Tag Archives: contest

Share Your Method of Analysis Without Sharing Your Data

How the author has used sad/loving/joyful emoji on Twitter over time.

This visualization from an example Personal Data Notebook shows how someone used various emoji on Twitter over time.

We are happy to have a guest post from Bastian Greshake Tzovaras, the director of research at the Open Humans project, on a new way to share personal data analysis methods. Read to the end to learn about a data analysis contest happening this month. Bastian can be found online at @gedankenstuecke. -Steven

The Quantified Self community builds its collective knowledge from individuals sharing insights gleaned from their own n-of-1 data. Not only do we learn from these projects, we also get inspired to do the same or similar projects of our own. But it’s easy to get tripped up when trying to do the same analysis on your own data. Is your input data in the same format? Are you running the code on the same operating system? Can you get all the dependencies installed? What if you have never really written code before or executed analysis scripts?

In the realm of academic science these issues are grouped under the label “reproducibility”. A solution to many of these issues are Jupyter Notebooks, which can be used to share code for analyzing data. JupyterHubs make it easy to host these notebooks online and overcome the difficulties that come with different operating systems, software packages, etc. Open Humans, a non-profit foundation that helps people donate their data to research, is using this technology to make the analysis of self-collected data reproducible for other members of the Quantified Self community.

We just released Open Humans’ Personal Data Notebooks. These are run in the browser and give people access to the data that they have stored in Open Humans. Data from Fitbit, Apple Health, Moves, Twitter, and a selection of genetic data providers is currently supported. People can write their personal data analysis in Python, R or Julia right in their web browser and see the results there – without having to worry about installing any local packages on their own computer. If you are proficient in any of these programming languages, it is easy to write your data analysis from scratch. If you are unfamiliar with coding in general – or with Python, R or Julia, in particular – the Personal Data Notebooks offer well-documented example notebooks which can be run without any prior knowledge as no modifications are needed and can serve as a great way to start coding.

Code from an example Personal Data Notebook.

For all notebooks the resulting analysis and visualizations can be shared easily with other users who then plug in their own data. We have made it easy to decouple the data analysis from the underlying data. You can share your data analysis code without having to share your personal data itself. Since data sources inside Open Humans are standardized, someone else’s Fitbit data will work just as well as your own.

There are step-by-step guides to get started with Personal Data Notebooks and example notebooks which can analyze your activity data from Fitbit and Apple Health or perform a  sentiment analysis of your Twitter data.

To celebrate the launch of the Personal Data Notebooks, Open Humans and Quantified Self are running a notebook competition.

To take part, all you have to do is:

Gary Wolf, Steven Jonas, and Azure Grant of Quantified Self will judge and rank the submitted notebooks. The most interesting notebooks will be highlighted and added to the set of existing samples that are preinstalled for each user. The winning notebooks will be featured here, on the Quantified Self blog. If you want to share and discuss your notebook ideas, The Open Humans community on Slack is eager to have you.

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Contests for Data Gurus

I recently came across three contests relevant to the QS community, and wanted to pass them along.

1. data in sight: making the transparent visual
This is a hands-on data visualization competition held June 25th and 26th, 2011, at the Adobe Systems, Inc. offices in San Francisco’s SoMa District. Open to coders, programmers, developers, designers, scientists, members of the media—anyone who believes that data is divine and has ideas for bringing it to life. Data sets will be provided, or bring your own. (Thanks to Indhira Rojas for sending this in!)

2. Health 2.0 Developer Challenge: Washington, DC Code-a-thon
On June 11, 2011, developers, designers and other stakeholders will be given an overview of health care issues, tools and data sets, and asked to creatively design new tools for the health care space. Developers are encouraged to use OpenGov data sets as well as private data sets to create their application. At the end of the day, developers present their application to the group, and the best solution is awarded.

3. CureTogether Health Data Discovery Contest
Over the past 3 years, CureTogether has gathered millions of patient-reported data points on symptoms and treatments for over 500 conditions. But on a larger scale, how well does CureTogether data represent the general population? In this contest, stats-minded people are asked to challenge the dataset and see whether or not it holds up to existing research studies. There are cash prizes, and the deadline for joining the contest is June 29, 2011.

4. sanofi-aventis U.S. Innovation Challenge: Data, Design, Diabetes
Starting July 1, 2011, innovators can submit their best data-inspired and human centered concepts for people living with diabetes. 5 semi-finalists will receive $20,000 and professional mentoring to develop a working prototype. Following a demo day, 2 finalists will be selected to receive an additional $10,000 to test their solution in a real life diabetes community. The final winner will receive $100,000 and a month stay at the RockHealth incubator in San Francisco to turn their prototype in to a scalable solution for people living with diabetes. (Thanks to Steve Dean for sending this in!)

Good luck! If you know of any other QS-related contests, please leave a comment.

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Quantified Self Tool Contest on Cool Tools

Here’s your chance to demonstrate your knowledge of self-tracking hardware and win a free ticket to the QS conference in 2 weeks. Kevin Kelly’s site Cool Tools is running a contest:

In particular, we are looking for the best self-tracking hardware (we will still accept reviews of software but you are going to have to really convince us why it’s better than the copious competition). For inspiration, the following is a list of potential reviews we would be interested in: Ant+ enabled scales or tools, a better calorie counter, a wireless vitals monitor, an improved sleep tracker, an updated energy consumption monitor, productivity tools, etc.

If you don’t have any self-tracking tools to review but are still interested in winning a ticket to the conference send us a review of a tool you think should qualify you and explain why.

As usual, we are looking for the following in a review:

1) a succinct narrative description of what the tool/tip/fix is,
2) how it changed your behavior,
3) why Cool Tools should run it,
4) why it is superior, and
5) why we should believe you.

Submissions will be accepted for one week until Friday, May 20th. Reviews can be submitted through email to editor@cool-tools.org or through this link.

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Personal Data Visualization Contest

givingupcoke_small.gifYesterday was a red letter day in the world of self-quantification, as Nathan Yau declared the winner in his summer-long personal data visualization context.

The winner is Tim Graham, whose data blog is an entertaining record of personal data that shows how much narrative (courage, hope, risk, disappointment) can  be packed into a single graph. (Fortunately, the “coke” at issue in this tragic tale of a failed quit attempt is Coca-Cola, not cocaine.)

Tim tips his hat to the inspiration of Nicholas Felton of Megafone whose amazing annual report about himself is a great artifact from the future being produced right now.

Nathan’s blog Flowing Data is wonderful, and in yesterday’s post he linked to lots of the entries in his context, all of which are entertaining and some of which contain good ideas for aspiring self-quantifiers. Thanks, Nathan!

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