1. Initial pre-processing steps could be as critical to an analysis final results as the actual predictive model building. Secondly, even if the data is not really in the really big range where you need a distributed file system, some of the machine learning approaches require significant processing power. Staging the data somewhere where they can be operated on directly (e.g. S3) seems useful. I do agree that the fork-pull loop of Git is very nice. We’ve started looking at storing code in Git and possibly we can store analysis records in Git as well. Building another application that uses Git is different than reusing GitHub for a new user community.
2. Our R package is designed to capture a complete R session so it can be later reconsituted. We are working for lightweight ways to record sessions as steps in a workflow, e.g. recording the data checked out of Synapse to start the session, and giving users simple R commands to save results and scripts back to Synapse to capture the session. This could be turned into a dependency graph of analytical steps showing the logical flow through a complex analysis. This is different than a lot of workflow tools which assume you have a pre-existing library of steps, and the purpose of the workflow tool is to help you wire those steps together in a complete sequence so you can run the whole thing together.
3. Note that Sage Bionetworks is a non-profit organization. The mission is to experiment with ways to catalyze what we see as a needed cultural change. I think we’d have a hard time selling this to a VC. If you’re looking for the best ROI on your investment dollars you’d best look elsewhere!

That said, Colbert is not why you wrote the article and not why I am replying… despite it being a great show. To me it’s a branding issue that the life sciences has, not just a marketing issue. A small difference but essential in my opinion. I’m no marketer or brander but having that conversation is worthwhile.

There are plenty of brands out there in open-source software but where are the brands in life sciences that communicate. I think what is needed is a brand inside the life sciences that says “we share data”., “we play well with others”. “we value your privacy”.

What do you think of when you think of “Intel”? “Bum Bum Bum Bum! Intel Inside.” People want to know if they are buying a computer with an Intel processor inside. Why? Most probably don’t even know, but that branding/marketing works. When you provide your email address to a company you want to know they aren’t going to send you spam. You look for that privacy policy.

To me, when people choose to participate in any sort of life science type test that collects personal data they need a brand to make them feel safe. On websites today we see all sorts of things that communicate this, which is why I imagine that you want a UI person. What I think you need to do is find people in life sciences that have similar values that you have, and make a brand. This brand will have rules to be able to use it on anything, one of which is, the person who collects the data must share the data to others who agree to follow the rules. With better branding and standardized data collection and privacy policies, processes, enforcement mechanisms, and punishments, I think more people will participate in studies and with that… you will have more scientists willing to play by these rules you so badly want. May the best brand win?