CUAHSI Skills Certificate Pilot - Reproducible Workflows with Open Water Data: NASA, NOAA, USGS River Data Access
November 3, 2026 - December 4, 2026 ET
Registration Deadline 10.23.2026
Location: Virtual and Synchronous
Register Here
Workshop Description:
This course was developed with support from NASA, NOAA, and USGS.
Water science researchers rely on datasets from federal agencies, but few have been trained in how to access this data using recommended and sustainable patterns. This course builds data literacy skills for programmatically accessing datasets from NASA, NOAA, and USGS, and understanding the strengths of each in water research applications. This pilot course is Python-based and has been developed with the recommendations of the data providers themselves.
This pilot training course will cover the following data sets:
The Fall 2026 Pilot of this program will consist of the following Modules:
Module 1: Data Best Practices in Water Research
Module 2: Federal Water Data Landscape
Module 3: Federal Water Data Access & Retrieval
Module 4: Federal Water Data Synthesis
Participant Commitment:
You should expect to spend approximately 20 hours over the 4 week duration of this course, which includes 14 hours of synchronous curriculum, and additional work outside of the virtual classroom hours. This course will also include office hours and opportunities for you to ask questions and receive feedback. To receive the completion certificate for this course, participants must pass assessments with hard deadlines, including short “check your understanding” quizzes, and a final presentation.
Eligibility and Requirements:
This course is designed for graduate students, postdocs, early-career researchers, and professionals who use federal water data in their research.
Participants are expected to have intermediate experience using Python, and should understand core concepts (variables, loops, functions) and be adapt example code to install and use libraries, load data into common structures, apply basic cleaning and plotting techniques, write short scripts, organize code in a Jupyter notebook, and troubleshoot common errors. If these skills are new to you, we recommend building them before enrolling.
Learning Objectives:
By the end of this training, learners will be able to:
- Explain open science, FAIR principles, and reproducibility best practices, and their benefits to science products and the science community.
- Outline the key elements of a data management plan and articulate the benefits of publishing scientific data in an appropriate repository.
- Characterize the river data products from NASA SWOT, NOAA NWM, and USGS WDFN, including relevant variables, derivation, spatial and temporal resolution, and known limitations.
- Adapt provided Python scripts to retrieve data from NASA SWOT, NOAA NWM, and USGS WDFN using their recommended APIs and libraries.
- Select the most appropriate programmatic approach for downloading data at large temporal and spatial scales for different hydrologic applications.
- Compare and describe the capabilities and limitations of NASA SWOT, NOAA NWM, and USGS WDFN data for a single flood event at the appropriate scales and resolutions.
Location:
The course will be delivered virtually.
A subset of the content will also be presented in person at the CUAHSI Water Science Exchange, November 12–14, 2026, at the University of Missouri, for attendees interested in learning more about the program.
Getting Started:
This Fall 2026 Pilot Cohort will accept 30 participants. After registration, participants will receive information on how to connect to the synchronous zoom meetings, and access to the classroom management system where they will submit assignments at least 1 week ahead of the start of the program.
Agenda:
Registration Cost:
Those affiliated with CUAHSI member institutions receive a 20% discount on registration for this event. See if your institution is a member.
Student, CUAHSI Member - $160
Student, Non-Member - $200
Professional, CUAHSI Member - $320
Professional, Non-Member - $400
Questions? Contact Lindsay Platt, email hidden; JavaScript is required.