The role of Research Software Engineers (RSEs) has become critical for progress in many research domains, as they build the computational tools that drive discovery. But with AI’s rapid growth in research, their future is in question. Will AI simply automate RSEs’ work, or will it prove their value as crucial partners? I argue that while AI can generate code, it lacks the human expertise to design robust systems, validate complex solutions, and provide the oversight needed for responsible research software.I explore the possibilities for RSEs to play either a limited role or a leading one. By examining corporate narratives about automation, new AI-driven research platforms, shifts in the workforce, and major national projects like the U.S. National AI Research Resource (NAIRR), I assume that the profession’s future is not set. The conclusion is that survival requires intentional action, including recognizing software as scholarship, investing in workforce development, and proactively addressing the policy implications of AI in research.

Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models. This paper suggests 10 simple rules for you to both (i) ensure you share models in a way that is at least "good enough," and (ii) enable others to lead the change towards better model-sharing practices.

Abstract Science gateways have been widely utilized by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on‐demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e‐learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection, and deployment of cloud‐based reference architectures that can be automatically instantiated and executed even by nontechnical users. The framework also incorporates a knowledge repository exchange and learning module that provides embedded e‐learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere, and thermosphere research communities.

Summary Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next‐generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE‐IG) of the Research Data Alliance. Thus, community‐driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations.

HUBzero© is a well-used science gateway framework actively developed for over a decade. The needs of its community are one of the primary driving forces guiding the team behind HUBzero©. For example, requirements in the community led to the integration of JupyterHub, RStudio and the provision of Docker containers for the tool submission environment. Besides its community driven requirements, the team behind HUBzero© continuously analyzes the existing science gateway landscape, the usage of instances of HUBzero© as well as trends in the usage of computational platforms in general to keep HUBzero© robust, scalable, and sustainable. HUBzero© has begun development of a science gateway platform called Open Science Place (OSP). OSP is a SaaS-based (Softwareas-a-Service) solution to give researchers a way to execute, share, and archive their research in a publically accessible venue. The poster goes into detail for the different aspects of sustainability addressed in OSP such as a community-hosting concept, flexible financing models, interoperability and scalability of tools.

The main goal of the US Science Gateways Community Institute (SGCI) is to serve science gateways to achieve sustainability and growth. Science gateways allow science and engineering communities to access shared data, software, computing services, instruments, educational materials, and other resources specific to their disciplines. Thus, science gateways are a subgroup of scientific software and the means for addressing software sustainability are also suitable for science gateways and vice versa, e.g., best practices for software engineering. Since science gateways are tailored to specific communities, understanding users' requirements is critical for sustainability. SGCI consists of five service areas that closely interact with each other. The Incubator acknowledges the value of business strategy to inform well-designed science gateways and offers two main types of services: individualized consultancy, tailored to specific challenges a gateway faces, and the Science Gateways Bootcamp. The cornerstone of the Bootcamp is a one-week onsite intensive workshop where participants create their own roadmap for a sustainable science gateway via sessions with experts, hands-on exercises, and group work. This paper offers an overview of the work of the Incubator and shares lessons learned from the inaugural session of the Bootcamp in April 2017.

BACKGROUND: Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks. There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free -an aspect that could potentially drive away members of the scientific community. RESULTS: We have developed a set of tools that enables the scientific community to benefit from workflow interoperability. We developed a platform-free structured representation of parameters, inputs, outputs of command-line tools in so-called Common Tool Descriptor documents. We have also overcome the shortcomings and combined the features of two royalty-free workflow engines with a substantial user community: the Konstanz Information Miner, an engine which we see as a formidable workflow editor, and the Grid and User Support Environment, a web-based framework able to interact with several high-performance computing resources. We have thus created a free and highly accessible way to design workflows on a desktop computer and execute them on high-performance computing resources. CONCLUSIONS: Our work will not only reduce time spent on designing scientific workflows, but also make executing workflows on remote high-performance computing resources more accessible to technically inexperienced users. We strongly believe that our efforts not only decrease the turnaround time to obtain scientific results but also have a positive impact on reproducibility, thus elevating the quality of obtained scientific results.

The runtime configuration of parallel and distributed applications remains a mysterious art. To tune an application on a particular system, the end-user must choose the number of machines, the number of cores per task, the data partitioning strategy, and so on, all of which result in a combinatorial explosion of choices. While one might try to exhaustively evaluate all choices in search of the optimal, the end user's goal is simply to run the application once with reasonable performance by avoiding terrible configurations. To address this problem, we present a hybrid technique based on regression models for tuning data intensive bioinformatics applications: the sequential computational kernel is characterized empirically and then incorporated into an ab initio model of the distributed system. We demonstrate this technique on the commonly-used applications BWA, Bowtie2, and BLASR and validate the accuracy of our proposed models on clouds and clusters.

