The UK's publicly-funded scientific research is going open access.
This is a Very Good Thing. And I'm quite impressed that the UK government has been pretty decisive about this; I was emailed by the MRC (my main funder) a while ago, telling me that publishing in an open access way was now a condition of funding.
Leaving aside the potential practical complications in making this happen (which other people have considered more than I have), why is this such a good thing? There are a number of reasons.
Firstly, there's the basic consideration of who's paying. In this case, it's the tax-paying UK public. So it seems entirely reasonable that they should have access to the research that they've paid for. It actually seems faintly ridiculous that this was ever not the case (although this was because pre-Web, the cost of distribution wasn't insignificant).
Secondly, and very importantly, it accelerates the pace of scientific research (which relates to this blog post). When I write a scientific paper, I want it to be accessible as rapidly as possible to as many people as possible, and as easy to access as possible. This means that my research can be read, assessed and acted upon as rapidly as possible, which means that people can benefit from my work and/or find ways to refine the ideas therein. Faster is better.
Thirdly, there's a very important consideration of public engagement with science. Science and scientific research are getting more and more complex with time, both because we're already discovered a lot of the easy stuff and also because we come up with progressively more clever ways in which to advance. This is great, but it does mean that it's increasingly difficult for the non-specialist to understand a lot of the good science that goes on. There are all sorts of efforts that are going on to try to address this, but one really good one is to try to make sure that anyone can access any piece of research.
My anecdotal view is that governments are in general pretty rubbish at having a clue about internet-related developments. Politicians are very busy people, making it hard to keep up with developments. I also suspect that the demographic profile of the current generation of politicians means there is a high proportion of Hapless Techno Weenies. But in this instance, they seem to have correctly identified an important internet trend and acted on it. Kudos for that.
Showing posts with label science as a subject. Show all posts
Showing posts with label science as a subject. Show all posts
Tuesday, 17 July 2012
Tuesday, 30 November 2010
Predictive power and explanatory power
Recently, I've been thinking about the goals of science. Science isn't about discovering Truth. This is for the very simple, practical reason that Truth is very hard to positively identify - how would you establish that a prospective Truth wasn't just a very good approximation? Not very helpful...
There are all sorts of philosophical discussions to be had here, but we can sidestep these and take a more practical approach. Specifically, we can choose to care about Predictive Power and Explanatory Power.
Predictive Power is the ability of a given theory to allow us to make predictions about the natural world. We know that Newtonian gravity is an approximation (to General Relativity, at the very least), but it's very good at predicting where the planets in our solar system will be. This is really a flat-out practical consideration - if a theory can't make predictions, it's not very useful (and some would argue it's not even science).
Explanatory Power is the quality of a theory that gives us some deeper understanding of what's going on in a physical system. For example, knowing about atomic electron orbitals allows us to make sense of the periodic table and chemical interactions. It gives us ways to develop other theories.
So, what we're looking for from a scientific theory is the ability to make predictions and for some explanatory insight as to why something happens, so that we can use that insight to develop further theories.
This is also relevant for statistical modeling (and hence data-intensive science), because we can build our models to address either or both of these. Predictive algorithms such as neural networks can perform very well, but the structure of the model is often hard to interpret in any kind of explanatory way. Conversely, a linear regression model might tell us a lot about which variables are important, but may not make good predictions. Ideally, it would be nice to build models that are useful for both prediction and explanation.
Saturday, 13 June 2009
Science in the 21st century
The nature of scientific research changes with time. Each epoch has its own particular characteristics, the result of a blend of factors ranging from our current state of knowledge and available technologies to the particular needs of our society and the world at that time. The early 21st century (ie. "right now") is no different.
Big data
The sum total of human data is growing more-or-less exponentially and scientific data are no exception. A decade ago, gigabyte-scale data-sets were the sort of thing the large physics experiments were producing. Currently, scientists talk about terabytes quite happily and it's far from stupid to be talking aboutpeta- and exa -scale data-sets. After all, we'll be able to handle routinely that size of data in the next decade of two (organisations like Google probably already do so).
The key point about big data is this: modern scientific data-sets are typically too large to fit in a human brain.
