Recommendations for repositories and scientific gateways from a neuroscience perspective

Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF (International Neuroinformatics Coordinating Facility) has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective. These recommendations have neurosciences as their primary use case but are often general. Considering the perspectives of researchers and providers of repositories as well as scientific gateways, the recommendations harmonize and complement existing work on criteria for repositories and best practices. The recommendations cover a range of important areas including accessibility, licensing, community responsibility and technical and financial sustainability of a service.Comment: 10 pages, submitted to Scientific Dat

Putting the Trust into Trusted Data Repositories: A Federated Solution for the Australian National Imaging Facility

The National Imaging Facility (NIF) provides Australian researchers with state-of-the-art instrumentation—including magnetic resonance imaging (MRI), positron emission tomography (PET), X-ray computed tomography (CT) and multispectral imaging – and expertise for the characterisation of animals, plants and materials. To maximise research outcomes, as well as to facilitate collaboration and sharing, it is essential not only that the data acquired using these instruments be managed, curated and archived in a trusted data repository service, but also that the data itself be of verifiable quality. In 2017, several NIF nodes collaborated on a national project to define the requirements and best practices necessary to achieve this, and to establish exemplar services for both preclinical MRI data and clinical ataxia MRI data. In this paper we describe the project, its key outcomes, challenges and lessons learned, and future developments, including extension to other characterisation facilities and instruments/modalities

A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility

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

Distributed ant: a system to support application deployment in the grid

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

Application deployment over heterogeneous grids using distributed ant

The construction of large scale e-Science grid experiments presents a challenge to e-Scientists because of the inherent difficulty of deploying applications over large scale heterogeneous grids. In spite of this, user-oriented application deployment has remained unsupported in grid middleware. This lack of support for application deployment is strongly detrimental to the usability, evolution, uptake and continual development of the grid. This paper presents our motivation, design and implementation of the Distributed Ant user-oriented application deployment system, including recent extensions to support application deployment over heterogeneous grids. We also present a significant Distributed Ant deployment case study, demonstrating how a user-oriented application deployment system enables e-Science experiments. 1

Motor: a virtual machine for high performance computing

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 e-Scientists. 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

Distributed Ant: A system to support application deployment

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. 1

Motor: a virtual machine for high performance computing’, The

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 e-Scientists. 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. 1

An infrastructure for the deployment of e-Science applications

Recent developments in grid middleware and infrastructure have made it possible for a new generation of scientists, e-Scientists, to envisage and design large-scale computational experiments. However, while scheduling and execution of these experiments has become common, developing, deploying and maintaining application software across a large distributed grid remains a difficult and time consuming task. Without simple application deployment, the potential of grids cannot be realized by grid users. In response, this paper presents the motivation, design, development and demonstration of a framework for grid application deployment. Using this framework, e-Scientists can develop platform-independent parallel applications, characterise and identify suitable computational resources and deploy applications easily