Introduction to Open Science

Exploring Reproducibility, Transparency, and Collaboration in Data Science

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

  • 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.

Instructor Information

Instructor: Nathan Alexander, PhD

Office Hours: TBD (On-site or virtual)

Contact: nathan.alexander@howard.edu

Course Outline

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

Assessment and Deliverables

  • Weekly reflection posts via GitHub Discussions or Quarto Blogs
  • Reproducible research exercises (5 short labs)
  • Open Project Portfolio (GitHub repository with README, data, code, and reproducible report)
  • Peer review of one classmate’s open workflow