2025 CDI Workshop: Cultivating a Data-Centric Culture
The 2025 CDI Workshop was held online in two parts from April 29-May 2 and August 13-14. The theme of the workshop was "Cultivating a Data-Centric Culture."
The 2025 Workshop had 477 registrants, 14 breakout sessions, 3 plenaries, 1 community session, and 20 lightning talks. The workshop was held virtually online.
The online agenda for the workshop is available on Sched: Part 1 Agenda, Part 2 Agenda.
Plenary Sessions
| Title | Session Speakers |
|---|---|
| Plenary 1 Welcome and Keynote on Data-Centric Culture | Leslie Hsu, Paul Exter, Julia Lowndes |
| Plenary 2 State of the Culture | Leslie Hsu, Mike Tischler, Lisa Zolly, Ricardo McClees, Tamar Norkin, Edgar Hernandez, John Bechtell, Alejandra Angulo |
| Plenary 3 Expanding on our Data-Centric Culture: Opportunities for the Future | Raleigh Martin, Susan Shingledecker, Megan Orlando, Margaret Goldman, Joshua Rosera |
| Community Session: Cultivating our Data-Centric Culture: Planting Seeds | Leslie Hsu and Leah Colasuonno |
Breakout Sessions
| Title | Session Lead(s) |
|---|---|
| Exploring Responsible AI for Effective Data Management | Tara Bell and Madison Langseth |
| Advanced Research Computing: getting started and accelerating discovery | Kyle Moran and Jay Laura |
| AI/ML Data & Model Development in the Cloud | Joe Bretz |
| Alteryx Hackathon | Stuart Wilson |
| Cloud AMA (Ask Me Anything!) | Robert Djurasaj, Johanna Ruff |
| Data quality control for everyone: a listening and discussion session | Adrian Moore, Gregor Siegmund |
| Joys and pitfalls of integration of data and records management | Matthew Arsenault, Madison Langseth, Angela Brennan |
| Leveraging the Power of Knowledge Graphs | Helen Turvene |
| Operational Data Systems | David Watkins |
| Preserving Legacy Data for the Public and Next Generation of Scientists | Laura McDuffie |
| Reading, Publishing, and Open Access to Enhance USGS Scientific Impact | Rob Thieler |
| The Future of USGS Supercomputing: Data and Compute | Kyle Moran |
| Unlocking Alteryx: How Alteryx Drives Data Solutions | Stuart Wilson |
| Using Python for working with USGS Analysis Ready Data | Bojan Milinic |
Lightning Talks
The order is loosely based on the USGS Data Strategy goals.
Goal 1: Maximize the utility of USGS data with FAIR practices
- Jess LeRoy: Leveraging the NWM (National Water Model) to drive FluEgg simulations
- Richard Inman: Scaling-up phenological date matching for invasive species mapping: a free opensource workflow
- Collin Roland: py-bedform-morphometrics: a modular Python toolbox for exhaustive measurements of bedform shape
- Ryan Anderson: Terrestrial Remote Sensing I/O With PyHAT
- Brianna Williams: Building a relative heat index of tire dust sources for the conterminous U.S.
Goal 2: Foster innovation in data and technology
- Marcelle Caturia: Introduction to USGS Map Ready Data
- Chuck Hansen: Seeing Below the Surface: Geospatial Delivery of Hi-Res Hydrologic and Aquatic Ecosystem Data from Autonomous Underwater Vehicles
- Raymond LeBeau: A Point Cloud-Based Workflow for Geomorphic Change Detection and Sediment Budget Analysis
- Helen Turvene: Harnessing the Power of AI in Geospatial Systems
Goal 3: Coordinate common data policies, methods, and standards
- Stephanie James: Advancing the Geophysical Survey (GS) data standard and GSPy toolbox
- Jeffrey Kwang: Geospatial Data Retrieval Alignment Workflows
- Chris Merkes: Understanding and streamlining environmental DNA QA/QC analysis
- Kyle Puls: wren: Model Development Using R Shiny
- Carol Morel: Where Do I Start? Using Microsoft Planner to Create a Data Management Training Plan
Goal 4: Build a strong, scalable data infrastructure
- Elise Hinman: Building a sturdy status page for a delicate database
Goal 5: Strengthen the data-centric culture
- Bojan Milinic: Fast-Track USGS Insights using Python and Analysis Ready Data (ARD)
- Gregor Siegmund: Data quality control for everyone: a course and recipes for well-documented data workflows in R
- Andy McAliley: Training: Introduction to Snakemake
- Kim Shaffer: Bridging the learning gap in R and R Shiny using Water Quality Data
- Jess Driscoll: Team Science
Go back to CDI 2025 Activities.