Cyberinfrastructure for Marine 'Omics (2013 version)

I just finished attending a 1-day workshop on Cyberinfrastructure for Marine 'Omics down in DC. It was a meeting organized by the Gordon and Betty Moore Foundation but attended by program managers from about a dozen different agencies and divisions (NSF BIO, NSF GEO, etc.); a bunch of pretty serious marine biogeochemists (?) also attended. And me.

The workshop was both really interesting and thought provoking, and very depressing. The problems are hard, and the solutions aren't forthcoming.

An imperfect summary and set of thoughts, below.

Links

My talk slides

A Storify from my occasionally sarcastic side commentary on Twitter from the workshop, and responses

A not-that-well written thing about how to write better metagenomics pipelines

Yes, we should use Service Oriented Architectures. Please reread the Yegge platform rant.

Building "Cyberinfrastructure" for specific domains is hard

The term "cyberinfrastructure" encompasses hardware, networking, and software across all levels - the metaphor used in the workshop was "roads and cars", as in, without roads, cars aren't that useful; without cars, roads aren't that useful. We need both.

Personally, I'm mostly focused on the question of "how do we build useful software, fairly close to the research, that can help enable domain experts to get their work done?"

The three approaches seem to be (warning, mild sarcasm below):

give money to computational folk, who then build low level infrastructure and focus on premature optimization of specific workflows. End result: infrastructure that doesn't address the actual problem.
give money to domain scientists, who then use it to do research. End result: software that does awesome stuff, but has unknown generalizability and often can't be used by anyone else (see: The Ladder of Scientific Software Notsuck)
recognize that there are cross-domain-cutting problems that could be addressed by coordinated funding, and try to leverage that funding to actually address those cross-cutting problems. (I would argue that metagenome assembly is one such problem shared by many fields.)

For examples of the 3rd, see iPlant and DOE KBase. My big problem with these is that they are not run as open source projects so there's a lot of opaque development focus, opaque money flow, and potentially wasted development. iPlant (at least) has an open source mandate although I am withholding judgment on reusability until they pay me a lot of money to consult. Haha, no, seriously, I want to see someone else install their stuff in the Amazon cloud. Then I'll believe them when they say it's reusable ("trust, but verify").

A fourth way would be to embrace the Open Source/Open Platform Way and start building kick-ass reusable components that could then be combined into analysis pipelines by whomever wants to do so. As I said during my talk, if the biggest problem we have in 3 years is how to combine all the awesome tools that are available, I will be a happy man...

This is also the point where companies could step in and make use of the components (thus avoiding the CLC Workbench problem of "everything is secret!") to build integrated pipelines. I can name a few companies doing this in other areas.

This 4th way is what we would like to do with khmer. Hell, it's what we are doing with khmer: one of the personally best things to see at the conference was the public acknowledgment by Susan Gregurick of KBase that they were using khmer somewhere in their metagenome assembly pipeline.

We need a new way to fund research software

Another thing I took away from the workshop was that we simply need a new way to fund research software. As with my Dear Abby post on hiring computational biologists, the domain specialists will win in any reasonable funding competition, and the computational people will be kicked to the curb.

Is it really that dire? I think so.

First, the funding cycles are slow and not particularly results driven. It may take two tries to get a 3 year grant, and at the end of the grant everyone will want to see a few papers. The question of whether or not you actually built anything useful rarely comes up - it's all measured by pubs.

Second, everyone wants to fund science, not software. The fact that some science requires software does not escape funders, but if you give an ocean geochemist reviewer a choice between a potentially really cool set of experiments and a potentially really cool software platform, I bet they'll choose the experiments 9 times out of 10.

Third, funding is flat or negative overall, so fairly frequently the overall size of the pie has effectively decreased and the reviewers are going to have to decide which of 5 awesome projects to cut. In combination with #2, you can bet that new software stuff isn't going to be funded. (Yes, given that increasingly science requires software, this bodes ill for the future.)

Fourth, software is (in theory) too broadly useful, and everyone wants to leverage other people's funding. Why would we fund 'omics platforms for biogeochemistry from NSF Geo, when we need basically the same thing over in NSF Bio? They should fund that! Bounce that around enough and voila, nobody actually gets any initial funding to leverage.

Fifth, funding for building software is rarely available: algorithms, yes. Data structures, yes. But implementations? Yawn. (This is for a pretty good reason: scientists basically don't know how to implement, either, and the salaries we can offer don't bring in serious software engineers.) The discussion of the three-track ABI program by Peter McCartney was awesome in this regard -- they seem to be doing it at least partly "right".

Sixth, we lack senior people who know how to build software, which is one reason it's not getting built. (I'm increasingly feeling like the token "guy who knows about github" in these CI meetings :). I guess that's a good change from 5 years ago when there wouldn't have been anyone, but the fact remains that you need people who are domain scientists and programmers in the room for these conversations.

