mind, though: Genetics is complicated. It's not deterministic. Pretending it is only undermines our understanding and our health.
Due to genetics, you might have a higher risk of heart attacks.
Due to genetics, you can get dark circles under your eyes.
"Happiness is 50% due to genetics"
Due to genetics, some athletes are simply better at doping without getting caught. (This article was amusing for all the pictures of athletes who did get caught doping, as if somehow this proves their point)
Your enlarged pores may be due to genetics.
Other articles that came up on this search discussed autism, breastfeeding, stretch marks, and Larry Summers' views on women in science (if you're not familiar with them and you just have too much good feeling for humanity, google it). Clearly a lot of our health, in every sense of the term, is presented as being "due to genetics".
Genetics is certainly important, and an understanding of it should be a priority for science educators. It's one of the areas of science that directly impacts people's understanding of themselves and their health. I worry, though, that the dominant perception of genetics is one that is far simpler and more deterministic than the reality. To quote the heart attack risk article linked to above, "When it comes to certain diseases, preventing the onset might be almost impossible. Due to genetics and other factors involved, certain people have a greater risk of developing certain health conditions." Genetics here is simple: genes=heart attack. It's also deterministic: preventing heart attack "might be almost impossible". Genetics, though, is neither simple nor deterministic.
Simple
First, simplicity: genetics is complicated. As genetics prof John H. McDonald points out, a large number of "canonical" examples from high school biology, from eye colour to earlobes to hitchhiker's thumbs are simply wrong. Two parents with blue eyes can, in fact, have a brown eyed child.
Why is genetics so complicated? It's a question that is still a subject of academic research. One answer, though, comes from looking at what DNA actually does in a cell. What DNA does is surprisingly simple: DNA codes for proteins. That's it. That's the only thing it does. Each three base pairs in your DNA code for one amino acid, and a string of amino acids forms a protein. Everything else that DNA is responsible for is the result of those proteins interacting with other parts of the cell, such as other proteins, lipids, DNA itself, etc.
In order to go from a relatively simple mechanism (DNA-->proteins) to extremely complicated outcomes (the huge variety of cells in your body, which work together to form your nervous system, your immune system, and you) requires a lot of feedback loops. What I mean by that is that DNA codes for proteins, which then interact with the DNA to affect the way it codes for other proteins.
The interaction between your DNA and your environment, in the broad sense, is composed of many, many of these feedback loops. DNA interacts with proteins inside a cell, which interact with proteins embedded in the cell wall, which interact with proteins outside the cell (and also with the lipids that make up the cell wall), which interact with proteins on other cell walls, in a chain of microscopic links that stretches from deep inside you to the surface of your lungs, skin, or eyes. Is it any wonder this chain of interactions is hard to understand in simple terms?
What these complicated interactions mean, among other things, is that you can't in general apply average results to an individual. So if gene X causes people on average to be 50% more likely to die of a heart attack than people without the gene, that won't hold true across all sub-groups. More concretely, a vegetarian who is also an avid jogger with gene X isn't necessarily 50% more likely to die of a heart attack than a vegetarian jogger who lacks gene X. It could be larger or smaller; the 50% on its own doesn't tell us. The complicated interactions of DNA with the environment--keeping in mind that what you eat and what you do are part of "the environment"--means average results may not apply to a particular group, or worse: they may not apply to any group, and only be relevant when everyone is added together. Still useful, perhaps, but not to you as an individual.
Deterministic
Genetics is also less deterministic than it's made out to be. Part of this is because it's complicated. Another part is because you inherit more than DNA from your parents.
I don't just mean that metaphorically; I'm not saying here that you also pick up a variety of behaviours and ideas from them, though that's obviously true as well. No, I mean that you inherit, in a biological sense, more than just the information encoded in the base pairs of your DNA.
People have known that this is technically true for a long time. The building block of a human isn't just a piece of DNA; it's a cell, with a cell wall and organelles and structural elements and an environment. This much has been known for a while. What's changing is our appreciation for how that early environment can actually reverberate through a child's development to affect their life as an adult. (The grammarians out there should note that a child's development also effects the life of an adult.)
The interactions DNA has with the cell environment not only change the way it behaves in the short term, they can also change the DNA in ways that are passed on to daughter cells. DNA methylation is one such way. Here a particular piece of a molecule called a methyl group is attached to part of the DNA. Not only does it change the way the DNA codes for proteins, it's a modification that gets passed on when the cell divides--even, potentially, when the cells in the reproductive system divide, combine, and go on to form a new organism.
