Topic Archives: QS Gallery

2015 QS Visualization Gallery: Round 4

We’re excited to share another round of personal data visualizations from our QS community. Below you’ll find another five visualizations of different types of personal data. Make sure to check out Part 1Part 2, and Part 3 as well!

daily habits Name: Damien Catani
Description: This is an overview of how I have been doing today against my daily habit targets. Yes, I had a good sleep!
Tools: I used a website I’ve been building for the purpose of setting and tracking all goals in life: goalmap.com

 

tock_b_tock_goal_page Name: Bethany Soule
Description: This is my pomodoro graph. I average four 45 minute pomodoros per day on my work, and I track them here. This is where most of my productivity occurs! There’s some give and take.
Tools: The graph is generated by Beeminder. I use a script I wrote to time my pomodoros and submit them to Beeminder when I complete them. The script also announces them in our developer chat room, so there’s also some public accountability there as well.

 

 

qs1 Name: Steven Zhang
Description: This plot shows the time I first go to sleep, against quality of day (a subjective metric I plot at the end of every day). What this tells me is that if I get a full night’s sleep of 8 hours, for every hour I got to bed, I can expect a .16 decrease in my QoD rating, which, given my range of QoD around 2 to 4, is about a 5% decrease in quality of day.
Tools: Sleep as Android to track sleep and some python scripts for ETL.

 

qs2Name:Steven Zhang
Description: Log of all my sleep for the last 6 months, labeled by the types of sleep I most often encounter

  1.  Normal sleep
  2. Napping
  3. 3. Trying to achieve normal sleep, but failing to

Tools: Tableau for visualization. Sleep as Android for logging sleep.

 

Digits
Name: Eric Jain
Description: Benford’s Law states that the most significant digits of numbers tend to follow a specific distribution, with “1″ being the most common digit, followed by “2″ etc. But my daily step counts show a slightly different distribution: The fall-off from “1″ to “2″ is larger than expected, and the frequency of digits larger than “5″ increases rather than decreases. Is this pattern typical for step counts? Could suspicious distributions be used to detect cheaters?
Tools: Fitbit, Zenobase, Tableau

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

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2015 QS Visualization Gallery: Part 3

We’re excited to share another round of personal data visualizations from our QS community. Below you’ll find another five visualizations of different types of personal data. Make sure to check out Part 1 and Part 2 as well!

eddie-flights Name: Edward Dench
Description: All recorded flights I’ve taken.
Tools: Manual entry into openflights.org (there is an interface using TripIt though).

 

QS Visualization Name: Siva Raj
Description: After 6 months of regular exercise failed to improve my fitness and blood pressure levels, I switched to training above my endurance limit (anaerobic threshold). This was higher intensity but half the cycling time, yet my fitness and blood pressure improved within weeks.
Tools:Revvo – tracking fitness and intensity of workout; Withings – weight; iHealth BP Monitor – BP. Visualization created by overlaying Revvo screenshot with other information in photoshop.

 

Screenshot 2015-06-05 08.07.14 Name: Kurt Spindler
Description: Grafana is a common tool in the Software community to create beautiful dashboards to visualize server health (network, requests, workers, cpu, etc.) and therefore more easily diagnose problems. I created a custom iOS app that allows me to publish metrics to the same backend as Grafana, giving me Grafana dashboards for my personal health.
Tools:Custom iOS app, Grafana, Graphite
RyanODonnell_PagesReadPerMonthName: Ryan O’Donnell
Description: This semi-logarithmic graph is called the Standard Celeration Chart (SCC). It’s beauty is that anything a human does can be placed on this chart (i.e., standardized display). This also allows for cool metrics to be developed that lend well to predictability. I charted the number of pages that I read for my field of study, Behavior Analysis. I wrote a blog post on the display to speak some to the reading requirements suggested by professionals in the field. There were many variables that led to variations in reading rate, but the point of this work was to try and establish a steady reading repertoire. A recent probe in May of 2015 was at 2800 pages read. Essentially, I learned how to incorporate reading behavior analytic material almost daily in my life, which indirectly aids in the effectiveness I have as a practitioner and supervisor.
Tools: Standard Celeration Chart and paper-based data collection system (pages read each day on a sheet of paper).

 

Graph4_red_black Name: Francois-Joseph Lapointe
Description: This *Microbial Selfie* depicts the gene similarity network among various families of bacteria sampled from my gut microbiome (red) and oral microbiome (black). Two bacteria are connected in the network when their gene sequences are more similar than a fixed threshold (80%). The different clusters thus identify bacterial families restricted to a single body site (red or black) versus those inhabiting multiple body sites (red and black).
Tools: In order to generate this data visualization, samples of my oral and gut microbiome have been sequenced on a MiSeq platform by means of 16S rRNA targeted amplicon sequencing, and the resulting data have been analyzed using QIIME, an open-source bioinformatics pipeline for performing microbiome analysis. The gene similarity network was produced with the open graph viz platform Gephi, using the Fruchterman–Reingold algorithm.

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

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2015 QS Visualization Gallery: Part 2

We’re back again with another round of visualizations from our QS15 Conference and Expo attendees. In today’s batch you’ll see a variety of representations of different tracking projects, from tracking biometrics while watching a movie to running distance over nearly 13 years. Enjoy!

interstellar-hr-hrv-gsr-1024x635 Name: Bob Troia
Description: I tracked my heart rate, HRV, and galvanic skin response while watching the movie Interstellar (in IMAX!), then plotted the data to understand how my body reacted during the 3+ hour movie. (Check Bob’s blog post about this data here!)
Tools: Polar H7 chest strap, SweeBeat Life app (iPhone), Basis B1 band, Excel.

 

Sleep for a week Name: Tahl Milburn
Description: This shows sleep over a week. The overall height of the bar is the time in bed. The part above the baseline is actual sleep whereas the part below 0 is restless sleep or awakening during the night. The line above the bars is the goal number of hours. The bar itself is green is all okay, turns yellow if overall duration is short or awakened too much. Red is even worse.
Tools: Google Charts with data from Fitbit.

 

LifeGauge Name: Tahl Milburn
Description: This is a very simple but powerful chart. T his is a “Life Gauge” which show how much of my statistical life has already been used. The ultimate age is based on the consensus estimate from several sources. Note the yellow and red markings indicating that one might be running out of life soon.
Tools: Google Charts for the graph itself. Several sources for computing the ultimate age.

 

BigGraph Name: Julie Price
Description: My running miles per week plus marathons since 2002.
Tools: Tracked running miles using various methods and recorded both on paper and, in the past few years, on a Google sheet. Summarized & graphed in Excel before manually adding in marathons.

 

IMG_8244 Name: Allan Caeg
Description: ”How much did you win today?” is one of the most important questions I ask myself every day. This pre-sleep question constantly gets me to reflect on what I did with my free will, inspiring me to ensure that I’d make the most out of every day.
Tools: Reporter

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

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2015 QS Visualization Gallery: Part 1

In 2013, just prior to our our Quantified Self Global Conference, we asked conference attendees to send us examples of their own personal data visualizations that they found especially meaningful. We were blown away by what everyone shared with us. From visualizations of blood glucose readings to GPS traces and plots of time tracking and productivity, the range of visualizations was astounding (you can view some of those visualization by searching the blog for the QS Gallery tag).

This year, we sent out the request once again to attendees of our QS15 Conference and Expo. Once again, our inbox immediately started to fill up with images, graphs, and visualizations describing the tracking experiences of our amazing community. Today, we’re excited to start sharing those visualizations with you here.

Beau Name: Beau Gunderson
Description: A homemade polysomnogram with a Zephyr Bioharness as the only data
source.
Tools: IPython, matplotlib, pandas, seaborn, numpy.

 

Seasonal compliance Name: Shannon Conners
Description: This graph shows what initially looks like an interesting trend in my activity data. I seem to be less active during the summer months, but when I pair my activity and wear time for the BodyMedia FIT armband I used to generate the data, the real reason for the drop becomes clear. I’m wearing the armband less in the summer months to avoid upper arm strap tan! I know my own device usage patterns, so when I graphed the two measures together, it was immediately clear to me what was going on. To me, this is a simple example that illustrates one of the big challenges of looking at activity monitor data in the absence of data about device usage. Usage patterns can and do change over time and the reasons for these changes may not be as obvious as the change of the seasons. For example, something as simple as breaking the clip-on case you use to carry the phone that counts your steps could greatly impact how often you carry it, and therefore the quality of the data you collect. Some monitors don’t even record a usage metric with which to compare activity data. I like this graph as a reminder that interesting patterns may in fact be data collection or data quality issues in disguise.
Tools: BodyMedia FIT Core BW, JMP

 

HeadsUp Name:: David Korsunsky
Description: Mashing data from my favorite wearables, my medical records as well as data I track manually into a custom dashboard.
Tools: Heads Up Health is software that can enable anyone to create their own custom configurations.

 

4fcfb36b86f2241013000002_graph Name:: Daniel Reeves
Description: Number of (read) messages in my inbox over time.
Tools: Beeminder’s GmailZero.com

 

QSHRVSeasonalTrend Name:: Jo Beth Dow
Description: Trend analysis of my HRV over a 2.5 year period. Displays a stunning seasonal trend.
Tools: iPhone running SweetBeatLife app to measure clinical grade HRV on a daily basis.

