Revision as of 17:24, 9 June 2014

What is Organic Data Science?

We are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open framework that facilitates ad-hoc participation and entice collaborators based on attractive science goals. Read more about Organic Data Science.

Contributing to Organic Data Science

We are using a semantic wiki framework with significant extensions to structure collaboration processes.

Read more about how this framework works and how to participate and contribute.

Learn how to use this wiki.

Our Science Goal: The Age of Water and Carbon

We focus on long-standing problems of coupled water and carbon budgets through development of a new scientific paradigm, The Age of Water and Carbon, that melds theory and practice from limnology and hydrology within the new collaborative paradigm of Organic Data Science. We are integrating analytical frameworks from two communities – hydrology and isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER. This foundation will serve as a nexus for participation by multiple communities and will seed the growth of additional science through shared ideas, knowledge, and data.

Currently Active Tasks

• Modeling connectivity in the hydroscape.
• Science questions about lake carbon cycling.
• The lake organic carbon models.
• Documentation for the PIHM model.
• Lake modeling support.
• Framework Design

• Setting up lake model for North Temperate Lakes
• Calibrate lake model

Today's Highlights

There is a growing set of contributors to the project. Here are some highlights about their expertise and affiliations. Help us fill in the blanks by editing their pages:

Picture	Name	Expertise	Affiliation
	Chris Duffy	Hydrology	Pennsylvania State University
	Craig Snortheim	Hydrodynamic modeling	Center for Limnology, University of Wisconsin-Madison
	David da Motta Marques	Hydrology, Hydrodynamic modeling	Universidade Federal do Rio Grande do Sul
	Hilary Dugan	Physical limnology	University of Wisconsin-Madison, Center for Limnology
	Jordan Read	Hydrodynamic modeling, Physical limnology	Center for Integrated Data Analytics, U.S. Geological Survey
	Matt Hipsey	Ecosystem modeling, Hydrodynamic modeling	University of Western Australia
	Michael Pace		University of Virginia
	Patricia Soranno	Landscape limnology	Michigan State University
	Paul Hanson	Carbon cycling	Center For Limnology, University of Wisconsin - Madison
	Steve Jepsen		University of California Merced
	Tom Harmon		University of California Merced
	Xuan Yu		Pennsylvania State University
	Yolanda Gil	AI planning and collaborative problem solving, Workflows, Semantic Web, Semantic wikis, Social computing	Information Sciences Institute, University of Southern California

We are starting to describe models, help us fill in the blanks:

Acknowledgments

This work is supported by the National Science Foundation through the INSPIRE program with grant number IIS-1344272.