Data Literacy

Explore your Internet habits with open data in a safe and accessible way

⛶ Full screen

It can be hard for people to understand the value, role and consequences of data, due to its abstract character. It is hard to imagine being more than just a consumer (end-user) of a complex service like a social network. Students may not have yet had the opportunity to have empowering, open interactions with digital data services yet - even though they play such a crucial part in today's society.

Learnings At the Open Education Hackdays we worked on a learning concept that helps students to grasp and work with their own data in a safe and accessible way. It is an instrument to also teach about connecting to and contributing to sources of open data: the basis for a "short and fun" data literacy module, which allows students to 1) gather first experiences with data as well as 2) get a more concrete understand of data and its role in today's society. This could be used in a school activity, the school curriculum, or within the wider community.

Achievements We have a working prototype of an application that aggregates your Internet usage patterns (currently the Mozilla Firefox browser history), uses open data sources to analyse potential safety risks, presents a readable report as well as a Data Package that can be used to contribute to future community initiatives and research projects. The main application is written in Python, with report generation in JavaScript using Bootstrap and Vega-Lite.

Screenshot of desktop application


Team members


  • Pauline
  • Moritz
  • Sophia


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Tracks to Digital Awareness

A work in progress. More details can be found in the wiki


1) Install all the libs:

pip install -Ur requirements.txt

2) Close your browser and run our graphical client:


3) Push the button.

Console instructions

You can also use our tool on the command line:

1) Close your browser, then run:


Optionally put a copy of your cached SQLite database in the private folder if you want to keep your browser running.

2) Now pipe the resulting extracted content into our data packager, and save the resulting output, like this:

cat private/websites.csv | python datapackage > datapackage/data/places_100.csv

3) Look at the contents of the datapackage folder for the resulting (anonymized) data and report.

4) ???

5) Profit!!!

24.03.2019 11:30


Worked on by oleg

23.03.2019 13:30

Hackathon finished

22.03.2019 10:59

Team forming

oleg has joined

22.03.2019 10:59


Worked on by oleg

22.03.2019 10:00

Hackathon started

01.03.2019 11:05

Team forming

Juerg has joined

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Creative Commons LicenceThe contents of this website, unless otherwise stated, are licensed under a Creative Commons Attribution 4.0 International License.