MOTIVATION: Since its launch in 2010, Identifiers.org has become an important tool for the annotation and cross-referencing of Life Science data. In 2016, we established the Compact Identifier (CID) scheme (prefix: accession) to generate globally unique identifiers for data resources using their locally assigned accession identifiers. Since then, we have developed and improved services to support the growing need to create, reference and resolve CIDs, in systems ranging from human readable text to cloud-based e-infrastructures, by providing high availability and low-latency cloud-based services, backed by a high-quality, manually curated resource. RESULTS: We describe a set of services that can be used to construct and resolve CIDs in Life Sciences and beyond. We have developed a new front end for accessing the Identifiers.org registry data and APIs to simplify integration of Identifiers.org CID services with third-party applications. We have also deployed the new Identifiers.org infrastructure in a commercial cloud environment, bringing our services closer to the data. AVAILABILITYAND IMPLEMENTATION: https://identifiers.org.

SUMMARY: Dispersed across the Internet is an abundance of disparate, disconnected training information, making it hard for researchers to find training opportunities that are relevant to them. To address this issue, we have developed a new platform-TeSS-which aggregates geographically distributed information and presents it in a central, feature-rich portal. Data are gathered automatically from content providers via bespoke scripts. These resources are cross-linked with related data and tools registries, and made available via a search interface, a data API and through widgets. AVAILABILITY AND IMPLEMENTATION: https://tess.elixir-europe.org.

Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshaling and permitting errors of omission and commission because dataset members are not explicitly specified. We address these issues by proposing simple methods and tools for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (BDBags), data descriptions (Research Objects), and simple persistent identifiers (Minids) to create a powerful ecosystem of tools and services for big data analysis and sharing. We present these tools and use biomedical case studies to illustrate their use for the rapid assembly, sharing, and analysis of large datasets.

Cloud computing for data-driven science and engineering* During the past decade, data-driven science and engineering have emerged as a key paradigm for performing scientific research, enabling innovations through new kinds of experiments that were earlier impossible.Today's science has access to advanced instruments like next generation genome sequencers, gigapixel survey telescopes, and networks of sensors that monitor cyber-physical systems, and these are generating datasets that are growing exponentially in complexity and data volume.Big Data, across all dimensions of volume, velocity, variety, and veracity, are offering unique opportunities to enable scientific discovery as well as novel challenges to scientific platforms.Such dynamic, distributed, and data-intensive applications hold the solutions to vital scientific and societal problems of the 21st century.In order to achieve breakthrough in new knowledge, there is a need to develop data-driven system models, perform analytics at large scales, manage data from instruments and analyses, and share and visualize the results with scientific peers and the society at large.To this end, cloud computing offers a computing model for running such data-intensive scientific and engineering applications.Clouds have democratized resource access to underserved disciplines, making it possible to perform nontrivial scientific explorations for just a few hundred dollars.Clouds are particularly cost-effective for Big Data applications due to their co-location of elastic compute resources with data, and their use of commodity hardware, which economizes on costs for non-high performance computing (HPC) workloads.Many contemporary Big Data platforms that have emerged from online enterprises such as Google and Twitter are also optimized for such commodity hardware, as found in their own data centers.Of course, there are costs associated with data transfer and storage, in keeping with the pay-as-you-go model, that may not be well suited for applications requiring frequent transfer of and long-term storage of terabytes of data.Likewise, it is valuable to understand how data-intensive or even HPC applications that have been developed for computing grids, at one end, and applications developed for workstation tools like MATLAB and R, at the other end, can be effectively run on clouds.These are some of the practical realities that are worth exploring on the relevance of clouds for data-driven scientific applications.In this special issue, we have compiled a set of articles that discusses new research, development, and deployment efforts in running eScience and eEngineering workloads on cloud infrastructures and platforms.The open solicitation, which followed the 3rd Workshop on Scientific Cloud Computing (ScienceCloud), invited research and case studies on a variety of topics relevant to data-driven scientific computing on clouds: use of cloud-based technologies to address innovative compute and data-driven scientific problems that are not well served by current HPC clusters and grids, programming platforms for elastic and Big Data applications, performance and cost-effective computing on clouds, and gaps in diverse cloud fabrics and service offerings, among others.In all, the special issue received 28 articles, of which six were selected for publication after multiple rounds of reviews and revisions.The special issue starts with two articles that explore the runtime platform support required for executing Big Data science on clouds.In TomusBlobs: Scalable Data-Intensive Processing on Azure Clouds [1], the authors address the limitations of data storage within IaaS clouds such as Amazon S3 and Microsoft Azure BLOBs that are, while co-located in the data center, not present in the virtual machines (VMs) and need to be accessed over the network.Their distributed storage on VMs is optimized for concurrent access and elastic scaling, even *Corrections added on 5 February 2016, after first online publication: references to the paper "Pilot-abstractions for distributed data-intensive cloud applications" have been removed.

In the paradigm of service oriented science, scientific computing applications and data are all wrapped as web accessible services. Scientific workflows further integrate these services to answer complex research questions. However, our earlier study conducted on myExperiment has revealed that although the sharing of service-based capabilities opens a gateway to resource reuse, in practice, the degree of reuse is very low. This finding has motivated us to propose ServiceMap to provide navigation facility through the network of services to facilitate the design and development of scientific workflows. This paper proposes ReputationNet as an enhancement of ServiceMap, to incorporate the often-ignored reputation aspects of services/workflows and their publishers, in order to offer better service and workflow recommendations. We have developed a novel model to reflect the reputation of e-Science services/workflows, and developed heuristic algorithms to provide service recommendations based on reputations. Experiments on myExperiment have illustrated a strong positive correlation (with Pearson correlation coefficient 0.82) between the reputation scores computed and the actual performance (i.e. usage frequency) of the services/workflows, which demonstrates the effectiveness of our approach.

Scientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understand what the workflows aim to achieve or to re-enact them. To gain an understanding of the workflow, and how it may be used and repurposed for their needs, scientists require access to additional resources such as annotations describing the workflow, datasets used and produced by the workflow, and provenance traces recording workflow executions. In this article, we present a novel approach to the preservation of scientific workflows through the application of research objects—aggregations of data and metadata that enrich the workflow specifications. Our approach is realised as a suite of ontologies that support the creation of workflow-centric research objects. Their design was guided by requirements elicited from previous empirical analyses of workflow decay and repair. The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate the application of the ontologies for building Workflow Research Objects with a case-study that investigates Huntington’s disease, performed in collaboration with a team from the Leiden University Medial Centre (HG-LUMC). Finally we present a number of tools developed for creating and managing workflow-centric research objects.

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.

Scientific workflows management systems are increasingly used by scientists to specify complex data processing pipelines. Workflows are represented using a graph structure, where nodes represent tasks and links represent the dataflow. However, the complexity of workflow structures is increasing over time, reducing the rate of scientific workflows reuse. Here, we introduce DistillFlow, a tool based on effective methods for workflow design, with a focus on the Taverna model. DistillFlow is able to detect "anti-patterns" in the structure of workflows (idiomatic forms that lead to over-complicated design) and replace them with different patterns to reduce the workflow's overall structural complexity. Rewriting workflows in this way is beneficial both in terms of user experience and workflow maintenance.