Distributed data life cycles consist of data sources, data and computing components as well as data sinks and user facing elements. The complexity of the underlying systems is ever rising with the increasing heterogeneity and distribution of components and environments. Researchers would like to focus on their specific research topic without the need to learn these systems in detail. Accessible data life cycles enable scientists to do better science more efficiently and obtain results, which would not have been possible without these advanced technologies. For this objective, abstraction to hide complexity and automation to avoid manual tasks are a necessity. These are embodied in the three conceptual data life cycle challenges, namely data, computing and utilization. Concepts and technologies to manage these challenges are explored and exemplified on the basis of the general data life cycle and MoSGrid (Molecular Simulation Grid) science gateway. In this context, we especially focus on teaching in drug design and quantum chemistry research use cases. Further cases are presented elucidating various challenges in adapting the concepts and technologies to wind energy data analysis and the XSEDE research infrastructure.

Logical workflow management systems provide a user-friendly portal through which data can be processed using a sequence of standard tools. These logical workflows are a natural way to express the high level intent of the user, and to share the structure and the results with other users. However, logical workflows are not necessarily suited to expressing parallelism for very large runs. As the amount of data is scaled up, the run time of each node in the logical workflow may become extreme. We propose a technique of job expansion to solve this problem. When job expansion is applied to a logical workflow, each node in the workflow is itself expanded into a large performance workflow that may consist of hundreds to thousands of tasks that can be executed in parallel, thus enabling high concurrency and scalability. From the user's perspective, nothing has changed and the logical workflow remains in its original form. To demonstrate this technique, we have applied job expansion to a selection of bioinformatics applications running in the Galaxy workflow management system. Each job in the workflow is expanded into a highly parallel workflow executed using Makeflow, which is well suited to express high levels of parallelism. Work Queue is then utilized for execution because of its ability to quickly dispatch tasks and cache files for later reuse. After applying job expansion, we improve the execution time of BWA 18X and GATK 402X, with a total speedup of 61.5X on the workflow. We also take a look at the systems behavior since its launch to analyze its effectiveness.

The Grid and Cloud User Support Environment (gUSE) enables users convenient and easy access to grid and cloud infrastructures by providing a general purpose, workflow-oriented graphical user interface to create and run workflows on various Distributed Computing Infrastructures (DCIs). Its arrangements for creating and modifying existing workflows are, however, non-intuitive and cumbersome due to the technologies and architecture employed by gUSE. In this paper, we outline the first integrated web-based workflow editor for gUSE with the aim of improving the user experience for those with industrial data workflows and the wider gUSE community. We report initial assessments of the editor's utility based on users' feedback. We argue that combining access to diverse scalable resources with improved workflow creation tools is important for all big data applications and research infrastructures.

