Michael Nielsen

Cameron Neylon is a scientist at the UK Science and Technology Facilities Council, an open notebook scientist, and one of the most thoughtful advocates of open science (blog, Twitter). In an email interview I asked Cameron a few questions about practical steps that can be taken toward open science:

Q1: Suppose you’ve just been invited by the head of a major funding agency to advise them on open science. They’re asking for two or three practical suggestions for how they can help move us toward a more open scientific culture. What would you tell them?

For me the key first question is to ask what they see as their mission to maximise, and then seek to measure that effectively. I think there are two different main classes of reason why funders support science. One is to build up knowledge, and the other is to support the generation of economic and social outcomes from research and innovation. A third (but often brushed under the carpet) target is prestige – sometimes the implicit target of small research funders, or those from emerging and transitional economies, seeking to find a place on the global research stage. Nothing wrong with this but if that is the target you should optimise for that. If you want other outcomes you should optimise for that.

Our current metrics and policies largely optimise for prestige rather than knowledge building or social outcomes. On the assumption that most funders would choose one of these two outcomes as their mission I would say that the simple things to do are to actively measure and ask fundees to report on these things.

For knowledge building: Ask about, and measure the use and re-use of research outputs. Has data been re-used, is software being incorporated into other projects, are papers being cited and woven tightly into the networks of influence that we can now start to measure with more sophisticated analysis tools?

For social and economic outcomes: Similar to above but look more explicitly for real measurable outcomes. Evidence of influence over policy, measure of real economic activity generated by outputs (not just numbers of spin out companies), development of new treatment regimes.

Both of these largely seek to measure re-use as opposed to counting outputs. This is arguably not simple but as the aim is to re-align community
attitudes and encourage changes in behaviour its not going to be simple. However this kind of approach takes what we are already doing, and the direction it is taking us in terms of measuring “impact” and makes it more sophisticated.

Asking researchers to report on these, and actively measuring them, will in and of itself lead to greater consideration of these broader impacts and change behaviour with regard to sharing. For some period between 18 months and three years simply collect and observe. Then look at how those who are doing best on specific metrics and seek to capture best practice and implement policies to support it.

Throughout all of this accept that as research becomes less directed or applied that the measurement becomes harder, the error margins larger, and picking of winners (already difficult) near impossible. Consider mechanism to provide baseline funding at some low level, perhaps at the level of 25-50% of a PhD studentship or technician, direct to researchers with no restrictions on use, across disciplines with the aim of maintaining diversity, encouraging exploration, and maintaining capacity. This is both
politically and technically difficult but could have large dividends if the right balance is found. If it drops below an amount which can be useful when combined between a few researchers it is probably not worth it.

Q2: Suppose a chemist early in their career has just approached you. They’re inspired by the idea of open science, but want to know what exactly they can do. How can they get involved in a concrete way?

Any young researcher I speak to today I would say to do three things:

1) Write as much as possible, online and off, in as many different ways as possible. Writing is the transferable skill and people who do it well will always find good employment.

2) Become as good a programmer/software engineer/web developer as possible. A great way to contribute to any project is to be able to take existing tools and adapt them quickly to local needs.

3) Be as open as you can (or as your supervisor will allow you to) about communicating all of the things you are doing. The next stage of your career will depend on who has a favourable impression of what you’ve done. The papers will be important, but not as important as personal connections you can make through your work.

In concrete terms:

1) Start a blog (ask for explicit guidelines from supervisors and institutions about limitations on what you should write about). Contribute to wikipedia. Put your presentations on slideshare, and screencasts and videos of talks online.

2) To the extent that it is possible maintain your data, outputs, and research records in a way that when a decision is taken to publish (whether in a paper, informally on the web or anything in between) that it is easy to do so in a useful way. Go to online forums to find out what tools others find useful and see how they work for you. Include links to real data and records in your research talks

3) Get informed about data licensing and copyright. Find the state of the art in arguments around scooping, data management, and publication, and arm yourself with evidence. Be prepared to raise issues of Open Access publication, data publication, licensing and copyright in group discussions. Expect that you will rarely win these arguments but that you are planting ideas in people’s heads.

Above all, to the extent that you can, walk the walk. Tell stories of successes and failures in sharing. Acknowledge that its complicated but provide access to data, tools, software and records where you can. Don’t act unilaterally unless you have the rights to do so but keep asking whether you can act and explaining why you think its important. Question the status quo.

Q3: One of the things that has made the technology startup world so vibrant is that there’s an enormous innovation ecoystem around startups – they benefit from venture capital, from angel investors, from open source, from University training of students, and so on. That’s part of the reason a couple of students can start Google in a garage, and then take it all the way to being one of the largest companies in the world. At the moment, there is no comparably successful innovation ecosystem in science. Is there a way we can develop such an innovation ecosystem?

