What We Are Reading

We have a great list for you today. Special thanks to all those who are reaching out via Twitter to send us articles, links, and other bits of interestingness. Keep ‘em coming!

Articles
Self-Experimentation: Crossing the Borders Between Science, Art, and Philosophy, 1840–1920 by Katrin Solhdju. This brief essay lays out a great foundation for anyone interesting in the history and philosophy of science, with an obvious focus on the self-experiment. This essay is hosted at the Max Plank Institute for the History of Science, at which I highly recommend spending some time clicking around and reading the wonderful essays and articles.

After the Data Confessional: interview with Ellie Harrison by Stephen Fortune. A very interesting and thought-provoking interview with artist Ellie Harrison. For six years self-tracking data was the core component of Ellie’s work as an artist. Then she decided to stop and reconsider her tracking practices and what it meant to her and her work.

Data is the New “___” by Sara M. Watson. “What do we talk about when we talk about data?” is the question Sara posses here to frame a wonderful piece on how our use of metaphors influences our view of data.

A brief history of big data everyone should read by Bernard Marr. If we’re going to talk about how we talk about data it is probably useful to have some historical context. Great timeline here of data in society.

Baby Lucent: Pitfalls of Applying Quantified Self to Baby Products [PDF] by Kevin Gaunt, Júlia Nacsa, and Marcel Penz. An interesting article here from three Swedish design students that looks at current baby and parenting tracking technology. They also conducted a design process to develop a future tracking concept to better understand parent’s reactions to baby tracking. I thought there were a few interesting finding from their interviews.

Hey, Nate: There Is No ‘Rich Data’ In Women’s Sports by Allison McCann. It only seems fitting that a few days before this weekend’s MIT Sloan Conference on Sports Analytics Conference, the “it” place to learn about and discuss sports data, that we learn about the amazing dearth of data collected and published about women’s sports.

Show&Tell
AustinWaterstop-25-word-frequency
Analyzing Email Data by Austin G. Waters. A great deep dive into the 23,965 emails that Austin has collected in his personal account since 2009. I won’t spoil it, but this post just keeps getting better and better as you scroll. Bonus points to Austin for describing his methods and open-sourcing the code he used to conduct this analysis.

The App That Tricked My Family Into Exercising by Adam Weitz. Not a lot of data in this post, but I enjoyed the personal and social changes Adam described through his use the Human activity tracking app.

Visualizations

Smart Art by Natasha Dzurny. Using IFTTT and a few littleBits modules Natasha created a piece of artwork that reflects how often she goes to the gym. Would love to seem more DIY data reflections like this!

SleepCycle_US-Wake-Up-Mood
How does weather affect U.S. sleep patterns? by Sleep Cycle. Sleep Cycle analyzed 142,272 sleep reports from their users (recorded in January of 2015) to explore mood upon awakening, stress levels before bed, and sleep quality. Fascinating stuff.

Access Links
HHS Expands Its Approach to Making Research Results Freely Available For the Public
Many Patients Would Like To Hide Some Of Their Medical Histories From Their Doctors
Doctors say data fees are blocking health reform

From the Forum
Best ECG/EKG Tool for Exercise
BodyMedia API – Anyone have an active key/application?
Sleep monitor recommendations for research on sleep in hospitals
Simplified nutrition, alertness, mood tracking

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Greg Kroleski: Six Years of Tracking My Time

Greg Kroleski has been tracking his time for the past six years, starting when he was 20-years old. Using a spreadsheet he designed himself he collects how much time he spends in eight different categories: Survival, Labor, Social, Spiritual, Mind, Expression, Body, and Distraction. In this talk, presented at the San Francisco QS meetup group, Greg describes the data he’s collected and what he’s learned about where his time goes. If you’re interested in applying his tracking methodology he’s graciously put his spreadsheet template online here.

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QS Access: Data Donation Part 2

In our Access Channel we’re trying to expose ideas, efforts, and insights about personal data access and it’s role in both generating personal and public insights. The last time we wrote about data donation we mentioned a few different projects that allowed you to collect and/or publish your self-tracking data for others to view and access. Today we’re going to showcase a few research-focused projects that collect personal data, but also allow participants to access the data they contribute. This seemingly minor addition, participant access to data, is actually a process not commonly employed by research studies. We’re very interested in new participatory models of research that respect participant’s rights to fully understand and access the data they contribute. If you know of others please get in touch and we’ll add them to the list.

