Although reproducibility--the idea that a valid scientific experiment can be repeated with similar results--is integral to our understanding of good scientific practice, it has remained a difficult concept to define precisely. Across scientific disciplines, the increasing prevalence of large datasets, and the computational techniques necessary to manage and analyze those datasets, has prompted new ways of thinking about reproducibility. We present findings from a qualitative study of a NSF--funded two-week workshop developed to introduce an interdisciplinary group of domain scientists to data-management techniques for data-intensive computing, with a focus on reproducible science. Our findings suggest that the introduction of data-related activities promotes a new understanding of reproducibility as a mechanism for local knowledge transfer and collaboration, particularly as regards efficient software reuse.

The convergence of sophisticated social networking, digital library tools and persistent digital archive frameworks with data grid technology is providing strong incentives for sharing digital data across time frames that can span decades. To provide scalable solutions, a service-oriented federation framework is demonstrated that can help not only federate data and services across disciplines and societal communities but also in support of technological evolution and long-term fiscal sustainability for current and future usage.

Community Remote Sensing (CRS) is an emerging field where information is collected about the environment by the general public and then integrated into collections to provide a holistic view of the environment with local details. We argue the need for a common architecture for the cyber-infrastructure that will be necessary to cater to the needs of Community Remote Sensing systems. We identify the challenges that such a cyber infrastructure (CRS-CI) has to meet and also proposed five principles as solutions to meet these challenges. Finally, we also describe the integrated Rule Oriented Data System, a data grid middleware that is built upon these principles which provides an ideal and exemplar implementation for CRS-CI.

International data grids are now being built that support joint management of shared collections. An emerging strategy is to build multiple independent data grids, each managed by the local institution. The data grids are then federated to enable controlled sharing of files. We examine the management issues associated with maintaining federations of production data grids, including management of access controls, coordinated sharing of name spaces, replication of data between data grids, and expansion of the data grid federation.

As sensor networks become denser and more widely deployed, the potential develops for interconnecting these networks to combine datasets, share technological solutions, and to conduct cross-disciplinary research and monitoring operations that rely on several signal domains simultaneously. To that end, the real-time observatories, applications and data management network (ROADNet) research project is connecting multiple sensor networks deployed by collaborating research projects into a single network in order to support a variety of research topics including coastal ocean observing, microclimatology and seismology. This paper gives a brief overview of the ROADNet project and discusses some of the implementation challenges we uncovered while building and maintaining the ROADNet system. We encountered challenges on several fronts including building effective programming abstractions for sensor networks, building tools for managing large-scale data in a scalable manner, and building efficient tools for deploying and managing hundreds of sensors. We discuss how these challenges were addressed and some of the lessons learned from collaborations with domain scientists using our network to conduct their research

The integration of grid, data grid, digital library, and preservation technology has resulted in software infrastructure that is uniquely suited to the generation and management of data. Grids provide support for the organization, management, and application of processes. Data grids manage the resulting digital entities. Digital libraries provide support for the management of information associated with the digital entities. Persistent archives provide long-term preservation. We examine the synergies between these data management systems and the future evolution that is required for the generation and management of information.

Data Grids are being built across the world as the next generation data handling systems to manage peta-bytes of inter organizational data and storage space. A data grid (datagrid) is a logical name space consisting of storage resources and digital entities that is created by the cooperation of autonomous organizations and its users based on the coordination of local and global policies. Data Grid Management Systems (DGMSs) provide services for the confluence of organizations and management of inter-organizational data and resources in the datagrid.The objective of the tutorial is to provide an introduction to the opportunities and challenges of this emerging technology. Novices and experts would benefit from this tutorial. The tutorial would cover introduction, use cases, design philosophies, architecture, research issues, existing technologies and demonstrations. Hands on sessions for the participants to use and feel the existing technologies could be provided based on the availability of internet connections.

Data grids enable next generation scientific explorations that require intensive computation and analysis of petabyte-scale shared data collections. Apart from the challenge in creation and management of the data, another major challenge is the discovery of derived data products that have already been created. This work addresses the later challenge in minimizing response time and conserving the computing cycles in the grids by using "virtual services" to access derived data products. We analyze the role of virtual services for long running services in a service-oriented architecture and contribute a technique called "semantic mirror". A prototype was developed to discover derived data products of grid services using SRB-MCAT technology and GriPhyN Virtual Data Language (VDL).

Data Grids are becoming increasingly important in scientific communities for sharing large data collections and for archiving and disseminating them in a digital library framework. The Storage Resource Broker (SRB) provides transparent virtualized middleware for sharing data across distributed, heterogeneous data resources separated by different administrative and security domains. The MySRB is a Web-based interface to the SRB that provides a user-friendly interface to distributed collections brokered by the SRB. In this paper we briefly describe the use of the SRB infrastructure as tools in the data grid architecture for building distributed data collections, digital libraries, and persistent archives. We also provide details about the MySRB and its functionalities.