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Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Exchange of research data and samples in biomedical research has become a common phenomenon, demanding for their effective quality assessment. At the same time, several reports address reproducibility of research, where history of biological samples (acquisition, processing, transportation, storage, and retrieval) and data history (data generation and processing) define their fitness for purpose, and hence their quality. This project aims to develop a comprehensive W3C PROV based provenance information standard intended for the biomedical research domain. The standard is being developed by the working group 5 (“data processing and integration”) of the ISO (International Standardisation Organisation) technical committee 276 “biotechnology”. The outcome of the project will be published in parts as international standards or technical specifications. The poster informs about the goals of the standardisation activity, presents the proposed structure of the standards, briefly describes its current state and outlines its future development and open issues.

The objective of the EOSCpilot data interoperability task (6.2) is to demonstrate how to ensure availability of research data to services and users through an open cloud infrastructure. To do so, this task has produced a set of recommendations driving a coherent strategy, as well as a set of technical solutions to help users and programmatic services to find and access datasets across several disciplines, enabling the EOSC to be built around a more coordinated and aligned data ecosystem. The six recommendations driving our proposed strategy are: - The data guidelines and technical solutions proposed by the EOSC should be specific, common, simple, lightweight and collaborative. - All the data resources contributing to the EOSC should expose structured metadata. - The EOSC should reuse or build upon existing standards and formats, and promote the use of common best practices across scientific domains. - The EOSC should propose minimum information guidelines across scientific domains especially targeting key operational metadata which is important for services consuming data. - The EOSC should support an interconnected ecosystem of metadata catalogues as a fundamental service component to facilitate data discovery. - The EOSC should implement a monitoring service to validate standards and recommendations proposed by the EOSC. The four technical solutions that comprise the data strategy are: - A common minimum information metadata guideline for datasets in the EOSC. - Recommendations to establish a coordinated and interconnected ecosystem of dataset metadata catalogues. - Adoption of ResearchSchemas as a means to drive research data discoverability and accessibility. - A set of recommendations for common properties across multiple data types.

The NeuroPsyGrid Project aims to build an ontology of psychosis that will be usable by practitioners and scientists across a range of different disciplines as a common and formalised knowledge framework. This paper provides a socio-technical perspective on the collaborative work involved in the development of such an ontology of psychosis. After introducing the context and nature of the project, we will elaborate on the challenges of such an undertaking, focussing on the process of ontology building and the socio-technical aspects of collaboration, including the meaningfulness of documents as boundary objects.

We describe a semantic information service that aggregates metadata from a large number of information sources of a large-scale Grid infrastructure. It uses an ontology-based information integration architecture (ActOn) suitable for the highly dynamic distributed information sources available in Grid systems, where information changes frequently and where the information of distributed sources has to be aggregated in order to solve complex queries. These two challenges are addressed by a Metadata Cache that works with an update-on-demand policy and by an information source selection module that selects the most suitable source at a given point in time. We have evaluated the quality of this information service, and compared it with other similar services from the EGEE production testbed, with promising results. © 2009 Elsevier B.V. All rights reserved. 26 3 324 336 Cited By :6

Software is fundamental to academic research work, both as part of the method and as the result of research. In June 2016 25 people gathered at Schloss Dagstuhl for a week-long Perspectives Workshop and began to develop a manifesto which places emphasis on the scholarly value of academic software and on personal responsibility. Twenty pledges cover the recognition of academic software, the academic software process and the intellectual content of academic software. This is still work in progress. Through this lightning talk, we aim to get feedback and hone these further, as well as to inspire the WSSSPE audience to think about actions they can take themselves rather than actions they want others to take. We aim to publish a more fully developed Dagstuhl Manifesto by December 2016.

The FAIRDOMHub is a repository for publishing FAIR (Findable, Accessible, Interoperable and Reusable) Data, Operating procedures and Models (https://fairdomhub.org/) for the Systems Biology community. It is a web-accessible repository for storing and sharing systems biology research assets. It enables researchers to organize, share and publish data, models and protocols, interlink them in the context of the systems biology investigations that produced them, and to interrogate them via API interfaces. By using the FAIRDOMHub, researchers can achieve more effective exchange with geographically distributed collaborators during projects, ensure results are sustained and preserved and generate reproducible publications that adhere to the FAIR guiding principles of data stewardship., Nucleic Acids Research, 45 (D1), ISSN:1362-4962, ISSN:0301-5610

As the quantity and complexity of Life Science data continues to grow, so too does its availability through the web. This can cause potential integration issues where individual record or dataset identification was not conceived for use in a global data landscape. This can manifest in issues such as identifier or namespace collisions, resulting in erroneous integration of data, or simply as “link rot” and “content drift”. As a consequence, there is often a need to provide additional and potentially costly procedures for the source-dependent processing of data, or else the provision of alternative mapping solutions. BioMedBridges, in association with an international community of partners, have begun to determine the common technical bridges required to allow data integration across the biological domain. Based on our experience we describe ten simple rules for best practice in the provision and reuse of identifiers for web-based Life Science data. We further elicit feedback from the community, through the use of an interactive exercise, to determine both their perceived ranking of these rules, and their actual experiences when encountering web-based data.

We provide a detailed report of a reproduction study of a paper published in the International Journal of Medical Sciences (IJMS). We first use the PROV-O ontology to model our reconstruction of the computational workflow of the original experiment and to systematically explicate all information that is needed for an reproduction study. We then identify which part of the required information is published in the IJMS paper and what part is missing. We then discuss our reproduction of this workflow, following the original as much as possible. Again, we use PROV-O to precisely define our version of the workflow, including our version of the information that was missing in the IJMS paper of the study. Finally, we generalize from the specific cased described in the original paper by providing a web service that allows mining for arbitrary drug-adverse event pairs.

The Linked Data cloud has grown to become the largest knowledge base ever constructed. Its size is now turning into a major bottleneck for many applications. In order to facilitate access to this structured information, this paper proposes an automatic sampling method targeted at maximizing answer coverage for applications using SPARQL querying. The approach presented in this paper is novel: no similar RDF sampling approach exist. Additionally, the concept of creating a sample aimed at maximizing SPARQL answer coverage, is unique. We empirically show that the relevance of triples for sampling (a semantic notion) is influenced by the topology of the graph (purely structural), and can be determined without prior knowledge of the queries. Experiments show a significantly higher recall of topology based sampling methods over ran- dom and naive baseline approaches (e.g. up to 90% for Open-BioMed at a sample size of 6%).

When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied. In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

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. ©2012 IEEE.

A workflow-centric research object bundles a workflow, the provenance of the results obtained by its enactment, other digital objects that are relevant for the experiment (papers, datasets, etc.), and annotations that semantically describe all these objects. In this paper, we propose a model to specify workflow-centric research objects, and show how the model can be grounded using semantic technologies and existing vocabularies, in particular the Object Reuse and Exchange (ORE) model and the Annotation Ontology (AO).We describe the life-cycle of a research object, which resembles the life-cycle of a scienti?c experiment.

In this paper we suggest that the full scientific potential of workflows will be achieved through mechanisms for sharing and collaboration, empowering scientists to spread their experimental protocols and to benefit from those of others. To facilitate this process we have designed and built the m yExperiment Virtual Research Environment for collaboration and sharing of workflows and experiments. In contrast to systems which simply make workflows available, m yExperiment provides mechanisms to support the sharing of workflows within and across multiple communities. It achieves this by adopting a social web approach which is tailored to the particular needs of the scientist. We present the motivation, design and realisation of m yExperiment. © 2008 Elsevier B.V. All rights reserved.