This project started in the summer 2015, and there have been several opportunities to do pulse checks (Feb '16, Dec '16) of progress along the way. A 2020+ reboot process recently started at Opendata.ch, to revisit the original Manifest and formulate a new agenda for the association. This will be relevant to the future of the School of Data working group, and I hope this post helps put things into perspective.
To give it some structure, this post outlines our starting-gate commitments, discussing how well we are meeting the goals defined in the Memorandum of Understanding co-signed in May 2016 with School of Data HQ and Open Knowledge.
a. Translate and adapt the online content of the School of Data into local language and context as well as creating new training materials as appropriate.
Workshop participants and student groups around the country doing open data projects with our support and references - the School of Design and University of Applied Sciences in Bern, the Federal Institute of Technology Lausanne, and University of Applied Sciences and Arts of Southern Switzerland being ones I am most closely involved with - are providing us with regular feedback. A group of students at the Informatikmittelschule (college of information technology) working on OGD projects in Frauenfeld with our support and Toolbox, will be a particularly relevant point of reference early next year.
In our workshops we adapt some content to a German/French/Italian-speaking audience, and to the Swiss context (locally relevant datasets, events, etc.). And we have initiatives like the R project and Toolbox which create new content. Our link to the School of Data has proven useful in lectures at schools and universities, in collaboration with government departments on the OGD Handbook, and as preparation for Hackathons.
We should also renew discussions with our neighbour schools (e.g. Datenschule, École des données, and so on) as well as related initiatives (SchoolMaps.ch, Data Carpentry, and so on); and get some funding to work on publishing multilingual (or at least better translated) content on a regular basis – as well as just to use our website to collect all the presentations and notes of all the different activities in one place.
b. Organise a minimum of 5 “School of Data” events per year, following School of Data principles
In the events forum and on our web site we can see at a glance how many events we are involved in. That glance tells us that we are not managing to organise 5 events of our own per calendar year, dedicated to the principles, so far. Last year @florianwieser and I wrote a business case for getting a program of events going, but failed to push it forward. Events are central to what we do and this point absolutely needs to be nailed down if the School is to continue into a third year. However, 5 is an arbitrary number, the definition of ‘event’ is up for discussion, and most of all our principles or philosophies, need to be continuously revisited.
Workshops this year included:
- [14.1] Food Data Expedition
- [3-4.2] Make Zurich 2017
- [1-5.3] 1001^2 @ MuDA
- [10.7] Virtual Reality Workshop
- [14.9] Wikidata Zurich Workshop
- [19.10] Tourism Data Workshop
c. Report key metrics such as number of training events and number of people trained at each event
Making our Chapter more data driven, collecting the data points and doing some reporting could be a good first task for someone who would like to help with the project. I did some rudimentary analysis one year ago, but we are overdue for an update. And most of all need a better sense for the people we are training, and training with, going forward.
I always wanted to build a place of practice, akin to a Data Dojo, and a good chunk of my year was invested into building such a space. I’m likely not the best person to lead the project into the direction of a more formal institution, for that I look to my colleagues and contributors here. However, adhering more systematically to these goal, measuring our progress, and applying our own instruments to develop the project would make the best sense in any format. I would start with a brainstorming of what content to focus on - there is a refresh going on internationally, and by content I mean anything from online courses to Q&A responses, as well as emerging platforms.
d. Commit to continuously share lessons and knowledge with the other School of Data Network members. Be an active member of the community including blogging, community meetings, mailing lists, social media, answer questions on ask.schoolofdata.org, etc.
We are committed, but inconsistent. In 2016 we organised many lots of calls with other members, take chances to interact with the network, and we post “lessons and knowledge” to our site and forum. But again this could be significantly improved, with a lot more exchange possible with our fellow chapters.
A prolonged, successful knowledge exchange this year was certainly the work of @heidi on the R learning materials project, which should serve as a model for others too - get a grant relevant to your data interest, work with the community, share your experiences and get support.
There are also questions as to where the community “lives virtually”. ask.schoolofdata.org is not active, and most of our exchanges with the School of Data network are today on a closed Slack platform, instead of in an open forum. Our local website should be updated much more often, and I am thinking this forum needs to be made useful and more accessible (languages and structure are an issue) to the rest of the Opendata.ch community.
As for myself, all my open data projects - hack.opendata.ch / dribdat,
handbook.opendata.swiss, Frictionless Data, Datalets.ch clients - should be supported with data literacy programs. All could be potential partners for a School of Data. Kicking things up a notch with an operative and effective data literacy program remains a personal priority.
I ask you, dear reader, to again critically evaluate the School of Data itself. We should never get locked-on to a single initiative. And indeed, I think we should always try to fork the good things, leave out the less-good things, and do our own creative interpretations of the things we are involved in. Especially in our free time. I am an eager follower of all the great online schools and blogs and tutorial sites, and of course the learning happening at events like Museomix and hackathons, in the incredible data art and data science happening out there. If you scroll through time and have a quick look at the blog posts or Slack channels, you should see what I mean.
Focus is essential, but from time to time we have to revisit the fundamentals. I believe that the cultural shifts going on around us, making people not just empowered but really at home with data - in general and open data in particular - are really exciting. And that is, I hope, the subject of a future conversation together.