Lessons from JSTOR: User Behavior and Faculty Attitudes

Author(s):  
Kevin M. Guthrie
2010 ◽  
Author(s):  
Laura Aoki ◽  
Mary E. Kite ◽  
Mary Ellen Dello Stritto

Author(s):  
Markus Wust

This qualitative study investigates how faculty gather information for teaching and research and their opinions on open access approaches to scholarly communication. Despite generally favorable reactions, a perceived lack of peer review and impact factors were among the most common reasons for not publishing through open-access forums.Cette étude qualitative examine comment les membres du corps professoral recueillent l’information pour l’enseignement et la recherche, et leurs opinions envers les approches de la communication scientifique à libre accès. Malgré des réactions généralement favorables, le manque perçu de révision par les pairs et les facteurs d’impact comptent parmi les motifs habituellement évoqués pour ne pas publier sur ces tribunes à libre accès. 


2013 ◽  
Author(s):  
David Darmon ◽  
Jared Sylvester ◽  
Michelle Girvan ◽  
William M. Rand

2010 ◽  
Author(s):  
Mohamed Husain ◽  
Amarjeet Singh ◽  
Manoj Kumar ◽  
Rakesh Ranjan

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.


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