There are two problems with taking the silicon valley model into science. Firstly capital and consumable costs are much higher. Particularly today with on demand consumer services it is cheap and easy to scale a web based startup. Secondly the timeframes are much longer. A Foursquare or a Yelp can be expected to start demonstrating revenue streams in 18-24 months whereas research is likely to take much longer. A related timeframe issue is that the expertise required to contribute across these web based startups is relatively common and widespread in comparison with the highly focussed and often highly localised expertise required to solve specific research problems.

Some research could fit this model, particularly analytical tool development, and data intensive science, and certainly it should be applied where it can. More generally applying this kind of model will require cheap access to infrastructure and technical capacity (instruments and materials). Some providers in the biosciences are starting to appear and Creative Commons’ work on MTAs [materials transfer agreements] may help with materials access in the medium term.

The most critical issue however is rapid deployment of expertise to specific problems. To apply a distributed rapid innovation model we need the means to rapidly identify the very limited number of people with appropriate expertise to solve the problem at hand. We also need to rethink our research processes to make them more modular so that they can be divided up and distributed. Finally we need capacity in the system that makes it possible for expertise to actually be rapidly deployed. Its not clear to me how we achieve these goals although things like Innocentive, CoLab, Friendfeed, and others are pointing out potential directions. We are a long way from delivering on the promise and its not clear what a practical route there is.

Practical steps: more effective communication mechanisms will be driven by rewarding people for re-use of their work. Capacity can be added by baseline funding. Modularity is an attitude and a design approach which we will probably need to build into training and will be hard to do in a community where everything is bespoke and great pride is taken in eating our own dogfood but never trusting anyone else’s…

More from Pierre Levy’s book Collective Intelligence: mankind’s emerging world in cyberspace, translated by Robert Bononno.

One reason the book is notable is that, so far as I know, it was the first to really develop the term “collective intelligence”. Levy was writing in the mid-1990s, and others had, of course, both used the term before, and also developed related notions. But Levy seems to be the first to have really riffed on the term collective intelligence. Here’s Levy’s definition, and some additional commentary:

What is collective intelligence? It is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills… My initial premise is based on the notion of a universally distributed intelligence. No one knows everything, everyone knows something, all knowledge resides in humanity… New communications systems should provide members of a community with the means to coordinate their interactions within the same virtual universe of knowledge. This is not simply a matter of modeling the conventional physical environment, but of of enabling members of delocalized communities to interact within a mobile landscape of signification… Before we can mobilize skills, we have to identify them. And to do so, we have to recognize them in all their diversity… The ideal of collective intelligence implies the technical, economic, legal, and human enhancement of a universally distributed intelligence that will unleash a positive dynamic of recognition and skills mobilization.

Here’s Levy on the future of the economy:

What remains after we have mechanized agriculture, industry and messaging technologies? The economy will center, as it does already, on that which can never be fully automated, on that which is irreducible: the production of the social bond, the relational… Those who manufacture things will become scarcer and scarcer, and their labour will become mechanized, augmented, automated to a greater and greater extent…. The final frontier will be the human itself, that which can’t be automated: the creation of sensible worlds, invention, relation, the continuous recreation of the community… What kind of engineering will best meet the needs of a growing economy of human qualities?

It’s a provocative thought, although I don’t find it convincing. It’s true that the social bond is increasing in importance, as some other things become less scarce, but other scarcities remain as well.

I liked the following comment of Levy on democracy – it’s incidental to his main point, but nicely distilled an idea for me:

[Democracy] is favored not because it establishes the domination of a majority over a minority, but because it limits the power of government and provides remedies against the arbitrary use of power.

A final quote:

The greater the number of collective intellects with which an individual is involved, the more opportunities he has to diversify his knowledge and desire.

The downside of this may be a kind of glorified dilettantism. But the upside – as so often, the more interesting aspect of events – is the possibility of becoming deeply familiar with many more communities of practice.

From Pierre Levy’s book Collective Intelligence: mankind’s emerging world in cyberspace:

Groups learn even more slowly than individuals.

The flipside to this is that sometimes groups learn things that individuals can’t or won’t.

