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We propose a use and reuse driven big data management approach that fuses the data repository and data processing capabilities in a co-located, public cloud. It answers to the urgent data management needs from the growing number of researchers who don't fit in the big science/small science dichotomy. This approach will allow researchers to more easily use, manage, and collaborate around big data sets, as well as give librarians the opportunity to work alongside the researchers to preserve and curate data while it is still fresh and being actively used. This also provides the technological foundation to foster a sharing culture more aligned with the open source software development paradigm than the lone-wolf, gift-exchanging small science sharing or the top-down, highly structured big science sharing. To materialize this vision, we provide a system architecture consisting of a scalable digital repository system coupled with the co-located cloud storage and cloud computing, as well as a job scheduler and a deployment management system. Motivated by Virginia Tech's Goodwin Hall instrumentation project, we implemented and evaluated a prototype. The results show not only sufficient capacities for this particular case, but also near perfect linear storage and data processing scalabilities under moderately high workload.

In 2012, University Libraries at Virginia Tech participated in this event for the first time to raise awareness of open access in general, and to promote services offered by the library for preserving and providing access to digital scholarship and research. By sharing our experiences and lessons learned coordinating a week-long series of lectures, panel sessions, workshops, and promotional activities, our intention is to encourage others to look for opportunities at their own institutions to share information about OA and the value and impact of open publishing

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Abstract. We describe a use and reuse driven digital repository integrated with lightweight data analysis capabilities provided by the Docker framework. Using building sensor data collected from the Virginia Tech Goodwin Hall Living Laboratory, we perform evaluations using Amazon EC2 and Container Service with a Fedora 4 repository backed with storage in Amazon S3. The results confirm the viability and benefits of this approach.