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1. Expert range maps of global mammal distributions harmonised to three taxonomic authorities

   https://doi.org/10.1111/jbi.14330  

   Marsh, Charles J.; Sica, Yanina V.; Burgin, Connor J.; Dorman, Wendy A.; Anderson, Robert C.; del Toro Mijares, Isabel; Vigneron, Jessica G.; Barve, Vijay; Dombrowik, Victoria L.; Duong, Michelle; et al (May 2022, Journal of Biogeography)
2. Transforming Teams of Experts into Expert Teams: Eight Principles of Expert Team Performance

   https://doi.org/ISSN 2573-2773  

   Bisbey, Tiffany; Traylor, Allison; Salas, Eduardo (January 2021, Journal of expertise)

   Anders Ericsson’s seminal research on expert performance spurred a number of streams of research across psychological disciplines. Though his work was primarily focused on expert individual performance, there has been increasing interest over the past several decades on the factors underlying expert teamwork. This paper advances eight principles of expert team performance based on decades of team science research: shared mental models, learning and adaptation, role clarity, shared vision, dynamic leadership, psychological safety, cooperation and coordination, and resilience. In addition, we review a number of team development interventions aimed at building team expertise including team training, simulation, coaching, and debriefing. Accordingly, this paper is divided into three sections addressing (1) how expert teams perform, (2) interventions to develop expert team performance, and (3) a reflection on the role Anders Ericsson’s work has played in team science, including a personal reflection from Eduardo Salas on deliberate and guided practice. 

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3. Transforming Teams of Experts into Expert Teams: Eight Principles of Expert Team Performance

   Bisbey, Tiffany; Traylor, Allison; Salas, Eduardo (January 2021, Journal of expertise)
4. Prediction with expert advice applied to the problem of prediction with expert advice

   https://doi.org/10.1007/s11229-022-03809-5  

   Herrmann, Daniel A. (July 2022, Synthese)

   Abstract We often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice (PWEA) framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well. 

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5. REMatch: Research Expert Matching System

   https://doi.org/10.1109/BDVA.2018.8534021  

   Hossain, Md Iqbal; Kobourov, Stephen; Purchase, Helen; Surdeanu, Mihai (October 2018, International Symposium on Big Data Visual and Immersive Analytics (BDVA))

   We describe a system designed to process, analyze and visualize academic data, from research papers and research proposals to list of courses taught, consulting, internal and external service. This can be helpful in identifying experts in a given field for future collaborations, as well as in putting together strong multi-disciplinary teams to apply for future research funding. Our REMatch system aims to support such tasks by leveraging natural language processing, machine learning, and interactive visualization. Specifically, REMatch provides a functional system that implements in-the-browser, map-based interactive navigation of a large underlying network, supporting semantic zooming, panning, searching, and map overlays. A prototype of the system is evaluated with a small-scale case study. 

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