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1. Tapis-CHORDS Integration: Time-Series Data Support in Science Gateway Infrastructure

   Cleveland, SB; Jamthey, A; Padhyy, S; Powelly, J; Stubbs, J; Daniels, MD; Pierce, SA; Jacobs, GA (October 2019, Conference: Science Gateways 2019At: San Diego, CA)

   The explosion of IoT devices and sensors in recent years has led to a demand for efficiently storing, processing and analyzing time-series data. Geoscience researchers use time-series data stores such as Hydroserver, VOEIS and CHORDS. Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. Tapis's (formerly known as Agave) platform as a service provides a way to support researchers in a way that they are not responsible for the infrastructure and can focus on the science. The University of Hawaii (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop a new API integration that combines Tapis with the CHORDS time series data service to support projects at both institutions for storing, annotating and querying time-series data. This new Streams API leverages the strengths of both the Tapis platform and CHORDS service to enable capabilities for supporting time-series data streams not available in either tool alone. These new capabilities may be leveraged by Tapis powered science gateways with needs for handling spatially indexed time-series data-sets for their researchers as they have been at UH and TACC. 

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2. Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway Application

   S. Sivarathri, S.; Calyam, P.; Zhang, Y.; Pandey, A.; Chen, C.; Xu, D.; Joshi, T.; Nair, S.S. (January 2019, 14th Gateway Computing Environments Conference)

   Neuroscientists are increasingly relying on high performance/throughput computing resources for experimentation on voluminous data, analysis and visualization at multiple neural levels. Though current science gateways provide access to computing resources, datasets and tools specific to the disciplines, neuroscientists require guided knowledge discovery at various levels to accomplish their research/education tasks. The guidance can help them to navigate them through relevant publications, tools, topic associations and cloud platform options as they accomplish important research and education activities. To address this need and to spur research productivity and rapid learning platform development, we present “OnTimeRecommend”, a novel recommender system that comprises of several integrated recommender modules through RESTful web services. We detail a neuroscience use case in a CyNeuro science gateway, and show how the OnTimeRecommend design can enable novice/expert user interfaces, as well as template-driven control of heterogeneous cloud resources. 

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3. Enabling rich data sharing for Science Gateways via the SeedMeLab platform

   https://doi.org/10.17605/OSF.IO/WV3BF  

   Chourasia, Amit; Nadeau, David; Luo, Jiaping; Chen, Tony; Miller, Mark; Brookes, Emre H (September 2019, Proceedings of Gateways 2019)

   Abstract Science Gateways provide an easily accessible and powerful computing environment for researchers. These are built around a set of software tools that are frequently and heavily used by large number of researchers in specific domains. Science Gateways have been catering to a growing need of researchers for easy to use computational tools, however their usage model is typically single user-centric. As scientific research becomes ever more team oriented, the need driven by user-demand to support integrated collaborative capabilities in Science Gateways is natural progression. Ability to share data/results with others in an integrated manner is an important and frequently requested capability. In this article we will describe and discuss our work to provide a rich environment for data organization and data sharing by integrating the SeedMeLab (formerly SeedMe2) platform with two Science Gateways: CIPRES and GenApp. With this integration we also demonstrate SeedMeLab’s extensible features and how Science Gateways may incorporate and realize FAIR data principles in practice and transform into community data hubs. 

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4. Collections Management and High-Throughput Digitization using Distributed Cyberinfrastructure Resources

   https://doi.org/10.3897/biss.2.25643  

   Motz, Gary; Zimmerman, Alexander; Cook, Kimberly; Bancroft, Alyssa (July 2018, Biodiversity Information Science and Standards)

   Collections digitization relies increasingly upon computational and data management resources that occasionally exceed the capacity of natural history collections and their managers and curators. Digitization of many tens of thousands of micropaleontological specimen slides, as evidenced by the effort presented here by the Indiana University Paleontology Collection, has been a concerted effort in adherence to the recommended practices of multifaceted aspects of collections management for both physical and digital collections resources. This presentation highlights the contributions of distributed cyberinfrastructure from the National Science Foundation-supported Extreme Science and Engineering Discovery Environment (XSEDE) for web-hosting of collections management system resources and distributed processing of millions of digital images and metadata records of specimens from our collections. The Indiana University Center for Biological Research Collections is currently hosting its instance of the Specify collections management system (CMS) on a virtual server hosted on Jetstream, the cloud service for on-demand computational resources as provisioned by XSEDE. This web-service allows the CMS to be flexibly hosted on the cloud with additional services that can be provisioned on an as-needed basis for generating and integrating digitized collections objects in both web-friendly and digital preservation contexts. On-demand computing resources can be used for the manipulation of digital images for automated file I/O, scripted renaming of files for adherence to file naming conventions, derivative generation, and backup to our local tape archive for digital disaster preparedness and long-term storage. Here, we will present our strategies for facilitating reproducible workflows for general collections digitization of the IUPC nomenclatorial types and figured specimens in addition to the gigapixel resolution photographs of our large collection of microfossils using our GIGAmacro system (e.g., this slide of conodonts). We aim to demonstrate the flexibility and nimbleness of cloud computing resources for replicating this, and other, workflows to enhance the findability, accessibility, interoperability, and reproducibility of the data and metadata contained within our collections. 

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5. Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway

   https://doi.org/10.1002/cpe.6080  

   Vekaria, K.; Calyam, P.; Sivarathri, S.; Wang, S.; Zhang, Y.; Pandey, A.; Chen, C.; Xu, D.; Joshi, T.; Nair, S.S. (January 2020, Wiley Concurrency and Computation: Practice and Experience)

   Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity,we present a novel “OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). 

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