Publication Date:
2024-06-25
Description:
This paper introduces ForametCeTera, a pioneering dataset designed to address the challenges
associated with automating the analysis of benthic foraminifera in sediment cores. Foraminifera are
sensitive sentinels of environmental change and are a crucial component of carbonate-denominated
ecosystems, such as coral reefs. Studying their prevalence and characteristics is imperative in
understanding climate change. However, analysis of foraminifera contained in core samples currently
requires washing, sieving and manual quantification. These methods are thus time-consuming and
require trained experts. To overcome these limitations, we propose an alternative workflow utilizing
3D X-ray computational tomography (CT) for fully automated analysis, saving time and resources.
Despite recent advancements in automation, a crucial lack of methods persists for segmenting and
classifying individual foraminifera from 3D scans. In response, we present ForametCeTera, a diverse
dataset featuring 436 3D CT scans of individual foraminifera and non-foraminiferan material following
a high-throughput scanning workflow. ForametCeTera serves as a foundational resource for generating
synthetic digital core samples, facilitating the development of segmentation and classification methods
of entire core sample CT scans.
Keywords:
Biodiversity
;
Bioinformatics
;
Climate-change ecology
;
Marine biology
;
Microbial biooceanography
Repository Name:
National Museum of Natural History, Netherlands
Type:
info:eu-repo/semantics/article
Format:
application/pdf
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