What this means is that environmental factors can alter genes for several generations before dying out. Since both the alteration and the time it takes for it to go away depends on the environment, trying to describe a deterministic role to genes is mistaken at best.
The field that looks at stuff is epigenetics. It's the field that studies the way the environment influences genetic expression, and it's currently a buzzword in science reporting. In many ways it is still a controversial field. What's clear, though, is that the environment does influence expression, even if we're still not sure exactly how it all pans out.
The amount of data available about both individual and group genomes is huge, and only growing larger. Soon people will be asked to make health decisions not only on current diagnosis but also on what their genes say about likely future scenarios. We need to keep this in mind, though: Genetics is complicated. It's not deterministic. Pretending it is only undermines our understanding and our health.
Wednesday, 28 August 2013
Lie-to-children
I admit, the first time I saw this phrase in print, I was a little put off. Lie-to-children? That sounds like the kind of thing that gets you a lot of extra time in purgatory.
Then I learned more about it and decided that perhaps it wasn't so bad after all. Well, maybe. It still could be bad.
I should explain all that. The first thing I thought when I saw the phrase "Lie-to-children" was that people were talking about the kind of thing that you tell kids because it's convenient, even if in the long run it doesn't help them at all. That's not what it actually is.
What lie-to-children refers to is the type of simplification that happens when you are teaching someone (anyone, it turns out: doesn't have to be a child) about physics, or math, or chess, or any field of human endeavour with a deep and complicated body of knowledge. The idea is that throwing the full force of, say, quantum electrodynamics at a beginner will only turn them off the field all together, so you teach them simplified forms (in this case, simplified electricity and magnetism) that you know aren't entirely correct. Since it wouldn't really be good teaching practice to emphasis their incorrectness at each turn, you're sort of lying. Hence, lie-to-children.
In this sense the concept has some merit. I've come to realize, though, that there's two different types of simplification. One is a type that, while simplified, gives students the right intuition about how the more complicated process works. The other type does the opposite: it is a subject simplified in such a way that students either don't make any progress towards understanding the fuller ideas.
Here's an example of the good type. In high school and early undergraduate physics, we teach students a theory of friction. In this theory, friction forces depend on the materials rubbing past each other (eg rubber on concrete or skin on carpet) and the force pushing them together (gravity in most cases)--and the dependence of friction on the force pushing the two objects together is linear (double the one force, double the other). There's no dependence on the size or shape of the contact area, or any other factors.
Clearly this can't be the complete theory of friction. If it was, then all cars with the same material in their tires would have the same stopping distance, and sports cars wouldn't need fat tires or good suspension for good handling--skinny tires would work just as well. But it works as a lie-to-children because it lets students figure out things like force, energy, and work in ways that serve them well as they move on to more complete forces. (As an aside, the wikipedia article about friction is terrible. Please don't read it unless you want to be seriously confused and misled).
An example of the bad type of lie-to-children is how we teach uncertainty estimates. The most common way of introducing students to measurement uncertainty in high-school and first year labs is to tell them to look at the four or five data points we've told them to collect, and subtract the largest from the smallest to get a range of uncertainty.
Why is this so terrible? For starters, it gives students the idea that a range of uncertainty on a reported value means that the true value cannot possibly be outside of that range. That's an unfortunate idea, though one that even professional scientists sometimes seem to have. It's not the worst of it, though. The worst part of calculating uncertainty this way is that the uncertainty goes up the more measurements you take. Taking more measurements gives you a higher chance of having a particularly large or particularly small one, which makes an uncertainty based on max minus min get larger. This is bad; we want students to get an intuitive feel for uncertainty as a measure of the confidence in a set of data, then we give them a way of calculating it that implies that the more data you have, the less confident you are in it.
I'm not going to go into how I think uncertainty should be introduced in high school. All I want to do here is point out that we need to shift the question from "how can we simplify this body of knowledge?" to "does this simplified version build students' (or readers', depending on context) intuition in the right direction?" If we can do that, the lie-to-children will be a little less of a lie.
Then I learned more about it and decided that perhaps it wasn't so bad after all. Well, maybe. It still could be bad.
I should explain all that. The first thing I thought when I saw the phrase "Lie-to-children" was that people were talking about the kind of thing that you tell kids because it's convenient, even if in the long run it doesn't help them at all. That's not what it actually is.
