Data, data everywhere

A laundry list of data I produce in a 24 hour period:

  • Times blinked
  • Breaths taken
  • Number of heartbeats
  • Blood pressure
  • Steps walked
  • Calories burned/consumed
  • Daily [insert important OR unhealthy vitamin/nutrient] intake
  • Cups of water drank per day
  • Energy/carbon footprint
  • Trash generated
  • Words spoken
  • Words typed (WPM?)
  • Number of people interacted with
  • Emails, texts, IMs read/written
  • Minutes on phone
  • Number of times glancing at phone
  • Cellular data usage
  • Internet data usage
  • Internet browsing data, ad trackers/cookies
  • Location services/geolocation data
  • Time spent doing things/where (i.e. calendar data)
  • Minutes (hours, really) of video watched (episodes of television, YouTube videos)
  • Minutes of music listening
  • Minutes spent surfing the internet
  • Minutes spent playing video games
  • Total time spent looking at screens
  • Money spent (cash/credit)
  • Purchases made/businesses they were made at
  • Apps used
  • Websites visited
  • Tabs open
  • Search engine queries
  • Links shared
  • Tweets written
  • Facebook likes
  • Social media impressions (likes, mentions, retweets)
  • Photons received
  • Hours slept
  • Visits to the bathroom
  • Weight gain/loss
  • Height gain/loss
  • Hours worked
  • Salary earned
  • Mail received
  • Word frequency (“actually”, “obviously”, “literally”, “classic”)
  • Emojis/stickers used
  • Lines of code written

A [snow] day of data

Note: For the sake of brevity, there are a few pieces of data that are generated continuously.

Things like:

  • Checking email (email read data, email read receipts) – I check my email extremely frequently.
  • Pedometer data (Health data on iPhone 6)
  • Checking social (Facebook, snapchat, GroupMe, Slack)

Main log:

  • Wake up – check smartwatch for daily notifications
  • In-depth email checking on iPad
  • Check Facebook (generate view data)
  • Send a snapchat (read receipts)
  • Check Groupme (read receipts)
  • Google search (“Is the T running today”)
    • Google Chrome anonymous browser statistics generated
  • Make a payment with MITFCU debit card
  • Tap ID on senior house, added to visitor log
  • Check email
  • Google search (variations on “seinfeld episodes”)
  • Send text messages
  • send Facebook messages
  • Watch Seinfeld episodes (Generate views on website)
  • Send Facebook messages
  • Download episodes of ‘Better Call Saul’ (Generate internet traffic statistics, views)
  • Send Text messages
  • Open Computer (generating usage statistics)
  • Using browser (usage statistics)
  • Searching Piazza (view data)
  • Search email (search results
  • Login to 6.815, change password (view data, sign-in log, change in db)
  • Writing code (IntelliJ usage statistics)
  • Git commits and pushes (creating commits, trackable history online)
  • Watching ABC with xfinity on-campus (login log, TV view information) (Fresh off the Boat)
  • Purchase a pizza (Creating an order, payment log on MITFCU)
  • Tap ID for EC (Entry log)
  • Tap ID for Senior house (Entry log)
  • Watch Youtube (view data, added to recommendations for user)
  • Visit a blog (view data)
  • Play music on Spotify (usage statistics, playback logs)


A Day of Tracking down Data

Date: 2/11/2015 (Wed)


  • check time using my iphone and go back to sleep


  • Waken up by alarm, snoozed the alarm, drank water (~three gulps out of a pitcher)
  • Ate a yogurt, two eggs for breakfast. While eating, look outside through window and realized it started to snow again. i.e. gathered information about today’s weather.
  • Attempted to check the weather/temperature using iphone, but it took too long (perhaps it was also trying to get out of its sleep mode too) and I didn’t really need/want to know the temperature, so I closed my phone.


  • check time again and rush down to BC gym for  morning exercise.


  • Started biking: the machine kept track of the calories, resistance, speed, heart rate, muscle used throughout my workout.


  • Checked the time, logged into gmail using my iphone
  • Took an elevator to get back to 5th floor.


  • Used bathroom: some system in the dorm must have been keeping track of how much water I used, the electricity used to light the bathroom and heat up the water.


  • Bought a cup of coffee and payed using my credit card.


