Tag Archives: data

What We Are Reading

Philosophy, bicycles and brains, opinions on tracking sleep, learning from actually tracking sleep, and visualizing work through vigilant self-report – all these and more in our reading list below. Enjoy!

Articles
Sleep apps and the quantified self: blessing or curse? by Jan Van den Bulck. Here at QS Labs, we’re very interested in how the academic and research world is colliding with those of us using tools of measurement previously restricted to science. In this Letter to the Editor, published in the Journal of Sleep Research, the author lays out an interesting set of opinions about the increasing availability and use of commercial sleep tracking devices. (You can access the full pdf here.)

Mindrider

Measuring Brainwaves to Make a New Kind of Bike Map for NYC by Alex Davies. Readers of the QS website may remember a great show&tell talk we featured back in May of 2014. In that talk, Arlene Ducao discussed her MindRider Project, an EEG tracking bicycle helmet. In this short piece, we learn that Arlene has continued this awesome work and has produced MindRider Maps Manhattan, exposing the brain data of 10 cyclists as they transversed New York City.

Big Data and Human Rights, a New and Sometimes Awkward Relationship by Kathy Wren. Earlier this year the AAAS Science and Human Rights Coalition held a meeting to discuss the intersection of personal data collection and human rights. This short article describing some of the key discussion points is a great place to start if you’re exploring what “big” and personal data means to you and your use of the tools and services that collect it. (Videos of the meeting are also available.)

How Theory Matters: Benjamin, Foucault, and Quantified Self—Oh My! by Jamie Sherman. A very interesting and thought-provoking essay here on the nature of self-tracking and data collection framed against the works of Michel Foucault and Walter Benjamin. We count ourselves lucky to have Jamie as an active member and observer of our QS community.

But taken together, Foucault and Benjamin suggest that the penetration of data into daily life is part of a larger shift underway, and that changes we can already see in social life, politics, and labor are not unrelated, but rather intimately linked.

Compulsory Quantified Self by Gwyneth Olwyn. I think it’s good practice to try and expose ourselves to all sides of the conversation around self-tracking, the positive and the negative. In this blog post Gwyneth describes a few ideas about the purpose and outcomes of self-tracking, especially when the self is superseded by the demands of others (such as in a workplace wellness program).

Show&Tells
RyanQuan_sleep-cycle-analysis-03
Sleep Data Analysis with R by Ryan Quan. Ryan has been tracking his sleep with the Sleep Cycle app for the last two years. In this excellent post he explores and plots his data (yay export!) to see when he goes to sleep, how long he sleeps, and what really makes up “quality sleep.” Love the fact that he included his R code and sample data. Go Ryan!

Quantifying Goals Using Key Performance Indicators (KPIs) by Bob Troia. No data in this post, but I found it particularly inspiring to see how Bob was planning on keeping track of his goals for this year. If you’re looking for ideas for tracking your 2015 goals and Key Performance Indicators this is a great place to start.

Visualizations

EricBoam_Resume
The Resume Of The Future by Eric Boam. The above is one of the two beautiful visualizations created by Eric to explore his daily work activity and interactions. This visualization shows what he was actually spending his time on. How did he collect the data? Well, he used the Reporter App to ask himself three questions: “where are you, what are you doing, and who are you with?” Make sure to read his post, he developed very interesting insights through collecting this data.

JawboneWeightLoss
Weight Loss: What Really Works? by Emi Nomura and Laura Borel. Another fascinating data analysis project here by the Jawbone data science team. They examined the behaviors of a group of users who lost at least 10% of their starting weight vs users with no weight loss and found that the biggest difference in behavior was tracking meals.

ER_RunRide2013-14
Mapping my Last Two Years of Runs and Rides
While browsing the r/dataisbeautiful subreddit I stumbled upon this interesting tool/company that visualizes the maps of your runs and bike rides by connecting to your Runkeeper or Strava account. Above I’ve included my 2013 and 2014 maps. Clearly I need to find some new running routes in my neighborhood. (click through to enlarge)

QS Access Links
As part of our new work highlighting stories, issues, and innovations related to personal data access we’re going to start publishing a short collections links in this space. As this works grows be on the lookout for a new Access Newsletter from QS Labs.

