Lawrence Sun’s Data Log

This is a summary of types of data I created and were captured in digital form on 5/1.

At 9 AM I wake up to my phone’s alarm. I briefly check my e-mail before heading downstairs to grab some breakfast. Already I am creating data: by using Google chrome on my phone several entities are tracking my behavior. Google is logging my behavior due to using Chrome, MIT because I am connected to their routers and checking their e-mail servers. We could go further and say MIT’s ISP, DNS servers, etc. are also logging data but at that point they don’t know the data is me, Lawrence Sun, browsing the internet.

At breakfast I swipe my ID at the registrar. This is logged by MIT’s techcash and dining services. While eating, I catch up on various things on my phone. Reddit, Gmail, Quora, and the New York Times are all logging data about my visit.

I leave my dorm for class. Because I am now moving with my phone, Google is tracking my location with my phone’s GPS.

After class, I get a burrito at Anna’s and I pay for it with my credit card. Both Anna’s accounting services and my bank (Bank of America) log this transaction.

I then go to my afternoon class. I open my laptop and start browsing the course website; it is hosted by CSAIL and the course notes are being hosted by NB. Both CSAIL and NB are logging my behavior.

After my classes are over, I return back to my dorm and stay off the grid for a few hours. Dinner time comes around and again I swipe my ID and my meal is logged. After dinner, I work on some work for my classes. I need to read a paper for one of my courses so I visit arXiv to retrieve the paper. After I finish reading the paper, I submit answers to some questions to an MIT PDOS website. Both arXiv and PDOS are logging my activity. After this I visit MIT Stellar to browse the upcoming homework for another one of my classes. Finally, I am left writing this blog post, leaving another data footprint at WordPress in this case.

Siyang Jing’s Data Log

8:00_wake up and check Facebook, WeChat, news on the phone and chatting with my friends on line for a while.

8:30_turn on a Reading book App while doing the exercise. 4

9:00_checking the email while having breakfast

10:00_Checking out the book in the library

12:00_Watch the TV show while having lunch at home

14:00_Use the Harvard App to Check the M2 shuttle to go to MIT

15:00_From a discussion group by using google PPT, google Doc and other group chatting softwares.

18: 00_go to the Gym running while listening to music

19:00_Using an food making App to teach me prepare for the food for tomorrow

20:00_Chatting with my team through wechat and sometimes facetime with them

21:00_Doing homework and searching the materials on line

23:00_make a phone call to my mother or friends.

00:00_Check Facebook, WeChat and News online

Most often used: Wechat, google chrome, office 365

Niki Waghani’s Data Log

9am – Added an event on my Google Calendar. Was surprised when I realized someone else’s travel calendar and schedule was showing up on my phone, just because we had been traveling together earlier.

10am – Looked up all sorts of information on movies about the Oscars on Safari. Google stores my last few searches.

11am – Tapping in to the EECS lounge with my card. They probably keep track of how many people come in and out. It’s possible they could even keep track of who.

12pm – Listened to music on Youtube. It now knows what kinds of songs I like and makes suggestions based off of that.

1pm – Facebook: Tracks where I like to online shop and the times of day I’m most active on the Internet

2pm – Allowed Google Maps to use my location.

6pm – Uber: Tracks where I am and where I’m going.

7pm – Uploaded video for UAT to the class website. Have also uploaded other psets to class websites earlier in the day.

9pm – Netflix knows the last show I was watching and kept track of where it stopped. It also still remembers the names and viewing information of a few friends who shared my account nearly two years ago.

Data Log 2/21: A Day Observing My Data

In this day and age, it is very difficult to go through one’s daily routine in a networked economy without generating digital data in some way. Indeed, you almost have to go out of your way to consciously avoid certain activities, tasks, and behaviors that are routinely tracked and captured in digital form.

The following activity log illustrates both the frequency and breadth of the data that I generated in a single day.

  • 7AM: Wake up. My smartwatch senses that I am moving and provides a summary of my sleep.
  • 7:30AM: Turn on cable TV while eating breakfast, and select and save programs to watch later.
  • 9AM: Go on the internet and read news articles, check and send email, and update my calendar.
  • 11AM: Walk to MIT campus. My smartwatch provides a summary of my walk.
  • 1PM: Listen to music on my smartphone. I receive recommendations based on my listening habits.
  • 2PM: Order grocery delivery online and receive automatic email notification.
  • 2:15PM: Purchase books on Amazon and receive automatic email notification.
  • 2:30PM: Play guitar and record my playing on an iPad app, which sends the information to the Cloud.
  • 6:30PM: Exercise on treadmill, which tracks my workout (along with my smartwatch).
  • 7PM: Use CharlieCard to board the T and travel from Cambridge to Boston.
  • 7:15PM: Visit store and use smartwatch to purchase items.
  • 7:30PM: Visit convenience store and use credit card to purchase items.
  • 8PM Use Uber app on my smartphone to request a pickup to go home.
  • 10:30PM: Change thermostat setting in my apartment.
  • 11PM: Watch TV and update my saved programs.