  • Swiped my ID in order to get into the Athena Cluster
  • Logged into the computer using my ID and password


  • Logged into my gmail, read emails, filter out spams, added two important events to attend on the google calendar.


  • Create a google doc to record this document


  • Used my BOA online account to make transfer between my accounts
  • Bought lunch from Anna’s and used my credit card to pay
  • Checked my Facebook and replied to messages: FB kept track of not only the messages I wrote but also in which city and at what time I wrote the messages


  • In 6.033 Lecture, I answered an in-class vote


  • Swiped my ID to get into the Margarat Cheney’s room


  • Used an elevator in Rotch
  • Used a printer to print a problem set for 6.045 and readings for 6.033

6:50pm – 9pm

  • Answered an application survey on Piazza Career
  • Emailed a9 interviews and answered their questions
  • Searched on google a bunch of things (such as ‘what is UNIX’, ‘Trailing dot in DNS’, and ‘Regular Expression distributive?’)
  • Logged into Stellar to check psets and readings


  • Swiped my ID to get back into my dorm
  • Signed on the “get-better” card for Peggy
  • Set up an alarm for tomorrow morning
  • Practiced some javascript and saved the script on my laptop’s memory
  • Posted a short daily write-up on the blog
  • Listened to Pandora: marked “thumbs-up” or “thumbs-down” to indicate my preference based on which Pandora recommended the subsequent songs

Snowday-pocalypse Data Log: 2/9/15


I tooled at my computer from midnight to ~2:59am doing the following:

– visited Stellar to download materials

– updated google calendar to account for snow day

– checked Tumblr several times

– checked emails

– created this document in google docs

– watched 10 videos of dance performance consecutively on youtube

3am ~ 11:30am

sleeep zzz


Woke up after snoozing two alarms

Skip breakfast in the dining hall,

eat a bowl of instant oatmeal

All on iPhone:

– manually update apps

– download three apps after browsing the app store

– check FB messenger, Groupme

– check Gmail, calendar

Wore glasses instead of contacts

12pm ~ 2:45pm

Played Hay Day (real-time “farming” iOS game) for most of this time, but I also played a few rounds of Bejeweled Blitz and Two Dots. I also responded immediately to messages on Groupme and Messenger that were received on my phone

2:45pm ~ 7pm

This time is spent almost continuously on my laptop using the internet. Sites/activities included:

– Gmail

– Google Calendar

– Atlas

– reading Stellar assignments

– Re-enable Facebook News Feed and scroll through for the first time in several days (was previously using Kill News Feed on Chrome to block it)

– reblog a few posts on Tumblr

– respond to text messages on laptop

I received about 10 notifications from Hay Day, and checked the game each time. I eventually disabled the app from sending notifications, and did not play it for the rest of the evening.

9pm ~ 11pm

Download and watch movie for a HASS assignment

Update google calendar to reflect Tuesday school closing

Check facebook and tumblr before going to bed


A few totals:

– emails sent: 5

– emails received: 117

– emails trashed: 45

– text messages sent/received: around 50

– time spent online: ~10 hours



Deborah’s data log

Sunday, February 8

11am – 12pm: Check email on phone and laptop, respond to gchats, use water in bathroom

12pm – 1pm: Call two people, check email again.

1pm – 2pm: Check MIT shuttles & Nextbus for grocery shuttle on phone (uses phone location), forward and reply to emails (TA duties)

2pm – 3pm: Eat lunch at grocery store (utensils), use credit card to pay for groceries, obtain grocery receipt, check shuttle app multiple times, read a Medium essay while waiting. When I get home, tap my MIT ID into dorm, send more emails, call my sister, check Facebook, and update Stellar for class I TA.

3p – 4pm: Respond to Doodle poll, copy a google spreadsheet to assign grades, enter them into Stellar.

4pm – 5pm: Catch up on Internet reading – various sites (Atlantic, Wikipedia, HackerNews), call my family

5pm – 6pm: Search artist on Spotify iPad app, put on an album.

6pm – 7pm: Respond to intermittent gchats, check Facebook, look at website for class I’m taking.

7pm – 8pm: Get started on lab1 – lots of google searches, piazza, github, stackoverflow, various blogs. Look at sandwich recipe on AllRecipes, turn on oven, check Facebook, eat.