What lie-to-children refers to is the type of simplification that happens when you are teaching someone (anyone, it turns out: doesn't have to be a child) about physics, or math, or chess, or any field of human endeavour with a deep and complicated body of knowledge. The idea is that throwing the full force of, say, quantum electrodynamics at a beginner will only turn them off the field all together, so you teach them simplified forms (in this case, simplified electricity and magnetism) that you know aren't entirely correct. Since it wouldn't really be good teaching practice to emphasis their incorrectness at each turn, you're sort of lying. Hence, lie-to-children.
In this sense the concept has some merit. I've come to realize, though, that there's two different types of simplification. One is a type that, while simplified, gives students the right intuition about how the more complicated process works. The other type does the opposite: it is a subject simplified in such a way that students either don't make any progress towards understanding the fuller ideas.
Here's an example of the good type. In high school and early undergraduate physics, we teach students a theory of friction. In this theory, friction forces depend on the materials rubbing past each other (eg rubber on concrete or skin on carpet) and the force pushing them together (gravity in most cases)--and the dependence of friction on the force pushing the two objects together is linear (double the one force, double the other). There's no dependence on the size or shape of the contact area, or any other factors.
Clearly this can't be the complete theory of friction. If it was, then all cars with the same material in their tires would have the same stopping distance, and sports cars wouldn't need fat tires or good suspension for good handling--skinny tires would work just as well. But it works as a lie-to-children because it lets students figure out things like force, energy, and work in ways that serve them well as they move on to more complete forces. (As an aside, the wikipedia article about friction is terrible. Please don't read it unless you want to be seriously confused and misled).
An example of the bad type of lie-to-children is how we teach uncertainty estimates. The most common way of introducing students to measurement uncertainty in high-school and first year labs is to tell them to look at the four or five data points we've told them to collect, and subtract the largest from the smallest to get a range of uncertainty.
Why is this so terrible? For starters, it gives students the idea that a range of uncertainty on a reported value means that the true value cannot possibly be outside of that range. That's an unfortunate idea, though one that even professional scientists sometimes seem to have. It's not the worst of it, though. The worst part of calculating uncertainty this way is that the uncertainty goes up the more measurements you take. Taking more measurements gives you a higher chance of having a particularly large or particularly small one, which makes an uncertainty based on max minus min get larger. This is bad; we want students to get an intuitive feel for uncertainty as a measure of the confidence in a set of data, then we give them a way of calculating it that implies that the more data you have, the less confident you are in it.
I'm not going to go into how I think uncertainty should be introduced in high school. All I want to do here is point out that we need to shift the question from "how can we simplify this body of knowledge?" to "does this simplified version build students' (or readers', depending on context) intuition in the right direction?" If we can do that, the lie-to-children will be a little less of a lie.
Friday, 23 August 2013
A new feature
In addition to doing science and writing about science (and writing about how we write about science...) I occasionally indulge in my artistic side. It's not much, mind you, but every so often I sit down with an art notebook and sketch out some science-y stuff. Since I'm writing this blog anyway, I've decided that every so often I'm going to upload those sketches for the world to see.
So far what I've discovered is that it's a lot of work to turn a sketch in a book into something that looks decent on the screen. And I don't mean looks like a professional painting, I just mean good enough that you can read the writing and follow what's going on.
Anyway, this next post is the first entry in My Scientific Notebook. Enjoy!
So far what I've discovered is that it's a lot of work to turn a sketch in a book into something that looks decent on the screen. And I don't mean looks like a professional painting, I just mean good enough that you can read the writing and follow what's going on.
Anyway, this next post is the first entry in My Scientific Notebook. Enjoy!
Wednesday, 21 August 2013
The Worlds of Biophysics
The other day I received a brochure from the Biophysical Society, the world's largest association of biophysicists, with a call for papers for their 58th annual meeting. The brochure contained a world map with the number of Biophysical Society members listed for each region. Since I'm far more interested in maps than in whatever society business the rest of the brochure was trying to tell me about, this is the section that I actually paid attention to.
Here's what I learned: there's a lot of biophysicists in the US. Not so much in Africa. The list of Biophysical Society members by region, sorted by which region has the most members, is as follows:
US: 5,823
Europe: 1,790
Asia: 762
Canada: 348
Latin America: 151
Australia/New Zealand: 99
Middle East/Africa: 95
That's right, the Middle East and Africa together have less biophysicists than Australia and New Zealand. I suspect that the comparison would be even worse if the Middle East and Africa were considered as separate regions, since Israel and Iran are both relative science powerhouses.
