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The Future of Science

2008-07-17T19:36:08Z

Building a better collective memory

In your High School science classes you may have learnt Hooke’s law, the law of physics which relates a spring’s length to how hard you pull on it. What your High School science teacher probably didn’t tell you is that when Robert Hooke discovered his law in 1676, he published it as an anagram, “ceiiinossssttuv”, which he revealed two years later as the Latin “ut tensio, sic vis”, meaning “as the extension, so the force”. This ensured that if someone else made the same discovery, Hooke could reveal the anagram and claim priority, thus buying time in which he alone could build upon the discovery.

Hooke was not unusual. Many great scientists of the age, including Leonardo, Galileo and Huygens, used anagrams or ciphers for similar purposes. The Newton-Leibniz controversy over who invented calculus occurred because Newton claimed to have invented calculus in the 1660s and 1670s, but didn’t publish until 1693. In the meantime, Leibniz developed and published his own version of calculus. Imagine modern biology if the human genome had been announced as an anagram, or if publication had been delayed thirty years.

Why were Hooke, Newton, and their contemporaries so secretive? In fact, up until this time discoveries were routinely kept secret. Alchemists intent on converting lead into gold or finding the secret of eternal youth would often take their discoveries with them to their graves. A secretive culture of discovery was a natural consequence of a society in which there was often little personal gain in sharing discoveries.

The great scientific advances in the time of Hooke and Newton motivated wealthy patrons such as the government to begin subsidizing science as a profession. Much of the motivation came from the public benefit delivered by scientific discovery, and that benefit was strongest if discoveries were shared. The result was a scientific culture which to this day rewards the sharing of discoveries with jobs and prestige for the discoverer.

This cultural transition was just beginning in the time of Hooke and Newton, but a little over a century later the great physicist Michael Faraday could advise a younger colleague to “Work. Finish. Publish.” The culture of science had changed so that a discovery not published in a scientific journal was not truly complete. Today, when a scientist applies for a job, the most important part of the application is their published scientific papers. But in 1662, when Hooke applied for the job of Curator of Experiments at the Royal Society, he certainly was not asked for such a record, because the first scientific journals weren’t created until three years later, in 1665.

The adoption and growth of the scientific journal system has created a body of shared knowledge for our civilization, a collective long-term memory which is the basis for much of human progress. This system has changed surprisingly little in the last 300 years. The internet offers us the first major opportunity to improve this collective long-term memory, and to create a collective short-term working memory, a conversational commons for the rapid collaborative development of ideas. The process of scientific discovery - how we do science - will change more over the next 20 years than in the past 300 years.

This change will not be achieved without great effort. From the outside, scientists currently appear puzzlingly slow to adopt many online tools. We’ll see that this is a consequence of some major barriers deeply embedded within the culture of science. The first part of this essay is about these barriers, and how to overcome them. The second part of the essay illustrates these ideas, with a proposal for an online collaboration market where scientists can rapidly outsource scientific problems.

Part I: Toward a more open scientific culture

How can the internet benefit science?

How can the internet improve the way we do science? There are two useful ways to answer this question. The first is to view online tools as a way of expanding the range of scientific knowledge that can be shared with the world:

Many online tools do just this, and some have had a major impact on how scientists work. Two successful examples are the physics preprint arXiv, which lets physicists share preprints of their papers without the months-long delay typical of a conventional journal, and GenBank, an online database where biologists can deposit and search for DNA sequences. But most online tools of this type remain niche applications, often despite the fact that many scientists believe broad adoption would be valuable. Two examples are the Journal of Visualized Experiments, which lets scientists upload videos which show how their experiments work, and open notebook science, as practiced by scientists like Jean-Claude Bradley and Garrett Lisi, who expose their working notes to the world. In the coming years we’ll see a proliferation of tools of this type, each geared to sharing different types of knowledge:

There is a second and more radical way of thinking about how the internet can change science, and that is through a change to the process and scale of creative collaboration itself, a change enabled by social software such as wikis, online forums, and their descendants.

There are already many well-known but still striking instances of this change in parts of culture outside of science [1]. For example, in 1991 an unknown Finnish student named Linus Torvalds posted a short note in an online forum, asking for help extending a toy operating system he’d programmed in his spare time; a volunteer army responded by assembling Linux, one of the most complex engineering artifacts ever constructed. In 2001 another young unknown named Larry Sanger posted a short note asking for help building an online Encyclopedia; a volunteer army responded by assembling the world’s most comprehensive Encyclopedia. In 1999, Garry Kasparov, the greatest chessplayer of all time, played and eventually won a game of chess against a “World Team” which decided its moves by the votes of thousands of chessplayers, many rank amateurs; instead of the easy victory he expected, he got the most challenging game of his career, a game he called “the greatest game in the history of chess”.

These examples are not curiosities, or special cases; they are just the leading edge of the greatest change in the creative process since the invention of writing.

Science is an example par excellence of creative collaboration, yet scientific collaboration still takes place mainly via face-to-face meetings. With the exception of email, few of the new social tools have been broadly adopted by scientists, even though it is these tools which have the greatest potential to improve how science is done.

Why have scientists been so slow to adopt these remarkable tools? Is it simply that they are too conservative in their habits, or that the new tools are no better than what we already have? Both these glib answers are wrong. We’ll resolve this puzzle by looking in detail at two examples where excellent online tools have failed to be adopted by scientists. What we’ll find is that there are major cultural barriers which are preventing scientists from getting involved, and so slowing down the progress of science.

A failure of science online: online comment sites

Like many people, when I’m considering buying a book or electronic gadget, I often first browse the reviews at amazon.com. Inspired by the success of amazon.com and similar sites, several organizations have created comment sites where scientists can share their opinions of scientific papers. Perhaps the best-known was Nature’s 2006 trial of open commentary on papers undergoing peer review at Nature. The trial was not a success. Nature’s final report terminating the trial explained:

There was a significant level of expressed interest in open peer review… A small majority of those authors who did participate received comments, but typically very few, despite significant web traffic. Most comments were not technically substantive. Feedback suggests that there is a marked reluctance among researchers to offer open comments.

