scholarly journals Using Multiple Data Sources on Species Distribution for Biodiversity Assessment: The Prarie Crocus (Anemone Patens) as a Case Study

Blue Jay ◽  
2015 ◽  
Vol 73 (3) ◽  
pp. 102-122 ◽  
Author(s):  
Vladimir V. Kricsfalusy ◽  
Meng Li ◽  
Charu Gupta
Keyword(s):  
Species Distribution ◽  
Data Sources ◽  
Biodiversity Assessment ◽  
Multiple Data Sources ◽  
Multiple Data

scholarly journals Tocilizumab in rheumatoid arthritis: A case study of safety evaluations of a large postmarketing data set from multiple data sources

2015 ◽  
Vol 44 (4) ◽  
pp. 381-388 ◽  
Author(s):  
Jeffrey R. Curtis ◽  
Susana Perez-Gutthann ◽  
Samy Suissa ◽  
Pavel Napalkov ◽  
Natasha Singh ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Data Sources ◽  
Data Set ◽  
Multiple Data Sources ◽  
Multiple Data

scholarly journals Exploring Interprofessional, Interagency Multimorbidity Care: Case Study Based Observational Research

2017 ◽  
Vol 7 (1) ◽  
pp. 64-78 ◽  
Author(s):  
Eileen M. McKinlay ◽  
Sonya J. Morgan ◽  
Ben V. Gray ◽  
Lindsay M. Macdonald ◽  
Susan R.H. Pullon
Keyword(s):  
Case Studies ◽  
Help Seeking ◽  
Chronic Illnesses ◽  
Data Sources ◽  
Observational Research ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Collaborative Interactions ◽  
Coordination And Collaboration

Background The increase in multimorbidity or co-occurring chronic illnesses is a leading healthcare concern. Patients with multimorbidity require ongoing care from many different professionals and agencies, and often report a lack of integrated care. Objective To explore the daily help-seeking behaviours of patients with multimorbidity, including which health professionals they seek help from, how professionals work together, and perceptions and characteristics of effective interprofessional, interagency multimorbidity care. Design Using a case study observational research design, multiple data sources were assembled for four patients with multimorbidity, identified by two general practitioners in New Zealand. In this paper, two case studies are presented, including the recorded instances of contact and communication between patients and professionals, and between professionals. Professional interactions were categorized as consultation, coordination, or collaboration. Results The two case studies illustrated two female patients with likely similar educational levels, but with different profiles of multimorbidity, social circumstances, and personal capabilities, involving various professionals and agencies. Engagement between professionals showed varying levels of interaction and a lack of clarity about leadership or care coordination. The majority of interactions were one-to-one consultations and rarely involved coordination and collaboration. Patients were rarely included in communications between professionals. Conclusion Cases constructed from multiple data sources illustrate the complexity of day-to-day, interprofessional, interagency multimorbidity care. While consultation is the most frequent mode of professional interaction, targeted coordinated and collaborative interactions (including the patient) are highly effective activities. Greater attention should be given to developing and facilitating these interactions and determining who should lead them.

A statistical approach for estimating fish diet compositions from multiple data sources: Gulf of California case study

2010 ◽  
Vol 20 (8) ◽  
pp. 2188-2202 ◽  
Author(s):  
Cameron H. Ainsworth ◽  
Isaac C. Kaplan ◽  
Phillip S. Levin ◽  
Marc Mangel
Keyword(s):  
Gulf Of California ◽  
Statistical Approach ◽  
Data Sources ◽  
Fish Diet ◽  
Multiple Data Sources ◽  
Multiple Data

scholarly journals Integrating multiple data sources in species distribution modeling: a framework for data fusion*

Ecology ◽  
2017 ◽  
Vol 98 (3) ◽  
pp. 840-850 ◽  
Author(s):  
Krishna Pacifici ◽  
Brian J. Reich ◽  
David A. W. Miller ◽  
Beth Gardner ◽  
Glenn Stauffer ◽  
...  
Keyword(s):  
Data Fusion ◽  
Species Distribution ◽  
Species Distribution Modeling ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Distribution Modeling ◽  
Multiple Data

scholarly journals Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

2020 ◽  
Author(s):  
Lijing Wang ◽  
Aniruddha Adiga ◽  
Srinivasan Venkatramanan ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Sources ◽  
Learning Models ◽  
Multiple Data Sources ◽  
Multiple Data
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