Gamers, get your folding on
May 9, 2008
Technology Review was the first place I saw it, then someone put it up on Friendfeed and now Andrew Perry has a great post on Foldit. Foldit comes out of the lab of a bbgm favorite, David Baker, right here at the University of Washington.
Foldit combines gaming with protein structure prediction. It’s an interesting approach to spreading scientific problems. Folding@home built upon the success of Seti@home and the geek cred of running on gaming consoles and has built quite a following. Will Foldit, which presents a simple, fun interface to get people interested in protein structure (and the existence of Folding@home makes this somewhat familiar to geeks everywhere) be an example of how we can leverage crowdsourcing? Andrew makes some interesting points (which I agree with) on weighting crowdsourcing, although that’s always a hard thing to do, but I’d like to see karma, etc come into play here.
It’s good to see protein structure getting some attention and continuing to be creative. It’s always been my favorite scientific subject. The field lends itself to “pretty pictures”, so getting non-experts involved is a possibility.
The site and server have had connectivity issues since I’ve been trying, so perhaps they need help with web resources, cause lots seem to be interested.
Here is a list of people supporting the project: UW Animation Research Labs, UW Baker Lab, DARPA, Microsoft, and Adobe. Nice list.
Image via Wikipedia
Technorati Tags: David Baker, Foldit, Protein Folding, Protein Structure Prediction, Gaming, Crowdsourcing
New platforms = cool stuff
April 23, 2008
Andrew Perry joins the rank of biogeeks leveraging the various platforms in the cloud. He has announced the release of ResolveRef, which is essentially a RESTful way of querying PubMed. ResolveRef has been built on top of Google’s AppEngine
It’s good to see these kinds of projects, and I suspect this is hardly the last of what we’ll see.
Technorati Tags: ResolveRef, AppEngine
Thinking data
April 16, 2008
Lots of talk on data lately. Paul Kedrosky, for example, wants to bring on the data blogs. So in that spirit I wanted to mention some cool data-related sites and APIs and put up some data of my own.
Google has its charts API. Swivel and Many Eyes are also familiar to many people who read this blog. To that list I would like to Trendrr, which I first read about on ReadWriteWeb. Haven’t quite tried it out yet, but it definitely looks very interesting. There are a lot of public datasets available, and while accessing them or finding them isn’t as easy as one would like, and the fact that more data should be openly available is always there, it is becoming increasingly available to do things with those data via APIs or services like Freebase (I think that’s where Freebase really shines as evidenced by the stuff that Pierre has been doing, and you just have to see this post/video by Toby Segaran).
In case you haven’t noticed Euan has been posting a lot of data as well. Two of my favorite data/visualization blogs are Data Mining and Information Aesthetics. It goes without saying that anything Ben Fry does will be on the shortlist as well.
Moving on to the data
Here is a graph from Trendrr (it’s simple enough)
And how about some data that I quickly looked up on Google Trends.
It’s interesting to see when Google started indexing open science
It’s also clear that 23andme has spikes in mindshare. I am pretty sure those spikes can be correlated to things like Wired articles (or maybe getting onto Digg and/or TechCrunch).
Perhaps of most interest to me is to see how much Illumina has caught up with Affymetrix
Hopefully more data in the future.
Please see the standard disclaimer
Technorati Tags: Data, Visualization
A bio-twitterverse and some thoughts on aggregation
April 14, 2008
When I started using Twitter, it was a bit lonely. There just weren’t too many life science types there. Well things are changing (and I have a feeling FriendFeed has something to do with that).
Earlier today, Attila took things one step further by setting up a twitter account called biotecher, which aggregates various “bio” Twitterers. Follow “biotechers” on Twitter and always feel free to follow me (mndoci).
Just thinking a little ahead, you have Friendfeed, newsgang, twitlinks, etc. These collect links posted by various people, as opposed to clustering around stories like techmeme and postgenomic do. This river of information approach is an interesting one and is a source of a lot of information, since one can easily skim. It’ll be interesting how the ways we consume information keeps changing. The fear I always have is losing one of my favorite aspects of the web, accidental discovery.
Image by marilink via Flickr
Technorati Tags: Twitter, Attila Csordas, Information
Aggregators and Meta-conversations
March 9, 2008
I don’t know who here has used Friendfeed, but it provides an interesting opportunity, which I am trying to re-position in a more interesting use-case and have failed thus far.
Essentially, Friendfeed allows people to aggregate their content. It’s what I call my lifestream, where I aggregate blog posts, shared bookmarks, youtube video’s last.fm etc. One thing it enables is annotation, either by the author or by someone else (Jeremy Zawodny does this very effectively) e.g. here’s how I annotate google reader shared items
Perhaps the most interesting use that I have seen is what might be called meta-conversations. I follow a bunch of people (not too many yet), either those whose entries across various networks are always high quality (e.g. Jeremy), or people I don’t follow on Twitter or on the blogosphere, but do post some interesting material (e.g. Dave Winer). Often enough a comment thread breaks out. While that thread does not capture the conversation at the source, you do get a meta-conversation going, which can be quite interesting.
What does all this mean? Not really sure, especially since what we are seeing now are essentially experiments in how information can be aggregated and people really haven’t figured out how to use them. I would love to see, perhaps somewhat automatically, similar aggregation around scientific tastes, e.g. your Connotea or CiteULike bookmarks, your Scintilla favorites, etc etc. Some more intelligence in the systems will be needed if we are to leverage them more effectively. For now these are the realm of geeks anyway.
Update: Neil has a great post on this subject as well
Technorati Tags: Friendfeed, Meta-conversation
