Navarro Edgardo L, Estebenet M Sol González, Guler M Verónica, Fuentes Sabrina, Cuitiño José Ignacio, Palazzesi Luis, Panera Juan Pablo Pérez, Barreda Viviana
Comisión de Investigaciones Científicas (CIC)-CGAMA, Departamento de Geología, Universidad Nacional del Sur (UNS), San Juan 670, 8000 Bahía Blanca, Buenos Aires, Argentina.
Instituto Geológico del Sur (INGEOSUR-CONICET), Universidad Nacional del Sur (UNS), San Juan 670, 8000 Bahía Blanca, Argentina.
Data Brief. 2021 Mar 18;36:106975. doi: 10.1016/j.dib.2021.106975. eCollection 2021 Jun.
The data presented here are related to the research article "Miocene Atlantic transgressive-regressive events in northeastern and offshore Patagonia: A palynological perspective" (Guler et al. 2021; https://doi.org/10.1016/j.jsames.2021.103239). A total of 60 drilled cutting samples from a 580 m-thick subsurface stratigraphic section (YPF.Ch.PV.es-1 borehole) in Península Valdés, Chubut Province, Argentina, collected every 10 m, were processed for palynological analysis. The quantitative data were statistically evaluated. In detail, the database contain: 1) raw palynological data - proxy data - from counting under transmitted light microscope; 2) four paleoenvironmental variables selected to conduct a multivariate analysis: terrestrial/marine ratio, acritarchs, outer neritic dinocyst taxa and warm-water dinocyst taxa; 3) transformed variables used for the Principal Component Analysis (PCA) and 4) the principal component scores obtained, stratigraphically ordered from the top to bottom of the borehole. Data from future studies in new sites combined with here presented data, can be useful to refine paleoenvironment models applied to basin analysis.
此处呈现的数据与研究论文《巴塔哥尼亚东北部及近海地区中新世大西洋海侵-海退事件：孢粉学视角》（古勒等人，2021年；https://doi.org/10.1016/j.jsames.2021.103239）相关。从阿根廷丘布特省瓦尔德斯半岛一个580米厚的地下地层剖面（YPF.Ch.PV.es - 1钻孔）中，每隔10米采集共60个钻屑样本，进行孢粉学分析处理。对定量数据进行了统计评估。具体而言，数据库包含：1）来自透射光显微镜下计数的原始孢粉学数据——替代数据；2）为进行多变量分析而选择的四个古环境变量：陆地/海洋比率、疑源类、浅海浮游甲藻分类群和暖水浮游甲藻分类群；3）用于主成分分析（PCA）的变换变量；4）从钻孔顶部到底部地层顺序排列得到的主成分得分。来自新地点未来研究的数据与本文呈现的数据相结合，可有助于完善应用于盆地分析的古环境模型。