Personal Genome Project: Harvard
Probably the most well-known of these research projects is the ongoing Personal Genomes Project based at Harvard University (PGP). Led by George Church and an outstanding team, the PGP is an ongoing research project recruiting participants to “share their genetic, health, and trait data in a public and non-anonymous manner. Participation is free.

American Gut
Much like the project above, the American Gut project is an open call for participant to collect and share their data. In this case it is human microbiome data. Although enrollment is not free (they request donations starting at $99 to participate) data is returned to participants. (If you’re interested in participating in microbiome research, but live in Europe see the British Gut project)

Dynamics of the Human Microbiota
This new project, based out of Stanford, is also exploring the human microbiome. This study includes a variety of different perturbations and longitudinal data collection. Participants are compensated for their participation, their data is made accessible to them, and they have the opportunity to discuss their results with the study staff.

For those of you interested in research methods and ethics we recommend reading this brief article by Jeantine E. Lunshof, George M. Church, and Barbara Prainsack: Raw Personal Data: Providing Access

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QS15 Conference Preview: Julie Price on Long-term Weight Tracking

On June 18-20 we’ll be hosting the QS15 Conference & Activate Expo in San Francisco at the beautiful facilities at the Fort Mason Center. This will be a very special year with three days of inspiring talks, demos, and discussion with your fellow self-trackers and toolmakers. As we start to fill out our program we’ll be highlighting speakers, discussion leaders, sponsors, and attendees here.

JuliePhoto2Julie Price is a long time member of our Bay Area QS meetup group and will be attending the QS15 Conference & Activate Expo to share her self-tracking story. Julie has been using and experimenting with a wide range of self-tracking tools and behavioral techniques to understand herself. Previously she’s shared her experiences using commercial tools and self-designed methods to understand and improve her marathon training.

Over the past 4 years, Julie has tracked her weight as it moved within a 30 pound range, varying wildly within each year. In December, Julie shared the factors that influenced her weight the most: family visits, distance road races, and a variety of weight loss tactics. As part of our show&tell program, Julie will share an update that includes her newest insights into her weight fluctuations as well as what interventions have made the greatest impact on her weight.

JP_Weight

We’re excited to have Julie joining us and asked her a few questions about herself and what she’s looking forward to at the conference.

QS: What is your favorite self-tracking tool (device, service, app, etc)?

Julie: Hands-down, I love the Whistle to measure my dog’s activity. I use Basis Peak and my husband uses Jawbone Up. Both seem well-designed for certain scenarios and not for others. Between all the wearables we’ve tried, the Whistle has been the most successful in influencing our behavior.

QS: What are you most looking forward to at the conference?

Julie: I’m looking forward to meeting interesting people, learning from their stories, and learning from their creative experiments and observations. But, I’m most looking forward to exploring new ideas that impact the behavior of people who don’t necessarily enjoy data.

QS: What should people come talk to you about at the conference?

Julie: I’m an expert in UX, interaction design, usability, health behavior change, and fitness. I’d love to talk about creative tactics for eliciting behavior change and a process for ensuring the right product and experience is designed for the right person. I also love to talk about health gaming and the complexities of the space.

QS: What tools, devices, or apps do you want to see at the conference?

Julie: Any product in health and fitness that is truly different or thought through from the perspective of the user. I’d love to see any product built with a process that continually validates their direction with target users.

QS: What topic do you think that Quantified Self community is not talking enough about?

Julie: We should explore more innovative ways to meet people where they are and creatively influence them gradually in a way that is meaningful and lasting. It would be great to talk more about what progressive techniques could be applied in order to create impact over both short and long periods of time.

Julie’s session is just one of the many hands-on, up-to-date, expertly moderated sessions we’re planning for the QS15 Global Conference and Exposition. We’ve made some early bird tickets available for readers of the Quantified Self blog (for a limited time): Register here!

