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Data Journalism Project

Finding Important Stories in Data

Data Journalism Project

The fact that a faulty GM ignition switch was causing cars to crash -- with many deaths -- went unnoticed for years in publicly available data. While investigative journalists and other watchdog organizations have access to increasing amounts of data, they have been hindered by available tools in gaining insights from large volumes of often messy data.

For this project, the Data Impact Lab is partnering with professional journalists at organizations including the Center for Investigative Reporting and the Wall Street Journal. We are applying new tools to the real datasets of emerging and potentially important stories. These tools, including some developed at Stanford, apply machine learning and other techniques to the challenge of working with large, messy datasets.

Our partners can put better tools to almost immediate use, to gain insights and to find stories that might otherwise be missed.

The faculty involved with this project are

  • Bill Behrman, ICME
  • Cheryl Phillips, Stanford Journalism Program

During this project, the students on the Tech Team used and evaluated a range of data tools. To help others doing similar work, they summarized their data tool experience in the Tech Team Report.