On February 13, 2015, Stanford University will host Data Driven: Coding and Writing Transportation’s Future. The conference will focus on the changing landscape of vehicle and transportation related data, and the opportunities arising from it.
Click here to RSVP for the Data Driven Conference.
Hosted by the Stanford Journalism Program, the REVS Program at Stanford and the Center for Automotive Research at Stanford, the conference brings together professionals from a range of sectors who are working at the frontier of liberating and utilizing vehicular data.
Car manufacturers, tech companies, city government officials, academics, and journalists will all contribute to discussions about data and the evolution of automobility. The conference will feature three panels: data in the car, communication from or about the car, and car data as sensors of institutions. Invited panelists will share their experiences with data and cars, followed by moderated discussions involving the audience. The day concludes with a reception at Stanford’s Arrillaga Alumni Center.
Questions? Email Peter Johnson at email@example.com
Remarks by Jay Hamilton, Director of the Stanford Journalism Program.
Panelists: James Buczkowski (Director, Electrical and Electronics Systems, Research and Advanced Engineering at Ford Motor Company), Liz Jensen (CEO at Road Rules), Aaron Kessler (Investigative reporter, data journalist at New York Times)
Moderated by Paul Ingrassia (Managing editor and automotive author at Reuters)
This panel focuses on data use in the car. Topics for this panel include identifying and describing the landscape of car data, introducing the concept of vehicle to vehicle communication technology and self-driving cars, and discussing the societal and consumer benefits of vehicular data. Questions for this panel include: What data are being generated from the vehicle right now? In the near future? In the more distant future? How is this data useful? How much of it will it be shared with consumers, the public, the government? Why or why not? What are the benefits of allowing vehicular data to be opened up? What are the benefits of vehicle to vehicle communication? What are the roadblocks to V2V?
Panelists: Adam Altman (Head of Product at Automatic Labs), Charlie Catlett (Director at the Urban Center for Computation and Data
at University of Chicago, Argonne National Laboratory),
Di-Ann Eisnor (VP Platform & Partnership at Waze)
Moderated by Reilly Brennan (Executive Director at The Revs Program at Stanford)
This panel focuses on communication about or from the car, such as data on location, traffic, and accidents. Questions pertinent to this panel are: What are the major hurdles facing local governments right now in terms of making city data more available and accessible? How can agreements between companies with traffic data and governments benefit communities? In general, how can more open vehicular/traffic data improve a city? What new business opportunities are available if car companies or governments are more open and cooperative about sharing data. How can the average citizen navigate open data websites for practical uses?
Panelists: Robert Benincasa (Producer, Investigations Unit at NPR), Paul Ingrassia (Managing editor and automotive author at Reuters), Danielle Ivory (Investigative reporter, data journalist at New York Times), John Maines (Computer-assisted reporting specialist at Florida Sun-Sentinel), Michael Morisy (Co-founder at MuckRock), Maurice Tamman (News editor, data jockey at Reuters)
Moderated by: Cheryl Phillips (Hearst Professional in Residence at Stanford Computational Journalism Lab)
This panel will feature journalists who have successfully used transportation or vehicle-related data to describe how institutions, both public and private, are working. Panelists will discuss the story behind their stories. They will talk about the process of accessing, analyzing and ultimately expressing the data used in their work. Relevant questions: What is the landscape of transportation data like right now for journalists doing investigative work? How are data limited, abundant, messy, or useable in covering transportation? In what ways can opening data from Panels 1 and 2 aid investigative reporting?