10 citations as recorded by crossref.

1. Reviews and syntheses: A trait-based approach to constrain controls on planktic foraminiferal ecology – key trade-offs and current knowledge gaps K. Edgar et al. https://doi.org/10.5194/bg-22-3463-2025
2. The effects of shell size and mass on trace element incorporation in different species of planktonic foraminifera E. Hathorne et al. https://doi.org/10.3389/fmars.2026.1792234
3. µ-Computed tomographic data of fossil planktonic foraminifera from the western Pacific Ocean: a dataset concerning two biostratigraphic events during the Early Pleistocene W. Chen et al. https://doi.org/10.3389/fevo.2023.1171891
4. A trait-based modelling approach to planktonic foraminifera ecology M. Grigoratou et al. https://doi.org/10.5194/bg-16-1469-2019
5. Ten recommendations for scanning foraminifera by X-ray computed tomography A. Searle-Barnes et al. https://doi.org/10.5194/jm-44-107-2025
6. Test-size evolution of the planktonic foraminifer Globorotalia menardii in the eastern tropical Atlantic since the Late Miocene T. Friesenhagen https://doi.org/10.5194/bg-19-777-2022
7. Distribution and Abundances of Planktic Foraminifera and Shelled Pteropods During the Polar Night in the Sea-Ice Covered Northern Barents Sea K. Zamelczyk et al. https://doi.org/10.3389/fmars.2021.644094
8. Analysing planktonic foraminiferal growth in three dimensions with foram3D: an R package for automated trait measurements from CT scans A. Brombacher et al. https://doi.org/10.5194/jm-41-149-2022
9. Reviews and syntheses: Review of proxies for low-oxygen paleoceanographic reconstructions B. Hoogakker et al. https://doi.org/10.5194/bg-22-863-2025
10. Automated analysis of foraminifera fossil records by image classification using a convolutional neural network R. Marchant et al. https://doi.org/10.5194/jm-39-183-2020