Science gateways are a solution for user communities to access applications and data via a graphical user interface. These graphical user interfaces hide the underlying infrastructure, as far as feasible and as far as desired by the users. In general, science gateways offer a single point of entry to create and/or analyze domain-specific data. Their core goal is to increase the usability and accessibility of computational tools and digitized data as well as to leverage reproducibility of scientific processes. While the user interfaces are especially tailored to the specific demands of a user community, the underlying infrastructures, for example, national or international distributed computing infrastructures (e.g., XSEDE), are mainly applicable for a wide range of use cases. Thus, science gateway frameworks and science gateway APIs, which offer building blocks for the management of jobs and data within such infrastructures, ease the implementation of science gateways for developers. The latter can focus on the domain-specific demands while reusing or extending available building blocks. The contributions to this special issue present the current state-of-the-art research and elucidate trends in the area of science gateways as well as demonstrate available solutions for the users. Submissions are grouped in five general areas: science gateway use and sustainability, generic development frameworks, novel workflow-oriented approaches, data management, and use cases from diverse domains. The statistics illuminate among other topics the increased usage of science gateways, which is also reflected in high number of submissions demonstrating specific use cases. Consequently, sustainability approaches have found their way into the special issue reflected not only in a submission about a model for sustainability but also in numerous submissions on developments and enhancements for generic building blocks of diverse existing mature science gateway frameworks and APIs. While novel approaches for workflow management and data management can be also considered under the enhancements for generic building blocks addressing new technologies such as mobile applications, they have already been core subjects for a couple of years and are presented in own sections emphasizing their importance for the science gateway community. The close collaboration between user communities and developers as well as providers is crucial for developing and offering effective science gateways widely used by science communities. Insights about the demands of user communities and about possibilities to support science gateway developers can immensely improve both the efficiency of creating science gateways and their long-term sustainability. Lawrence et al. 1 present results from a large-scale user survey of nearly 5000 researchers from diverse research domains and computer science departments involved in science gateway provisioning, who answered a questionnaire about demands on and existing courses for developing science gateways. Major topics include the support of user communities, aiding developers in choosing a suitable science gateway technology and involving specific expertise. The paper also notes that for the first time in the National Science Foundation (NSF's) supercomputing program, more users have accessed resources via gateways than by using the command line. The manuscript ‘Reflections on Science Gateways Sustainability Through the Business Model Canvas: Case Study of a Neuroscience Gateway’ 2 goes into detail on sustainability approaches for science gateways applying a methodology from lean business development, the Business Model Canvas. The authors adopted the model for the Amsterdam Medical Center Computational Neuroscience Gateway and suggest using the model as draft for science gateways in general for structuring various factors, which have to be considered not only by industry but also by providers of science gateways in academia. In the last 10 years, quite a few mature and reliable science gateway frameworks and APIs have evolved, which aid developers with building blocks for generic tasks such as authentication as well as job, data, and workflow management. Thus, these tasks can be efficiently implemented in diverse science gateways without the need to develop them for each science gateway from scratch. Marru et al. 3 present the science gateway API Apache Airavata and its roadmap. The open-source API offers rich features from connectors for multiple infrastructures (clusters, cloud, and grids) through workflow support, as well as data management capabilities to multi-language support. The use of Apache Airavata as middleware is described in ‘The GenApp Framework Integrated with Airavata for Managed Compute Resource Submissions’ 4. The GenApp Framework is designed for creating flexible user interfaces in general and for science gateways while considering aspects like re-submission. Thus, via the integration with Apache Airavata, the authors deliver a full science gateway framework for all layers of a science gateway—frontend, middleware, and connectors to diverse infrastructures. Cholia et al. 5 aspire to ease the development of science gateways by targeting the backend of science gateways. They demonstrate the Nice and Easy Web Toolkit (NEWT) platform, which consists of standards-based RESTful services for the application of HPC infrastructures. Once the services are integrated within High-Performance Computing (HPC) infrastructures, they allow for exploiting the resources via a common web API. NEWT has been applied at the National Energy Research Scientific Computing Center since 2010. A similar approach is followed by Caballer et al. 6. They have developed services for scientific virtual infrastructures on the cloud provisioned as Infrastructure-as-a-Service. The services allow for flexible allocation of resources with features for sharing them with other users, deploying and undeploying them and adding or removing resources dynamically. The manuscript goes into detail for three successful use cases. ‘Enabling Cloud Bursting for Life Sciences within Galaxy’ 7 targets also the provision of cloud services. The authors describe the ongoing efforts in creating a ubiquitous platform capable of simultaneously utilizing dedicated as well as on-demand cloud resources. While Galaxy is widely used especially by the life sciences community, the developed technologies are applicable for diverse research domains. The use of Galaxy as generic employable science gateway framework is also tackled in 8. The authors describe a domain-independent, cloud-based science gateway platform, the Globus Galaxies platform, which provides a set of hosted services that directly address the needs of science gateway developers and deliver