Head Start: Improving Academic Literature Search with Overview Visualizations based on Readership Statistics (original) (raw)

Visualization of co-readership patterns from an online reference management system

In this paper, we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating knowledge domain visualizations. First, we investigate the distribution of subject areas in user libraries of educational technology researchers on Mendeley. The results show that around 69% of the publications in an average user library can be attributed to a single subject area. Then, we use co-readership patterns to map the field of educational technology. The resulting visualization prototype, based on the most read publications in this field on Mendeley, reveals 13 topic areas of educational technology research. The visualization is a recent representation of the field: 80% of the publications included were published within ten years of data collection. The characteristics of the readers, however, introduce certain biases to the visualization. Knowledge domain visualizations based on readership statistics are therefore multifaceted and timely, but it is important that the characteristics of the underlying sample are made transparent.

Building for users not for experts: designing a visualization of the literature domain

2007

Abstract As researchers we are constantly working with academic literature. Literature data is growing exponentially. Interacting with this huge amount of information has been a challenge for the field of HCI for years. The goal is to assist users in making sense of this information by producing usable designs. Information Visualization (InfoVis) augments users' cognition when interacting with complex data structures.

Supporting the Exploration of Semantic Features in Academic Literature using Graph-based Visualizations

2020

Literature search and recommendation systems have traditionally focused on improving recommendation accuracy through new algorithmic approaches. Less research has focused on the crucial task of visualizing the retrieved results to the user. Today, the most common visualization for literature search and recommendation systems remains the ranked list. However, this format exhibits several shortcomings, especially for academic literature. We present an alternative visual interface for exploring the results of an academic literature retrieval system using a force-directed graph layout. The interactive information visualization techniques we describe allow for a higher resolution search and discovery space tailored to the unique feature-based similarity present among academic literature. RecVis - the visual interface we propose - supports academics in exploring the scientific literature beyond textual similarity alone, since it enables the rapid identification of other forms of similarit...

Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization

Journal of the American Society for Information Science and Technology, 2012

Keeping up with rapidly growing research fields, especially when there are multiple interdisciplinary sources, requires substantial effort for researchers, program managers, or venture capital investors. Current theories and tools are directed at finding a paper or website, not gaining an understanding of the key papers, authors, controversies, and hypotheses. This report presents an effort to integrate statistics, text analytics, and visualization in a multiple coordinated window environment that supports exploration. Our prototype system, Action Science Explorer (ASE), provides an environment for demonstrating principles of coordination and conducting iterative usability tests of them with interested and knowledgeable users. We developed an understanding of the value of reference management, statistics, citation text extraction, natural language summarization for single and multiple documents, filters to interactively select key papers, and network visualization to see citation patterns and identify clusters. A three-phase usability study guided our revisions to ASE and led us to improve the testing methods.

Harnessing user library statistics for research evaluation and knowledge domain visualization

2012

Abstract Social reference management systems provide a wealth of information that can be used for the analysis of science. In this paper, we examine whether user library statistics can produce meaningful results with regards to science evaluation and knowledge domain visualization. We are conducting two empirical studies, using a sample of library data from Mendeley, the world's largest social reference management system.

Evaluating visual and statistical exploration of scientific literature networks

2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2011

Action Science Explorer (ASE) is a tool designed to support users in rapidly generating readily consumable summaries of academic literature. It uses citation network visualization, ranking and filtering papers by network statistics, and automatic clustering and summarization techniques. We describe how early formative evaluations of ASE led to a mature system evaluation, consisting of an in-depth empirical evaluation with four domain experts. The evaluation tasks were of two types: predefined tasks to test system performance in common scenarios, and user-defined tasks to test the system's usefulness for custom exploration goals. The primary contribution of this paper is a validation of the ASE design and recommendations to provide: easy-to-understand metrics for ranking and filtering documents, user control over which document sets to explore, and overviews of the document set in coordinated views along with detailson-demand of specific papers. We contribute a taxonomy of features for literature search and exploration tools and describe exploration goals identified by our participants.

Visualizing a Discipline: An Author Co-Citation Analysis of Information Science, 1972-1995

Journal of the American Society for …, 1998

in articles, regardless of which of their works are cited. discipline-information science-in terms of its au-ACA synthesizes many such counts. Now that 15 years thors. Names of those most frequently cited in 12 key have passed since it was introduced by White and Griffith journals from 1972 through 1995 were retrieved from So-(1981), the present writers wish to explore this literaturecial Scisearch via DIALOG. The top 120 were submitted based technique as a means for contributing to intellectual to author co-citation analyses, yielding automatic classifications relevant to histories of the field. Tables and history. As in that earlier article, we shall use authors graphics reveal: (1) The disciplinary and institutional affrom information science to illustrate that, although ACA filiations of contributors to information science; (2) the is applicable in any discipline (Bayer, Smart, & Mcspecialty structure of the discipline over 24 years; (3)

Metadata visualization of scholarly search results

Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies - i-KNOW '12, 2012

Studies of online search behaviour have found that searchers often face difficulties formulating queries and exploring the search results sets. These shortcomings may be especially problematic in digital libraries since library searchers employ a wide variety of information seeking methods (with varying degrees of support), and the corpus to be searched is often more complex than simple textual information. This paper presents Bow Tie Academic Search, an interactive Webbased academic library search interface aimed at supporting the strategic retrieval behaviour of searchers. In this system, a histogram of the most frequently used keywords in the top search results is provided, along with a compact visual encoding that represents document similarities based on the co-use of keywords. In addition, the list-based representation of the search results is enhanced with visual representations of citation information for each search result. A detailed view of this citation information is provided when a particular search result is selected. These tools are designed to provide visual and interactive support for query refinement, search results exploration, and citation navigation, making extensive use of the metadata provided by the underlying academic information retrieval system.

LRV: A Tool for Academic Text Visualization to Support the Literature Review Process

Computers, Materials & Continua, 2019

Text visualization is concerned with the representation of text in a graphical form to facilitate comprehension of large textual data. Its aim is to improve the ability to understand and utilize the wealth of text-based information available. An essential task in any scientific research is the study and review of previous works in the specified domain, a process that is referred to as the literature survey process. This process involves the identification of prior work and evaluating its relevance to the research question. With the enormous number of published studies available online in digital form, this becomes a cumbersome task for the researcher. This paper presents the design and implementation of a tool that aims to facilitate this process by identifying relevant work and suggesting clusters of articles by conceptual modeling, thus providing different options that enable the researcher to visualize a large number of articles in a graphical easy-to-analyze form. The tool helps the researcher in analyzing and synthesizing the literature and building a conceptual understanding of the designated research area. The evaluation of the tool shows that researchers have found it useful and that it supported the process of relevant work analysis given a specific research question, and 70% of the evaluators of the tool found it very useful.