In January of 2009, Tim Gowers initiated an experiment in massively collaborative mathematics, the Polymath Project. The initial stage of this project was extremely successful, and led to two scientific papers: “A new proof of the density Hales-Jewett theorem” and “Density Hales-Jewett and Moser numbers”. The second of these papers will soon appear in a birthday volume in honour of Endre Szemeredi. The editor of the Szemeredi birthday volume, Jozsef Solymosi, invited me to submit an introduction to that paper, and to the Polymath Project more generally. The following is a draft of my introductory piece. I’d be very interested in hearing feedback. Note that the early parts of the article briefly discuss some mathematics, but if you’re not mathematically inclined the remainder of the article should be comprehensible. Many of the themes of the article will be discussed at much greater length in my book about open science, “Reinventing Discovery”, to be published early in 2011.

At first appearance, the paper which follows this essay appears to be a typical mathematical paper. It poses and partially answers several combinatorial questions, and follows the standard forms of mathematical discourse, with theorems, proofs, conjectures, and so on. Appearances are deceiving, however, for the paper has an unusual origin, a clue to which is in the name of the author, one D. H. J. Polymath. Behind this unusual name is a bold experiment in how mathematics is done. This experiment was initiated in January of 2009 by W. Timothy Gowers, and was an experiment in what Gowers termed “massively collaborative mathematics”. The idea, in brief, was to attempt to solve a mathematical research problem working entirely in the open, using Gowers’s blog as a medium for mathematical collaboration. The hope was that a large number of mathematicians would contribute, and that their collective intelligence would make easy work of what would ordinarily be a difficult problem. Gowers dubbed the project the “Polymath Project”. In this essay I describe how the Polymath Project proceeded, and reflect on similarities to online collaborations in the open source and open science communities. Although I followed the Polymath Project closely, my background is in theoretical physics, not combinatorics, and so I did not participate directly in the mathematical discussions. The perspective is that of an interested outsider, one whose main creative interests are in open science and collective intelligence.

Gowers began the Polymath Project with a description of the problem to be attacked (see below), a list of rules of collaboration, and a list of 38 brief observations he’d made about the problem, intended to serve as starting inspiration for discussion. At that point, on February 1, 2009, other people were invited to contribute their thoughts on the problem. Anyone with an interest and an internet connection could follow along and, if they wished, contribute their ideas in the comment section of Gowers’s blog. In just the first 24 hours after Gowers opened his blog up for discussion, six people offered 24 comments. In a sign of things to come, those contributors came from four countries on three continents, and included a high-school teacher, a graduate student, and four professors of mathematics. A collaboration was underway, a collaboration which expanded in the weeks that followed to involve more than twenty people.

The problem originally posed by Gowers was to investigate a new approach to a special case of the density Hales-Jewett theorem (DHJ). Let me briefly describe the statement of the theorem, before describing the special case Gowers proposed to attack. Let be the set of all length strings over the alphabet . A combinatorial line is a set of points in , formed by taking a string with one or more wildcards (““) in it, e.g., 14x1xx3, and replacing those wildcards by , respectively. In the example I’ve given, the resulting combinatorial line is: . The density Hales-Jewett theorem says that as becomes large, even very low density subsets of must contain a combinatorial line. More precisely, let us define the density Hales-Jewett number to be the size of the largest subset of which does not contain a combinatorial line. Then the density Hales-Jewett theorem may be stated as:

Theorem (DHJ): .

DHJ was originally proved in 1991 by Furstenberg and Katznelson, using techniques from ergodic theory. Gowers proposed to find a combinatorial proof of the case of the theorem, using a strategy that he outlined on his blog. As the Polymath Project progressed, that goal gradually evolved. Four days after Gowers opened his blog up for discussion, Terence Tao used his blog to start a discussion aimed at understanding the behaviour of for small . This discussion rapidly gained momentum, and the Polymath Project split into two subprojects, largely carried out, respectively, on Gowers’s blog and Tao’s blog. The first subproject pursued and eventually found an elementary combinatorial proof of the full DHJ theorem. The results of second subproject are described in the paper which follows, “Density Hales-Jewett and Moser Numbers”. As mentioned, this second subproject began with the goal of understanding the behaviour of for small . It gradually broadened to consider several related questions, including the behaviour of for small and , as well as the behaviour of the Moser numbers, , defined to be the size of the largest subset of which contains no geometric line. As for a combinatorial line, a geometric line is defined by taking a strinq in with one or more wildcard characters present. But unlike a combinatorial line, there are two distinct types of wildcards allowed (“” and ““), with taken to vary over the range , and . So, for example, generates the geometric line .

Both subprojects of the Polymath Project progressed quickly. On March 10, Gowers announced that he was confident that the polymaths had found a new combinatorial proof of DHJ. Just 37 days had passed since the collaboration began, and 27 people had contributed approximately 800 mathematical comments, containing 170,000 words. Much work remained to be done, but the original goal had already been surpassed, and this was a major milestone for the first subproject. By contrast, the goals of the second subproject were more open-ended, and no similarly decisive announcement was possible. Work on both continued for months thereafter, gradually shifting to focus on the writeup of results for publication.