Who Should Have Access to Your DNA?
What FDA developments in Diabetes mean for FDA approval in Digital Health
Open consent, biobanking and data protection law: can open consent be ‘informed’ under the forthcoming data protection regulation?
WTF! It Should Not Be Illegal to Hack Your Own Car’s Computer
Unique in the shopping mall: On the reidentifiability of credit card metadata
Majority of Consumers Want to Own the Personal Data Collected from their Smart Devices
Who Owns Patient Data
Los Angeles County Supervisors OK Creation of Open-Data Website

From the Forum
Jawbone Up
How to find all major volunteer bioscience projects I can partake in?
Bluetooth pulse oximeters…
Best Heart Rate Monitor that syncs with Withings Ecosystem

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QS Access: Precision, Patients, and Participation

This morning President Barack Obama announced a new Precision Medicine Initiative, a key $215 million piece of the proposed 2016 budget. Much has been written since last week’s State of the Union, when this initiative was first mentioned by President Obama. In brief, the initiative is an investment in new programs and funding initiatives at major government bodies that influence the current and future health of all Americans, including the National Institutes of Health (NIH), the Food and Drug Administration (FDA), and the Office of the National Coordinator for Health Information Technology (ONC). These programs will focus on developing “a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.”

There is a lot of information being circulated about this new initiative, and we’ve collected some links below, but we’d like to highlight something directly related to our interests in self-tracking data, personal data access, and new models of participatory research. In this morning’s announcement President Obama mentioned a long-term goal of creating a participatory research cohort comprised of 1 million volunteers who will be called upon to share personal medical record data, genetic samples, biological samples, and diet and lifestyle information. This is truly an ambitious goal and we are happy to see the President take care to mention the importance of including patients and the individuals who collect this data in the decision making and research process. For example, here is the description of this specific program from the NIH Precision Medicine Infographic

NIH_PM_Participation

Here at QS Labs, we’re dedicated to helping create and grow a culture that enables everyone to generate personal meaning from their personal data. Sharing, participation, and exploring new models of discovery are a core themes we’re exploring as part of our QS Access work. We’ll be following this initiative as it moves from today’s announcement to tomorrow’s reality. Be sure to stay tuned to our QS Access Channel for more updates as we learn more.

Learn more about the Precision Medicine Initiative
NIH mini site describing the initiative
White House Blog: The Precision Medicine Initiative: Data-Driven Treatments as Unique as Your Own Body
FACT SHEET: President Obama’s Precision Medicine Initiative
A New Initiative on Precision Medicine by Francis Collins and Harold Varmus (New England Journal of Medicine).

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What We Are Reading

Below you’ll find this week’s selection of interesting bits and pieces from around the web. Enjoy!

Articles
Open Books: The E-Reader Reads You by Rob Horning. A fantastic essay about the nature of delight and discovery, and how that may (is) changing due to data collected from e-readers. For those interested in books and data this article By Buzzfeed’s Joseph Bernstein is also an interesting read.

Flashing lights in the quantified self-city-nation by Matthew W. Wilson. Quantified Self, smart cities, and Kanye West quotes – this commentary in the Regional Studies, Regional Science journal has it all. Read closely, especially the final paragraph, which gives space to think about the role the institutions and companies that provide cities with the means to “be smart” have in our in social and urban spaces.

Most Wearable Technology Has Been a Commercial Failure, Says Historian by Madeleine Monson-Rosen. This is a interesting book review for Susan Elizabeth Ryan’s Garments of Paradise which had me thinking about the nature of wearables, customization, and expression.

‘The Cloud’ and Other Dangerous Metaphors by Tim Hwang and Karen Levy. This was mentioned so many times over the last few days by so many smart friends and colleagues that I had to set aside time to read it. It was time well spent. The authors make the case that how we talk about data (personal, public, mechanical, and bioligical) is tied to the metaphors we use, and how those metaphors can either help or hinder the broader ethical and cultural questions we find ourselves grappling with.

Why the Internet Should Be a Public Resource by Philip N. Howard. This isn’t the first, nor will it be the last, argument for changing the way we think about and regulate the Internet. Worth reading the whole things, but in case you don’t consider this point:

And then we might even imagine an internet of things as a public resource that donates data flows, processing time, and bandwidth to non-profits, churches, civic groups, public health experts, academics, and communities in need.