BACKGROUND: Scientific workflows management systems are increasingly used to specify and manage bioinformatics experiments. Their programming model appeals to bioinformaticians, who can use them to easily specify complex data processing pipelines. Such a model is underpinned by a graph structure, where nodes represent bioinformatics tasks and links represent the dataflow. The complexity of such graph structures is increasing over time, with possible impacts on scientific workflows reuse. In this work, we propose effective methods for workflow design, with a focus on the Taverna model. We argue that one of the contributing factors for the difficulties in reuse is the presence of "anti-patterns", a term broadly used in program design, to indicate the use of idiomatic forms that lead to over-complicated design. The main contribution of this work is a method for automatically detecting such anti-patterns, and replacing them with different patterns which result in a reduction in the workflow's overall structural complexity. Rewriting workflows in this way will be beneficial both in terms of user experience (easier design and maintenance), and in terms of operational efficiency (easier to manage, and sometimes to exploit the latent parallelism amongst the tasks). RESULTS: We have conducted a thorough study of the workflows structures available in Taverna, with the aim of finding out workflow fragments whose structure could be made simpler without altering the workflow semantics. We provide four contributions. Firstly, we identify a set of anti-patterns that contribute to the structural workflow complexity. Secondly, we design a series of refactoring transformations to replace each anti-pattern by a new semantically-equivalent pattern with less redundancy and simplified structure. Thirdly, we introduce a distilling algorithm that takes in a workflow and produces a distilled semantically-equivalent workflow. Lastly, we provide an implementation of our refactoring approach that we evaluate on both the public Taverna workflows and on a private collection of workflows from the BioVel project. CONCLUSION: We have designed and implemented an approach to improving workflow structure by way of rewriting preserving workflow semantics. Future work includes considering our refactoring approach during the phase of workflow design and proposing guidelines for designing distilled workflows.

Computing systems are rapidly changing with multicore, graphics processing units (GPUs), clusters, volunteer systems, clouds, and grids offering a confusing dazzling array of opportunities. New programming paradigms such as Google MapReduce and many-task computing have joined the traditional repertoire of workflow and parallel computing for the highest performance systems. Meanwhile, the life sciences are continuing to expand in data generated with continuing improvement in the instruments for high-throughput analysis. This ‘fourth paradigm’ (data driven science) is joined by complex systems or biocomplexity that can build phenomenological models of biological systems and processes. This special issue for Emerging Computational Methods for the Life Sciences Workshop ECMLS2012 1, juxtaposes these trends seeking those computational methods that will enhance scientific discovery. Within this overall scope, this special issue encouraged researchers to submit and present original work related to the latest trends in parallel and distributed high-performance systems applied to life science problems. Weber et al. 2 note that GPUs and multicore processors are now pervasive in computational sciences and high-performance computing. Their high-arithmetic throughput and memory bandwidth combined with their ever increasing programmability make them suitable for a widening variety of applications. They give a high-level overview of Specmaster, a MyriMatch port that can use every Open Computing Language (OpenCL) device available in a machine to identify peptides in tandem mass spectrometry data. Then they highlight device-specific optimizations for multicore CPUs and GPUs as well as describing framework for implementing these optimizations while still using a single code-base. They also provide performance results of Specmaster running on four different architectures and compare these numbers to MyriMatch. Finally, they improve on their existing work by showing Specmaster dynamically load balancing on 3 AMD Radeon 7970s and 32 AMD Opteron Interlagos 6272 cores as well as comparing the quality of their search results to MyriMatch. Yang et al. 3 study a difficulty in building a mechanistic model of biological systems coming from determination of correct parameter values. Their paper proposes a novel parameter estimation method to infer unknown parameters, such as kinetic rates, from noisy experimental observations. Derived from the ABC sequential Monte Carlo algorithm, their method predicts the distribution of each parameter rather than a single value via several intermediate distributions. Motivated by the computational intensity of the method, they improve the ABC sequential Monte Carlo method in two aspects. First, to increase efficiency, a windowing method is developed to reduce the parameter searching space, and an adaptive sampling weight mechanism is introduced to make the intermediate distributions converge to the target distributions in a much quicker manner. Second, to speedup the estimation process, they implement their method in a parallel computing environment to speedup the sampling process. Elllingson et al. 4 present the current state of high-throughput virtual screening. They describe a case study of using a task-parallel MPI version of Autodock4 to run a virtual high-throughput screen of one million compounds on the Jaguar Cray XK6 Supercomputer at Oak Ridge National Laboratory. The paper includes a description of scripts developed to increase the efficiency of the predocking file preparation and postdocking analysis. A detailed tutorial, scripts, and source code for this MPI version of Autodock4 are available online at http://www.bio.utk.edu/baudrylab/autodockmpi.htm Hamacher et al. 5 present a massively parallel implementation of the computation of coevolutionary signals from biomolecular sequence alignments based on mutual information (MI) and a normalization procedure to neutral evolution. The MI is computed for two-point and three-point correlations within any multiple sequence alignment. The high-computational demand in the normalization procedure is met efficiently with an implementation on GPUs using NVIDIA's CUDA framework. In particular, the normalization of the MI for three-point ‘cliques’ of amino acids or nucleotides requires large sampling numbers in the normalization that is achieved by using GPUs. GPU computation serves as an enabling technology here insofar as MI normalization is also possible using traditional computational methods or cluster computation, but only GPU computation makes MI normalization for sequence analysis feasible in a statistically sufficient sample and in acceptable time given affordable commodity hardware. They illustrate a) the computational efficiency and b) the biological usefulness of two-point and three-point MI by applications to the well-known protein calmodulin and the variable surface glycoprotein of Trypanosoma brucei which are subject to involved evolutionary pressure. Here, they find striking coevolutionary patterns and distinct information on the molecular evolution of these molecules that question previous work that relied on inefficient MI computations. Cushing et al. 6 note that task farming is often used to enable parameter sweep for exploration of large sets of initial conditions for large scale complex simulations. Such applications occur very often in life sciences. Available solutions enable us to perform parameter sweep by creating multiple job submissions with different parameters. This paper presents an approach to farm workflows employing service oriented paradigms using the WS-VLAM workflow manager from University of Amsterdam, which provides ways to create, control, and monitor workflows applications and their components. They present two service oriented approaches for workflow farming: task-level, whereby task harness acts as services by being invoked on which task to load, and data-level where the actual task is invoked as a service with different chunks of data to process. An experimental evaluation of the presented solution is performed with a biomedical application for which 3000 simulations were required to perform a Monte Carlo study. Finally, Stanberry et al. 7 note that modern biology is experiencing a rapid increase in data volumes that challenges analytical skills and existing cyberinfrastructure. Exponential expansion of the protein sequence universe (PSU), the protein sequence space, together with the costs and complexities of manual curation creates a major bottleneck in life sciences research. Existing resources lack scalable visualization tools that are instrumental for functional annotation. They describe a new visualization tool using multidimensional scaling to create a 3D embedding of the protein space. The advantages of the proposed PSU method include the ability to scale to large numbers of sequences, integrate different similarity measures with other functional and experimental data, and facilitate protein annotation. They apply the method to visualize the prokaryotic PSU by using sequence alignment scores. As an annotation example, they use an interpolation approach to map the set of annotated archaeal proteins into the prokaryotic PSU. Transdisciplinary approaches akin to the one described in this paper are urgently needed to quickly and efficiently translate the influx of new data into tangible innovations and groundbreaking discoveries We would like to thank the authors for contributing papers on their research on latest trends in data intensive technologies and applications for this special issue, and thank all the reviewers for providing constructive reviews and in helping to shape this special issue. Finally, we would like to thank the editors of Concurrency and Computation: Practice and Experience for providing us an opportunity to bring this special issue to the research community.

The Open PHACTS Discovery Platform aims to provide an integrated information space to advance pharmacological research in the area of drug discovery. Effective drug discovery requires comprehensive data coverage, i.e. integrating all available sources of pharmacology data. While many relevant data sources are available on the linked open data cloud, their content needs to be combined with that of commercial datasets and the licensing of these commercial datasets respected when providing access to the data. Additionally, pharmaceutical companies have built up their own extensive private data collections that they require to be included in their pharmacological dataspace. In this paper we discuss the challenges of incorporating private and commercial data into a linked dataspace: focusing on the modelling of these datasets and their interlinking. We also present the graph-based access control mechanism that ensures commercial and private datasets are only available to authorized users.