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Meetups This Week

This week will bring four fascinating Quantified Self meetups. St. Louis will have a “quantified romance”-themed meetup including a toolmaker talk with the creator of a service that helps with romantic decisions.  In Ashland, they will be getting together to discuss n=1 study designs. This is the same group that started the Individual Metabolic Research Group (iMeRG) that we featured a couple of days ago.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own! If you organize a QS meetup, please post pictures of your event to the Meetup website. We love seeing them.

Tuesday, February 24
St. Louis, Missouri

Wednesday, February 25
Cincinnati, Ohio

Thursday, February 26
Ljubljana, Slovenia

Saturday, February 28
Ashland, Oregon

Also, enjoy these photos from last week’s meetup in New York, as well as, last month’s meetup in Vienna.
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What We Are Reading

We hope you enjoy this week’s What We’re Reading list!

Articles
The Wow of Wearables by Joseph Kvedar. An excellent post here in the wake of the “Smartphones vs. Wearables” hype in the past weeks. Favorite part:

“I’d have to say that reports of the death of wearables have been greatly exaggerated. The power of sensor-generated data in personal health and chronic illness management is simply too powerful to ignore.”

Survival of the Fittest: Health Care Accelerators Evolve Toward Specialization by Lisa Suennen. If you’re at all interested in the recent surge in health and healthcare focused accelerators this is for you. Excellent reporting. (Thanks for sharing Maarten!)

Your Brain Is Primed To Reach False Conclusions by Christie Aschwanden. Fascinating piece here about the nature of the “illusion of causality.”

A Few Throughs About Patient Health Data by Emil Chiauzzi. Emil, Research Director at PatientsLikeMe, lays out four point to consider when thinking about how to best use and grow self-collected patient data.

Having Parkinson’s since I was 13 has made me an expert in self-care by Sara Riggare.

I am the only person with the whole picture. To me, self-care is everything I do to stay as healthy as possible with a disease that is a difficult life companion. It entails everything from making sure I take my medication in the optimal way, to eating healthily, getting enough sleep, to making sure I stay physically active. I also make an effort to learn as much as I can about my condition; my neurologist says that I know more about Parkinson’s research than he does. I don’t find that odd, since he needs to try to stay on top of research in probably hundreds of neurological diseases, whereas I focus on just one.

From Bathroom to Healthroom: How Magical Technology will Revolutionize Human Health by Juhan Sonin. A beautifully written and illustrated essay on the design of our  personal healthcare future.

Show&Tell
Experimenting with sprints at the end of exercise routines by Gustavo M. Gustavo is a person with type 1 diabetes. After reading that post-exercise high intensity exertion might have an effect on blood glucose he put it to the test.

On Using RescueTime to Monitor Activity and Increase Productivity by Tamara Hala. Tamara walks us through the last three years of her RescueTime data and how she used that information to understand her work and productivity.

How Do You Find Time to Write? by Jamie Todd Rubin. Jamie has been writing for 576 consecutive days. How does he do it? A mixture of data and insight of course!

Visualizations
ILoveYouMaps Say “I Love You” With Mapping by Daniel Rosner. Wonderful to see CHI papers ending up on Medium. This seems like a fun self-tracking/art project.

ShannonConnors_4yearsfood Cleaning up and visualizing my food log data with JMP 12 by Shannon Conners. Once again, Shannon displays a wonderful ability to wow us with her data analysis and visualization. Above is four years of food tracking data!


Two Trains: Sonification of Income Inequality on the NYC Subway by Brian Foo. Brian created this data-driven musical composition based on income data from neighborhoods the border the 2 train. Beautiful work.

Access Links
Walgreens adds PatientsLikeMe data on medication side effects
How Open Data Can Reveal—And Correct—The Faults In Our Health System
Big Data is our Generation’s Civil Rights Issue, and We Don’t Know It.

From the Forum
Creating Scales for Quantifying Action
Sharing Anonymized Data

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Introducing iMeRG: The Individual Metabolic Research Group

Here at QS Labs we take great pride in supporting a worldwide network of meetup groups. From Bucharest to the Bay Area, we have over 100 groups meeting to discuss self-tracking, share experiences, and learn from each other.

