Setting the Framework of E-Collaboration for E-Science
Andrea Bosin (Università degli Studi di Cagliari, Italy), Nicoletta Dessì (Università degli Studi di Cagliari, Italy), Maria Grazia Fugini (Politecnico di Milano, Italy), Diego Liberati (Italian National Research Council, Italy) and Barbara Pes (Università degli Studi di Cagliari, Italy)
Copyright: © 2008
Collaboration for e-science, namely executing experiments in a cooperative way by sharing data, tools, and expertise towards a common scientific goal, is becoming more and more appealing in a context like the scientific community. In such a context, a critical mass is needed to address very important and complex new questions arising because of the increasing availability of experimental data made possible by continuous technological achievements. An effective collaboration can be set in place by using the increasing empowerment of the Internet to perform such distributed laboratory. By designing a lab environment able to involve accredited actors, public research centers could benefit from technologies and tools, and could become the first promoters and the key players of collaboration initiatives. Moreover, a possible interest by private actors in adhering to such labs should be stimulated in a proactive way by contracting the mutual beneficial and burdens in such a way that both kinds of actors, as well as the civil society (which eventually funds these initiatives) could all take advantage of such arrangements. In addition, privacy concerns that could arise in this type of environment are instead easily granted under appropriate rules that leave public-only scientific data, while keeping both individual and sensitive data and information protected by copyright reserved.
Key Terms in this Chapter
Web Services: Software paradigm enabling peer-to-peer computation in distributed environments based on the concept of “service” as an autonomous piece of code published in the network.
Cooperative Information Systems: Independent, federated information systems that can either autonomously execute locally or cooperate for some tasks towards a common organizational goal.
Grid Computing: Distributed computation over a grid of nodes dynamically allocated to the process in execution. Interoperability: Possibility of performing computation in a distributed heterogeneous environment without altering the technological and specification structure at each involved node.
Workflow: Stream of information within the network related to the accomplishment of every single orchestrated task.
Bio-Informatics: The application of the ICT tools to advanced biological problems, like transcriptomics and proteomic, involving huge amounts of data.
E-Science: Modality of performing experiments in silico in a cooperative way by resorting to information and communication technology (ICT).