This repository contains three reproducible workflows as Jupyter Notebook using the Pyleoclim software package to serve as fully executable companions to the Pyleoclim manuscript, currently in review at Paleoceanography & PaleoClimatology.
The repository contains three folders, each representing a study using Pyleoclim:
- Orbital Cycles contains a workflow describing spectral and wavelet analysis of a marine record convering the past 5 million years/
- MD Confrontation contains a notebook reproducing the study by Zhu et al. (2019) looking at how well climate models capture the continnum of global-average temperature variability.
- CrystalCorrelations contains a notebook looking at the pitfall of correlations for paleoclimate study following the study by Hu et al. (2017).
The notebooks are fully executable through MyBinder (click on the badge at the top of this read me).
Pyleoclim is a Python package geared towards timeseries analysis of time-uncertain data.
The package can (but do not necessarily have to) directly work with data in the Linked Paleo Data (LiPD) format. The advantage of working with that format is that the code contains automated data transformation, making working with paleoclimate data easier and faster.
v0.4 of the notebooks require v0.9.1 of Pyleoclim and associated requirements detailed in the environment.yml file. A built container is available on Quay.io:
docker pull quay.io/linkedearth/pyleoclim:5b1148342325
All notebooks herein are provided under an Apache 2.0 license.
We needn't tell you that making research tools accessible requires time and effort. If you find any of these resources useful and use them in your own research, please do us the kindness of one or more citations. Notebooks in this collection is registered on Zenodo, and associated with a digital object identifier (DOI). A ready-to-use citation is provided on this GitHub repository in APA and BibTex (in the About section on the right panel, click on "Cite this repository"). If you use any of the standards (LiPD) or the software (Pyleoclim), please cite them as well. It will make us (and NSF!) very happy to hear that these investments spawned more research.
If you use any of the data on this repository, please cite the original authors of the study.