Hypercuration

Hypercuration is a research practice that extends traditional curation by integrating advanced computational methods, automation, and data management techniques to enhance the discovery, organization, and reuse of research outputs. The term was introduced in 2016 by researchers at the University of California, San Diego, to describe a systematic approach to managing the complex, interconnected data generated in modern scientific research. Unlike traditional curation, which often focuses on selecting and preserving individual artifacts like datasets or publications, hypercuration emphasizes the curation of entire research workflows, including raw data, software, methods, and metadata, within their broader context.

The core idea behind hypercuration is to create a comprehensive, machine-readable record of a research project, enabling better reproducibility, collaboration, and long-term accessibility. This involves annotating and linking data with relevant metadata, such as provenance information (who created it, when, and how), citations to related works, and technical specifications. By embedding these details within the research process, hypercuration aims to reduce the "data dark matter"—the vast amount of research data that remains unused due to poor documentation or accessibility.

Tools and platforms supporting hypercuration often incorporate features like automated metadata extraction, version control, and integration with workflow management systems. These technologies help researchers capture and preserve the dynamic nature of scientific inquiry, where insights emerge from iterative analysis and collaboration. While hypercuration is particularly relevant in fields like genomics, astronomy, and climate science—where datasets are large and complex—its principles are increasingly applied across disciplines to improve research transparency and efficiency.

Critics note that hypercuration requires significant effort and expertise, potentially adding overhead to research workflows. However, proponents argue that the long-term benefits, such as enhanced reproducibility and reduced waste of resources, outweigh the initial challenges. As research becomes more data-intensive, hypercuration is seen as a critical strategy for ensuring that scientific knowledge remains discoverable, interpretable, and useful for future generations.