It’s ironic. I spend a lot of time and energy flying around the world talking about new forms of collaboration, but one would think those forms would actually allow me to fly less and not more. But every talk seems to turn into three more talks, and everyone wants to meet in person. Highlights of the year so far include the amazing bunch at CC Korea and the burgeoning connections we’re creating with folks in the US national cancer grid – they’re doing good work there, with some real progress.

This all comes at a cost. I’ve flown 110,000 miles already this year for work – add another 30,000 to cover personal flights to Brazil (my fiancee is from Sao Paulo) – and it just seems to get worse. For the most part we’ve shut down big work travel til September but then it’s back down in the hole until December, with trips coming up to Cuba, Australia, Japan, Colombia, Scotland, and various domestic destinations.

The road is a great place in many ways. It’s where the collaborations actually take form, where the ideas bounce and reverberate. Blogs are great, as are online videos in lieu of speaking engagements. But until we can cross the uncanny valley the big collaborations still seem to be face-time driven, rather than facebook driven.

There’s a stored body of post ideas, including: why journals as we know them aren’t going anywhere in the short term, elsevier’s grand challenge, chris anderson’s (ill-advised, i think) comments on the scientific method, the relationship of access cultures to innovation cultures, and more. Hopefully the coming weeks of downtime will be rich for the blog. Email me if I owe you a response to anything!

Cameron Neylon has a must-read post on the this aspect of the data commons :

So I started with the notion of paying researchers to make data available, originally phrased as ‘pay the journals to buy papers’. What I really meant was paying people to put research results somewhere useful. So let us imagine we can pay people to deposit data (we’ll figure out how later). We don’t want to be swamped with rubbish so the data has to be well structured, tagged up and machine readable. If we’re paying for it, we set the standards. We also want to encourage re-use of data, perhaps by paying a premium for the deposition of data that re-uses other data. And in turn, perhaps pay a premium to those whose data is re-used.

Funders are putting hundreds of millions into data centres that no-one is too sure what to do with. Maybe that money could be used more effectively to drive data deposition quality. Some funders may also see this as a good model for direct funding. Putting money in to drive the generation of specific data set. Channel funding through the foundation to pay groups to deposit the results rather than pay them to do the research. For small foundations or charitable concerns this may be a much more effective means of driving the outcomes they want.

I need to come back to this point, and I’m actually going to sit down with some lawyers and prepare an “appellate brief” on this topic (thanks Jordan Hatcher for the suggestion). But I want to get this into the discussion right now.

The public domain is not an “unlicensed commons”. The public domain does not equal the BSD. It is not a licensing option.

It is the natural legal state of data.

It is a damn shame that we no longer think of the public domain as an option that is attractive. It’s a sign of the victory of the content holders that the free licensing movements work against that something without a license – something that is truly free, not just just free “as in” – is somehow thought to be worse. We’ve bought into their games if we allow the public domain to be defined as the BSD. The idea of the public domain has been subjected to continuous erosion thanks to both the big content companies and our own movements, to the point where we think freedom only comes in a contract.

The public domain is not contractually constructed. It just is. It cannot be made more free, only less free. And if we start a culture of licensing and enclosing the public domain (stuff that is actually already free, like the human genome) in the name of “freedom” we’re playing a dangerous game.

There’s a lot more to get at here. For one thing, queries to a set of databases create a data product – which means that naive web users have to keep track of licensing of their Google results – unlike code, where as a naive user I don’t worry about GPL v. BSD. For another, the EU database directive and the UK tradition of “sweat of the brow” can make the public domain harder to achieve. And yet third, no one knows how moral rights relate to this – those are rights that emerge from the classical idea of an author’s rights, but how can someone claim moral rights on a fact of nature? Fourth, what’s the difference between fundamental data (GPS coordinates or species genome sequences) and “state change data” (like when you probe the genome with a drug, which might well trigger a right akin to moral rights)? Fifth, not all data can even be in the public domain or virally licensed due to privacy rights (anything collected from a person can’t be made free by any magic contract, privacy trumps contract like paper covers rock).

Speaking as someone who got into this two years ago convinced SA was the way for data, this stuff is complicated.

I don’t know the answers. But I do know that if we start to frame “public domain data v. viral data” as “BSD v. GPL” that we’ve already lost the debate, because we’ll have bought into the erosion of the public domain that led to the need for commons licensing in the first place.

