Welcome to the IdeaMatt blog!

My rebooted blog on tech, creative ideas, digital citizenship, and life as an experiment.


"Tell me, Betty. Has your husband always been..."

[Reet Pappin] Tell me, Betty. Has your husband always been this, well, ... bitter?

[Betty Armstrong] No, he wasn't at all. I think it's something new he's trying.

This is from the absurd The Lost Skeleton Returns Again, one of the gems I found on the new Amazon prime instant video. (Learning about this new benefit was a delight, since I've been a Prime subscriber for years to feed by reading habit.) I perked up when I heard Betty's response, which is a lovely Think, Try, Learn perspective. Not an experiment I personally want to try, though.

I love the work of this indie movie team, though I think the first movie, The Lost Skeleton of Cadavra (official site here), was better. Here are a few gems on YouTube from this same team (I enjoy how they play with language):

I've embedded them below. You can find more at moontaurus's YouTube channel.

I'm curious

If this is your sort of humor, what else do you like? I need a lift!


How to get your Edison updates to go to Twitter

rss icon

An Edison user asked about getting his experiments' observations to route to his Twitter account. Here's how I did it for the @thinktrylearn Twitter account. Basically you use Edison's RSS feature, which is available for both an individual experiment or for all of your experiments. (The @thinktrylearn one uses the third RSS feature, all experiments.) I used twitterfeed.com, though there are other similar tools.

To find the various RSS feeds on Edison:

There you have it! Anyone want to give it a try?

(Note: Currently the feed for group experiments is not showing. The work-around for the motivated user is to use the first type of pattern. For example, the RSS feed for the group experiment corresponding to my above peer experiment - bottle good feelings (Group) - is http://edison.thinktrylearn.com/experiments/show/367.rss.)


How to Bottle Up Good Feelings (Group Experiment)

Glass Vase 01

When I told a friend about some recent great news (a LinkedIn invitation from a self-experimentation leader), she suggested I "bottle the feeling." This struck a chord with me because I tend to focus on the negative, i.e., when things don't feel like they are going well. So I decided to do an experiment where I do just that - capture when I feel especially good because of an event. My "bottle" in this case is my big-arse text file. All I've been doing is:

  1. Notice when something good happens to me,
  2. Write it down in the file, tagging it with BottleTheFeeling, and
  3. Review the file when I'm feeling down.

So far I've been doing the first two, but I haven't implemented a reviewing practice. Also, I had not set up a proper Edison experiment, so I thought I'd make a group experiment and ask if you'd like to join me. You can learn more about it at bottle good feelings (Group Experiment). To join immediately:

Here's the summary, taken from the experiment:

What can we do to save up good things that happen to us when we need them on emotionally rainy days? A gratitude journal is a traditional approach, but in this experiment we try something simpler and more immediate - "bottling" the feeling for later.

This is similar to the common gratitude journal idea [1], but feels to me a little lighter to implement. It's also similar to my hack, Use Gmail's "star" to highlight your good news, but more specific.

Why don't you join me? It'll be fun!


  • [1] Here are two useful links. First, How to Keep a Gratitude Journal | eHow.com. Second, from What is a gratitude journal?:
    [In your journal] you will write 5 things that you are grateful for that day. You can do this right before bed or in the evening sometime. These don't have to be big earth shattering revelations, as most things you are grateful for are not. These entries are what matter to you and what you are grateful for. This is a very personal venture. The only 2 rules with the gratitude journal are:
    1. Everything in it must be positive and;
    2. You can only list something you're grateful for once - no repeating entries.

Announcing Edison 2, with Group Experiments and Quantitative Data!


I am absolutely thilled to let you know that Edison 2 is live. It adds two major features: Group experiments and quantitative data. A little about each follows below. I've also added a list view for experiments, which helps to get a sense of the different kinds of activity going on, especially what new group experiments have been created, and which ones you might want to join.

I threw together some documentation on the thinktrylearn.com wiki at EdisonHelp, with screencasts hopefully coming soon. As an experiment, I'm playing with using Edison itself to document how to use it, in the form of an Official Group Experiment. (Note: If you're interested in volunteering, I have plenty of fun ways you can help with Edison, including documentation and screencasts.)


For the development philosophy, I worked hard to adopt the edict of "As simple as possible, but no simpler"[1], part of the lean startup ideas I'm learning about. Here I kept the functionality and designs of the two features to a minimum, including converging on a super simple data model. Like any good experiment, the results will tell me how I did and where to go next.

A huge thanks to my brilliant back end programmer Andy O'Shea, and to and my talented designer and front end coder Zinj Guo. They are both excellent, and made this ambitious release go smoothly.

