Principles for Open Data Curation: A Case Study with the New York City 311 Service Request Data

TL;DR

This paper analyzes the challenges of open data curation using NYC's 311 Service Request dataset and proposes principles to improve data quality, consistency, and usability for government open data initiatives.

Contribution

It introduces tailored data curation principles addressing validity, consistency, and efficiency, based on a detailed case study of NYC's open data practices.

Findings

Harmonized field definitions improve data comparability

Automated quality checks enhance data reliability

Streamlined storage increases curation efficiency

Abstract

In the early 21st century, the open data movement began to transform societies and governments by promoting transparency, innovation, and public engagement. The City of New York (NYC) has been at the forefront of this movement since the enactment of the Open Data Law in 2012, creating the NYC Open Data portal. The portal currently hosts 2,700 datasets, serving as a crucial resource for research across various domains, including health, urban development, and transportation. However, the effective use of open data relies heavily on data quality and usability, challenges that remain insufficiently addressed in the literature. This paper examines these challenges via a case study of the NYC 311 Service Request dataset, identifying key issues in data validity, consistency, and curation efficiency. We propose a set of data curation principles, tailored for government-released open data, to address these challenges. Our findings highlight the importance of harmonized field definitions, streamlined storage, and automated quality checks, offering practical guidelines for improving the reliability and utility of open datasets.