Democratizing data, Uni Bern

#1

My talk on 27 June 2016 called “making machine-usable information also more human-usable” at the University of Bern for the 2nd meeting of the Data Visualization Group is available as online slides here. Thanks to Janik Endtner for the invitation.

For the talk I imported and geocoded the #euref data in this Google Fusion Table.

References:

In a future talk I’d like to go into more detail on the topic of intent + consequence, and explore these six keys to data literacy:

✓ source
✓ license
✓ link
✓ code
✓ logic
✓ context

To find out more about Open Knowledge visit okfn.org, and check out our project to start a School of Data in Switzerland.

The Swiss Open Cultural Hackathon takes place again in Basel this week. Later this month I’m running a course at SUPSI called Adventures in Data visualization.

“Oh God, the terrible tyranny of the majority. We all have our harps to play. And it’s up to you to know with which ear you’ll listen.”
― Ray Bradbury, Fahrenheit 451

Year in review and what's next for School of Data CH
#2

Notes from my talk on May 23, 2018 - on the program:

There was a brief introduction to the project I’ve been working with Open Knowledge on, showing how we have been making use of Data Packages and the various lightweight tools at https://frictionlessdata.io/field-guide/ to promoting open data at hackathons - notably through schoolofdata.ch workshops, boilerplates, datacentral and dribdat.

I shared my affinity for julialang.org (high performance! R/MATLAB/Python inspired! machine learning! Jupyter notebooks! multiple dispatch! ok, maybe that last one didn’t come across so well…), and talked about my experience porting Data Packages. I did my best (it worked better last month, when I had more time) to explain analogies of data and software containerization.

Then I ran through a quick demo of the smartuse.ch project, for which I’ve built a Flask app to upload and annotate GeoJSON using the WIP geospatial profile, the datasets - actually models, because they are outputs of various analyses we are running to mock up the platform. I’m aiming at taking the basic principle of geocat and combining it with DataHub.io - the “distributed CKAN”.