The resulting activity log is interesting in several regards. First, since it captures only the activity that can be tracked digitally, it can result in an inaccurate portrayal of how one’s time is spent (or more generally, how a system functions or behaves). In my case, for example, I spent more than five hours during the day reading and studying, yet that activity was not captured digitally.

Second, the data that is generated can be categorized in many different ways: location tracking, motion detection, and transactional, for example. Data can also be captured for different purposes: health, entertainment, operational efficiency, convenience, surveillance.

A less obvious attribute of the data log above, however, is the degree of awareness associated with the capturing of data in each activity. Some behaviors (such as requesting an Uber ride) are active and require more consciousness and explicit consent about the data that is being tracked. Other activities (such as walking around in areas that have IP surveillance cameras) are more passive and subconscious with regard to data, and consent is usually implicit. In all cases, however, vast amounts of data are being captured digitally.


Paul’s Data Log – Living a live with Google

Android smartphone, Chrome browser, Google Maps, and Gmail. Regardless you recognize it or not, using an iPhone or Galaxy; Google is near to us more than we are expecting. Even Google is playing a crucial role to make our lives much easier, many people are unsure how much and how often they are using it and which kind of data are provided for. A simple way to check is the Google Dashboard where we can see our data and account activities. I want to illustrate how my typical day looks like with Google services.

9 AM – Managed schedule (Google relation: Google Calendar, Android phone)

10 AM – Ordered food visiting Instacart (Chrome browser)

11 AM – Watched YouTube videos (YouTube, Android phone)

11:30 AM – Visited to check my order status (Chrome)

12:00 PM – Checked email (Gmail, Android phone)

1:00 PM – Visited to learn Finance languages and added the site to the bookmark bar (Chrome)

2:00 PM – Searched the route to a Japanese restaurant in Alston (Google maps, Android phone)

3:00 PM – Made a call to confirm dinner (Contacts, Android phone)

4:00 PM – Ordered battery for smoke alarm replacement (Chrome)

5:00 PM – Read articles (Chrome, Android tablet)

6:00 PM – Went to Japanese restaurant using Uber (Google maps, Android phone)

9:00 PM – Visited Stellar (Chrome)

9:45 PM – Updated apps in my phone (Play Store, Android phone)

10:30 PM – Updated course folders downloading new materials (Chrome, Google Drive)

Willie Zhu Data Log


My iPhone is constantly on, and it’s probably both a) the device I use the most, and b) the device I have on my person the most. It tracks:

  • My sleep—when I sleep (based on when I stop using it)
  • Location—even though I’ve turned as many permissions off as I can, it’s still tracking my location. Good when I need Find My iPhone, but otherwise…
  • My contacts—who I call/text/message, and when
  • App usage—it knows all the time I’ve wasted on Reddit
  • Cell network—every cell tower I connect to
  • Apps—the apps I use definitely track my actions. My most used: Inbox, Spotify, Reddit, Messenger
  • Wifi—my phone is a walking data beacon that spews probe requests, uniquely identifying myself to anyone willing to listen


My Macbook, Windows desktop, Athena workstations, and so on

  • Operating systems—Windows 10 collects all kinds of data without my permission, and macOS probably does too
  • Intel ME—pretty much all of my computers have Intel processors
  • SSH logs—connecting to remote computers leaves logs behind
  • Athena—MIT probably tracks just about everything I do on Athena


  • Browsing history—this goes without saying. I’m pretty unique, according to
  • Security cameras—MIT has cameras everywhere
  • Card taps—Similarly, there’s all kinds of places I need to tap to get in. It goes without saying that MIT records all of them
  • Purchase history—every time I buy something with a credit card or TechCash

Income Inequality Data Visualization


I (re)watched this video on income inequality recently, and found it to be both compelling and informative. Its narrative is clear: that income inequality is an issue is common knowledge, but the magnitude of income inequality is far greater than the public believes.

The video displays three charts: one demonstrating what people want the income distribution in the US to be, one showing what people actually think it is, and one of what it actually is. Personally, I was taken aback at the true magnitude of the income gap, and how staggering the difference between the poorest and wealthiest Americans actually was. The video’s strategy of reducing the US population to 100 people made it a lot more relatable—for many, it’s a lot easier to picture a room of 100 people than to talk about generic percentiles.

