Teaching Resource in Jupyter Notebooks for Accessing and Analyzing Large Research Databases in Earth and Planetary Science

Communities in Earth and planetary sciences have built large databases for research purposes over many years. These databases can be important resources for inquiry-based learning and project-based courses. However, for students with little background in coding, the usual workflow has been to access the databases through a browser, learn how to define a search, and download the data required. The workflow cannot utilize the full potential of the large databases as teaching resources. For education, the value of these large databases can be dramatically enhanced if students can download and analyze the large number of data points. Furthermore, capability to combine multiple databases will open new opportunities in education. We have constructed a series of codes and documents in Jupyter Notebooks with Python programming language for accessing and analyzing large research databases. The format of Jupyter Notebooks is also excellent for teaching coding at the same time, as they provide instructions between the chunks of code and allow the user to modify code and instantly see the result. The notebooks we have constructed guide the user with a rudimentary understanding of Python to access large databases. It starts with a basic explanation of what an API is and how to use it and progresses into defining search criteria, executing searches, filtering the results, and plotting and analyzing the datasets. Some of the datasets used are Hypatia Catalog, providing stellar compositions; PetDB, a geochemical database; Pymatgen accessing MaterialsProject, for crystallography and materials science; and RRUFF, for mineralogy and crystallography. The notebooks will be uploaded to github.com and serve as an open-source teaching module library. A student or researcher can download the files and depending on their specific database needs, and run through it while reading the instructions. The open-source files allow for modification and personalization of searches for teaching in classes or workshops. The knowledge gained can be applied to other databases not covered in our notebooks and also serve as a launching point to learning how to use Python and programming in research and study.