Towards team-centered informatics: accelerating innovation in multidisciplinary scientific teams through visual analytics (Record no. 510329)

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Personal name Bhavnani, Suresh K. et al
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Title Towards team-centered informatics: accelerating innovation in multidisciplinary scientific teams through visual analytics
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Date of publication, distribution, etc 2019
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Extent p.50-72.
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Summary, etc A critical goal of multidisciplinary scientific teams is to integrate knowledge from diverse disciplines for the purpose of developing novel insights and innovations. For example, multidisciplinary translational teams (MTTs) which typically include physicians, biologists, statisticians, and informaticians, aim to integrate biological and clinical knowledge leading to innovations for improving health outcomes. However, such teams face numerous barriers in integrating multidisciplinary knowledge, which is further exacerbated by the explosion of molecular and clinical data generated from millions of patients. Here, we explore the use of a visual analytical representation to help MTTs integrate molecular and clinical data with the goal of accelerating translational insights. The results suggest that the visual analytical representation functioned as a “computational evolving boundary object” which (a) evolved through several emergent states that progressively helped integrate diverse disciplinary knowledge, (b) enabled team members to play primary and supportive roles in evolving the data representation resulting in a more egalitarian team structure, and (c) enabled the team to arrive at novel translational insights leading to domain and methodology publications. However, the interventions also revealed limitations in the approach motivating new visual analytical approaches. These results suggest (a) implications for theory related to modeling computational evolving boundary objects (CEBOs) as an instance of team-centered informatics, and (b) implications for practice related to the design and use of interactive features that enable teams to fluidly evolve CEBOs through emergent states, with the goal of deriving novel insights from large multiomics datasets. - Reproduced.
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Topical term or geographic name as entry element knowledge
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Topical term or geographic name as entry element Innovation
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Main entry heading Journal of Applied Behavioral Science
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Subject DIP Interdisciplinary approach
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Item type Articles
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent location Current location Date acquired Serial Enumeration / chronology Barcode Date last seen Koha item type
          Indian Institute of Public Administration Indian Institute of Public Administration 2019-08-11 55(1), Mar, 2019: p.50-72. AR120224 2019-08-11 Articles

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