SUMMARY The increase in volume and complexity of biological data has led to increased requirements to reuse that data. Consistent and accurate metadata is essential for this task, creating new challenges in semantic data annotation and in the constriction of terminologies and ontologies used for annotation. The BioSharing community are developing standards and terminologies for annotation, which have been adopted across bioinformatics, but the real challenge is to make these standards accessible to laboratory scientists. Widespread adoption requires the provision of tools to assist scientists whilst reducing the complexities of working with semantics. This paper describes unobtrusive ‘stealthy’ methods for collecting standards compliant, semantically annotated data and for contributing to ontologies used for those annotations. Spreadsheets are ubiquitous in laboratory data management. Our spreadsheet‐based RightField tool enables scientists to structure information and select ontology terms for annotation within spreadsheets, producing high quality, consistent data without changing common working practices. Furthermore, our Populous spreadsheet tool proves effective for gathering domain knowledge in the form of Web Ontology Language (OWL) ontologies. Such a corpus of structured and semantically enriched knowledge can be extracted in Resource Description Framework (RDF), providing further means for searching across the content and contributing to Open Linked Data ( http://linkeddata.org/ ). Copyright © 2012 John Wiley & Sons, Ltd.

Linked Data holds great promise in the Life Sciences as a platform to enable an interoperable data commons, supporting new opportunities for discovery. Minimum Information Checklists have emerged within the Life Sciences as a means of standardising the reporting of experiments in an effort to increase the quality and reusability of the reported data. Existing tooling built around these checklists is aimed at supporting experimental scientists in the production of experiment reports that are compliant. It remains a challenge to quickly and easily assess an arbitrary set of data against these checklists. We present the MIM (Minimum Information Model) vocabulary and framework which aims to provide a practical, and scalable approach to describing and assessing Linked Data against minimum information checklists. The MIM framework aims to support three core activities: (1) publishing well described minimum information checklists in RDF as Linked Data; (2) publishing Linked Data against these checklists; and (3) validating existing “in the wild” Linked Data against a published checklist. We discuss the design considerations of the vocabulary and present its main classes. We demonstrate the utility of the framework with a checklist designed for the publishing of Chemical Structure Linked Data using data extracted from Wikipedia as an example.

While workflow technology has gained momentum in the last decade as a means for specifying and enacting computational experiments in modern science, reusing and repurposing existing workflows to build new scientific experiments is still a daunting task. This is partly due to the difficulty that scientists experience when attempting to understand existing workflows, which contain several data preparation and adaptation steps in addition to the scientifically significant analysis steps. One way to tackle the understandability problem is through providing abstractions that give a high-level view of activities undertaken within workflows. As a first step towards abstractions, we report in this paper on the results of a manual analysis performed over a set of real-world scientific workflows from Taverna and Wings systems. Our analysis has resulted in a set of scientific workflow motifs that outline i) the kinds of data intensive activities that are observed in workflows (data oriented motifs), and ii) the different manners in which activities are implemented within workflows (workflow oriented motifs). These motifs can be useful to inform workflow designers on the good and bad practices for workflow development, to inform the design of automated tools for the generation of workflow abstractions, etc.

The interpretation and integration of experimental data depends on consistent metadata and uniform annotation. However, there are many barriers to the acquisition of this rich semantic metadata, not least the overhead and complexity of its collection by scientists. We present RightField, a lightweight spreadsheet-based annotation tool for lowering the barrier of manual metadata acquisition; and a data integration application for extracting and querying RDF data from these enriched spreadsheets. By hiding the complexities of semantic annotation, we can improve the collection of rich metadata, at source, by scientists. We illustrate the approach with results from the SysMO program, showing that RightField supports the whole workflow of semantic data collection, submission and RDF querying in Systems Biology. The RightField tool is freely available from http://www.rightfield.org.uk, and the code is open source under the BSD License.

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.

Scientific workflows are increasingly gaining momentum as the new paradigm for modeling and enacting scientific experiments. The value of a workflow specification does not end once it is enacted. Indeed, workflow specifications encapsulate knowledge that documents scientific experiments, and are, therefore, worth preserving. Our experience suggests that workflow preservation is frequently hampered by the volatility of the constituent service operations when these operations are supplied by third-party providers. To deal with this issue, we propose a heuristic for locating substitutes that are able to replace unavailable service operations within workflows. The proposed method uses the data links connecting inputs and outputs of service operations in existing workflow specifications to locate operations with parameters compatible with those of the missing operations. Furthermore, it exploits provenance traces collected from past executions of workflows to ensure that candidate substitutes perform tasks similar to those of the missing operations. The effectiveness of the proposed method has been empirically assessed.

MOTIVATION: In the Life Sciences, guidelines, checklists and ontologies describing what metadata is required for the interpretation and reuse of experimental data are emerging. Data producers, however, may have little experience in the use of such standards and require tools to support this form of data annotation. RESULTS: RightField is an open source application that provides a mechanism for embedding ontology annotation support for Life Science data in Excel spreadsheets. Individual cells, columns or rows can be restricted to particular ranges of allowed classes or instances from chosen ontologies. The RightField-enabled spreadsheet presents selected ontology terms to the users as a simple drop-down list, enabling scientists to consistently annotate their data. The result is 'semantic annotation by stealth', with an annotation process that is less error-prone, more efficient, and more consistent with community standards. AVAILABILITY AND IMPLEMENTATION: RightField is open source under a BSD license and freely available from http://www.rightfield.org.uk

The increasing number of available online services demands distributed architectures to promote scalability as well as semantics to enable their precise and efficient retrieval. Two common approaches toward this goal are Semantic Overlay Networks (SONs) and Distributed Hash Tables (DHTs) with semantic extensions. This paper presents ERGOT, a system that combines DHTs and SONs to enable semantic-based service discovery in distributed infrastructures such as Grids and Clouds. ERGOT takes advantage of semantic annotations that enrich service specifications in two ways: (i) services are advertised in the DHT on the basis of their annotations, thus allowing to establish a SON among service providers, (ii) annotations enable semantic-based service matchmaking, using a novel similarity measure between service requests and descriptions. Experimental evaluations confirmed the efficiency of ERGOT in terms of accuracy of search and network traffic.

Scientific data stands to represent a significant portion of the linked open data cloud and science itself stands to benefit from the data fusion capability that this will afford. However, simply publishing linked data into the cloud does not necessarily meet the requirements of reuse. Publishing has requirements of provenance, quality, credit, attribution, methods in order to provide the \emph{reproducibility} that allows validation of results. In this paper we make the case for a scientific data publication model on top of linked data and introduce the notion of \emph{Research Objects} as first class citizens for sharing and publishing.

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.

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.

BACKGROUND: The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources. RESULTS: Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis. CONCLUSIONS: Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

BACKGROUND: The Open Source movement and its technologies are popular in the bioinformatics community because they provide freely available tools and resources for research. In order to feed the steady demand for updates on software and associated data, a service infrastructure is required for sharing and providing these tools to heterogeneous computing environments. RESULTS: The Debian Med initiative provides ready and coherent software packages for medical informatics and bioinformatics. These packages can be used together in Taverna workflows via the UseCase plugin to manage execution on local or remote machines. If such packages are available in cloud computing environments, the underlying hardware and the analysis pipelines can be shared along with the software. CONCLUSIONS: Debian Med closes the gap between developers and users. It provides a simple method for offering new releases of software and data resources, thus provisioning a local infrastructure for computational biology. For geographically distributed teams it can ensure they are working on the same versions of tools, in the same conditions. This contributes to the world-wide networking of researchers.