We wanted to highlight a new group, based in southern Oregon, that is using self-tracking to expand and influence medical knowledge within the healthcare system. Dr. Dawn Lemanne, a board certified and practicing oncologist, has started the new Individual Metabolic Research Group (iMeRG) to develop, test, and explore inexpensive way to prevent and treat chronic diseases related to lifestyle, through rigorous N of 1 research methods.

iMeRG

Currently the iMERG is a composed of physicians and other health care professionals frustrated by the rising rates of lifestyle driven chronic disease, and the failure of the large randomized controlled trial (RCT) to provide effective interventions. Inspired by QS, they are working together to develop and use rigorous N of 1 research designs, while using themselves (not their patients) as subjects. Members propose projects, and together they figure out how to do it. QS devices and philosophies play a major role in the data collection and analysis methods being talked about at the group.  Current proposals have included:

  • How best to measure the effect of combining intermittent fasting and exercise on blood ketone levels and inflammatory markers in a sedentary postmenopausal woman
  • The clinical manifestations of Familial Mediterranean Fever gene heterozygosity.

Join the group! If you hold a license to practice a health profession (MD, DO, DDS, DMD, RN, NP, PA, DC, ND, LAc, etc.), you’re interested in N of 1 research design and methods, and you’d like to be involved, please contact Dawn. All individuals are welcome, regardless of geographic location. If you’re in the southern Oregon area you can join their meetup group on February 28th. We’ll be posting updates from the group as their research progresses.

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Do It Yourself Diabetes

DanaScott_Header

Dana Lewis and Scott Leibrand are the creators of the amazing “Do-It-Yourself-Pancreas-System,” also known by #DIYPS. We had a few question for them.

Ernesto: Why build your own pancreas?

IMG_3561Dana: I’ve had Type 1 diabetes for about 12 years. I use an insulin pump and a continuous glucose monitor (CGM), but the devices are separate. They don’t talk to each other. I have to look at the data from the CGM and then make decisions about my insulin. I have to make about 300 decisions per day on average. It’s really fatiguing. So we created some algorithms that took my blood glucose data, the amount of insulin that I’ve given myself, and the amount of carbohydrates that I’ve decided that I’ve eaten, and ran them over and over again to give me a prediction of what my blood sugar was going to be and whether I need to take any action. Instead of having to constantly do the math myself, our system will push an alert to my phone or watch.

Ernesto: Does it dose you automatically?

Dana: Originally no, but more recently we’ve built a full closed loop version of #DIYPS, that is essentially an artificial pancreas, that talks to my pump and adjusts to give me a little more or a little less insulin.

Ernesto: Who writes the code?

Scott: I’m doing all the coding. I’m sure Dana could, but she has a lot going on and designs the algorithms. My title is Chief Spaghetti-Coder. This is the bleeding edge. It doesn’t need to be elegant code.

Ernesto: What have you learned from building your own pancreas?

Dana: The beauty of a CGM is that it gives you a data point every five minutes. Over the past year I’ve produced more than 130,000 data points of blood sugar levels alone. That gives me an incredible picture of what’s happening. With a traditional meter, it’s rare to find somebody who tests up to even 10 times a day. And the standard use for an insulin pump is very much “set it and forget it.” The #DIYPS allows me to customize without having to constantly adjust my insulin pump manually, and that frees me up to live my life, work, and do whatever it is that I want to do.

A visualization of Dana’s Data over the first year of the #DIYPS system.

A visualization of Dana’s Data over the first year of the #DIYPS system.

Ernesto: How did this project start?

Dana: We first started building the system just to make the alarms on the device louder, to wake me up because I would sleep through them. The device manufacturers didn’t seem to have a solution. Then we started looking at getting the data onto a computer so Scott would be able to view it. At the time, we had recently started dating, and he lives 20 miles away. I wanted him to be able to see what my blood glucose level was, so if it was low, he could text me; and if I didn’t respond, he could call 911. But we didn’t have a way to get the data off of the device.

Scott: The key moment was when we saw a tweet from John Costik, who was working on the Nightscout Project. Nightscout is open source code that helps people transmit their CGM data to other devices. I tweeted John right away: “Hey it would be awesome if we could get access to this code.” That’s really where it started. And along the way the whole process has been extremely public. We’ve been tweeting, blogging, and making everything we’ve been doing completely visible.

