2016 Data Storytelling Studio @ MIT

• Road the bus and the T: data tracked via swipes of my Charlie card
• Google searched: number of article research, my minds own curiosity.
• Podcasts: downloaded podcasts, iTunes for sure tracks how many podcasts I download and whether I’ve listened to them recently (If I haven’t it’ll stop downloading new ones of the same podcast).
• General web surfing: link clicks.
• Facebook: length of data sent through messaging, messages written but not sent, whose profiles I looked at, what content I liked, and what if any links I clicked on.
• Sent emails: using Gmail and MIT’s web based outlook, recorded data on who I sent the message to (to help improve spam filters), likely the content of the message (checking for “trigger” words that indicate problematic content, either spam worthy or you know terrorist watch list worthy).
• Twitter: made tweets, re-tweeted, and liked content.
• What’s app message: messaged friend in Canada.
• Gchat: number of people I chatted with, length of messages
• Google map: location data ( I get lost a lot)
• Netflix: streaming video data on what shows I watch
• Plex: streaming video data on how many “home videos” I watch (I don’t sync my video data to metadata so it’s unclear how much Plex actually knows about the content of what I watch vs the quantity)Spam data (how successful was my spam filter)
• Diet bet app: my weight.
• Grocery shopping: consumer spending data recorded twice in this case, by the supermarket where I made the purchase and by my credit company which also tracks that data.
• Phone call:  location data (based on the cell phone tower that my conversation pinged off of), duration of call, and whether it was domestic or long distance (it was domestic).
• Youtube: which videos I watched, and which ones I watched to completion, also their ads – did I stick it out to the end or did I click on the countdown link. Uses the data to suggest other videos I’ll watch.
• Pandora: which songs I listened to the end, which ones I liked, which songs I skipped, and which ones I asked them to never play for me again.

Basically, every waking moment of my lie is quantified except for what I eat, and my apartment’s thermostat. However, the electric company is tracking the kW of electricity my home is using, while the gas company is tracking how much gas I use.

In chronological order:

I took Saturday off to cook food but still generated a wide variety of data.

• Query, temporal, and text data of my online actions on the Google platform: checked, cleaned out, and sent email via Gmail; watched some videos on YouTube; used Google Search; and recorded this list on a Google doc.
• Address data for grocery delivery by Peapod: attempted to buy groceries online through the Peapod website (before realizing I needed them today, not tomorrow).
• Fare transaction data for the MBTA: took the 1 Bus to and from Shaw’s/Star Market.
• Coupon selection history data for coupons.com: Looked at coupons for good deals while on the bus.
• (Inadvertent) picture data for Snapchat: Was very likely captured in photos when the person in front of me on the bus decided to take Snapchat selfies that included everyone behind her.
• Music history data for Apple Music: Listened to a mix of old and new songs on the bus.
• Credit card financial data: Paid for groceries at Shaw’s/Star Market.
• Time and location access data for my MIT ID: Tapped ID to get back into Burton Conner.
• SMS/iMessage data: Sent texts to friends and family throughout the day.
• Call detail record data: Generated for Verizon when I called my sister in the afternoon.

On my way from Providence, RI (spending the day at HackAtBrown), I logged my data (or, at least as much of it as I could).

Travel

• Location on Public Transit: Tapped MIT ID to use T
• Purchase Information: Used a credit card to buy train tickets.
• Geo-location apps also know my GPS location

Hackathon

• Check In: Signed into the hackathon in the early afternoon
• Internet Traffic: Using my computer at the computer throughout the day tracked by Brown and their ISP
• Progress by teammates: My teammates tracked the progress of my portion of the project
• Music over time: I listened to Apple Music throughout the day while working

Throughout the Day

• Heart rate over time: Through the Apple Watch, that I wear on a daily basis
• Step Count over time: Also through the Apple Watch
• Motion Tracking: The Apple Watch also does motion tracking, and records how often I stand up throughout the day
• Emails & Text Messages over time: My communication logs with people not near me through email and texting on my phone and computer.
• Various Internet services: Youtube, Netflix, and Twitter track my usage, which occur throughout the day