Masterpiece is a data visualization and analysis tool that identifies patterns in data of a user's habits.

Inspired by the idea of a New Year's Resolution, Masterpiece considers data within the window of one year, and frames it within the cycle of a week, allowing users to understand their habits in relation to time at large.


Explore a variety of filters, statistics, graphs, and methods of anaylsis. I hope you're horrified by what you discover!



Or, you can upload your own file. Your data will not be stored.
     








Busiest Ranges Longest Interstices
measures density identifies gaps
This algorithm identifies range(s) of the chosen length that contain the highest frequency of data relative to all other equally-sized ranges. Ties are unbroken.




 This algorithm identifies range(s) that contain no data and have a length that is above the chosen threshold percentile.
           
Masterpiece is also an experiment in recognizing the bias that is implicitly packaged together with self-reported data. This bias is created in a self-fulfilling prophecy that is inextricable from manual data entry: as time progresses and data is recorded, an individual becomes accustomed to, self-aware of, and eventually hyper-aware of the habit they are tracking. And what is the relationship between my simultaneous creation of the tool, and the data that feeds it?

The effect is almost Pavlovian as the feeling of performing a habit becomes a necessary precursor to the feeling of recording that habit as data. By extension, it is not unreasonable to think that the order of events might flip: could an individual perform a habit for the pleasure of recording it? Might an individual avoid performing a habit in order to modify the data to their liking? Perhaps it is inevitable that as we become closer with our data, we become farther from ourselves.

Is data any more honest, then, when it is automated? Automation can be inaccurate, but is it any worse than the effects of our human biases? One year into this experiment, I find myself stuck with (what I hope is a false) binary: imperfect (and human-designed) automation, or increasingly biased self-reported data. There may not be a Goldilocks option, and I'm beginning to think that it may be impossible to collect unbiased data about the self when the very act of recording data is a form of passive data analysis.

Jack Adam, Dec. 2022
And on GitHub...
Upload a .csv file with headers:

Month,Day,Date,Hour,Minute
View sample data file