Ernesto: I’ve seen you tweet using the hashtag #wearenotwaiting. What does that mean?

Dana: #WeAreNotWaiting is a hashtag that was coined at a conference hosted by an online diabetes advocacy and information sharing community called DiabetesMine.com. For me it means that we’re not waiting for traditional device manufacturers to come out with the product. In three to ten years there’ll be devices like our artificial pancreas systems out in the market, being sold by companies approved by the FDA. I need to be alive when that system gets out in the market in, perhaps, five years.

I need to be alive when a cure becomes available.

Scott: Right about the time that we started working on #DIYPS, the Nightscout Project started to grow really quickly. There are now over 10,000 people in the CGM in the Cloud group. Over 2,000 people are using Nightscout to view their own or their loved ones’ blood sugar levels remotely on phones, watches, and other devices. This is real stuff that’s making a real difference in the world. And that’s only going to accelerate as more people do more interesting things like this closed loop that we’ve just done.

Ernesto: You’ve written about “data as free speech.” What do you mean? How can data be speech?

Dana: People often don’t understand why its legal for us to ‘hack’ a CGM and an insulin pump. (Note that hacking isn’t a negative thing; we’re just sharing the data across devices!) They assume that because all my DIY gadgets are not FDA-approved to use them the way I’m using them is somehow against the rules. But I can treat my own body, my own diabetes, the way I want to. And if I share my data, that’s obviously a kind of speech. But if we decide to share our code? I think the FDA sees this as a gray area. We very much want to continue our conversations with regulators.

Ernesto: Where do you see your project going?

Dana: I feel that every time I answer this question my answer changes, because my understanding of its potential is constantly changing. I never would have thought that any of what we’ve done was possible. Right now one of our goals is to make sure that the knowledge we gained about diabetes through our work with #DIYPS is adopted by clinicians, and that patients have access to this new information for treating diabetes. We’re also taking #DIYPS to a new level with #OpenAPS, an open and transparent effort to make safe and effective basic Artificial Pancreas System (APS) technology widely available to more quickly improve and save as many lives as possible and reduce the burden of Type 1 diabetes.

Dana with the #OpenAPS system.

Dana with the #OpenAPS system.

Scott: A few of months ago, at a conference convened by the advocacy group DiabetesMine, we got up and talked about our project, and I said: “I’m putting a stake in the ground that we’re going to make a closed loop artificial pancreas by August 1st, which is the date we’re getting married.” Everybody applauded and thought that was awesome. Then we went home. And we had it done in two weeks.

Dana: For anybody who wants to get involved in this, we would love to talk to you. There are so many people with diabetes and there is so much data that drives the management of this disease.

But there’s not a lot of awareness of how many diseases, including diabetes, could have their care revolutionized just by having better access to data.

That’s the thread of Quantified Self that I’m most interested in. The diabetes community happens to be one of the first to take advantage of what’s possible.

Dana tweeted her blood glucose data during this interview.

We invite you to share your data access stories, and this Access Conversation with the #qsaccess hashtag and follow along here in our Access Channel quantifiedself.com and @quantifiedself.

RWJF_Logo_Support2

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Quantified Homescreens

Quantifying-Homescreens

Betaworks recently announced that they had collected data from over 40,000 users who shared their iPhone homescreens through their apptly named #homescreen app. As they stated in their announcement, the apps people keep on their homescreen are often the apps they use the most. Being a data-minded individual I thought, “I wonder what kind of questions you could ask of this kind of data?” Of course, I immediately jumped to using the data to try and understand the landscape of self-tracking and quantified self app use. Let’s dive in.

Methods

I didn’t do anything fancy here. I used the search function to look up specific applications that I either use myself or have heard of. I also used the “in folders with” and “on homescreens with” lists to find additional applications that weren’t on my initial list. Each of the apps I found went into a Google Spreadsheet along with the “on % of homesceens” value reported by #homescreen. Additionally I categorized each of the apps into one of seven very broad categories: Activity, Fitness, Diet, Sleep, General Tracking, General Health, and Other.

Screen Shot 2015-02-14 at 9.52.05 AM

If you search for an app this is the data that is returned.

