w4s - strategizing for the future

This is one of a bunch of posts on science and the Web. Start here for an overview.

I've been reading Michael Nielsen's book Reinventing Discovery, which is an awesome and inspirational book about (among other things) accelerating scientific discovery using the Internet. Highly recommended.

From my position within academia, it's clear that we're not making anywhere near full use of the Internet. What's less clear to me is how to accelerate the process of integrating the Internet into research. Here are a bunch of longish-term strategies and ideas. (The short-term ones are over in tech wanted.)

  1. Focus on tech things that are so useful that scientists have no choice but to pick them up, because the learning curve is worth it.

    Nielsen comes up with a lot of ideas about how scientists could use networked collaboration technology, but he only lightly covers our motivation for using it. Without strong, explicit incentives -- which are unlikely to emerge in the short term -- I think it's unlikely that most scientists would invest the learning time necessary to become usefully proficient in collaboration tech. Speaking only for myself, I can tell you that I am constantly challenged for "thinking time", and so I'm perennially scared of new technology that has a learning curve (...one reason I like the IPython notebook so much -- no learning curve!) What can we do?

    Well, if we look at which part of collaboration technologies we actually use now, it's clear that some things are too useful not to adopt. I'm thinking about things like e-mail, Skype, and online journals here -- active researchers have virtually no choice but to use these technologies. Dropbox, too.

    So, if we want to sucker scientists into the long hall of improving their lives through somewhat more outre collaboration tech, we need to make darn sure it's extraordinarily useful and pretty easy to dip into. (Put that way, it sounds obvious, right?)

    For example, reproducible research is a great and popular concept, but we haven't built an enabling tool that's both really easy to learn and just solves the problem. How could we do that?

  2. Better ways for scientists to share data and process.

    Most scientists use e-mail, physical media, or Dropbox to share data. "Process", by which I largely mean the computational processes used in data analysis and modeling, is shared by personal contact and e-mail interaction. This blocks tracking of provenance, limits communication of process improvements, and is otherwise really inefficient.

    If we could make it easy (or at least easier) to share and explore data with collaborators, and to explicitly communicate process to collaborators, I think a lot of scientists would jump on it. This is another reason I like IPython Notebook so much, but it's only part of the solution -- we need more than a notebook, we need associated data and compute!

    There are some pretty interesting attempts to do this explicitly -- check out RunMyCode. Are they working? If not, why not? I'd be interested in finding out.

  3. Better ways for scientists to collaborate on and teach their process and practice.

    Researchers necessarily embrace lifelong learning. In addition to collaborating on data and process, people need to learn new techniques and apply them to their own data. This requires training materials. So far most training materials are a disappointing gemisch of text, static diagrams, and opaque videos.

    If we could mark up process (see point 2) and share that, and provide entry points into and annotations on video... well, that would be a good start. What would be better would be ways for scientists to better communicate and manage large-scale interactions, including through online real-time collaborations.

  4. Better ways for scientists to create and participate in ad hoc collaborative interactions.

    "Hey, bub, take a look at this data -- what do you think is going on?" But across continents.

    Again, IPython Notebook is going to make this possible, but we need a rich ecosystem of open collaboration tools so that we can easily tie this into video conferencing and phone calls.

  5. Offer opportunities for hands-on expectation-free training, e.g. sabbatical opportunities in tech-enabled environments.

    Lots of scientists are interested in this stuff, but very few have the time to devote to learning new tech (see point 1). Support 3- or 6-month sabbaticals, or summers, in environments with enough tech-savvy at-risk youth and other practicing researchers to show off blogging, online forums, and the rest. Sucking in young scientists with summer funding a la GSoC might also work -- if it was on their research rather than some side project.

  6. Venues for in-person collaboration and discussion by scientists on tech stuff.

    We need venues where crazy people can get together and talk about the process that they'd like to be using for their research. There just aren't many (if any) -- I tend to have these conversations in the hallway at conferences with grad students and faculty, for example.

  7. Tech and venues for "living publications", bridging the gap between blogs and publications and source code and data.

    Publications are currently very formal and also very dead. We should fix that -- first, make it easy to cite blogs; and second, make it easy to link pubs to source code and data, online and interactive. PLoS is very interested in this.

  8. Data integration across and within data sets; enable remote interaction with and exploration of large data sets.

    The "living publications", above, could be one end of a spectrum that would include sites with opportunities for exploration, annotation, and publication of "views" of data.

  9. "Reddit for scientists" a la Pickrell.

    Joe Pickrell has a great blog post (and a follow-up) on this.

    Briefly, Joe proposes immedate publication, rapid social recommendation of interesting papers connected to existing networks, and strong automated recommendations of papers that take into account the opinion of existing field-specific social networks. I think reddit, only mildly modified, would be able to serve this purpose quite nicely.

    To put it another way, once you decouple publication from perceived future impact like PLoS One has, you instantly realize that all that's left is winnowing out papers of interest to you. How can we facilitate this?

  10. Negative results and open data repositories specifically to combat selective publication, bad statistics, and fraud.

    Retraction Watch, but on steroids. Encourage a culture of posting data without publication, and (in some cases) require that any registered study of, say, a proposed pharmaceutical, post its data.

    Yeah, this requires policy changes. But why not be ambitious?


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