A Web application for creating and sharing visual bibliographies

The amount of information provided by peer-reviewed scientific literature citation indexes such as Scopus, Web of Science (WOS), CrossRef and OpenCitations is huge: it offers to the users a lot of metadata about publications, such as the list of papers written by a specific author, the editorial and content details of a paper, the list of references and citations. But, for a researcher could be interesting also to: extract in real-time these data in order to create bibliographies, for example, starting with a small set of significant papers or a restricted number of authors, and progressively enriching them by exploring cited/citing references; dispose of a graphical and aggregate representation for them; be able to easily share them with other interested researchers. With these main intents, we modelled and realized VisualBib, a Web application prototype, which enables the user to select sets of papers and/or authors in order to create customized bibliographies, and in real-time visualize them, aggregating data from different sources, in a comprehensive, holistic graphical view. The bibliographies are displayed using time-based visualizations, called narrative views, which contain explicit representations of the authorship and citing relations. These views may help users to: describe a research area; disseminate the research on a specific topic through the sharing of personal points of view; show in a fresh look the entire production of a researcher, or research groups, in order present or evaluate it

A “Look” into the IS Discipline through the Lens of MIS Quarterly: A Visual Examination of Scholar Characteristics

Academic disciplines naturally form their own knowledge cultures. This research examines characteristics of knowledge creators by investigating a subset of information systems (IS) researchers, namely those who have published in one of the field’s premier journals – MIS Quarterly. Author characteristics (pedigree, gender, various location data, etc.) are examined and reported on. Additionally, with the aid of modern visualization tools such as Tableau and/or Microsoft Power BI, influential scholarly foci (knowledge centers) are analyzed. Findings suggest an increase of women, international scholars, and locations over time, are adding to the richness and diversity of the IS field. Furthermore, this research presents some discussion and presentation of the migratory pattern of IS researchers utilizing dynamic mapping visualizations

Applications of Mash-ups for a Digital Journal

The WWW is currently experiencing a revolutionary growth due to numerous emerging tools, techniques and concepts. Digital journals thus need to transform themselves to cope with this evolution of the web. With their growing information size and access, conventional techniques for managing a journal and supporting authors and readers are becoming insufficient. Journals of the future need to provide innovative administrative tools in helping its managers to ensure quality. They also need to provide better facilities for assisting authors and readers in making decisions regarding their submission of papers and in providing novel navigational features for finding relevant publications and collaborators in particular areas of interest. In this paper, we explore an innovative solution to address these problems by using an emerging Web 2.0 technology. We explore the application of mash-ups for J.UCS - the Journal of Universal Computer Science and encourage readers and authors to try out the applications (see section 11 Conclusions). J.UCS can then serve as a model for contemporary electronic journals