The Nature trial is just one of many attempts at comment sites for scientists. The earliest example I’m aware of is the Quick Reviews site, built in 1997, and discontinued in 1998. Physics Comments was built a few years later, and discontinued in 2006. A more recent site, Science Advisor, is still active, but has more members (1139) than reviews (1008). It seems that people want to read reviews of scientific papers, but not write them [2].

The problem all these sites have is that while thoughtful commentary on scientific papers is certainly useful for other scientists, there are few incentives for people to write such comments. Why write a comment when you could be doing something more “useful”, like writing a paper or a grant? Furthermore, if you publicly criticize someone’s paper, there’s a chance that that person may be an anonymous referee in a position to scuttle your next paper or grant application.

To grasp the mindset here, you need to understand the monklike intensity that ambitious young scientists bring to the pursuit of scientific publications and grants. To get a position at a major University the most important thing is an impressive record of scientific papers. These papers will bring in the research grants and letters of recommendation necessary to be hired. Competition for positions is so fierce that 80 hour plus work weeks are common. The pace relaxes after tenure, but continued grant support still requires a strong work ethic. It’s no wonder people have little inclination to contribute to the online comment sites.

The contrast between the science comment sites and the success of the amazon.com reviews is stark. To pick just one example, you’ll find
approximately 1500 reviews of Pokemon products at amazon.com, more than the total number of reviews on all the scientific comment sites I described above. The disincentives facing scientists have led to a ludicrous situation where popular culture is open enough that people feel comfortable writing Pokemon reviews, yet scientific culture is so closed that people will not publicly share their opinions of scientific papers. Some people find this contrast curious or amusing; I believe it signifies something seriously amiss with science, something we need to understand and change.

A failure of science online: Wikipedia

Wikipedia is a second example where scientists have missed an opportunity to innovate online. Wikipedia has a vision statement to warm a scientist’s heart: “Imagine a world in which every single human being can freely share in the sum of all knowledge. That’s our commitment.” You might guess Wikipedia was started by scientists eager to collect all of human knowledge into a single source. In fact, Wikipedia’s founder, Jimmy Wales, had a background in finance and as a web developer for an “erotic search engine”, not in science. In the early days few established scientists were involved. Just as for the scientific comment sites, to contribute aroused suspicion from colleagues that you were wasting time that could be spent writing papers and grants.

Some scientists will object that contributing to Wikipedia isn’t really science. And, of course, it’s not if you take a narrow view of what science is, if you’ve bought into the current game, and take it for granted that science is only about publishing in specialized scientific journals. But if you take a broader view, if you believe science is about discovering how the world works, and sharing that understanding with the rest of humanity, then the lack of early scientific support for Wikipedia looks like an opportunity lost. Nowadays, Wikipedia’s success has to some extent legitimized contribution within the scientific community. But how strange that the modern day Library of Alexandria had to come from outside academia.

The challenge: achieving extreme openness in science

These failures of science online are all examples where scientists show a surprising reluctance to share knowledge that could be useful to others. This is ironic, for the value of cultural openness was understood centuries ago by many of the founders of modern science; indeed, the journal system is perhaps the most open system for the transmission of knowledge that could be built with 17th century media. The adoption of the journal system was achieved by subsidizing scientists who published their discoveries in journals. This same subsidy now inhibits the adoption of more effective technologies, because it continues to incentivize scientists to share their work in conventional journals, and not in more modern media.

The situation is analogous to the government subsidies for corn-based ethanol in the United States. In the early days these seemed to many people to be a good idea, encouraging the use of what people hoped would be a more efficient fuel. But now we understand that there are more energy-efficient alternatives, such as grass-based cellulose ethanol. Unfortunately, the subsidies for corn-based ethanol are still in place, and now inhibit the adoption of the more efficient technologies.

We should aim to create an open scientific culture where as much information as possible is moved out of people’s heads and labs, onto the network, and into tools which can help us structure and filter the information. This means everything - data, scientific opinions, questions, ideas, folk knowledge, workflows, and everything else - the works. Information not on the network can’t do any good.

Ideally, we’ll achieve a kind of extreme openness. This means: making many more types of content available than just scientific papers; allowing creative reuse and modification of existing work through more open licensing and community norms; making all information not just human readable but also machine readable; providing open APIs to enable the building of additional services on top of the scientific literature, and possibly even multiple layers of increasingly powerful services. Such extreme openness is the ultimate expression of the idea that others may build upon and extend the work of individual scientists in ways they themselves would never have conceived.

The challenge of achieving a more open culture is also being confronted in popular culture. People such as Richard Stallman, Lawrence Lessig, Yochai Benkler, Cory Doctorow, and many others have described the benefits openness brings in a networked world, and developed tools such as Creative Commons licensing and free and open source software to help promote a more open culture, and fight the forces inhibiting it. As we have seen, however, science faces a unique set of forces that inhibit open culture - the centuries-old subsidy of old ways of sharing knowledge - and this requires a new understanding of how to overcome those forces.

How can we open up scientific culture?

To create an open scientific culture that embraces new online tools, two challenging tasks must be achieved: (1) build superb online tools; and (2) cause the cultural changes necessary for those tools to be accepted. The necessity of accomplishing both these tasks is obvious, yet projects in online science often focus mostly on building tools, with cultural change an afterthought. This is a mistake, for the tools are only part of the overall picture. It took just a few years for the first scientific journals (a tool) to be developed, but many decades of cultural change before journal publication was accepted as the gold standard for judging scientific contributions.

None of this is to discount the challenge of building superb online tools. To develop such tools requires a rare combination of strong design and technical skills, and a deep understanding of how science works. The difficulty is compounded because the people who best understand how science works are scientists themselves, yet building such tools is not something scientists are typically encouraged or well suited to do. Scientific institutions reward scientists for making discoveries within the existing system of discovery; there is little place for people working to change that system. A technologically-challenged Head of Department is unlikely to look kindly on a scientist who suggests that instead of writing papers they’d like to spend their research time developing general-purpose tools to improve how science is done.

What about the second task, achieving cultural change? As any revolutionary can attest, that’s a tough order. Let me describe two strategies that have been successful in the past, and that offer a template for future success.

