Revision as of 01:03, 16 July 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. 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: a science approach to tackle the problem of the age of water, a technical substrate that facilitates transdisciplinary collaborations, and a social approach to engage the community:

1. Technical approach: Human Computing to Support Organic Team Science. We are pursuing a social computing approach that takes into account human aspects such as incentives and participation, while providing the fabric for representing and coordinating tasks involved in accomplishing science goals. Our approach will openly expose science tasks, facilitating inspection and engagement of new potential contributors. The collaboration will grow in an organic way, drawing in people and other contributions from existing data providers and cyberinfrastruture resources.
2. Science approach: Focus on the Lake-Catchment Isoscape. We intend to apply the notion of a catchment isoscape for stable isotopes of water to examine the implication of age and residence time on biogeochemical cycles of coupled catchment-lake systems. This will require integrating the analytical frameworks developed within 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 with existing data and resources from two research sites, the Shale Hills CZO and the GLEON member site, North Temperate Lakes LTER. This foundation will serve as a nexus for participation by multiple communities in the proposed science and will seed the growth of additional science through shared ideas, knowledge, and data.
3. Social approach: Engaging the Community. Tremendous potential resides in the collective resources of the highly distributed science community. We can realize that potential by creating an exciting and engaging environment in which individuals can contribute their unique resources, and gain in return, additional resources, including knowledge, credit, and an expanded network. We are collaborating with investigators from diverse areas of research committed to supporting information sharing for advancing Earth and environmental sciences on a scale much larger than we could accomplish individually or even through any one discipline.

Read more about how to participate and contribute.

This is a preliminary article about this work:

• “Organic Data Sharing: A Novel Approach to Scientific Data Sharing.” Gil, Y.; Ratnakar, V.; and Hanson, P. In Second International Workshop on Linked Science: Tackling Big Data (LISC), held in conjunction with the International Semantic Web Conference (ISWC), Boston, MA, 2012. Available as a preprint.

Read more about Organic Data Science.

Our Science Goal: The Age of Water and Carbon

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.

Today's Highlights

Browse through some of the currently active tasks:

• Set up the Catchment Hydrodynamic Model at Field Sites.
• Set up the Lake Hydrodynamic Model at Field Sites.
• Develop Coupled Lake - Catchment Hydrodynamic Model.
• Calibrate lake and catchment models
• Develop "Age" model for coupled lake-catchment
• Lake carbon questions.
• The lake organic carbon models.
• Documentation for the PIHM model.
• Lake modeling support.
• Framework Design

• Setting up lake model for North Temperate Lakes

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
80px	Craig Snortheim	Hydrodynamic modeling
	David da Motta Marques	Hydrology, Hydrodynamic modeling	Universidade Federal do Rio Grande do Sul
	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.