Small Data for big insights in ecology

Todman, L. C. ORCID: https://orcid.org/0000-0003-1232-294X, Bush, A. and Hood, A. S. C. ORCID: https://orcid.org/0000-0003-3803-0603 (2023) Small Data for big insights in ecology. Trends in Ecology and Evolution, 38 (7). pp. 615-622. ISSN 0169-5347 doi: 10.1016/j.tree.2023.01.015

Abstract/Summary

Big Data science has significantly furthered our understanding of complex systems by harnessing large volumes of data, generated at high velocity and in great variety. However, there is a risk that Big Data collection is prioritised to the detriment of ‘Small Data’ (data with few observations). This poses a particular risk to ecology where Small Data abounds. Machine learning experts are increasingly looking to Small Data to drive the next generation of innovation, leading to development in methods for Small Data such as transfer learning, knowledge graphs and synthetic data. Meanwhile, meta-analysis and causal reasoning approaches are evolving to provide new insights from Small Data. These advances should add value to high quality Small Data catalysing future insights for ecology.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/110311
Identification Number/DOI	10.1016/j.tree.2023.01.015
Refereed	Yes
Divisions	Life Sciences > School of Agriculture, Policy and Development > Department of Sustainable Land Management > Centre for Agri-environmental Research (CAER)
Publisher	Elsevier
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