Scientific information gathering and processing for engineering research


Course description

This course will make you a better researcher and provide crucial project management training for work life. The focus is on Information retrieval, Information processing, Project management, and Technical report writing.


According to studies conducted by Prof. Russell Korte at three U.S. organizations—a global automobile manufacturing company, a manufacturer of computer components, and a government transportation agency—20-50% of newly graduated employees quit within 24 months, mainly due to factors related to the team they were placed in, such as relationships, collaboration, mentoring, acceptance, leadership, and assignment (Korte et al. 2015).


In this course, you will get to practice project management using both Waterfall and Agile. You will get to experience group development and conflicts, while solving a challenging software engineering task related to information retrieval and processing. After the course, you know how to search for scientific information systematically, e.g. in Scopus and Web of Science. You will have experience working in a team, using Scrum for project management. You have written a summary of an article and a section of a technical report. You have contributed to writing open source software in Python or JavaScript to automate information extraction and processing.


這堂課將會使你成為更好的研究者,並提供你工作所需的專案管理能力,此課程著重於資料檢索、資料分析、管理專案以及論文撰寫。根據Prof. Russell Korte在三間美國企業的研究:一間跨國的汽車製造公司、一間電腦零件製造公司以及一間貨運代理公司,顯示20%到50%的社會新鮮人會在24個月內辭掉工作,其中問題的主因源自於團隊合作能力不足,如人際關係、合作能力、協助指導、容忍度、領導能力以及作業分工(Korte et al. 2015),有鑒於此,在此課程你會有機會使用瀑布式與敏捷式的專案管理技巧,並學習在開發資料搜尋軟體時會碰到的組織發展和衝突的應對方法。


Course Objectives

  • Be able to search for scientific information systematically
  • Have experience extracting information from some reference database platforms
  • Be able to explain the current state of the art of information retrieval
  • Be able to analyse and select scientific articles for a specific question
  • Write a summary of an article with relation to a specific question
  • Write a technical report
  • Know the difference between waterfall and agile project management
  • Be able to use scrum for project management
  • Have experience using Git for version control and writing in Latex
  • Judge if the criteria of a task have been met
  • Discuss scientific results
  • Give a progress report orally
  • Contribute towards open source software development
  • Be able to outline a system for automated information processing

Teaching Strategies

How this course is going to be taught

  • Lecture 80% 80%
  • Discussion 15% 15%
  • Video/Music Appreciation 5% 5%

Course material

Selected scientific articles, books, and technical documentation (see the references), and last year’s technical report.

Course outline (2019)
  1. Introduction and team formation
  2. Group development and tools for information retrieval
  3.  Individuals in teams, effective feedback, and comparison of reference databases
  4. Team building and summary writing
  5.  Latex and GIT
  6. Project management, Waterfall vs. Agile, and Scrum
  7. Brainstorming
  8. Backlog creation, sprint planning, first sprint
  9. Sprint review & retrospective, sprint planning, second sprint
  10. Team maturity, sprint review & retrospective, sprint planning, third sprint
  11. Sprint review & retrospective, sprint planning, fourth sprint
  12. Software solution stack, testing, and documentation
  13. Sprint review & retrospective, sprint planning, fifth sprint
  14. UML diagrams and code review
  15. Trends in Data Science and Computer Science, Sprint review & retrospective, sprint planning, sixth sprint
  16. Technical report writing
  17. Final presentation, sprint review & retrospective.


  1. Attwood, T. K. et al. Calling International Rescue: knowledge lost in literature and data landslide! Biochem. J. 424, 317–333 (2009).
  2. Sanderson, M. & Croft, W. B. The History of Information Retrieval Research. Proc. IEEE. 100, Special Centennial Issue, 1444–1451, (2012).
  3. Korte, R. et al. (Mis)Interpretations of Organizational Socialization: The Expectations and Experiences of Newcomers and Managers. Human Resource development quarterly, 26(2), 185-208, (2015). DOI: 10.1002/hrdq.21206
  4.  Schwaber, K. & Sutherland, J. The Scrum Guide. (2013).
  5.   Hartley, J. Academic Writing and Publishing, (2008), New York, USA: Routledge.
  6.  Rougier, N. P. et al. Ten simple rules for better figures. PLoS Computational Biology, 10(9), pp. 1–7. doi: 10.1371/journal.pcbi.1003833.
  7.  Shaw, Z. Learn Python the Hard way, 3rd Ed. (2013).,
  8.  Van Rossum, G. et al. PEP 8 — Style Guide for Python Code, (2013),
  9.    Provost & Fawcett. Data Science for Business, (2013), ISBN 9781449361327
  10.  Larose. Data mining methods and models, (2006), ISBN 139780471666561

Course policy

This course is building on the pedagogics of collaborative learning, project based learning, and problem based learning. Every student is assigned to a group that is given an open-ended problem that the group members together solve and implement as open-source software in Python or Javascript. It is learning through trial and error by doing. Attendance 70% (counted based on lecture attendance, the percentage of committed code lines to the software and report). Individual oral presentation 10% (each student must present the groups progress once). Group project report 20% (each group is responsible for a section in the technical report).

This course will both make you a better researcher and prepare you for industrial teamwork. Students from all departments of NCKU are encouraged to participate, because the skills that you will learn are applicable in all fields of science. If you take this course during your first year in graduate school or your last year as an undergraduate student, then you will have the largest benefit from it.

We will continue building upon the previous results available under the Apache 2.0 open source license at:

In 2016, a majority of the participants considered it to be one of the most difficult but rewarding courses, and 19 out of 20 students passed this course with an average grade of 80. In 2017, 67% of the participants stated that they have learnt more than on average and 100% that they will remember the things longer than things learnt in all other courses this semester, and all students passed with an average grade of 89. In 2018, 84.2% of the participants stated that they have learnt more than on average and 94.7% that they will remember the things longer than things learnt in all other courses this semester. 94.7% of the students think the things will be more useful than on average, and 20 out of 21 students passed with an average grade of 86 (one student registered but never showed up and thus failed).
Warning: You are expected to work outside of class so if you don’t reserve at least 12 hours per week for this class, please, do not sign up for it.
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