In the next week or two I'll be announcing a contest with prizes for various categories, such as experiment with the most participants, most data, or experiment with the most surprising results. Stay tuned, and Happy Experimenting!

Group Experiments


There are now basically two experiment types - Individual Experiments and Group Experiments. They are created by the "Create an Experiment" button that's at the top of the right sidebar on most pages, which now pops up a choice of which type you want to create. Individual experiments haven't changed in concept, but they now - like any experiment - have the option of defining measurements that are used for capturing data about your experiment.

Group experiments, on the other hand, are community experiments that multiple people participate in. The way they work is straightforward. You create a group experiment (describing its details and defining its measurements), which people can join by clicking the big "Join" button at the top right of the group experiment's page. Participants who join get an personal instance of the group experiment (called a "peer" experiment) that works like an individual one, but which is linked to the group. In this way you get to make observations and record data on your own experiment page, but you can also interact with the rest of the group by making comments on the group experiment's page. In other words, it's the community page for the group experiment itself.

You can read more in the docs. Again, comments are always welcome.

Quantitative Data


The other big feature is the ability to define what measurements you'd like to take in your experiment. The way you do this is to edit the experiment's details and use the Measurements widget at the bottom to specify each thing you want to track. The data model I decided on is simple:

From DesigningExperiments: Each measurement has four components:

  • Name: A few words at most
  • Type: Either Number or List
  • Units: What units the measurement is in (optional)
  • Description: What the measurement is, what it's used for, how and when the measurement is taken, etc. (optional)

This should cover 90% of all measurement tasks, with some work-arounds for the others. (Again, the results of the 2.0 release will tell me a lot.) Once you've defined your measurements you can enter data anywhere you could enter a text observation before. There's also a dedicated Data page for browsing, editing, and deleting.


The Big Bucket Personal Informatics Data Model

Bucket-headed dog

Seth Robert's post on Personal Science (especially about "data exhaust" [1]) got me thinking about big data and the implications for the self-tracking work we do. What evidence is there that big data will infiltrate self-experimenting? Under what conditions will self-tracking move from "small data", or "data poor" (a few hundred or a few thousand data points) to "big data" or "data rich" (terminology from The Coming Data Deluge)? Let me share some thoughts and get yours.

First, what does "big data" mean [2]? From Wikipedia:

Big data are datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing.

This identifies an important problem. While it is natural to throw all our personal data into one big database, there are costs associated with doing so. I don't mean those associated with capture (clearly we will solve the technical and cultural challenges), but the costs in sensemaking - turning data into actionable wisdom. Let's put the problem into context and assume the future for personal science looks something like this (help me here):

  1. Many of our personal artifacts will be instrumented to know something about us (find many body-oriented ones in Walter's Health Internet of Things), but the sky's the limit. (For some great examples of work data we might capture, see Gary's comment on my post The Quantified Worker.) The idea is that these things will be smart enough to answer questions needed for our experiments, like "How much water did I drink?", "How active was I today?", or "Did I raise my voice this week?"
  2. These artifacts will seamlessly transmit data to a central place that each individual owns and has complete control over. Also contributing data are medical professionals and any other person or organization that learns something about us. They will be contractually obligated to share it.
  3. This data is augmented by self-tracking tricorders that we may wear, which capture other personal data channels like cognitive states and life events.
  4. From all that data the citizen scientist will periodically reflect and analyze via triggers such as periodic reminders, natural events from experiments (e.g., when a question is answered or an experiment ends), or opportunistic situations such as encountering a problem or having a friend asking how we're doing.
  5. Finally, the experimenter applies the results by integrating them into new mental models or behaviors, and continues this cycle of thinking up experiments, trying them out, and learning from them.

(Note that these steps are non-linear and are happening in parallel.)

Given this flow, I argue that the hard work is in the final two steps - sensemaking and behavior change. Leaving the latter for now (Ian Ayres on Carrots and Sticks addresses that well), how can we do these effectively when we are collecting a lifetime's worth of data? I don't know, but a few things come to mind including using advanced statistical tools, Visual analytics, and possibly the most important, collaboration. After all, successful researchers know that science works best when collaborating with others. In fact, given this possible future, our relationships with professions may move more in this direction.

What do you think?


  • [1] My reply: Exhaust usually means waste that's a byproduct of production. However, in our case data is the means of self-improvement. It's like a catalyst for making a change in ourselves. Plus, unlike exhaust, it has value after its use. While factories may capture waste products for other uses, they don't treat the waste as intrinsically useful. That's a big difference.
  • [2] Two additional resources you might find helpful are Wired's The End of Theory: The Data Deluge Makes the Scientific Method Obsolete and Nature's Special on Big Data.