BACKGROUND: In biological and medical domain, the use of web services made the data and computation functionality accessible in a unified manner, which helped automate the data pipeline that was previously performed manually. Workflow technology is widely used in the orchestration of multiple services to facilitate in-silico research. Cancer Biomedical Informatics Grid (caBIG) is an information network enabling the sharing of cancer research related resources and caGrid is its underlying service-based computation infrastructure. CaBIG requires that services are composed and orchestrated in a given sequence to realize data pipelines, which are often called scientific workflows. RESULTS: CaGrid selected Taverna as its workflow execution system of choice due to its integration with web service technology and support for a wide range of web services, plug-in architecture to cater for easy integration of third party extensions, etc. The caGrid Workflow Toolkit (or the toolkit for short), an extension to the Taverna workflow system, is designed and implemented to ease building and running caGrid workflows. It provides users with support for various phases in using workflows: service discovery, composition and orchestration, data access, and secure service invocation, which have been identified by the caGrid community as challenging in a multi-institutional and cross-discipline domain. CONCLUSIONS: By extending the Taverna Workbench, caGrid Workflow Toolkit provided a comprehensive solution to compose and coordinate services in caGrid, which would otherwise remain isolated and disconnected from each other. Using it users can access more than 140 services and are offered with a rich set of features including discovery of data and analytical services, query and transfer of data, security protections for service invocations, state management in service interactions, and sharing of workflows, experiences and best practices. The proposed solution is general enough to be applicable and reusable within other service-computing infrastructures that leverage similar technology stack.

Abstract By making research content more reusable, and providing a social infrastructure that facilitates sharing, the human aspects of the scholarly knowledge cycle may be accelerated and ‘time‐to‐discovery’ reduced. We propose that the key to this is the sharing of methods and processes. We present myExperiment, a social web site for discovering, sharing and curating Scientific Workflows and experiment plans, and describe how myExperiment facilitates the management and sharing of research workflows, supports a social model for content curation tailored to the researcher and community, and supports Open Science by exposing content and functionality to the users' tools and applications. Based on this, we introduce the notion of the Research Object—the work objects that are built, transformed and published in the course of scientific experiments—and suggest that by encapsulating methods with results we can achieve research that is more reusable and repeatable and hence rapid and robust. Copyright © 2010 John Wiley & Sons, Ltd.

With the emergence of "service oriented science," the need arises to orchestrate multiple services to facilitate scientific investigation-that is, to create "science workflows." We present here our findings in providing a workflow solution for the caGrid service-based grid infrastructure. We choose BPEL and Taverna as candidates, and compare their usability in the lifecycle of a scientific workflow, including workflow composition, execution, and result analysis. Our experience shows that BPEL as an imperative language offers a comprehensive set of modeling primitives for workflows of all flavors; while Taverna offers a dataflow model and a more compact set of primitives that facilitates dataflow modeling and pipelined execution. We hope that this comparison study not only helps researchers select a language or tool that meets their specific needs, but also offers some insight on how a workflow language and tool can fulfill the requirement of the scientific community.

Resource-intensive and complex medical imaging applications can benefit from the use of scientific workflow technology for their design, rapid implementation and reuse, but at the same time they require a grid computing infrastructure to execute efficiently. In this paper we describe a technical architecture that bridges the gap between the Taverna workflow management system and the EGEE grid infrastructure. This is achieved through a novel Taverna gLite activity plugin that makes the interface between the multi-threaded, centralized workflow enactor and the massively distributed, batch-oriented grid infrastructure. The plugin significantly increases the performance of Medical Imaging workflows over an equivalent plain Taverna workflow.

In scientific collaboration platforms such as caGrid, workflow-as-a-service is a useful concept for various reasons, such as easy reuse of workflows, access to remote resources, security concerns, and improved execution performance. We propose a solution for facilitating workflow-as-a-service based on Taverna as the workflow engine and gRAVI as a service wrapping tool. We provide both a generic service to execute all Taverna workflows, and an easy-to-use tool (gRAVI-t) for users to wrap their workflows as workflow-specific services, without developing service code. The signature of the specific service is identical to the corresponding workflow's input/output definition and is therefore more self-explained to workflow users. These two categories of services are useful in different scenarios, respectively. We use a tumor analysis workflow as an example to demonstrate how the workflow-as-a-service approach benefits the execution performance. Finally a conclusion is drawn and future research opportunities are discussed.

The soaring number of available online services calls for distributed architectures to promote scalability, fault- tolerance and semantics; to provide meaningful descriptions of services; and to support their efficient retrieval. Current approaches exploit either Semantic Overlay Networks (SONs) or Distributed Hash Tables (DHTs) sweetened with some ”semantic sugar.” SONs enable semantic driven query answering but are less scalable than DHTs, which on their turn, feature efficient but semantic-free query answering based on ”exact” match. This paper presents the ERGOT system combining DHTs and SONs to enable distributed and semantic-based service discovery. A preliminary evaluation of the system performance shows the suitability of the approach both in terms of recall and number of messages.

Abstract Automation in science is increasingly marked by the use of workflow technology. Thesharingof workflows through repositories supports the verifiability, reproducibility and extensibility of computational experiments. However, the subsequentdiscoveryof workflows remains a challenge, both from a sociological and technological viewpoint. Based on a survey with participants from 19 laboratories, we investigate the current practices in workflow sharing, re‐use and discovery among life scientists chiefly using the Taverna workflow management system. To address their perceived lack of effective workflow discovery tools, we go on to develop benchmarks for the evaluation of discovery tools, drawing on a series of practical exercises. We demonstrate the value of the benchmarks on two tools: one using graph matching and the other relying on text clustering. Copyright © 2009 John Wiley & Sons, Ltd.

There seems to be a general consensus on the crucial role metadata can play for enhancing the functionalities of scientific workflows systems, e.g., workflow and service discovery, composition and provenance browsing, among others. However, in most cases their management is under-specified, if not left unaddressed at all. A step in this direction, the main contribution of the work presented in this paper is an overview of metadata and their management in the Taverna workflow system. In Taverna, we consider metadata to be a first class citizen in the system, in the sense that we fully cover their life cycle from their creation, through their use and curation until their eventual removal. We present the main steps of this cycle and present the models used for metadata specification. In doing so, we distinguish two classes of metadata: metadata that describe workflow related entities, such as services, workflows and sub- workflows, and metadata that describe workflow executions, also known as workflow provenance.

The myExperiment virtual research environment supports the sharing of research objects used by scientists, such as scientific workflows. For researchers it is both a social infrastructure that encourages sharing and a platform for conducting research, through familiar user interfaces. For developers it provides an open, extensible and participative environment. We describe the design, implementation and deployment of myExperiment and suggest that its four capabilities - research objects, social model, open environment and actioning research - are necessary characteristics of an effective Virtual Research Environment for e-research and open science.

In bioinformatics, we are familiar with the idea of curated data as a prerequisite for data integration. We neglect, often to our cost, the curation and cataloguing of the processes that we use to integrate and analyse our data. Programmatic access to services, for data and processes, means that compositions of services can be made that represent the in silico experiments or processes that bioinformaticians perform. Data integration through workflows depends on being able to know what services exist and where to find those services. The large number of services and the operations they perform, their arbitrary naming and lack of documentation, however, mean that they can be difficult to use. The workflows themselves are composite processes that could be pooled and reused but only if they too can be found and understood. Thus appropriate curation, including semantic mark-up, would enable processes to be found, maintained and consequently used more easily. This broader view on semantic annotation is vital for full data integration that is necessary for the modern scientific analyses in biology. This article will brief the community on the current state of the art and the current challenges for process curation, both within and without the Life Sciences.