Results

I was able identify 65 unique applications reported as being present on #homescreen users iPhone homescreens. While in no way a complete list, I think it’s a good sample to see what people are using in their everyday life. So, how does the data actually break down?

Category Representation

categoryrepresentationThe most popular category was Activity with 20 apps (30.8%) followed by Fitness (15 / 23.1%), General Tracking (11 / 16.9%), a tie between General Health and Diet (6 / 9.2% each), Sleep (5 / 7.7%), and Other (2 / 3.1%).

Frequency of Homescreen Appearance

Perhaps unsurprisingly, Apple’s Health app tops the list here with a whoping 23.45%. No other self-tracking application even comes close to that level of homescreen penetration. The next most frequent application is Day One (a journaling app) with 8.45%.

homescreenfreq_WHealth

Clearly Apple Health skews the data so let’s look at homescreen frequency without it in the data set:

homescreenfreq_woHealth

When you exclude Apple Health (a clear outlier) the average percentage for homescreen frequency is 0.83% with a standard deviation of 1.39%. This data is skewed by a high number of applications that appear very infrequently. How skewed?

freqHistogram

If we disregard all apps that fall below this 1% cutoff what do we find? Fourteen apps meet this criteria: Coach.me, Day One, Fitbit, Health Mate (Withings), Moves, MyFitnessPal, Nike+, Pedometer++, Runkeeper, Runtastic Pro, Sleep Better, Sleep Cycle, Strava, and UP (Jawbone):

homescreenfreq_more1percent

Interestingly, MyFitnessPal is the only “Diet” app that made this >1% list, but it has the second highest appearance percentage at 4.36. Sleep Cycle is not far behind at 3.98%.

Discussion

Let’s start with the caveats. Obviously you can’t take these simple findings and generalize it to all individuals with smartphone, especially because this is only capturing iPhone users (many of the apps in this list have Android versions as well). Second, this data is based on a relatively small sample size of 40,000 individuals that are using the #homescreen app. Third, users of #homescreen are probably not representative of the general population of self-tracking application users. Lastly, it’s hard to draw conclusions about actual app use from this data. Like many people (myself included), I’m sure this data set has more than a few users who don’t regularly change their homescreen configuration when apps fall out of favor.

With that out of the way, what did I actually find interesting in this data set?

I found that sleep tracking, or having sleep tracking apps on a homescreen, was more popular than I thought it would be. Of the top 15 apps, 2 were sleep. When exploring by category, sleep had the highest mean appearance percentage (when also excluding Apple Health) at 1.32% (n = 5 apps).

For connected tracking devices, Fitbit (3.47%) is the clear winner, far outpacing it’s wearable rivals. The next closest application that is at least partially dedicated to syncing with a wearable device was Health Mate by Withings (2.14%).

I was reluctant to include Day One in this analysis. It isn’t commonly thought of as a “self-tracking” or “quantified self” application, but journaling and daily diaries are a valid form of tracking a life. Clearly it resonates with the people in this data set.

This was a fun exercise, but I’m sure there are many more questions that can be answered with this data set. I’ve compiled everything in an open google spreadsheet. I’ve enabled editing in the spreadsheet so feel free to add apps I might have missed or create additional charts and analysis.

And in the spirit of full disclosure, he’s my current homescreen.

This post first appeared on our Medium publication “Notes on Numbers, where we’re starting to share some of our thoughts and editorial experiments. Follow along with us there

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Meetups This Week

There will be six Quantified Self meetups occurring in two countries this week. Thessaloníki­ in Greece will be having their very first meetup. New York is a must attend event for they always have excellent talks. Chicago will have a toolmaker talk from the maker of an under-desk elliptical trainer that tracks usage, while southern Florida will have two meetups in Miami and Naples.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own! If you organize a QS meetup, please post pictures of your event to the Meetup website. We love seeing them.

Tuesday, February 17
Chicago, Illinois

Wednesday, February 18
Denton, Texas
New York City, New York

Thursday, February 19
Miami, Florida
Thessaloníki­, Greece

Friday, February 20
Naples, Florida

Also, here are some photos from Cambridge‘s meet up last week:
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