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Analyzing social media networks with NodeXL : insights from a connected world
Analyzing Social Media Networks with NodeXL offers backgrounds in information studies, computer science, and sociology. This book is divided into three parts: analyzing social media, NodeXL tutorial, and social-media network analysis case studies. Part I provides background in the history and concepts of social media and social networks. Also included here is social network analysis, which flows from measuring, to mapping, and modeling collections of connections. The next part focuses on the detailed operation of the free and open-source NodeXL extension of Microsoft Excel, which is used in all exercises throughout this book. In the final part, each chapter presents one form of social media, such as e-mail, Twitter, Facebook, Flickr, and Youtube. In addition, there are descriptions of each system, the nature of networks when people interact, and types of analysis for identifying people, documents, groups, and events.
*Walks you through NodeXL, while explaining the theory and development behind each step, providing takeaways that can apply to any SNA *Demonstrates how visual analytics research can be applied to SNA tools for the mass market *Includes case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and wikis *Download companion materials and resources at https://nodexl.codeplex.com/documentation
eBook
Practical Tableau : 100 tips, tutorials, and strategies from a Tableau zen master
\"Whether you have some experience with Tableau software or are just getting started, this manual goes beyond the basics to help you build compelling, interactive data visualization applications. Author Ryan Sleeper, one of the world's most qualified Tableau consultants, complements his web posts and instructional videos with this guide to give you a firm understanding of how to use Tableau to find valuable insights in data.\"--Back cover.
Book
Challenges and Strategies for Educational Virtual Reality
Virtual reality (VR) is a rich visualization and analytic platform that furthers the library’s mission of providing access to all forms of information and supporting pedagogy and scholarship across disciplines. Academic libraries are increasingly adopting VR technology for a variety of research and teaching purposes, which include providing enhanced access to digital collections, offering new research tools, and constructing new immersive learning environments for students. This trend suggests that positive technological innovation is flourishing in libraries, but there remains a lack of clear guidance in the library community on how to introduce these technologies in effective ways and make them sustainable within different types of institutions. In June 2018, the University of Oklahoma hosted the second of three forums on the use of 3D and VR for visualization and analysis in academic libraries, as part of the project Developing Library Strategy for 3D and Virtual Reality Collection Development and Reuse(LIB3DVR), funded by a grant from the Institute of Museum and Library Services. This qualitative study invited experts from a range of disciplines and sectors to identify common challenges in the visualization and analysis of 3D data, and the management of VR programs, for the purpose of developing a national library strategy.
Journal Article
Weed Your Budget
This paper presents a case study about opportunities for libraries to elevate reporting on their organization's financial status, transactions, and available funds using data visualization, specifically with Power BI, based on work at University of Oregon Libraries. The Power BI dashboard aggregates data from the university’s financial system and integrated library system to provide a consolidated view, and to tell the library's story in a more engaging way. The author proposes using this type of business intelligence technology to provide a transformative impact on financial operations of libraries.
Journal Article
Three-Dimensional Image Visualization under Photon-Starved Conditions Using IN/I Observations and Statistical Estimation
In this paper, we propose a method for the three-dimensional (3D) image visualization of objects under photon-starved conditions using multiple observations and statistical estimation. To visualize 3D objects under these conditions, photon counting integral imaging was used, which can extract photons from 3D objects using the Poisson random process. However, this process may not reconstruct 3D images under severely photon-starved conditions due to a lack of photons. Therefore, to solve this problem, in this paper, we propose N-observation photon-counting integral imaging with statistical estimation. Since photons are extracted randomly using the Poisson distribution, increasing the samples of photons can improve the accuracy of photon extraction. In addition, by using a statistical estimation method, such as maximum likelihood estimation, 3D images can be reconstructed. To prove our proposed method, we implemented the optical experiment and calculated its performance metrics, which included the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), peak-to-correlation energy (PCE), and the peak sidelobe ratio (PSR).
Journal Article