Computers Are Learning How To Treat Illnesses By Playing Poker And Atari by Oliver Roeder. How does research into algorithms and AI intended for winning poker games morph into something that can optimize insulin treatment? An interesting exploration on the background and future implications of computers that can learn how to play games.

Data Stories #45 With Nicholas Felton. by Enrico Bertini and Moritz Stefaner. In this episode of the great Data Stories podcast Nicholas Felton talks about his background, his interest in typography, and what led him to start producing personal annual reports. Super fun to listen to them geek out about the tools Nicholas uses to track himself.

Increasingly, people are tracking their every move by Mark Mann. A great peak into some of our QS Toronto community members and how they use self-tracking.

Quantified Existentialism by Ernesto Ramirez. I’m putting this last here because it feels a bit self-congratulatory. Earlier this week I took some time to examine how common it is for people to express their relationship with what counts when they use self-tracking tools. It was a fun exercise.

Show&Tell
Insights From User Generated Heart Rate Variability Data by Marco Altini. While not a personal show&tell (however, I’m sure his data is in there somewhere), this great post details what Marco was able to learn about HRV based on 230 users and 13,758 recordings of HRV.

Quantify This Thursday: No Coding Required by Kerri MacKay. A bit different post here, more of a how-to, but I found it really compelling the lengths Kerri went to get get her Fitbit data to show up on he Pebble watch. I was especially drawn to her explanation of why this method is important to her:

The reality is, getting nudges every time I look at the clock or dismiss a text notification on my Pebble (via my step count) is yet another way to make the wearing-a-wearable less passive and the data meaningful.

Correlating Weight with Blood Pressure by Sam. A short and simple post detailing how Sam used Zenobase and his iHealth devices to see how weight loss was associated with his blood pressure.

Visualizations
WithingHolidays
The Effect of End of Year Festivities on Health Habits by Withings. The above is just one of four great visualizations from Withings exploring how the holidays affect how users sleep, move, and weight themselves. Unsurprisingly people are less likely to weight themselves on Christmas day (I looked at my data, I am among those non-weighers).

SimonData
Simon Buechi: In Pure Data by Simon Buechi. A simple, elegant dashboard intended to represent himself to the world.

MatYancy_Coding
Grad School Coding Analysis by Matt Yancey. The above is just a preview of two fantastic visualizations that summarize the coding Matt did while enrolled in the Northewestern Masters of Analytics program.

Fitbit_NewYears_Steps
News Year’s Eve Celebration in Steps by Lenna K./Fitbit. A fun visualization describing differences in how people in different age groups moved while celebrating the new year.

From The Forum
How do I visualize information quickly? (mobile app)
Monitoring Daily Emotions
Best Heartrate Monitor that syncs with Withings Ecosystem
Is the BodyMedia Fit still alive?
Capture Online Activities (and More) into Day One Journal Software (Mac/iOS)

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How to Represent a Year in Numbers

As the calendar turns over to a new year, it’s useful to look back and see what the last 365 days have been all about. Looking back is always easier when you have something to look back on, and, no surprise here, self-tracking is a great help for trying to figure out how things went. That’s what makes this time of year so interesting for someone like myself. I spend a good deal of my time trying to track down real-world examples of people using personal data to explore their lives. Sometimes it’s easy, and sometimes it’s hard finding people willing to expose themselves and their data. However, when late December rolls around, I perk up because this is the time for those yearly reviews.

I’ve spent the last few weeks gathering up some great examples from individuals from all over the world. I hope the following examples inspire you to track something new in 2015 and maybe share it with the QS community in person at a local meetup, at our QS15 Global Conference, or in our social channels. Okay, let’s dive in!

My Year 2014 in Numbers #QuantifiedSelf by Ragnar Heil. A brief, but fun post detailing a year of music, travel, and location checkins.

