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In a single generation, technology and economic conditions have radically altered the pace and practice of research. Once manageable in a laboratory notebook, the scale and complexity of scientific data in the life sciences has exploded. The number of software packages and distributed computational resources available to scientists for data storage and analysis has undergone similar expansion. Once solitary, research is now increasingly team-based, spanning cross-disciplinary and cross-institutional collaborations. Collaboration requiring specialized scientific computing resources magnifies the challenges of integrating raw data and maintaining analysis provenance. Consequently, the full potential of these resources can only be realized if the entire pipeline from data collection to analysis can readily capture the annotations and intuition of each distributed collaborator. Currently, few tools exist that integrate data management, provenance tracking and collaborative infrastructure into a package palatable to all stakeholders in this growing, distributed team.
Ovation™ (http://ovation.io) is a distributed and eventually consistent data management and collaboration platform. Ovation’s data model, interface and API are closely matched to the mental model of researchers, facilitating adoption by experimental and computational research teams. Ovation integrates with researchers’ existing acquisition and analysis tools including Matlab, Python, R and Mathematica. The Ovation platform helps individual scientists organize their data and track provenance, and empowers collaborative project teams through sharing of data, annotations and analyses. I will share our experience in deploying Ovation to research groups in the life sciences and discuss the potential of deeper integration with computational resources such as those at the UW eScience institute.