Think about that for a moment. As soon as you can't fit all your data in your brain at once, you need to start doing something new or you're going to have to start throwing data (and hence information) away. This leads to whole new areas of research into how to handle any given type of big data.
Computers
Computers can do things that people cannot. Ever tried adding a million numbers together in a faction of a second? Exactly.
There are two ways of looking at this. The first is that we need computers because otherwise we would be unable to handle the Big Data we are now generating. The other is that computers give us possibilities that didn't exist before, for example there are many applications of Bayesian statistical inference nowadays that were always technically possible, but were simply impractical in terms of the amount of computation required. That is often no longer a problem.
Computational science (ie. doing science using a computer) has become a whole distinct area of scientific research, which means that computing skill-sets have become valuable in a scientific context. In much the same way that scientists with specialist lab skills, mathematical skills, electrical engineering skills (eg. building the big physics experiments) and the like are vital to modern science, this is also true of scientists with specialist programming and other computer skills.
The flow of knowledge...
How rapidly scientific knowledge flows is key to the rate at which science advances. A world-changing idea will likely only do so once it's reached a substantial number of people. The Internet has become a game-changer for this. 20 years ago, a new paper would only typically become available when the hard copy of the journal reached your university library. Now I can scan the abstracts of a hundredpre-prints a day over a cup of coffee, via an RSS feed, months before they appear in the journals. A literature search that might occupy days of library time can now get under way in seconds via Google Scholar and the websites of other academics, and be completed in short order via downloadedPDFs. And even if I can't make it to a conference, there's a good chance I can access the slides online (or even see a webcast of the talk) and email the speaker if I have any questions.
All of this removes overheads from the process of learning about new scientific knowledge. And that makes a big difference to the amount of science you can get done.
Interdisciplinarity
In some sense, science has always featured what we now called "interdisciplinarity". Some of the best new ideas simply span more than one discipline. However, it seems to me that this is particularly true right now. The body of scientific knowledge has become large enough that no one person can know even a moderate proportion of it. This means that 'Eureka!' moments involving ideas from different disciplines are harder to find. So it's become very important to have people who are expert in one discipline who go and talk to people in other disciplines. This even extends to interdisciplinary centres, which have the benefit of putting people from different disciplines in the same office/meeting/seminar on a daily basis. A lot of science is driven by the conversations you have over coffee...
Advancing rapidly...
If I had to pick one thing to characterise modern science, it would be rapidity of its advance. Driven both by the speed of communication and by the rate of improvement of underlying technologies (eg. computers, the cost-per-bit of to generate useful data), we're making new discoveries at an amazing rate. And one of the most striking features is the speed with which new discoveries can be applied to, for example, new technologies - consider Moore's law for an obvious example.
The need for multiple skill-sets
Science is a large field, nowadays. Gone are the days when a gentleman scientist could be the master of all disciplines. Today there are many distinct specialisms, each of which benefits from (often requires) professional-level skill-sets. For example:
Weight of numbers....
I don't have any concrete numbers for this, but my guess is that we have more scientists now than at any point in history. There are several reasons for this intuition. Firstly, the world's population is bigger that it's ever been (and growing...). Secondly, more countries have developed economies to the point where they can afford significant programmes of scientific research. Thirdly, there are big private companies that have programmes of scientific research.
I would love to see some properly researched numbers on this. And I wonder what a graph of total-science-budget versus time would look like for the world as a whole...
In conclusion...
In the 20th century, science discovered some of the fundamental laws of nature (eg relativity and quantum theory), developed the standard models of cosmology and particle physics, unravelled the secrets of life (DNA) and beat the majority of infectious diseases (antibiotics). We know how to turn lead into gold (in a nuclear reactor) and while eternal life is trickier, theUK's life expectancy has risen by 30 years over the course of the last century. And science is now progressing faster than it did in the last century (maybe a lot faster).
Anyone else excited by the possibilities...?
Big data
The sum total of human data is growing more-or-less exponentially and scientific data are no exception. A decade ago, gigabyte-scale data-sets were the sort of thing the large physics experiments were producing. Currently, scientists talk about terabytes quite happily and it's far from stupid to be talking aboutpeta- and exa -scale data-sets. After all, we'll be able to handle routinely that size of data in the next decade of two (organisations like Google probably already do so).
