Reproducibility Starts at the Source: R, Python, and Julia Packages for Retrieving USGS Hydrologic Data

Authors

Timothy O. Hodson, U.S. Geological Survey Central Midwest Water Science CenterFollow
Laura A. DeCicco, U.S. Geological Survey Upper Midwest Water Science CenterFollow
Jayaram A. Hariharan, U.S. Geological Survey Water Mission Area
Lee F. Stanish, U.S. Geological Survey Water Mission AreaFollow
Scott Black, Consortium of Universities for the Advancement of Hydrologic Science, Inc.Follow
Jeffery S. Horsburgh, Utah State UniversityFollow

Document Type

Article

Author ORCID Identifier

Timothy O. Hodson https://orcid.org/0000-0003-0962-5130

Laura A. DeCicco https://orcid.org/0000-0002-3915-9487

Jayaram A. Hariharan https://orcid.org/0000-0002-1343-193X

Lee F. Stanish https://orcid.org/0000-0002-9775-6861

Scott Black https://orcid.org/0009-0003-9687-2996

Jeffery S. Horsburgh https://orcid.org/0000-0002-0768-3196

Journal/Book Title/Conference

Water

Volume

15

Issue

24

Publisher

MDPI AG

Publication Date

12-9-2023

First Page

1

Last Page

10

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users.

Recommended Citation

Hodson, T.O.; DeCicco, L.A.; Hariharan, J.A.; Stanish, L.F.; Black, S.; Horsburgh, J.S. Reproducibility Starts at the Source: R, Python, and Julia Packages for Retrieving USGS Hydrologic Data. Water 2023, 15, 4236. https://doi.org/10.3390/w15244236