8pm – 9pm: More gchats, google state of Snowpocalypse, check Twitter.

9pm – 10pm: Look at bread recipes on food blogs, read about various foods (mostly first two pages of Google results)

10pm – 12am: Intermittent gchats, Internet wandering – check my news sites (Atlantic, nytimes, New Yorker, Hacker News, Gawker), watch Youtube, check Piazza, email

Tuyen’s 24hours data activity log

I was in New York last weekend so this relates the trip back home that shows a more data generated activity than a regular quiet weekend.

Sunday Feb 8

11:00 woke up in the hotel room no alarm clock

11:00 checked texts on my iphone to see if friends responded to my texts about D’angelo concert feedback.

11:30 took a shower, used water from the hotel

12:00 check out of the hotel room and use the hotel computer

12:05 booked some tickets for a Broadway show via credit card, on the hotel computer received confirmation email.

12:15 booked a table via Opentable for 2 for lunch (but never went)

12:45 went to a random Japanese restaurant instead

13:15 checked facebook and online reviews on last night concert

14:00 restaurant paid by credit card and got printed receipt.

14:30 walked on Times Square and took some pictures – my iphone tags the location of the picture

14:33 a camera on times square took my picture and shows on a gigantic marketing board for Revlon

14:40 arrived at the theater and pick up tickets at registration, barcode tickets were scanned.

15:00 show starts.

16:00 intermission, bought drinks with my credit card

17:30 going back to the hotel by subway, paid $10 metro card refill by credit card at the subway station

18:34 took the train at central park to West Haven. Tickets were bought the day before via credit card and were checked manually byan agent

18:40 listened to music on the train with Spotify on my phone

19:25 received text from MassArt closed on Monday

20:15 arrived at West Haven and picked up the car – security cameras in the train station.

20:20 used Google maps app for directions to Home

20:30 received email from MIT alert. School closed

21:00 collected a ticket for the toll

22:30 filled up the car with gas on Mobil station with the Speedpass – will get the bill at the end of the month

22:45 ordered some food and paid credit card

23:30 paid the toll $2.75 in cash – probably security cameras

00:00 arrived home safe, brush teeth- data will be reflected on water bill, city of Arlington

00:15 put the heater in the room – will be reflected on electricity bill

00:30 turned on Netflix to watch a movie, I searched by category foreign movies – and felt asleep.


Monday Feb, 9

08:30 woke up naturally– no alarm.

08:45 brushed my teeth and bathroom use- reflected on water bill, city of arlington

09:02 received texts on my phone, date and time tagged checked facebook, instagram

09:18 put the Netflix movie back- right where I stopped it the day before

09:30 replying to emails- connecting to home network (Xfinity)

09:33 received email from Opentable saying “Sorry we missed you at the restaurant”

10:00 weight myself – records the weight on the digital scale

10:17 received email from my bank asking why I transferred huge amount of money on Dec 8 and Jan 21

10:21 looked at my bank account HBSC App on my phone, bank tags last time I logged in.

10:34 opened a new word document, saved it several times on my laptop

10:45 downloaded the Txto app on my mac – browsed all your texts ready for printing.

11:00 watched French TV show on with login and password

A Day’s Worth of Data

I wake up at 9:00a, make breakfast, snap a picture of it on my phone and post it to Instagram with relevant hashtags. [Data generated: photographic data – which is also synced between my Dropbox as well as my iCloud, timestamp data, and social engagement data via likes on Instagram – note: I do not choose to geotag my photos, although who knows what data Instagram is actually relaying to its servers…] In the meantime, I catch up on the day’s headlines on NYTimes Now – creating data on the articles that I read, and the links that I click through.

I receive a few texts from a friend, as well as from a colleague who is going to meet up with me this morning to film some short scenes for a documentary. I reply to the texts, creating metadata (timestamp, to/from) and content within the texts – all donated to my wireless carrier and iMessage.

I meet up with the colleague, and we film around my neighborhood – her camera records video and time data. We also take a bus, and I swipe my Charliecard as I board, creating a few more data points on my bus usage – the stop I got on at, and perhaps how many times I’ve swiped within this month (I have a monthly pass instead of a top-up card).

