powers of four

We came to the conclusion that technology is finally at a point that if we don’t use it now, then we are holding back the progress of science. And what exactly are we to use technology on? Open science/data/access.

When I first heard about Free Software in 1998 I was mesmerized by it’s potential. Sure, back then the software was complex and some would argue ugly, but underneath the rough edges was a thing of beauty — the opportunity for people to come together to make new things, and anyone with the inclination and energy could take part.

Jono's description really rings home for me. When I first really started hearing about FLOSS, I was 18. I'd been using Linux and Unix for seven years (but only on dial-in BBSes and the like) but I'd never considered using it on my desktop. And then I picked up a copy of SUSE 6.1, installed it, and started looking beneath the surface. I still believe in the principles, but it's hard not to get frustrated with where computing is going. I've tried to use those principles in my research, pushing my code and my data into the open while encouraging my colleagues and collaborators to do so as well. We've been successful so far, but the Grand Experiment (as my current mentor likes to call it) is just beginning. (More on that as it occurs.) In many ways the FLOSS community and the Scientific communities are very similar, but the cross-talk is sadly limited.

To answer the prompt from Jono that I didn't quote above, I'm passionate about Free Software and Open Source software because I want my kids to have the same opportunities and the same encouragements that I did. And because I think it's really guided me, my personal and my professional interactions in many ways, and I want to make sure that spirit of collaboration, of openness, of tinkering and problem solving never dies out.

Checking data collected at Brookhaven National Laboratory on Long Island and the Federal Physical and Technical Institute in Germany, they came across something even more surprising: long-term observation of the decay rate of silicon-32 and radium-226 seemed to show a small seasonal variation. The decay rate was ever so slightly faster in winter than in summer.

It was thus a catalyst in the rise of the personal computer, offering home users inexpensive data storage at a time when floppy disk drives cost around $1000.

Hit the original article for what "It" was. I used this for Scott Adams-brand (no, not that Scott Adams, the other one) adventure games.

Spatial indexing is increasingly important as more and more data and applications are geospatially-enabled. Efficiently querying geospatial data, however, is a considerable challenge: because the data is two-dimensional (or sometimes, more), you can't use standard indexing techniques to query on position. Spatial indexes solve this through a variety of techniques. In this post, we'll cover several - quadtrees, geohashes (not to be confused with geohashing), and space-filling curves - and reveal how they're all interrelated.

Includes a handy Python function for converting to a Hilbert curve.

The real energy hog, it turns out, is not industrial agriculture at all, but you and me. Home preparation and storage account for 32 percent of all energy use in our food system, the largest component by far.

The most critical issue however is rapid deployment of expertise to specific problems. To apply a distributed rapid innovation model we need the means to rapidly identify the very limited number of people with appropriate expertise to solve the problem at hand. We also need to rethink our research processes to make them more modular so that they can be divided up and distributed. Finally we need capacity in the system that makes it possible for expertise to actually be rapidly deployed. Its not clear to me how we achieve these goals although things like Innocentive, CoLab, Friendfeed, and others are pointing out potential directions. We are a long way from delivering on the promise and its not clear what a practical route there is.

Practical steps: more effective communication mechanisms will be driven by rewarding people for re-use of their work. Capacity can be added by baseline funding. Modularity is an attitude and a design approach which we will probably need to build into training and will be hard to do in a community where everything is bespoke and great pride is taken in eating our own dogfood but never trusting anyone else’s…

I find this particularly compelling, especially in light of some of the growing pains yt has been having. We're trying to build something that's useful, without losing sight of our own personal goals, and it's difficult at times. But we've been having some amazing successes, and I think it's worth it.

However, a fair number of computational science approaches require resources with scheduling flexibility and rapid turnaround. "By focusing on core counts of 1,024 or less, Trestles is designed to serve a much larger number of users while simultaneously improving their productivity as measured by turnaround and the number of jobs completed," said Allan Snavely, associate director of SDSC and also a co-PI for the new system.

Meso-scale computing. Rad.

The team performed a series of expensive high-fidelity simulations on the Ranger supercomputer to generate a small “reduced model” which was transferred to a Google Android smart phone. They were then able to solve problems on the phone and visualize the results on the fly.