Although the Polymath Project is unusual from the perspective of current practice in mathematics, there is another perspective from which it does not appear so unusual. That is the tradition of open source software development in the computer programming community. Perhaps the best known example of open source software is the Linux operating system. Begun by Linus Torvalds in 1991 as a hobby project, Linux has since grown to become one of the world’s most popular operating systems. Although not as widely used in the consumer market as Microsoft Windows, Linux is the primary operating system used on the giant computer clusters at companies such as Google, Yahoo! and Amazon, and also dominates in markets such as the movie industry, where it plays a major role at companies such as Dreamworks and Pixar.

A key feature of Linux is that, unlike proprietary software such as Microsoft Windows, the original source code for the operating system is freely available to be downloaded and modified. In his original message announcing Linux, Torvalds commented that “I’ve enjouyed [sic] doing it, and somebody might enjoy looking at it and even modifying it for their own needs. It is still small enough to understand, use and modify, and I’m looking forward to any comments you might have.” Because he had made the code publicly available, other people could add features if they desired. People began emailing code to Torvalds, who incorporated the changes he liked best into the main Linux code base. A Linux kernel discussion group was set up to co-ordinate work, and the number of people contributing code to Linux gradually increased. By 1994, 80 people were named in the Linux credits file as contributors.

Today, nearly twenty years later, Linux has grown enormously. The kernel of Linux contains 13 million lines of code. On an average day in 2007 and 2008, Linux developers added 4,300 lines of code, deleted 1,800 lines, and modified 1,500 lines. The social processes and tools used to create Linux have also changed enormously. In its early days, Linux used off-the-shelf tools and ad hoc social processes to manage development. But as Linux and the broader open source community have grown, that community has developed increasingly powerful tools to share and integrate code, and to manage discussion of development. They have also evolved increasingly sophisticated social structures to govern the process of large-scale open source development. None of this was anticipated at the outset by Torvalds – in 2003 he said “If someone had told me 12 years ago what would happen, I’d have been flabbergasted” – but instead happened organically.

Linux is just one project in a much broader ecosystem of open source projects. Deshpande and Riehle have conservatively estimated that more than a billion lines of open source software have been written, and more than 300 million lines are being added each year. Many of these are single-person projects, often abandoned soon after being initiated. But there are hundreds and perhaps thousands of projects with many active developers.

Although it began in the programming community, the open source collaboration process can in principle be applied to any digital artifact. It’s possible, for example, for a synthetic biologist to do open source biology, by freely sharing their DNA designs for living things, and then allowing others to contribute back changes that improve upon those designs. It’s possible for an architect to do open source architecture, by sharing design files, and then accepting contributions back from others. And, it’s possible to write an open source encyclopedia, by freely sharing the text of articles, and making it possible for others to contribute back changes. That’s how Wikipedia was written: Wikipedia is an open source project.

The Polymath Project is a natural extension of open source collaboration to mathematics. At first glance it appears to differ in one major way, for in programming the open source process aims to produce an artifact, the source code for the desired software. Similarly, in synthetic biology, architecture and the writing of an encyclopedia the desired end is an artifact of some sort. At least in the early stages of the Polymath Project, there was no obviously analogous artifact. It’s tempting to conclude that the two papers produced by the polymaths play this role, but I don’t think that’s quite right. In mathematics, the desired end isn’t an artifact, it’s mathematical understanding. And the Polymath process was a way of sharing that understanding openly, and gradually improving it through the contributions of many people.

The Polymath Project’s open approach to collaboration is part of a broader movement toward open science. Other prominent examples include the human genome project and the Sloan Digital Sky Survey, which use the internet to openly share data with the entire scientific community. This enables other scientists to find ingenius ways of reusing that data, often posing and answering questions radically different to those that motivated the people who originally took the data.

An example which gives the flavour of this reuse is the recent work by Boroson and Lauer, who used a computer algorithm to search through the spectra of 17,000 quasars from the Sloan Digital Sky Survey, looking for a subtle signature that they believed would indicate a pair of orbiting black holes. The result was the discovery of a candidate quasar containing a pair of supermassive black holes, 20 million and 800 million times the mass of the sun, respectively, and a third of a light year apart, orbiting one another roughly once every 100 years. This is just one of more than 3,000 papers to have cited the Sloan data, most of those papers coming from outside the Sloan collaboration.