In short, it’s a demonstration of how confusing data licenses make the position of data providers essentially untenable. From my perspective, the answer is either go public domain, or don’t. If you don’t, please make the metadata public domain. Anything is simply too confusing to figure out, and it’s going to be worse.

Part of the problem is that we have created a cargo cult around licenses. A contract will come from the heavens and make us free! But in data we’ve got the public domain right there to teach us. All we have to do is look up from the lawyer’s desk and follow the yellow brick road…er, the NCBI’s lead.

jtw

>>>>>>>>>>>>>>>

I tried to post a comment but don’t know if it got through.

I did not intend for my comments to become public – that was a post to an advisory board list, intended to highlight precisely how this issue demonstrates the difficulty providers have in understanding licensing of data.

Creative Commons licenses were built for cultural works, like this blog or a website or music. They weren’t built for data. Data has different qualities and characteristics and thus requires different licensing approaches.

I would recommend you read the official CC position on this, which is the Science Commons Open Access Data Protocol (http://sciencecommons.org/projects/publishing/open-access-data-protocol/) and that you look at the best available legal tool to achieve the protocol (http://www.opendatacommons.org/odc-public-domain-dedication-and-licence/). These are regimes that facilitate data integration, unlike the CC BY SA license.

Please know that I salute your intent here and don’t want to slander you – you’re trying to share, and you’re confused on how to do so. I do believe that in our conversations I did indeed recommend to you the idea of releasing an RDF dump of your database in the public domain, using only the NCBI approach listed on this very blog. That’s essentially what we recommend at CC, as you’d see in the protocol.

Again, it was not my intent for this to go public before I could reach you, and I’m very sorry for that. It is never fun to make a decision and get pummeled for it, and from my perspective you don’t deserve the pummeling.

I’ll cross post this to my blog to make sure it gets online.

I agree fully with Jamie’s writing here. He does a very elegant job in noting that the patent system is itself not the cause of the lack of research into diseases of the global poor, but that the drug companies that depend on patents are also blocking the creation of new incentive systems that might address market failures in global disease research.

His key point for me is that patents are like religion. Boy howdy, is that ever my experience.

I’ve had the temerity to suggest on occasion that patents aren’t the big problem in global disease research – that our research at science commons indicates the problems are much more to do with fundamental knowledge gaps, process failures, non-patent-related transaction costs. That isn’t popular in many places.

I’ve also had the temerity to suggest that prize models need to be accompanied by systematic changes in the fundamentals of drug discovery: knowledge sharing as a first principle, investment in biobanks to move materials around the world smoothly, investments in web infrastructure to make a scientist in Brazil as powerful as a scientist in Pfizer’s Groton offices. Also not popular in some places (actually not popular either in the prize advocate community or the pharma community, oddly).

I don’t believe prize models are going to scale as long as the fundamental uncertainty of drug discovery persists – we have to change the game at the same time we change the rewards granted to the winners. Remember how little we know about the body: we don’t know how Tylenol works, much less how to predict what a new drug is going to do.

In all the costs of drug discovery there’s one big one, clinical trials, and it comes from knowledge gaps. We don’t know if Magic Drug X is going to kill people or not until we give it to humans and watch to see how many die. That knowledge gap costs money – lots and lots and lots of money. We have to eliminate that knowledge gap and lower the cost so that more can play, and more can fund – and in turn, so that we can have process competition at the funding level like prize models without an $80B pot of gold needing to be located. Otherwise, only rich people will play, and they’ll play by market rules. That’s what markets do, as Jamie points out. The question is how to manage the places where the markets fail, and we can do that only if we lower the chance of failure by bridging the knowledge gaps, and doing so in a public and open access manner.

We’ll know we’ve been successful not when a prize model replaces the pharma dependence on patents, but when it sits alongside and outcompetes the patent system. I am personally looking forward to that day. To get there we’ve got a lot of roads to walk, and we need to get away from patents as religion – whether we’re pro or con on the topic – and we need to focus on filling the knowledge gaps that drive up costs and steal precious research cycles.