After filming, I return home to answer emails, and work on rescheduling many meetings and other engagements that have been cancelled due to the snow storm. In the process, I generate data through my email conversations (text content + metadata + geolocation through my ip), and through my calendar updates (schedule content and time data on when changes are made).

Once I get hungry, I make lunch and Instagram it, again generating more photographic data, timestamp data, and social engagement data.

I return to working on my laptop, editing documents and spreadsheets on Google docs while collaborating with other students on a research project. I leave behind a data trail of my document edits – both the content and timing of the edits. In the process, I also get in touch with my collaborators via Gtalk – generating more text data and metadata.

Taking a small break before transitioning to my next work-related task, I log into Pinterest (a guilty pleasure!) and browse for a bit. I notice that Pinterest is now showing me suggested posts (instead of only the posts curated by the people I choose to follow), and I update my settings to suppress these superfluous pins. I realized that no user setting will get rid of the “picked for you” pins, and I use Google to attempt to find another workaround (creating search data) – however, this effort is not successful and I end up closing all of the tabs that I opened related to this break – and in the meantime, generating more browser history data.

Later on, I finalized a paper submission – this created a few updates to my Dropbox history as I completed some last minute document and content edits, and reformatted a few images. I submitted the paper, and generating data about my submission – both the actual content submitted, as well as the metadata of when I submitted, the file names and types that I submitted, etc.

I take another break to watch an episode of “Modern Family” on Hulu, creating data (linked, unfortunately, to my Facebook account – since at some point in history I had linked my Hulu and Facebook accounts together) on my viewing behavior, on which ads I watched, and also on my click behavior, as well as adding more to my browser history data.

I go back to emails again, sending more emails and generating more email and conversation history as I confirm the meetings that I have tomorrow.

Finally, as I’m writing this blog post, I’m generating data via the hyperlinks that I add, and also the content that I’m generating (metadata on the post category, revision data, as well as the actual text)!

Data Log

As I started this assignment, I was skeptical about how much data I would create, especially on a day in which I was not particularly busy. However, I quickly realized that passively, just through receiving texts and emails, I was already creating a substantial amount of data. I also realized, after looking back on the data I created, that, while there was a lot of data I created, it would be easy to misinterpret some of the data and end up with a relatively inaccurate guess about how I spent my day.


Data Log from 12am to 11:59pm on February 8th, 2015



Watched YouTube videos

Sent/received >100 texts


Watched YouTube videos

Sent/Received ~50 texts


Watched TV

Watched YouTube videos


Took pictures

Sent ~25 texts

Watched YouTube videos

Sent emails to ~50 people


Logged out of Google account

Logged into different Google account

Edited 6 Google docs

Updated MIT mailing list


Created Google doc and spreadsheet

Sent emails to ~10 people


Plugged in computer and phone

Set alarm for 10am




Received phone call

Wrote note on iPhone Notes App

Sent/received ~75 texts


Snoozed alarm 3 times

Sent emails to about 100 people

Updated MIT mailing list

Deleted note on iPhone

Received phone call

Sent ~25 texts

Made 50 phone calls


Swiped into MIT Dining Hall

Logged out of Google Account

Logged into Google Account

Logged out of Google Account

Logged into Google Account

Edited 2 Google Docs

Sent/received ~50 texts


Left dorm (footage on security cameras)

Turned on lights in class room

Received ~10 texts


Swiped into dorm

Received ~10 texts


Swiped into MIT dining hall

Sent/Received ~75 texts

Sent emails to about 1500 people


Went of Facebook

Logged out of Google account

Logged into Google account

Updated Google docs/spreadsheets


Printed ~12 pages

Updated MIT mailing list

Sent/received ~50 texts

Sent emails to about 50 people


Went of Facebook

Watched YouTube videos

Sent/received ~20 texts

Sent emails to ~50 people


Signed into Stellar

Downloaded 2 assignments from Stellar

Submitted assignment on Stellar

Set alarm for 11:59pm


Live streamed TV

Sent/received ~25 texts

Sent emails to ~100 people

Alarm went off



Totals for day:

Texts sent/received: >500

People reached via email: ~2000

Number of emails received: ~200

Phone calls made: 50

Phone calls received: 2

Number of different Google accounts signed into: 4

Data Log During the Snow-Pocalypse

How much data can really be logged on a snow day when you spend the whole day in one building and mostly on the same floor? Turns out quite a lot.

