Shifting Paradigms in Information Flow: An Open Science Framework (OSF) for Knowledge Sharing Teams

Abstract

Aim/Purpose: This paper explores the implications of machine-mediated communication on human interaction in cross-disciplinary teams. The authors explore the relationships between Open Science Theory, its contributions to team science, and the opportunities and challenges associated with adopting open science principles. Background: Open Science Theory impacts many aspects of human interaction throughout the scholarly life cycle and can be seen in action through various technologies, which each typically touch only one such aspect. By serving multiple aspects of Open Science Theory at once, the Open Science Framework (OSF) serves as an exemplar technology. As such it illustrates how Open Science Theory can inform and expand cognitive and behavioral dynamics in teams at multiple levels in a single tool. Methodology: This concept paper provides a theoretical rationale for recommendations for exploring the connections between an open science paradigm and the dynamics of team communication. As such theory and evidence have been culled to initiate a synthesis of the nascent literature, current practice and theory. Contribution: This paper aims to illuminate the shared goals between open science and the study of teams by focusing on science team activities (data management, methods, algorithms, and outputs) as focal objects for further combined study. Findings: Team dynamics and characteristics that will affect successful human/machine assisted interactions through mediators of workflow culture, attitudes about ownership of knowledge, readiness to share openly, shifts from group-driven to user-driven functionality, group-organizing to self-organizing structures, and the development of trust as teams regulate between traditional and open science dissemination. Recommendations for Practitioners: Participation in open science practices through machine-assisted technologies in team projects/scholarship should be encouraged. Recommendation for Researchers: The information provided highlights areas in need of further study in team science as well as new primary sources of material in the study of teams utilizing machine-assisted methods in their work. Impact on Society: As researchers take on more complex social problems, new technology and open science practices can complement the work of diverse stakeholders while also providing opportunities to broaden impact and intensify scholarly contributions. Future Research: Future investigation into the cognitive and behavioral research conducted with teams that employ machine-assisted technologies in their workflows would offer researchers the opportunity to understand better the relationships between intelligent machines and science teams’ impacts on their communities as well as the necessary paradigmatic shifts inherent when utilizing these technologies.

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