The problem is, it sprawled from the original 1000 words to, well, a lot more. I’m going to hack it way down and it’ll be a totally different article. But I thought this one deserved its own URI as well. As such, I’m eating my own preprint dogfood. It’s online in Nature Precedings under CC BY 3.0 as The Control Fallacy: Why OA Out-Innovates the Alternative and I welcome comments either to me directly, here at this blog, or on the Precedings page for the paper.

Money quote: if you have limited resources, add more data rather than fine-tuning the weights on your fancy machine-learning algorithm.

Harumph. With apologies to George Clinton, Free Your Data ...And Your Searchers Will Follow.

(edited post to add in link to slashdot thread)

The author is a scientist and asks a good question about one of the points I make in the talk, regarding the relationship between copyright and creative works and scholarly publishing. It’s a point I have actually removed from my talks recently because I was finding it misconstrued – it’s a little subtle and hard to grok sometimes, and it’s an example of how hard it is for the lawyers and the scientists to understand each other.

But it’s a perfect conversation for the blogworld, and I’ll be pinging the author at PA to engage in a conversation. I’m still figuring out how to engage the community and this seems a good place to start :-)

More after the jump…

In Australia, another post is fermenting, but the day job is calling. I met up with Scott Page and Karim Lakhani in Oxford last week and have some good thoughts on wisdom of crowds / open innovation in science that came out of those discussions.

My short version is: science is harder than culture. Science takes a level of exactness and accuracy that other fields don’t (and in many ways harms cultural creativity). Science is institutional in a way that other fields aren’t. And last, the technical and social infrastructures for cultural creativity simply aren’t very well tuned to support science – we can’t just point biologists at web 2.0 or “get data online” and expect things to change.

more after the jump.

From Chemical & Engineering News (am I really linking to an ACS site? you bet…)

“Working with mutant fruit flies, the researchers found that accumulation of the abnormal huntingtin within presynaptic neurons increases levels of Ca2+ in the neurons. The extra calcium induces the presynaptic neurons to release excess neurotransmitters, which contributes to the development of the disease (Neuron 2008, 57, 27).

[...]

The results also suggest that treatments could be developed to delay the onset and progression of the disease. Examples include compounds that would modulate neurotransmission or that would block the membrane pores through which calcium enters neurons.”

Let’s remember the HD gene was patented in the 1980s. And we still don’t even know how it relates to the disease, and this research is coming out of the fruit fly world…so it’s probably a long way from a treatment. At current rates, it’d be something in the year 2025.

Knowledge gaps, people. Knowledge gaps.

There’s a lot of passion. Smart people disagree on the real cost of discovering a drug, but everyone tends to agree that it’s insanely high. I actually believe the $1B number bandied about isn’t far off, and I also believe that the cost of failure is a fair part of the number of the costs of successes. The time for successes isn’t hard to argue though…seventeen years.

The success of “open source” licensing in changing software and culture gets a lot of folks excited about open source biology. Now, I believe in open science. And I have made my career choice in open science. I’m a believer in the power of open approaches.

But I am very skeptical that the law is the key, single solution here. Mainly because I believe the law is only a symptom of the big problem. I do believe that the commons itself is the solution, but not because of intellectual property.

It’s the solution because it’s an incredibly efficient way to generate knowledge rapidly, at low costs. And knowledge is what we need. A commons is the infrastructure for distributed collaboration and innovation in the life sciences, and we should be thinking about the law in terms of how to expose and integrate as much knowledge as possible at the lowest possible transaction cost.

The primary reason drugs are so expensive to discover and develop is that we just don’t know very much about the human body. The problem isn’t the law. To paraphrase Bill Gates, the problem is complexity (read the whole thing, it’s worth it).

This has got me thinking about the knowledge gap – the things we don’t know that cause us to fail so regularly at discovering drugs – and the way that gap interacts with the commons of knowledge and research tools.

More after the jump.

((Life trumps blogging warning: I won’t be back posting here until after the new year – whatever you celebrate, I hope you have fun celebrating it))

Over at the work blog yesterday, I announced the release of the Science Commons Open Access Data Protocol – as the title indicates, it’s not a license, but a guide to how to make data and databases available under terms inspired by Open Access.

The best blog response I’ve seen so far is from Glyn Moody , who said that “Something calling itself a “Protocol for Implementing Open Access Data” sounds about as exciting as a list of ingredients for paint.”

