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‘Small Data’ for big insights in ecology

Research output: Contribution to Journal/Magazine › Review article › peer-review

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‘Small Data’ for big insights in ecology. / Todman, Lindsay C.; Bush, Alex; Hood, Amelia S.C.
In: Trends in Ecology and Evolution, Vol. 38, No. 7, 31.07.2023, p. 615-622.

Research output: Contribution to Journal/Magazine › Review article › peer-review

Harvard

Todman, LC, Bush, A & Hood, ASC 2023, '‘Small Data’ for big insights in ecology', Trends in Ecology and Evolution, vol. 38, no. 7, pp. 615-622. https://doi.org/10.1016/j.tree.2023.01.015

APA

Todman, L. C., Bush, A., & Hood, A. S. C. (2023). ‘Small Data’ for big insights in ecology. Trends in Ecology and Evolution, 38(7), 615-622. https://doi.org/10.1016/j.tree.2023.01.015

Vancouver

Todman LC, Bush A, Hood ASC. ‘Small Data’ for big insights in ecology. Trends in Ecology and Evolution. 2023 Jul 31;38(7):615-622. Epub 2023 Feb 14. doi: 10.1016/j.tree.2023.01.015

Author

Todman, Lindsay C. ; Bush, Alex ; Hood, Amelia S.C. / ‘Small Data’ for big insights in ecology. In: Trends in Ecology and Evolution. 2023 ; Vol. 38, No. 7. pp. 615-622.

Bibtex

@article{953f74d8b1764fb1a0798d73f6b363a1,
title = "{\textquoteleft}Small Data{\textquoteright} for big insights in ecology",
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 {\textquoteleft}Small Data{\textquoteright} (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.",
keywords = "Ecology, Evolution, Behavior and Systematics",
author = "Todman, {Lindsay C.} and Alex Bush and Hood, {Amelia S.C.}",
year = "2023",
month = jul,
day = "31",
doi = "10.1016/j.tree.2023.01.015",
language = "English",
volume = "38",
pages = "615--622",
journal = "Trends in Ecology and Evolution",
issn = "0169-5347",
publisher = "ELSEVIER SCIENCE LONDON",
number = "7",

}

RIS

TY - JOUR

T1 - ‘Small Data’ for big insights in ecology

AU - Todman, Lindsay C.

AU - Bush, Alex

AU - Hood, Amelia S.C.

PY - 2023/7/31

Y1 - 2023/7/31

N2 - 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.

AB - 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.

KW - Ecology

KW - Evolution

KW - Behavior and Systematics

U2 - 10.1016/j.tree.2023.01.015

DO - 10.1016/j.tree.2023.01.015

M3 - Review article

VL - 38

SP - 615

EP - 622

JO - Trends in Ecology and Evolution

JF - Trends in Ecology and Evolution

SN - 0169-5347

IS - 7

ER -