2014: A Year in Review with iPhone Pedometer Data by Geoffrey Litt. I really enjoyed this very thorough exploration of a year’s worth of pedometer data gathered from the Argus app (iOS). Not satisfied with just looking at his total step count for the year, Geoffrey ran a series of data explorations. Among my favorite, his visualization of his daily rhythms:

GeoffreyLit_StepHours

2014 in Numbers – My Life Behind the Command Line by Quincy Larson. Work, wellness (sleep and running) and reading – it’s all here. I like the idea of tracking what you’ve read by writing one tweet per book.

2014, Quantified by Sarah Gregory. Sarah does an amazing job of capturing and showcasing her 2014 activities in this beautifully simple post. With a balance of pure quantitative information and qualitative insights I found this review especially compelling. (It was also nice to see that she used our How to Download Your Fitbit Data tutorial.)

2014 in Numbers by Donald Noble. Speaking of our Fitbit data download tutorial, here’s a short post about a year’s worth of steps – 4.15 million steps to be precise.

Three Years of Running Data: 1,153km with Nike+ and Mind by Todd Green. As you can see from the title, this post details three years of running, but as a runner myself I always like peeking into other runner’s data. (Todd also has a fantastic post from early 2014 about tracking every penny he spent in 2013.)

Food, Glorious Food by Peter Chambers. A fun post detailing what Peter and his family ate for dinner nearly every day of 2014. One juicy bit – the most common meal? Chili – Peter’s favorite!

2014 in Numbers by Jill Homer. With the help of her Strava app, Jill details her cycling and running from 2014. Click for the numbers, stay for the gorgeous photos.

I wrote every day in 2014: Here’s an #infographic by Jamie Todd Rubin. It’s great fun following Jaime’s blog. He’s relentless on his journey of daily writing (and is quite the active Fitbit user as well). What was 2014 like for his writing? Over 500,000 words – almost enough to take on Tolstoy’s War and Peace. Plus, the visualization is great (click through for the full version):

JamieTR_Writing

2014 Stats by Dan Goldin. Amazing data gathered from a self-designed Google spreadsheet that includes mood, sleep, food, and drink.

Tracking My Life in 2014 by Mike Shea. Mike tracks his life using his own custom designed “Lifetracker app.” This includes his rating on six aspects of his life, daily activities, media, and location. In this post he turns his 8,400 rows of data into elegant visualizations and interesting analysis:

MikeShea_2014

A Year in Review of Personal Data, Should be, well, Personal. By Chris Dancy. As always, Chris has an interesting and entertaining post about his 2014 data and how it compares to 2013.

Tiny Preview By Lillian Karabaic. If her previous work is any indication this year’s review is going to be great. Keep in mind this is just a place holder until the full post is up.

Why #DIYPS N=1 data is significant (and #DIYPS is a year old!) by Dana Lewis. Along with her co-investigator, Scott Leibrand, Dana has been on a journey to better control, understand, and generate knowledge about her type 1 diabetes through augmenting CGM data, devices, and alerts. What started as project to make alarms more clear and useful has morphed into a full on DIY closed loop pancreas. In this post, Dana explores what they’ve learned over the last year of data collection. Truly inspiring work:

My Quantified Self Lessons Learned in 2014 by Paul LaFontaine. In this post Paul recounts what he’s learned from his various QS experiments during 2014, with a focus on stress and hear rate variability. Make sure to also take a peak at his 2014 Review and Gear Review.

2014 Year in Webcam and Screenshots by Stan James. We’ve featured Stan and his great LifeSlice project here on QuantifiedSelf.com before. It’s an ingenious little lifelogging application that tracks your computer use through webcam shots, self-assessments, and screenshots. Check out this post to see a fun representation of his data.

2014 by Kyle McDonald. A very interesting diary of a year.

What 2439 Reports Taught Me by Sam Bew. We highlighted this great post in our What We’re Reading a few weeks ago, but it deserves another mention here. Sam analyzes the data collected from using the Reporter iOS app and writes about what he learned.

2014 Personal Annual Report by Jehiah Czebotar. Coffee, travel, Citi bike trips, software development, laptop battery life, and webcam shots – all included in this amazing page. Presented without narrative or explanation, but meaningful nonetheless. The coffee consumption visualization is not to be missed (click through for the interactive version):JC_Coffee2

2014: My Year in Review by Sachin Monga. A mix of quantitative and qualitative data from Sachin.