BACKGROUND: There has been a dramatic increase in the amount of quantitative data derived from the measurement of changes at different levels of biological complexity during the post-genomic era. However, there are a number of issues associated with the use of computational tools employed for the analysis of such data. For example, computational tools such as R and MATLAB require prior knowledge of their programming languages in order to implement statistical analyses on data. Combining two or more tools in an analysis may also be problematic since data may have to be manually copied and pasted between separate user interfaces for each tool. Furthermore, this transfer of data may require a reconciliation step in order for there to be interoperability between computational tools. RESULTS: Developments in the Taverna workflow system have enabled pipelines to be constructed and enacted for generic and ad hoc analyses of quantitative data. Here, we present an example of such a workflow involving the statistical identification of differentially-expressed genes from microarray data followed by the annotation of their relationships to cellular processes. This workflow makes use of customised maxdBrowse web services, a system that allows Taverna to query and retrieve gene expression data from the maxdLoad2 microarray database. These data are then analysed by R to identify differentially-expressed genes using the Taverna RShell processor which has been developed for invoking this tool when it has been deployed as a service using the RServe library. In addition, the workflow uses Beanshell scripts to reconcile mismatches of data between services as well as to implement a form of user interaction for selecting subsets of microarray data for analysis as part of the workflow execution. A new plugin system in the Taverna software architecture is demonstrated by the use of renderers for displaying PDF files and CSV formatted data within the Taverna workbench. CONCLUSION: Taverna can be used by data analysis experts as a generic tool for composing ad hoc analyses of quantitative data by combining the use of scripts written in the R programming language with tools exposed as services in workflows. When these workflows are shared with colleagues and the wider scientific community, they provide an approach for other scientists wanting to use tools such as R without having to learn the corresponding programming language to analyse their own data.

Abstract The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract Taverna is a workflow workbench developed as part of the UK's myGrid project. Taverna's provenance model captures both internal provenance locally generated in Taverna and external provenance gathered from third‐party data providers. This model also supports overlaying secondary provenance over the primary logs and lineage. This design is motivated by the particular properties of bioinformatics data and services used in Taverna. A Semantic Web of provenance, Ouzo, is built to combine the above different provenance by means of semantic annotations. This paper shows how Ouzo can be mined by a provenance usage component, Provenance Query and Answer (ProQA). ProQA supports provenance retrievals as well as provenance abstraction, aggregation, and semantic reasoning. ProQA is implemented as a suite APIs which can be deployed as provenance services to compose system provenance workflows that analyse experiment results using the provenance records. We show how these features of Taverna's provenance support us in answering the questions from the provenance challenge workshop and a set of additional provenance queries. Copyright © 2007 John Wiley & Sons, Ltd.

Semantic annotations of web services can support the effective and efficient discovery of services, and guide their composition into workflows. At present, however, the practical utility of such annotations is limited by the small number of service annotations available for general use. Manual annotation of services is a time consuming and thus expensive task, so some means are required by which services can be automatically (or semi-automatically) annotated. In this paper, we show how information can be inferred about the semantics of operation parameters based on their connections to other (annotated) operation parameters within tried-and-tested workflows. Because the data links in the workflows do not necessarily contain every possible connection of compatible parameters, we can infer only constraints on the semantics of parameters. We show that despite their imprecise nature these so-called loose annotations are still of value in supporting the manual annotation task, inspecting workflows and discovering services. We also show that derived annotations for already annotated parameters are useful. By comparing existing and newly derived annotations of operation parameters, we can support the detection of errors in existing annotations, the ontology used for annotation and in workflows. The derivation mechanism has been implemented, and its practical applicability for inferring new annotations has been established through an experimental evaluation. The usefulness of the derived annotations is also demonstrated.

Workflows systems are steadily finding their way into the work practices of scientists. This is particularly true in the in silico science of bioinformatics, where biological data can be processed by Web services. In this paper we investigate the potential of evolving the users' interaction with workflow environments so that it more closely relates to the mode in which their day to day work is carried out. We present the Data Playground, an environment designed to encourage the uptake of workflow systems in bioinformatics through more intuitive interaction by focusing the user on their data rather than on the processes. We implement a prototype plug-in for the Taverna workflow environment and show how this can promote the creation of workflow fragments by automatically converting the users' interactions with data and Web services into a more conventional workflow specification.

Many scientific workflow systems have been developed and are serving to benefit science. In this paper we look beyond individual systems to consider the use of workflows within scientific practice, and we argue that the tremendous scientific potential of workflows will be achieved through mechanisms for sharing and collaboration - empowering the scientist to spread their experimental protocols and to benefit from the protocols of others. We discuss issues in workflow sharing, propose a set of design principles for collaborative e-Science software, and illustrate these principles in action through the design of themyExperiment Virtual Research Environment for collaboration and sharing of experiments.

This paper presents the formal syntax and the operational semantics of Taverna, a workflow management system with a large user base among the e-Science community. Such formal foundation, which has so far been lacking, opens the way to the translation between Taverna workflows and other process models. In particular, the ability to automatically compile a simple domain-specific process description into Taverna facilitates its adoption by e-scientists who are not expert workflow developers. We demonstrate this potential through a practical use case.

This article offers a briefing in one of the knowledge management issues of in silico experimentation in bioinformatics. Recording of the provenance of an experiment-what was done; where, how and why, etc. is an important aspect of scientific best practice that should be extended to in silico experimentation. We will do this in the context of eScience which has been part of the move of bioinformatics towards an industrial setting. Despite the computational nature of bioinformatics, these analyses are scientific and thus necessitate their own versions of typical scientific rigour. Just as recording who, what, why, when, where and how of an experiment is central to the scientific process in laboratory science, so it should be in silico science. The generation and recording of these aspects, or provenance, of an experiment are necessary knowledge management goals if we are to introduce scientific rigour into routine bioinformatics. In Silico experimental protocols should themselves be a form of managing the knowledge of how to perform bioinformatics analyses. Several systems now exist that offer support for the generation and collection of provenance information about how a particular in silico experiment was run, what results were generated, how they were generated, etc. In reviewing provenance support, we will review one of the important knowledge management issues in bioinformatics.

Abstract Scientific workflows are becoming a valuable tool for scientists to capture and automate e‐Science procedures. Their success brings the opportunity to publish, share, reuse and re‐purpose this explicitly captured knowledge. Within the $^{my}$ Grid project, we have identified key resources that can be shared including complete workflows, fragments of workflows and constituent services. We have examined the alternative ways that these resources can be described by their authors (and subsequent users) and developed a unified descriptive model to support their later discovery. By basing this model on existing standards, we have been able to extend existing Web service and Semantic Web service infrastructure whilst still supporting the specific needs of the e‐Scientist. The $^{my}$ Grid components enable a workflow life‐cycle that extends beyond execution to include the discovery of previous relevant designs, the reuse of those designs and their subsequent publication. Experience with example groups of scientists indicates that this cycle is valuable. The growing number of workflows and services mean more work is needed to support the user in effective ranking of search results and to support the re‐purposing process. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract In a virtual organization environment, where services and data are provided and shared among organizations from different administrative domains and protected with dissimilar security policies and measures, there is a need for a flexible authentication framework that supports the use of various authentication methods and tokens. The authentication strengths derived from the authentication methods and tokens should be incorporated into an access‐control decision‐making process, so that more sensitive resources are available only to users authenticated with stronger methods. This paper reports our on‐going efforts in designing and implementing such a framework to facilitate multi‐level and multi‐factor adaptive authentication and authentication strength linked fine‐grained access control. The proof‐of‐concept prototype is designed and implemented in the Shibboleth and PERMIS infrastructures, which specifies protocols to federate authentication and authorization information and provides a policy‐driven, role‐based, access‐control decision‐making capability. Copyright © 2006 John Wiley & Sons, Ltd.

