The Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) is a national imaging and visualization facility established by Monash University, the Australian Synchrotron, the Commonwealth Scientific Industrial Research Organization (CSIRO), and the Victorian Partnership for Advanced Computing (VPAC), with funding from the National Computational Infrastructure and the Victorian Government. The MASSIVE facility provides hardware, software, and expertise to drive research in the biomedical sciences, particularly advanced brain imaging research using synchrotron x-ray and infrared imaging, functional and structural magnetic resonance imaging (MRI), x-ray computer tomography (CT), electron microscopy and optical microscopy. The development of MASSIVE has been based on best practice in system integration methodologies, frameworks, and architectures. The facility has: (i) integrated multiple different neuroimaging analysis software components, (ii) enabled cross-platform and cross-modality integration of neuroinformatics tools, and (iii) brought together neuroimaging databases and analysis workflows. MASSIVE is now operational as a nationally distributed and integrated facility for neuroinfomatics and brain imaging research.
Imaging technologies are used throughout the life and biomedical sciences to understand mechanisms in biology and diagnosis and therapy in animal and human medicine. We present criteria for globally applicable guidelines for open image data tools and resources for the rapidly developing fields of biological and biomedical imaging.
There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.
e-Science has much to benefit from the emerging field of grid computing. However, construction of e-Science grids is a complex and inefficient undertaking. In particular, deployment of user applications can present a major challenge due to the scale and heterogeneity of the grid. In spite of this, deployment is not supported by current grid computing middleware or configuration management systems, which focus on a super-user approach to application management. Hence, individual users with limited resource control deploy applications manually which is not a grid scalable solution. This paper presents our motivation, design and implementation of a grid scalable, user-oriented, secure application deployment system, Distributed Ant (DistAnt). DistAnt extends the Ant build file environment to provide a flexible procedural deployment description and implements a set of deployment services.
High performance application development remains challenging, particularly for scientists making the transition to a Grid environment. In general areas of computing, virtual environments such as Java and .Net have proved successful in fostering application development. Unfortunately, these existing virtual environments do not provide the necessary high performance computing abstractions required by eScientists. In response, we propose and demonstrate a new approach to the development of a high performance virtual infrastructure: Motor is a virtual machine developed by integrating a high performance message passing library directly within a virtual infrastructure. Motor provides high performance application developers with a common runtime, garbage collection and system libraries, including high performance message passing, whilst retaining strong message passing performance.
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