WaterSciCon24 Workshop Materials: Advancing Open Data Science and Analytics for Water

Authors:
Jeffery S. HorsburghDavid BlodgettTim HodsonAnthony M. CastronovaIrene Garousi-NejadAmber JonesLaura DeCiccoLee Stanish
Owners: Jeffery S. Horsburgh
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Created: Apr 26, 2024 at 6:03 p.m.
Last updated: Jul 30, 2024 at 11:06 p.m.
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Abstract

Water science and management challenges require synthesis of diverse data. Many data analysis tasks are difficult because data are large or complex; standard formats are not always agreed upon or mapped to efficient structures for analysis; scientists may lack training for tackling large and complex datasets; and it can be difficult to share, collaborate around, and reproduce scientific work. Access to computing for running and sharing data science or modeling workflows and structuring them in a way that they can be reproduced can also be challenging. Overcoming these barriers can transform the way water scientists work. Participants will learn how to use multiple data science tools, including data retrieval packages for easy access to data from the United States Geological Survey’s (USGS) National Water Information System (NWIS) and tools associated with the CUAHSI HydroShare repository and linked JupyterHub environment available to assist scientists in building, sharing, and publishing more reproducible scientific workflows following Findable, Accessible, Interoperable, and Reusable (FAIR) principles. We will demonstrate how the technical burden for scientists associated with creating a computational environment for executing analyses can be reduced and how sharing and reproducibility of analyses can be enhanced through the use of these tools.

This HydroShare resource includes all of the materials presented in a workshop at WaterSciCon24.

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Related Resources

The content of this resource references Chegini et al., (2021). HyRiver: Hydroclimate Data Retriever. Journal of Open Source Software, 6(66), 3175, https://doi.org/10.21105/joss.03175
The content of this resource references Blodgett, D., Johnson, J.M., 2022, nhdplusTools: Tools for Accessing and Working with the NHDPlus, https://doi.org/10.5066/P97AS8JD
The content of this resource references Horsburgh, J. S., A. S. Jones, S. S. Black, T. O. Hodson (2022). USGS dataretrieval Python Package Usage Examples, HydroShare, http://www.hydroshare.org/resource/c97c32ecf59b4dff90ef013030c54264
The content of this resource references Horsburgh, J. S., S. S. Black (2021). HydroShare Python Client Library (hsclient) Usage Examples, HydroShare, http://www.hydroshare.org/resource/7561aa12fd824ebb8edbee05af19b910
The content of this resource references Hodson, T. S., DeCicco, L. A., Hariharan, J. A., Stanish, L. F., Black, S., Horsburgh, J. S. (2023). Reproducibility Starts at the Source: R, Python, and Julia Packages for Retrieving USGS Hydrologic Data, Water, 15(24), 4236, https://doi.org/10.3390/w15244236.

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
U.S. National Science Foundation Collaborative Research: Elements: Advancing Data Science and Analytics for Water (DSAW) 1931297

How to Cite

Horsburgh, J. S., D. Blodgett, T. Hodson, A. M. Castronova, I. Garousi-Nejad, A. Jones, L. DeCicco, L. Stanish (2024). WaterSciCon24 Workshop Materials: Advancing Open Data Science and Analytics for Water, HydroShare, http://www.hydroshare.org/resource/c0184e9db2714782a03520ce1efde7ba

This resource is shared under the Creative Commons Attribution CC BY.

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