Tag Archives: Coffee

What We Are Reading


I’m filling in for Ernesto. I hope you enjoy this week’s list of articles and visualizations!


Don’t Relax: Uncomfortability Is The New Convenience by Adele Peters. This article looks at some products where a tolerable level of inconvenience is built into the design that prompts healthy actions or occasions for reflection.

Using Biometric Data to Make Simple Objects Come to Life by Liz Stinson. A whimsical project on display at Dublin Science Gallery’s Life Logging exhibition uses household objects to reflect and amplify the signals from your body.

The High Price of Precision Healthcare by Joseph Guinto. This is a fairly in-depth article on the relationship between drug and insurance companies and what happens when drug companies are given incentives for developing medicine for smaller populations. Not a breezy read by any means, but important for understanding the unintended consequences of changes made to the American healthcare system.

If Algorithms Know All, How Much Should Humans Help? by Steve Lohr. An exploration of a quandary that arises from machine learning methods. At what point do the automatic, self-learning processes mature to the point where any human intervention for correction is seen as injecting sullying “human bias.”


Networking the Coffee Maker by David Taylor. A fun, little project using an ElectricImp micro-controller to track when the office coffee pot was brewing. The author helpfully includes his code.

Using 750words.com and self-quantification by Morris Villarroel. Morris has been using 750words.com for the past three months and reflects on his previous attempts to use the service consistently and how he uses it now.



My brain on electricity: a 130 day tDCS experiment. This is a fascinating self-experiment where the author tries different tDCS montages while doing thirty minutes of dual n-back training.

My Path to Sobriety by ERAU. From Reddit, the poster shares the data from an effort to reduce one’s alcohol consumption.

Access Links

Open Humans Aims to Be the Social Network for Science Volunteerism
Los Angeles Unveils Dashboard to Measure Sustainability Efforts
Who Owns Your Data?

From the Forum

Hardware Startup: Tracking Your Hydration
Five years of weight tracking
QS Research – 5 minute survey!
Zeo Sleep Monitor
Google Fit

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What We Are Reading

We’re back after missing last week (sorry!) with a bit longer list than usual. Enjoy!

Thoughts on Quantified Self for Modifying Long Term Life Goals by Mark Krynsky. Mark, a member of our QS Los Angeles meetup group, is consistently putting together interesting ideas in the QS space. In this short post he explore how QS tools might be used to understand long-term life goals.

Open Data for Open Lands by Alyssa Ravasio. The value of data isn’t confined to what we can understand about ourselves. There is so much beneficial information out there, especially when it comes to public data. In this post, Alyssa makes the case for protecting and promoting open data ideas and concepts regarding out most precious public spaces – the national parks system.

Art at the Edge of Tomorrow: Lillian Schwartz at Bell Labs by Jer Thorpe. A wonderful biographical piece about Lillian Schwartz, a pioneer in the field of computational art and exploration.

Terms of Service by Michael Kelller and Josh Neufeld. A reporter and nonfiction cartoonist team up to use a comic to tell us about the new world of data and privacy we currently inhabit. Interesting format and compelling content!

Narrative Camera by Morris Villarroel. Morris has been wearing a Narrative personal camera for six months. In this short post he explains what he’s learned and experienced over that time.

Where my 90 Hours of Mobile Screen Time in September Went by Bob Stanke. Bob used an app (Trackify) on his Android phone to track how much time he was spending on his phone and what apps he used the most.

Quitting Caffeine by Andrei-Adnan Ismail. Andrei wasn’t happy with his relationship with coffee and caffeine so he he decide to try and quit. Using tracking and really interesting use of “sprints” to gradually reduce his consumption, Andrei was able to quit. Great post here describing his process and the data he gathered along the way (including how his change affected his sleep).

Twitter Pop-up Analytics by Myles Harrison. Myles takes us through the process of downloading, visualizing, and analyzing personal data from Twitter.

Seven Months of Sleep by Eric Boam. A bit of an old one here, but beautiful and informative nonetheless. Make sure to read the accompanying piece by Eric. (I’m also looking forward to seeing more about this dataviz of his Reporter app data soon.)

CalorieWeight_vizMy latest effort to visualize my calorie intake and weight loss by reddit user bozackDK. Using data collected from MyFitness pal, bozackDK has created this great visualization of his data. I asked what was learned from making this graph and received this wonderful response:

“I make graphs like these to keep myself going. I need some kind of proof that I’m doing alright, in order to keep myself wanting to go on – and a graph showing that I can (somewhat) stay within my set limits, and at the same time showing that it actually works on my weight, is just perfect.”

From the Forum
Using Facial Images to Track Mood?
Can You Track Inner Peace?
Different Approach to ZEO Headband
How Far Are You Quantified?
Google Fit

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Numbers from Around the Web: Round 3

Will Lam loves coffee. As the chief blogger over at Indie Coffee Blog he’s constantly trying new roasts, new places and letting his readers know about good coffee spots around his hometown of Toronto. In 2011 he decided to go a step further and really track his coffee habits. Let’s look at a few of his awesome insights:

Number of Cups of Coffee Consumed (total and by type):

Frequency of Coffee Consumption:

Total Spending by Location on Coffee:

I highly recommend reading his fascinating blog post about what he learned by tracking his coffee habits over an entire year. You can find that here. Will also used his new found love of data collection to steer him to his local QS Meetup. Way to go Will and thanks for letting us learn from your data!

We got such great feedback on the orignal NFATW post that we decided to turn it into a regular feature. Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.

