The integration of grid and peer-to-peer to support scientific collaboration

There have been a number of e-Science projects which address the issues of collaboration within and between scientific communities. Most effort to date focussed on the building of the Grid infrastructure to enable the sharing of huge volume of computational and data resources. The ‘portal’ approach has been used by some to bring the power of grid computing to the desk top of individual researchers. However, collaborative activities within a scientific community are not only confined to the sharing of data or computational intensive resources. There are other forms of sharing which can be better supported by other forms of architecture. In order to provide a more holistic support to a scientific community, this paper proposes a hybrid architecture, which integrates Grid and peer-to-peer technologies using Service Oriented Architecture. This platform will then be used for a semantic architecture which captures characteristics of the data, functional and process requirements for a range of collaborative activities. A combustion chemistry research community is being used as a case study

Police Knowledge Exchange: Summary Report

[Executive Summary] This report draws on research commissioned by the Association of Police and Crime Commissioners (APCC), the National Police Chiefs Council (NPCC) and the Home Office to investigate cultural aspects of knowledge sharing across the police service. The research reviews literature and police perceptions to identify the enablers and barriers to effective knowledge exchange and sharing within and between police forces and police partners, including the public. Data were collected from 11 police forces; 42 in-depth interviews/focus groups and 47 survey responses. The literature-guided analysis identified four core research themes: who, why, what and how we share. Detailed findings are presented in the full report; this summary report presents the core research findings. Recommendations from this study will inform the next phase of activity for the Board. The research identified that cross-force, cross-organisation, national and international sharing relies on a culture supporting individuals who have an independent and reflective sharing approach. A key enabler to police sharing is that, regardless of police rank and role, they all have a strong collaborative nature, through a deep motivation to share, that benefits the wider social community. This collaborative nature is driven by processes that reveal reciprocal benefit and safe sharing, as well as how to effectively ‘get the job done’ and foster professional learning. A key barrier to police sharing is a strong hierarchical culture that does not encourage the independent nature of sharing. Whilst police officers and staff act independently within the confines of their prescribed roles, they rarely independently share beyond this. This hierarchical culture means that innovations in sharing are often initiated or approved top-down and tied to leadership. Hierarchical structures are seen to support a competitive culture combining concepts of risk aversion and blame. The hierarchical culture is also perceived as providing poor clarity on what is of value to share and how to effectively share. There are two key recommendations to overcome this barrier: one long-term and one short-term. Long-term: ‘Become independent sharers’ by changing the nature and culture of the police to encourage this independent nature, so that specific sharing barriers are effectively solved by individuals. Professionalising the police and working collaboratively with academia are steps towards this long-term goal. Short-term: ‘Guide and authorise independent sharing’ by using the hierarchy to scaffold/support and direct police towards effective and approved sharing approaches. This will show the police, through the hierarchy, how and why this independent sharing nature is safe, effective and valued

The shared work of learning: lifting educational achievement through collaboration

This report argues that leaving the momentum of educational improvement to the status quo will result in widening inequality and stagnation in Australia. Key findings: Overall, student performance in Australia is not improving. But some schools in Australia, serving highly disadvantaged students and families, are successfully using collaboration to support student achievement. Common features of the practices in these diverse schools can be applied to strategies for wider, systemic change. This research examines how the schools and their partners use: Professional collaboration to support, sustain, evaluate and refine professional learning, and to access expertise, data and relevant practice. Local collaboration with other schools, universities, employers and community organisations to provide structure and resources for student achievement. Collaboration with students, parents and local community to build trust and social capital. Collaboration – the sharing of effort, knowledge and resources in the pursuit of shared goals – is created through a wide range of flexible, trust-based relationships. The high impact schools featured in this research: actively seek connections and resources that create value for students; develop ‘local learning systems’ to translate connections and resources into concrete actions; and apply a consistent rationale, focused on student learning, to choose and prioritise collaborative projects and relationships

Balancing Access to Data And Privacy. A review of the issues and approaches for the future

Access to sensitive micro data should be provided using remote access data enclaves. These enclaves should be built to facilitate the productive, high-quality usage of microdata. In other words, they should support a collaborative environment that facilitates the development and exchange of knowledge about data among data producers and consumers. The experience of the physical and life sciences has shown that it is possible to develop a research community and a knowledge infrastructure around both research questions and the different types of data necessary to answer policy questions. In sum, establishing a virtual organization approach would provided the research community with the ability to move away from individual, or artisan, science, towards the more generally accepted community based approach. Enclave should include a number of features: metadata documentation capacity so that knowledge about data can be shared; capacity to add data so that the data infrastructure can be augmented; communication capacity, such as wikis, blogs and discussion groups so that knowledge about the data can be deepened and incentives for information sharing so that a community of practice can be built. The opportunity to transform micro-data based research through such a organizational infrastructure could potentially be as far-reaching as the changes that have taken place in the biological and astronomical sciences. It is, however, an open research question how such an organization should be established: whether the approach should be centralized or decentralized. Similarly, it is an open research question as to the appropriate metrics of success, and the best incentives to put in place to achieve success.Methodology for Collecting, Estimating, Organizing Microeconomic Data

Enabling collaborative numerical modeling in earth sciences using knowledge infrastructure

Knowledge Infrastructure is an intellectual framework for creating, sharing, and distributing knowledge. In this paper, we use Knowledge Infrastructure to address common barriers to entry to numerical modeling in Earth sciences: computational modeling education, replicating published model results, and reusing published models to extend research. We outline six critical functional requirements: 1) workflows designed for new users; 2) a community-supported collaborative web platform; 3) distributed data storage; 4) a software environment; 5) a personalized cloud-based high-performance computing platform; and 6) a standardized open source modeling framework. Our methods meet these functional requirements by providing three interactive computational narratives for hands-on, problem-based research demonstrating how to use Landlab on HydroShare. Landlab is an open-source toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for the sharing of data and models. We describe the methods we are using to accelerate knowledge development by providing a suite of modular and interoperable process components that allows students, domain experts, collaborators, researchers, and sponsors to learn by exploring shared data and modeling resources. The system is designed to support uses on the continuum from fully-developed modeling applications to prototyping research software tools

HydroShare: Sharing Diverse Environmental Data Types and Models as Social Objects with Application to the Hydrology Domain

The types of data and models used within the hydrologic science community are diverse. New repositories have succeeded in making data and models more accessible, but are, in most cases, limited to particular types or classes of data or models and also lack the type of collaborative and iterative functionality needed to enable shared data collection and modeling workflows. File sharing systems currently used within many scientific communities for private sharing of preliminary and intermediate data and modeling products do not support collaborative data capture, description, visualization, and annotation. In this article, we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. This article describes the generic data model and content packaging scheme for diverse hydrologic datasets and models used by a new hydrologic collaborative environment called HydroShare to enable storage, management, sharing, publication, and annotation of the diverse types of data and models used by hydrologic scientists. The flexibility of HydroShare\u27s data model and packaging scheme is demonstrated using multiple hydrologic data and model use cases that highlight its features