의학 연구에서의 과학적 증거의 활용을 위한 시각적 분석 시스템 디자인

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.Evidence-based medicine, "the conscientious, explicit, and judicious use of current best evidence in healthcare and medical research" [98], is one of the most widely accepted medical paradigms of modern times. Searching, reviewing, and synthesizing reliable and high-quality scientific evidence is the key step for the paradigm. However, despite the widespread use of the EBM paradigm, challenges remain in applying Evidence-based medicine protocols to medical research. One of the barriers to applying the best scientific evidence to medical research is the severe literature and clinical data overload that causes the evidence-based tasks to be tremendous time-consuming tasks that require vast human effort. In this dissertation, we aim to employ visual analytics approaches to address the challenges of searching and reviewing massive scientific evidence in medical research. To overcome the burden and facilitate handling scientific evidence in medical research, we conducted three design studies and implemented novel visual analytics systems for laborious evidence-based tasks. First, we designed PLOEM, a novel visual analytics system to aid evidence synthesis, an essential step in Evidence-Based medicine, and generate an Evidence Map in a standardized method. We conducted a case study with an oncologist with years of evidence-based medicine experience. In the second study, we conducted a preliminary survey with 76 medical doctors to derive the design requirements for a biomedical literature search. Based on the results, We designed EEEVis, an interactive visual analytic system for biomedical literature search tasks. The system enhances the PubMed search result with several bibliographic visualizations and PubTator annotations. We performed a user study to evaluate the designs with 24 medical doctors and presented the design guidelines and challenges for a biomedical literature search system design. The third study presents GeneVis, a visual analytics system to identify and analyze gene expression signatures across major cancer types. A task that cancer researchers utilize to discover biomarkers in precision medicine. We conducted four case studies with domain experts in oncology and genomics. The study results show that the system can facilitate the task and provide new insights from the data. Based on the three studies of this dissertation, we conclude that carefully designed visual analytics approaches can provide an enhanced understanding and support medical researchers for laborious evidence-based tasks in medical research.근거중심의학(Evidence-Based Medicine)이란 "임상 치료 및 의학 연구에서 현재 존재하는 최고의 증거를 양심적이고, 명백하며, 분별 있게 이용하는 방법론"이며 [98], 현대 의학에서 가장 널리 받아들여지는 의학 패러다임이다. 신뢰할 수 있는 고수준의 과학적 근거를 검색, 검토, 합성하는 것이야 말로 근거중심의학의 핵심이다. 하지만, 근거중심의학이 이미 광범위하게 사용되고 있음에도 불구하고, 의학 연구에 근거중심의학의 프로토콜을 실천하는 데에는 여전히 많은 어려움이 따른다. 의료 문헌 정보, 임상 정보 및 유전체학 정보까지 연구자가 검토해야 할 근거의 양은 방대하며 광범위하다. 또한 의학과 기술의 발전으로 인해 점차 더 빠른 속도로 늘어나고 있기에, 이를 모두 엄밀히 검토하기 위해서는 막대한 양의 시간과 인력이 있어야 한다. 본 논문은 시각적 분석 방법론을 접목하여 의학 연구에서 방대한 과학적 증거를 검색하고 검토할 시 발생하는 막대한 인적 자원의 과부하 문제를 완화하고자 한다. 이를 위하여 근거중심의학의 절차 중 특히 인력 소모가 막심한 절차들을 선정하고, 이러한 난관을 극복하고 보다 효율적이고 효과적으로 데이터에서 유의미한 정보를 도출할 수 있게끔 보조하는 세 가지 시각적 분석 시스템들을 구현하였으며, 각각의 시스템에 관한 디자인 연구를 수행하였다. 우선 첫 디자인 연구에서는 근거중심의학 연구에 있어 필수적 단계인 근거 합성 방법론의 하나인 근거 매핑(Evidence Mapping) 과정을 지원하기 위한 시각적 분석 시스템 PLOEM을 설계했다. 그리고 이를 검증하기 위해 다년간의 근거 기반 의료 경험이 있는 종양학자와 함께 사례 연구를 수행했다. 두 번째 디자인 연구에서는 의학 문헌 검색 시스템의 요구사항 분석을 위해 총 76명의 의사를 상대로 설문조사를 진행하였고, 이러한 분석을 바탕으로 대화형 시각적 분석 시스템인 EEEVis를 설계했다. 이 시스템은 여러 종의 서지 정보 시각화 인터페이스와 PubTator의 주석 정보를 활용하여 PubMed 검색 엔진의 검색 결과를 증강하는 시스템이며, 이를 평가하기 위해 총 24명의 의사와 함께 사용자 연구를 수행하였다. 이 연구 결과를 바탕으로 의학 문헌 검색 시스템에 대한 설계 지침과 과제를 제시한다. 마지막으로 세 번째 디자인 연구에서는 임의의 유전자군의 유전자 발현 패턴을 주요 암 유형에 따라 시각화하고 분석할 수 있는 시스템인 GeneVis를 설계하였다. 암 유형에 따른 유전자 발현 패턴의 분석과 비교는 암 연구자들이 정밀 의학에서 생체 지표(Biomarker)를 발견하기 위해 빈번히 수행하는 작업이다. 우리는 종양학 전문가 및 유전체학 전문가 총 4인을 대상으로 사례 연구를 진행하였고, 그 결과 GeneVis가 해당 작업을 더 수월하게 수행하는 것과 기존의 데이터에서 새로운 정보를 도출하는 것에 도움이 되었음을 확인하였다. 위의 세 디자인 연구의 결과를 바탕으로, 본 논문은 사용자 분석과 작업 분석을 동반한 시각적 분석 방법론이 의학 연구의 근거 관련 작업의 어려움을 해소하고, 분석 데이터에 대한 보다 나은 이해를 제공하는 것이 가능하다고 결론 내린다.CHAPTER1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Outline 5 CHAPTER2 Related Work 7 2.1 Evidence Mapping: Graphical Representation for a Scientific Evidence Landscape 7 2.2 Scientific Literature Visualizations and Bibliography Visualizations 9 2.3 Visual Anlytics Systems for Genomics Data sets and Research Tasks 10 CHAPTER3 PLOEM: An Interactive Visualization Tool for Effective Evidence Mapping with Biomedical literature 12 3.1 Introduction 12 3.2 Visual Representations and Interactions of PLOEM 14 3.2.1 Overview of the PICO Criteria 14 3.2.2 Trend Visualization with the Timeline view 17 3.2.3 Representing the PICO Co-occurrence with the Relation view 20 3.2.4 Study detail view 22 3.3 Usage Scenarios: Visualizing Various Study Sizes with PLOEM 23 3.4 Conclusion 24 CHAPTER4 EEEvis: Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system 26 4.1 Introduction 26 4.1.1 Motivation 26 4.1.2 Preliminary Survey: A Questionnaire on conventional literature search methods 28 4.1.3 Design Requirements for Biomedical Literature Search Systems 36 4.2 System and Interface Implementation of EEEVis 37 4.2.1 System Overview 37 4.2.2 Bibliography Filters 40 4.2.3 Timeline View 41 4.2.4 Co-authorship Network View 43 4.2.5 Article List and Detail View 44 4.3 User Study 46 4.3.1 Participants 46 4.3.2 Procedures 48 4.3.3 Results and Observations 50 4.4 Discussion 54 4.4.1 Design Implications 56 4.4.2 Limitations and Future Work 57 4.5 Conclusions 59 CHAPTER5 GeneVis: A Visual Analytics Systemfor Gene Signature Analysis in Cancers 68 5.1 Motivation 68 5.2 System and Interface Implementation 69 5.2.1 System Overview 69 5.2.2 Gene Expression Detail View 71 5.2.3 Gene Vector Projection View 72 5.2.4 Gene x Cancer Type Heatmap view 74 5.2.5 User Interaction in Multiple Coordinated Views 76 5.3 Case Studies 76 5.3.1 Participants 76 5.3.2 Task and Procedures 76 5.3.3 Case1: Identifying SimilarGeneSignatures with TGFB1in Hallmark Gene Sets 80 5.3.4 Case2: Identifying Cluster Patterns in the HRD data set 81 5.3.5 Results 82 5.4 Summary 85 CHAPTER6 Conclusion and future work 86 6.1 Conclusion 86 6.2 Future Work 87 Abstract (Korean) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102박

