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1. Interactive Demonstrations and Hands-On Use of thenet.science Cyberinfrastructure for Network Science Chairs’ Welcome and Tutorial Summary

   https://doi.org/10.1145/3462741.3466679  

   Barrow, Golda; Kuhlman, Chris J.; Machi, Dustin; Ravi, S. S. (June 2021, 13th ACM Web Science Conference 2021)

   null (Ed.)

   Networks are readily identifiable in many aspects of society: cellular telephone networks and social networks are two common examples. Networks are studied within many academic disciplines. Consequently, a large body of (open-source) software is being produced to perform computations on networks. A cyberinfrastructure for network science, called net.science, is being built to provide a computational platform and resource for both producers and consumers of networks and software tools. This tutorial is a hands-on demonstration of some of net.science’s features. 

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2. Science DMZ Networks: How Different Are They Really?

   Mutter, E; Shannigrahi, S (October 2024, 2024 IEEE 50th Conference on Local Computer Networks (LCN))

   The Science Demilitarized Zone (Science DMZ) is a network environment optimized for scientific applications. The Science DMZ model provides a reference set of network design patterns, tuned hosts and protocol stacks dedicated to large data transfers and streamlined security postures that significantly improve data transfer performance, accelerating scientific collaboration and discovery. Over the past decade, many universities and organizations have adopted this model for their research computing. Despite becoming increasingly popular, there is a lack of quantitative studies comparing such a specialized network to conventional production networks regarding network characteristics and data transfer performance. But does a Science DMZ exhibit significantly different behavior than a general-purpose campus network? Does it improve application performance compared a to general-purpose network? Through a two-year-long quantitative network measurement study, we find that a Science DMZ exhibits lower latency, higher throughput, and lower jitter behaviors. We also see several non-intuitive results. For example, a DMZ may take a longer route to external destinations and experience higher latency than the campus network. While the DMZ model benefits researchers, the benefits are not automatic, careful network tuning based on specific use cases is required to realize the full potential of Science DMZs. 

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3. 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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4. 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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5. Enabling Data Streaming-based Science Gateways through Federated Cyberinfrastructure

   Rodero, I; Qin, Y; Valls, J; Simonet, A; Villalobos, J.J.; Parashar, M; Youn, C; Wang, C; Thareja, K; Ruth, P; et al (January 2019, Gateways 2019)

   Large scientific facilities are unique and complex infrastructures that have become fundamental instruments for enabling high quality, world-leading research to tackle scientific problems at unprecedented scales. Cyberinfrastructure (CI) is an essential component of these facilities, providing the user community with access to data, data products, and services with the potential to transform data into knowledge. However, the timely evolution of the CI available at large facilities is challenging and can result in science communities requirements not being fully satisfied. Furthermore, integrating CI across multiple facilities as part of a scientific workflow is hard, resulting in data silos. In this paper, we explore how science gateways can provide improved user experiences and services that may not be offered at large facility datacenters. Using a science gateway supported by the Science Gateway Community Institute, which provides subscription-based delivery of streamed data and data products from the NSF Ocean Observatories Initiative (OOI), we propose a system that enables streaming-based capabilities and workflows using data from large facilities, such as the OOI, in a scalable manner. We leverage data infrastructure building blocks, such as the Virtual Data Collaboratory, which provides data and comput- ing capabilities in the continuum to efficiently and collaboratively integrate multiple data-centric CIs, build data-driven workflows, and connect large facilities data sources with NSF-funded CI, such as XSEDE. We also introduce architectural solutions for running these workflows using dynamically provisioned federated CI. 

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