The Taverna workflow tool suite (http://www.taverna.org.uk) is designed to combine distributed Web Services and/or local tools into complex analysis pipelines. These pipelines can be executed on local desktop machines or through larger infrastructure (such as supercomputers, Grids or cloud environments), using the Taverna Server. In bioinformatics, Taverna workflows are typically used in the areas of high-throughput omics analyses (for example, proteomics or transcriptomics), or for evidence gathering methods involving text mining or data mining. Through Taverna, scientists have access to several thousand different tools and resources that are freely available from a large range of life science institutions. Once constructed, the workflows are reusable, executable bioinformatics protocols that can be shared, reused and repurposed. A repository of public workflows is available at http://www.myexperiment.org. This article provides an update to the Taverna tool suite, highlighting new features and developments in the workbench and the Taverna Server.

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The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud

Scipion: A software framework toward integration, reproducibility and validation in 3D electron microscopy.

Describes the Wings intelligent workflow system that assists scientists with designing computational experiments by automatically tracking constraints and ruling out invalid designs, letting scientists focus on their experiments and goals.

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뀀Ƞ WINGS:뀀ȠIntelligent뀀ȠWorkflow-Based뀀ȠDesign뀀Ƞ뀀Ƞ of뀀ȠComputational뀀ȠExperiments뀀Ƞ

Due to the diversity of natural language processing (NLP) tools and resources, combining them into processing pipelines is an important issue, and sharing these pipelines with others remains a problem. We present DKPro Core, a broad-coverage component collection integrating a wide range of third-party NLP tools and making them interoperable. Contrary to other recent endeavors that rely heavily on web services, our collection consists only of portable components distributed via a repository, making it particularly interesting with respect to sharing pipelines with other researchers, embedding NLP pipelines in applications, and the use on high-performance computing clusters. Our collection is augmented by a novel concept for automatically selecting and acquiring resources required by the components at runtime from a repository. Based on these contributions, we demonstrate a way to describe a pipeline such that all required software and resources can be automatically obtained, making it easy to share it with others, e.g. in order to reproduce results or as examples in teaching, documentation, or publications.

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A broad-coverage collection of portable NLP components for building shareable analysis pipelines

Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud Functions

Workflows provide a popular means for preserving scientific methods by explicitly encoding their process. However, some of them are subject to a decay in their ability to be re-executed or reproduce the same results over time, largely due to the volatility of the resources required for workflow executions. This paper provides an analysis of the root causes of workflow decay based on an empirical study of a collection of Taverna workflows from the myExperiment repository. Although our analysis was based on a specific type of workflow, the outcomes and methodology should be applicable to workflows from other systems, at least those whose executions also rely largely on accessing third-party resources. Based on our understanding about decay we recommend a minimal set of auxiliary resources to be preserved together with the workflows as an aggregation object and provide a software tool for end-users to create such aggregations and to assess their completeness.

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Why workflows break — Understanding and combating decay in Taverna workflows

We describe a corpus of provenance traces that we have collected by executing 120 real world scientific workflows. The workflows are from two different workflow systems: Taverna [5] and Wings [3], and 12 different application domains (see Figure 1). Table 1 provides a summary of this PROV-corpus.

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A workflow PROV-corpus based on taverna and wings

Scientific workflows play an important role in computational research, as the essential artifacts for communicating the methods used to produce the research findings. We are witnessing a growing number of efforts of treating workflows as first-class artifacts for sharing and exchanging actual scientific knowledge, either as part of scholarly articles or as stand-alone objects. However, workflows are not born to be reliable, which can seriously damage their reusability and trustworthiness as knowledge exchange instruments. Scientific workflows are commonly subject to decaying, which consequently undermines their reliability. In this paper, we propose the hypothesis that reliability of workflows can be notably improved by advocating scientists to preserve a minimal set of information that is essential to assist the interpretations of these workflows and hence improve their reproducibility and reusability. By measuring and monitoring the completeness and stability of this information over time, we are then able to indicate the reliability of scientific workflows, which is critical for establishing trustworthy reuse of these important scientific artifacts and supporting the claims in related publications.

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How reliable is your workflow:monitoring decay in scholarly publications

Scientific workflows play an important role in computational research as essential artifacts for communicating the methods used to produce research findings We are witnessing a growing number of efforts that treat workflows as first-class artifacts for sharing and exchanging scientific knowledge, either as part of scholarly articles or as stand-alone objects However, workflows are not born to be reliable, which can seriously damage their reusability and trustworthiness as knowledge exchange instruments Scientific workflows are commonly subject to decay, which consequently undermines their reliability over their lifetime The reliability of workflows can be notably improved by advocating scientists to preserve a minimal set of information that is essential to assist the interpretations of these workflows and hence improve their potential for reproducibility and reusability In this paper we show how, by measuring and monitoring the completeness and stability of scientific workflows over time we are able to provide scientists with a measure of their reliability, supporting the reuse of trustworthy scientific knowledge

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When History Matters - Assessing Reliability for the Reuse of Scientific Workflows

Research Objects (ROs) are semantically enhanced aggregations of resources associated to scientific experiments, such as data, provenance of these data, the scientific workflow used to run the experiment, intermediate results, logs and the interpretation of the results. As the number of ROs increases, it is becoming difficult to find ROs to be used, reused or re-purposed. New search and retrieval techniques are required to find the most appropriate ROs for a given researcher, paying attention to provide an intuitive user interface. In this paper we show CollabSpheres, a user interface that provides a new visual metaphor to find ROs by means of a recommendation system that takes advantage of the social aspects of ROs. The experimental evaluation of this tool shows that users perceive high values of usability, user satisfaction, usefulness and ease of use. From the analysis of these results we argue that users perceive the simplicity, intuitiveness and cleanness of this tool, as well as this tool increases collaboration and reuse of research objects.

Aleix Garrido

Papers

Why workflows break — Understanding and combating decay in Taverna workflows

A workflow PROV-corpus based on taverna and wings

How reliable is your workflow:monitoring decay in scholarly publications

When History Matters - Assessing Reliability for the Reuse of Scientific Workflows

Collaboration Spheres: a Visual Metaphor to Share and Reuse Research Objects