DataLabs and discoverability: a summary report on the DataLabs enhancements project

Green, David Philip ORCID: https://orcid.org/0000-0002-3256-8640; Lord, Carolynne ORCID: https://orcid.org/0000-0003-3372-8420; Widdicks, Kelly ORCID: https://orcid.org/0000-0003-3502-9172; Roebuck, Jennifer; Gouldsbrough, Lily ORCID: https://orcid.org/0009-0000-0042-8419; Hollaway, Mike ORCID: https://orcid.org/0000-0003-0386-2696; Blair, Gordon ORCID: https://orcid.org/0000-0001-6212-1906. 2024 DataLabs and discoverability: a summary report on the DataLabs enhancements project. UK Centre for Ecology & Hydrology, 17pp. (Unpublished)

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Abstract/Summary

In this report, we draw on a two-part study that explores future directions for enhancing discoverability within and through DataLabs: The first part of the study is based on recent engagement with stakeholders in environmental science at UKCEH, conducted as part of a wider co-design strategy to better understand how DRI might be developed in the future. Taking a ‘bottom-up’ approach, this part of the study explored users’ perspectives on DRI and how these infrastructures could be enhanced in the future, including improvements to DataLabs and discoverability. The second part of the study, which reflects a more ‘top-down’ approach presents insights from the development of an experimental Large Language Model (LLM) designed to improve the discoverability of digital assets in DataLabs.

Item Type:	Publication - Report (Technical Report)
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-24)
Funders/Sponsors:	NERC
NORA Subject Terms:	Ecology and Environment Computer Science
Date made live:	11 Mar 2025 12:52 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539039

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