Opening the Net: Building a Distributed Team

2021 ◽  
pp. 111-124
Author(s):  
Alberto S. Silveira
Keyword(s):  
Distributed Team

Distributed Team Training: Effective Feedback

2010 ◽  
Author(s):  
Kevin B. Oden
Keyword(s):  
Team Training ◽  
Effective Feedback ◽  
Distributed Team

Supporting new online instructors and engaging remote learners during COVID-19: a distributed team teaching approach

2021 ◽  
pp. 1-21
Author(s):  
Vanessa P. Dennen ◽  
Lauren M. Bagdy ◽  
Ömer Arslan ◽  
Hajeen Choi ◽  
Zhichun Liu
Keyword(s):  
Team Teaching ◽  
Teaching Approach ◽  
Online Instructors ◽  
Distributed Team

Colossal, Scattered, and Chaotic (Planning with a Large Distributed Team)

2008 ◽  
Author(s):  
Wes Williams ◽  
Mike Stout
Keyword(s):  
Distributed Team

Distributed team performance in software development

2005 ◽  
Vol 37 (3) ◽  
pp. 357-357
Author(s):  
Martha L. Hause
Keyword(s):  
Software Development ◽  
Team Performance ◽  
Distributed Team

A Method for Rigorously Assessing Causal Mental Models to Support Distributed Team Cognition

2020 ◽  
pp. 115-135
Author(s):  
Jill L. Drury ◽  
Mark S. Pfaff ◽  
Gary L. Klein
Keyword(s):  
Mental Models ◽  
Team Cognition ◽  
Distributed Team

scholarly journals case study in distributed team science in research using electronic health records

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Jiao Song ◽  
Elizabeth Elliot ◽  
Andrew D Morris ◽  
Joannes J Kerssens ◽  
Ashley Akbari ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Oral Anticoagulants ◽  
Cost Effective ◽  
Study Cohort ◽  
Team Science ◽  
Data Types ◽  
Health Records ◽  
Distributed Team ◽  
Electronic Health ◽  
The Uk

IntroductionDue to various regulatory barriers, it is increasingly difficult to move pseudonymised routine health data across platforms and among jurisdictions. To tackle this challenge, we summarized five approaches considered to support a scientific research project focused on the risk of the new non-vitamin K Target Specific Oral Anticoagulants (TSOACs) and collaborated between the Farr institute in Wales and Scotland. ApproachIn Wales, routinely collected health records held in the Secure Anonymous Information Linkage (SAIL) Databank were used to identify the study cohort. In Scotland, data was extracted from national dataset resources administered by the eData Research & Innovation Service (eDRIS) and stored in the Scottish National Data Safe Haven. We adopted a federated data and multiple analysts approach, but arranged simultaneous accesses for Welsh and Scottish analysts to generate study cohorts separately by implementing the same algorithm. Our study cohort across two countries was boosted to 6,829 patients towards risk analysis. Source datasets and data types applied to generate cohorts were reviewed and compared by analysts based on both sites to ensure the consistency and harmonised output.  DiscussionThis project used a fusion of two approaches among five considered. The approach we adopted is a simple, yet efficient and cost-effective method to ensure consistency in analysis and coherence with multiple governance systems. It has limitations and potentials of extending and scaling. It can also be considered as an initialisation of a developing infrastructure to support a distributed team science approach to research using Electronic Health Records (EHRs) across the UK and more widely. KeywordsTeam science, cross-jurisdictional data linkage, electronic health records

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