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THE TEXT OF THE INTRO FOLLOWS. IT IS ABOUT ONE PAGE.

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 [Ribes and Finholt 2009; Bos et al 2007]. 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 quest (e.g., the Human Genome Project). Our work focuses on scientific collaborations that revolve around complex science questions that require:

• multi-disciplinary contributions, so that the participants belong to different communities with diverse practices and approaches
• significant coordination, where ideas, models, software and data need to be discussed and integrated to address the shared science goals
• unanticipated participants, so that the collaboration needs to grow over time and include new contributors that may bring in new knowledge, skills, or data

Such scientific collaborations do occur but are not very common. Unfortunately, they take a significant amount of effort to pull together and to sustain for the usually long period of time required to solve the science questions. Yet, these kinds of collaborations are needed in order to address major engineering and science challenges ahead (e.g., http://www.engineeringchallenges.org). Our goal is to develop a collaborative software platform that supports such scientific collaborations, and ultimately make them significantly more efficient and commonplace.

This paper presents an Organic Data Science framework to support scientific collaborations that revolve around complex science questions that require multi-disciplinary contributions to gather and analyze data, significant coordination to synthesize findings, and grow organically to accommodate new contributors as needed as the work evolves over time. Key features of this framework are: 1) it provides a task-oriented nexus driven by science goals that connects scientists together, organizing tasks to help scientists track where they can contribute and when, as well as their past contributions, 2) it incorporates principles from social sciences research on successful on-line collaborations, including best practices for retention and growth of the community, 3) it is open in that it exposes all tasks and activities publicly, so that newcomers can immediately see what work is being done and what tasks they can contribute to. The framework is still under development, and it evolves to accommodate user feedback and to incorporate new collaboration features.

There is a significant body of work on studying on-line collaboration [Kraut and Resnick 2011], notably Wikipedia and other wiki-style frameworks. The ability to sustain a community of contributors and include newcomers has been extensively demonstrated in wikis, notably in Wikipedia. Our work builds on the social design principles uncovered by this research. However, our belief is that scientific work is best organized around tasks, not topic pages.

There are a wide range of approaches that have been explored for collaboration, although they have not had much adoption in science practice. Argumentation interfaces facilitate the collaborative synthesis of diverse ideas [Conklin 1995], and have been used in the context of science [Filho et al 2010]. ADD MORE HERE.

The paper begins with a motivating scenario of a complex science task that we are currently pursuing using this framework. We then review prior work on social studies that discuss the nature and challenges of scientific collaborations, and on interfaces developed to support on-line collaboration.