Dataset Information

Updating a Paleogene magnetobiochronological time scale through graphical integration.

ABSTRACT: All studies focused on the evaluation of paleoecological variability over geological time must be linked to a specific age or time interval, which can be defined using different time scales (biostratigraphic, chronostratigraphic, geochronological or orbital). Therefore, integrated time scales are essential to allow comparisons of data from different locations and/or to assess evolutionary and other events through time. Here we use a new method to update a Paleogene magnetobiochronological time scale, with the following contributions:•The update of the Paleogene magnetobiochronological scale was made by graphical correlation with new age models and adding calcareous nannoplankton and planktonic foraminiferal biozones from different authors.•An excel file structure was proposed to plot any kind of data in MATLAB software, as long as they are associated with some of the scales shown in our updated version of Paleogene magnetobiochronology.•The excel file structure facilitates the analysis of long-term trends of taxonomic groups throughout the Paleogene, and of their evolution in a period characterized by intense climate variability.

SUBMITTER: Arreguin-Rodriguez GJ

PROVIDER: S-EPMC8374267 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Updating a Paleogene magnetobiochronological time scale through graphical integration.

Arreguín-Rodríguez Gabriela J GJ Trasviña-Moreno Carlos A CA Thomas Ellen E Alegret Laia L

MethodsX 20210228

All studies focused on the evaluation of paleoecological variability over geological time must be linked to a specific age or time interval, which can be defined using different time scales (biostratigraphic, chronostratigraphic, geochronological or orbital). Therefore, integrated time scales are essential to allow comparisons of data from different locations and/or to assess evolutionary and other events through time. Here we use a new method to update a Paleogene magnetobiochronological time s ...[more]

PMID: 34434811

Similar Datasets

Project description:BackgroundIdentifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using [Formula: see text]-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively.ResultsWe propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks.ConclusionsThe method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.

Dataset Information

Updating a Paleogene magnetobiochronological time scale through graphical integration.

Publications

Updating a Paleogene magnetobiochronological time scale through graphical integration.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets