Tag Archives: music
In this fascinating talk Rocio Chongtay shares her novel and thoughtfully designed experiments in using music to adjust her concentration and relaxation depending on what she’s doing. Using a consumer EEG device from Neurosky, Rocio tried different types of music while tracking the relaxation and concentration dimensions identified by the Neurosky algorithm. She had experience experimenting with Neurosky in her lab, and then turned these techniques on understanding something about her own mind.
A few notes up top here. First, if you haven’t yet checked it out please give our new QS Radio podcast a listen. We’d love to hear what you think!
Second, our QS15 Conference & Exposition is fast approaching. It’s going to be a wonderful and jam-packed three days of talks, sessions, and amazing demos. Our Early Bird tickets are almost gone. Register before Monday (May 11th) to get $200 off the regular price!
Now, on to the links!
Data (v.) by Jer Thorp. So many people in my network were sharing this over the last few days I had to give it a read, and I’m happy I did. Jer Thorp makes a succinct argument for turning the word “data” from a amorphous blob of a noun into a verb.
By embracing the new verbal form of data, we might better understand its potential for action, and in turn move beyond our own prescribed role as the objects in data sentences.
How Not to Drown in Numbers by Alex Peysakhovich and Seth Stephens-Davidowitz. In this great article, two data scientists make the case for “small data” – the surveys and rich contextual information from open-ended questions.
We are optimists about the potential of data to improve human lives. But the world is incredibly complicated. No one data set, no matter how big, is going to tell us exactly what we need. The new mountains of blunt data sets make human creativity, judgment, intuition and expertise more valuable, not less.
Data, Data, Everywhere, but Who Gets to Interpret It? by Dawn Nafus. We’ve been collaborating with Dawn and her team at Intel for quite a while, and we’ve learned a lot. Reading this wonderful piece lead to even more learning. Dawn uses this article to describe not only the community of individuals who track, but also why, and what happens when it comes time to interpret the data. (You can explore DataSense, the tool Dawn and her team have been working on, here: makesenseofdata.com)
Applying Design Thinking to Protect Research Subjects by Lori Melichar. Lori is a director at the Robert Wood Johnson Foundation and recently did some work related to how institutional review boards (IRBs) function. For those who don’t know, IRBs are the groups/committee that evaluate the benefits and harms of human subjects research. Their process hasn’t changed much in the few decades, but the face of research has. In this short post Lori describes the ideas that came from thinking about how we might re-design the current system.
ResearchKit and the Changing Face of Human Subjects Protections by Avery Avrakotos. As mentioned above, research is changing, and one of the big changes we’re currently seeing is the use of mobile systems like Apple’s ResearchKit. It’s not all sunshine and roses though, the popularity and excitement that goes along with these new methods also means we have to think hard about we protect those who choose to participate.
I measured my brain waves and task performance on caffeine- here’s what I found by John Fawkes. John was interested in how much caffeine he should be ingesting to help with his mental and physical performance. In this post he details some of what did, how he tested himself, and what he learned about how caffeine, and how much of it, affects different aspects of his life.
The Quantified Self & Diabetes by Tom Higham. Tom was diagnosed with diabetes in the late 80s. In this short post he details some of the different apps and tools he uses to “get my HbA1c down to the best levels it’s ever been.”
2014: A Year in New Music by Eric Boam. I had the pleasure of meeting Eric recently in Austin and was blown away by his ongoing music tracking project. I’m excited to see this new report and learn a bit more about what he’s discovered.
Apple Watch Heart Rate Comparison by Brad Larson. Brad used a simple script to export the heart rate values from his Apple Watch and compare it to two different heart rate measurement devices. Above is a comparison with the Mio Alpha, and he also compared is to a more traditional chest strap and found the readings to be “nearly identical.”
From the Forum
This week on QuantifiedSelf.com
The validity of consumer-level, activity monitors in healthy adults worn in free- living conditions: a cross-sectional study by Ty Ferguson, Alex Rowland, Tim Olds, and Carol Maher. A very interesting research study examining the accuracy of different consumer activity trackers when compared to “research-grade devices.” Free living only lasted a few days, but it’s a great start to what I hope to see more of in the research – actual use out in the wild.
The Healing Power of Your Own Medical Records by Steve Lohr. Steven Keating has a brain tumor. He also has over 70GB of his medical data, much of which is open and available for anyone to peruse. Is he showing us our future? One can hope.
Mr. Keating has no doubts. “Data can heal,” he said. “There is a huge healing power to patients understanding and seeing the effects of treatments and medications.”