Metadata visualization of scholarly search results

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. To address these problems, an interactive Web-based library search interface is presented, which has been designed to support strategic retrieval behaviour of library searchers. This system takes advantage of the rich metadata associated with academic documents and employs information visualization techniques to provide searchers with additional information-seeking tools. These tools are designed to facilitate visual and interactive query refinement, search results exploration, and citation navigation. User evaluations illustrate the potential benefits of the design choices in comparison to a list-based digital library search interface

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance

Understanding research trends in conferences using PaperLens

PaperLens is a novel visualization that reveals trends, connections, and activity throughout a conference community. It tightly couples views across papers, authors, and references. PaperLens was developed to visualize 8 years (1995-2002) of InfoVis conference proceedings and was then extended to visualize 23 years (1982-2004) of the CHI conference proceedings. This paper describes how we analyzed the data and designed PaperLens. We also describe a user study to focus our redesign efforts along with the design changes we made to address usability issues. We summarize lessons learned in the process of design and scaling up to the larger set of CHI conference papers

Visualizing & Exploring Networks Using Semantic Substrates

Visualizing and exploring network data has been a challenging problem for HCI (Human-Computer Interaction) Information Visualization researchers due to the complexity of representing networks (graphs). Research in this area has concentrated on improving the visual organization of nodes and links according to graph drawing aesthetics criteria, such as minimizing link crossings and the longest link length. Semantic substrates offer a different approach by which node locations represent node attributes. Users define semantic substrates for a given dataset according to the dataset characteristics and the questions, needs, and tasks of users. The substrates are typically 2-5 non-overlapping rectangular regions that meaningfully lay out the nodes of the network, based on the node attributes. Link visibility filters are provided to enable users to limit link visibility to those within or across regions. The reduced clutter and visibility of only selected links are designed to help users find meaningful relationships. This dissertation presents 5 detailed case studies (3 long-term and 2 short-term) that report on sessions with professional users working on their own datasets using successive versions of the NVSS (Network Visualization by Semantic Substrates, http://www.cs.umd.edu/hcil/nvss) software tool. Applications include legal precedent (with court cases citing one another), food-web (predator-prey relationships) data, scholarly paper citations, and U. S. Senate voting patterns. These case studies, which had networks of up to 4,296 nodes and 16,385 links, helped refine NVSS and the semantic substrate approach, as well as understand its limitations. The case study approach enabled users to gain insights and form hypotheses about their data, while providing guidance for NVSS revisions. The proposed guidelines for semantic substrate definitions are potentially applicable to other datasets such as social networks, business networks, and email communication. NVSS appears to be an effective tool because it offers a user-controlled and understandable method of exploring networks. The main contributions of this dissertation include the extensive exploration of semantic substrates, implementation of software to define substrates, guidelines to design good substrates, and case studies to illustrate the applicability of the approach to various domains and its benefits