Tag Archives: calendar
A long one this time. Enjoy the words, numbers, and images herein.
New biometric tests invade the NBA by Pablo S. Torre and Tom Haberstroh. Data and statistics are nothing new in professional sports. They’ve even made Academy Award nominated movies based the idea that data can help a team win. Until now data on players and teams has come from analysis of practices and gameplay. This great piece opens another discussion about collecting even more personal data about how players in the NBA live their lives off the court. Recall that athletes, coaches, and owners have been talking about out of game data tracking since 2012.
Misleading With Statistics by Eric Portelance. We’ve featured these type of articles before, but the example used here by Eric is not to be missed. So many times the data visualization trumps the actual data when a designer makes editorial choices. After reading this piece you’ll think critically the next time you see a simple line chart.
Handy Tools & Apps by Ray Maker. A great resource for athletes and exercisers who use a variety of tools to capture, export, and work with the activity and workout data we’re collecting.
Happiness Logging: One Year In by Jeff Kaufman. A great post here about what Jeff has learned about himself, what is means to log something like “happiness”, and the power of tagging data. After looking at his data, and a commenter’s from the r/quantifiedself subreddit, I’m wondering about the validity of 10-point scales for this type of self-tracking.
Redshit/f.lux Sleep Experiment by Gwern. Our esteemed friend and amazing experimenter is back with another analysis of his sleep data. This time he explains his findings from using a program that shifts the color temperature on his computer away from blue and towards red.
I ran a randomized experiment with a free program (Redshift) which reddens screens at night to avoid tampering with melatonin secretion and sleep from 2012-2013, measuring sleep changes with my Zeo. With 533 days of data, the main result is that Redshift causes me to go to sleep half an hour earlier but otherwise does not improve sleep quality.
Make sure to join the discussion on the forum!
Schedule Abstracted by Mike McDearmon.
Even a hectic schedule can have a sense of serenity with all text, labels, and interface elements removed.
Location History Visualizer by Theo Platt. The data above is actually my full Location History from Google Takeout. Theo made this simple and fast mapping visualization tool. Try is out yourself!
Lifelogging Lab. No visualizations here, but if you’re a designer, visualizer, or just have some neat data then you should submit it to this sure to amazing curated exhibition.
From the Forum
The ethics of QS
Call For Papers: HCI International 2015 Los Angeles
Pebble for Fitness Tracking
QS Business Models
QS, Light, Sleep, Reaction Timing, and the Quantified Us
Are you using your data to write a reference book or tell a story?
“If I look at this, I have these memories, and I remember this was a good year.”
Collect it and forget it. This could be be hidden mantra of many people engaged with self-tracking, myself included. I will readily admit to buying a device or application with the hope that I can collect enough information to generate a grand insight at some mythical point in the future where the intersection of free time, analytical knowledge, and sample size magically coalesce. Ulrich Atz encountered the same problem. He was tracking, but soon lost sight of the purpose. Rather than giving up he started a new tracking project.
Ulrich started by building on the popular habit and tracking theory, Don’t Break the Chain, based on consistency in behaviors you care about. He identified six major categories he wanted to understand and pay attention to: his evening ritual, fitness, nutrition, learning, sleep, and travel. Rather than using an passive tracking system like Foursquare of Sleep Cycle, he decided to keep track of it by writing on a large wall calendar. In this presentation, given at the London QS meetup group, Ulrich describes his methods and what he learned from this year-long process.
I gained a lot of insights from this heat map. The most obvious weight gain was no surprise — that’s when I periodically don’t track. In any case, the big picture patterns are easily identified with a heat map.
Realized looking at this heat map that the point of no return was mid-April 2012 — my data shows that was when I switched protein shakes with an egg based breakfast. I have since experimented and seen that protein shake in the morning seems to keep my blood sugar more stable and as a result my weight under control!
We invite you to take part in this project as we share our favorite personal data visualizations.If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along.