Syllabus
Introduction to Open Science
Course Information
- Course title: Introduction to Open Science
- Instructor: Nathan Alexander, PhD
- Meeting times: TBD
- Contact: nathan.alexander@howard.edu
- Office hours: TBD
Course Description
This course introduces the principles and practices of open science, with an emphasis on reproducible research workflows, transparent reporting, and collaborative scholarship. Students will critically examine reproducibility, explore tools that support openness (such as Git, GitHub, and Quarto), and apply these tools in hands-on assignments and a final open project. Students will also explore how open science practices vary across disciplines, including education, humanities, social sciences, industry and government, and STEM contexts. This course emphasizes applying open science principles to real-world data problems in alignment with the principles of producing reproducible research.
Learning Objectives
By the end of the course, students will be able to:
- Describe key concepts in open science, including transparency, reproducibility, and open scholarship.
- Use version control and hosted repositories to manage research projects collaboratively.
- Create reproducible analyses and reports using scripted workflows and literate programming tools for data science.
- Apply best practices for sharing data, code, and other research outputs with appropriate documentation.
- Use open datasets and public information responsibly to address practical problems in domains such as community organizing, education, cybersecurity, business intelligence, or public policy.
- Critically evaluate ethical, legal, and equity considerations in open science.
Required Materials
- Software:
- R and RStudio (or another suitable IDE)
- Git (with a GitHub account)
- Quarto for document and site authoring
- QGIS or another open-source GIS tool for geospatial projects
- R and RStudio (or another suitable IDE)
- Readings:
- Selected articles and chapters provided via the course site (all open access or openly licensed).
Course Schedule (Overview)
| Week | Topic | In-Class Focus | Major Due Dates |
|---|---|---|---|
| 1 | What is open science? | History, motivations, and myths | — |
| 2 | Reproducibility crisis | Replication and reform movements | Reflection 1 |
| 3 | Frameworks for openness | FAIR, TOP, and related standards | — |
| 4 | Research workflows | Project structure and documentation | Lab 1: Project skeleton |
| 5 | Version control with Git | Commits, branches, remotes | Lab 2: Git basics |
| 6 | GitHub for collaboration | Issues, pull requests, review workflows | Reflection 2 |
| 7 | Literate programming | R Markdown / Quarto basics | Lab 3: Reproducible report |
| 8 | Data management | Tidy data, metadata, README files; metadata, projections, and documenting data sources | Data & code checklist |
| 9 | Open science in Education, Humanities, and Social Science | Disciplinary norms, text data, large-scale quantitative data, archives | Reflection 3a |
| 10 | Open science in Industry, Business, and Government | Proprietary vs open data, NDAs, open-by-default policies, open data portals, OSINT-style collection and verification (e.g., social media, public records) | — |
| 11 | Open science in STEM | Lab notebooks, code and data standards, preprints, replication, geospatial and simulation-based workflows | Reflection 3b |
| 12 | Sharing data and code | Repositories, DOIs, OSF, licensing | Lab 4: Discipline-specific workflow |
| 13 | Licensing and attribution | Creative Commons and software licenses | Lab 5: Licensing plan |
| 14 | Project updates | Project presentations | Final projects |
Assignments and Evaluation
Participation and engagement (10%)
Regular attendance, preparation, and constructive contributions to discussions and activities.Reflections (20%)
Short written reflections (about 500 words) connecting course topics to your disciplinary context and research interests.Labs / Practical exercises (30%)
Hands-on tasks that build skills in version control, reproducible reporting, data management, and sharing.Open project (40%)
A small, self-contained research or demonstration project hosted in a public repository, including:- Clearly organized folder structure and documentation
- Reproducible analysis and report (for example, a Quarto document or site)
- Appropriate licensing and citation
- Class time in Weeks 9–11 will be used for discipline-specific application labs that support progress on your open project
- Short in-class presentation in Week 14
- Students are encouraged to choose an open project that aligns with their professional pathway
- Clearly organized folder structure and documentation
Open Project Requirements
Your open project should:
- Be grounded in one of the course’s disciplinary clusters (Education/Humanities/Social Science; Industry/Business/Government; or STEM)
- Live in a public GitHub repository under your account (or a course organization).
- Include a clear
READMEdescribing the project, requirements, and how to reproduce results.
- Provide data (or simulated/open data), code, and a rendered report or site. Projects may focus on tabular, text, or geospatial data (for example, an open GIS mapping project using QGIS and open spatial datasets).
- Use an appropriate license for code and content, documented in the repository.
Course Policies
Attendance and Participation
Active participation is essential to developing practical open science skills. More than a specified number of unexcused absences (define this in your local context) may affect your participation grade.
Late Work
Specify your policy here, such as a percentage deduction per day late up to a maximum, or acceptance only by prior arrangement. Clarify how this applies to labs, reflections, and the final project.
Academic Integrity
All work must comply with institutional policies on academic integrity. When reusing or adapting open materials, you must provide proper attribution and follow the terms of the original licenses.
Accessibility and Accommodations
Students requiring accommodations are encouraged to contact the instructor and the campus accessibility office as early as possible to ensure appropriate arrangements.
Technology and Data Ethics
Students are expected to follow ethical and legal guidelines in handling data. When using real-world datasets, ensure that data use complies with privacy and institutional or IRB policies where applicable.
Tentative Nature of the Syllabus
The instructor may adjust topics, readings, or assignments as needed to better meet course goals. Any changes will be communicated in class and on the course site.