The first is a top-down strategy that has been successfully used by the open access (OA) movement [3]. The goal of the OA movement is to make scientific research freely available online to everyone in the world. It’s an inspiring goal, and the OA movement has achieved some amazing successes. Perhaps most notably, in April 2008 the US National Institutes of Health (NIH) mandated that every paper written with the support of their grants must eventually be made open access. The NIH is the world’s largest grant agency; this decision is the scientific equivalent of successfully storming the Bastille.

The second strategy is bottom-up. It is for the people building the new online tools to also develop and boldly evangelize ways of measuring the contributions made with the tools. To understand what this means, imagine you’re a scientist sitting on a hiring committee that’s deciding whether or not to hire some scientist. Their curriculum vitae reports that they’ve helped build an open science wiki, and also write a blog. Unfortunately, the committee has no easy way of understanding the significance of these contributions, since as yet there are no broadly accepted metrics for assessing such contributions. The natural consequence is that such contributions are typically undervalued.

To make the challenge concrete, ask yourself what it would take for a description of the contribution made through blogging to be reported by a scientist on their curriculum vitae. How could you measure the different sorts of contributions a scientist can make on a blog - outreach, education, and research? These are not easy questions to answer. Yet they must be answered before scientific blogging will be accepted as a valuable professional scientific contribution.

A success story: the arXiv and SPIRES

Let’s look at an example illustrating the bottom-up strategy in action. The example is the well-known physics preprint arXiv. Since 1991 physicists have been uploading their papers to the arXiv, often at about the same time as they submit to a journal. The papers are made available within hours for anyone to read. The arXiv is not refereed, although a quick check is done by arXiv moderators to remove crank submissions. The arXiv is an excellent and widely-used tool, with more than half of all new papers in physics appearing there first. Many physicists start their day by seeing what’s appeared on the arXiv overnight. Thus, the arXiv exemplifies the first step for achieving a more open culture: it is a superb tool.

Not long after the arXiv began, a citation tracking service called SPIRES decided they would extend their service to include both arXiv papers and conventional journal articles. SPIRES specializes in particle physics, and as a result it’s now possible to search on a particle physicist’s name (example), and see how frequently all their papers, including arXiv preprints, have been cited by other physicists.

SPIRES has been run since 1974 by one of the most respected and highly visible institutions in particle physics, the Stanford Linear Accelerator Center (SLAC). The effort SLAC has put into developing SPIRES means that their metrics of citation impact are both credible and widely used by the particle physics community. It’s now possible for a particle physicist to convincingly demonstrate that their work is having a high impact, even if it has only been submitted to the arXiv, and has not been published in a conventional scientific journal. When physics hiring committees meet to evaluate candidates in particle physics, people often have their laptops out, examining and comparing the SPIRES citation records of candidates.

The arXiv and SPIRES have not stopped particle physicists from publishing in peer-reviewed journals. When you’re applying for jobs, or up for tenure, every ounce of ammunition helps, especially when the evaluating committee may contain someone from another field who is reluctant to take the SPIRES citation data seriously. Still, particle physicists have become noticeably more relaxed about publication, and it’s not uncommon to see a CV which includes preprints that haven’t been published in conventional journals. This is an example of the sort of cultural change that can be achieved using the bottom-up strategy. In the next part, we’ll see how far these ideas can be pushed in pursuit of new tools for collaboration.

Part II: Collaboration Markets: building a collective working memory for science

The problem of collaboration

Even Albert Einstein needed help occasionally. Einstein’s greatest contribution to science was his theory of gravity, often called the general theory of relativity. He worked on and off on this theory between 1907 and 1915, often running into great difficulties. By 1912, he had come to the astonishing conclusion that our ordinary conception of geometry, in which the angles of a triangle add up to 180 degrees, is only approximately correct, and a new kind of geometry is needed to correctly describe space and time. This was a great surprise to Einstein, and also a great challenge, since such geometric ideas were outside his expertise. Fortunately for Einstein and for posterity, he described his difficulties to a mathematician friend, Marcel Grossman. Grossman said that many of the ideas Einstein needed had already been developed by the mathematician Bernhard Riemann. It took Einstein three more years of work, but Grossman was right, and this was a critical point in the development of general relativity.

Einstein’s conundrum is familiar to any scientist. When doing research, subproblems constantly arise in unexpected areas. No-one can be expert in all those areas. Most of us instead stumble along, picking up the skills necessary to make progress towards our larger goals, grateful when the zeitgeist of our research occasionally throws up a subproblem in which we are already truly expert. Like Einstein, we have a small group of trusted collaborators with whom we exchange questions and ideas when we are stuck. Unfortunately, most of the time even our collaborators aren’t that much help. They may point us in the right direction, but rarely do they have exactly the expertise we need. Is it possible to scale up this conversational model, and build an online collaboration market [4] to exchange questions and ideas, a sort of collective working memory for the scientific community?

It is natural to be skeptical of this idea, but an extremely demanding creative culture already exists which shows that such a collaboration market is feasible - the culture of free and open source software. Scientists browsing for the first time through the development forums of open source programming projects are often shocked at the high level of the discussion. They expect amateur hour at the local Karaoke bar; instead, they find professional programmers routinely sharing their questions and ideas, helping solve each other’s problems, often exerting great intellectual effort and ingenuity. Rather than hoarding their questions and ideas, as scientists do for fear of being scooped, the programmers revel in swapping them. Some of the world’s best programmers hang out in these forums, swapping tips, answering questions, and participating in the conversation.

Innocentive

I’ll now describe two embryonic examples which suggest that collaboration markets for science may be valuable. The first is Innocentive, a service that allows companies like Eli Lilly and Proctor and Gamble to pose Challenges over the internet, scientific research problems with associated prizes for their solution, often many thousands of dollars. For example, one of the Challenges currently on Innocentive asks participants to find a biomarker for motor neuron disease, with a one million dollar prize. If you register for the site, it’s possible to obtain a detailed description of the Challenge requirements, and attempt to win the prize. More than 140,000 people from 175 countries have registered, and prizes for more than 100 Challenges have been awarded.

Innocentive is an example of how a market in scientific problems and solutions can be established. Of course, it has shortcomings as a model for collaboration in basic research. Only a small number of companies are able to pose Challenges, and they may do so only after a lengthy vetting process. Innocentive’s business model is aimed firmly at industrial rather than basic research, and so the incentives revolve around money and intellectual property, rather than reputation and citation. It’s certainly not a rapid-fire conversational tool like the programming forums; one does not wake up in the morning with a problem in mind, and post it to Innocentive, hoping for help with a quick solution.

