Tag Archives: data

We Need a Personal Data Task Force

Earlier today John Wilbanks sent out this tweet:

 

John was lamenting the fact that he couldn’t export and store the genome interpretations that 23&Me provides (they do provide a full export of a user’s genotype). By the afternoon two developers, Beau Gunderson and Eric Jain, had submitted their projects. (You can view them here and here).

We’ve doing some exploration and research about QS APIs over the last two years and we’ve come to understand that having data export is key function of personal data tools. Being able to download and retain an easily decipherable copy of your personal data is important for a variety of reasons. One just needs to spend some time in our popular Zeo Shutting Down: Export Your Data thread to understand how vital this function is.

We know that some toolmakers already include data export as part of their user experience, but many have not or only provide partial support. I’m proposing that we, as a community of people who support and value the ability to find personal meaning through personal data, work together to provide the tools and knowledge to help people access their data.

Would you help and be a part of our Personal Data Task Force*? We can work together to build a common set of resources, tools, how-to’s and guides to help people access their personal data. I’m listening for ideas and insights. Please let me know what you think and how you might want to help.

Replies on our forum or via email are welcomed.

*We’re inspired by Sina Khanifar’s work on the Rapid Response Internet Task Force.

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Rajiv Mehta on Tracking Reading

“Papa, what should I read?”

Sometimes a simple question can lead someone down an interesting path towards self-tracking and understanding. In the case of Rajiv Mehta, his daughter’s interest in reading led him to start tracking his own reading behavior. In this wonderful talk, Rajiv walks us through his method for tracking his reading as well as what he’s found out about his habits over the last four years.

This talk was filmed at our 2013 Quantified Self European Conference. We hope that you’ll join us this year for our 2013 Global Conference where we’ll have great talks, sessions, and discussions that cover the wide range of Quantified Self topics. Registration is now open so make sure to get your ticket today!

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Mark Wilson on Synthesizing Data

Data gave me power to talk about the issue.

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We highlight a lot of great show&tell talks here that focus on personal medical mysteries and understanding one’s own health. Well, this one really hit home for me. I’m a runner and I’m constantly battling minor injuries and recurring knee pain. It’s nothing terrible, but it’s at that level of annoying that really makes it hard to enjoy running as much as I should.

Mark Wilson was having similar issues. After running a half-marathon his knee started giving him trouble. The typical treatments didn’t work for him, but instead of giving up running he turned to self-tracking to understand his knee pain (you can see a snap shot of Mark’s running (blue) and knee pain (pink) over time in the header image of this post). In this show&tell talk, filmed at the QS San Francisco Meetup, Mark explains how he built a database that pulls information from different sources like Fitbit, Runkeeper, and his self-rated knee pain, and what he’s learned from that process.

I think most importantly putting all this data together and being able to look at it gave me power to talk about it. Because, I can’t really describe how much despair I was feeling just looking at my knee and thinking, “What the hell is wrong with you? Why is my knee hurting?” I felt like I was trying everything I could on my own and it just wasn’t working. So I wanted to collect a lot of evidence against my knee to indict it.

This data-backed indictment enabled him to have better and more productive conversations with his physical therapist and he began to understand how to move forward. Is it working? You’ll have to watch his great talk to find out:

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Peter Denman on Visualizing Data with Biomimicry

Biomimicry is an interesting topic and one that we’ve started to see creep into our Quantified Self tools and visualizations. While recovering from surgery, Pete Denman, an interaction designer at Intel, became inspired to start to explore biomimicry as a way to show data. In this short Ignite talk from our 2013 European Conference, Pete talks about  his inspiration and how he’s begun testing and learning about using “beautiful mathematics” to explore visualizing data.

Update:
Thanks to Gary Wolf, we were able to find a great presentation delivered by Pete that provides a bit more detail on his excellent work:

 

This talk was filmed at our 2013 Quantified Self European Conference. We hope that you’ll join us this year for our 2013 Global Conference where we’ll have great talks, sessions, and discussions that cover the wide range of Quantified Self topics. Registration is now open so make sure to get your ticket today!

 

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APIs: What Are The Common Obstacles?

