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.

Our Science Goal: The Age of Water and Carbon

The simulated catchment isoscape illustrating the 2009 annual mean spatial pattern for groundwater age at the Shale Hills CZO (Bhatt, 2012) based on calibration to CZO data. The overall space-time mean age is 217 days. Our goal is to develop a methodology to carry out similar analyses comprehensively in entire watersheds.

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.

Today's Highlights

Browse through some of the currently active tasks:

There is a growing set of contributors to the project. Here are some highlights about their expertise and affiliations. You can help us fill in the empty cells by editing their individual pages, once you do that the information will be shown here:

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