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Numbers From Around The Web: Round 1

We got such great feedback on the orignal NFATW post that we decided to turn it into a regular feature. Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.

Michael Allen Smith
Michael is an avid coffee drinker and contributor to the caffeine-obsessed blog I Need Coffee. He recently wrote up a nice post about his experimentation exploring how his coffee consumption and sleep quality. Using a simple spreadsheet, Michael tracked his daily coffee intake, the time of his last coffee, chocolate consumption and sleep quality (rated 1-5 after waking). He has a nice explanation for the simplicity in his tracking methodology:

What I discovered is that the more complicated you make the tracking, the less likely you’ll maintain the data.

So what did Michael find out? After a few months of data tracking it turns out he gets the best sleep when he drinks 3 cups of coffee per day and has a coffee after 1PM.
Data from Michael's coffee experimentMichaels's coffee intake from March 24, 2011 - December 24, 2011. The red line is a 3 day moving average.

Michael also leaves us with some great parting thoughts that we can all apply as we initiate and work through our own experiments:

Look at the data and dial in the level that works best for you. [...] Only you can answer these questions.

If you’re interested in coffee and caffeine, you might also enjoy this post by Robin BarooahThe False God of Coffee

Matt Danzico
Matt Danzico is a journalist (and self-described nerd) living and working in Washington DC. He took it upon himself to engage in a year-long experiment of sorts in 2011. Dubbed Time Hack, the project sought to explore the complex interplay between our actions and our perception of time.

The year-long project aims to test whether time itself is flexible and whether our brains measure time differently than the clocks around us.

While this may not seem like a strict QS self-experiment, I think it worth discussing. Time is something that everyone battles with. We want more time to do this or that, we track productivity, we keep calendars and to do lists handy at all time. Why? To conquer time of course. But what if time is relative (and not just in the Einstienian sense) and it our perception of time depends on our behavior? Matt explored this idea for a full year and has some really interesting – and quite fun – data to show for it. He actively engaged in new experiences every day and tracked his perception of time and compared it to objective measures of time (stopwatch, video, etc.). Even more interesting (in my opinion) than the measurement of time, he also recorded his perceptions of specific details that occurred during each event. I’ll highlight a few of my favorites here, but take some time and dig through his blog. It’s well worth it.

Day 345: Visit an airport dressed as a Star Wars character.

  • Estimated time: 0:49:41
  • Actual time: 0:57:55.8

Day 297: Wash clothes with a washboard.

  • Estimated time: 1:21:00
  • Actual time: 1:09:31.1

Time is a fascinating subject and I am eagerly awaiting Matt’s analysis of his year long experiment. Until then I suggest keeping yourself busy by listening to these two wonderful podcasts on time by the always interesting folks over at Radio Lab: Time and Beyond Time.

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The false god of coffee

This year I decided to stop drinking coffee, my only source of caffeine.  Anyone who knows me will recognize this as a radical step. I’ve been drinking coffee since age 10, and I’d developed quite an obsession for the perfect cup.

In the past, I’ve experimented with quitting a few times by simply going cold turkey. Each time, the physical withdrawal, basically headaches, was over within 10 days, but after a month or two I would become convinced that coffee was good for my concentration and start drinking it again.

coffee making.JPG My reason to quit this time was the growing suspicion that coffee was causing mood swings and crashes that are bad for my overall sense of well-being. For this experiment I decided to stop very gradually. I thought that if I allowed the psychological withdrawal to occur gradually alongside the physiological, I would be able to observe my ‘coffee-desire’ without acting on it, and learn the skill I would need to avoid relapsing in future.

I made the same amount of coffee each day, using a vac-pot. Although I didn’t measure caffeine content, I did control many factors including grind, age of beans, water temperature and water/coffee contact time. From this controlled pot of coffee, I used measuring cups to discard an additional 20ml per week. I used notebook software to keep some records of my progress and I started with a 3 cup pot in mid-April ’09. Towards the end of July I wrote “I am increasingly wanting to abandon this project altogether”, but I continued and on 8th August I was down to a half shot glass per day, and decided I was done.

Over the past few days (starting around 12th Oct), I noticed myself increasingly thinking “I am having trouble concentrating and coffee might help”. These thoughts came to a crescendo on Wednesday. This time, I was armed with data.

As part of a separate experiment, I have been keeping track of the amount of time I spend working on projects.  I work in 25 minute intervals which I time with a coffee timer, and I mark an X in a paper journal for each interval that I successfully complete.  If I get distracted, I don’t mark the X, and if I can’t concentrate, I abandon it and don’t mark an X rather than sitting out the timer. I’ve been doing this since the end of June, so I tabulated the data and created a graph* of my hours of concentration per day, and overlaid a bar showing when I drank my last coffee.

concentration-vs-coffee-chart.png Causality is a complex issue. Obviously this is an n=1 experiment and I am intentionally doing other things that may well be improving my concentration, but one thing is very clear; the amount of time I spend concentrating has not deteriorated since I quit coffee, so I can easily reject the hypothesis “I need coffee to help me concentrate.”

I see this as a success for self-quantification.  Whether or not it provides a general insight into the effects of caffeine, it validates the utility of self-tracking for making individualized personal decisions.

I will be doing more experiments.

*At the QS MeetUp someone correctly pointed out that I had an error in the labeling of my x-axis on the chart I showed there.  This meant that I’d placed the “quitting bar” in the wrong place – near to september 4th, happily this doesn’t affect the conclusion, and the graph shown here is the corrected version.

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