Even as it stands, though, the numbers are stark. Here's a variation on the list above: the number of people in each region per biophysicist:
US: 1 biophysicist per 54,000 people
Canada: 1 biophysicist per 95,000 people
Australia/NZ: 1 biophysicist per 282,000 people
Europe: 1 biophysicist per 413,000 people
Latin America: 1 biophysicist per 3,900,000 people
Asia: 1 biophysicist per 5,050,000 people
Africa/ME: 1 biophysicist per 13,700,000 people
When weighted by population, the US has 250 times as many biophysicists as Africa and the Middle East. That's a far larger disparity than the economic one--the richest countries in Africa have about a quarter of the GDP per capita of the US, which the poorest have around a tenth (based on a rough estimate from here and here).
Now, you may object that I'm conflating "biophysicist" and "member of the Biophysical Society" here. And you'd have a good point; the society makes no effort to survey everyone to make sure they've gotten all the biophysicists, and it's likely that rates of scientists working in biophysics that pay to join a society are lower in parts of the world where research funds are less available.
This brings up an interesting point. Why wouldn't someone want to be part of an organization like the Biophysical Society? Perhaps because the main point of joining is to gain access to the conferences. There's no professional designation granted by the society, and it's not really something to put on a CV. Sure, you get a subscription to Biophysical Journal, which is probably a motivator for a few people, but not many. No, the main reason to join is that each year the society hosts it's annual meeting, the largest gathering of biophysicists in the world, as well as a number of smaller, more focussed conferences.
In principle the organization is international, but the meetings are always held in the US. For researchers in Africa, this means that the society membership and meeting registration are the smallest costs of attending: a couple of hundred dollars isn't much compared to a flight and hotel. Quickly searching United Airlines tells me that a round trip flight from Nairobi to San Francisco, where the next annual meeting is to be held, will run you about $1,800 (and also take about 30 hours each way). That would be small change in the research budget of a large American group, but for researchers at cash-strapped African universities it puts the trip out of reach. And if you can't go to the conferences, there's really no point in becoming a member of the society.
Not being able to attend conferences is important because they are where a large part of scientific networking happens. I've written before about the role of social networks in scientific careers. By being priced out of conferences, African researchers are also being kept out of the social networks that are advancing the careers of their American "colleagues".
Beyond that, conferences are vital for simply keeping up with the field at the level required to contribute to it. Here's an example. This past March, I went to a conference and presented work I had done that we were "submitting next week" to a journal. That work was finally submitted a month later, revised, accepted, and will be published in a couple of months--October, maybe. I'm not under any illusion that anyone in any part of the world is racing to react to this particular work, but it sets a typical timeline: someone who is reliant on published research articles to build off of is about half a year behind someone who went to the conference and saw the work presented there. In some areas this won't matter so much; in others six months is an eternity.
This isn't a problem specific to biophysics. Across disciplines, high profile conferences tend to happen where there are concentrations of high profile researchers, leaving poorer regions on the outside. And obviously underlying this all is a whole host of economic and political systems that the Biophysical Society can hardly be held accountable for fixing. Still, there is one step that they could take. They could hold the occasional meeting in Africa. Or Asia, or Latin America. Anywhere outside the US or Europe. Ostensibly the organization is international, after all. Hold the occasional meeting in Africa and a bunch of researchers (and students!) suddenly have a small bit of access to the professional networks and early results they are typically denied.
Why don't they do this? The cost isn't the biggest concern; I've already mentioned that travel costs would be a small amount of a typical research group's budget. The travel time is probably far more significant--most faculty I've met would far sooner part with a few thousand dollars from their grant than with sixty hours.
The biggest obstacle is apathy. As far as I can tell there is next to no concern among the research communities in the West for those in the developing world. We, they say, are a biophysics group. We are not a development organization, nor is it our job to build a scientific community in countries that don't have the economic base to support one even if they had the talent.
Perhaps. But everyone who works in science should realize that the historical scientific community in Europe and later the US was nurtured by an economy that was deeply exploitative, in a way that the rest of the world has yet to recover from, particularly since in some areas the exploitation never stopped. Extending a hand to researchers in countries on whose backs we built our scientific enterprise is the absolute least we can do.
For the moment, though, it seems like we're not even doing that.
Here's what I learned: there's a lot of biophysicists in the US. Not so much in Africa. The list of Biophysical Society members by region, sorted by which region has the most members, is as follows:
US: 5,823
Europe: 1,790
Asia: 762
Canada: 348
Latin America: 151
Australia/New Zealand: 99
Middle East/Africa: 95
That's right, the Middle East and Africa together have less biophysicists than Australia and New Zealand. I suspect that the comparison would be even worse if the Middle East and Africa were considered as separate regions, since Israel and Iran are both relative science powerhouses.