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Today’s guest post come to us from Eric Jain, the lead developer behind Zenobase and a wonderful contributor to our community. 

At last month’s QS Europe 2013 conference, developers gathered at a breakout session to compile a list of common obstacles encountered when using the APIs of popular, QS-related services. We hope that this list of obstacles will be useful to toolmakers who have developed APIs for their tools or are planning to provide such APIs.

  1. No API, or incomplete APIs that exposes only aggregate data, and not the actual data that was recorded.
  2. Custom authentication mechanisms (instead of e.g. OAuth), or custom extensions (e.g. for refreshing tokens with OAuth 1.0a).
  3. OAuth tokens that expire.
  4. Timestamps that lack time zone offsets: Some applications need to know how much time has elapsed between two data points (not possible if all times are local), or what e.g. the hour of the day was (not possible if all times are converted to UTC).
  5. Can’t retrieve data points going back more than a few days or weeks, because at least one separate request has to be made for each day, instead of being able to use a begin/end timestamp and offset/limit parameters.
  6.  Numbers that don’t retain their precision (1 != 1.0 != 1.00), or are changed due to unit conversion (71kg = 156.528lbs = 70.9999kg?).
  7. No SSL, or SSL with a certificate that is not widely supported.
  8. Data that lacks unique identifies (for track-ability, or doesn’t include its provenance (if obtained from another service).
  9. No sandbox with test data for APIs that expose data from hardware devices.
  10. No dedicated channel for advance notifications of API changes.

This list is by no means complete, but rather a starting point that we hope will kick off a discussion around best practices.

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Doug Kanter on Data, Diabetes, and Marathon Training

Doug Kanter has been a Type 1 diabetic for 26 years. Through this time he’s come to learn more about his disease by using many data-gathering tools and his own work in visual analysis at the NYU ITP program. We’ve featured Doug’s compelling work here on the blog before and we were excited to hear him talk at the NY QS Meetup about his new project to understand how marathon training and running effect his blood sugar and insulin treatment.

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What We Are Reading

Here we are again. Another week and another great list of articles, projects, and posts. We hope you find these as interesting as we did.

Data Science of the Facebook World by Stephen Wolfram: “I’ve always been interested in people and the trajectories of their lives. But I’ve never been able to combine that with my interest in science. Until now.” Stephen Wolfram sets his mind and data crunching services and the mounds of data available through the Wolfram|Alpha Personal Analytics service.

There’s an App for That by John-Paul Flintoff:  While many people write about QS, every once in a while a piece stands out as a thoughtful and personal assessment of the meaning of self-tracking. The only major fault with the piece is the accompanying illustration which proclaims that “the overexamined life is not worth living,” a conclusion the article does not actually make.

Disciplinary Power, the Oligopticon and Rhizomatic Surveillance in Elite Sports Academies: Elite athletes and sports programs push Quantified Self tools to their extremes. This article from an academic journal about surveillance discusses the tracking mechanisms employed in elite sports academies that transform performance into a type of numerical language that contributes to new social norms, personas and senses of the self

Refugees of the Modern World by Joseph Stromberg: A common cultural signature in the world of the Quantified Self is the formation of loose-knit groups around common interests and conditions. So it was fascinating to learn of a tight-knit group that has formed around the choice of a common environment in which to live. This is the stort of a self-diagnosed group suffering from “electromagnetic hypersensitivity” who live together in an area of West Virginia in the U.S. National Radio Quiet Zone.

Body 01000010011011110110010001111001 by Stanza: Artists have been playing with connecting #quantifiedself and “smart city” technologies for several years. I think projects like this are useful for opening new channels of thought not yet constrained by utility.

Goggles Can Provide Vital Data and Distraction by Matt Ritchel: Google makers incorporate data streams into heads up displays. But why include text messages? That seems like a mistake.

Thanks  to Joshua Kauffman and Gary Wolf for contributing to this weeks post! If you’ve found something interesting be sure to send it to us and we can post it in the upcoming weeks.

