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151-8102-00L 4 Credits BSC , DR , MSC D-MAVT , D-BAUG
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Research Beyond the Lab: Open Science and Research Methods for a Global Engineer

Lecturers & Examiners: Prof. Dr. Elizabeth Tilley, Lars Schöbitz
VVZ CR n/a

Last Updated: 2026-02-05 16:37:59

Abstract

From the proverbial 'field' to the heart of Zurich, engineering research is guided by the same fundamental principles. With the goal to improve the human condition with technology, we designed this course to teach learners how to conduct a research project out of the lab, and apply open science principles to their data analysis projects.

Objective

By the end of the course, learners will be able to: • articulate a foundational understanding of 'research' • identify and implement an appropriate research paradigm for a given study • identify the importance of, and challenges related to research ethics • create a SMART research question • articulate appropriate research aims and objectives for specific questions • create survey questions using a variety of question types and understand the limitations and uses for each type of survey question • apply 12 principles for data organisation in spreadsheets in the layout of a collected dataset • clone a repository from GitHub into the Posit Cloud and can use the RStudio IDE to commit and push changes to GitHub • create a repository on GitHub and start a new R Project using the RStudio IDE on Posit Cloud • can use three different ways of getting support in solving coding problems online • can apply 10 functions from the dplyr R Package to generate a subset of data for use in a table or plot • use GitHub to publish their Course project report as a website • can use exported references from Zotero in Better BibTex Format to generate an automated reference list • cross-reference figures and tables within an R Markdown file

Content

Over the course of the semester, students will develop a research project and learn the necessary qualitative and quantitative methods required to collect data from people. We will use tidyverse R packages to work with data, and git and GitHub as tools for version control and collaboration. By the end of the course, students will have a complete overview of how a typical field-based research project is designed, implemented and communicated. Content will be delivered through lectures and tutorials. The success of the course will depend on the student's own willingness to engage with local challenges, stakeholders, citizens and agencies in order to develop a comprehensive body of work that answers a relevant, local problem. Topics covered include: - The data science life-cycle - Data organization in spreadsheets - Exploratory data analysis using visualization - Concept of tidy data and data tidying - Data transformation and descriptive statistics - Data communication using the Quarto open-source scientific and technical publishing system - Theory and foundations of field-based research - Research Design and implications for analysis

Resources

Lecture Notes

Distributed during the course.

Learning Materials (Links)

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 120 minutes
Aids
Any online and hardcopy resources can be used during the final exam.
There is a final written exam, which assesses the technical skills taught during the course. It contains programming exercises using the R programming language. The success of the exam depends on the effort put into the compulsory continuous performance assessment. The performance assessment consists of 10 weekly homework assignments worth 20% and a course project during the second half of the course (from Week 7) worth 30%. The course project is a hands-on research project that requires team work and concludes with the submission of a report one week after the end of of final lecture. The report is assessed for its technical parts and the intellectual framing of the results.Grading scheme in summary:Final written exam: 50%Compulsory continuous performance assessment: 50%, of whichHomework: 20% (n = 10)Course project: 30%, of whichTechnical bits of submitted report: 20%Intellectual framing of results: 10%

Registration & Places

Max Places
50

Course Components

Type Title Time & Place Hours
lecture with exercise Research Beyond the Lab: Open Science and Research Methods for a Global Engineer
  • Thu 12:15-15:00 (CAB G 59)
3 h weekly

Offered In