This event focuses on the practice of making sense of sensor data. Intended as a hands-on data lab, we will collaboratively explore different types of sensor data that are part of the empirical material of the participating projects. How are these data types structured? How can they be described, analysed, visualised, or otherwise made sense of? To what kind of research questions do these data types speak? How have these data been inscribed by the media, devices, and tools used to capture and store them?

Together we will explore Waymo’s open data set – “one of the largest and most diverse autonomous driving datasets ever released” (see also on github.com). This open data set was released as part of a public ‘challenge’ – and thus for public exploration and testing. It contains various types of data related to autonomous driving, including motion data, video captures, object data, map data, code, LIDAR-based sensor data, and labelled data. They will also explore a data set from Comma.ai, an open-source driver assist platform, incorporating 33 hours of commute data (video, GPS, CAN) from California.

All participating projects are invited to bring their own sensor-related data sets to explore during this event. There will be a collective opening session to introduce all the data sets brought in and consider possible approaches to make sense of them. Subsequently, we proceed in individual working groups to engage with these data sets in depth. In a collective closing session, each working group will present their exploration process and (preliminary) results. Each working group will include one information designer to support in the process.

The event is designed as an internal, hands-on data “lab” with selected external guests.

Organisation: subprojects A03, A04, A05, A06, B06, B08, and P03