The key point about big data is this: modern scientific data-sets are typically too large to fit in a human brain.
Think about that for a moment. As soon as you can't fit all your data in your brain at once, you need to start doing something new or you're going to have to start throwing data (and hence information) away. This leads to whole new areas of research into how to handle any given type of big data.
Computers
Computers can do things that people cannot. Ever tried adding a million numbers together in a faction of a second? Exactly.
There are two ways of looking at this. The first is that we need computers because otherwise we would be unable to handle the Big Data we are now generating. The other is that computers give us possibilities that didn't exist before, for example there are many applications of Bayesian statistical inference nowadays that were always technically possible, but were simply impractical in terms of the amount of computation required. That is often no longer a problem.
Computational science (ie. doing science using a computer) has become a whole distinct area of scientific research, which means that computing skill-sets have become valuable in a scientific context. In much the same way that scientists with specialist lab skills, mathematical skills, electrical engineering skills (eg. building the big physics experiments) and the like are vital to modern science, this is also true of scientists with specialist programming and other computer skills.
The flow of knowledge...
How rapidly scientific knowledge flows is key to the rate at which science advances. A world-changing idea will likely only do so once it's reached a substantial number of people. The Internet has become a game-changer for this. 20 years ago, a new paper would only typically become available when the hard copy of the journal reached your university library. Now I can scan the abstracts of a hundredpre-prints a day over a cup of coffee, via an RSS feed, months before they appear in the journals. A literature search that might occupy days of library time can now get under way in seconds via Google Scholar and the websites of other academics, and be completed in short order via downloadedPDFs. And even if I can't make it to a conference, there's a good chance I can access the slides online (or even see a webcast of the talk) and email the speaker if I have any questions.
All of this removes overheads from the process of learning about new scientific knowledge. And that makes a big difference to the amount of science you can get done.
Interdisciplinarity
In some sense, science has always featured what we now called "interdisciplinarity". Some of the best new ideas simply span more than one discipline. However, it seems to me that this is particularly true right now. The body of scientific knowledge has become large enough that no one person can know even a moderate proportion of it. This means that 'Eureka!' moments involving ideas from different disciplines are harder to find. So it's become very important to have people who are expert in one discipline who go and talk to people in other disciplines. This even extends to interdisciplinary centres, which have the benefit of putting people from different disciplines in the same office/meeting/seminar on a daily basis. A lot of science is driven by the conversations you have over coffee...
Advancing rapidly...
If I had to pick one thing to characterise modern science, it would be rapidity of its advance. Driven both by the speed of communication and by the rate of improvement of underlying technologies (eg. computers, the cost-per-bit of to generate useful data), we're making new discoveries at an amazing rate. And one of the most striking features is the speed with which new discoveries can be applied to, for example, new technologies - consider Moore's law for an obvious example.
The need for multiple skill-sets
Science is a large field, nowadays. Gone are the days when a gentleman scientist could be the master of all disciplines. Today there are many distinct specialisms, each of which benefits from (often requires) professional-level skill-sets. For example:
- chemical/biological/physics lab skills
- software engineering
- electrical/mechanical engineering (eg. building the big physics machines such as telescopes, particle accelerators)
Weight of numbers....
I don't have any concrete numbers for this, but my guess is that we have more scientists now than at any point in history. There are several reasons for this intuition. Firstly, the world's population is bigger that it's ever been (and growing...). Secondly, more countries have developed economies to the point where they can afford significant programmes of scientific research. Thirdly, there are big private companies that have programmes of scientific research.
I would love to see some properly researched numbers on this. And I wonder what a graph of total-science-budget versus time would look like for the world as a whole...
In conclusion...
In the 20th century, science discovered some of the fundamental laws of nature (eg relativity and quantum theory), developed the standard models of cosmology and particle physics, unravelled the secrets of life (DNA) and beat the majority of infectious diseases (antibiotics). We know how to turn lead into gold (in a nuclear reactor) and while eternal life is trickier, theUK's life expectancy has risen by 30 years over the course of the last century. And science is now progressing faster than it did in the last century (maybe a lot faster).
Anyone else excited by the possibilities...?
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