@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 {RN241,
	title = {Leveraging user access patterns and advanced cyberinfrastructure to accelerate data delivery from shared-use scientific observatories},
	journal = {Future Generation Computer Systems-the International Journal of Escience},
	volume = {122},
	year = {2021},
	pages = {14-27},
	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 = {Collaboratory, Cyberinfrastructure, Data pre-fetching, Distributed data sharing, Distributed facilities, Observatories, Virtual Data},
	issn = {0167-739X},
	doi = {10.1016/j.future.2021.03.004},
	author = {Qin, Y. B. and Rodero, I. and Simonet, A. and Meertens, C. and Reiner, D. and Riley, J. and Parashar, M.}
}
@article {RN172,
	title = {Obsea: A Decadal Balance for a Cabled Observatory Deployment},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	pages = {33163-33177},
	type = {Journal Article},
	abstract = {The study of the effects of climate change on the marine environment requires the existence of sufficiently long time series of key parameters. The study of these series allows both to characterize the range of variability in each particular region and to detect trends or changes that could be attributed to anthropogenic causes. For this reason, networks of permanent cabled observation systems are being deployed in the ocean. This paper presents a balance of a decade of activity at the OBSEA cabled observatory, as an example of ocean monitoring success and drawbacks. It is not the objective of this article to analyze the scientific and technical aspects already presented by the authors in different publications (Table 4). We will evaluate the overall experience by retracing the different steps of infrastructure deployment and maintenance, focusing on routines for in situ control, damages experienced, breakdowns and administrative constraints by local administrations. We will conclude by providing a set of guidelines to improve cabled observatories scientific outreach, societal projection, and economic efficiency. As a result of this work, a 10-years dataset has been published in Pangaea that is available for the community.},
	keywords = {Europe, Instruments, monitoring, Observatories, Oceans, Sea measurements, Underwater cables},
	doi = {10.1109/access.2020.2973771},
	url = {https://app.dimensions.ai/details/publication/pub.1125026314 https://ieeexplore.ieee.org/ielx7/6287639/8948470/08998177.pdf},
	author = {Del-Rio, Joaquin and Nogueras, Marc and Toma, Daniel Mihai and Mart{\'\i}nez, Enoc and Artero-Delgado, Carola and Bghiel, Ikram and Martinez, Marc and Cadena, Javier and Garcia-Benadi, Albert and Sarria, David and Aguzzi, Jacopo and Masmitja, Ivan and Carandell, Matias and Olive, Joaquim and Gomariz, Spartacus and Santamaria, Pep and L{\`a}zaro, Antoni M{\`a}nuel}
}
@article {RN78,
	title = {Data Cyber-Infrastructure for End-to-end Science: Experiences from the NSF Ocean Observatories Initiative},
	journal = {Computing in Science \& Engineering},
	year = {2019},
	pages = {1-1},
	type = {Journal Article},
	abstract = {Large-scale scientific facilities provide a broad community of researchers and educators with open access to instrumentation and data products generated from geographically distributed instruments and sensors. This paper discusses key architectural design, deployment, and operational aspects of a production cyberinfrastructure for the acquisition, processing, and delivery of data from large scientific facilities using experiences from the National Science Foundation{\textquoteright}s Ocean Observatories Initiative. This paper also outlines new models for data delivery and opportunities for insights in a wide range of scientific and engineering domains as the volumes and variety of data from facilities grow.},
	keywords = {Cloud computing, Data acquisition, Design methodology, Distributed databases, Information processing, Laboratories, Observatories, Oceanography, US Government},
	issn = {1521-9615},
	doi = {10.1109/MCSE.2019.2892769},
	author = {Rodero, I. and Parashar, M.}
}
