Creating a Data Portal for Ocean Science
Oceanography is a multidisciplinary field which involves synthesizing physical, chemical and biological data. Analyzing these observations often requires harmonizing data such as satellite measurements with model estimates.
Participant
Observations
Interviews
Surveys
Card Sorting
Affinity Mapping
Focus Groups
Andrew Neang
Charlotte P. Lee
9/2019 — Current
Handling satellite and model data sets is particularly challenging for the following reasons:
Are scattered all over the web and might not be trivial to retrieve or discover
Are massive in size
Have varying spatial and temporal dimensions and resolutions
As a part of a larger effort funded by the Simons Foundation called Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES), we are helping scientific stakeholders come together to design and develop shared understandings, priorities, practices, methods, software tools, and computer architectures that will lead to the development of a data repository. The project brings together a multi-disciplinary group of investigators from oceanography, statistics, data science, ecology, biogeochemistry and remote sensing among others.
We are undertaking “action research” which involves hands-on problem solving and intervention in the process. Drawing on participatory design methods we take time to identify a broad range of stakeholders and engage them in an interactive process of design.
The Simons collaborative marine atlas project is taking a truly socio-technical approach to design.
With social scientists, biologists, computer modelers, and computer scientists sitting at one table, our discussions necessarily range from how to identify and engage a broad but still practical range of stakeholders to current and future trends in computer architecture to the affects of real-time data analysis on the scientific process. This project will lead to a more community-driven system and will provide new insights on how to do sociotechnical design in support of collaboration-dependent and data-intensive innovation.