a science gateway as service. Science gateways are often tailor made to provide a harness for and interface to advanced workflow tools. Two submissions presented novel developments integrated within workflow solutions. In ‘Mobile Application Development Exploiting Science Gateway Technologies’ 9, the authors present a mobile application connected to a workflow-enabled framework. Mobile devices are, of course, increasingly common and can be invaluable in some domains, for example, those requiring extensive fieldwork. This paper describes how the capabilities of mobile devices can be extended by using distributed computing infrastructures for visualization and analysis of large astrophysics datasets and also highlights areas where web-based gateways must be adapted to be mobile friendly. The interfaces will be further adapted as usage by astrophysicists increases. ‘WorkWays: Interacting with scientific workflows’ 10 also discusses how human endeavors can be assisted by workflows. In this example, human interactions with the workflow happen through a dynamic IO model where users can insert data into or export data out of a continuously running workflow dynamically. Previous Workways papers have demonstrated interactivity in the analysis of Magnetic Resonance Imaging (MRI) images and in aerospace design optimization 10. This submission includes the incorporation of Paraview Web for visualization, which is used in the analysis of the fluid flow through a ‘micromixer’. Here, the user can actually steer the computation by selecting a parameter space and having the optimization workflow focus on only the selected region. Data management is an increasingly time-consuming task for scientists and can be fraught with error. Gateways again can provide a natural interface to managing data effectively. Our first submission, ‘Remote Storage Management in Science Gateways via Data Bridging’ 11 actually connects challenges in data management with the challenges of workflows and distributed computing infrastructures described in the preceding section. A data bridging service called Data Avenue has been integrated into the WS-PGRADE/gUSE portal framework to provide a common interface to the myriad storage resources, thus streamlining the use of workflows that use many different underlying infrastructures. While not strictly a data management application, Araport 12 is an open-source, online community resource for discovery of both data and applications that support the study of the Arabidopsis thaliana genome through an app store-like approach. Users can both choose tools from Araport and contribute their own. User registrations have doubled because the launch of the Science Apps Workspace with over 30 registered app developers, so with all of this parallel effort there, will be tremendous leverage to the original investment. Five submissions addressed science gateway use cases. For the editors, the practical use of science gateways in a variety of fields is often the highlight of these workshops. ‘FACE-IT: A Science Gateway for Food Security Research’ 13 develops a framework for crop and climate impact assessments. Data are ingested from diverse geospatial archives and often require regridding and additional processing. Large-scale climate simulations, including agricultural models, are then conducted and comparisons made between regional and global models. Workflows are executed through the Globus Galaxies platform, and outputs are captured in well-defined, reusable and comparable formats. FACE-IT will be used to achieve the goals by the Agricultural Model Intercomparison and Improvement Project at the Center for Robust Decision-making on Climate and Energy Policy. Hu et al. also address agricultural issues in their submission, ‘CyberGIS-BioScope: A Cyberinfrastructure-based Spatial Decision-Making Environment for Biomass-to-Biofuel Supply Chain Optimization’ 14, although here, we are looking at agricultural production in support of biofuels. Here, although, the entire supply chain must be considered. This requires collaborative data integration, model specification, analysis, and coordinated implementation and management. As in the FACE-IT paper, preprocessing of the data is crucial for interoperability of data sources. Here, this is between bioenergy models and geographic information systems. Next, interactive scenarios are developed for evaluation and sharing, and as a final step, the optimization problem can be solved. CyberGIS-BioScope is the resulting product that accomplishes these tasks. The result is an interface that is tailored to both agricultural scientists and decision makers. In a completely different field, the IMP Science Gateway 15 is used to further virtual experimental labs and their use in multiscale courses in e-learning. IMP uses WS-PGRADE and gUSE technologies for molecular dynamics simulations of nanostructures. Workflow components are used as Lego-style construction units for learning modules of various duration and complexity. These learning modules can be used in a variety of settings—such as lifelong learning and vocational training. The final two use case submissions come from the medical field. ‘Building a Medical Research Cloud in the EASI-CLOUDS Project’ 16 describes a European research project that supports a group at Charite University Hospital with a high demand for computation to analyze MRI images. In order to meet the needs of this customer, the gateway had to integrate, monitor, and manage services all within a defined service level agreement (SLA). More on how this influenced design and their experiences meeting the SLAs. Science gateways are also used in the medical field in the area of computer-aided drug design in a process called virtual drug screening. Large amounts of often difficult to manage high-throughput computation are needed in this process. The Docking gateway 17 was developed to allow biochemists to easily conduct these screenings. In a nice development, the authors were able to reuse generic layers developed for a neuroimaging gateway. Data and computation management as well as operational support processes could all be reused. This really should become one of the hallmarks of science gateways. The submission highlights the user-centered design process, a contribution that should be of interest to many developers, and also includes a performance assessment of three different analysis approaches—a gLite grid, Hadoop (running on the Dutch Hadoop cluster), and a local cluster. The editors are once again pleased with the quality and variety of submissions to IWSG14 and GCE14 and the interest in and effort taken to submit extended papers for this special issue. We are grateful to both authors and reviewers for their invaluable help in making this possible.

