Christian Martinez, "NYC Open Data Student Gallery" (Brooklyn College CUNY, 2026)
New Books in TechnologyShow Notes
About NYC Open Data
During the Fall 2025 semester, students in the M.S. program in Psychological Research at Brooklyn College completed the inaugural offering of Reproducible Psychological Research. Using the R programming language, students developed weekly R Markdown documents to solve simulated real-world analytical problems using authentic datasets, with an emphasis on transparency, documentation, and reproducibility.
For their final projects, students were tasked with conducting independent, original research using open data related to New York City. Rather than working with pre-cleaned or artificial datasets, students engaged directly with messy, real-world data and were responsible for every step of the analytical workflow—from data acquisition and cleaning to analysis, visualization, and interpretation. A majority of projects utilized data from the NYC Open Data Portal, though students were encouraged to explore any open NYC-based data source that aligned with their research questions.
Each project in this volume represents a complete, reproducible research artifact. Students were required to meet the following criteria:
The data must be openly available
The data must meaningfully relate to New York City
The research question, analysis, and interpretation must be original
Collectively, these projects demonstrate not only technical proficiency in R, but also the ability to ask meaningful questions about the city students live in, evaluate real-world data critically, and communicate findings in a clear, reproducible manner. This volume serves both as a showcase of student growth and as an example of how open data and open-source tools can be used to conduct rigorous, socially relevant research. Chapters are organized in alphabetical order of the student’s last names.
This volume is designed for students, educators, and practitioners interested in applied data analysis, reproducible research, and open data. Each chapter represents an independent research project and can be read on its own. Readers are encouraged to explore the accompanying code, reproduce analyses, and adapt methods for their own work. Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology
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