Even as it stands, though, the numbers are stark. Here's a variation on the list above: the number of people in each region per biophysicist:
US: 1 biophysicist per 54,000 people
Canada: 1 biophysicist per 95,000 people
Australia/NZ: 1 biophysicist per 282,000 people
Europe: 1 biophysicist per 413,000 people
Latin America: 1 biophysicist per 3,900,000 people
Asia: 1 biophysicist per 5,050,000 people
Africa/ME: 1 biophysicist per 13,700,000 people
When weighted by population, the US has 250 times as many biophysicists as Africa and the Middle East. That's a far larger disparity than the economic one--the richest countries in Africa have about a quarter of the GDP per capita of the US, which the poorest have around a tenth (based on a rough estimate from here and here).
Now, you may object that I'm conflating "biophysicist" and "member of the Biophysical Society" here. And you'd have a good point; the society makes no effort to survey everyone to make sure they've gotten all the biophysicists, and it's likely that rates of scientists working in biophysics that pay to join a society are lower in parts of the world where research funds are less available.
This brings up an interesting point. Why wouldn't someone want to be part of an organization like the Biophysical Society? Perhaps because the main point of joining is to gain access to the conferences. There's no professional designation granted by the society, and it's not really something to put on a CV. Sure, you get a subscription to Biophysical Journal, which is probably a motivator for a few people, but not many. No, the main reason to join is that each year the society hosts it's annual meeting, the largest gathering of biophysicists in the world, as well as a number of smaller, more focussed conferences.
In principle the organization is international, but the meetings are always held in the US. For researchers in Africa, this means that the society membership and meeting registration are the smallest costs of attending: a couple of hundred dollars isn't much compared to a flight and hotel. Quickly searching United Airlines tells me that a round trip flight from Nairobi to San Francisco, where the next annual meeting is to be held, will run you about $1,800 (and also take about 30 hours each way). That would be small change in the research budget of a large American group, but for researchers at cash-strapped African universities it puts the trip out of reach. And if you can't go to the conferences, there's really no point in becoming a member of the society.
Not being able to attend conferences is important because they are where a large part of scientific networking happens. I've written before about the role of social networks in scientific careers. By being priced out of conferences, African researchers are also being kept out of the social networks that are advancing the careers of their American "colleagues".
Beyond that, conferences are vital for simply keeping up with the field at the level required to contribute to it. Here's an example. This past March, I went to a conference and presented work I had done that we were "submitting next week" to a journal. That work was finally submitted a month later, revised, accepted, and will be published in a couple of months--October, maybe. I'm not under any illusion that anyone in any part of the world is racing to react to this particular work, but it sets a typical timeline: someone who is reliant on published research articles to build off of is about half a year behind someone who went to the conference and saw the work presented there. In some areas this won't matter so much; in others six months is an eternity.
This isn't a problem specific to biophysics. Across disciplines, high profile conferences tend to happen where there are concentrations of high profile researchers, leaving poorer regions on the outside. And obviously underlying this all is a whole host of economic and political systems that the Biophysical Society can hardly be held accountable for fixing. Still, there is one step that they could take. They could hold the occasional meeting in Africa. Or Asia, or Latin America. Anywhere outside the US or Europe. Ostensibly the organization is international, after all. Hold the occasional meeting in Africa and a bunch of researchers (and students!) suddenly have a small bit of access to the professional networks and early results they are typically denied.
Why don't they do this? The cost isn't the biggest concern; I've already mentioned that travel costs would be a small amount of a typical research group's budget. The travel time is probably far more significant--most faculty I've met would far sooner part with a few thousand dollars from their grant than with sixty hours.
The biggest obstacle is apathy. As far as I can tell there is next to no concern among the research communities in the West for those in the developing world. We, they say, are a biophysics group. We are not a development organization, nor is it our job to build a scientific community in countries that don't have the economic base to support one even if they had the talent.
Perhaps. But everyone who works in science should realize that the historical scientific community in Europe and later the US was nurtured by an economy that was deeply exploitative, in a way that the rest of the world has yet to recover from, particularly since in some areas the exploitation never stopped. Extending a hand to researchers in countries on whose backs we built our scientific enterprise is the absolute least we can do.
For the moment, though, it seems like we're not even doing that.
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