12  Final Project: NYC Open Data 2026 Proposal

Independent Civic Research • Reproducible Workflow • Public-Facing Communication

13 Overview

Your final project for this course is to design and conduct an original research project using NYC Open Data.

Your project will be written as a fully reproducible Quarto document and structured as though it were ready for public presentation at NYC Open Data Week.

This assignment is designed to:

  • Give you experience designing an independent research project
  • Provide hands-on work with open civic datasets
  • Produce a polished portfolio-ready artifact
  • Strengthen your reproducible workflow and data communication skills
  • Prepare you to submit to NYC Open Data Week 2026 if you choose

By the end of this course, you will have created a professional-quality research document suitable for public presentation.

14 Learning Objectives

By completing this project, you will be able to:

  • Formulate a clear, data-driven research question
  • Identify and justify appropriate open datasets
  • Build a fully reproducible data workflow
  • Clean, wrangle, and analyze civic data responsibly
  • Create clear and meaningful visualizations
  • Write for a public-facing, non-technical audience
  • Connect your work to broader open data principles

15 Textbook Connection

This final project synthesizes concepts in Reproducible Research Using R.

Students are encouraged to review the chapter before beginning this assignment, as it provides the conceptual foundation and reproducible workflow demonstrated here.

16 Project Requirements

16.1 1. Dataset Selection

You must:

  • Select at least one dataset from the NYC Open Data Portal (preferred).
  • Other publicly available civic datasets regarding NYC are acceptable with instructor approval.
  • Clearly cite and hyperlink all datasets used.
  • Justify why the dataset is appropriate for your research question.

Your project should demonstrate thoughtful dataset selection rather than convenience-based selection.

16.2 2. Reproducible Analysis (Quarto)

Your final submission must:

  • Be written in Quarto (.qmd) format.
  • Include narrative, code, output, and interpretation in one document.
  • Knit/render successfully without errors.
  • Suppress unnecessary warnings and messages in the final output.

Your workflow should clearly demonstrate:

  • Data import
  • Cleaning and transformation
  • Analysis aligned with your research question
  • Transparent decision-making

16.3 3. Proposal-Style Structure

Your document should be structured as if it were an Open Data Week proposal.

Include the following sections:

16.3.1 A. Title & Event Description

  • A clear, compelling project title.
  • A 1–2 paragraph overview explaining:
    • Your research question
    • Why it matters
    • What attendees would learn

Write this as though it were submitted to Open Data Week.

16.3.2 B. Dataset(s) Used

  • Describe the dataset(s).
  • Provide proper citations and links.
  • Explain how the dataset(s) connect to your research question.

16.3.3 C. Analysis

  • Present a reproducible workflow from raw data to results.
  • Clearly explain:
    • What you cleaned and why
    • What transformations were performed
    • What analyses were conducted
  • Align every analysis step with your research question.

16.3.4 D. Visualizations

Include at least two clear, well-labeled figures or tables created in R.

All visualizations must:

  • Include titles
  • Include axis labels
  • Include captions
  • Be readable in a public-facing setting

Explain what each visualization shows and why it matters.

16.3.5 E. Audience & Relevance

Identify:

  • Who would care about this project?
    • New Yorkers?
    • Policymakers?
    • Journalists?
    • Community organizations?
  • Why does this research matter in a civic context?

16.3.6 F. Connection to Open Data

Explain how your project:

  • Demonstrates transparency
  • Highlights accessibility of public data
  • Shows the value of open civic information
  • Reflects open data principles

17 Length & Formatting Requirements

  • Minimum 500 words of written narrative (excluding code).
  • Figures and tables must be clearly labeled.
  • Citations must be properly formatted.
  • The document must render cleanly to HTML or PDF.

Submit both:

  • Your .qmd file
  • Your knitted HTML or PDF output

18 Class Presentation

You will present your project during the final weeks of the semester.

Presentation Requirements:

  • 10–12 minutes
  • 2–3 minutes for Q&A
  • Use slides (Quarto, PowerPoint, etc.)

Your presentation should:

  • Clearly explain your research question
  • Highlight key findings
  • Show at least one visualization
  • Be understandable to a non-technical audience

This is practice for a potential Open Data Week 2026 submission.

19 Class Publication (Optional but Encouraged)

At the end of the semester, projects may be compiled into a collective class book via Posit Cloud.

Each student’s project may appear as a chapter.

Benefits:

  • A publication credit
  • A portfolio artifact
  • A public-facing research contribution

You may opt out if you prefer.

20 Grading (100 Points Total)

  • Dataset & Research Question (15 pts)
    Clarity, appropriateness, creativity

  • Reproducibility (20 pts)
    Working document, organized workflow, transparent code

  • Analysis (20 pts)
    Depth, correctness, alignment with question

  • Visualizations (10 pts)
    Clarity, design, relevance

  • Public Framing & Civic Relevance (10 pts)
    Event description, audience, connection to Open Data

  • Class Presentation (15 pts)
    Clarity, engagement, professionalism

  • Professionalism & Formatting (10 pts)
    Writing quality, citations, organization

21 Reproducibility Practice

This final project emphasizes transparent and repeatable workflows.

Your document must:

  • Load all required packages explicitly
  • Import data programmatically
  • Avoid manual editing of datasets
  • Clearly document cleaning decisions
  • Avoid hard-coding results in interpretation
  • Render cleanly without errors
  • Set a seed if using sampling, modeling, or randomness

The goal is that another analyst could reproduce your findings exactly.

22 Resources

  • NYC Open Data Portal: https://opendata.cityofnewyork.us/
  • Past Open Data Week Events: https://2025.open-data.nyc/

23 Submission

Required:

  • .qmd file
  • Knitted HTML or PDF output

24 Takeaway

By completing this final project, you will leave the course with:

  • An independent, reproducible research project
  • A polished presentation
  • A potential publication credit
  • A strong foundation for an Open Data Week 2026 submission