My Q4 2014 Data Review by Brandon Corbin. While not a full “year in review” here, I still found this post compelling. Brandon created his own life tracking application, Nomie, and then crunched the numbers from the 60 different things he is tracking. Some great examples of learning from personal data in here.

20140101 – 20141231 (2014). Noah Kalina started taking a photo of himself on January 11, 2000. On the 15th anniversary of his “everyday” project he published his 2014 photos.

Reading
When I was spending late nights searching for variations on “2014”+”data”+”my year in review” I stumbled upon quite a few posts detailing reading stats. Here’s a good selection of what I can only assume is a big genre:

2014 Reading Stats and Data Sheets by Kelly Jensen. A great place to start if you want to track your own reading in 2015. Kelly provides links to three excellent spreadsheet examples.

My Year in Reading by Jon Page. Short and to the point, but a great exploration of format, genre, and authors.

My Year in Reading: 2014 by Annabel Smith.

My Year in Books, Unnecessarily Charted by Jane Bryony Rawson.

Well, that it for now. Special thanks to Beau Gunderson, Steven JonasNicholas Felton (and many others) for sending in links and tips on where to find many of the above mentioned work. If you have a data-driven year in review please reach our via email or twitter and we’ll add it to the list!

If you’re interested in learning about how people generate meaning from their own personal data we invite you to join us for our QS15 Global Conference. It’s a great place to share your experience, learn from others, and get inspired by leading experts in the growing Quantified Self Community. Early bird tickets are on sale. We hope to see you there.

If you’ve made it this far here’s a fun treat: Warby Parker made neat little tool you can use to generate a silly personal annual report.

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How To Access & Export MyFitnessPal Data

MFP

MyFitnessPal is one of the leading dietary tracking tools, currently used by tens of millions of people all around the world to better track and understand the foods they consume every day. Their mobile apps and online tools allow individuals to enter foods and keep track of their micro- and macro-nutrient consumption, connect additional devices such as fitness trackers, and connect with their community – all in the name of weight management. However, there is no natively available method for easily accessing your dietary data for personal analysis, visualization, or storage.

With a bit of digging in the MyFitnessPal help section we can see that they have no official support for data export. However, they mention the ability to print reports and save PDF files that contain your historical data. While better than some services, a PDF document is far from easy to use when you’re trying to make your own charts or take a deeper look into your data.

We spent some time combing the web for examples of MyFitnessPal data export solutions over the last few days. We hope that some of these are useful to you in your ongoing self-tracking experiences.

Browser Extensions/Bookmarks

MyFitnessPal Data Downloader: This extension allows you to directly download a CSV report from your Food Report page. (Chrome only)

MyFitnessPal Data Export: This extension is tied to another website, FoodFastFit.com. If you install the extension, it will redirect you back to that site where your data is displayed and you can download the CSV file. (Chrome only)

ExportMFP: A simple bookmark that will open a text area with comma-separated values for weight and calories, which you can copy/paste into your data editor of choice.

MyFitnessPal Reports: A bookmarklet that allows you to generates more detailed graphs and reports.

 Web Apps/Tools

MyFitnessPal Analyser: Accesses your diet and weight data. It requires you to input your password so be careful.

Export MyFitnessPal Data to CSV: Simple web tool for exporting your data.

FreeMyDiary: A recently developed tool for exporting your food diary data.

Technical Solutions

MyFitnessPal Data Access via Python: If you’re comfortable working with the Python language, this might be for you. Developed by Adam Coddington, it allows access to your MyFitnessPal data programmatically

MFP Extractor and Trend Watcher: An Excel Macro, developed by a MyFitnessPal user, that exports your dietary and weight data into Excel. This will only work for Windows users.

Access MyFitnessPal Data in R: If you’re familiar with R, then this might work for you.

QS Access + Apple HealthKit

If you’re an iPhone user, you can connect MyFitnessPal to Apple’s HealthKit app to view your MyFitnessPal data alongside other data you’re collecting. You can also easily export the data from your Health app using our QS Access app. Data is available in hourly and daily breakdowns, and you should be able to export any data type MyFitnessPal is collecting to HealthKit.

