Dataset Information

Tree-ring data set for dendroclimatic reconstructions and dendrochronological dating in European Russia.

ABSTRACT: The data set presented represents 15 years of collection. It contains tree-ring width measurements from 64 sites of living trees and ten historical chronologies based on archaeological and construction wood up to year 572 CE, altogether 2909 tree-ring series and more than 450000 measured and cross-dated tree rings. It covers the vast territory of European Russia, including its forested northern and central parts, and the Northern Caucasus mountains. The potential use of these data include climatic reconstructions of regional and hemispheric scale, dendrochronological dating of historical and cultural wood, ecological and remote sensing studies.

SUBMITTER: Solomina O

PROVIDER: S-EPMC9237095 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Tree-ring data set for dendroclimatic reconstructions and dendrochronological dating in European Russia.

Solomina Olga O Matskovsky Vladimir V Dolgova Ekaterina E Kuznetsova Veronika V Semenyak Nadezhda N Bebchuk Tatiana T Mikhalenko Vladimir V Karpukhin Alexey A Khasanov Bulat B

Scientific data 20220627 1

The data set presented represents 15 years of collection. It contains tree-ring width measurements from 64 sites of living trees and ten historical chronologies based on archaeological and construction wood up to year 572 CE, altogether 2909 tree-ring series and more than 450000 measured and cross-dated tree rings. It covers the vast territory of European Russia, including its forested northern and central parts, and the Northern Caucasus mountains. The potential use of these data include climat ...[more]

PMID: 35760795

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Project description:MotivationAutomated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist's prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data.ResultsWe introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual.Availability and implementationDetailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss. Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Tree-ring data set for dendroclimatic reconstructions and dendrochronological dating in European Russia.

Publications

Tree-ring data set for dendroclimatic reconstructions and dendrochronological dating in European Russia.

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