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Today, I was teaching the second class of our Semantic Web class here at the VU University Amsterdam on RDF and RDFS. After the first half of the class in a very warm lecture room, the students were fading. After a quick poll, we decided to take the course outside. So I had the fun challenge of teaching off-the-cuff RDF Schema without a chalkboard and slides… and I think it actually worked. The students did a great job of participating and we managed to demonstrate a bit of rule based reasoning using a combination of coloured paper, students, and moving about. Here’s a photo of the class as we ended:

Note this post has been cross posted at The the International Collaboration of Early Career Researchers 

This past Thursday, I had the opportunity to participate in a mini-symposium held by the VU University Amsterdam (where I work) around open data for science titled Open Data for Science: Will it hurt or help?

The symposium consisted of three 15 minute talks and then some lively discussion with the audience of I think ~60 people from the university. We were lucky to have Jos Engelen the chairman of the NWO (the dutch NSF) discuss the perspective from research policy makers. The main take away I got from his presentation and the subsequent discussion is that open data (despite all reservations) is a worthy endeavor to pursue and something that research funders should (and will) encourage. Furthermore, just his presence means that policy makers are reaching out to see what the academic community thinks and that the community will have a say in how (open) data management policies will be rolled out in the Netherlands.

The most difficult talk to give was by Eco de Geus, who was asked to reflect on the more negative aspects of open data.  He presented important points about incentive structures (will I be scooped?), privacy, and the tendency towards one size fits all open data policies. These were important points. I think what made the reservations more poignant is that Prof. de Geus is not anti open data indeed he is deeply involved in large open data project  in his domain.

I talked about the view from a scientist starting out in their career. I told two stories:

  1.  how open data really benefited a collaborator of mine in her study of interdisciplinary work practices. As a consumer it really of data, open data really removes a number of barriers.
  2. in an analogy to open code, I discussed how an open source code I produced during my PhD led to more citations, a new collaboration, and others comparing there work to mine. However, these benefits were contrasted with the need to do support and having to be comfortable exposing my work practices.

I ended by making the following points about open data:

  1. Open data is a boon to young scientists when they are acting as consumers of data.
  2. It’s a more difficult position for producers of data. There are trade-offs including concerns about credit, time for support, and time to prepare data.
  3. Given 2, if we want to help scientists as consumers of data, we need to give support to producers.
  4. Clear simple guidelines for data publication are critical. Scientists shouldn’t need to be lawyers to either produce or consume data sets.
  5. Credit where credit is due. For open data to succeed, we need data citation on par with traditional citation.

You’ll find the slides to my talk below. Although they are a lot images so may not make much sense.

Overall, I thought the talks and discussion were excellent. It’s great to see this sort of discussion happening where I work. I hope it’s happening in many other institutions as well.

The VU University Amsterdam computer science department has been a pioneer at putting structured data and Semantic Web into the undergraduate curriculum through our Web-based Knowledge Representation. I’ve had the pleasure of teaching the class for the past 3 years. The class is done in a short block of 8 weeks (7 weeks if you give them a week for exams). It’s a fairly complicated class for second year undergraduates but each year the technology becomes easier making it easier for the students to ground the concepts of KR and Web-based data into applications.

The class involves 6 lectures covering the major ground of Semantic Web technologies and KR. We then give them 3 1/2 weeks to design and hopefully build a Semantic Web application in pairs. During this time we give one-on-one support through appointments. For most students, this is the first time they’ve come into contact with Semantic Web technologies.

This year they built applications based on The Times Higher Education 2011 World University rankings. They converted databases to RDF, developed their own ontologies, integrated data from the linked data cloud and visualized data using sparql. I was impressed with all the work they did and I wanted to share some of their projects. Here are four screencasts from the applications the students built.

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