However, I feel that the video could do more than merely presenting the information in a provocative manner (which it does well, both visually and psychologically). Instead, the video should end with a call to action, perhaps by providing links to organizations dedicated to fighting the income gap. Even though I had just watched the video, I felt no more empowered to actually do something about the problem.

Data Log 2/15 (ashwang)

  • 12:00 AM – Watching the Netflix show ‘Freaks and Geeks’ – my stopping point is saved, and Netflix records my ‘TV show preferences’.
  • 10:00 AM – iPhone
    • iPhone Alarm app saves my settings for a wakeup alarm, keeps track of how many times I snoozed.
    • iPhone Health app keeps track of how many steps I’ve taken, flights climbed, and whether I was running or biking.
  • 10:20 AM – Facebook, Gmail, etc
    • Facebook messenger tracks my conversation history, which messages I haven’t read/seen yet, etc.
    • Gmail’s Inbox app also tracks which messages I haven’t read yet, which ones I’ve started to respond to but haven’t sent yet (as drafts), and which ones I’ve labeled as ‘pinned’ or ‘later’.
  • 11:00 AM – Package addressed to me arrived at the New House desk. Its location was stored, an automated e-mail was sent out.
  • 1:00 PM – 6.813 Lecture
    • nanoquiz results submitted using Google Forms
    • Their Stellar site logs the last time I visited the site, as well as my answers to the reading questions.
    • Microsoft OneNote records notes I’ve taken throughout class
  • 3:00 PM – 18.065 Office Hours
    • WolframAlpha stores my last few searches
    • Gradescope records the pictures/pdfs I upload and when I upload them.
  • 5:00 PM – swiping into Dorm using MIT ID
    • Security camera captures image of me going in through the door
  • 7:00 PM – added myself to German House late plate list online
  • 11:00 PM – Used Hubway key in at the closest Hubway stop to check out a bike. The # bikes, # docks information changes on apps like Bos Bikes, and Hubway keeps track of my biking time.
  • 11:30 PM – Used Credit Card at Shaws to buy food.

Christian´s data log of 2/14/2017

08.00 Woke up.

Checked my Harvard and two private email accounts.

Checked Facebook, Twitter, Slack and WhatsApp accounts.

09.00 Sent WA messages in team group for European Conference.

09.15 Put on Nike AppleWatch with GPS sensor tracking how much I move.


09.30 Bought a coffee at Starbucks using my Debit Card.

09.45 Logged into Harvard Wifi network.

10.15 Used Maps on IPhone to figure out best way to MIT

10.30 Add value to my Charlie MBTA card using my Debit card.


11.00 Bought a coffee at MIT student center (Debit Card)

11.10 Logged into MIT Guest network, surfing the web (various news sites)

11.30 Shared a picture I shot earlier on my fellowships´s Slack channel.

14.30 Data story-telling studio: surfing the course´s blog for data viz homework.

16.15 Purchased book at MIT Coop with Debit card.


16.30 Book a trip with Uber paid with German credit card. Driven route is stored by company.

17.30 Logged into Harvard Wifi network. Posted tweets via Iphone app.


21.00 Bought movie for class with AmazonPrime, streamed it to my laptop.

23.00 Updated information on panel for European Conference via shared Google docs.

23.30 Watch German news broadcast via app.


In addition to that, I am pretty sure there is a lot of data collection that I don´t think about. I was recorded by CCTV cameras. Multiple apps on my phone collected data and exchanged with there servers. File on my laptop are also in iCloud or Google Drive.

Sam’s Data Log for 2/15/17

Ongoing throughout day:

Sending/receiving emails

Social media visits (Instagram, Facebook)

Text messaging

Google searches

Google docs/drive modifications

MIT website visits


Other activities

8:00 AM – Tapped MIT ID to get into Z-Center gym

8:50 AM – Tapped MIT ID to exit Z-Center gym

8: 55 AM – Purchase record at LaVerdes

10:00 AM – Downloaded Arduino

11:50 AM – Delivered tuition check to MIT Student Financial Services

11:55 AM – Purchase record at LaVerdes

1:30 PM – Accepted two Google calendar invitations

3:00 PM – Took ecological footprint survey online at

4:00 PM – Tapped MIT ID to get into Athena cluster

4:15 PM – Used MIT printer

4:30 PM – Joined Piazza site as a TA for 15.S50

5:00-7:00 PM – Constant internet use, various Google apps use, communication via text/email

7:00 PM – Attendance recorded in evening class

9:30 PM – Team GroupMe set up

9:55 PM – Checked MIT Saferide app and took Saferide (Maybe data collected?)

10:30 PM – Watched Netflix

Most used throughout day: Email, Google Calendar