Gallery#
PaleoBooks is a compendium of digital resources to bridge the gap between paleoclimate observations and models, and between past, present and future. The goal is as much to encourage adoption of open-source, free languages like R and Python by the observational paleoscience community as it is to encourage the modeling community to leverage the unique information coming from paleo archives.
The scholarly objects shown below are of several types:
Tutorials on how to overcome a particular technical difficulty (“Life Hacks”)
Tutorials on how to leverage open-source code to achieve easier, more transparent, or more powerful scientific workflows (“Science Bits”)
Examples of novel paleoscience using the Python ecosystem e.g. as turn-key supplementary material from recent publications (“Papers”)
In the following, “PaleoBooks” refers to curated collections of Jupyter Notebooks organized around various themes. “Chapters” refers to the components of these PaleoBooks. A system of tags allows both PaleoBooks and individual chapters to be associated with one another based on individual themes or methodologies, in addition to their host PaleoBook. These tags enable searching along 3 directions:
“Domains” refers to scientific themes or methodologies
“Formats” refers to the type of resource (Life Hacks, Science Bits, or Papers)
“Packages” refers to the packages being used in each notebook (e.g. Xarray)
The gallery below is a work in progress, and we encourage you to contribute your own analyses if you think they might bring enlightenment to, and/or ease the life of, fellow scientists.
PaleoBooks#

tracers coordinate systems Mystery Interval C14 Deglaciation data viz clustering machine learning Paleoceanography model output eNd oceanography watermass geometry, xarray scikit-learn seaborn matplotlib pandas intake cartopy, Science Bits Lifehacks

proxy composite temperature, numpy seaborn pyleoclim pandas cfr, Science Bits

tracers endmember mixing clustering watermass geometry, scipy matplotlib basemap pandas scikit-learn sqlalchemy, Science Bits

data viz, pyleoclim matplotlib ammonyte, Extras Main Analysis

correlation spectral analysis model-data comparison insolation paleoceanography cross-wavelet analysis model output age uncertainty paleoclimate wavelet analysis, xarray climlab pyleoclim pylipd cartopy, Science Bits

Sr/Ca coral calibration paleoceanography Bayesian Statistics Calibration Paleocenography Statistics, Great Tables pyleoclim matplotlib pymc arviz pandas statsmodel, Getting Started Calibration

PCA modeling paleoceanography, xarray pyleoclim matplotlib eofs pandas cartopy, Science Bits

PMIP PaMoDaCo Common Era statistics data viz data assimilation reconstruction paleoclimatology timeseries modeling, scipy seaborn pyleoclim matplotlib statsmodels pandas pens, Motivation Paleoclimate Applications Temporal Interpretation Plume Distance

data viz data analysis data wrangling, ammonyte pyLiPD matplotlib pyleoclim basemap pylipd, Main Analysis Exploring the Data Loading Data

correlation spectral analysis visualization PCA regression tidyverse wavelets LiPD calibration data wrangling uncertainty quantification ensemble mapping age uncertainty dimension reduction age modeling, dplyr oxcAAR readr ggplot2 lipdR purrr, Data Manipulation Core Methods Extensions
Chapters#

oceanography tracers data viz, xarray matplotlib cartopy pandas seaborn, Lifehacks

model output oceanography coordinate systems, xarray matplotlib cartopy pandas intake, Lifehacks

model output eNd, xarray matplotlib cartopy pandas, Science Bits

clustering machine learning watermass geometry, xarray matplotlib cartopy scikit-learn, Science Bits

C14 Paleoceanography Deglaciation Mystery Interval, xarray matplotlib cartopy pandas, Science Bits

AWS cloud-ready data data viz CMIP6, intake AWS xarray pandas, Lifehacks

model output CMIP6 coordinate systems, xarray matplotlib cartopy pandas, Lifehacks

model output LMR CMIP6, xarray matplotlib cartopy pandas pyleoclim, Science Bits

proxy composite temperature, numpy seaborn pandas pyleoclim cfr, Science Bits

watermass geometry tracers endmember mixing, basemap scipy matplotlib pandas sqlalchemy, Science Bits

clustering watermass geometry, basemap scikit-learn matplotlib, Science Bits

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Main Analysis

data viz, matplotlib pyleoclim ammonyte, Extras

data viz, matplotlib pyleoclim ammonyte, Extras

data viz, matplotlib pyleoclim ammonyte, Extras

spectral analysis wavelet analysis cross-wavelet analysis insolation paleoceanography, xarray pyleoclim pylipd climlab, Science Bits

model output spectral analysis model-data comparison paleoclimate, xarray pyleoclim, Science Bits

paleoclimate correlation age uncertainty, xarray pyleoclim cartopy pylipd, Science Bits

paleoceanography Sr/Ca coral calibration, matplotlib pyleoclim pandas, Getting Started

Statistics Calibration Paleocenography, statsmodel Great Tables, Calibration

Bayesian Statistics Calibration Paleocenography, pymc arviz, Calibration

paleoceanography PCA modeling, xarray pyleoclim cartopy matplotlib pandas eofs, Science Bits

paleoclimatology data assimilation data viz, matplotlib pens pyleoclim, Motivation

data assimilation PaMoDaCo, matplotlib pens, Temporal Interpretation

data assimilation timeseries modeling, matplotlib pens statsmodels seaborn, Temporal Interpretation

data assimilation timeseries modeling, matplotlib pens pyleoclim, Temporal Interpretation

statistics PaMoDaCo, matplotlib pens seaborn, Plume Distance

statistics PaMoDaCo, matplotlib pens seaborn, Plume Distance

reconstruction statistics Common Era, matplotlib seaborn pens scipy, Paleoclimate Applications

reconstruction statistics Common Era, matplotlib pens, Paleoclimate Applications

data assimilation PaMoDaCo PMIP, matplotlib pens pyleoclim pandas, Paleoclimate Applications

data wrangling, pyLiPD, Loading Data

data wrangling, pylipd pyleoclim, Loading Data

data viz data analysis, pyleoclim matplotlib, Exploring the Data

data viz data analysis, pyleoclim basemap, Exploring the Data

data viz data analysis, pyleoclim matplotlib, Main Analysis

data analysis data viz, matplotlib pyleoclim, Main Analysis

data viz data analysis, pyleoclim ammonyte matplotlib, Main Analysis

data viz data analysis, pyleoclim matplotlib, Main Analysis

age modeling uncertainty quantification, geoChronR lipdR, Core Methods

correlation age uncertainty, geoChronR ggplot2, Core Methods

regression calibration, geoChronR readr ggplot2, Core Methods

spectral analysis wavelets, geoChronR, Core Methods

PCA dimension reduction, geoChronR, Core Methods

visualization, geoChronR ggplot2, Data Manipulation

data wrangling mapping, geoChronR, Data Manipulation

data wrangling tidyverse, geoChronR dplyr purrr, Data Manipulation

age modeling, geoChronR oxcAAR, Extensions

LiPD ensemble, geoChronR, Extensions