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.

Summary GenApp generates applications on an extensible set of target languages for scientific modules. GenApp utilizes JavaScript object notation (JSON) format for all definition files. To create an application, definition files are created for global directives, menu, and modules. Target languages have definition files detailing the steps‐mapping code fragments to output. Modules must be wrapped to accept and produce JSON as defined in the module's definition file. Execution models are not defined by GenApp; they are included in target language code fragments. Previously, GenApp included target languages of HTML5/PHP, Qt3/C++, and Qt4/C++ with execution models of direct local execution, a web server, or a web server accessible resource. A Google Summer of Code (GSoC) 2014 student demonstrated Airavata‐managed execution in GenApp's current target languages. Subsequently, Airavata's API and GenApp have evolved. Two GSoC‐2015 students updated the previous Airavata integration to support the current API and extend target languages to include Qt5/C++, Qt5/Android, and Java. GenApp was initially developed to wrap modules utilized in the small angles scattering field but is not restricted to this discipline. The GenApp philosophy is to minimize effort of the researcher to deploy modules and insure preservation in an evolving software landscape. Generated applications are in production and used by small angle scattering researchers. Copyright © 2015 John Wiley & Sons, Ltd.

Summary A new framework (GenApp) for rapid generation of scientific applications running on a variety of systems including science gateways has recently been developed. This framework currently builds a GUI and/or web‐based user interface for a variety of target environments on a collection of executable modules. The method for execution of modules has limited framework restrictions: primarily the requirement of wrapping the application to accept input and output formatted in JavaScript Object Notation (JSON). Initial implementation supports direct execution on a user's workstation, a web server, or a compute resource accessible from the web server. After a successful initial workshop utilizing the framework to create a web‐based user interface wrapping a scientific software suite, it was discovered that long‐running jobs would sometimes fail, because of the loss of a Transmission Control Protocol (TCP) connection. This precipitated an improvement to the execution method with the bonus of easily allowing multiple web clients to attach to the running job. To support a diversity of queue managed compute resources, a Google ‘Summer of Code’ project was completed to integrate the Apache Airavata middleware as an additional execution model within the GenApp framework. New features of file management, job management with progress, and message box support are described. Concurrency and Computation: Practice and Experience, 2015.© 2015 Wiley Periodicals, Inc.

Frequently in the physical sciences experimental data are analyzed to determine model parameters using techniques known as parameter estimation. Eliminating the effects of noise from experimental data often involves Tikhonov or Maximum-Entropy regularization. These methods introduce a bias which smoothes the solution. In the problems considered here, the exact answer is sharp, containing a sparse set of parameters. Therefore, it is desirable to find the simplest set of model parameters for the data with an equivalent goodness-of-fit. This paper explains how to bias the solution towards a parsimonious model with a careful application of Genetic Algorithms. A method of representation, initialization and mutation is introduced to efficiently find this model. The results are compared with results from two other methods on simulated data with known content. Our method is shown to be the only one to achieve the desired results. Analysis of Analytical Ultracentrifugation sedimentation velocity experimental data is the primary example application.

We present a novel divide and conquer method for parallelizing a large scale multivariate linear optimization problem, which is commonly solved using a sequential algorithm with the entire parameter space as the input. The optimization solves a large parameter estimation problem where the result is sparse in the parameters. By partitioning the parameters and the associated computations, our technique overcomes memory constraints when used in the context of a single workstation and achieves high processor utilization when large workstation, clusters are used. We implemented this technique in a widely used software package for the analysis of a biophysics problem, which is representative for a large class of problems in the physical sciences. We evaluate the performance of the proposed method on a 512-processor cluster and offer an analytical model for predicting the performance of the algorithm.