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#
oceanography, watermass geometry, tracers, C14, Mystery Interval, clustering, eNd, model output, Deglaciation, Paleoceanography, machine learning, data viz, coordinate systems, scikit-learn, intake, matplotlib, pandas, seaborn, xarray, cartopy, Science Bits, C-iTRACE, Lifehacks
AWS, CMIP6, LMR, model output, Holocene climate, Volcanic Input, cloud-ready data, data viz, coordinate systems, AWS, intake, matplotlib, pandas, ipywidgets, xarray, pyleoclim, cartopy, Science Bits, LMR-CMIP6, Lifehacks
proxy composite, temperature, pandas, cfr, seaborn, numpy, pyleoclim, 2k Proxy Composite, Science Workflows
endmember mixing, watermass geometry, clustering, tracers, scikit-learn, sqlalchemy, basemap, matplotlib, scipy, pandas, Science Bits, AncientWMG
data viz, matplotlib, pyleoclim, ammonyte, Extras, Figures, Detecting Paleoclimate Transitions with LERM
insolation, paleoclimate, age uncertainty, cross-wavelet analysis, wavelet analysis, model output, spectral analysis, paleoceanography, correlation, model-data comparison, pylipd, xarray, pyleoclim, cartopy, climlab, Pyleoclim science, Science Workflows
Chapters#
oceanography, tracers, data viz, xarray, matplotlib, cartopy, pandas, seaborn, notebook, Lifehacks, C-iTRACE
model output, oceanography, coordinate systems, xarray, matplotlib, cartopy, pandas, intake, notebook, Lifehacks, C-iTRACE
model output, eNd, xarray, matplotlib, cartopy, pandas, notebook, Science Bits, C-iTRACE
clustering, machine learning, watermass geometry, xarray, matplotlib, cartopy, scikit-learn, notebook, Science Bits, C-iTRACE
C14, Paleoceanography, Deglaciation, Mystery Interval, xarray, matplotlib, cartopy, pandas, notebook, Science Bits, C-iTRACE
AWS, cloud-ready data, data viz, CMIP6, intake, AWS, xarray, pandas, notebook, Lifehacks, LMR-CMIP6
model output, CMIP6, coordinate systems, xarray, matplotlib, cartopy, pandas, notebook, Lifehacks, LMR-CMIP6
model output, ipywidgets, matplotlib, notebook, Lifehacks, LMR-CMIP6
model output, LMR, CMIP6, xarray, matplotlib, cartopy, pandas, pyleoclim, notebook, Science Bits, LMR-CMIP6
Volcanic Input, Holocene climate, LMR, CMIP6, xarray, matplotlib, cartopy, ipywidgets, notebook, Science Bits, LMR-CMIP6
proxy composite, temperature, numpy, seaborn, pandas, pyleoclim, cfr, notebook, Science Workflows, 2k Proxy Composite
watermass geometry, tracers, endmember mixing, basemap, scipy, matplotlib, pandas, sqlalchemy, notebook, Science Bits, AncientWMG
clustering, watermass geometry, basemap, scikit-learn, matplotlib, notebook, Science Bits, AncientWMG
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Figures, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Extras, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Extras, Detecting Paleoclimate Transitions with LERM
data viz, matplotlib, pyleoclim, ammonyte, notebook, Extras, Detecting Paleoclimate Transitions with LERM
spectral analysis, wavelet analysis, cross-wavelet analysis, insolation, paleoceanography, xarray, pyleoclim, pylipd, climlab, notebook, Science Workflows, Pyleoclim science
model output, spectral analysis, model-data comparison, paleoclimate, xarray, pyleoclim, notebook, Science Workflows, Pyleoclim science
paleoclimate, correlation, age uncertainty, xarray, pyleoclim, cartopy, pylipd, notebook, Science Workflows, Pyleoclim science