Knowledge has become increasingly important to support intelligent process automation and collaborative problem solving in large-scale science over the Internet. This paper addresses distributed knowledge management, its approach and methodology, in the context of grid application. We start by analyzing the nature of grid computing and its requirements for knowledge support; then, we discuss knowledge characteristics and the challenges for knowledge management on the grid. A semantic Web-based approach is proposed to tackle the six challenges of the knowledge lifecycle - namely, those of acquiring, modeling, retrieving, reusing, publishing, and maintaining knowledge. To facilitate the application of the approach, a systematic methodology is conceived and designed to provide a general implementation guideline. We use a real-world Grid application, the GEODISE project, as a case study in which the core semantic Web technologies such as ontologies, semantic enrichment, and semantic reasoning are used for knowledge engineering and management. The case study has been fully implemented and deployed through which the evaluation and validation for the approach and methodology have been performed

The importance of the World Wide Web for information dissemination is indisputable. However, the dominance of visual design on the Web leaves visually disabled people at a disadvantage. Although assistive technologies, such as screen readers, usually provide basic access to information, the richness of the Web experience is still often lost. In particular, traversing the Web becomes a complicated task since the richness of visual objects presented to their sighted counterparts are neither appropriate nor accessible to visually disabled users. To address this problem, we have proposed an approach called Dante in which Web pages are annotated with semantic information to make their traversal properties explicit. Dante supports usage of different annotation techniques and as a proof-of-concept in this article, pages are annotated manually which when transcoded become rich. We first introduce Dante and then present a user evaluation which compares how visually disabled users perform certain travel-related tasks on original and transcoded versions of Web pages. We discuss the evaluation methodology in detail and present our findings, which provide useful insights into the transcoding process. Our evaluation shows that, in tests with users, document objects transcoded with Dante have a tendency to be much easier for visually disabled users to interact with when traversing Web pages.

The paper describes the design of FAME (Flexible Access Middleware Extension) architecture aimed at providing multi-level user authentication service for Shibboleth, which is endorsed by the Joint Information Systems Committee (JISC) as the next generation authentication and authorisation infrastructure for the UK Higher Education community. FAME derives authentication assurance level based upon the strength of the authentication token and protocol used by the user when authenticating and feeds it to the PERMIS authorisation decision engine via Shibboleth to enable more fine-grained access control. In this way, access to resources is controlled according to the strength of the authentication method so that more sensitive resources may require users to identify themselves using a higher level of authentication.

Much has been written on the promise of Web service discovery and (semi-) automated composition. In this discussion, the value to practitioners of discovering and reusing existing service compositions, captured in workflows, is mostly ignored. This paper presents one solution to workflow discovery. Through a survey with 21 scientists and developers from the myGrid workflow environment, workflow discovery requirements are elicited. Through a user experiment with 13 scientists, an attempt is made to build a gold standard for workflow ranking. Through the design and implementation of a workflow discovery tool, a mechanism for ranking workflow fragments is provided based on graph sub-isomorphism matching. The tool evaluation, drawing on a corpus of 89 public workflows from bioinformatics and the results of the user experiment, finds that the average human ranking can largely be reproduced

Abstract Life sciences research is based on individuals, often with diverse skills, assembled into research groups. These groups use their specialist expertise to address scientific problems. The in silico experiments undertaken by these research groups can be represented as workflows involving the co‐ordinated use of analysis programs and information repositories that may be globally distributed. With regards to Grid computing, the requirements relate to the sharing of analysis and information resources rather than sharing computational power. The myGrid project has developed the Taverna Workbench for the composition and execution of workflows for the life sciences community. This experience paper describes lessons learnt during the development of Taverna. A common theme is the importance of understanding how workflows fit into the scientists' experimental context. The lessons reflect an evolving understanding of life scientists' requirements on a workflow environment, which is relevant to other areas of data intensive and exploratory science. Copyright © 2005 John Wiley & Sons, Ltd.

What is the semantic grid? How can e-Science benefit from the technologies of the semantic grid? Can we build a semantic Web for e-Science? Would that differ from a semantic grid? Given our past experiences with scientists, grid developers and semantic Web researchers, what are the prospects, and pitfalls, of putting semantics into e-Science applications and grid infrastructure?

The Taverna e-Science Workbench is a central component of myGrid, a loosely coupled suite of middleware services designed to support in silico experiments in biology. Taverna enables the construction and enactment of complex workflows over resources on local and remote machines, allowing the automation of otherwise labour-intensive multi-step bioinformatics tasks. As the Taverna user community has grown, so has the demand for new features and additions. This paper outlines the functional requirements that have become apparent over the last year of working with domain scientists, along with the solutions implemented in both the Taverna workbench and the Freefluo enactment engine to address concerns relating to workflow construction and enactment, respectively

The convergence of the Semantic Web and Grid technologies has resulted in the Semantic Grid. The Semantic Grid should be service-oriented, as the Grid is, so the formal description of Grid Services (GS) turns to be a crucial issue. In this paper we present our approach for this issue. ODESGS Framework will enable the annotation of all the aspects of a GS and the design, discovery and composition Semantic Grid Services (SGS).

Currently, the vast majority of web sites do not support accessibility for visually impaired users. Usually, these users have to rely on screen readers: applications that sequentially read the content of a web page in audio. Unfortunately, screen readers are not able to detect the meaning of the different page objects, and thus the implicit semantic knowledge conveyed in the presentation of the page is lost. One approach described in literature to tackle this problem, is the Dante approach, which allows semantic annotation of web pages to provide screen readers with extra (semantic) knowledge to better facilitate the audio presentation of a web page. Until now, such annotations were done manually, and failed for dynamic pages. In this paper, we combine the Dante approach with a web design method, WSDM, to fully automate the generation of the semantic annotation for visually impaired users. To do so, the semantic knowledge gathered during the design process is exploited, and the annotations are generated as a by-product of the design process, requiring no extra effort from the designer.

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in myGrid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.

In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population, and reuse on the grid. Its objective is to evolve the Grid toward the Semantic Grid with the ultimate purpose of facilitating problem solving in e‐Science. The framework uses ontology as the conceptual backbone and adopts the service‐oriented computing paradigm for information‐ and knowledge‐level computation. We further present a semantics‐based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation, and composition. The framework and approach has been applied to a UK e‐Science project—Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology‐enabled problem solving environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported.

Movement, or mobility, is key to the accessibility, design, and usability of many hypermedia resources (websites); and key to good mobility is context and preview by probing. This is especially the case for visually impaired users when a hypertext anchor is inaccurately described or is described out of context. This means confusion and disorientation. Mobility is similarly reduced when the link target of the anchor has no relationship to the expected information present on the hypertext node (web-page). We suggest that confident movement with purpose, ease, and accuracy can only be achieved when complete contextual information and an accurate description of the proposed destination (preview) are available. Our past work (1) deriving mobility heuristics from mobility models, (2) transforming web-pages based on these heuristics, and(3) building tools to analyse and access these transformed pages; has shown us that a tool to expand context and preview would be useful. In this paper we describe the development of such a middleware tool to automatically and dynamically annotate web-pages with additional context information present within the page, and preview information present within hypertext link destinations found on the page.

