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The future for science gateways warrants exploration as we consider the possibilities that extend well beyond ’science’ and high-performance computing into new interfaces, applications and user communities. In this paper, we look retrospectively at the successes of representative gateways thus far. This serves to highlight existing gaps gateways need to overcome in areas such as accessibility, usability and interoperability, and in the need for broader outreach by drawing insights from technology adoption research. We explore two particularly promising opportunities for gateways - computational social sciences and virtual reality – and make the case for the gateway community to be more intentional in engaging with users to encourage adoption and implementation, especially in the area of educational usage. We conclude with a call for focused attention on legal hurdles in order to realize the full future potential of science gateways. This paper serves as a roadmap for a vision of science gateways in the next ten years.

We present an open source extensible web framework for the analysis of different farm practices and programs and easy dissemination of their results to the users. Currently, this framework is being applied to two use cases --- a web-based decision support system for cover crop management and a web-based farm program analysis tool to assist farmers, academics, and policymakers to understand programs and policies surrounding the Farm Bill. Through the first use case, we address the problem of bridging the gap between the scientific research that happens in labs and experimental plots and the day to day practices and decisions taken by the farmers in the fields. Specifically, this use case focuses on the practice of cover crops, their management, and the impact on reducing nutrient runoff into water bodies. Through the second use case, we address the problem of predicting the expected payment amounts and measured risk or probability of payment for different government insurance programs authorized by the 2018 Farm Bill, namely the Agriculture Risk Coverage (ARC) and Price Loss Coverage (PLC). This helps the farmers compare these programs based on forecasted crop yields and prices. In this paper, we describe the overall architecture of the framework and its major components, the use cases that are currently benefiting from using this framework and share screenshots of the web applications developed using this framework for those use cases. We also share our plans for future work and conclusions about applying this framework to the two use cases.

Brown Dog is a data transformation service for auto-curation of long-tail data. In this digital age, we have more data available for analysis than ever and this trend will only increase. According to most estimates, 70--80% of this data is unstructured, and together with unsupported data formats and inaccessible software tools, in essence, this data is not either easily accessible or usable to its owners in a meaningful way. Brown Dog aims at making this data more accessible and usable by auto-curation and indexing, leveraging existing and novel data transformation tools. In this paper, we discuss the recent major component improvements to Brown Dog including transformation tools called extractors and converters; desktop, web and terminal-based clients which perform data transformations; libraries written in multiple programming languages which integrate with existing software and extend their data curation capabilities; an online tool store for users to contribute, manage and share data transformation tools and receive credit for developing them; cyberinfrastructure for deploying the system on diverse computing platforms leveraging scalability via Docker swarm; workflow management service for creatively integrating existing transformations to generate custom, reproducible workflows which meet research needs, and its data management capabilities. This paper also discusses data transformation tools developed to support some scientific and allied use cases, thereby benefiting researchers in diverse domains. Finally, we briefly discuss our future directions with regard to production deployments as well as how users can access Brown Dog to manage their un-curated unstructured data.