Taverna: a tool for building and running workflows of services

Hull, Duncan; Wolstencroft, Katy; Stevens, Robert; Goble, Carole; Pocock, Matthew; Li, Peter; Oinn, Tom

doi:10.1093/nar/gkl320

Cited by 809 publications

(554 citation statements)

References 22 publications

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“…Field phenotyping magnifies the organizational challenges compared to greenhouse-based systems, which usually include pre-packaged workflow and data management systems [42]. There have been several generations of both workflow and interoperability systems, but to the best of our knowledge their application so far has been limited to molecular data collected in the laboratory [43][44][45][46][47][48].…”

Section: Use By Biologistsmentioning

confidence: 99%

An opinion on imaging challenges in phenotyping field crops

Kelly

Vatsa

Mayham

et al. 2015

Machine Vision and Applications

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Almost all the world's food is grown in open fields, where plant phenotypes can be very different from those observed in greenhouses. Geneticists and agronomists studying food crops routinely detect, measure, and classify a wide variety of phenotypes in fields that contain many visually distinct types of a single crop. Augmenting humans in these tasks by automatically interpreting images raises some important and nontrivial challenges for research in computer vision. Nonetheless, the rewards for overcoming these obstacles could be exceptionally high for today's 7 billion people, let alone the 9.6 billion projected by 2050 (United Nations Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2012 Revision). To stimulate dialog between researchers in computer vision and those in genetics and agronomy, we offer our views on three computational challenges that are central to many phenotyping tasks. These are disambiguating one plant from another; assigning an individual plant's organs to it; and identifying field phenotypes from those shown in archival images. We illustrate these challenges with annotated photographs of maize highlighting the regions of interest. We also describe some of the experimental, logistical, and photographic constraints on image collection and processing. While collecting the data sets needed for algorithmic experiments requires sustained collaboration and funding, the images we show and have posted should allow one to consider the problems, think of possible approaches, and decide on the next steps.

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Section: Use By Biologistsmentioning

confidence: 99%

An opinion on imaging challenges in phenotyping field crops

Kelly

Vatsa

Mayham

et al. 2015

Machine Vision and Applications

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“…SciCumulus is an engine that is designed to distribute scientific workflow activities (or even entire scientific workflows) dispatched from an SWfMS, such as, VisTrails [21], Taverna [22], into a cloud environment, e.g. Amazon EC2 [23] to be executed in parallel.…”

Section: Scicumulus Engine and Algebraic Workflow Representationmentioning

confidence: 99%

“…There is also Janus [30], an extension of the Provenir ontology for modeling domain-specific provenance data. It is used in Taverna [22], focusing on the semantics of a specific domain on a provenance graph.…”

mentioning

confidence: 99%

Using Domain-Specific Data to Enhance Scientific Workflow Steering Queries

Gonçalves

Oliveira

Ocaña

et al. 2012

Lecture Notes in Computer Science

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Abstract. In scientific workflows, provenance data helps scientists in understanding, evaluating and reproducing their results. Provenance data generated at runtime can also support workflow steering mechanisms. Steering facilities for workflows is considered a challenge due to its dynamic demands during execution. To steer, for example, scientists should be able to suspend (or stop) a workflow execution when the approximate solution meets (or deviates) preset criteria. These criteria are commonly evaluated based on provenance data (execution data) and domain-specific data. We claim that the final decision on whether to interfere on the workflow execution may only become feasible when workflows can be steered by scientists using provenance data enriched with domain-specific data. In this paper we propose an approach based on specialized software components, named Data Extractor (DE), to acquire domain-specific data from data files produced during a scientific workflow execution. DE gathers domain-specific data from produced data files and associates it to existing provenance data on the provenance repository. We have evaluated the proposed approach using a real bioinformatics workflow for comparative genomics executed in SciCumulus cloud workflow parallel engine.

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“…The derivation process is based on the concept of cartridges, i.e. when scientists are deriving their conceptual workflows (represented in our object model) into concrete ones they have to choose one cartridge in a set of available cartridges, where each cartridge of this set generates concrete workflows for different representation language and also in XPDL [7] which is agnostic from SWfMS, (iii) Import: imports concrete workflows from Kepler [8], Taverna [9], and VisTrails [3]. This import process is also based on the concept of cartridges.…”

Section: Gexplinementioning

confidence: 99%

GExpLine: A Tool for Supporting Experiment Composition

Oliveira

Ogasawara

Seabra

et al. 2010

Lecture Notes in Computer Science

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Abstract. Scientific experiments present several advantages when modeled at high abstraction levels, independent from Scientific Workflow Management System (SWfMS) specification languages. For example, the scientist can define the scientific hypothesis in terms of algorithms and methods. Then, this high level experiment can be mapped into different scientific workflow instances. These instances can be executed by a SWfMS and take advantage of its provenance records. However, each workflow execution is often treated by the SWfMS as independent instances. There are no tools that allow modeling the conceptual experiment and linking it to the diverse workflow execution instances. This work presents GExpLine, a tool for supporting experiment composition through provenance. In an analogy to software development, it can be seen as a CASE tool while a SWfMS can be seen as an IDE. It provides a conceptual representation of the scientific experiment and automatically associates workflow executions with the concept of experiment. By using prospective provenance from the experiment, GExpLine generates corresponding workflows that can be executed by SWfMS. This paper also presents a real experiment use case that reinforces the importance of GExpLine and its prospective provenance support.

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Taverna: a tool for building and running workflows of services

Cited by 809 publications

References 22 publications

An opinion on imaging challenges in phenotyping field crops

An opinion on imaging challenges in phenotyping field crops

Using Domain-Specific Data to Enhance Scientific Workflow Steering Queries

GExpLine: A Tool for Supporting Experiment Composition

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