Tables remain a persistent problem for visually impaired people using screen readers. Tables are complex structures that are widely used for different purposes such as spatial layout or data summarisation. The multi-dimensional nature of tables challenges the linear interaction styles typically supported by screen readers. To read a table, a user needs to maintain coherency of, and interact with more than one dimension. In this paper, we first characterise why tables are useful in print, but difficult to read in the audio. We present a survey of the relationship between table structure, intention and the reading styles employed to use the content of tables. We then present two different approaches for interacting with tables non-visually. These approaches are designed to support the characteristics of tables that make them such a popular and useful means of conveying information. The first approach provides a small table browser called EVITA (Enabling Visually Impaired Table Access), whose aim is to enable non-visual table browsing and reading in an analogous manner to the print medium. The second approach provides a table lineariser to transform tables into a form such that they can be easily read by screen readers.

The future success of Grid-enabled e-Science depends on the availability of semantic/knowledge-rich resources on the Grid, i.e., the so-called semantic Grid. This requires not only novel knowledge modelling and representation formalisms but also a shift of knowledge acquisition and population from a limited number of specialised knowledge engineers to resource providers. To this end we have developed a lightweight ontology-enabled tool, "Function Annotator", to support resource providers in capturing and publishing resource semantics and knowledge. Function Annotator takes a different line to most knowledge acquisition tools in that it is designed for use by resource providers, probably in the absence of a knowledge engineer. Its aim is to facilitate large scale knowledge population on the Grid. Function Annotator is built on the state-of-the-art of semantic web technologies, such as ontologies, OWL, instance store and DL-based reasoning, thus ensuring flexibility and scalability on the Grid. This paper describes the tool's role in a Grid-oriented resource lifecycle, its underlying technologies and implementation. It also illustrates the usage of the tool in the context of engineering design search and optimisation.

My Grid is an e-Science Grid project that aims to help biologists and bioinformaticians to perform workflow-based in silico experiments, and help them to automate the management of such workflows through personalisation, notification of change and publication of experiments. In this paper, we describe the architecture of my Grid and how it will be used by the scientist. We then show how my Grid can benefit from agents technologies. We have identified three key uses of agent technologies in my Grid: user agents, able to customize and personalise data, agent communication languages offering a generic and portable communication medium, and negotiation allowing multiple distributed entities to reach service level agreements.

Movement, or mobility, is a key to the accessibility, design, and usability of many Web sites. While some peripheral mobility issues have been addressed few have centered on the mobility problems of visually impaired users. We use our past work to address these issues and derive mobility heuristics from mobility models, use these heuristics to place mobility objects within a Web page, and describe the construction of a prototype mobility instrument, in the form of a Netscape plug-in, to process these objects. Our past work extends the notion of movement to include environment, feedback and the purpose of the current travel task. Specifically, we likened web use to travelling in a virtual space, compared it to travelling in a physical space, and introduced the idea of mobility - the ease of travel - as opposed to travel opportunity.

Users make journeys through the Web. Web travel encompasses the tasks of orientation and navigation, the environment and the purpose of the journey. The ease of travel, its mobility, varies from page to page and site to site. For visually impaired users, in particular, mobility is reduced; the objects that support travel are inaccessible or missing altogether. Web development tools need to include support to increase mobility. We present a framework for finding and classifying travel objects within Web pages. The evaluation carried out has shown that this framework supports a systematic and consistent method for assessing travel upon the Web. We propose that such a framework can provide the foundation for a semi-automated tool for the support of travel upon the Web.

MOTIVATION: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or 'semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repertoire of analyses. RESULTS: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. AVAILABILITY: Software available from http://www.russet.org.uk.

The growing quantity and distribution of bioinformatics resources means that finding and utilizing them requires a great deal of expert knowledge, especially as many resources need to be tied together into a workflow to accomplish a useful goal. We want to formally capture at least some of this knowledge within a virtual workbench and middleware framework to assist a wider range of biologists in utilizing these resources. Different activities require different representations of knowledge. Finding or substituting a service within a workflow is often best supported by a classification. Marshalling and configuring services is best accomplished using a formal description. Both representations are highly interdependent and maintaining consistency between the two by hand is difficult. We report on a description logic approach using the web ontology language DAML+OIL that uses property based service descriptions. The ontology is founded on DAML-S to dynamically create service classifications. These classifications are then used to support semantic service matching and discovery in a large grid based middleware project [Formula: see text]. We describe the extensions necessary to DAML-S in order to support bioinformatics service description; the utility of DAML+OIL in creating dynamic classifications based on formal descriptions; and the implementation of a DAML+OIL ontology service to support partial user-driven service matching and composition.

We are developing a Grid Enabled Optimisation and Design Search system (GEODISE). It offers grid-based access to a state-of-the-art collection of optimisation and design search tools, industrial strength analysis codes, and distributed computing and data resources.

This paper presents an approach to answering queries over an ontology modelled using a description logic. The ontology acts as a global schema, providing a declarative description of the concepts of the domain, the instances of which are stored in (potentially many) object-wrapped sources. Queries are expressed using terms from the rich vocabulary of the ontology, and are translated into an equivalent calculus expression, which references only the objects available in the source databases. The query is then optimized on the basis of information from the ontology and the source databases. Distinctive features of the approach include: the use of the expressive ALCQI description logic, which supports both ontology definition and query expression; the adoption of a global-as-view approach to relating the ontology to the sources; and the use of the ontology to direct semantic optimization of queries phrased over specific sources. The approach is being developed in, and is illustrated using examples from, bioinformatics.

The complex questions and analyses posed by biologists, as well as the diverse data resources they develop, require the fusion of evidence from different, independently developed, and heterogeneous resources. The web, as an enabler for interoperability, has been an excellent mechanism for data publication and transportation. Successful exchange and integration of information, however, depends on a shared language for communication (a terminology) and a shared understanding of what the data means (an ontology). Without this kind of understanding, semantic heterogeneity remains a problem for both humans and machines. One means of dealing with heterogeneity in bioinformatics resources is through terminology founded upon an ontology. Bioinformatics resources tend to be rich in human readable and understandable annotation, with each resource using its own terminology. These resources are machine readable, but not machine understandable. Ontologies have a role in increasing this machine understanding, reducing the semantic heterogeneity between resources and thus promoting the flexible and reliable interoperation of bioinformatics resources. This paper describes a solution derived from the semantic web [a machine understandable world-wide web (WWW)], the ontology inference layer (OIL), as a solution for semantic bioinformatics resources. The nature of the heterogeneity problems are presented along with a description of how metadata from domain ontologies can be used to alleviate this problem. A companion paper in this issue gives an example of the development of a bio-ontology using OIL.

This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualization is encoded using the ontology inference layer (OIL), a knowledge representation language that combines the modeling style of frame-based systems with the expressiveness and reasoning power of description logics (DLs). This paper is the second of a pair in this special issue. The first described the core of the OIL language and the need to use ontologies to deliver semantic bioinformatics resources. In this paper, the early stages of building an ontology component of a bioinformatics resource querying application are described. This ontology (TaO) holds the information about molecular biology represented in bioinformatics resources and the bioinformatics tasks performed over these resources. It, therefore, represents the metadata of the resources the application can query. It also manages the terminologies used in constructing the query plans used to retrieve instances from those external resources. The methodology used in this task capitalizes upon features of OIL-The conceptualization afforded by the frame-based view of OIL's syntax; the expressive power and reasoning of the logical formalism; and the ability to encode both handcrafted, hierarchies of concepts, as well as defining concepts in terms of their properties, which can then be used to establish a classification and infer relationships not encoded by the ontologist. This ability forms the basis of the methodology described here: For each portion of the TaO, a basic framework of concepts is asserted by the ontologist. Then, the properties of these concepts are defined by the ontologist and the logic's reasoning power used to reclassify and infer further relationships. This cycle of elaboration and refinement is iterated on each portion of the ontology until a satisfactory ontology has been created.