People practicing open notebook science have carried this open data approach to its logical conclusion, sharing their entire laboratory record in real time. The Polymath Project and the open data and open notebook projects are all examples of scientists sharing information which, historically, has not been openly available, whether it be raw experimental data, observations made in a laboratory notebook, or ideas for the solution of a mathematical problem.

There is, however, a historical parallel to the early days of modern science. For example, when Galileo first observed what would later be recognized as Saturn’s rings, he sent an anagram to the astronomer Kepler so that if Kepler (or anyone else) later made the same discovery, Galileo could disclose the anagram and claim the credit. Such secretive behaviour was common at the time, and other scientists such as Huygens and Hooke also used devices such as anagrams to “publish” their discoveries. Many scientists waited decades before genuine publication, if they published at all. What changed this situation – the first open science revolution – was the gradual establishment of a link between the act of publishing a scientific discovery and the scientist’s prospects for employment. This establishment of scientific papers as a reputational currency gave scientists an incentive to share their knowledge. Today, we take this reputational currency for granted, yet it was painstakingly developed over a period of many decades in the 17th and 18th centuries. During that time community norms around authorship, citation, and attribution were slowly worked out by the scientific community.

A similar process is beginning today. Will pseudonyms such as D. H. J. Polymath become a commonplace? How should young scientists report their role in such collaborations, for purposes of job and grant applications? How should new types of scientific contribution – contributions such as data or blog comments or lab notebook entries – be valued by other scientists? All these questions and many more will need answers, if we are to take full advantage of the potential of new ways of working together to generate knowledge.

The singer Avril Lavigne’s third hit was a ballad titled “I’m With You”. Let me pose what might seem a peculiar question: should the second word in her song title – “With” – be capitalized or uncapitalized? This seems a matter of small moment, but to some people it matters a great deal. In 2005 an edit war broke out on Wikipedia over whether “With” should be capitalized or not. The discussion drew in a dozen people, took more than a year to play out, and involved 4,000 words of discussion. During that time the page oscillated madly back and forth between capitalizing and not capitalizing “With”.

This type of conflict is not uncommon on Wikipedia. Other matters discussed at great length in similar edit wars include the true diameter of the Death Star in Return of the Jedi – is it 120, 160 or 900 kilometers in diameter? When one says that U2 “are a band” should that really be “U2 is a band”? Should the page for “Iron Maiden” point by default to the band or to the instrument of torture? Is Pluto really a planet? And so on.

Don’t get me wrong. Wikipedia works remarkably well, but the cost in resolving these minor issues can be very high. Let me describe for you an open source collaboration where problems like this don’t occur. It’s a programming competition run by a company called Mathworks. Twice a year every year since 1999 Mathworks has run a week-long competition involving more than one hundred programmers from all over the world. At the start of the week a programming problem is posed. A typical problem might be something like the travelling salesman problem – given a list of cities, find the shortest tour that lets you visit all of those cities. The competitors don’t just submit programs at the end of the week, they can (and do) submit programs all through the week. The reason they do this is because when they submit their program it’s immediately and automatically scored. This is done by running the program on some secret test inputs that are known only to the competition organizers. So, for example, the organizers might run the program on all the capital cities of the countries in Europe. The score reflects both how quickly the program runs, and how short a tour of the cities it finds. The score is then posted to a leaderboard. Entries come in over the whole week because kudos and occasional prizes go to people at the top of the leaderboard.

What makes this a collaboration is that programs submitted to the competition are open. Once you submit your program anyone else can come along and simply download the code you’ve just submitted, tweak a single line, and resumbit it as their own. The result is a spectacular free-for-all. Contestants are constantly “stealing” one another’s code, making small tweaks that let them leapfrog to the top of the leaderboard. Some of the contestants get hooked by the instant feedback, and work all week long. The result is that the winning entry is often fantastically good. After the first contest, in 1999, the contest co-ordinator, Ned Gulley, said: “no single person on the planet could have written such an optimized algorithm. Yet it appeared at the end of the contest, sculpted out of thin air by people from around the world, most of whom had never met before.”

Both Wikipedia and the Mathworks competition use open source patterns of development, but the difference is striking. In the Mathworks competition there is an absolute, objective measure of success that’s immediately available – the score. The score acts as a signal telling every competitor where the best ideas are. This helps the community aggregate all the best ideas into a fantastic final product.

In Wikipedia, no such objective signal of quality is available. What allows Wikipedia to function is that on most issues of contention – like whether “With” should be capitalized – there’s only a small community of interest. A treaty can be beaten out by members of that community that allows them to reach consensus and move forward. Constructing such treaties takes tremendous time and energy, and sometimes devolves into neverending flame wars, but most of the time it works okay. But while this kind of treaty-making might scale to tens or even hundreds of people, we don’t yet know how to make it scale to thousands. Agreement doesn’t scale.