FriendFeed

FriendFeed is a much more fluid tool which is being used by scientists as a conversational medium to discuss scientific research problems. What FriendFeed allows users to do is set up what’s called a lifestream. As an example, my lifestream is set up to automatically aggregate pretty much everything I put on the web, including my blog posts, del.icio.us links, YouTube videos, and several other types of content:

I also subscribe to a list of about one hundred or so “friends” (a few are listed on the right in the screenshot above) whose lifestreams I can see aggregated into one giant river of information - all their Flickr photos, blog posts, and so on. These people aren’t necessarily real friends - I’m not personally acquainted with my “friend” Barack Obama - but it’s a fantastic way of tracking a high volume of activity from a large number of people.

As part of the lifestream, FriendFeed allows messages to be passed back and forth in a lightweight way, so communities can form around common interests and shared friendships. In April 2008, Cameron Neylon, a chemist from the University of Southampton, used FriendFeed messaging to post a request for assistance in building molecular models. Pretty quickly Pawel Szczesny replied, and said he could help out. A scientific collaboration was now underway. The original request and discussion is shown here:

FriendFeed is a great service, but it suffers from many of the same problems that afflict the comment sites and Wikipedia. Lacking widely accepted metrics to measure contribution, scientists are unlikely to adopt FriendFeed en masse as a medium for scientific collaboration. And without widespread adoption, the utility of FriendFeed for scientific collaboration will remain relatively low.

The economics of collaboration

How much is lost due to inefficiencies in the current system of collaboration? To answer this question, imagine a scientist named Alice. Like most scientists, many of Alice’s research projects spontaneously give rise to problems in areas in which she isn’t expert. She juggles hundreds or thousands of such problems, re-examining each occasionally, and looking to make progress, but knowing that only rarely is she the person best suited to solve any given problem.

Suppose that for a particular problem, Alice estimates that it would take her 4-5 weeks to acquire the required expertise and solve the problem. That’s a long time, and so the problem is on the backburner. Unbeknownst to Alice, though, there is another scientist in another part of the world, Bob, who has just the skills to solve the problem in less than a day. This is not at all uncommon. Quite the contrary; my experience is that this is the usual situation. Consider the example of Grossmann, who saved Einstein what might otherwise have been years of extra work.

Do Alice and Bob exchange questions and ideas, and start working towards a solution to Alice’s problem? Unfortunately, nine times out of ten they never even meet, or if they meet, they just exchange small talk. It’s an opportunity lost for a mutually beneficial trade, a loss that may cost weeks of work for Alice. It’s also a great loss for the society that bears the cost of doing science, a loss that must run to billions of dollars each year in total. Expert attention, the ultimate scarce resource in science, is very inefficiently allocated under existing practices for collaboration.

An efficient collaboration market would enable Alice and Bob to find this common interest, and exchange their know-how, in much the same way eBay and craigslist enable people to exchange goods and services. However, in order for this to be possible, a great deal of mutual trust is required. Without such trust, there’s no way Alice will be willing to advertise her questions to the entire community. The danger of free riders who will take advantage for their own benefit (and to Alice’s detriment) is just too high.

In science, we’re so used to this situation that we take it for granted. But let’s compare to the apparently very different problem of buying shoes. Alice walks into a shoestore, with some money. Alice wants shoes more than she wants to keep her money, but Bob the shoestore owner wants the money more than he wants the shoes. As a result, Bob hands over the shoes, Alice hands over the money, and everyone walks away happier after just ten minutes. This rapid transaction takes place because there is a trust infrastructure of laws and enforcement in place that ensures that if either party cheats, they are likely to be caught and punished.

If shoestores operated like scientists trading ideas, first Alice and Bob would need to get to know one another, maybe go for a few beers in a nearby bar. Only then would Alice finally say “you know, I’m looking for some shoes”. After a pause, and a few more beers, Bob would say “You know what, I just happen to have some shoes I’m looking to sell”. Every working scientist recognizes this dance; I know scientists who worry less about selling their house than they do about exchanging scientific information.

In economics, it’s been understood for hundreds of years that wealth is created when we lower barriers to trade, provided there is a trust infrastructure of laws and enforcement to prevent cheating and ensure trade is uncoerced. The basic idea, which goes back to David Ricardo in 1817, is to concentrate on areas where we have a comparative advantage, and to avoid areas where we have a comparative disadvantage.

Although Ricardo’s work was in economics, his analysis works equally well for trade in ideas. Indeed, even were Alice to be far more competent than Bob, Ricardo’s analysis shows that both Alice and Bob benefit if Alice concentrates on areas where she has the greatest comparative advantage, and Bob on areas where he has less comparative disadvantage. Unfortunately, science currently lacks the trust infrastructure and incentives necessary for such free, unrestricted trade of questions and ideas.

An ideal collaboration market will enable just such an exchange of questions and ideas. It will bake in metrics of contribution so participants can demonstrate the impact their work is having. Contributions will be archived, timestamped, and signed, so it’s clear who said what, and when. Combined with high quality filtering and search tools, the result will be an open culture of trust which gives scientists a real incentive to outsource problems, and contribute in areas where they have a great comparative advantage. This will change science.

Acknowledgments

Based on a keynote talk by Michael Nielsen at the New Communication Channels for Biology workshop, San Diego, June 26 and 27, 2008. Thanks to Krishna Subramanian and John Wooley for organizing the workshop, and all the participants for an enjoyable event. Thanks to Eva Amsen, Jen Dodd, Danielle Fong, Peter Rohde, Ben Toner, and Christian Weedbrook for providing feedback that greatly improved early drafts of this essay.

Footnotes

[1] Clay Shirky’s “Here Comes Everybody” is an excellent book that contains much of interest on new ways of collaborating.

[2] An ongoing experiment which incorporates online commentary and many other innovative features is PLoS ONE. It’s too early to tell how successful its commentary will be.

[3] I strongly recommend Peter Suber’s Open Access News as a superb resource on all things open access.

