I just left the NAS meeting on Integrating Environmental Health Data to Advance Discovery, where I was an invited speaker. It was a pretty interesting meeting, with presentations from speakers who worked on chemotoxicity data, pollution data, exposure data, and electronic health records, as well as a few "outsiders" from non-environmental health fields who deal with big data and data integration issues. I was one of the outsiders.
The entire meeting was recorded and should soon be available, in which case I'll update this post with links to those materials. Rather than blog the entire thing, I'm going to try to distill out three main take-homes. (Think "lossy compression," heh.)
tl; dr? It's all about incentives; nobody really cares about education, but it's really really important; and standards and ontology are the really hard technical problems.
1. It's all about incentives
A number of people quite convincingly pointed out that it was not in the direct interests of scientists to share data well, and that cooperation between groups was not particularly rewarded in general. Stephen Friend's entire talk was about incentives, in a way; incentives to share, incentives to collaborate, incentives to release data, incentives to come up with better analyses.
These are not new arguments, of course.
But what came out of this meeting is the very clear sense that the slowest people to change (apart from the larger part of the mass of scientists themselves) are the mid- and upper-level bureaucrats at universities. Since universities are where most basic research happens, this is A Problem: not only do they not encourage change, they actively block researchers from doing things differently by firing ("failing to give tenure to") those who do. The particular problem we're confronting here is the scientific equivalent of how to get enough street cred to get tenure -- a topic of some personal interest to me :).
If anyone can crack this problem in a general way, the research world will change. One hopes it will be for the better.
A number of people made the point that the most likely pivot point for manipulating institutional incentives was funding; if the NSF and NIH provide the appropriate incentives to change, then change will happen, because MOOLAH. (This is why I'm so amazed by the set of reviews I recently got, because it rather directly went against common wisdom in the university.) Stephen Friend invoked Elinor Ostrom's work on how to change institutions, and Michael Neilsen (via Twitter) strongly suggested her book "Governing the Commons".
On further reflection, though, this is a chicken-and-egg problem. Funding agencies have different incentives than universities, but are largely staffed by academics on furlough. Moreover, grants are typically reviewed by guess who? By academics, of course, who can subvert the stated intent of a grant program by reviewing and ranking the grants however they please. We're not going to get change very fast unless we can figure out how to drive the grant review process differently. (See above amazement, again. ;) There was a lot of discussion of data analysis competitions, gamification, and incentive systems that rewarded progress directly, and I have a hunch that those will result in faster change than is possible via the standard grant mechanism -- and again, Stephen Friend presented some convincing experiences in this direction.
Stephen also made the surprising prediction that in some number of years (like 10 -- soon!) that most research dollars will come from crowdfunding solutions. (This occasioned some skepticism on Twitter.) Interesting prediction; I suggest we all watch Ethan Perlstein's experiment veeeeeeeeeeery closely, and not just because I'm concerned about the meth-crazed yeast.
Barbara Wold had a really interesting and (to me, novel) idea for incentivizing the production and distribution of good data. She suggested that in addition to just providing an archive of all data, an additional archive of only high-quality data -- data that passed some set of benchmarks and quality controls -- be created. If this data became regarded as more trustworthy than the default archive by reviewers, work based on the data could be more highly regarded; given citation mechanisms for data this could result in regular producers of good quality data gaining cred. This idea passes my smell test for being something that could be quietly subversive and effective, in part because Barbara is both smart and understands the system very well from all sides.
2. Education is important and undervalued
There's a weird blank spot amongst biology faculty about education. On the one hand, everyone acknowledges that grad students, or postdocs (who come from grad students, note), do most of the work. But there's a strong bias against teaching them anything outside of the lab, 'cause it's a waste of time when they could be doing research. So most of what they learn, they learn in the lab which is necessarily very focused and directed towards the research the lab is doing. The question is, in an era where most labs lack computational skills, how do students become more broadly knowledgeable and capable beyond what is already done in the lab?
Personally, I think this situation is an implicit indictment of the graduate classes we offer, which often lack practicality (I've certainly gotten this feedback from people who go into industry, BTW). In my previous blogging about this, I came to the surprising conclusion that we should only force bio grad students to learn stuff they don't know they should learn, and leave the rest to survey courses, seminars, and self-education. Maybe.
