Revision as of 17:24, 21 July 2014

What is Organic Data Science?

Organic data science diagram goes here.

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. Organic Data Science]] allows scientists to formulate and resolve science processes through an open framework that facilitates ad-hoc participation and entice collaborators based on attractive science goals.

Accomplishing this requires three elements:

1. a science approach to tackle the problem of the age of water,
2. a technical substrate that facilitates transdisciplinary collaborations, and
3. a social approach to engage the community.

Read more about Organic Data Science.

Our Science Goal: The Age of Water and Carbon

Organic team science would enable the creation of “virtual overlay networks” over existing cyberinfrastructure and other related activities.

This study focuses 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. Read more about Modeling the Age of Water and Carbon in Lake-Catchment Systems. or Read more about the PIHM catchment model.

Ongoing Activities

Our project goal is to predict pathways and age of water and carbon isotopes for lake-catchment systems. To that end, we are working with the community in several major activities:

• Develop a computational model for water and carbon isotopes in lake-catchment systems
• Select core lake and catchment models
• Implement the catchment model for North Temperate Lakes
• Implement the lake model for North Temperate Lakes
• Couple the lake and catchment models

Models

The models that we are considering include:

Contributing to Organic Data Science

We have Special Information for Newcomers to catch up with what we have been doing so far and our plans for the future.

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.

Get an account, and learn how to use this wiki.

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:

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

Acknowledgments

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