ABSTRACT:

This resource contains the Jupyter notebooks and conda environment for Reproducible Workflows with Open Water Data: NASA, NOAA, USGS River Data Access, a CUAHSI skills certificate course for graduate students, postdocs, early-career researchers, and professionals who use federal water data in their research. The notebooks accompany the course’s slides and are where learners run the code.

Module 3 has one notebook per agency: retrieving NASA SWOT river data, NOAA National Water Model (NWM) streamflow data, and USGS Water Data for the Nation (WDFN). Module 4 brings the products together for a single flood event and takes a first step with additional federal products. Each notebook follows the same habits: find what exists before downloading it, describe what comes back using the course’s shared concepts, and choose an access pattern that scales with the question.

All notebooks share one conda environment, defined in environment.yml. To use the materials, download the resource, create the environment, and open the notebooks in your choice of Jupyter-compatible editor; the accompanying setup guide walks through each step. Downloading SWOT and NWM data requires a free NASA Earthdata Login account; a USGS Water Data API key is optional but raises rate limits.

ABSTRACT:

This HydroShare resource is an example dataset created for workshop and training purposes. All data are simulated and do not represent measurements from any real location or monitoring program.
The resource includes a comma-separated values file containing synthetic water quality measurements collected at three fictional monitoring sites (Site A, Site B, Site C) over a single sampling season (April through October 2023). Variables include water temperature, pH, dissolved oxygen, turbidity, nitrate, total phosphorus, and specific conductance. Sampling frequency is biweekly, with values designed to reflect plausible seasonal and longitudinal patterns in a small stream system.
A companion Jupyter Notebook demonstrates a basic analysis workflow in Python, including data loading and inspection, summary statistics, time series visualization, and correlation analysis.
This resource is intended to help researchers practice publishing, documenting, and accessing data on HydroShare using a realistic but consequence-free dataset.

ABSTRACT:

The CUAHSI CUAHSI Water Learning Commons (https://water-learning-commons.cuahsi.org/) is an open learning platform that brings together educational materials for water research. The portal includes tutorials, reusable workflows, and instructional content designed to support students, researchers, and practitioners working with water data.

This platform organizes diverse educational materials into five distinct categories for water researchers, bringing them together in a single, accessible space. Materials are designed to be reusable, transparent, and adaptable for teaching and research applications.

This HydroShare resource serves as a collection that references the full portal and links to individual collections and pages that are archived as separate, citable resources.