(he does go on to praise the protocol after that lede, thankfully)

My official position on all of this is of course the position on the SC blog. But I thought I’d reveal a little of the personal story that led us to the position that we reached. It’s long, and a little self indulgent, but hey – it’s a blog!

More after the jump…

I’m talkin’ web 1.0 here, people. Basic stuff like Amazon and eBay – tools that allow for low-cost, low-friction connections between buyers and sellers, with feedback systems and fulfillment, secure orders and acknowledgment and ratings and more.

We’re so far away from this in academic life sciences it’s laughable. Taxpayer dollars flow into labs, and enormous amounts of expertise and effort go into making biological materials for research purposes. Plasmids. Cell lines. Mice. Fish.

And most of these materials, this treasure trove of implicit knowledge and encoded experience, live inside refrigerators, never to be shared. At best they’re hoarded to guarantee the owning lab priority for more publications. At worst they simply decay from neglect, left unlabeled and unloved when a grad student moves to a postdoc elsewhere, or a postdoc takes a job in industry.

Can you imagine the increase in our ability to build on published research if the scientist reading the paper didn’t have to recapitulate all the experimental construction of tools? If instead you could search the web, drop in a credit card number, and get a cell line via fedex in four days? If, yaknow, e-commerce worked for science?

It’s possible. The technology is easy. It’s the culture that’s hard. Getting to one-click for this stuff requires a lot of people to change at the same time.

More after the jump.

Paraphrasing slightly, if all 60,000 neuroscientists took up arms and began a round of tagging, collaborative filtering, and more…would that be enough to mark up the literature?

John and many others call for mass Social Web, because they believe the crowds are big enough to be wise as crowds qua crowds. I call for the Research Web, because I believe the task is so incredibly large – so much more complex than wikipedia, even – that even an army of neuroscientists could not map the information in a meaningful way, much less 60,000.

Sadly, I think my greater point about the interdependence of Research and Social Webs upon each other tends to get lost in the details of the argument. I think neither one is sufficient, and I think both are necessary. The two are far greater than the sum of their parts.

As for scooping – which was another interesting set of comments – I’ll try to address that in an upcoming post.

More on Seeding the Social Web after the jump.

Let’s start with a tautology. The wisdom of crowds depends on the existence of crowds.

And let’s take neuroscience as an example of a scientific discipline. It sits inside of life sciences but it’s big enough to qualify (the Society for Neuroscience claims 38,000 members, and after attending the SfN meeting this year, I agree – they filled up the San Diego convention center).

Neuroscience itself subdivides into lots of specializations. From the wikipedia entry on neuroscience “studies may include the structure, function, evolutionary history, development, genetics, biochemistry, physiology, pharmacology, and pathology of the nervous system.” There’s a lot more after that top-level definition including the enormous cross-disciplinary interest in neuroscience.

So I think it’s worth asking: are there enough people who understand this stuff to follow to the water hole? Is the crowd big enough to be wise?

I think I was the only one in the room focused on research (everybody else was patient-oriented, which is great – but the drugs do have to come from somewhere). I talked about the Semantic Web. It went ok, but the questions and the general response got me thinking about phrasing and messaging.

We need to start talking about the Research Web, which is the reality of what we’re building here. Just as the Social Web uses, but is more than, AJAX) , the Research Web will use the Semantic Web, RDF and other technologies and strategies to accomplish something we deeply care about: Making medical research more effective so that we can cure and relieve the suffering of patients.

We need the Research Web because the existing Web doesn’t work for research. Here’s what I mean: Googling a phrase like signal transduction genes in pyramidal neurons doesn’t get you a list of genes. It should get you a list of genes. No amount of collaborative filtering makes it easy to read 188,000 papers – and this is stuff where you tend to want experts moreso than the “wisdom of crowds” – advice from someone who doesn’t understand signal transduction tends to be less reliable than from someone who does.

The Research Web is about integrating lots of stuff that wasn’t designed to be integrated with anything. It’s about getting precise answers to complicated questions instead of a mess of Web pages. It’s about the move to industrialize the way scientists annotate data. The Research Web is about making the Web work in a complex data environment, where machines make and transmit terabytes of content that humans have to interpret.