Summary Quantum chemical workflows can be built up within the science gateway Molecular Simulation Grid. Complex workflows required by the end users are dissected into smaller workflows that can be combined freely to larger meta‐workflows. General quantum chemical workflows are described here as well as the real use case of a spectroscopic analysis resulting in an end‐user desired meta‐workflow. All workflow features are implemented via Web Services Parallel Grid Runtime and Developer Environment and submitted to UNICORE. The workflows are stored in the Molecular Simulation Grid repository and ported to the SHIWA repository. Copyright © 2014 John Wiley & Sons, Ltd.

This special issue represents an active collaboration between the organizers of two science gateway workshops.The International Workshop on Science Gateways 2013 has taken place in June 2013 in Zurich and the Science Gateway Institute Workshop 2013, held in conjunction with IEEE Cluster in September 2013 in Indianapolis.The workshops attracted together over 100 international researchers and have led to excellent presentations and publications on science gateway developments, workflow-centered enhancements, science gateway infrastructures, and developments in specific research domains such as the life sciences and health applications.Authors of accepted submissions to the workshops have been invited to submit extended versions for a special issue.This special issue consists of the accepted papers of a further peer review process.The increasing complexity of scientific study and the increasingly digital nature of data have resulted in a myriad of community-developed solutions.Advanced Web portals, also called science gateways, have emerged in many domains.They assemble the computational resources, data collections, visualization capabilities, collaboration tools, and even access to instruments that scientists need to conduct their research.Development of these gateways is also increasingly complex, and developers often find it quite valuable to learn from one another, even across domains.This special issue highlights accepted papers from two workshops.The Fifth annual International Workshop on Science Gateways, held June 2013 in Zurich and the Science Gateway Institute workshop, held in conjunction with IEEE Cluster held September 2013 in Indianapolis.Both workshops incorporated a peer review process.Authors of top papers from both events were invited to submit extended versions of their work for publication in this special issue.The purpose of these workshops is to provide a forum to showcase science gateway projects and related technologies.Developers can learn from one another and learn about new technologies, and principal investigators can keep abreast on the state of the field.This special issue features contributions in the areas of technologies for building gateways, workflows to enhance the capabilities of gateways, and infrastructures that support science gateways.Also featured are ready-to-use gateways in the life sciences and health applications fields.Providers of science gateway technologies aim at offering generic frameworks to ease the development of science gateways for a specific research domain while providers of distributed computing infrastructures work on supporting communities with computing and data resources.Such infrastructures require policies on usage and security, and the close collaboration with developers of domain-specific science gateways elucidates the demands in the specific research domain.Users of such a domain want to focus on their research questions and create and analyze data in an intuitive and efficient way-regardless of whether the underlying infrastructure provides resources in cloud, grid, or cluster infrastructures. TECHNOLOGIES FOR SCIENCE GATEWAY DEVELOPMENTThe development of science gateways can be distinguished in two main tasks.Firstly, the generic part, which is concerned with security features, accesses to underlying infrastructures and job, workflow, and data management.This part can be very similar for diverse science gateways.Frameworks or APIs support developers with building blocks so that there is not the need to develop such features

Scientific data life cycles are becoming more and more demanding. Data amounts are seen to be ever increasing while at the same time advanced IT infrastructures become more common resulting in a much broader user group demanding a high usability. These challenges are met with a distributed science gateway infrastructure, which in turn is complex in nature. In this situation a main part is missing, a generic metadata management concept. The approach and architecture of such a concept, the current status, an evaluation, and an outlook will be described in this publication. The main goals are to enable scientists to easily manage and use large amounts of data and provide a generic way to enable the fast transfers to a multitude of scientific use cases.

Workflow Management Systems, such as Galaxy and Taverna, provide a portal through which data can be processed using a sequence of different tools. This sequence allows for the creation of a logical workflow that describes the process. However, when the data workload becomes large enough the time spent in each logical step increases making it difficult to run the workflow fast and efficiently. The proposed solutions is to use task level expansion. Task expansion aims to take each step of the logical workflow and expand it into a new self-contained workflow. These workflows would allow for greater scalability and concurrency by creating more tasks. The resulting workflows will be used indistinguishably from the original tool, but perform more quickly and efficiently. The concept was applied to the BWA tool in Galaxy and we were able to see a 7.36 times speedup in runtime on our 32 GB dataset.

Big Data applications in science are producing huge amounts of data, which require advanced processing, handling, and analysis capabilities. For the organization of large scale data sets it is essential to annotate these with metadata, index them, and make them easily findable. In this paper we investigate two scientific use cases from biology and photon science, which entail complex situations in regard to data volume, data rates and analysis requirements. The LSDMA project provides an ideal context for this research, combining both innovative R&D on the processing, handling, and analysis level and a wide range of research communities in need of scalable solutions. To facilitate the advancement of data life cycles we present preferred metadata management strategies. In biology the Open Microscopy Environment (OME) and in photon science NeXus/ICAT are presented. We show that these are well suited for the respective data life cycles. To facilitate searching across communities we discuss solutions involving the Open Archive Initiative - Protocol for Metadata Harvesting (OAI-PMH) and Apache Lucene/Solr.

SUMMARY State‐of‐the‐art research in a variety of natural sciences depends heavily on methods of computational chemistry, for example, the calculation of the properties of materials, proteins, catalysts, and drugs. Applications providing such methods require a lot of expertise to handle their complexity and the usage of high‐performance computing. The MoSGrid (molecular simulation grid) infrastructure relieves this burden from scientists by providing a science gateway, which eases access to and usage of computational chemistry applications. One of its cornerstones is the molecular simulations markup language (MSML), an extension of the chemical markup language. MSML abstracts all chemical as well as computational aspects of simulations. An application and its results can be described with common semantics. Using such application, independent descriptions users can easily switch between different applications or compare them. This paper introduces MSML, its integration into a science gateway, and its usage for molecular dynamics, quantum chemistry, and protein docking. Copyright © 2013 John Wiley & Sons, Ltd.

