LeapFROGS modules

[khider]

Modules to be considered for LeapFROGS:

• Python
• R
• time series analysis
• github
• docker/Binder/myBinder
• principle of FAIR publishing (geoscience paper of the future)

[CommonClimate]

TSA content:

• harmonic analysis DAiEES
• detrending
• filtering: theory & practice
• interpolation: theory & practice
• spectral analysis theory & practice
• wavelet analysis theory & practice
• coherency analysis: cross-wavelet here
• changepoint detection??

[DaveEdge1]

autocorrelation
arima modeling

[khider]

• Correlation (which would have autocorrelation)

[khider]

Python Modules:

1. Basic Python:
   • https://learnpython.trinket.io/ - Useful for getting started with basic operations. https://docs.trinket.io/getting-started-with-python#/welcome/where-we-ll-go
     • Exercises (DSCI510):
       • Temperature conversion from Celsius to Fahrenheit
       • Area of a circle given radius
       • Check whether a given year is a leap year
       • Acceptable password
       • Caesar Cypher
   • Need to find information about functions and classes
     • Exercises (DSCI510)
       • Create an Item class
   • Web scrapping? [Low priority]
   • Using Web APIS
     • Exercises (DSCI510)
       • Return weather data
       • Top 3 restaurants from Yelp- Replace with https://waterdata.usgs.gov/blog/api_catalog/
2. Jupyter and Jupyter Notebooks (Already in?)
3. The Scientific Python Stack
   • Project Pythia: https://foundations.projectpythia.org/landing-page.html
     • NumPy
     • Matplotlib
     • Cartopy
     • Datetime -> Will need to be extended
     • Pandas
     • Xarray
   • GeoPandas: https://medium.com/@data95/unlocking-geospatial-data-analysis-with-geopandas-a-comprehensive-python-tutorial-and-analysis-6e131c44dda3
   • Scikit-learn (ML): https://towardsdatascience.com/exploring-use-cases-of-machine-learning-in-the-geosciences-b72ea7aafe2
     • List of subjects can be found here: https://docs.rcd.clemson.edu/training/workshop_catalog/python/machine_learning_python/

[khider]

Publishing resources:

SPF: https://zenodo.org/records/4744365
FAIR Software: https://fair-software.nl/ / FAIR data: https://www.go-fair.org/fair-principles/ /FAIR workflow: https://workflows.community/groups/fair/
EarthCube presentation: doi.org/10.6084/m9.figshare.25246069

1. Motivation
2. How to manage data
3. How to manage software
4. How to manage Workflow

Relevant Papers:

1. FAIR data: https://www.nature.com/articles/sdata201618
2. FAIR software: https://www.nature.com/articles/s41597-022-01710-x
3. FAIR workflow: https://direct.mit.edu/dint/article/2/1-2/108/10003/FAIR-Computational-Workflows