Why the DIY part of OpenAPS is important by Dana Lewis. Always great to read Dana’s thoughts on the ever evolving ecosystem of data and data-systems for people living with diabetes.
Why I Don’t Worry About a Super AI by Kevin Kelly. I, for one, am super excited for advancements in artificial intelligence. There are some that aren’t that excited. In this short post our QS co-founder, Kevin Kelly, lays out four reasons why he, and maybe why all of us, shouldn’t be fearful of AI now or into the future.
Responding to Mark Cuban: More is not always better by Aaron Carroll. Earlier this week Mark Cuban started a bit of an kerfuffle by tweeting out, “1) If you can afford to have your blood tested for everything available, do it quarterly so you have a baseline of your own personal health.” What followed, and is still ongoing, is a great discussion about the usefulness of longitudinal medical testing. I’m not sure I agree with the argument made here in this piece, but interesting nonetheless.
My Quantified Email Self Experiment: A failure by Paul Ford. Paul takes a look at his over 450,000 email messages dating back 18 years. He find out a lot, but states that he doesn’t learn anything. I disagree, but then again, I’m not Paul. Still fascinating regardless of the outcome.
Filling up your productivity graph by Belle Beth Cooper. Want to understand your productivity, but not sure where to start? This is a great post by Belle about how she uses Exist and RescueTime to track and understand her productive time.
2014: An Interactive Year In New Music by Eric Boam. We’ve featured some of Eric’s visualization work here before, but this one just blew me away. So interesting to see visualization of personal data, in this case music listening information, turned into something touchable and engaging.
“Women and Children First” by Alice Corona. A fascinating deep data dive into the Titanic disaster. Was the common refrain, “Women and children first!” followed? Read on to find out.
HHS Expands Its Approach to Making Research Results Freely Available For the Public
European Food Safety Authority (EFSA) Grants Public Access to Data through Scientific “Data Warehouse”
FDA ‘Taking a Very Light Touch’ on Regulating the Apple Watch
Selling your right of privacy at $5 a pop
From the Forum
In 2009 Tim Ngwena switched on Last.fm and he’s been running in across all his devices ever since. Earlier this year he decided to take a deep dive into his listening data to see what he could learn.
I realized that I was listening to the same old thing and I began to think about changing what I was listening to. But how can I change? Where can I start? I also wanted to learn something about my music, what I was listening to and who was behind the sounds. I decided to focus on music because it was doable.
In this talk, presented at the London QS meetup group, Tim explains how he was able to make sense of almost five years of data and learn more about himself and his listening habits.
What Did Tim Do?
Tim explored his music data along side additional information such as location data from Moves to learn about his musical tastes, listening habits, and explore new visualization and data analysis techniques.
How Did He Do It?
Tim exported his data, used the Last.fm API and some data cleaning and organizational tools to create a simplified and extensive database of his music listening history and associated data. He then visualized that data using Tableau.
What Did He Learn?
Tim learned a lot about himself and what the music he listens to says about him. He describes a few of the most interesting below,
Basically 80% of my listening comes form 10% of the artists that I have in my library.
I’ve listened to Erykah Badu for over a week (7.2 days). It led me to ask what is she saying to me?
Monday is my jam time. I’m listening from the morning into the evening.
I listen to music mostly when I’m walking.
Tim also learned a lot through the process of designing and creating his data visualization. The visualization, which you can explore here, made him think about being able to see the big picture when he has so much linked data.
I think context is important and you need to see all that information in one place and the tools I’m using allows me to do this.
Douglas Mason didn’t know who the Beatles were until he went to grad school. As a classically trained musician, he was blown away when he saw their unique chord choices. He started to investigate why the Beatles’ music sounded so good. Douglas created a shorthand musical notation to represent songs as strings and analyze things like melody, time signatures, chord changes, and lyrics. He describes his project and what he has learned in the video below. (Filmed by the Boston QS Show&Tell meetup group.)
Remko Siemerink tells his personal story of health insights through accidental lifelogging. He has bipolar disorder, and has been using last.fm over the past 7 years to track his music listening and compare it with his friends’ music patterns. He talks about insights he has gained using various tools that make use of last.fm’s API. For example, Remko discovered a pattern of listening intensely when he’s feeling good, and not listening to music when he is feeling depressed, usually in the summer. He also suggests it would be great to have a similar service for groceries, so you could correlate your mood with foods you eat. Watch his engaging story below. (Filmed at Amsterdam QS Show&Tell #3.)