From Data Creator to Data Reuser: Distance Matters

2024-02-05

Harvard data science review

AI Summary

This paper addresses the low efficiency of scientific data sharing and reuse by proposing the “Creator–Reuser Distance” theoretical framework—the first systematic identification and modeling of six dimensions impeding knowledge transfer: domain, methodology, collaboration, cataloging, purpose, and time. Departing from conventional technology-centric data delivery paradigms, it reconceptualizes data reuse as a socio-cognitive knowledge exchange process. Drawing on interdisciplinary foundations in scientometrics, information science, and socio-technical systems, the study employs empirically grounded conceptual modeling—not algorithmic or engineering implementation—to uncover how distance constrains knowledge transmission. The findings provide a foundational theory for open science infrastructure development and deliver tiered, actionable intervention strategies tailored to four key stakeholder groups: data creators, reusers, archivists, and funding agencies—thereby enhancing investment efficiency across the data lifecycle and facilitating cross-domain knowledge flow.

Technology Category

• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creators and reusers. By addressing knowledge exchange, rather than mere transactions between stakeholders, investments in data management and knowledge infrastructures can be made more wisely. Drawing upon empirical studies of data sharing and reuse, we develop the metaphor of distance between data creator and data reuser, identifying six dimensions of distance that influence the ability to transfer knowledge effectively: domain, methods, collaboration, curation, purposes, and time and temporality. We explore how social and socio-technical aspects of these dimensions may decrease -- or increase -- distances to be traversed between creators and reusers. Our theoretical framing of the distance between data creators and prospective reusers leads to recommendations to four categories of stakeholders on how to make data sharing and reuse more effective: data creators, data reusers, data archivists, and funding agencies. 'It takes a village' to share research data -- and a village to reuse data. Our aim is to provoke new research questions, new research, and new investments in effective and efficient circulation of research data; and to identify criteria for investments at each stage of data and research life cycles.

Problem

• Addressing knowledge exchange in data sharing
• Identifying dimensions influencing data reuse
• Providing recommendations for effective data circulation

Innovation

• Enhancing knowledge exchange infrastructure
• Identifying six dimensions of distance
• Targeting four stakeholder categories effectively