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Research Data Access and Management in Modern Libraries
Handling and archiving data should be done in a highly professional and quality-controlled manner. For academic and research libraries, it is required to know how to document data and support traceability, as well as to make it reusable and productive. However, these institutions have different requirements relating to the archiving and reusability of data. Therefore, a comprehensive source of information is required to understand data access and management within these organizations. Research Data Access and Management in Modern Libraries is a critical scholarly resource that delves into innovative data management strategies and strategy implementation in library settings and provides best ...
Tolbooths and Town-houses
Illustrated throughout, this book includes detailed architectural and historical articles on over 90 surviving examples, and brief entries on about 100 demolished or altered buildings.
Project Team Rewards
This book gives you a comprehensive introduction to rewards in general and project team rewards in particular. Motivation theories and their impact on designing a reward system are explained. Throughout the book six so-called 'reward questions' are considered that need to be answered for designing a reward system. These reward questions are: Rewarding or not rewarding? Whom to reward? What to reward? What kind of reward? How much reward? When to reward? In addition, impacts of variable factors that may influence the answers to the reward questions are identified and explained. Some of those factors are employee's age, the company's culture but also project characteristics such as goal clarity, applied success criteria, project duration or member fluctuation. Please note that this book originally was written as a Master's Thesis. Accordingly, you should not expect to read a 'normal' text book but a Master's Thesis. Visit www.project-team-rewards.com for more details.
Recommender Systems
In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
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