Checking date: 30/04/2023

Course: 2023/2024

Information visualization
Master in Libraries, Archives and Digital Continuity (Plan: 500 - Estudio: 335)

Coordinating teacher: OLMEDA GOMEZ, CARLOS

Department assigned to the subject: Library and Information Sciences Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
There are no specific course prerequisites for this course
BASIC SKILLS CB9 That students know how to communicate their conclusions and the latest knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way GENERAL COMPETENCES CG 5 Recognize the growing importance of teamwork in the workplace and demonstrate adaptability and integration in different work environments, maintaining fluid relationships and communications. CG 9 Integrate knowledge, formulate judgments and communicate their conclusions, as well as the latest knowledge and reasons that support them, to specialized and non-specialized audiences in a clear and unambiguous way. CG 11 Ability to interpret, apply and innovate in context methodologies, technologies and new methods of analysis, treatment and information retrieval. SPECIFIC COMPETENCES CE 7 Representation of scientific knowledge and epistemic communities, using data mining and network analysis techniques. CE 9 Acquire the necessary knowledge to prepare the Master's Final Project, academic papers, reports or similar documents, in an appropriate way, both from the formal point of view and from the content perspective. LEARNING OUTCOMES 1. Apply the fundamental principles of data retrieval, through the manipulation of bibliographic databases of indexed scientific literature. 2. Constructs science maps and designs and implements projects to support research into a real community or issue.
Skills and learning outcomes
Description of contents: programme
1. Infovis overview. 2. Statistical visualisation types. 3. Topic visualizations. 4. Network visualizations.
Learning activities and methodology
TRAINING ACTIVITIES OF THE CURRICULUM RELATED TO SUBJECTS AF1 Individual work for the study of theoretical and practical materials developed and contributed by the teacher AF2 Individual work for problem solving and case studies AF3 Theoretical-practical classes AF4 Tutorials AF5 Active participation in forums in the educational platform Code Activity Total hours Onsite Hours % Onsite AF1 125(45) 0 0 AF2 80(32) 0 0 AF3 12(3) 12(3) 100 AF4 10(2) 0 0 AF5 124(0) 0 0 AF6 5(2) 0 0 AF7 4(1) 0 0 TOTAL SUBJ.(COURSE) 360(90) 12(3) 3,3 LA 1. Individual work for the study of theoretical and practical materials developed and contributed by the teacher LA 2. Individual work for problem solving and case studies LA 3. Video-tutorials LA 4. Active participation in forums enabled by the teacher in the virtual educational platform LA 5. Conducting self-evaluation tests to review content TEACHING METHODOLOGIES M 1. Explanations of the teacher with support of computer and audiovisual media, in which the main concepts of the subjects are developed M 2. Critical reading of texts recommended by the professor of the subject M 3. Resolution of practical cases and problems raised by the teacher in an individual way M 4. Reading of theoretical and practical teaching materials TUTORIALS The schedules of the tutorials, adjusted to the provisions of the University, It will be able to be consulted in the own space of the subject in the platform of teaching and learning (Aula Global). It will include two modes: One to attend face-to-face and the other for online handle. Furthermore students can request and arrange with the teacher tutorials online or offline outside of these schedules.
Assessment System
  • % end-of-term-examination 50
  • % of continuous assessment (assigments, laboratory, practicals...) 50
Calendar of Continuous assessment
Basic Bibliography
  • Mazza, Ricardo. Introduction to Information visualization. Springer. 2009
  • Tufte, Edward R.. The visual display of quantitative information. 2nd ed.. Graphic Press. 2007
  • Yau, Nathan. Data points: visualization that means something. John Wiley & Sons. 2013
Additional Bibliography
  • Börner, K., Chen, C., & Boyack, K.W. (2003). Visualizing knowledge domains.. Annual Review of Information Science and Technology, 37(1), 179¿255.
  • Chen, Ch. (2017). Science mapping. A systematic review of the literature. . Journal of Data and Information Science, Vol. 2 No. 2, 2017 pp 1¿40.
  • Kim, M.Ch., Zhu, Y., Chen, Ch (2016). How are they different? A quantitative domain comparison of information visualization and data visualization (2000-2014). Scientometrics (2016) 107, pp. 123 165.
  • Nardi P, Di Matteo G, Palahi M, Scarascia Mugnozza G.(2016). Structure and Evolution of Mediterranean Forest Research: A Science Mapping Approach.. PLoS ONE 11(5): e0155016..
  • Olmeda-Gómez, C., Ovalle-Perandones, MªA., Perianes-Rodríguez, A. (2017). Co-word analysis and thematic landscapes in Spanish information science literature, 1985-2014. Scientometrics 113 (1), 195-217.
  • Olmeda-Gómez, C., Romá-Mateo, R., Ovalle-Perandones, Mª A. (2019). Overview of trends in global epigenetic research (2009-2017). Scientometrics, 119 (3), 1545-1574.
  • Vargas-Quesada; B; Chinchilla-Rodríguez, Z. & Rodriguez, N. (2017). Identification and Visualization of the Intellectual Structure in Graphene Research. Frontiers in Research Metrics and Analytics. Vol 2. , pp. 1-22..
  • White, H.D., & McCain, K.W. (1997).. Visualization of literatures. Annual Review of Information Science and Technology, 32, 99-168..
  • van Eck, N.J., Waltman, L (2014). Visualizing bibliometric networks. . En Measuring scholarly impact (285-320). Heidelberg: Springer

The course syllabus may change due academic events or other reasons.