# From Data Creator to Data Reuser: Distance Matters

arXiv: https://arxiv.org/abs/2402.07926 · HTML: https://mlsanity.com/paper/2402.07926/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [Towards Contextual Sensitive Data Detection](https://mlsanity.com/paper/2512.04120/) (2025)
- [Measuring research data reuse in scholarly publications using generative artificial intelligence: Open Science Indicator development and preliminary results](https://mlsanity.com/paper/2604.28061/) (2026)
- [From Queries to Narratives: Cultural Heritage Data Stories for Knowledge Graph Exploration and Quality Assessment](https://mlsanity.com/paper/2609.11403/) (2026)

## Abstract (arXiv)

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.