Explore the Quirky World of Self-tracking Experimenters

We have the ability today to collect so much data on ourselves. With this data we might be able to discover new things about ourselves and to do something with our discoveries. But this explosion of opportunity has difficulties of its own. How do we manage all of these sensors and data? What will we track? And what should we do with anything we find?

From data to information to connection

Currently we track the most basic of events: steps, calories, restlessness during sleep, heart-rate maybe. We collect this data and try to piece together a picture of our health or self or life. But even this simple data can start to become overwhelming.

Finding meaning in data is difficult. The more types of data available, the more ways in which it can be correlated. Just collecting data haphazardly and looking for patterns leads to a swamp of statistics. Finding what is relevant after the fact is next to impossible.

It is better to collect data with a clear goal in mind. Ask a question and choose the data you will need to collect. With so many new sensors we have exponentially more combinations of data to explore. How do we know what data to collect? What questions should we ask? What do we really want to know?

The answers to these questions are not clear - they are different for every person. But perhaps there is another way. We can turn over the generation and collection of these questions to the crowd...

What makes an Experimenter an Experimenter?

Rather than tracking themselves in hopes of creating a better life or higher performance, or motivating themselves by setting and meeting arbitrary goals, Experimenters are motivated to self-track because they are curious. They may want to answer some obscure questions about themselves or they may simply be curious to see if such questions are answerable. They believe in a better world through technology and want to be the first ones there. They don't want to miss an opportunity to take advantage of technology to make themselves better.

Experimenters are optimists

Experimenters believe that someday soon there will be a device or service that will help them make sense out of their data. Their data will be valuable then, at some future date, to answer an important, unasked question. For this reason experimenters hoard data. Collecting it for that later, as yet unknown, use.

Experimenters are self-motivated

When a typical Experimenter has a question, he is motivated to research it. When he has a question about himself, he will look to discover an answer to it. Sometimes these questions can be quite creative.

Experimenters are technically savvy

Experimenters are comfortable in the use of the latest technologies. They often have knowledge or experience in statistics, computer coding, or electronics. They are on the leading edge of the adoption curve. They are comfortable with messy data and interfaces, and are willing to work to create their own solutions.

Experimenters are curious

Experimenters are a quirky crew. They seek to measure anything. They don't always have a plan for the data. They just want to know. This curiosity shows some of the deeper motivations for their habits. The Experimenter is less concerned with whether he gains useful information about himself than with learning in general. Even if his experiment fails he wins, as long as he has learned something in the process.

Listen to Experimenters

"It'd be fantastic to bring in correlations of daily barometric pressure, phases of the moon. Who knows what's going to play into this, right?"

- Brian

"I heard about the quantified self movement and I thought this is made for me ... I thought it would be this little fringe group of unique individuals like myself."

- Scott

"I designed these eight-minute cognitive tests that I would take at the exact same time every day."

- Elizabeth

"I'm still starting out ... Coming to this [QS meetup] gives me more motivation."

- Ilan

"I had a bunch of different pictures on the screen, they were all of sad faces, and one was a happy face, and I would try to rapidly click the happy face."

- Adam

Three Types of Activities

Through interviews and research, we identified three ways in which a self-tracker experiments:

The "Recipe"

Experimenters share “recipes” of their experiments within the community.


Creating a way to answer a question and then gathering and combining all of the pieces to make it possible to answer that question repeatedly. It involves selecting data sources, and deciding output method, among other things. It ends with the presentation of your findings.


Reduce learning time

Easy to iterate

Reduce waste of data

Easily find relevant methodology


Either looking for recipes that can be modified for your needs, finding ways to mix and match your current data sets, or simply looking for a recipe to use for yourself.


Find correlations and support

Articulate needs through questions

Make use of their data

Expand tracking scope


Customizing an existing recipe to make something custom to your needs. It begins with searching for and possibly using a recipe, and ends with building your own custom solution.


Ability and knowledge to design reasonable experiments

Build on someone else’s experiment

Help to initiate experiments

Quantified Self Meetup

A model of our service can be seen in the Quantified Self meetup. A QS meetup is a gathering of trail-blazing self-trackers who make their own tracking experiments. At a meetup several people present their current activities and get feedback from the community. Everyone learns about new methods and gets ideas for data that they might collect.

