[Paper Review] Citizen Science: An Information Quality Research Frontier

Roman Lukyanenko, Andrea Wiggins+1 more|arXiv (Cornell University)|May 20, 2019

Species Distribution and Climate Change176 references1 citations

TL;DR

This paper positions citizen science as a transformative frontier for information quality (IQ) research, arguing that crowdsourced scientific data from non-experts presents unique challenges and opportunities for IQ management. By integrating citizen science into broader IQ frameworks, the authors advocate for unified, interdisciplinary approaches to ensure data quality in large-scale, open scientific research.

ABSTRACT

The rapid proliferation of online content producing and sharing technologies resulted in an explosion of user-generated content (UGC), which now extends to scientific data. Citizen science, in which ordinary people contribute information for scientific research, epitomizes UGC. Citizen science projects are typically open to everyone, engage diverse audiences, and challenge ordinary people to produce data of highest quality to be usable in science. This also makes citizen science a very exciting area to study both traditional and innovative approaches to information quality management. With this paper we position citizen science as a leading information quality research frontier. We also show how citizen science opens a unique opportunity for the information systems community to contribute to a broad range of disciplines in natural and social sciences and humanities.

Motivation & Objective

• To establish citizen science as a critical, emerging frontier for information quality (IQ) research within the information systems (IS) discipline.
• To address the gap in IQ research focused on user-generated content (UGC) from non-expert contributors, particularly in scientific contexts.
• To extend traditional IQ frameworks—originally designed for corporate data—to accommodate the complexities of citizen science data.
• To promote interdisciplinary collaboration between IS researchers and natural/social scientists and humanists to improve data quality in open science.
• To call for the development of a unified IQ framework that integrates emerging technologies like citizen science with established IQ methodologies.

Proposed method

• Theoretical synthesis of existing IQ frameworks from information systems and applies them to citizen science contexts.
• Analysis of citizen science as a form of user-generated content (UGC) with unique characteristics such as voluntary participation, diverse contributor expertise, and open access.
• Comparative assessment of IQ challenges across UGC domains (e.g., Wikipedia, social media, crowdsourcing platforms) to position citizen science within the broader IQ landscape.
• Identification of under-researched IQ dimensions in citizen science, such as motivation, context, and quality control mechanisms.
• Proposal for extending traditional IQ models to include non-corporate, non-organizational data production contexts.
• Call for future research to develop generalizable IQ frameworks that unify traditional and emerging data sources, including citizen science.

Experimental results

Research questions

• RQ1 How can information quality (IQ) frameworks be extended to accommodate citizen science as a form of user-generated content (UGC)?
• RQ2 What are the key differences in IQ challenges between traditional corporate data and citizen science data?
• RQ3 In what ways can information systems (IS) research contribute to improving data quality in open, citizen-driven scientific projects?
• RQ4 How can existing IQ models be adapted to account for the motivations, anonymity, and contextual diversity of non-expert contributors?
• RQ5 What role can IS researchers play in building sustainable, high-quality data infrastructures for citizen science and interdisciplinary research?

Key findings

• Citizen science represents a critical frontier for information quality (IQ) research due to its scale, openness, and reliance on non-expert contributors.
• Traditional IQ frameworks, designed for controlled, organizational data, are insufficient for managing the quality of citizen science data due to contributor diversity and lack of credentials.
• The quality of citizen science data is influenced by motivation, context, and the absence of formal oversight, necessitating new quality control mechanisms.
• There is a significant research gap in understanding how content moderation, editing, and collaborative production affect IQ in citizen science, compared to platforms like Wikipedia.
• Integrating citizen science into broader IQ research can lead to more robust, unified frameworks that span traditional and emerging data sources.
• Information systems researchers are well-positioned to contribute to sustainable, high-quality data ecosystems in citizen science by applying their expertise in data quality, adoption, and system design.