Bioinformatics is a discipline that performs analyses, modeling, and simulations of complex biological systems by using a computer science approach, which typically means dealing with huge amounts of data. Although the most powerful supercomputers in the world are heavily involved in computational biology research, scalability, portability, integration, and usability of bioinformatics software still represent open issues. Currently, the possibility of parallelizing algorithms and analysis techniques exploiting various high-performance computing (HPC) techniques and platforms is receiving an even increasing interest. Examples include the porting of legacy applications to clusters, such as those for genome analysis, and the use of distributed technologies like grid and cloud computing for large, embarrassingly parallel computations. Also, performance acceleration using on-chip supercomputing, such as graphic processing units (GPUs) and massively parallel architectures for the processing of large data sets, is becoming largely exploited. This trend is motivated by the lightening improvement of novel molecular biology high-throughput technologies, such as next generation sequencing, which allow the analysis of inter personal variations in genomics and transcriptomics, but also to the development of mass spectrometry techniques for proteomics and metabolomics profiles. This clearly calls for novel solutions in the field of HPC. Also in the field of structural biology, in silico molecular dynamic simulations, ligand screening projects for neglected and complex diseases, and drug discovery campaigns are examples of the great advantages that HPC can provide to medicine and healthcare. Many are the projects aiming at developing technologies in the field of high-performance computational biology worldwide. Examples are the EU-funded FP7 projects Venus-C 1 and scientific gateway based user interface (SCI-BUS) 2, and the project Extreme Science and Engineering Discovery Environment (XSEDE) 3 funded by the US National Science Foundation. These projects aim to improve the quality of HPC services for diverse research areas and support a large number of applications and projects in computational biology, for example, Biodrugscore 4 in XSEDE and the Swiss Proteomics Gateway 5 in SCI-BUS. Furthermore, the FP7 projects BioHPC 6 and Elixir 7 are specifically dedicated to the life sciences. Whereas BioHPC offers a suite of applications in a science gateway, Elixir functions as data hub for life sciences organizations. As regards for the most important national initiatives, the Flagship project Interomics 8, Laboratory for Interdisciplinary Technologies in Bioinformatics (LITBIO) 9, and Italian Bioinformatics Network (Italbionet)10 in Italy and IdeeB 11, Grid Support for Bioinformatics 12 and Renabi 13 in France are concerned with the development of bioinformatics HPC infrastructures. Also, in the UK, there are very important initiatives in this sense, such as MyGrid 14, which is very active in the development of tools for e-Science. In Germany, the D-Grid project MediGRID 15 was focused especially on grid services for biomedical research. Its work is continued via Technologie und Methodenplattform für die vernetzte medizinische Forschung e.V. 16, which is the umbrella organization for networked medical research in Germany. The D-Grid project MoSGrid 17 offers a complete solution for the molecular simulation community supporting HPC infrastructures via a web-based science gateway. It is being further developed via SCI-BUS. WeNMR 18 follows a similar approach like MoSGrid and it supplies a worldwide e-Infrastructure for NMR and structural biology. All these projects have prompted the diffusion of parallel and distributed solutions for bioinformatics, which also promoted the discussion of these topics in a number of conferences. In particular, this special issue of Concurrency and Computation: Practice and Experience is partially a follow-up to some special sessions/workshops held in the context of international conferences in the field of HPC, such as the special session ‘Grid, Parallel and Distributed Bioinformatics Applications’ of the ‘Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)’ 19. European Grid Infrastructure 20 has organized each year two forums as successors of the Enabling Grids for e-Science in Europe conferences 21 for the HPC community with dedicated sessions to the life sciences since 2009. Also, smaller events are launched very successfully in this field like the workshop series International Workshop on Science Gateways for Life Sciences (IWSG) 22 extended to IWSG for a wider community or the Black Forest Grid Workshop23. Accelerating applications using specific paradigms of computations, in particular for scientific computations, is a long-standing task and usually involves the use of custom-designed solutions. Traditionally, parallel computing has been employed for addressing bioinformatics problems that would otherwise be impossible to solve. A first memorable example has been the computational assembly of the human genome as post analysis of the whole genome shotgun sequencing proposed by Craig Venter, which challenged the long-standing Human Genome Project, arriving very close to an unpredictable victory 24. The point is that using parallel computing implies rethinking the whole application to exploit multiple processors, shared or distributed memory resources, and the network itself. The role of software architect is therefore of primary importance to maximize performance beside, and of course, the possibility of accessing a large computational facility 25. In this context, the cost of buying and maintaining an in-house cluster is very important, and this explains why the grid computing paradigm gained a great success in the mid-1990s 26. The term grid was used in analogy to the electric power grid to indicate the main goal to make the access to computing power as easy as the access to electricity. Ian Foster extended his definition of grid computing by a three-point checklist, which emphasizes that a grid manages distributed resources, uses standard, open, and generic-purpose protocols and interfaces, and carries out nontrivial quality of services 27. Grid middlewares are responsible for all the aspects related to the efficient management of the available computing power: the authentication of users, the submission and monitoring of jobs, and the data movement. Accordingly, the underlying hardware, the involved operating systems, batch systems, and file systems are hidden from the users. Grid was a very innovative paradigm of distributed and collaborative computing, but it often requires users to adapt their code for being able to run in this environment. Ten years later, cloud computing was presented as a more flexible solution 28. Cloud computing overcomes the idea of volunteer computing for resource sharing by proposing an ‘on-demand’ paradigm in which users pay for what they use. Cloud computing providers offer their services according to several fundamental models: infrastructure as a service (IaaS), platform as a service, and software as a service, where IaaS is the most basic model whereas the other provide higher level of abstraction 29. Furthermore, in the last decade, new paradigms have emerged in parallel computing, in particular concerning the exploitation of massively parallel architectures (GPUs) 30 such as CUDA 31 and OpenCL 32. Users can presently exploit up to 10 TFlops on a single workstation equipped with multiple CPUs and accelerators, with a cost of less than $5000 33. This allows also low-budget research groups to achieve very high computational performance for many compute-intensive applications in the field of computational biology 34. It is widely recognized that the biology research field is data driven because of