As part of a recent workshop entitled "Imagining Tomorrow's University", we were asked to visualize the future of universities as research becomes increasingly data-and computationdriven and to identify a set of principles characterizing pertinent opportunities and obstacles presented by this shift. To establish a holistic view, we take a multilevel approach and examine the impact of open science on individual scholars as well as on the university as a whole. Generally, we agree that increased transparency in the scientific process can broaden and deepen scientific inquiry, understanding, and impact. However, the realization of these outcomes will require significant time, effort, and aptitude to convey the means by which data are transformed into knowledge. We propose that open science can most effectively enable this evolution when it is conceptualized as a multifaceted pathway that includes: the provision of accessible and welldescribed data, along with information about its context [1], the methodology and mechanisms necessary to reproduce data analyses, and training products that provide transparent understanding of how the data can be applied to answer questions. Thus, impactful open science requires investments on the part of individual researchers that are often greater than might be needed for "non-open" science. At the university level, open science presents a double-edged sword: when well executed, open science can accelerate the rate of scientific inquiry across the institution and beyond; however, haphazard or half-hearted efforts are likely to squander valuable resources, diminish university productivity and prestige, and potentially do more harm than good. Here, we present our perspective on the varying roles open science.
Open science enables low-barrier collaborationsFor some university researchers, open science can be both powerful and transformative. Imagine the research program that generates not only publications but also programmatic code that can quickly reproduce each analysis and publishable figure with a minimal amount of manual intervention. This structure can provide continuity in a project and accelerate the research enterprise by allowing researchers to rapidly repeat the same analysis on new datasets, all while lowering training and other human capital investments. Included with a publication, this "research notebook" and accompanying datasets (e.g., [2]), could be compiled into a tutorial for others in the field who could then repeat this work with their own data -all without the need for formal collaborations. Such approaches can benefit not only the initiating research group but also an entire scientific discipline.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2781v1 | CC BY 4.0 Open Access | rec: