Reproducible Research on Kailas Venkitasubramanian

Embracing multilingualism in data science

Thu, 10 Apr 2025 00:00:00 +0000

Both of those efforts — reproducibility and pipelines — rest on a more basic question: which programming languages should a small research team actually use? In the previous posts of this series, I covered why reproducibility matters and how we are designing reproducible data pipelines at the UNC Charlotte Urban Institute. This post is about the layer underneath both.

Specifically, I want to argue that embracing multilingualism—fluency in both R and Python, rather than loyalty to one—has quietly done more for our team’s output than almost any other choice we’ve made.

Designing Reproducible Data Pipelines for Community Research

Sat, 09 Mar 2024 00:00:00 +0000

In the first post of this series, I argued that reproducibility is not a technical luxury for community research institutions—it is an ethical and operational obligation. In this post, I want to move from philosophy to plumbing—because this is where reproducibility becomes real.

Specifically: what does it mean to design reproducible data pipelines in a community research environment?

At the UNC Charlotte Urban Institute, this question became concrete as we built the Quality of Life Explorer, developed deposit and extraction pipelines for the Charlotte Regional Data Trust, and began orchestrating workflows using Apache Airflow in an AWS environment.

Releasing v1.0 of the Charlotte-Mecklenburg Quality of Life Explorer Data Pipeline

Thu, 16 Mar 2023 00:00:00 +0000

A successful wrap

It’s a great day today.

We completed the version 1.0 of the Charlotte-Mecklenburg Quality of Life Explorer automation project. It’s hard to put in words how exciting and rewarding this feels but I’ll try anyways.

We managed to automate most of the data and computational processes needed to generate the 80-odd quality of life indicators featured in the explorer and create functional data pipelines to serve the application. Through this work, we’ve accomplished a significant reduction of our project workload and fundamentally transformed the nature of our engagement in this project. The completion of this work also revitalizes our team’s vision to building a reproducible data science framework at the Urban Institute and a unified data ecosystem. Let me tell you how all this worked out.

Towards reproducible data science for community and policy research - An experiential roadmap

Mon, 06 Mar 2023 00:00:00 +0000

On developing a reproducible data science framework and practice at the Charlotte Urban Insitute

Charlotte Regional Data Trust - Technical Operations Manual

Sat, 06 Aug 2022 00:00:00 +0000

On how we developed the technical operations manual at the Charlotte Regional Data Trust