[4] Shirley Wu and Cameron Neylon have stimulating blog posts where they propose ideas closely related to collaboration markets.

ShareThis

Big News (Warning: Legal Content)

2008-08-13T17:46:22Z

Larry Lessig posts wonderful news

In his own words: I am very proud to report today that the Court of Appeals for the Federal Circuit (THE “IP” court in the US) has upheld a free (ok, they call them “open source”) copyright license, explicitly pointing to the work of Creative Commons and others.

Short version is that if you violate the terms of a CC license, the license goes away, and you’re a copyright infringer. This is what we’ve always argued, and it’s great to see the courts agree. Contracts like the CC suite are kind of like uncompiled code before they get into court. This decision means the code in fact compiles.

Get arrested and never get lost again.

2008-04-17T02:25:00Z

This comprehensive collection of DNA samples obtained from individuals arrested by an agent of a federal law enforcement agency will have several remarkable consequences. For example, if an information altruist, such as a volunteer for the Personal Genome Project, puts put on the web a substantial fraction of her genome, federal authorities will be able to trivially run a search program to see if any of them match the genomic characteristics of one of the previously arrested individuals. High-throughput genomics finally meets high-throughput forensics.

Photosynth / Infosynth

2008-03-09T17:55:10Z

Photosynth is the coolest technology I have seen in recent memory. It is also the most graphic example I know of that demonstrates the power of context accumulation.

As I noted in this post last week, the only way to make any real sense of the big picture is to first stitch together all of the atomic-level observations (puzzle pieces) into context (pictures).

With this in mind, take a look at this exceptional video about “Photosynth” presented by Blaise Aguera y Arcas at a TED conference.

Now imagine the process of stitching together not just digital images … but all available data – across disparate data types (e.g., structured, unstructured text, images, video, audio, etc.).

When I speak of Context, this is exactly what I mean.

So after seeing Photosynth ... this made me think “n” sensor context accumulation could just as easily be called “Infosynth.”

These context engines will be used to deliver laser-precise relevance in support of next generation: user search; customer service; risk mitigation; ultra-weak signal detection; and so much more.

PRIVACY RAMIFICATIONS? Big time. Nonetheless, consumers are going to love the new industry offerings that context engines will enable. Stay tuned for a forthcoming post on my Responsible Innovation thread about what technologists might want to "design in" to these next generation systems in hopes of making these future systems more palatable from the privacy and civil liberties perspective.

Algorithms At Dead-End: Cannot Squeeze Knowledge Out Of A Pixel

Context: A Must-Have and Thoughts on Getting Some …

Federated Discovery vs. Persistent Context – Enterprise Intelligence Requires the Later

To Know Semantic Reconciliation is to Love Semantic Reconciliation

More Data is Better, Proceed With Caution

Accumulating Context: Now or Never

Big Breakthrough in Performance: Tuning Tips for Incremental Learning Systems

Responsible Innovation: Staying Engaged with the Privacy Community

Tiny projects keep it new

2008-03-12T14:44:00Z

When do we do our best work? When we’re excited about something. Excitement morphs into motivation. We do our best work when we’re motivated. A great way to stay motivated is to work on something new. No one likes being stuck on a project that never seems to end.

The typical project

The typical project starts out great but then our motivation and interest wanes as time goes on. It’s natural. Staying interested in a project over a long period of time is a challenge for anyone. The longer the project the thinner the tail. You’re not going to do your best work in the tail.

The ideal project

When you break a big project into smaller chunks — into tiny projects — you stand a better chance at maintaining motivation and rekindling interest. When you have a pile of tiny projects you get the chance to work on something new more often. We do our best work when we’re excited about starting something new.

Break problems down to their atomic level

The best way to optimize for new is to break features and projects down to their atomic level. Keep breaking them down until you all you have left are a lot of small project elements instead of a few big project molecules.

For example, we just introduced bulk delete in Highrise. The UI and underpinnings we built for this will eventually (probably) be used for bulk tagging and other bulk actions. But instead of trying to shove all the other potential bulk actions into this release — ultimately turning a one week project into a 4 week project — we decided to just tackle bulk delete first. It took a few days from inception to public launch. Now we can get excited about the next phase since it’s a new tiny project again.

Bottom line: Shatter big projects into little pieces. Finish and launch one piece at a time. Introduce value now. Over time you can recombine these pieces into the one big feature you had planned. Working on, finishing, and launching one little piece at a time will help you stay motivated because you’re always working on something new. Your best work is in the bursts, not in the tails.

[Credit for the waveform concept goes to Jim Coudal]

YTranscript: Using the brand new YouTube chromeless, scriptable player

2008-03-12T15:25:54Z

We saw a very exciting YouTube launch for developers today. People have long wanted to customize the YouTube player, and now you have complete control with a chromeless player that has JavaScript access so you can start, stop, go to a timing, and much more.

I took this API and implemented a simple DSL that allows me to build a simple transcript table of contents that lets you jump to a particular chapter in the video.

The HTML has special attributes and CSS classes, ending up like this:

PLAIN TEXT

HTML:

<div id="ytplayer" class="ytplayer" url="http://www.youtube.com/v/2SgMHjmZO60"></div>
<div id="ytranscript" class="ytranscript" for="ytplayer">
<b>Click on the item you want to skip too</b>
<ul>
<li starttime="0.0">Hand, hand, fingers, thumb</li>
<li starttime="8.0">Drumming and drumming</li>
<li starttime="28.0">Time to pick the apples and the plums</li>
<li starttime="42.0">Enter Jake</li>
<li starttime="44.0">Enter Jack</li>
<li starttime="51.0">The monkeys say bye bye</li>
<li starttime="60.0">Now they play bangos and fiddles</li>
<li starttime="78.0">Wooah, millions of monkeys!</li>
</ul>
</div>

My JavaScript trivially attaches behaviour to the list to talk to the player. It adds timing information from the DSL to the HTML content, and uses seekTo to get the player to the point you need.