8:30 – My alarm goes off. I told someone I would meet them at breakfast by 9:00. I check my phone, read my emails, answer a few messages. The keyboard on my phone (Swift) requested permission to send back data about what I typed to its servers, so presumably it’s doing that the whole time. Google is reading my emails now, since it now shows me my flight information and other ‘convenient’ things, so it was probably doing that all night. I open up Clash of Clans to collect my gold and elixer that accumulated overnight – they’re probably tracking that as you can’t play the game without an internet connection. Apple probably actually collects data about my alarm going off, and wether I turned it off immediately or hit snooze – they already collect data about how far I walk, how many flights of stairs I climb, where I go, etc all day.

9:00 – I take the elevator down to breakfast. The elevator probably made note of the journey and possibly the weight somewhere, for maintenance purposes. I swipe my ID to get into dining – that action was definitely recorded and used by MIT, Bon Appetite, and was stored to prevent me from overeating my share of meals this week. I toast a bagel, as I do every morning. The cafeteria manager is probably keeping track of the total bagels consumed so that she knows how many to order in the next food shipment. I make tea with a Tazo teabag and she probably collects similar data about this too.

9:45 – I’ve finished breakfast and I go to do laundry. The laundry machine records my load, both for maintenance purposes (probably) and to deduct $1.00 from my TechCash account. The water and electricity usage is probably also recorded. I go back to my room to take a nap, not sure how much information about me can be recorded while I’m napping…

10:30 – I switch my clothes to the dryer (similar data is recorded as from the washer).

10:35 – I start working on my 6.046 pset. This includes many internet searches and the occasional facebook (or other form of social media) distraction. The websites that I view undoubtedly record my visit, and probably leave cookies in my computer too. It’s possible that they even recorded my keystrokes while browsing their site.

11:45 – I collect my warm clothes from the dryer. The laundry machine system is aware of this because it will now show that dryer as available in their online interface. I fold my clothes and continue with my pset.

1:00 – Lunch time! Normally I would venture out into Kendall or Central square to buy myself a sandwich with my debit card (while simultaneously contributing to data that the restaurants keep about their daily profit, number of customers, and most popular items), but today I eat a Luna bar and an apple I took from dining while in the company of my pset.

1:30 – I exchange text messages with a friend, asking him if he wants to work on the 6.046 pset with me. The data from that conversation was probably stored somewhere, maybe even by the NSA.

1:35 – My friend arrives and we work on the pset for a solid 2 hours together, talking things out and drawing pictures on paper. I’m not using my computer very much, but I will have to use LaTex to type up the answers at some point, and that will contribute to my computer usage data.

4:00 – It’s time for a break. I pull out my laptop again to watch an episode of Hulu before dinner. Hulu has a list of all of the shows I’ve watched, which ones are my favourites, and what it recommends for me, so my pset break definitely does not go unrecorded. After my episode finishes, I do a bit of internet browsing and get distracted by a cool new belt. Retailers love to collect data about me, so these actions did not go unnoticed either.

5:15 – Dinner time. I normally eat dinner much later, but because of my lackluster lunch, I got hungry early today. Anther elevator ride, another dining swipe, more data about me. During dinner we also get an email from MIT saying that Tuesday will also be a snowday, the dining hall is alight with excitement.

6:15 – I return from dining and open facebook again. I see adds for the belt that I had been viewing earlier, which means that my data went through as many as 3 companies: the original website, a third party advertising company, and now facebook.

6:30 – I return to working, but switch gears to finish up my 6.033 pset. After more googling and consulting a friend, I finish and submit my answers online. My submission was sent to a server somewhere probably in CSAIL so that the course staff can grade my answers.

7:30 – I decide to watch Unbroken with a friend, we’ve been trying to watch all the movies nominated for Best Picture. Since the movie is still in theatres and there’s a Massachusetts State of Emergency declared, we turn to the internet to provide entertainment. The site that we land on is no doubt full of malicious links and is trying to collect data on me for the next 2.5 hours, but it provides video nonetheless and we’re watching it through the incognito window.

10:00 – The night is no longer young, and I turn to reading and internet browsing (more cookies and advertising) for a long time before taking a shower (water usage), setting my alarms (apple data?) for the next morning, and going to bed.