17 Association of Research Libraries Research Library Issues 298 2019 Common Themes and Distinctive Paths Each MSDSE institution focused on interdisciplinarity and collaboration—attributes that data science fosters and requires. Each also initially established or built up a data science education program outside of existing departments (but in collaboration with them) in order to encourage institution-wide cohesion. In addition to local endeavors, the three institutional groups also came together frequently, through focused meetings on a particular topic, and in an annual summit to talk about research, education, careers, and the progress of the grant itself. There were core themes, goals, and structure to the three-site design. Each institution participated in six cross-institutional working groups focused on recognized challenges to data-driven science, including (1) careers, (2) education and training, (3) tools and software, (4) reproducibility and open science, (5) working spaces and culture, and (6) data science studies. NYU, UC Berkeley and UW approached curriculum-building differently— some starting with graduate and others with undergraduate courses—and each emphasized the six themes to greater or lesser degree. NYU’s focus on openness and reproducibility UC Berkeley’s development of Data 8, an entry- level course designed for students in any major and UW’s widely adopted data science “options” across the curriculum are examples of their individual distinction. All three institutions have made many of their research and education materials open to the public. For example, the Data 8 course can be viewed online and freely reused at http://data8.org/, UW has three massively open online Core to the development of data science at the three MSDSE universities was a posture of non- competitiveness with existing programs and disciplines...and a belief that the set of tools and methods that comprise data science are critical to unlocking new discoveries across all research domains.
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