PLAIN TEXT

JAVASCRIPT:

window.onload = function() {
$$('.ytranscript').each(function(e) {
var player = e.readAttribute("for");
var playerEmbed = player + "Embed";
initPlayer(player, playerEmbed);
var lis = e.getElementsByTagName("li");
var odd = 1;
$A(lis).each(function(li) {
var starttime = li.readAttribute('starttime');
li.innerHTML = li.innerHTML + " <span class='timing'>" + parseInt(starttime) + " secs</span>";
li.addClassName( (odd++ % 2) ? "odd" : "even");
li.writeAttribute("title", "Send the player to this location");
li.onclick = function() {
makeSureVideoIsPlaying($(playerEmbed));
$(playerEmbed).seekTo(starttime, true);
};
});
});
}
function makeSureVideoIsPlaying(playerEmbed) {
if (playerEmbed.getState && $(playerEmbed).getState() == -1) { // -1 unstarted
playerEmbed.playVideo(); // play if we haven't started
}
}
function initPlayer(player, playerEmbed) {
var url = $(player).readAttribute('url');
var so = new SWFObject(url + "&enablejsapi=1&playerapiid=my" + playerEmbed, playerEmbed, 432, 400, 8);
so.addParam("AllowScriptAccess", "always");
so.write(player);
}
function onYouTubePlayerReady(playerId) {}

The one gotcha is that seekTo goes to the nearest keyframe, which can be a few seconds off. Hopefully it will at least go to the nearest one before the timing, but that isn't the case right now.

Learn more

We have an interview with the engineers, and a bunch of documentation.

Check out the JavaScript API and chromeless player reference to find out more.

UPDATE: Christian Heilmann has written a video captioning system that lets you annotate as you pause.

The Current Landscape for Direct to Consumer Genetic Testing: Legal, Ethical, and Policy Issues

2008-01-12T00:13:14Z

Annual Review of Genomics and Human Genetics Volume 9, September 2008. This chapter surveys the developing market for direct-to-consumer genetic tests, examining the range of companies and tests available, the regulatory landscape, the concerns which have been raised both about DTC testing and the calls for enhanced ...

Genetic Screening for Low-Penetrance Diseases

2008-01-12T00:13:17Z

Annual Review of Genomics and Human Genetics Volume 9, September 2008.

Next-Generation DNA Sequencing Methods

2008-01-12T00:13:17Z

Annual Review of Genomics and Human Genetics Volume 9, September 2008. Recent scientific discoveries that resulted from the application of next-generation DNA sequencing technologies highlight the striking impact of these massively parallel platforms on genetics. These new methods have expanded previously focused readouts ...

The moral manipulation of Gattaca

2008-03-06T06:58:00Z

A man is given strong medical evidence that he will die from a heart attack if exposed to the exertions of space travel, thus risking his own life and the lives of his crewmates. Ignoring this evidence, he fakes his way into astronaut training - and inexplicably, we cheer him on. How did the makers of Gattaca steer us towards this bizarre response? Philosopher Neven Sesardic explains, in a fascinating essay (PDF) that touches on issues highly relevant to personal genomics.

One quote to chew over:

Contrary to what the movie is trying to tell us, a more detached analysis leads us back to the common sense belief that a more detailed knowledge of our genetic predispositions would indeed severely narrow our choices. Were this kind of information to become massively available, it would be rational for many people to abandon their previous career plans and reconsider what they want to do with their lives.

Obviously we don't know enough yet about genetics to be advising people on future careers based on a genome scan - but at some point, it's likely that we'll be able to make at least some probabilistic inferences about predispositions and skills very early in life based on genetic information. So long as we ensure that we rely only on accurate information, this isn't a bad thing. It simply means that people will have more information with which to make important decisions.

HT: Black Belt Bayesian.

Subscribe to Genetic Future.

Spit Kit Giveaway

2008-01-27T16:32:25Z

23andMe co-founders Linda Avey and Anne Wojcicki were in Switzerland last week for the World Economic Forum. When they announced on Monday that they would be providing the 23andMe service free to 1,000 conference attendees, Michael Arrington of TechCrunch was a little bummed – he had already purchased the service.

Arrington asked for a refund, but our fearless leaders wouldn’t go for it. Instead they struck a deal – they’d give him a free kit that he could then give away to a TechCrunch reader. Arrington made a contest of it, saying he would choose whoever posted the best comment explaining why knowing their genetic background is important to them.

One reader, Jorel, wanted the service in order to share the data with others, because there “still seems to be a bit of confusion about what kinds of things the kit can determine…”

Many of the other responses – there were more than 200 – illustrated some of that confusion. So we’d like to clear a few things up by commenting on a few of them.

First, let’s cover the basics. 23andMe is a personal genotyping service. The process starts when you send a saliva sample to our contracted laboratory. They extract DNA from the cells that are floating around in your spit and then run that genetic material on a “SNP chip.” This little device queries more than 500,000 spots in the genome that have been found to vary between people. The data are then sent to 23andMe, where we put them into a secure account that helps you put the information in context.

But what does all of this really mean? What can and can’t we give you? To try and answer these questions, let’s see what some of the TechCrunch readers had to say.

(more…)

ShareThis

Vitamin D not so good for health?

2008-01-27T16:30:08Z

Long time readers of this weblog will know I have an interest in Vitamin D. It has been hypothesized to be one of the major causal factors in generating human skin color variation, and we know from evolutionary genomics that the genes which underly skin color have been under very recent & powerful selection pressures. There is also data that Vitamin D levels may have a relationship to endemic diseases such as flu, and chronic ones such as arthritis. And then we find out nuggets such as the fact that most non-whites in Canada are Vitamin D deficient.

But what if we're putting the cart before the horse? Vitamin D Deficiency Study Raises New Questions About Disease And Supplements:

"Our disease model has shown us why low levels of vitamin D are observed in association with major and chronic illness," Marshall added. "Vitamin D is a secosteroid hormone, and the body regulates the production of all it needs. In fact, the use of supplements can be harmful, because they suppress the immune system so that the body cannot fight disease and infection effectively."

Marshall's research has demonstrated how ingested vitamin D can actually block VDR activation, the opposite effect to that of Sunshine....

...

Vitamin D deficiency, long interpreted as a cause of disease, is more likely the result of the disease process, and increasing intake of vitamin D often makes the disease worse. "Dysregulation of vitamin D has been observed in many chronic diseases, including many thought to be autoimmune," said J.C. Waterhouse, Ph.D., lead author of a book chapter on vitamin D and chronic disease.

