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. referencemarchant, 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-39-183-2020adebayo, 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).

本数据集中所包含的浮游有孔虫图像源自于热带印度洋的柱状样，该柱状样是通过RV Marion Dufresne和Barat 94航次在RV Baruna Jaya I上进行采集。样品存档于法国的环境地球科学研究和教育中心（CEREGE）以及气候和环境科学实验室（LSCE）。数据库中的浮游有孔虫标本均来自大于150微米的筛分部分，图像通过CEREGE开发的最先进微化石分选机MISO捕获。由于用于图像捕获的望远镜头具有约90微米的视场深度，因此无法完全捕捉到视图中的大多数有孔虫，因此所捕获的图像被融合以生成Z堆栈图像。堆栈中图像之间的间隔为70微米。所有图像均以每毫米1159.4像素的分辨率输出。一款卷积神经网络模型（基础循环16），同样由CEREGE开发（Adebayo等，已提交至G3），被用于自动识别和分类图像。通过将机器模型的分类结果与21个类别中既不属于训练集也不属于测试集的柱状样的人类分类结果进行比较，验证了模型的准确率。结果显示机器标签的准确率为98%。有关图像获取、迁移学习、输入维度、训练、模型评估和CNN选择的系统及流程的更多详细信息，请参阅Marchant等（2020）。本目录包含185,222张图像，属于36个浮游有孔虫物种分类，包括有孔虫碎片。参考文献：Marchant, R., Tetard, M., Pratiwi, A., Adebayo, M. and de-Garidel Thoron, T.（2020）. 利用卷积神经网络对有孔虫化石记录进行自动分析. 微古生物学，39，183–202. https://doi.org/10.5194/jm-39-183-2020。Adebayo, M. B., Bolton, C., Marchant, R., Bassinot, F., Conrod, S. 和 de-Garidel Thoron, T.（待提交）。热带印度洋近期浮游有孔虫大小分布的环境控制：一项校准研究。地球化学，地球物理学，地球系统（待提交）。