Effective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. For example, when analysing gene expression data, it may be useful to have access to the sequences upstream of the genes, or to the cellular location of their protein products. Such information is currently stored in different formats at different sites in a way that does not readily allow integrated analyses. The Genome Information Management System (GIMS) is an object database that integrates genome sequence data with functional data on the transcriptome and on protein-protein interactions in a single data warehouse. We have used GIMS to store the Saccharomyces cerevisiae (yeast) genome and to demonstrate how the integrated storage of diverse kinds of genomic data can be beneficial for analysing data using context-rich queries and analyses. GIMS allows data to be stored in a way that reflects the underlying mechanisms in the organism, and permits complex questions to be asked of the data. This paper provides an overview of the GIMS system and describes some analyses that illustrate its use for analysing functional data sets for S. cerevisiae.

There have been several attempts at addressing the problem of annotating sequence data computationally. Annotation generation can be considered a pipeline of processes: first harvesting data from a variety of data sources, then distilling and transforming it into a form more appropriate for the end database. This task is usually performed by human annotators, a solution that is clearly not scaleable. There have been several attempts to mimic some of these pipelines in software. However, these have generally focused on low level annotation, such as database cross-references, or by harvesting data from computational techniques such as gene finding or similarity searches. Higher level annotation such as that seen in the PRINTS database is usually formed from data that is free text, or only partly structured. This presents a much greater computational challenge. Therefore we studied the pipeline that is used to generate annotation for the PRINTS database, and have developed prototype software that reflects and automates this pipeline. As this software operates primarily on data culled from the SWISS-PROT database, we have called it PRECIS (Protein Reports Engineered from Concise Information in SWISS-PROT). This software is currently being used to generate annotation for the prePRINTS database. As the output is a structured report detailing the function, structure and disease associations of a protein, and providing literature references and keywords we believe it will be of more generic use. The software is available on request from [email protected].

Article Conceptual linking: ontology-based open hypermedia Share on Authors: Leslie Carr Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKView Profile , Wendy Hall Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK Intelligence, Agents, Multimedia, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKView Profile , Sean Bechhofer Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UKView Profile , Carole Goble Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK Information Management Group, Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UKView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 334–342https://doi.org/10.1145/371920.372084Online:01 April 2001Publication History 93citation1,575DownloadsMetricsTotal Citations93Total Downloads1,575Last 12 Months12Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Abstract Motivation: This paper reports on a survey of bioinformatics tasks currently undertaken by working biologists. The aim was to find the range of tasks that need to be supported and the components needed to do this in a general query system. This enabled a set of evaluation criteria to be used to assess both the biology and mechanical nature of general query systems. Results: A classification of the biological content of the tasks gathered offers a checklist for those tasks (and their specialisations) that should be offered in a general bioinformatics query system. This semantic analysis was contrasted with a syntactic analysis that revealed the small number of components required to describe all bioinformatics questions. Both the range of biological tasks and syntactic task components can be seen to provide a set of bioinformatics requirements for general query systems. These requirements were used to evaluate two bioinformatics query systems. Contact: [email protected] Supplementary information: The questionnaire, responses and their summaries may be found at http://img.cs.man.ac.uk/tambis/questionnaire/bio-queries.html * To whom correspondence should be addressed.

Model-based user interface development environments show promise for improving the productivity of user interface developers, and possibly for improving the quality of developed interfaces. While model-based techniques have previously been applied to the area of database interfaces, they have not been specifically targeted at the important area of object database applications. Such applications make use of models that are semantically richer than their relational counterparts in terms of both data structures and application functionality. In general, model-based techniques have not addressed how the information referenced in such applications is manifested within the described models, and is utilised within the generated interface itself. This lack of experience with such systems has led to many model-based projects providing minimal support for certain features that are essential to such data intensive applications, and has prevented object database interface developers in particular from benefiting from model-based techniques. This paper presents the Teallach model-based user interface development environment for object databases, describing the models it supports, the relationships between these models, the tool used to construct interfaces using the models and the generation of Java programs from the declarative models. Distinctive features of Teallach include comprehensive facilities for linking models, a flexible development method, an open architecture, and the generation of running applications based on the models constructed by designers. © 2001 Elsevier Science B.V. All rights reserved.

Much of biology works by applying prior knowledge ('what is known') to an unknown entity, rather than the application of a set of axioms that will elicit knowledge. In addition, the complex biological data stored in bioinformatics databases often require the addition of knowledge to specify and constrain the values held in that database. One way of capturing knowledge within bioinformatics applications and databases is the use of ontologies. An ontology is the concrete form of a conceptualisation of a community's knowledge of a domain. This paper aims to introduce the reader to the use of ontologies within bioinformatics. A description of the type of knowledge held in an ontology will be given.The paper will be illustrated throughout with examples taken from bioinformatics and molecular biology, and a survey of current biological ontologies will be presented. From this it will be seen that the use to which the ontology is put largely determines the content of the ontology. Finally, the paper will describe the process of building an ontology, introducing the reader to the techniques and methods currently in use and the open research questions in ontology development.

Abstract Motivation: Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. Results: This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms. Availability: Most of the information models presented here have been implemented by the authors using an object database. The implementation of a public interface to this database is in progress. We hope to have a public release in the autumn of 2000, available from http://img.cs.man.ac.uk/gims. Contact: [email protected]

The TAMBIS project aims to provide transparent access to disparate biological databases and analysis tools, enabling users to utilize a wide range of resources with the minimum of effort. A prototype system has been developed that includes a knowledge base of biological terminology (the biological Concept Model), a model of the underlying data sources (the Source Model) and a 'knowledge-driven' user interface. Biological concepts are captured in the knowledge base using a description logic called GRAIL. The Concept Model provides the user with the concepts necessary to construct a wide range of multiple-source queries, and the user interface provides a flexible means of constructing and manipulating those queries. The Source Model provides a description of the underlying sources and mappings between terms used in the sources and terms in the biological Concept Model. The Concept Model and Source Model provide a level of indirection that shields the user from source details, providing a high level of source transparency. Source independent, declarative queries formed from terms in the Concept Model are transformed into a set of source dependent, executable procedures. Query formulation, translation and execution is demonstrated using a working example.

Conducting bioinformatic analyses involves biologists in expressing requests over a range of highly heterogeneous information sources and software tools. Such activities are laborious, and require detailed knowledge of the data structures and call interfaces of the different sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. In TAMBIS, queries are expressed in terms of an ontology implemented using a description logic, and queries over the ontology are rewritten to a middleware level for execution over the diverse sources. The paper describes query processing in TAMBIS, focusing in particular on the way source-independent concepts in the ontology are related to source-dependent middleware calls, and describing how the planner identifies efficient ways of evaluating user queries.

Conducting bioinformatic analyses involves biologists in expressing requests over a range of heterogeneous information sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. TAMBIS is available at.

MOTIVATION: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. RESULTS: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. AVAILABILITY: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.

In this paper we describe Kaleidoquery, a visual query language for object databases with the same expressive power as OQL. We will describe the design philosophy behind the filter flow nature of Kaleidoquery and present each of the language's constructs, giving examples and relating them to OQL. The Kaleidoquery language is described independent of any implementation details, but a brief description of a 3D interface currently under construction for Kaleidoquery is presented. The queries in this implementation of the language are translated into OQL and then passed to the object database O2 for evaluation.

It is argued that current clinical information systems incorporate oversimplistic, prescriptive data models that are not faithful to clinicians' observations. A non-prescriptive descriptive semantic formalism, Structured Meta Knowledge (SMK), which unifies a terminological knowledge base with controlled assertional capabilities with the medical record and supports the semantic control necessitated by such an approach, is proposed. The three-layer model of categories, individuals, and occurrences described is more appropriate to medical applications than the two layers of classes and instances. The application of SMK in predictive data entry is considered.>