AI Article Synopsis
- - Big Data has enhanced our understanding of complex systems through the collection and analysis of large, diverse datasets, but it risks overshadowing the importance of Small Data.
- - Small Data refers to datasets with fewer observations, which are particularly abundant in fields like ecology and are gaining attention for their potential insights.
- - Innovative approaches in machine learning, such as transfer learning and synthetic data, along with evolving meta-analysis techniques, are poised to leverage Small Data, ultimately benefiting ecological research and insights.
Article 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.
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| http://dx.doi.org/10.1016/j.tree.2023.01.015 | DOI Listing |
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