@article {RN189,
	title = {Open Data, Collaborative Working Platforms, and Interdisciplinary Collaboration: Building an Early Career Scientist Community of Practice to Leverage Ocean Observatories Initiative Data to Address Critical Questions in Marine Science},
	journal = {Frontiers in Marine Science},
	volume = {7},
	year = {2020},
	type = {Journal Article},
	abstract = {Ocean observing systems are well-recognized as platforms for long-term monitoring of near-shore and remote locations in the global ocean. High-quality observatory data is freely available and accessible to all members of the global oceanographic community{\textemdash}a democratization of data that is particularly useful for early career scientists (ECS), enabling ECS to conduct research independent of traditional funding models or access to laboratory and field equipment. The concurrent collection of distinct data types with relevance for oceanographic disciplines including physics, chemistry, biology, and geology yields a unique incubator for cutting-edge, timely, interdisciplinary research. These data are both an opportunity and an incentive for ECS to develop the computational skills and collaborative relationships necessary to interpret large data sets. Here, we use observatory data to demonstrate the potential for these interdisciplinary approaches by presenting a case study on the water-column response to anomalous atmospheric events (i.e., major storms) on the shelf of the Mid-Atlantic Bight southwest of Cape Cod, United States. Using data from the Ocean Observatories Initiative (OOI) Pioneer Array, we applied a simple data mining method to identify anomalous atmospheric events over a four-year period. Two closely occurring storm events in late 2018 were then selected to explore the dynamics of water-column response using mooring data from across the array. The comprehensive ECS knowledge base and computational skill sets allowed identification of data issues in the OOI data streams and technologically sound characterization of data from multiple sensor packages to broadly characterize ocean-atmosphere interactions. An ECS-driven approach that emphasizes collaborative and interdisciplinary working practices adds significant value to existing datasets and programs such as OOI and has the potential to produce meaningful scientific advances. Future success in utilizing ocean observatory data requires continued investment in ECS education, collaboration, and research; in turn, the ECS community provides feedback, develops knowledge, and builds new tools to enhance the value of ocean observing systems. These findings present an argument for building a community of practice to augment ECS ocean scientist skills and foster collaborations to extend the context, reach, and societal utility of ocean science.},
	keywords = {big data, COVID-19 pandemic, data mining, early career researchers, middle Atlantic bight, ocean observing, ocean response, remote collaboration},
	doi = {10.3389/fmars.2020.593512},
	author = {Levine, R. M. and Fogaren, K. E. and Rudzin, J. E. and Russoniello, C. J. and Soule, D. C. and Whitaker, J. M.}
}
@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}
}
