- Networks, Crowds, and Markets — network theory (graph analysis), small worlds, network effects, power laws, markets, voting, property rights, and more. A book that came out of a Cornell course by ACM-lauded Jon Kleinberg.
- Qu — a framework for building data APIs. From a government department, no less. (via Nelson Minar)
- Three Most Common M-Commerce Questions Answered (Facebook) — When we examined basket sizes on an m-site versus an app, we found people spend 43 cents in app to every $1 spent on m-site. (via Alex Dong)
- Phonelabs — science labs with mobile phones. All open sourced for maximum spread.
"open data" entries
Personal wellness data should be shared as freely as water and air.
Register for the free webcast, “Life Streams, Walled Gardens, and the Internet of Living Things.” Brigitte Piniewski and Hagen Finley will discuss the Internet of Living Things, what makes sensoring and monitoring data emanating from our bodies unique, and why we should elect to participate in this seemingly Orwellian mistake of open-sourcing our personal health data.
We are at a threshold in the history of personal data. Sensors and apps are making it possible to generate digital data signatures of important aspects of healthy living, such as movement, nutrition, and sleep. However, we are rapidly losing the opportunity to erect a Linux-like open “living-well” data system steeped in open commons principles. We can either join together to ensure enlightened open source and crowdsourced discovery practices become the norm for our living-well data footprints, or we can passively allow this data to be sequestered into one of the walled gardens offered by health systems, funded research, or big business.
Why this is important?
Living-well data provides the map by which vast amounts of preventable human suffering can be prevented. Everyone can benefit from the health journeys of those who lived before us because our modern societies are no longer “accidentally well.” Decades ago, parents had no need to question the nutrition a child was offered or concern themselves with how much activity a child engaged in. No deliberate use of devices was needed to track these important health contributors. Reasonable access to whole foods (farm foods) and reasonable amounts of activity were provided, as it were, by default — in other words, by accident. This resulted in remarkably low rates of chronic disease. Today, communities cannot take those healthy choices for granted — we are no longer accidentally well. Read more…
If you really want to understand the effect data is having, you need the models.
Writing my post about AI and summoning the demon led me to re-read a number of articles on Cathy O’Neil’s excellent mathbabe blog. I highlighted a point Cathy has made consistently: if you’re not careful, modelling has a nasty way of enshrining prejudice with a veneer of “science” and “math.”
Cathy has consistently made another point that’s a corollary of her argument about enshrining prejudice. At O’Reilly, we talk a lot about open data. But it’s not just the data that has to be open: it’s also the models. (There are too many must-read articles on Cathy’s blog to link to; you’ll have to find the rest on your own.)
You can have all the crime data you want, all the real estate data you want, all the student performance data you want, all the medical data you want, but if you don’t know what models are being used to generate results, you don’t have much. Read more…