There’s a lot of phrases out there for this. Network Science. Cyberinfrastructure. E-Science. I like Research Web a lot more, because it ties into the ideas of the Web and what it means to us day to day. Research Web means that search engines work for research, like they do for pizza. Research Web means that biological materials can be ordered in a setting closer to one-click than pre-industrial artisanal fulfillment. It means open source tools, pipes that run on data like they run on music, and much more.

This isn’t about grids or ontologies (though it definitely builds on them). It’s about making the user experience for research on the Web more like…well, the Web for other stuff.

It doesn’t require anything deeply novel to make a Research Web. The “and then a miracle happens” problem doesn’t surface. The Research Web doesn’t tell you anything that isn’t yet known. We’re just talking about re-designing information that is already digital into a format that works better for research – a format with more context and more structure.

To be totally open, I’ve been waiting for Creative Commons licenses to come to the Nature Network before investing myself into blogging. It’s hard for us at CC to work otherwise. And while there is movement on the copyright aspects of this site, it’s not there yet.

However, I’ve been itching to start talking here anyway, and in the short term, I’ll be posting the fulltext of my comments into Nature Precedings under CC-BY 3.0, so that the text itself will indeed be available under an open sharing license.

Thanks again to Nature Network for working this out with me.

I am far from qualified to judge the situation. Though I’m across the street from most of these folks, the Computer Science & Artificial Intelligence Lab is a world away. I don’t know any of them.

But I am utterly fascinated by the interplay of policy and personality, competition and collaboration. It speaks to the issues we’re working on at Science Commons in a unique way: we got into this research thinking it was about the law and using the law to help people collaborate and innovate – like Creative Commons did for culture. But in science, it’s so much more complex than that. Prestige and competition appear to play a greater role in the collaboration, or non-collaboration, between laboratories, than do for example…patents…copyrights…contracts. It’s one of the reasons we’re also focused on the integration of technology, law, and policy to make scientists see the benefits of an open approach by giving them questions they wouldn’t otherwise be able to ask. Apropos of this discussion, we’re doing a lot of work in neuroscience on this front.

“Pressing scientists to collaborate”. Makes me think of collaboration as a constantly shifting piece of game theory. Is it worth collaborating with you? A few roleplaying scenarios:

1. No, if you can’t help me, or if you can hurt me
2. Yes, if you can help me, or are more famous than I am
3. Yes, if by not collaborating with you, you choose to work with a different lab with the same tools and get a paper out ahead of me

This doesn’t even count the two graduate students or postdocs who are surreptitiously trading antibodies and animal models, taking the T with an eppendorf tube and some shaved ice between MIT and the Farber. In that case, the collaboration is deniable unless the two involved get enough data to make a publicly acknowledged collaboration worthwhile.

So what is the impact of adding to this complex calculus, which scientists seem to learn through guild and rites in graduate school, an institutional policy of collaboration? What would that policy look like? How would you track its success and impact? I’m curious and eager to watch and see what happens here.

I’m behind in blogging at my regular job location, so I’ll probably be doing a fair amount of reposting of material from that blog to this one. And I’ll make some Boston specific posts here as well.

First off, I’ll introduce myself. I run the Science Commons project at Creative Commons. I’ve got a background as a software entrepeneur and in technology policy, with a focus on the intersection of intellectual property rights and S&T policy. I’m happy to be blogging here.

Second, I’d like to thank the folks at Nature for taking a very restrictive original copyright policy off the site, and making it much more amenable to open licensing regimes like Creative Commons. But I will be agitating on these pages for open licensing until it arrives.

It doesn’t make a lot of sense to run a blogging site or a community site with restrictive licensing. If you look at the Web 2.0 sites that have really taken off, they don’t have massively restrictive terms of use. Wikipedia and YouTube come to mind. And I think it’s even more important in the sciences to let that reuse be as frictionless as possible.

It’s an interesting experiment to see just how far a science blogger is willing to go in terms of talking about science. Maybe for now it’s just complaining about reagent quality or a vendor. Maybe next year it’s describing a quandary or a hypothesis. Maybe trackbacks and pings will establish enough priority to start affecting authorship, to start affecting fear of scoops.

More likely not in the short term though. If we knew that was going to happen we’d know what kind of licenses would work here. But this is a dream. And placing early limits on dreams has a nasty habit of constraining creativity when it’s most needed.

I’ll try to blog weekly and start describing the Science Commons project space…

Thanks again to the editors for letting a contrarian into the compound.