Many of the crucial problems of governance have large communities of interest, and it can be very difficult to get even two people to agree on tiny points of fact, much less values. As a result, we can’t simply open source policy documents in a location where they can be edited by millions of people. But, purely as a thought experiment, imagine you had a way of automatically scoring policy proposals for their social utility. You really could set up a Policyworks where millions of people could help rewrite policy, integrating the best ideas from an extraordinarily cognitively diverse group of people.

The question I have is how we can develop tools that let us scale such a process to thousands or even millions of people? How can we get the full benefit of cognitive diversity in problem-solving, without reaching deadlock? Are there clever new ways we can devise for signalling quality in the face of incomplete or uncertain information? We know some things about how to do this in small groups: it’s the art of good facilitation and good counselling. Is it possible to develop scalable mechanisms of agreement so we can open source key problems of governance?

Let me conclude by floating a brief, speculative idea for a Policyworks. In the one minute I have left there’s not time to even begin discussing the problems with the idea, let alone potential solutions. But hopefully it contains the kernel of something interesting. The idea is to allow open editing of policy documents, in much the same way the Mathworks competition allows open editing of computer programs. But each time you make an edit, it’s sent to a randomly selected jury of your peers – say 50 of them. They’re invited to score your contribution, and perhaps offer feedback. They don’t all need to score it – just a few (say 3) is enough to start getting useful information about whether your contribution is an improvement or not. And, perhaps with some tweaking to prevent abuse, and to help ensure fair scoring, such a score might be used as a reliable way of signalling quality in the face of incomplete or uncertain information. My suspicion is that – as others have said of Wikipedia – this may be one of those ideas that works better in practice than it does in theory.

You can subscribe to my blog here.

This post is based on some brief remarks I made about open architecture democracy at the beginning of a panel on the subject, moderated by Tad Homer-Dixon, and with co-panelists Hassan Masum and Mark Tovey. One day, I hope to expand this into a much more thorough treatment.

I’m deeply engrossed in finishing my book at the moment, but wanted to mention two events which readers of this blog might enjoy hearing about, and perhaps attending.

The first event is a panel on open source democracy that’s being run at the University of Waterloo (just outside Toronto) on February 22. It’s about how and whether ideas like collective intelligence and mass collaboration will have any impact on governance in the 21st century. The panel is being run by Tad Homer-Dixon, and the panelists are Mark Tovey, Hassan Masum, and myself. After some short initial presentations it’s going to be (we hope) very interactive, with people there from a wide variety of backgrounds. I’m looking forward to it!

If you’re interested in open science, Science Commons is organizing a Science Commons Symposium on February 20, in Seattle, at the Microsoft Campus. They’ve organized a great group of speakers, and if I wasn’t chained to my desk writing I’d be on a plane to Seattle!

Update: The open source democracy panel is on Feb 22, not Feb 20, as I originally wrote.

• Smart phones blessed in Canon David Parrott’s 3G church service
  • “Yesterday, in the church of the City of London Corporation, he presented an updated version of Plow Monday, an observance that dates from medieval times. On this day, the first Monday after Twelfth Night, farm labourers would bring a plough to the door of the church to be blessed… Men and women coming to [the modern] church no longer used ploughs; their tools were their laptops, their iPhones and their BlackBerries. So he wrote a blessing and [delivered] it before a congregation of 80, the white heat of technology shining from his every pronouncement. “I invite you to have your mobile phone out … though I would like you to put it on silent,” he said. This was Church 2.0. Behind him, the altar resembled a counter at PC World. Upon it, laid out like holy relics, were four smart phones, one Apple laptop and one Dell. When he [delivered] his sermon, the melody of a million ringtones played on the organ. One almost expected Canon Parrott to bellow: “Hello! I’m just giving a service!”"
• Software Carpentry
  • For the past few years, Greg Wilson at the University of Toronto has run an interesting project called Software Carpentry. It’s a boot camp for scientists to learn the skills they need to do scientific computing, introducing powerful techniques that programmers (but not scientists) often learn, like source control, automated testing, and so on. Greg’s hoping to scale the course up in a big way, and this post has some details of what he’s planning, and what he needs to get this done.
• Engelbartbookdialogues’s Blog
  • “In the spirit of Creative Commons non commercial license we are posting the ENTIRE text of the book “The Engelbart Hypothesis: Dialogs with Douglas Engelbart” in this blog.”
• Chemistry Seminar – Science 2.0
  • Dan Gezelter’s course on Science 2.0. Looks like fun, and has lots of great links on the subject, many of them new to me.
• Science in the Open: What can be done? What should be done?
  • Excellent talk from Cameron Neylon.
• The Total Growth of Open Source
  • Fascinating paper from Amit Desphande and Dirk Riehle studying the growth of open source software. Suggests that the total volume of code is now well over 1 billion lines, and growing at a rate of several hundred million lines per year.
• Modern Physics: A Complete Introduction | Open Culture
  • Very interesting: 120 hours of lectures from Lenny Susskind, covering the basics of modern physics.
• Stefanie Bowles on Dan Ariely on people’s choices
  • “We don’t know our preferences very well as humans. We like to think that we decide everything, but we function within choice architectures which impact what we chose. He then asked the audience if they could think of 3 or 10 reasons why they love their significant other. Turns out people can usually only think of 3, and that most people run out of reasons! People are very influenced in the moment and get confused with too much data—he likes to use this on his students to ask them 15 ways the class can be improved. People’s preferences are not fully formed.”

