Ten simple rules for starting (and sustaining) an academic data science initiative

The past decade has seen an explosion of data science centers, institutes, and programs appearing across the U.S. as universities increasingly recognize the importance and promise of data science to university research and education. It has been, and continues to be, an exciting time. But there are systemic challenges faced by these initiatives in the context of the higher education system. Some, but not all, of these challenges center around funding. Campuses fortunate enough to receive initial funding, often as a result of philanthropy or private sector investment, have some measure of sustainability, especially if these funds are in the form of an endowment. However, at most smaller colleges and universities, or those without a lucrative alumnus or local industry investor, just getting started with very limited funding can be daunting. And yet, every school is facing the reality that to truly prepare their student body for the expectations of 21st century employers, they must find a way to incorporate core critical thinking and data-intensive skills into nearly every discipline. This call to action challenges traditional disciplinary silos and begs for new models of higher education. As data science initiatives attempt to breakdown old barriers, they each face a unique set of challenges due to their campus’ political, financial, and structural environments. Our consideration of these challenges and those we have faced directly ourselves lead us to recognize a set of global commonalities. We capture them here in the familiar Ten Simple Rules (TSR) format for simplicity, recognizing much more could be said.