@article {qin_toward_2022,
	title = {Toward Democratizing Access to Facilities Data: A Framework for Intelligent Data Discovery and Delivery},
	journal = {Computing in Science \& Engineering},
	volume = {24},
	number = {3},
	year = {2022},
	note = {Conference Name: Computing in Science \& Engineering},
	month = {may},
	pages = {52{\textendash}60},
	abstract = {Data collected by large-scale instruments, observatories, and sensor networks (i.e., science facilities) are key enablers of scientific discoveries in many disciplines. However, ensuring that these data can be accessed, integrated, and analyzed in a democratized and timely manner remains a challenge. In this article, we explore how state-of-the-art techniques for data discovery and access can be adapted to facilitate data and develop a conceptual framework for intelligent data access and discovery.},
	keywords = {Data collection, Data models, Data transfer, Distributed databases, Observatories},
	issn = {1558-366X},
	doi = {10.1109/MCSE.2022.3179408},
	author = {Qin, Yubo and Rodero, Ivan and Parashar, Manish}
}
@article {RN208,
	title = {Leveraging user access patterns and advanced cyberinfrastructure to accelerate data delivery from shared-use scientific observatories},
	journal = {Future Generation Computer Systems},
	year = {2021},
	type = {Journal Article},
	abstract = {With the growing number and increasing availability of shared-use instruments and observatories, observational data is becoming an essential part of application workflows and contributor to scientific discoveries in a range of disciplines. However, the corresponding growth in the number of users accessing these facilities coupled with the expansion in the scale and variety of the data, is making it challenging for these facilities to ensure their data can be accessed, integrated, and analyzed in a timely manner, and is resulting significant demands on their cyberinfrastructure (CI).

In this paper, we present the design of a push-based data delivery framework that leverages emerging in-network capabilities, along with data pre-fetching techniques based on a hybrid data management model. Specifically, we analyze data access traces for two large-scale observatories, Ocean Observatories Initiative (OOI) and Geodetic Facility for the Advancement of Geoscience (GAGE), to identify typical user access patterns and to develop a model that can be used for data pre-fetching. Furthermore, we evaluate our data pre-fetching model and the proposed framework using a simulation of the Virtual Data Collaboratory (VDC) platform that provides in-network data staging and processing capabilities. The results demonstrate that the ability of the framework to significantly improve data delivery performance and reduce network traffic at the observatories{\textquoteright} facilities.},
	keywords = {Cyberinfrastructure, Data pre-fetching, Distributed data sharing, Distributed facilities, Observatories, Virtual Data Collaboratory},
	doi = {10.1016/j.future.2021.03.004},
	url = {https://app.dimensions.ai/details/publication/pub.1136656679},
	author = {Qin, Yubo and Rodero, Ivan and Simonet, Anthony and Meertens, Charles and Reiner, Daniel and Riley, James and Parashar, Manish}
}
@article {RN166,
	title = {The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research},
	journal = {Computing},
	volume = {22},
	number = {3},
	year = {2020},
	pages = {79-92},
	type = {Journal Article},
	abstract = {The Virtual Data Collaboratory is a federated data cyberinfrastructure designed to drive data-intensive, interdisciplinary, and collaborative research that will impact researchers, educators, and entrepreneurs across a broad range of disciplines and domains as well as institutional and geographic boundaries.},
	keywords = {big data, Cloud computing, Collaboration, Computer architecture, Education, Software engineering, Virtual reality},
	doi = {10.1109/mcse.2019.2908850},
	url = {https://app.dimensions.ai/details/publication/pub.1113521625},
	author = {Parashar, Manish and Simonet, Anthony and Rodero, Ivan and Ghahramani, Forough and Agnew, Grace and Jantz, Ron and Honavar, Vasant}
}
@article {RN94,
	title = {Submarine: A subscription-based data streaming framework for integrating large facilities and advanced cyberinfrastructure},
	journal = {Concurrency and Computation Practice and Experience},
	year = {2019},
	pages = {e5256},
	type = {Journal Article},
	abstract = {Large scientific facilities provide researchers with instrumentation, data, and data products that can accelerate scientific discovery. However, increasing data volumes coupled with limited local computational power prevents researchers from taking full advantage of what these facilities can offer. Many researchers looked into using commercial and academic cyberinfrastructure (CI) to process these data. Nevertheless, there remains a disconnect between large facilities and CI that requires researchers to be actively part of the data processing cycle. The increasing complexity of CI and data scale necessitates new data delivery models, those that can autonomously integrate large-scale scientific facilities and CI to deliver real-time data and insights. In this paper, we present our initial efforts using the Ocean Observatories Initiative project as a use case. In particular, we present a subscription-based data streaming service for data delivery that leverages the Apache Kafka data streaming platform. We also show how our solution can automatically integrate large-scale facilities with CI services for automated data processing.},
	doi = {10.1002/cpe.5256},
	url = {https://app.dimensions.ai/details/publication/pub.1113381084},
	author = {Zamani, Ali Reza and AbdelBaky, Moustafa and Balouek-Thomert, Daniel and Villalobos, J. J. and Rodero, Ivan and Parashar, Manish}
}
@article {RN13,
	title = {The Ocean Observatories Initiative},
	journal = {Oceanography},
	volume = {31},
	number = {1},
	year = {2018},
	pages = {16-35},
	type = {Journal Article},
	abstract = {The Ocean Observatories Initiative (OOI) is an integrated suite of instrumented platforms and discrete instruments that measure physical, chemical, geological, and biological properties from the seafloor to the sea surface. The OOI provides data to address large-scale scientific challenges such as coastal ocean dynamics, climate and ecosystem health, the global carbon cycle, and linkages among seafloor volcanism and life. The OOI Cyberinfrastructure currently serves over 250 terabytes of data from the arrays. These data are freely available to users worldwide, changing the way scientists and the broader community interact with the ocean, and permitting ocean research and inquiry at scales of centimeters to kilometers and seconds to decades.},
	issn = {10428275},
	doi = {10.5670/oceanog.2018.105},
	author = {Smith, Leslie and Barth, John and Kelley, Deborah and Plueddemann, Al and Rodero, Ivan and Ulses, Greg and Vardaro, Michael and Weller, Robert}
}