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Self Expression From Performance Data

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Typically when we think about Quantified Self and the associated collection and visualization of personal data we’re left struggling in the world of charts, graphs, and other well-worn visualizations. That’s not to disparage those of you who love spending some time tinkering in Excel. Those are valuable tools for understanding and there is a good reason we rely on them to tell us the stories of our data. It’s important to realize that those stories rooted in data aren’t always just about finding trends, searching for correlations, or teasing out significant changes. Sometimes data can represent something more visceral and organic – the expression of a unique experience.

Vincent Boyce is a an artist and designer who spends his free time riding on asphalt and water. Those experiences on his longboard and surfboard led him to starting thinking about how his rides, his performances, could be used as inputs for generating art and “exposing the hidden narrative.” After some tinkering with hardware and software Rideware Labs was born. Vincent has designed and built a prototype sensor pack and custom interface that ingests data from his riding and outputs unique visual representations. As you can see above, these aren’t your typical bar charts.

In his great talk filmed at the New York QS Meetup Vincent describes his motivation behind building his prototype system and his goals for future versions.

This is a great first step in turning data rooted in performance into artistic representations of self-expression. What do you think? What kind of data would you like to see hanging on your wall as works of art? Let us know in the comments!

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How To Make A Sparktweet

Update: Want to make your own Sparktweet? We made a simple tool that you can use. Check it out here!

I was stumbling around Twitter the other day when I was confronted with something new and different:

Apparently that little data representation is not all that new and different. Way back in 2010 Alex Kerin figured out that Twitter was accepting unicode and decide to play around and see if it could represent data. Lo and behold it could and a SparkTweet was born:

Before we get into how you too can start populating your Twitter feed and Facebook (I checked and it worked there as well) with representations of your own Quantified Self data let’s dive into some history.

The data visualization theorist and pioneer, Edward Tufte, is primarily responsible for the widespread use of sparklines. In his wonderful his book, Beautiful Evidence, Tufte describes sparklines as

a small intense, simple, word-sized graphic with typographic resolution. Sparklines mean that graphics are no longer cartoonish special occasions with captions and boxes, but rather sparkline graphic can be everywhere a word or number can be: embedded in a sentence, table, headline, map, spreadsheet, graphic.

In another wonderful book, The Visual Display of Quantitative InformationTufte describes sparklines as “datawords: data-intense, design-simple, word-sized graphics.“ Of course, those of us in the QS community are deeply interested not only in data, but also in how data operates in society, what is means as a cultural artifact that is discussed and exchanged in language both written and verbal. This interest iswhat initially  piqued my curiosity.  The movement of data and a dataword distributed among text and publicly expressed in a tweet. I can’t help but wonder, what does this mean for how we think about and express data about our world?*

How To

Update:Thanks to our QS friend, Stan James, you can now make Sparktweets right here on Quantifiedself.com. Just head over to our Sparktweet Tool page and start making your own “data words.”

If you want display quantitative data in your Twitter stream it shouldn’t take you all that long to get started. Lucky for us Alex Kerin has provided a nifty little Excel workbook that will generate the unicode that can be pasted into your tweet. Just download this workbook and follow the simple instructions! Soon you’ll be able to send out tweets just like this:

For those of you with a bit more technical skill Zack Holman has made a very neat command line tool that will quickly generate the unicode for sparklines.

Now you’re ready and able to go forth and tweet your data! If you use a sparktweet to express your Quantified Self data be sure to let us know in the comments or tweet at us with #sparktweet and/or #quantifiedself.

*Of course the use of sparktweets is not without controversy in the world of data visualization. For more discussion on sparktweets and their utility I suggest you start here.

 

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How To Download Fitbit Data Using Google Spreadsheets: An Update

If you’re like me, then you’re always looking for new ways to learn about yourself through the data you collect. As a long time Fitbit user I’m always drawn back to my data in order to understand my own physical activity patterns. Last year we showed you how to access your Fitbit data in a Google spreadsheet. This was by far the easiest method for people who want to use the Fitbit API, but don’t have the programming skills to write their own code. As luck would have it one of our very own QS Meetup Organizers, Mark Leavitt from QS Portland, decided to make some modifications to that script to make it even easier to get your data. In this video below I walk you through the steps necessary to setup your very own Fitbit data Google spreadsheet.

Step-by-step instructions after the jump. Continue reading

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