Me myself, I had the luck to be in the lab of someone who helped found the field of developmental molecular biology and genomics and insisted that we learn a lot of background, but this is hard to pull off as a young prof -- trust me. I also came into bio from a weird background of computing, math, and physics. Institutionalizing either of these things is impossible.
The meta-problem, of course, is that if we really want to bring computing into biology, or Big Data into wide use, we need to confront not just the manpower gap but the meta-gap: the lack of training programs in this area. The NSF has offered an IGERT-CIF21 training program grant in Big Data for just this reason, but they're only going to award 2 this year -- two. And each will train only about ~30 grad students in 5 years.
For comparison, we received 169 applications for 24 spots in our NGS course last year, including applications from more than 20 tenure-line faculty. I could almost do an IGERT-CIF21 just with the faculty applicants. (Assuming they wanted to get another PhD, of course. Hmmmmmmm.)
Another problem is that even if the NSF offered lots more money for training, these grants come with little institutional overhead (which is what universities value); one chair told me that if I'd asked before getting into it, he'd have discouraged me from participating in the IGERT, because it is likely to be a lot of work for little respect or reward (see: Incentives, above). I'm already running into this with my Analyzing Next Generation Sequencing Data course, which is lot of work for little institutional reward. Both of my chairs were downright discouraging about a $200k education supplement that I got, because it looked like I was "going educational", which, as everyone knows, isn't good honest work; and I have encountered a number of obstacles putting this grant into practice -- "sabotage" is maybe too strong a word, but I'm certainly not being officially encouraged in any of these endeavors. And let's not even talk about Software Carpentry, which is clearly a useless effort, right, because no one gets credits for it?
<whipes froth from lips> Sorry, sometimes I just get going...
The solution, of course, is money. Money money money.
There are rumbles that more significantly funded training efforts will emerge from NIH, who has realized that just producing lots of data is not as useful as producing lots of data and then analyzing it really well, for which they need lots of trained graduate students and postdocs. I await with baited breath! (That's not sarcasm -- I am really hopeful!)
A point I tried to make at the NAS workshop panel was that since we have this massive Big Data worker shortage, and the NSF wants to change that, we need both undergrad and grad training programs, the bigger the better; if Big Data really is important to research, those people will have a competitive advantage in academia as well as in industry, and everyone will be desperate to hire them. So it should be a win-win-win.
3. Ontologies and standards are the real technical problem
Or, "Give me but a set of primary keys, and I will pivot the world around them."
It's no surprise that entity resolution is probably the single most pressing challenge when integrating heterogeneous data, and making sure that THIS value in THIS database means the same thing as THAT value in THAT database is a fundamental semantic challenge. In my talk I pointed out that the first 90% of bioinformatics is identifier munging, while the second 90% of bioinformatics is figuring out what each different database means, exactly, by the term "gene".
In my talk, I inadvertently appeared to recommend completely ditching ontologies and standards. Ann Richards rightly took me to task (at length) for this in the Q&A session ;). Ewan Birney and Barbara Wold said what I should have said: there is a sweet spot between rigor and "winging it" in ontology and standard development, and one of the best ways to find the sweet spot is to get real live practitioners to engage in this development. Or, to paraphrase Stephen Friend, individual researchers working on a particular problem come up with a partial solution, and then iterating, seem to be better at standards development than are committees.
I have avoided standards and ontologies as much as possible, and will probably continue to do so; I have never heard someone talk about their time on an ontology development project with enthusiasm, and I am personally much better at winging it then at any sort of advance planning. So I don't have much more to say other than that it seems like a really hard problem. But I asked around: Before the meeting, I tapped into an old friend of mine who works in data quality in a non-academic field -- Clint Bidlack of ActivePrime. He gave me the lay of the land, and pointed out that the general problem of heterogeneous data integration appears to rival the development of truly autonomous robots in difficulty. I will therefore be expecting fully automated solutions to heterogeneous data integration when my robot bartender first brings me a gin & tonic when I want one, without my having asked. But Clint did point me at an academic research field that looked very promising: Unsupervised Feature Learning. He bade me watch this video, but pointed out that this is still a deep research topic with limited practical applicability so far.
Overall, a very interesting meeting that got me thinking hard about some interesting problems.