"We have found that vitamin D supplementation, even at levels many consider desirable, interferes with recovery in these patients."

"We need to discard the notion that vitamin D affects a disease state in a simple way," Marshall said. "Vitamin D affects the expression of over 1,000 genes, so we should not expect a simplistic cause and effect between vitamin D supplementation and disease. The comprehensive studies are just not showing that supplementary vitamin D makes people healthier."

Read the rest of this post... | Read the comments on this post...

Database leaks are as immortal and toxic as nuclear spills -- let's start acting like it

2008-01-22T18:15:16Z

My latest Guardian column is online: "Personal data is as hot as nuclear waste," which looks at the immortality of databases -- just as it's impossible for the Internet to scourge itself of Paris Hilton's terrible genitals, it is likewise impossible that the personal information hemorrhaged by the likes of Her Majesty's Revenue and Customs (25 million records!) will ever go away. In the era of infinite copying, this information is like a nuclear disaster, immortal and terrible in its consequence. The only way to contain future spills is to make every person who gathers information on his neighbours pay in advance for the long-term handling and storage of that undying, toxic sludge:

If we are going to contain every heap of data plutonium for 200 years, that means that every single person who will ever be in a position to see, copy, handle, store, or manipulate that data will have to be vetted and trained every bit as carefully as the folks in the rubber suits down at the local fast-breeder reactor.
Every gram - sorry, byte - of personal information these feckless data-packrats collect on us should be as carefully accounted for as our weapons-grade radioisotopes, because once the seals have cracked, there is no going back. Once the local sandwich shop's CCTV has been violated, once the HMRC has dumped another 25 million records, once London Underground has hiccoughed up a month's worth of travelcard data, there will be no containing it.
And what's worse is that we, as a society, are asked to shoulder the cost of the long-term care of business and government's personal data stockpiles. When a database melts down, we absorb the crime, the personal misery, the chaos and terror.

Link

Update on Societe Generale rogue trader Jerome Kerviel

2008-01-25T23:58:24Z

From The Daily Mash via Megan McArdle:

Friends of rogue trader Jerome Kerviel last night blamed his $7 billion losses on unbearable levels of stress brought on by a punishing 30 hour week.

Kerviel was known to start work as early as nine in the morning and still be at his desk at five or even five-thirty, often with just an hour and a half for lunch.

One colleague said: "He was, how you say, une workaholique. I have a family and a mistress so I would leave the office at around 2pm at the latest, if I wasn't on strike.

"But Jerome was tied to that desk. One day I came back to the office at 3pm because I had forgotten my stupid little hat, and there he was, fast asleep on the photocopier.

"At first I assumed he had been having sex with it, but then I remembered he'd been working for almost six hours."

As the losses mounted, Kerviel tried to conceal his bad trades by covering them with an intense red wine sauce, later switching to delicate pastry horns.

At one point he managed to dispose of dozens of transactions by hiding them inside vol-au-vent cases and staging a fake reception...

The ethical challenges of whole-genome sequencing part 1

2008-01-22T23:11:00Z

A recent perspective article in Nature Reviews Genetics (sorry, subscriber only!) discusses the issues arising from the advent of individual whole-genome sequencing. The authors discuss three major issues facing researchers using this new technology: the return of data to participants, obligations to participants' relatives, and potential future uses of samples and data. Although I don't agree with all of the authors' arguments, it's great to see some informed discussion of these issues in advance of whole-genome sequencing technologies becoming widely available.

I'll start with a bit of a background on whole-genome sequencing technology and discuss the first of these ethical challenges today; I plan to discuss the other two problematic areas in a separate post.

The power of whole-genome sequencing
It's important to remember just how difficult it was for us humans to obtain the first (almost) complete sequences of the DNA that resides within each of our cells - the sequences generated by the public Human Genome Project and by the private company Celera, both published in 2001. It took almost a decade and cost somewhere in the vicinity of $3 billion to obtain the public human genome sequence, a vast amount of money by any standards.

Over the last seven years the price of genome sequencing has plummeted. A private company, Knome, will now sequence your genome for the comparatively paltry sum of $350,000, and the notion that we will see a $1000 genome sequence within the next decade has become a cliché. Over the last year we saw the publication of several brand new genome sequences: those of James Watson (right) and J. Craig Venter, as well as a Chinese volunteer analysed by the Beijing Genomics Institute.

The advantages of whole-genome sequencing (WGS) for research and for medicine are enormous. Your full genome sequence - including both the nuclear DNA you inherited from both your parents, and the mitochondrial DNA you inherited from your mother - contains all of the genetic information that resulted, through a complex process of interaction with your environment, in your adult form. This gives researchers a complete catalogue of the genetic differences between you and the people around you, a far superior data-set to the limited collection of common variants provided by genotyping methods (like those employed by 23AndMe and deCODEme). This is because at least some of the differences between people are due to rare variations, perhaps found only in them and their immediate family, that simply won't show up at all on even the densest genotyping chips but will be revealed by complete sequencing.

How important are those rare variants? It's still difficult to say, but at least for some traits these uncommon genetic quirks probably play a major role. For instance, we know that variation in height is around 80% determined by our genes, but the common variants identified by recent (and quite well-powered) genome-wide scans for height differences explain only about 1% of this variation. It's likely that much of the residual variation is made up of less common variants that each confer only a small proportion of the total effect.

The same is likely to be true for many common diseases, for which genome scans to date have uncovered only a fraction of the total genetic risk. Such rare, small-effect variants could only realistically be identified by sequencing, either of a selected set of "candidate genes", or - more comprehensively - by whole-genome sequencing.

Return of genome data to participants
I suspect that the majority of the three readers of this blog (one of whom is me) would be very interested in getting a free copy of their own genome sequence, should we be fortunate enough to be part of a study in which this was generated. Indeed, the authors of the NRG review drily note that in the age of 23AndMe and hugely popular genetic ancestry sites, "the desire for information and the expectations of research participants for receiving their results are likely to increase."