As always, we’re interested to hear your stories and learn about your experiences with exploring your data. Feel free to leave comments here or get in touch via twitter or email.

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What We Are Reading

Have a great time exploring these links, posts, and visualizations!

Articles
At Quantified Self, I forget I have Parkinson’s by Sara Riggare. Sara is a longtime member of our worldwide QS community and this heartfelt post about her experience at our conferences was wonderful to read. Experience the conference yourself and meet Sara at our QS15 Global Conference and Exposition. Register here 

Standards for Scientific Graphic Presentation by Jure Triglav. Jure is a doctor, developer, and researcher interested in how data is presented in the sciences. In this post he goes back in time to look at previous standards for presenting data that have largely been forgotten.

Painting with Data: A Conversation with Lev Manovich by Randall Packer. In this great interview, researcher, artist, and visualization expert, Lev Manovich, explains his latest work on exposing a window onto the world through photos posted to popular social apps.

Big Data, LIke Soylent Green is Made of People by Karen Gregory. A thoughtful essay here on automation, algorithmic living, and the change in value of human experience.

“In the production of these massive data sets, upon which the promise of “progress” is predicated, we are actually sharing not only our data, but the very rhythms, circulations, palpitations, and mutations of our bodies so that the data sets can be “populated” with the very inhabitants that animate us.”

When Fitbit Is the Expert Witness by Kate Crawford. I almost didn’t include this article in this week’s list. The story has been circulated so many times around the web this week, mostly without any real thought or examination. However, I found that Kate Crawford did a good job putting this news in context without resorting to sensationalism.

How California’s Crappy Vaccination Policy Puts Kids At Risk by Renee DiResta. A bit of a sensational title, but a great post that uses a variety of open data sources to showcase a growing concern about childhood vaccination policies in California.

Show&Tell
How I Used RescueTime to Baseline My Activity in 2014 and Set Goals for 2015 by Jamie Todd Rubin. I’ve been a big fan of Jamie’s writing since I found it earlier this year. He’s voracious self-tracker, mostly related to his tracking and understanding his writing, and this post doesn’t disappoint.

Sleeping My Way to Success with Data by Pamela Pavliscak. A great post by Pamela here about her experience starting tracking her sleep with the Sleep Cycle app. A great combination of actual data experience and higher-level thoughts on what it means to interface with personal data. I especially love this quote referencing her experience interacting with other sleep trackers,

“And they are doing the same thing that I’m doing — creating data about themselves, for themselves.”

Visualizations
IntoTheOkavango
Into the Okavango by The Office for Creative Research. A really neat interactive project by researchers, scientist, and the local community to document an expedition into the Okavango Delta in Botswana.

Strava
A Day in the Bike Commuting Life by Strava. The data science team at Strava put together a neat animation comprised of one-day of cycling commutes in San Francisco. Unsurprisingly, the Golden Gate Bridge is quite popular among cyclists.

From the Forum
Sleep tracking for new parents
Different Approach to ZEO Headband
Hello Everybody!
MyStress
New Self-Quantifier

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What We Are Reading

We hope you enjoy this week’s list!

Articles
Are Google making money from your exercise data?: Exercise activity as digital labour by Christopher Till. Christopher describes his recent paper, Exercise as Labour: Quantified Self and the Transformation of Exercise into Labour, which lays out a compelling argument for considering what happens when all of our exercise and activity data become comparable. Are we destined to become laborers producing an expanding commercialization of our physical activities and the data they produce?

How Big is the Human Genome? by Reid J. Robinson. Prompted by a recent conversation at QS Labs, I went looking for information about the size of the human genome. This post was one of the most clear descriptions I was able to find.

Show&Tell

VacationGPS
Visualizing Summer Travels by Geoff Boeing. A mix of Show&Tell and visualization here. Geoff is a graduate student and as part of his current studies he’s exploring mapping and visualization techniques. If you’re interested in mapping your personal GPS data, especially OpenPaths data, Geoff has posted a variety of tutorials you can use.

Visualizations

SymptomViz
Symptom Portraits by Virgil Wong. For 30 weeks Virgil met with patients and helped them turn their symptoms into piece of art work and data visualization.