Click here for all of my del.icio.us bookmarks.

• Carl Zimmer: Science Writing Workshop Later This Month
  • Carl Zimmer is teaching a one-week science writing workshop for science grad students.
• Martin Rees: A Level Playing Field
  • “In 2002, three Indian mathematicians (Manindra Agrewal, and his two students Neeraj Kayal and Nitin Saxena) invented a faster algorithm for factoring large numbers — an advance that could be crucial for code-breaking. They posted their results on the Web. Such was the interest that within just a day, 20000 people had downloaded the work, which became the topic of hastily-convened discussions in many centres of mathematical research around the world.

    This episode — offering instant global recognition to two young Indian students — offers a stark contrast with the struggles of a young Indian genius a hundred years ago. Srinivasa Ramanujan, a clerk in Bombay, mailed long screeds of of mathematical formulae to G H Hardy, a professor at Trinity College, Cambridge.”

• Brian Eno: The ‘Authentic’ Has Replaced The Reproducible
  • “I notice that, as the Net provides free or cheap versions of things, ‘the authentic experience’ — the singular experience enjoyed without mediation — becomes more valuable. I notice that more attention is given by creators to the aspects of their work that can’t be duplicated. The ‘authentic’ has replaced the reproducible.

    I notice that almost all of us haven’t thought about the chaos that would ensue if the Net collapsed.

    I notice that my daily life has been changed more by my mobile phone than by the Internet.”

• Clay Shirky on how the internet is changing the way we think
  • “It is our misfortune to live through the largest increase in expressive capability in the history of the human race, a misfortune because surplus always breaks more things than scarcity. Scarcity means valuable things become more valuable, a conceptually easy change to integrate. Surplus, on the other hand, means previously valuable things stop being valuable, which freaks people out…. Given what we have today, the Internet could easily become Invisible High School, with a modicum of educational material in an ocean of narcissism and social obsessions. We could, however, also use it as an Invisible College, the communicative backbone of real intellectual and civic change, but to do this will require more than technology. It will require that we adopt norms of open sharing and participation, fit to a world where publishing has become the new literacy.”
• Danny Hillis on how the net is changing how we think
  • “More and more decisions are made by the emergent interaction of multiple communicating systems, and these component systems themselves are constantly adapting, changing the way they work. This is the real impact of the Internet: by allowing adaptive complex systems to interoperate, the Internet has changed the way we make decisions. More and more, it is not individual humans who decide, but an entangled, adaptive network of humans and machines.

    To understand how the Internet encourages this interweaving of complex systems, you need to appreciate how it has changed the nature of computer programming. Back in the twentieth century, a programmer had the opportunity to exercise absolute control within a bounded world with precisely defined rules. They were able to tell their computers exactly what to do. Today, programming usually involves linking together complex systems developed by others, without understanding exactly how they work.”

Click here for all of my del.icio.us bookmarks.