So I'm not feeling particularly positive here.

Are there any solutions?

Dunno. Everyone seems enamored of the "let's throw more money at it, oh, wait, we have no more money, drat" conversation, and a strong recommendation of the workshop was to have another workshop to address whether more workshops about having workshops would be useful, with the end goal of having another workshop to produce a white paper that would inform the next set of workshops. (You think I'm kidding, but only slightly -- this wasn't actually the final recommendation, because we recognized the absurdity. And, by the way, I'm getting burnt out on workshops.) Best part of the conversation: when one of the organizers said, "the point of this workshop isn't just to update the recommendations from the 2007, 2009, and 2011 workshops. It's to come up with a specific recommendation for the next workshop." heh.

There was a lot of discussion about how to get this on the NSF's radar by holding community meetings. But, as one experienced program manager pointed out, you simply end up robbing Peter to pay Paul unless new money comes in. It's not clear anyone will actually go for that, although I'm all for trying.

I think there are a lot of cheap things that could be tried by the more experimentally minded, though.

I'd love to see a Sandpit in the area of "meta-omics". Sandpits are brainstorming sessions to develop grant ideas, and I think one on components would be really welcome. The more general idea is Coopetition -- get a bunch of smart people in a room and let them figure out what should be done, and the fund it. As long as the smart people don't actually like each other that much, you can avoid collusion ;).

Another thought is that if the Moore Foundation really wants to address some of the missing components, they need to fund bioinformaticians. I know that some of the MMI investigators are having a hard time with bioinformatics (I partly know this because I am collaborating with some of them), and I'm increasingly critical of the very concept of funding data gathering without concomitantly funding data analysis. (Then again, this recognition is one reason the workshop was being held, so I'm probably being too mean. But they did reject me for MMI :) And yes, this is a self serving suggestion, but it needs to be said.

I would particularly welcome "collaboration grants" where funding agencies provide, say, a computational grad students' salary for some period of time to work with an existing Moore investigator, so that they can work on building more sustainable infrastructure centered on a very specific biological problems. i.e. Take the specific and make it more general.

Another idea -- it would be great to have rapidly proposed, rapidly funded, and rapidly evaluated 6- or 12-month software projects. I'm not sure if this could work well in isolation, but if you did this as an open call to build and test components that both iPlant and KBase could use (for example - maybe add Galaxy in here, too), then you'd virtually be guaranteed to end up with something useful. My guess is the money wouldn't be big enough to actually put in the required time, but there's an obvious solution to that ;). And if you required that the components be open source, well, at least you'd end up with some product.

Ginger Armbrust talked about a meeting that sounded like a great hackfest-like workshop, where biologists and computational people got together to work on problems. We did something similar for the HMP, nucleated by Rob Knight and the NIH. More of this kind of communal workshop would be great, because it makes the problems clearer on both sides. (Sprints FTW.)

Final thoughts

Building software at the interface of research and computation is really hard, no doubt about it. People are leery of pouring more money into what has so often been a failed enterprise. End Comment.

It's no surprise that resolving the tangle of academia, funding, career incentives, and training in order to build software effectively is seemingly intractable. When I put up my "other things I'm doing slide" (see my talk, slide 7) it struck me that I'm trying to address exactly this tangle. Maybe obvious to people, but there you are. It's nice to find unifying themes to one's work.

It was really nice to hear an NSF program manager complaining that they don't like seeing fixed-term faculty on research grants, because to the NSF it seems like the university isn't putting itself on the line at all. Basically, if you hire somebody conditionally on them finding grants to pay for themselves and all their resources, then the NSF rightly has no faith that the university will support them in any way if they run into trouble. I'd never thought about it that way.

Program managers and funders really don't like hearing themselves described as making top-down design decisions about computational pipelines. I said something like that early on and then had to defend it (privately) to about 5 different people. They did see my point, though, when I explained it like this: if you have enough money for only one project, and you write the RFP, find the reviewers, evaluate the reviews, and pick one specific project, it's hard to argue that this is a "bottom up" driven process. Their point is also well taken: "top down" depends on where you are in the hierarchy of decision making. I just happen to be at the bottom, which means that everything is "top down" from my perspective :)

I asked for examples of successful CI, and people cited the Protein Data Bank and one other that I've now forgotten.

All in all, an interesting workshop. We'll see what happens. I look forward to the next one :).

--titus

p.s. Update: one reader suggests that we not ask people what they want. See: https://www.helpscout.net/blog/why-steve-jobs-never-listened-to-his-customers/. Money quote, "If I had asked people what they wanted, they would have said faster horses." -- from Henry Ford.

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