An Insider’s Take on Data Curation: Context, Quality, and Efficiency

Skylar Hawthorne

doi:10.7191/jeslib.2021.1200

Abstract

This commentary describes how context, quality, and efficiency guide data curation at the University of Michigan's Inter-university Consortium for Political and Social Research (ICPSR). These three principals manifest from necessity. A primary purpose of this work is to facilitate secondary data analysis but in order to so, the context of data must be documented. Since a mistake in this work would render any results published from the data inaccurate, quality is paramount. However, optimizing data quality can be time consuming, so automative curation practices are necessary for efficiency. The implementation of these principles (context, quality, and efficiency) is demonstrated by a recent case study with a high-profile dataset. As the nature of data work changes, these principles will continue to guide the practice of curation and establish valuable skills for future curators to cultivate.

Highlights

I curate data for the world’s largest social science data archive
At Inter-university Consortium for Political and Social Research (ICPSR), researchers and institutions affiliated with hundreds of universities entrust us with their data because our fastidious curation process increases the data’s value, posterity, and potential for discovery and citation
There is no room for error in data curation

Summary

Skylar Hawthorne University of Michigan

Let us know how access to this document benefits you. Follow this and additional works at: https://escholarship.umassmed.edu/jeslib Part of the Archival Science Commons, Cataloging and Metadata Commons, Scholarly. An Insider’s Take on Data Curation: Context, Quality, and Efficiency.

Introduction

Documenting Data Context

Assuring Data Quality

Efficiency in Curation