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

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Visualizing Our Quantified Self

At our 2013 Quantified Self Global Conference we were excited to share a variety of beautiful and insightful data visualizations from our community. In the months leading up to the conference we asked attendees to send in their own personal data visualizations along with a short description. In our 6 years of hosting Quantified Self meetups and events, as well as running this website, our forum, and social channels, we’ve seen the power of data visualization as a story telling medium. We exist in part to help people tell their stories – about the data they collect, the changes they create, and the insights and new knowledge they’re excited to share.

Today we’re sharing a few of our favorite visualizations from past conferences. The images and descriptions below represent a wide a variety of tracking experiences and techniques, and we hope to showcase eve more unique personal data projects at our upcoming QS15 Conference & Exposition.

Tracking Sleep by Anita Lillie

This is concatenation of screenshots from my sleep app. Most sleep apps don’t let you zoom out like this and still see daily/nightly detail, so I just made it myself. I like that it shows how almost-consistent I am with my sleep, and made me ask new questions about the “shape” of a night of sleep for me.



2.5 Years of My Weight by Mette Dyhrberg

I gained a lot of insights from this heat map. The most obvious weight gain was no surprise — that’s when I periodically don’t track. In any case, the big picture patterns are easily identified with a heat map. Realized looking at this heat map that the point of no return was mid-April 2012 — my data shows that was when I switched protein shakes with an egg based breakfast. I have since experimented and seen that protein shake in the morning seems to keep my blood sugar more stable and as a result my weight under control!



One Month of Blood Sugar by Doug Kanter

This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle).



Tracking Productivity by Nick Winter

My percentile feedback graph of my development productivity helps my motivation.



Six Months of My Life by David El Achkar

This is my life during the past six months. Each square = 15 minutes. Each column = 1 day. This picture represents 138 days or 3,000+ activities.



My Thesis Self Portrait by Sara M. Watson

Here’s a period of a few days of webcam images taken using Stan James’ LifeSlice during the final days of editing my thesis on Quantified Self uses of personal data. Serious business!



Sleep and Meaningful Work by Robby Macdonell

In an average work day, I don’t consider communication (email, instant message, etc) to be terribly meaningful work. I’d much rather be working on building software. Getting more sleep the night before increases the amount of meaningful work I’m likely to do in a day.



70 Days of Pulse by Laurie Frick

Pulse rate over 24 hours for 70 days from my Basis watch. Grey=null, blues=85

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QS Gallery: John Caddell

Today’s QS Gallery image comes to use from John Caddell who states,

This is a simple graph showing the trend in the ratio of work accomplishments to setbacks, month over month. According to the book “The Progress Principle” by Teresa Amabile and Steven Kramer, to feel as if you’re making overall progress at work, you need a ratio of 2 or 3 to 1 in accomplishments to setbacks. Less than that will result in a feeling of stagnation or even regression. This chart shows that I keep above that level for the most part, with the exception of July, which was a lousy month for sure. This graph helps me make sense of my feelings about work – am I happy? Headed in the right direction? If the trend shows me dipping below 70% for several months in a row, it’s a sign I need to change up what I’m doing.

This was put together with a web tool that I worked on with my friend Dave Kaylor called 3Minute Journal. It captures a daily text journal entry (answering “what happened today that you’ll remember most?”) and several questions about the entry. One of the questions classifies the entry – two responses are “accomplishment” and “setback” – and these are used for the visualization.

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QS Gallery: David El Achkar

We thought it would be nice to post David’s 138 days of activities visualization here. Make sure to watch his talk from the 2013 QS Global Conference to learn how he created this and what he’s learned from tracking his time.

This is my life during the past six months. Each square = 15 minutes. Each column = 1 day. This picture represents 138 days or 3,000+ activities.
- David El Achkar

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QS Gallery: Nick Winter

Nick Winter is a tracker, self-experimenter, and builder of popular tools (like Quantified Mind). Nick sent us this amazing visualization of his percentile feedback system he uses to keep track of his work efficiency.

My percentile feedback graph of my development productivity helps my motivation
-Nick Winter

Continue reading

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QS Gallery: Doug Kanter

Doug Kanter shared this beautiful and unique visualization of his blood glucose with us. Be sure to take a peak at his other great visualizations and his wonderful talk at the 2013 Quantified Self Global Conference.

This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle).
-Doug Kanter

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QS Gallery: Eric Jain

Today’s gallery image comes to us from Eric Jain. Eric is the creator of Zenobase a neat data aggregation and tracking system. He’s also been a great contributor to our community at meetups in Seattle, our conferences, and on the forum.

This map shows my outdoor trips in the Pacific Northwest since 2008. Red is driving, yellow is hiking or paddling. The map doesn’t just help me remember past trips, but also helps me decide what areas to explore next. The tracklogs were recorded with a Garmin GPS device, processed with a simple script and uploaded to Google Fusion Tables with additional meta data stored for each trip in my Zenobase account.

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