• Remnants of the Biosphere
  • Extraordinary images from Biosphere 2.
• …My heart’s in Accra » Yemen and the problems of ADD journalism
  • “In a print age, media pack behavior made slightly more sense. Most readers read only a daily newspaper or watched a specific newscast. If that news outlet didn’t report on Michael Jackson’s death, their viewers wouldn’t have this critical bit of cultural information – it made sense for all the outlets to flock to the key stories. But it’s a maladaptive behavior in an internet age. If the Times is all over Yemen like white on rice, I don’t need the Post to be as well – in fact, I’d probably benefit if they were able to turn their attention to another part of the world, one not at the top of the news agenda today, but likely to be important in the future. Or if they used the shoebomber story to explore other related issues – Muslim/Christian tensions in Nigeria, the fact that the alleged bomber was the child of great privlege in Nigeria (characteristic of many terrorists, countering the narrative that terrorist cells prey on the weak, disadvantaged and ignorant)…”
• Christmas Tree Rocketry
  • Launch your tree.
• Kurt Vonnegut on Writing
  • Interesting tidbits from Vonnegut
• Tim Bray: Doing It Wrong
  • “What I’m writing here is the single most important take-away from my Sun years, and it fits in a sentence: The community of developers whose work you see on the Web, who probably don’t know what ADO or UML or JPA even stand for, deploy better systems at less cost in less time at lower risk then we see in the Enterprise. This is true even when you factor in the greater flexibility and velocity of startups.

    This is unacceptable. The Fortune 1,000 are bleeding money and missing huge opportunities to excel and compete. I’m not going to say that these are low-hanging fruit, because if it were easy to bridge this gap, it’d have been bridged. But the gap is so big, the rewards are so huge, that it’s worth buckling down and grinding away at. I don’t know what my future is right now, but it seems by far the most important thing for my profession to be working on.”

• Peter Diamandis: Energetic Fundraising
  • Interesting short video from the founder of the X-prize on raising money.
• Marginal Revolution: The economics of advice
  • “1. You don’t know what a person really thinks until you hear his or her advice. Along these lines, if you really want to know what a person thinks, ask for advice and he or she will open up.

    2. In philanthropy there is a saying: “Ask for money and you will get advice. Ask for advice and you will get money.”

    3. There are many exacting scholars who should be locked in a room, asked for advice of various kinds, and forced to speak into a tape recorder with no edits allowed. The advice-giving mode mobilizes insights which otherwise remain dormant, perhaps for fear of falsification or ridicule or of actually influencing people. All of the transcripts should be put on The Advice Website, with an open comments section, to limit the actual influence of the advice. Some famous people would be revealed as foolish in critical regards. The contents would be most interesting as non-advice and the site would carry a government warning that the advice is not to be taken seriously.”

• A Map of the Universe
  • A fun 2003 paper about producing maps of the Universe: “We have produced a new conformal map of the universe illustrating recent discoveries, ranging from Kuiper belt objects in the Solar system, to the galaxies and quasars from the Sloan Digital Sky Survey. This map projection, based on the logarithm map of the complex plane, preserves shapes locally, and yet is able to display the entire range of astronomical scales from the Earth’s neighborhood to the cosmic microwave background. The conformal nature of the projection, preserving shapes locally, may be of particular use for analyzing large scale structure. Prominent in the map is a Sloan Great Wall of galaxies 1.37 billion light years long, 80% longer than the Great Wall discovered by Geller and Huchra and therefore the largest observed structure in the universe. “
• Why I’d Rather Be Enthusiastic Than Confident.
  • “There’s a dark tendency in human nature to mock or attack other people’s enthusiasms. It’s easy to make fun of ping-pong or Barry Manilow or Star Trek or wine-tasting — but why do it? I remind myself to Shield my joyous ones. I draw energy and cheer from the joyous ones, from the enthusiastic ones, and I need to encourage and join them, not drag them down. “

Click here for all of my del.icio.us bookmarks.

• How We Miscalculated | Print Article | Newsweek.com
  • Andy Grove on the Intel floating-point bug. Interesting throughout, including this comment on how insulated most CEOs are: “But most CEOs are in the center of a for-titled palace, and news from the outside has to percolate through layers of people from the periphery where the action is. For example, I was one of the last to understand the implications of the Pentium crisis. It took a barrage of relentless criticism to make me realize that something had changed and that we needed to adapt to the new environment. We could change our ways and embrace the fact that we had become a household name and a consumer giant, or we could keep our old ways and not only miss an opportunity to nurture new customer relationships but also suffer damage to our reputation and well-being.”
• Dive Into HTML5
  • From Mark Pilgrim, of the superb “Dive Into Python”.
• Industry of Change: Linux Storms Hollywood
  • How Linux won in Hollywood.
• Linux at the movies
  • In Hollywood, Linux has apparently almost completely won: “In this upside-down world where Windows and Mac are minority operating systems, Linux evangelists would be hard-pressed to find anyone left to convert. The free operating system built by the people for the people has been embraced foremost by film studios.”
• Magnus Carlsen’s Blog
  • Arguably the strongest chess player in the world, Magnus Carlsen has a blog.
• How I Work: Bill Gates
  • I was surprised by how email-centric his life sounds.

Click here for all of my del.icio.us bookmarks.