Year: 2023
Type: review
Abstract: 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.
Source: Trends in Ecology & Evolution
Authors: Lindsay C. Todman, Alex Bush, Amelia S.C. Hood
Institutions: University of Reading, Lancaster University
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
SDG: Life on land
Open Access status: hybrid
Estimated APC paid: $6,830
OpenAlex ID: W4320922951