Tropical Indian ocean annotated planktonic foraminifera image dataset from surface sediments

The planktonic foraminifera images contained in this dataset come from coretops sampled in the tropical Indian Ocean using the RV Marion Dufresne and the BARAT 94 cruise onboard the RV Baruna Jaya I. The samples are archived at Centre Européen de Recherche et d'Enseignement de Géosciences de l'Environnement (CEREGE, France) and Laboratoire des Sciences du Climat et de l'Environnement (LSCE, France). The planktonic foraminifera specimens in this database are from the >150 μm sieve fraction and images were captured using MiSo, a state-of-the-art microfossil sorting machine developed at CEREGE. Since the telecentric lens used for image capturing had a field depth of approximately 90 μm and therefore cannot fully capture most foraminifera under view, the captured images were fused to produce Z-stack images. A 70 μm separation between images was adopted for the stack. All images were outputted at a resolution of 1159.4 pixels per millimetre.  A Convolutional Neural Network model (Base Cyclic 16), also developed at CEREGE (Adebayo et al., submitted to G3), was used to automatically identify and classify the images. Model accuracy was confirmed by comparing machine model classification with results from classification by human classifiers on coretop samples that were neither part of the training nor testing sets across 21 classes. Result shows 98% accuracy in the machine labels. For more details on the systems and procedures for image acquisition, transfer learning, input dimensions, training, model evaluation, and CNN selection, please see Marchant et al. (2020). This directory contains 185, 222 images belonging to 36 taxa classes of planktonic foraminifera, including foraminifera fragments.  
Reference
Marchant, R., Tetard, M., Pratiwi, A., Adebayo, M. and de-Garidel Thoron, T. (2020). Automated analysis of foraminifera fossil records by image classification using a convolutional neural network. Micropalaeontology, 39, 183–202. https://doi.org/10.5194/jm-39-183-2020
Adebayo, M. B., Bolton, C., Marchant, R., Bassinot, F., Conrod, S., and de-Garidel Thoron, T. Environmental controls on size distribution of recent planktonic foraminifera in the tropical Indian Ocean: A calibration study. Geochemistry, Geophysics, Geosystems (Submitted).

本数据集包含的浮游有孔虫图像，采自热带印度洋的岩芯顶部样品，采样工作由“马里恩·迪弗雷斯纳号（RV Marion Dufresne）”科考船以及“巴鲁纳·查亚一号（RV Baruna Jaya I）”科考船执行的BARAT 94航次完成。所有样品目前归档于法国环境地球科学研究与教育中心（Centre Européen de Recherche et d'Enseignement de Géosciences de l'Environnement, CEREGE）以及法国气候与环境科学实验室（Laboratoire des Sciences du Climat et de l'Environnement, LSCE）。本数据库中的浮游有孔虫标本均取自粒径大于150μm的筛分级分，图像由CEREGE研发的先进微古生物分选设备MiSo拍摄。由于本次成像所用的远心镜头景深约为90μm，无法完整捕捉多数有孔虫的全貌，因此通过图像融合生成Z堆叠（Z-stack）图像，堆叠时采用70μm的层间距。所有图像的输出分辨率均为1159.4像素/毫米。本数据集使用了由CEREGE研发的卷积神经网络（Convolutional Neural Network, CNN）模型Base Cyclic 16（Adebayo等，投稿至《G3》），对图像进行自动识别与分类。为验证模型精度，研究团队将模型自动分类结果与人工分类结果进行对比，对比样本为未参与模型训练与测试集的岩芯顶部样品，共涵盖21个类别，最终模型标注的准确率达98%。若需了解图像采集、迁移学习、输入维度、模型训练、评估以及卷积神经网络选型等系统与流程的更多细节，请参阅Marchant等（2020）的研究。本目录共包含185222张图像，涵盖36个浮游有孔虫类群（包含有孔虫碎片）。
参考文献：
1. Marchant, R., Tetard, M., Pratiwi, A., Adebayo, M. and de-Garidel Thoron, T. (2020). 基于卷积神经网络图像分类的有孔虫化石记录自动化分析. 微体古生物学, 39, 183–202. https://doi.org/10.5194/jm-39-183-2020
2. Adebayo, M. B., Bolton, C., Marchant, R., Bassinot, F., Conrod, S., and de-Garidel Thoron, T. 热带印度洋现代浮游有孔虫粒径分布的环境控制：定标研究. 《地球化学、地球物理学、地球系统》（已投稿）。