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2019 Data Fellowship

Program Description: 


The 2019 Center for Health Journalism Data Fellowship was designed for skilled journalists who want to learn to mine data sources to reveal key insights essential to high-impact journalism. 

The program offered professional reporters an opportunity to learn to acquire, analyze and produce visualizations of data that can help their audiences understand key health and child welfare developments.  Center for Health Journalism Data Fellows received four days of intensive training on data acquisition, cleaning, analysis and visualization, as well as an introduction to important data sets that can serve as the basis for groundbreaking journalism.  They heard from leading data journalism experts about how to make successful Freedom of Information Act (FOIA) requests and gain insights on how to pair original data analysis with compelling narratives.

The 2019 Data Fellowship provided two training tracks tailored to the skill levels of participating journalists. The lead trainers were award-wining investigative and data joruanlists who are some of the best in the business: Meghan Hoyer, data editor at The Associated Press; Paul Overberg, data reporter at the Wall Street Journal; and Cheryl Phillips, Lorey I. Lokey Visiting Professor in Professional Journalism in the Department of Communication at Stanford University and a founding member of the California Civic Data Coalition.

Each applicant was required to propose an ambitious investigative or explanatory reporting project to undertake in the six months following the training. Fellows returned home from the training with grants of $2,000 to support reporting and data acquisition costs.   For six months, Fellows received guidance from our expert data journalism mentors as they complete ambitious explanatory or investigative Fellowship projects built around data – reporting that impacts policy and spurs new community discussions. 

Click here to see a list of the 2019 Data Fellows and links to their profiles and reporting projects.

The Data Fellowship identified priority areas for projects:

For National Applicants (Outside California)

The Center sought proposals for projects that explore child welfare, juvenile justice and child health and well-being issues, including, but not limited to, the impact of chronic stress, poverty and childhood trauma on child development; juvenile justice; the intersection between partner violence and child abuse; the role of policy in improving prospects for children; community violence; child illness, injury and mortality trends; the intersection of race/ethnicity and/or class in child and family outcomes; strengths-based approaches to improving outcomes for vulnerable children and families; creative financing and cross-agency strategies to treat and prevent the impacts of child maltreatment on children and families; and innovative solutions. 

For California Applicants

The Center sought proposals for projects that explore how community conditions influence health and well-being including health-related environmental justice issues; the impact of community violence on health and well-being; racial, ethnic, economic and geographic health disparities; access to care for underserved communities, the performance of California’s safety net; the possible consequences in California of the federal effort to rollback the Affordable Care Act and current social supports for low-income people; health and mental health challenges for immigrants; and innovative solutions to the state's public health and health care challenges. We welcome project ideas that explore public policies -- or failings of public policies -- to address the high cost of housing, transportation challenges, air pollution, and neighborhood safety. Projects can explore the school environment and the emotional health of children, including trauma-informed approaches and efforts to ensure successful paths for youth. Proposals from California journalists can focus on children and families, but they should also meet one of the above criteria. 









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