The myExperiment Open Repository for Scientific Workflows

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PresentationsDate: 2009-05-19 10:00 AM – 11:30 AMmyExperiment is an open repository solution for the born-digital items arising in contemporary research practice, in particular scientific workflows and experiment plans. Launched in November 2007, the public repository (myexperiment.org) has established a significant collection of scientific workflows, spanning multiple disciplines and multiple workflow systems, which has been accessed by over 16,000 users worldwide. Built according to Web 2.0 design principles, myExperiment demonstrates the success of blending modern social curation methods with the demands of researchers sharing hard-won intellectual assets and research works within a scholarly communication lifecycle. myExperiment is an important component in the revolution in creating, sharing and publishing scientific results, and has already established itself as a valuable and unique repository with a growing international presence.JISC; EPSRC; Microsoft Corporatio

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

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The Evolution of myExperiment

The myExperiment social website for sharing scientific workflows, designed according to Web 2.0 principles, has grown to be the largest public repository of its kind. It is distinctive for its focus on sharing methods, its researcher-centric design and its facility to aggregate content into sharable 'research objects'. This evolution of myExperiment has occurred hand in hand with its users. myExperiment now supports Linked Data as a step toward our vision of the future research environment, which we categorise here as '3rd generation e-Research'

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page

Quality Flow : uma plataforma colaborativa orientada a qualidade para experimentos em eScience

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Muitos procedimentos de pesquisa científica dependem da análise de dados obtidos de fontes de dados heterogêneas. A validade dos resultados de pesquisa depende, entre outros, da qualidade dos dados - um tópico recorrente na pesquisa em computação há décadas. Embora existam muitas propostas para a avaliação da qualidade de dados, ainda há problemas em aberto, como mecanismos flexíveis para a avaliação de qualidade e maneiras para derivar a qualidade dos dados. O objetivo desta dissertação é trabalhar nesses problemas. A principal contribuição da dissertação é a criação do QualityFlow: uma plataforma colaborativa para avaliação de qualidade para experimentos em eScience. As principais contribuições são: suportar à criação de workflows científicos com parâmetros de qualidade, permitindo a adição de atributos de qualidade a workflows, permitindo ao mesmo tempo que usuários disintos definam métricas de qualidade específicas para o mesmo workflow; permitir aos usuários manter o histórico de diferentes avaliações de qualidade para um mesmo processo, provendo assim melhor compreensão do real valor dos dados e workflows; e permitir aos cientistas customizar dimensões de qualidade de dados e métricas de qualidade colaborativamente. O QualityFlow foi desenvolvido como um protótipo web, e executado para dois experimentos ¿ um baseado em dados reais e o outro em um workflow de exemploAbstract: Many scientific research procedures rely upon the analysis of data obtained from heterogeneous sources. The validity of the research results depends, among others, on the quality of data. Data quality is a topic that has pervaded computer science research for decades. Though there are many proposals for data quality assessment, there are still open problems such as mechanisms to support flexible quality assessment and ways to derive data quality. The goal of this dissertation is to work on these issues. The main contribution of this dissertation is the proposal of QualityFlow: a quality-aware collaborative platform for experiments in eScience. The following contributions were accomplished: to support the creation of quality-aware scientific workflows, allowing the addition of quality attributes to workflows, while at the same time letting distinct users define their specific quality metrics for the same workflow; to allow users to keep track of different quality assessments for a given process, thereby providing insights into the actual value of data and workflow; and to allow scientists to customize data quality dimensions and quality metrics collaboratively. QualityFlow was developed as a web prototype, and executed in two experiments - one based upon a real problem and the other on a sample workflowMestradoCiência da ComputaçãoMestre em Ciência da Computaçã