Contents
- 1 Teaching Reproducible Research
- 1.1 Content overview
- 1.2 Learning objectives
- 1.3 Completion criteria
- 1.4 Audience
- 1.5 Level
- 1.6 Prerequisite skills, expertise and experience
- 1.7 Overall likely time commitment
- 1.8 Event dates and times
- 1.9 Registration deadline
- 1.10 Training partner
- 1.11 Location
- 1.12 Cost to institution per participant
- 1.13 Maximum number of attendees
- 1.14 Application
Teaching Reproducible Research
Content overview
This workshop will introduce attendees to Project TIER’s principles and practices of integrating reproducible methods into teaching and research.
The focus of the workshop is practical: the objective is to help instructors develop plans for teaching reproducible research practices that will be feasible and effective in their particular contexts, and prepare them to implement the methods presented at the workshop in their classes and research supervision.
Course program includes:
- Workflows for reproducible research;
- Teaching Strategies;
- File-sharing Platforms;
- Pedagogical Benefits.
Learning objectives
- Understand core principles of computational reproducibility:
- Work with data by writing and executing scripts, not interactively
- Establish a well-defined directory structure
- Make explicit choices about the working directory
- Use relative directory paths to identify file locations
- Understand practical implementation of these principles to create a comprehensive, fully documented reproduction package
- Understand the purposes/benefits of computational reproducibility
- Scientific benefits for professional researchers
- Pedagogical benefits for undergraduate and graduate students
- Develop strategies for incorporating reproducibility into quantitative methods instruction
- For a wide range of projects–from simple data exercises to complete theses or dissertations
- At all levels of the curriculum–from introductory through graduate-level
- Be prepared to create new curriculum that emphasizes reproducibility
Completion criteria
During the workshop, attendees will create an output (such as a lab exercise or instructions for a reproducible research project) based on principles they learn in the workshop that they can use in their own teaching in the upcoming academic year.
Attendees may also be expected to complete a short piece of work reflecting on what they learned and how it could be implemented (UKRN will provide a template for this).
Audience
- Faculty who teach courses involving statistics and data analysis, and/or supervise student research.
- Staff of libraries, interdisciplinary centres for research and education, or IT departments with responsibility for training or support of student research.
- Graduate students and post-docs who currently serve as instructors or TAs, or anticipate doing so in the future.
- Any other individuals in a position to use the methods presented at the workshops in some way that promotes transparent and reproducible methods in the research training of students in quantitative fields.
Individuals who teach or advise quantitative research methods are welcome, regardless of their disciplines. Most participants in past workshops have been social scientists, but the number of individuals from mathematics, statistics, and the natural sciences has been growing. Further increases in the diversity of the disciplines represented at the workshops would be welcome.
Level
While the training is mainly introducing new concepts and methods to build reproducibility into teaching workflows, more intermediate skills commensurate with teaching or supervising data analysis (such as lesson design or data analysis methods) are expected and not directly taught on this course. The taught strategies are software-agnostic and you will be asked which software you use (e.g. R, SPSS, STATA, MATLAB, SAS, etc) during the application process.
Important note: Despite this neutrality with respect to which particular software is used, the use of some programmable statistical package is essential. The methods presented at the workshop are not applicable in settings in which students work with their data interactively in Excel or other spreadsheets, or in which students rely on the drop-down menus available in some programs.
Instructors whose students use spreadsheets or drop-down menus, but who wish to wean them from those tools and teach them to work with editable scripts instead, are welcome at the workshop and may find much of it valuable. The workshop does not have much to offer for situations in which switching from spreadsheets and drop-down menus to editable scripts is not feasible.
Prerequisite skills, expertise and experience
Attendees should have some experience teaching courses involving applied data analysis and/or supervising data-based student research projects, as well as plans to teach such a course again in the near future.
Other more general prerequisites
- Familiarity with data analysis
- Experience of using a software tool that allows scripting (and being open to implementing it)
Overall likely time commitment
Two-day workshop
Event dates and times
- 5th June 2024
- 6th June 2024
Registration deadline
8th March 2024
Training partner
Location
University of Glasgow
Cost to institution per participant
Registration is free. Travel and accommodation to be paid from institutional project budget.
Maximum number of attendees
12