OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. Results We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies. Conclusions Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Accelerating Science: A Computing Research Agenda

The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion). This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.Comment: Computing Community Consortium (CCC) white paper, 17 page

Functional units: Abstractions for Web service annotations

Computational and data-intensive science increasingly depends on a large Web Service infrastructure, as services that provide a broad array of functionality can be composed into workflows to address complex research questions. In this context, the goal of service registries is to offer accurate search and discovery functions to scientists. Their effectiveness, however, depends not only on the model chosen to annotate the services, but also on the level of abstraction chosen for the annotations. The work presented in this paper stems from the observation that current annotation models force users to think in terms of service interfaces, rather than of high-level functionality, thus reducing their effectiveness. To alleviate this problem, we introduce Functional Units (FU) as the elementary units of information used to describe a service. Using popular examples of services for the Life Sciences, we define FUs as configurations and compositions of underlying service operations, and show how functional-style service annotations can be easily realised using the OWL semantic Web language. Finally, we suggest techniques for automating the service annotations process, by analysing collections of workflows that use those services.</p

What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page

Reprodutibilidade e reuso de experimentos em eScience : workflows, ontologias e scripts

Orientadores: Claudia Maria Bauzer Medeiros, Yolanda GilTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Scripts e Sistemas Gerenciadores de Workflows Científicos (SGWfC) são abordagens comumente utilizadas para automatizar o fluxo de processos e análise de dados em experimentos científicos computacionais. Apesar de amplamente usados em diversas disciplinas, scripts são difíceis de entender, adaptar, reusar e reproduzir. Por esta razão, diversas soluções têm sido propostas para auxiliar na reprodutibilidade de experimentos que utilizam ambientes baseados em scripts. Porém, estas soluções não permitem a documentação completa do experimento, nem ajudam quando outros cientistas querem reusar apenas parte do código do script. SGWfCs, por outro lado, ajudam na documentação e reuso através do suporte aos cientistas durante a modelagem e execução dos seus experimentos, que são especificados e executados como componentes interconectados (reutilizáveis) de workflows. Enquanto workflows são melhores que scripts para entendimento e reuso dos experimentos, eles também exigem documentação adicional. Durante a modelagem de um experimento, cientistas frequentemente criam variantes de workflows, e.g., mudando componentes do workflow. Reuso e reprodutibilidade exigem o entendimento e rastreamento da proveniência das variantes, uma tarefa que consome muito tempo. Esta tese tem como objetivo auxiliar na reprodutibilidade e reuso de experimentos computacionais. Para superar estes desafios, nós lidamos com dois problemas de pesquisas: (1) entendimento de um experimento computacional, e (2) extensão de um experimento computacional. Nosso trabalho para resolver estes problemas nos direcionou na escolha de workflows e ontologias como respostas para ambos os problemas. As principais contribuições desta tese são: (i) apresentar os requisitos para a conversão de experimentos baseados em scripts em experimentos reprodutíveis; (ii) propor uma metodologia que guia o cientista durante o processo de conversão de experimentos baseados em scripts em workflow research objects reprodutíveis. (iii) projetar e implementar funcionalidades para avaliação da qualidade de experimentos computacionais; (iv) projetar e implementar o W2Share, um arcabouço para auxiliar a metodologia de conversão, que explora ferramentas e padrões que foram desenvolvidos pela comunidade científica para promover o reuso e reprodutibilidade; (v) projetar e implementar o OntoSoft-VFF, um arcabouço para captura de informação sobre software e componentes de workflow para auxiliar cientistas a gerenciarem a exploração e evolução de workflows. Nosso trabalho é apresentado via casos de uso em Dinâmica Molecular, Bioinformática e Previsão do TempoAbstract: Scripts and Scientific Workflow Management Systems (SWfMSs) are common approaches that have been used to automate the execution flow of processes and data analysis in scientific (computational) experiments. Although widely used in many disciplines, scripts are hard to understand, adapt, reuse, and reproduce. For this reason, several solutions have been proposed to aid experiment reproducibility for script-based environments. However, they neither allow to fully document the experiment nor do they help when third parties want to reuse just part of the code. SWfMSs, on the other hand, help documentation and reuse by supporting scientists in the design and execution of their experiments, which are specified and run as interconnected (reusable) workflow components (a.k.a. building blocks). While workflows are better than scripts for understandability and reuse, they still require additional documentation. During experiment design, scientists frequently create workflow variants, e.g., by changing workflow components. Reuse and reproducibility require understanding and tracking variant provenance, a time-consuming task. This thesis aims to support reproducibility and reuse of computational experiments. To meet these challenges, we address two research problems: (1) understanding a computational experiment, and (2) extending a computational experiment. Our work towards solving these problems led us to choose workflows and ontologies to answer both problems. The main contributions of this thesis are thus: (i) to present the requirements for the conversion of script to reproducible research; (ii) to propose a methodology that guides the scientists through the process of conversion of script-based experiments into reproducible workflow research objects; (iii) to design and implement features for quality assessment of computational experiments; (iv) to design and implement W2Share, a framework to support the conversion methodology, which exploits tools and standards that have been developed by the scientific community to promote reuse and reproducibility; (v) to design and implement OntoSoft-VFF, a framework for capturing information about software and workflow components to support scientists manage workflow exploration and evolution. Our work is showcased via use cases in Molecular Dynamics, Bioinformatics and Weather ForecastingDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2013/08293-7, 2014/23861-4, 2017/03570-3FAPES

An investigation of how researchers in data intensive scientific fields use, process and curate data

This project was an Arcadia funded project, examining (i) whether research outputs from data-rich science (most typically software) were failing to be disseminated, both within and outside subject communities and (ii) whether there was a useful role for information specialists in this area.This work was conducted as part of the Arcadia Programme, a three year programme funded by a grant from the Arcadia Fund