Data Visualization Rules, 1915 by Ben Schmidt. In 1915, the US Bureau of the Census published a set of rules for graphic presentation. A great find by Ben here.

From the Forum
Moodprint
Measuring Cognitive Performance
Looking Forward to Experimenting

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Access Matters

Someday, you will have a question about yourself that impels you to take a look at some of your own data. It may be data about your activity, your spending at the grocery store, what medicines you’ve taken, where you’ve driven your car. And when you go to access your data, to analyze it or share it with somebody who can help you think about it, you’ll discover…

You can’t.

Your data, which you may have been collecting for months or years using some app or service that you found affordable, appealing, and useful, will be locked up inside this service and inaccessible to any further questions you want to ask it. You have no legal right to this data. Nor is there even an informal ethical consensus in favor of offering ordinary users access to their data. In many cases, commercial tools for self-tracking and self-measurement manifest an almost complete disinterest in access, as demonstrated by a lack of data export capabilities, hidden or buried methods for obtaining access, or no mention of data access rights or opportunities in the terms of service and privacy policy.

Now is the time to work hard to insure that the data we collect about ourselves using any kind of commercial, noncommercial, medical, or social service ought to be accessible to ourselves, as well as to our families, caregivers, and collaborators, in common formats using convenient protocols. In service to this aim, we’ve decided to work on a campaign for access, dedicated to helping people who are seeking access to their data by telling their stories and organizing in their support. Although QS Labs is a very small organization, we hope that our contribution, combined with the work of many others, will eventually make data access an acknowledged right.

The inspiration for this work comes from the pioneering self-trackers and access advocates who joined us last April in San Diego for a “QS Public Health Symposium.” Thanks to funding support from the Robert Wood Johnson Foundation, and program support from the US Department of Health And Human Services, Office of the CTO, and The Qualcomm Institute at Calit2, we convened 100 researchers, QS toolmakers, policy makers, and science leaders to discuss how to improve access to self-collected data for personal and public benefit.  During our year-long investigation leading up to the meeting, we learned to see the connection between data access and public health research in a new light.

If yesterday’s research subjects were production factors in a scientist’s workshop; and if today’s participants are – ideally – fully informed volunteers with interests worthy of protection; then, the spread of self-tracking tools and practices opens the possibility of a new type of relationship in which research participants contribute valuable craft knowledge, vital personal questions, and intellectual leadership along with their data.

We have shared our lessons from this symposium in a full, in-depth report from the symposium, including links to videos of all the talks, and a list of attendees. We hope you find it useful. In particular, we hope you will share your own access story. Have you tried to use your personal data for personal reasons and faced access barriers? We want to hear about it.

You can tweet using the hashtag #qsaccess, send an email to labs@quantifiedself.com, or post to your own blog and send us a link. We want to hear from you.

The key finding in our report is that the solution to access to self-collected data for personal and public benefit hinges on individual access to our own data. The ability to download, copy, transfer, and store our own data allows us to initiate collaboration with peers, caregivers, and researchers on a voluntary and equitable basis. We recognize that access means more than merely “having a copy” of our data. Skills, resources, and access to knowledge are also important. But without individual access, we can’t even begin. Let’s get started now.

An extract from the QSPH symposium report

[A]ccess means more than simply being able to acquire a copy of relevant data sets. The purpose of access to data is to learn. When researchers and self-trackers think about self-collected data, they interpret access to mean “Can the data be used in my own context?” Self-collected data will change public health research because it ties science to the personal context in which the data originates. Public health research will change self-tracking practices by connecting personal questions to civic concerns and by offering novel techniques of analysis and understanding. Researchers using self-collected data, and self-trackers collaborating with researchers, are engaged in a new kind of skillful practice that blurs the line between scientists and participants… and improving access to self-collected data for personal and public benefit means broadly advancing this practice.

Download the QSPH Report here.

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QSEU14 Breakout: Emotive Wearables

Today’s post comes to us from Rain Ashford. Rain is a PhD student, researcher, and hardware tinkerer who is interested in how personal data can be conveyed in new and meaningful ways. She’s been exploring ideas around wearable data and the hardware that can support it. At the 2014 Quantified Self Europe Conference, Rain led a breakout session on Emotive Wearables during which she introduced her EEG Visualizing Pendant and engaged attendees in a discussion around wearing data and devices. 

