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Achieving open science outcomes with multi-faceted teams: learning from the coal face

Authors: Specht, Alison;

Achieving open science outcomes with multi-faceted teams: learning from the coal face

Abstract

Addressing today���s complex, global research challenges, such as the threats posed by climate change and other environmental hazards associated with breaching our planetary health boundaries, has never been as urgent. Much of the data and information to tackle such problems is at our fingertips, the question is how to acquire, assemble and analyse diverse and complex information as expeditiously as possible to arrive at new understanding and recommendations for action or policies. Such cross-domain synthesis involves achieving four key components: (i) successful collaboration between experts and stakeholders across disciplinary, skillset, organisational and national boundaries, (ii) the availability of relevant, trusted, good quality data and code fit for use, (iii) access to the best infrastructure and tools available, wherever they may be, and (iv) openly-available results for critical review and future use. The presentation draws on examples from around the world to illustrate important key elements that can support effective collaboration, both at an individual and a team level. The means by which the researcher might identify good quality data and code for use is covered together with examples of the use of infrastructures of various sorts that may assist the team achieve their goal. The talk is given against the background of open science (UNESCO, 2021) and how the work of an individual and a project can be best presented within the academic community.

this talk was given in the Wichita State University Disaster Resilience Analytics Center Seminar Series

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Keywords

multi-factoral, open science, transdisciplinary, data science, cross-domain, collaboration

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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