The Global Impact of Science Gateways, Virtual Research Environments and Virtual Laboratories

Science gateways, virtual laboratories and virtual research environments are all terms used to refer to community-developed digital environments that are designed to meet a set of needs for a research community. Specifically, they refer to integrated access to research community resources including software, data, collaboration tools, workflows, instrumentation and high-performance computing, usually via Web and mobile applications. Science gateways, virtual laboratories and virtual research environments are enabling significant contributions to many research domains, facilitating more efficient, open, reproducible research in bold new ways. This paper explores the global impact achieved by the sum effects of these programs in increasing research impact, demonstrates their value in the broader digital landscape and discusses future opportunities. This is evidenced through examination of national and international programs in this field

A Dynamic web service scheduling and deployment framework for grid workflow

126 p.Grid computing boosts productivity by maximizing resource utilization and simplifying access to resources which are shared among virtual organizations. Recently, the Grid and Web Service communities have established a set of common interests and requirements. The latest version of the Globus Toolkit implements the Web Service Resource Framework (WSRF) specifications which have been formulated to cover these interests. However, it has some limitations in supporting the dynamic nature of large-scale Grid and data-intensive workflow executions. Dynamic Web Service deployment fits well into the dynamic nature of the Grid and opens new ways of managing workflow executions on the Grid.MASTER OF ENGINEERING (SCE

Rosemary: Make Complex Collaborative Big Data Analysis Easy

Fact sheet about the Rosemary Science Gateway Platform

Integrated support for neuroscience research: from study design to publication

Computational neuroscience is a new field of research in which neurodegenerative diseases are studied with the aid of new imaging techniques and computation facilities. Researchers with different expertise collaborate in these studies. A study requires scalable computational and storage capacity and information management facilities to succeed. Many virtual laboratories are proposed and developed to facilitate these studies, however most of them cover only the parts related to the computational data processing. In this paper we describe and analyse the phases of the computational neuroscience studies including the actors, the tasks they perform, and the characteristics of each phase. Based on these we identify the required properties and functionalities of a virtual laboratory that supports the actors and their tasks throughout the complete stud

Provenance for distributed biomedical workflow execution

Scientific research has become very data and compute intensive because of the progress in data acquisition and measurement devices, which is particularly true in Life Sciences. To cope with this deluge of data, scientists use distributed computing and storage infrastructures. The use of such infrastructures introduces by itself new challenges to the scientists in terms of proper and efficient use. Scientific workflow management systems play an important role in facilitating the use of the infrastructure by hiding some of its complexity. Although most scientific workflow management systems are provenance-aware, not all of them come with provenance functionality out of the box. In this paper we describe the improvement and integration of a provenance system into an e-infrastructure for biomedical research based on the MOTEUR workflow management system. The main contributions of the paper are: presenting an OPM implementation using relational database backend for the provenance store, providing an e-infrastructure with a comprehensive provenance system, defining a generic approach to provenance implementation, potentially suitable for other workflow systems and application domains and demonstrating the value of this system based on use cases presenting the provenance data through a user-friendly web interfac

Heritability and genome-wide association analyses of sleep duration in children: the EAGLE consortium

Study Objectives: Low or excessive sleep duration has been associated with multiple outcomes, but the biology behind these associations remains elusive. Specifically, genetic studies in children are scarce. In this study, we aimed to: (1) estimate the proportion of genetic variance of sleep duration in children attributed to common single nucleotide polymorphisms (SNPs), (2) identify novel SNPs associated with sleep duration in children, and (3) investigate the genetic overlap of sleep duration in children and related metabolic and psychiatric traits. Methods: We performed a population-based molecular genetic study, using data form the EArly Genetics and Life course Epidemiology (EAGLE) Consortium. 10,554 children of European ancestry were included in the discovery, and 1,250 children in the replication phase. Results: We found evidence of significant but modest SNP heritability of sleep duration in children (SNP h 0.14, 95% CI [0.05, 0.23]) using the LD score regression method. A novel region at chromosome 11q13.4 (top SNP: rs74506765, P = 2.27e-08) was associated with sleep duration in children, but this was not replicated in independent studies. Nominally significant genetic overlap was only found (r = 0.23, P = 0.05) between sleep duration in children and type 2 diabetes in adults, supporting the hypothesis of a common pathogenic mechanism. Conclusions: The significant SNP heritability of sleep duration in children and the suggestive genetic overlap with type 2 diabetes support the search for genetic mechanisms linking sleep duration in children to multiple outcomes in health and disease

Heritability and genome-wide association analyses of sleep duration in children: The EAGLE consortium

Study Objectives: Low or excessive sleep duration has been associated with multiple outcomes, but the biology behind these associations remains elusive. Specifically, genetic studies in children are scarce. In this study, we aimed to: (1) estimate the proportion of genetic variance of sleep duration in children attributed to common single nucleotide polymorphisms (SNPs), (2) identify novel SNPs associated with sleep duration in children, and (3) investigate the genetic overlap of sleep duration in children and related metabolic and psychiatric traits. Methods: We performed a population-based molecular genetic study, using data form the EArly Genetics and Life course Epidemiology (EAGLE) Consortium. 10,554 children of European ancestry were included in the discovery, and 1,250 children in the replication phase. Results: We found evidence of significant but modest SNP heritability of sleep duration in children (SNP h2 0.14, 95% CI [0.05, 0.23]) using the LD score regression method. A novel region at chromosome 11q13.4 (top SNP: rs74506765, P = 2.27e-08) was associated with sleep duration in children, but this was not replicated in independent studies. Nominally significant genetic overlap was only found (rG = 0.23, P = 0.05) between sleep duration in children and type 2 diabetes in adults, supporting the hypothesis of a common pathogenic mechanism. Conclusions: The significant SNP heritability of sleep duration in children and the suggestive genetic overlap with type 2 diabetes support the search for genetic mechanisms linking sleep duration in children to multiple outcomes in health and disease