the presence of high-throughput acquisition techniques and the increased level of details of simulated biological complex systems. The consequence is that there is the need to apply the most advanced HPC techniques and platforms to process the data and to turn them into real knowledge. This explains why all the available HPC solutions have been exploited to deal with the different kinds of analyses. Several kinds of analysis algorithms have been developed with very different behaviors: some of them are mainly CPU-intensive, whereas others require to access a large amount of data; some require a high number of communications between the parallel processes for each step of the computation, and some analysis require a stochastic approach where the accuracy of results is proportional to the number of runs. This is the reason why different parallelization paradigms, parallel, and distributed architectures have been considered in order to be able to achieve the highest possible performance figures. Salah and Kenli 35 proposed PAR-3D-BLAST, a parallel tool for protein structure comparison, where a two-level parallelism is exploited to improve the performance. Meier-Kolthoff et al. 36 describe a parallelized method to infer phylogenetic relationship using Genome Blast Distance Phylogeny on a reference set of microbial genome from the GEBA project. In particular, they developed an infrastructure in order to save computational time and data storage able to scale up the generation of phylogenetic tree. Even if the low-level details of the grid infrastructures are hidden via middlewares, often the application of bioinformatics methods on HPC facilities require specialized knowledge. Thus, researchers in this field currently need to deal especially with usability issues. On the one hand, these issues derive from the applied methods: the usability of many tools is limited and the implemented methods are very complex, reflecting the underlying complex theory. They thus require a lot of experience, also because the lack of user interfaces and of pre-configured settings deter novice users from them. It is clear that users have to become acquainted with the domain-related features, but the presence of a mere command line interface represents a big issue for a wide audience. On the other hand, the heterogeneity of the underlying distributed computing infrastructure augments the complexity, especially for users who do not have an information and communication technology background. A suitable solution to offer easy-to-use and intuitive access to applications are science gateways, which offer specific services tailored to the users' needs. Additionally, the users mostly do not only analyze and process data via single jobs but via workflows in computational biology. Thus, science gateways capable of managing workflows are essential for processing all the necessary steps. Kertesz et al. 37 introduced a case study on generating conformers via unconstrained molecular dynamics parallelizing single steps and creating a reusable workflow in the grid portal WS-PGRADE. The workflow can be performed significantly faster on European grid infrastructures than on single CPU machines. Grunzke et al. 38 addressed a further aspect of usability in workflow-enabled science gateways: metadata management via standards. They present Molecular Simulation Markup Language for the area of molecular simulations of small and large molecules. Molecular Simulation Markup Language allows for workflow-interoperability on data level and is supported in the grid workflows of the MoSGrid portal. Cloud computing is a model in which users access computational resources and storage facilities from a vendor over Internet, that is, the commercial Amazon Elastic Compute Cloud and Simple Storage Server 39. The user can exploit computers and storage for any task, such as serving websites or running computationally intensive parallel bioinformatics pipelines, being an administrator of its services and paying just for the time of effective usage. By instantiating many virtual resources, a parallel cluster can be deployed on demand, where common libraries such as the Message Passing Interface can be exploited. Also, batch-processing systems can be used to manage the different computations in a queue. Moreover, frameworks for distributed access to files such as Hadoop can be adapted to distributed programming paradigms such as MapReduce 40. The flexibility and the cost-effectiveness provided by cloud computing is extremely appealing for computational biology, in particular for small-medium biotechnology laboratories which need to perform bioinformatics analysis without coping with all the issues of having an in-house information and communication technology infrastructure 41. An intermediate solution is represented by Hybrid Clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones 42. Kiss et al. 43 investigated and analyzed how Windows Azure cloud can be applied for a virtual screening experiment relying on docking simulations, building a framework that exploits the generic worker concept. Kavasidis et al. 44 presented a bioinformatics knowledge discovery tool, BioWizard-C, for extracting and validating implicit associations between biological entities. The aim of the work is to demonstrate how porting a data-intensive application to the Cloud, affects positively its efficiency. Guerrero et al. 45 stated the importance of GPUs in Cloud Computing environments but also highlighted that the efficiency of such infrastructure, with respect to the use of local resources, should be evaluated on the size of the studied problem. In 2007, NVIDIA released the first version of the CUDA 31 programming tools, which enormously facilitated the exploitation of GPU hardware. Afterwards, other possibilities were presented (i.e. OpenCL 32 and OpenACC 46) aiming to provide users a simplified interface to the great computing capabilities offered by present graphics cards. In the field of bioinformatics, a huge number of applications has been ported to CUDA, and depending on the algorithm, the performance can be very competitive when compared with other platforms 47. In general terms, compute-intensive algorithms with simple mathematical operations are the ones that benefit the most from these computational architectures. NVIDIA cards can be installed in workstations quite easily, but they can be also exploited as part of distributed infrastructures such as grid and cloud platforms. Exploiting simple procedures, such as the Peripheral Component Interconnect Passthrough approach, NVIDIA cards can be mounted on virtual machines to provide the user more complete and customizable solutions, whose costs can be very competitive with respect to in-house implementations. Chessa and Pasquale 48 show in another problem of great relevance for biomedicine, how visual coding in the primary visual cortex can be modeled thanks to a well-designed parallel implementation, conveniently tuned to the GPU architecture. D'Agostino et al. 49 report dramatic speedup improvements obtained in local GPU machines for calculation of the molecular surface, problem of great relevance, which appears in many contexts of molecular sciences. Guerrero et al. 50 demonstrate how computational kernels for virtual screening applications can be ported on GPU architectures achieving both interesting energy efficiency and performance figures. We would like to thank the authors for contributing papers on their research on latest advances in Distributed, Parallel, and GPU-accelerated Approaches to Computational Biology for this special issue and all the reviewers for providing constructive reviews and in helping to shape this special issue. Finally, we would like to thank Prof. Geoffrey Fox for providing us an opportunity to bring this special issue to the research community.

