Difference between revisions of "Main Page"

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Over the last hundred years, science has become an increasingly collaborative endeavor. Scientific collaborations, sometimes referred to as “collaboratories” and “virtual organizations”, range from those that work closely together and others that are more loosely coordinated.  Some scientific collaborations revolve around sharing instruments (e.g., the Large Hadron Collider), others focus on a shared database (e.g., the Sloan Sky Digital Survey), others form around a shared software base (e.g., SciPy), and others around a shared scientific question (e.g., the Human Genome Project).  
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= What is Organic Data Science? =
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[[File:OrganicDataScience.png|thumb|left|upright|Organic Data Science will facilitate ad-hoc collaborations in science.]]
  
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Our work focuses on scientific collaborations that are driven by a shared scientific question that requires the integration of ideas, models, software, data, and other resources from different disciplines. These projects are particularly challenging because they require:
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We are investigating [[What_Is_Organic_Data_Science| 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. [[What_Is_Organic_Data_Science| 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.
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* significant organization and coordination, as people with diverse backgrounds are supposed to first discover one another and then find common ground to collaborate
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* retaining users over the long term, since people need clear incentives to remain involved for the long period of time that such projects are active
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* incrementally growing the community with unanticipated participants, as they bring in skills or resources needed as the project is fleshed out
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For all these reasons, even though such scientific collaborations do occur they are not very common. Yet, they are needed in order to address major engineering and science challenges in our future.
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Accomplishing this requires three elements:
  
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This project is developing the <b>Organic Data Science Framework (ODSF)</b> to support scientific collaborations that revolve around complex science questions that require significant coordination to synthesize multi-disciplinary findings, enticing contributors to remain engaged for extended periods of time, and continuous growth to accommodate new contributors as needed as the work evolves over time.
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# a science approach to tackle the problem of the age of water,  
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# a technical substrate that facilitates transdisciplinary collaborations, and  
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# a social approach to engage the community.
  
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<b><i>[[What_Is_Organic_Data_Science| Read more about Organic Data Science]].</i></b>
  
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ODSF addresses these challenges with a collaborative user interface that supports:
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= Technical and Social Aspects of Organic Data Science =
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# self-organization of the community through user-driven dynamic task decomposition,
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# on-line community support by incorporating social design principles and best practices,
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# an open science process by capturing new kinds of metadata about the collaboration that provide invaluable context to newcomers.
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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. 
  
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With ODSF, users formulate science tasks to describe the what, who, when, and how of the smaller activities pursued within the collaboration.  The interface is designed to entice contributors to participate and continue involved in the specific tasks they are interested in.  The framework is in its early stages of development, and it evolves to accommodate user feedback and to incorporate new collaboration features.
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=== Ongoing Technical Activities ===
  
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We are working on several major activities:
  
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For more information, please visit these sites:
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# [[Framework Design | Design the technical aspects of our organic data science framework]]
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* [http://www.organicdatascience.org/ageofwater We are using ODSF to investigate the isotopic age of water]
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# [[Human_Centered_Computing_to_Support_Organic_Data_Science | Understand the human-centered computing aspects of organic data science]]
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* [http://www.organicdatascience.org/framework The ODSF site where we are developing this framework]
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* [http://www.organicdatascience.org/training The ODSF site where newcomers can learn about how to use the framework]
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= Contributing to this project =
  
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[[File:nsf-logo.gif]] This work is supported by the National Science Foundation through the INSPIRE program with grant number IIS-1344272.
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<b>[[Contributors | There is a growing set of contributors to the project]].</b>
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We are testing this framework with a collaborative research project focused on the theoretical and experimental aspects of the isotopic age of water.  [http://www.organicdatascience.org/ageofwater Visit the project site].
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The contents of this wiki are accessible to everyone.  If you would like to contribute new content, please contact us to obtain an account by emailing us at <b>organic.data.science@gmail.com.</b>
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= Acknowledgments =
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This work is supported by the National Science Foundation through the INSPIRE program with grant number IIS-1344272.

Revision as of 07:07, 21 October 2014

What is Organic Data Science?

Organic Data Science will facilitate ad-hoc collaborations in 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:

  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.

Technical and Social Aspects of Organic Data 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.

Ongoing Technical Activities

We are working on several major activities:

  1. Design the technical aspects of our organic data science framework
  2. Understand the human-centered computing aspects of organic data science

Contributing to this project

There is a growing set of contributors to the project.

We are testing this framework with a collaborative research project focused on the theoretical and experimental aspects of the isotopic age of water. Visit the project site.

The contents of this wiki are accessible to everyone. If you would like to contribute new content, please contact us to obtain an account by emailing us at organic.data.science@gmail.com.

Acknowledgments

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

Yandex.Metrica
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