How might we support a community of Experimenters in creating and sharing their recipes?

Tinker.it is an online self-tracking data aggregator. It provides a platform for people to create services for self-tracking sensors. In addition to aggregating, correlating, and visualizing a user's tracked data, Tinker.it provides a community of like-minded people with whom you can share and explore new tracking possibilities.


Find useful recipes or new knowledge on how to make them.


Make the service that you wish you had for your self-tracking sensors.


Share knowledge by answering questions or creating presentations about your recipes.


In the Lab, you are provided with tools for creating your own recipes. Choose devices or data, collect and compare data, and create appropriate information output -- all in the service of answering your question. Digging deeper, you can code visualizations, run statistical analytics, or connect your recipes to the internet of things.

How does it work?


All of your activities are recorded on a personal timeline. This includes: recipes used, questions and comments to the community, and your own recipes. Your progression made in experiments is automatically recorded and can be used to create a more sharable presentation. Publish anything in your timeline to your profile.

How does it work?

Dave's story
Based on a true story

Dave is a self-tracker, who has been tracking and storing his running data for five years. He is also tracking other data like sleep patterns.

Dave has found that he has been kicking his blankets off at night.

As a self-tracker, he is wondering what he could do about it. He would like to know "Why do I kick-off my blankets?” and “Can I find a way to show me why?"

He visits Tinker.it to see if there are recipes and ideas. He is eager to have a try at his “blanket puzzle.”

He searches for an already crowd-sourced solution. He filters them by rank, devices, and time. Finally he finds one which was developed by Nick one year ago.

On Nick's profile page, he reads about Nick's reasons for creating the Keep my Blanket recipe, and his methods. Dave reads about Nick's other recipes and his general interests.

He connects his current sleep pattern tracking device to this recipe. For the body temperature, since Nick wrote his experiment several years ago, could there be a better sensor to use today?

He posts this question.

The next day several people have replied, including Nick himself! Nick suggests two options - buy an updated version of the first sensor he used, or buy a new device that tracks both sleep and body temperature together.

He finds a correlation between his room temperature and his body temperature.

That night he adjusts his room temperature.

But now he feels cold in the early morning.

So he decides to add a real-time adapting function, linking his body sensor directly to his thermostat.

He starts by trying a different combinations. Then he looks into multiple techniques. He adjusts his new recipe several times.

Soon his home thermostat adjusts to his body temperature and sleep patterns and Dave is getting the best sleep of his life.

He reviews his timeline, picks out some key moments to build a presentation on the next Quantified Self meetup. Other users who have the similar issues now can build on the result of his work: his new recipe - Peaceful Sleep.


Kate has a similar issue. She kicks her blankets off at night. She finds Dave’s Peaceful Sleep recipe and and would like to improve it. She too is planning other applications as she gains experience.

The Tinker.it Experience

Value Proposition

The Seed

The branching model is an important idea. It provides a structure for sharing between different using types.

The Extension

One recipe can be extended exponentially. Generating shared knowledge and providing for many.

Experimenters are the creative engine of the future self-tracking ecosystem

Tinker.it helps Experimenters by…

Tinker.it helps the self-trackers fulfil their curiosity by providing a flexible, easy to use environment for setting up and iterating on experiments. Our service ties deeply into the sharing mechanism of community such as gathering information, searching for innovative ideas, and presenting to the public.

Why is it important?

We created this platform for experimenters to play with their quirky ideas. What they develop and build will inform the future direction of the self-tracking device and service industry. And thus there are possibilities to benefit more groups beyond this small group, like consumer products and service, healthcare, scientific research, and so on.


For the individual, Tinker.it reduces the work involved by creating a place to build on other’s work and knowledge.


For industry, Tinker.it provides a place where future thinkers congregate, showing what the leading edge consumers want and generating ideas for new things.


Tinker.it a community of people continuously experimenting on themselves and refining their experiments. The data generated here will be valuable to researchers. They can see trends in the aggregated data, revealing novel methods and successful results.

Tinker.it attracts Experimenters - with the kind of sharing community that they love. It is a place to better store and communicate knowledge, with better tools for creating their experiments. As a platform that grows with the contributions of its users, Tinker.it also provides value to everyone.