The Science Gateway MoSGrid (Molecular Simulation Grid) allows to build up quantum chemical metaworkflows. End users mostly request complex workflows which are dissected into smaller workflows: Those can be combined freely to larger metaworkflows. Herein, we describe important general quantum chemical workflows which serve as toolbox for the real use case of a spectroscopic analysis as example for an end user desired metaworkflow. All workflow features are implemented via WS-PGRADE and submitted to UNICORE. The workflows are stored in the MoSGrid repository and ported to the SHIWA repository.

Abstract The topic ‘Portals for life sciences’ includes various research fields, on the one hand many different topics out of life sciences, e.g. mass spectrometry, on the other hand portal technologies and different aspects of computer science, such as usability of user interfaces and security of systems. The main aspect about portals is to simplify the user's interaction with computational resources that are concerted to a supported application domain. Copyright © 2010 John Wiley & Sons, Ltd.

Proteomics, the study of all the proteins contained in a particular sample, e.g., a cell, is a key technology in current biomedical research. The complexity and volume of proteomics data sets produced by mass spectrometric methods clearly suggests the use of grid-based high-performance computing for analysis. TOPP and OpenMS are open-source packages for proteomics data analysis, however, they do not provide support for Grid computing. In this work we present a portal interface for high-throughput data analysis with TOPP. The portal is based on Rapid, a tool for efficiently generating standardized port lets for a wide range of applications. The web-based interface allows the creation and editing of user-defined pipelines and their execution and monitoring on a Grid infrastructure. The portal also supports several file transfer protocols for data staging. It thus provides a simple and complete solution to high-throughput proteomics data analysis for inexperienced users through a convenient portal interface.