Emotive Wearables
By Rain Ashford

It was great to visit Amsterdam again and see friends at the 3rd Quantified Self Europe Conference, previously I have spoken at the conference on Sensing Wearables, in 2011 and Visualising Physiological Data, in 2013.

There were two very prominent topics being discussed at Quantified Self Europe 2014, firstly around the quantifying of grief and secondly on privacy and surveillance. These are two very contrasting and provocative areas for attendees to contemplate, but also very important to all, for they’re very personal areas we can’t avoid having a viewpoint on. My contribution to the conference was to lead a Breakout Session on Emotive Wearables and demonstrated my EEG Visualising Pendant. Breakout Sessions are intended for audience participation and I wanted to use this one-hour session to get feedback on my pendant for its next iteration and also find out what people’s opinions were on emotive wearables generally.

I’ve been making wearable technology for six years and have been a PhD student investigating wearables for three years; during this time I’ve found wearable technology is such a massive field that I have needed to find my own terms to describe the areas I work in, and focus on in my research. Two subsets that I have defined terms for are, responsive wearables: which includes garments, jewellery and accessories that respond to the wearer’s environment, interactivity with technology or physiological signals taken from sensor data worn on or around the body, and emotive wearables: which describes garments, jewellery and accessories that amplify, broadcast and visualise physiological data that is associated with non-verbal communication, for example, the emotions and moods of the wearer. In my PhD research I am looking at whether such wearable devices can used to express non-verbal communication and I wanted to find out what Quantified Self Europe attendees opinions and attitudes would be about such technology, as many attendees are super-users of personal tracking technology and are also developing it.

Demo-ing EEG Visualising Pendant

My EEG Visualising Pendant is an example of my practice that I would describe as an emotive wearable, because it amplifies and broadcasts physiological data of the wearer and may provoke a response from those around the wearer. The pendant visualises the brainwave attention and meditation data of the wearer simultaneously (using data from a Bluetooth NeuroSky MindWave headset), via an LED (Light Emitting Diode) matrix, allowing others to make assumptions and interpretations from the visualizations. For example, whether the person wearing the pendant is paying attention or concentrating on what is going on around them, or is relaxed and not concentrating.

After I demonstrated the EEG Visualising Pendant, I invited attendees of my breakout session to participate in a discussion and paper survey about attitudes to emotive wearables and in particular feedback on the pendant. We had a mixed gender session of various ages and we had a great discussion, which covered areas such as, who would wear this device and other devices that also amplified one’s physiological data? We discussed the appropriateness of such personal technology and also thought in depth about privacy and the ramifications of devices that upload such data to cloud services for processing, plus the positive and the possible negative aspects of data collection. Other issues we discussed included design and aesthetics of prominent devices on the body and where we would be comfortable wearing them.

I am still transcribing the audio from the session and analysing the paper surveys that were completed, overall the feedback was very positive. The data I have gathered will feed into the next iteration of the EEG Visualising Pendantprototype and future devices. It will also feed into my PhD research. Since the Quantified Self Europe Conference, I have run the same focus group three more times with women interested in wearable technology, in London. I will update my blog with my findings from the focus groups and surveys in due course, plus of course information on the EEG Visualising Pendant’s next iteration as it progresses.

A version of this post first appeared on Rain’s personal blog. If you’re interested in discussing emotive wearable we invite you to follow up there, with Rain on Twitter, or here in the comments. 

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Laurie Frick: Experiments in Self-tracking

As much as we talk about self-tracking being about health or fitness. . . I think it’s about identity. I think it’s about us. It’s about seeing something meaningful in who we are.

Laurie Frick is a self-tracker and visual artist. It this unique combination that has led her down a path of learning about herself while using the data she collects to inform her artistic work. What started with time and sleep tracking rapidly expanded to included other types of data. In this short talk, presented at the 2014 Quantified Self Europe Conference, Laurie explains how her past experiences have informed her new way of thinking about data, “Don’t hide. Get more.”

If you’re interested in Laurie’s artistic work I highly recommend spending some time browsing the gallery on her website.

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