Data Visualization as a Tool for Communicating Ocean Science Rob Bochenek Information Architect Axiom Consulting & Design

Presentation Summary o Explore why communicating ocean science data is difficult in a data rich world. o Review an example dataset and discuss communication issues. o Example visualizations. o How does data visualization improve science communication for everyone? o How an organization can prepare to rapidly and cost effectively visualize their data.

Why is communicating ocean science data difficult? o Data is complex (measurements spread over space and time). o The amount of data being created is increasing with time. o Raw data is not very accessible to scientists (and even less accessible to the public). o Summarized data in report form is condensed lacking granular access.

Example Dataset: NOAA Mussel Contaminants Dataset o Data is a simple table describing samples for lingering oil in the PWS oil spill affected area. o Multiple samples at multiple locations sampled through time.

Example Dataset: NOAA Mussel Contaminants Dataset o Example graphic from the final report. o Graphs display contaminant data grouped into “common name” to show change over time. o No way to tell where sampling events took place.

Google Earth o Google Earth Client is free and available. o Renders.kml or.kmz documents (very much like a word document). o 4 Dimensional Visualization (x,y,z,t). o Can interact with the web but is not required. o Can create data mosaics (layers from multiple sources).

Website Based Data Visualization o 2 D (x,y). o Requires Web Server. o Systems include : google maps, open layers, mapserver.

How to be Prepared to Visualize your Data o Require investigators to create descriptive metadata during (NOT five years after) data collection o Federal Geospatial Data Committee (FGDC) o Send them to the Friday workshop with USGS on metadata. o Require data fields for latitude, longitude and time for data which has a geospatial/temporal component (avoid the “Beaver Creek” ambiguity). o Make sure images and other information acquired during data collection are logically linked to sampling efforts. GEO DB Data Standardization Documentation Programming Visualizations