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(Ongoing Activities)
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* [[Couple the lake and catchment models]]
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= Models =
<b>We are starting to describe models, help us fill in the blanks:</b>
<b>The models that we are considering include:</b>
{{ #ask: [[Is a::model]]
{{ #ask: [[Is a::model]]

Revision as of 10:24, 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:


The models that we are considering include:

 AuthorSoftware licenseLanguage
Delft3dDeltares systems
GLM SoftwareCasper Boon
Louise Bruce
Matt Hipsey
IPH-ECODavid da Motta Marques
PIHM SoftwareGopal Bhatt
Lorne Leonard
Xuan Yu
Chris Duffy
Mukesh Kumar

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
resize Chris Duffy Hydrology Pennsylvania State University
80px Craig Snortheim Hydrodynamic modeling
resize David da Motta Marques Hydrology, Hydrodynamic modeling Universidade Federal do Rio Grande do Sul
resize Jordan Read Hydrodynamic modeling, Physical limnology Center for Integrated Data Analytics, U.S. Geological Survey
resize Matt Hipsey Ecosystem modeling, Hydrodynamic modeling University of Western Australia
resize Michael Pace University of Virginia
resize Patricia Soranno Landscape limnology Michigan State University
resize Paul Hanson Carbon cycling Center For Limnology, University of Wisconsin - Madison
resize Steve Jepsen University of California Merced
resize Tom Harmon University of California Merced
resize Xuan Yu Pennsylvania State University
resize Yolanda Gil AI planning and collaborative problem solving, Workflows, Semantic Web, Semantic wikis, Social computing Information Sciences Institute, University of Southern California


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