However, they also note that "in most jurisdictions there are still no definitive research ethics policies regarding the return of research results." In practice, there are a number of logistical and ethical hurdles that need to be overcome before researchers start handing back data to their unprepared research subjects:

Data format. A DVD containing gigabytes of text files of As, Ts, Cs and Gs is unlikely to satisfy most WGS research participants. However, providing fully annotated sequences (with lay descriptions of the meaning of every potential disease allele) would be far beyond the means of most research groups, as well as triggering potential regulatory restrictions and litigation risk.

This is a tricky dilemma, and the advice of the NRG authors is irritatingly vague: they simply suggest that any research project involving WGS "should be conducted under a formal research protocol, and ought to include the development of a data return and counselling policy".

That's pretty unhelpful to anyone actually trying to come up with such a policy. My suggestion: if researchers can't afford to provide the annotation themselves, they should at least return the data in a standardised format that makes it easy for participants to get that annotation from other sources. Over the next year or so we will see a profusion of private companies seeking to decipher our genomes for us, for a price; at the same time, I fully expect that online communities and publicly funded research institutes will set about designing browsers that will let us do the same thing gratis. If the research participant has their data in a standard format recognised by all these systems they can decide for themselves who they trust to peer inside their genes.

Clinical follow-up. Anyone who has their genome sequenced will almost certainly learn that they carry several recessive disease variants - variants which cause no harm to them, since they are each complemented by a normal healthy copy of the gene, but may result in severe deformity and disease in their children if they are unlucky enough to mate with someone who carries nasty versions of the same genes. In addition, each of us will carry any number of common variants which are associated with an increased risk of complex diseases such as coronary artery disease or diabetes. Finally, a few of us will find that we carry variants of the worst sort: things like a Huntington disease mutation, which will result in an incurable slide into dementia and death within a few decades. Either way, it's likely that all of us will need someone to explain what these things mean, and point us towards specialist care if this is needed.

The review notes that the medical community is massively unprepared for this: there is a serious shortage of clinicians with the required training to effectively communicate genetic risks. They recommend, quite reasonably, that governments invest in further training of primary care physicians to this end. Surprisingly, although the authors mention "an expanded role for geneticists and genetic counsellors", there is no discussion of increasing the number of university places for non-physician genetic counsellors - despite the fact that these individuals are likely to take on a substantial part of the burden.

Integration of data into medical records. Obviously genetic information that impacts on a study subject's health is just as important as other sources of information - cholesterol, blood pressure and the like. But equally obviously, if a variant has not been well-validated as a genetic risk factor, it shouldn't be described as such to a research participant, and it shouldn't be included in that person's medical records.

The NRG authors make some good recommendations:

only validated data of known clinical relevance should be included in the health record;
practice guidelines should be outlined for determining what constitutes validated and clinically relevant data; and
there should be a process by which health records are updated with new knowledge about the clinical relevance of specific genes.

The first two points are fairly self-evident, although it would have been great to see some realistic suggestions regarding those practice guidelines. The third recommendation, updateable records, will become more realistic if and when we start seriously moving into an era of centralised, electronic medical records.

That's more than enough for today. Later I'll discuss the other major ethical challenges discussed in the NRG review: obligations to close relatives of study participants, and future uses of samples and data.

Interview with Ben Heywood, CEO of PatientsLikeMe

2008-01-02T13:36:32Z

On the Health Business Blog, I interviewed PatientsLikeMe CEO and co-founder Ben Heywood. The online community enables patients with serious illnesses to build content-rich connections to similar patients. Patients are motivated to provide all the relevant information about themselves and to stick with the site over time to help one another. As a consequence, PatientsLikeMe users [...]

Nigel takes a pop at Aubrey

2008-01-10T20:28:00Z

In “Preserving Humanity - and Technology?”, Nigel Cameron’s response to Aubrey de Grey’s essay “Life Span Extension Research and Public Debate: Societal Considerations” by Aubrey D.N.J de Grey.

Unlike Kass and Fukuyama, Nigel finds it hard to argue against healthy life extension. So he tries to drive a wedge between the longevity dividend advocates and H+/SENS-ists:

..perhaps all this agitprop will get the attention of policymakers, so that the implications of emerging technologies can claim the place they deserve in our public attention. I think though it is more likely that transhumans and technophobes will feed on each other, a resonance that will scare off those in the middle - and thereby raise the risk involved not simply in technological investment but in serious public discussion of emerging technologies and their implications.

Nigel also gives Aubrey his favorite epithet for us H+ types: “pugilistic.”

Were I uncharitable I might suggest that de Grey’s worst enemy is not death, but his style, which is pugilistic,

I also received that judgment from Nigel.

Abstract: Aubrey de Grey’s enthusiasm may or may not be infectious, but it is certainly palpable. And it adds a dimension to the discussion the priority that should be given to life-extension/anti-ageing research of which he seems to be unaware. For on the cusp of developments in emerging technologies we find ourselves button-holed by enthusiasts whose ``transhumanist” visions importunately press upon us the most radical understanding of their implications. My suspicion is that the transhumanist mini-insurgency is partly responsible for the general failure of the policy establishment to summon up the courage and vision to address the implications of emerging technologies at all. The insurgents’ effort at ``branding” these technologies as transhumanist (like that of the Raelian flying-saucer cult, a decade ago, to claim cloning as their own) does no favors to the technology. The irony is that de Grey and his fellow-visionaries, far from generating consensus enthusiasm for emerging technology applications, are making them too hot to handle.

PCR - the music video

2008-01-12T01:19:01Z

Man oh man, if only they’d had this video when I was taking Cell and Molecular Bio in 1999 - it might have injected a wee bit of life into our 3 hour Thursday evening lectures (Anne and I resorted to pen wars to keep ourselves occupied).

Anyways. Please please watch the above. It’s like a ray of nerdy sunshine to the tune of a gospel choir, provided to us by Bio-Rad, who make PCR machines (I mean SHIT man, that is the best ad for lab equipment I have ever seen and I’ve seen a lot of them).

My favorite line: “It’s amazing what heating and cooling and heating will do...” Of course the chorus is pretty amazing too: “PCR when you need to detect mutation. PCR when you need to recombine. PCR when you need to find out who the daddy is.PCR when you need to solve a crime.”

Kary Mullis would be so very, very proud.

(Thanks Tom)

Phenotypes mapped by 23andMe & deCODEme

2008-01-12T11:47:49Z