Abstract
Open science is the idea that all those working to unlock solutions to the world’s most vexing problems will collaborate and share data, algorithms, tried and failed combinations, and more, so that new discoveries can be made more quickly. Increasingly, open science also means full appreciation of the value of data and opening virtual floodgates that allow massive amounts of data to flow freely within and across the health sector and healthcare systems. Data-intensive computing and analytics allow researchers to generate new insights and discoveries in ways that were almost unimaginable until recently. The possibilities for open science fueled by cognitive computing, advances in machine and deep learning, and burgeoning data are endless and exciting. However, there are a number of hurdles that must be overcome before open science is truly established. This chapter will provide a view of what is possible in the health sector in an era of augmented intelligence and cognitive computing committed to unprecedented collaboration and discovery. We will discuss what is involved in overcoming barriers and systemic inertia to achieve real-world adoption and engage, train, and expand the skills of a next-generation workforce.
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Klenk, J., Payne, P.R.O., Shrestha, R., Edmunds, M. (2019). Open Science and the Future of Data Analytics. In: Edmunds, M., Hass, C., Holve, E. (eds) Consumer Informatics and Digital Health. Springer, Cham. https://doi.org/10.1007/978-3-319-96906-0_18
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