Publications

[1]
E. A. Huerta et al., “Accelerated, scalable and reproducible AI-driven gravitational wave detection,” Nature Astronomy, Jul. 2021, doi: https://doi.org/10.1038/s41550-021-01405-0.
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R. Smith et al., “Longitudinal assessment of diagnostic test performance over the course of acute SARS-CoV-2 infection,” The Journal of Infectious Diseases, no. jiab337, Jun. 2021, doi: https://doi.org/10.1093/infdis/jiab337.
[1]
N. Kenyon et al., “Extended Survival vs Accelerated Rejection of Nonhuman Primate Islet Allografts:  Effect of Mesenchymal Stem Cell Source and Timing,” American Journal of Transplantation, Jun. 2021.
[1]
C. Kirkpatrick et al., “National and International Trends in Research Storage at Scale.” San Diego Super Computer Center, University of California, San Diego, 12-Mar-2021 [Online]. Available: http://library.ucsd.edu/dc/object/bb8676950x/_1.pdf
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A. Villarreal et al., “A High Performance Python-based Workflow for Sky Survey Simulations,” presented at the 17th IEEE International Conference on eScience 2021, 2021.
[1]
C. Kirkpatrick, K. Coakley, M. Cragin, J. Glasgow, and J. Goodhue, “Research Drivers and Capabilities.” San Diego Supercomputer Center, University of California, San Diego, 07-Dec-2020 [Online]. Available: http://library.ucsd.edu/dc/object/bb7106971q/_1.pdf
[1]
B. Stalder, K. Reil, C. Claver, M. Liang, S. Pietrowicz, and H.-K. Win, “Rubin commissioning camera: integration, functional testing, and lab performance,” SPIE 11447, vol. 11447, 2020, doi: 10.1117/12.2561132.
[1]
B. Galewsky, R. Gardner, L. Gray, M. Neubauer, and J. Pivarski, “ServiceX A Distributed, Caching, Columnar Data Delivery Service,” EPJ Web Conf. 245 (2020), 2020 [Online]. Available: https://www.epj-conferences.org/articles/epjconf/abs/2020/21/epjconf_chep2020_04043/epjconf_chep2020_04043.html
[1]
C. Blatti III et al., “Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform,” PLOS Biology, 2020, 2020 [Online]. Available: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000583
[1]
J. Zwart et al., “Reproducible Forecasting Workflows,” Ecological Forecasting Intiative Blog, 2020, 2020 [Online]. Available: https://ecoforecast.org/introducing-efi-task-views
[1]
L. Angrave et al., “Improving student accessibility, equity, course performance, and lab skills:How introduction of ClassTranscribe is changing engineering education at the University of Illinois,” ASEE 2020, 2020 [Online]. Available: https://www.asee.org/public/conferences/172/papers/29904/view
[1]
L. Shinn et al., “" Fecal Bacteria as Biomarkers for Predicting Food Intake in Healthy Adults,” Journal of Nutrition, October 2020, 2020 [Online]. Available: https://academic.oup.com/jn/advance-article/doi/10.1093/jn/nxaa285/5918305
[1]
B. Smith et al., “Identification of early liver toxicity gene biomarkers using comparative supervised machine learning,” Nature, November 2020, 2020 [Online]. Available: https://www.nature.com/articles/s41598-020-76129-8
[1]
D. Katz, J. Lee, and K. McHenry, “Research Software Sustainability: Lessons Learned at NCSA,” Hawaii International Conference on System Sciences (HICSS), 2020 [Online]. Available: http://hdl.handle.net/10125/71494
[1]
S. Brantley et al., “A Vision for the Future Low-Temperature Geochemical Data-scape,” EarthArXiv 2020, 2020 [Online]. Available: https://eartharxiv.org/repository/view/1839/
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L. Marini, C. Wehmeier, K. Hass, S. P. Satheesan, and M. Slavenas, “Supporting Broad Data Management and Sharing with the Clowder Framework,” Museum Big Data 2020, 2020.
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E. Huerta et al., “Convergence of artificial intelligence and high performance computing on NSF‑supported cyberinfrastructure,” Journal of Big Data 2020, no. Article 88, 2020 [Online]. Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00361-2
[1]
K. Rubin et al., “Recommended Standards and Specifications for EarthCube Projects,” EarthCube Leadership Council 2020, 2020 [Online]. Available: https://library.ucsd.edu/dc/object/bb7786965d
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K. McHenry, D. Fulker, D. Katz, M. Daniels, and C. Kirkpatrick, “From Papers to Notebooks: Submission, Review, Presentation & Publication of Notebooks at EarthCube’20,” JupyterCon 2020, 2020 [Online]. Available: https://cfp.jupytercon.com/2020/schedule/presentation/258/from-papers-to-notebooks-submission-review-presentation-publication-of-notebooks-at-earthcube20/
[1]
K. McHenry, D. Fulker, D. Katz, M. Daniels, and C. Kirkpatrick, “From Papers to Notebooks Submission, Review, Presentation & Publication of Notebooks at EarthCube’20,” Research Data Alliance 2020: BoF - Computational Notebooks, 2020.
[1]
McHenry, K., Fulker, D, Katz, D, and Kirkpatrick, C., “Jupyter Notebooks as a tool to increase Visibility of EarthCube Products,” American Geophysical Union Fall Meeting, 2020, 2020 [Online]. Available: https://agu.confex.com/agu/fm20/webprogram/Paper775046.html
[1]
Liljedahl, Anna et al., “Permafrost Discovery Gateway: A web platform to enable discovery and knowledge-generation of permafrost Big Imagery products,” American Geophysical Union Fall Meeting, 2020 [Online]. Available: https://agu.confex.com/agu/fm20/webprogram/Paper770791.html
[1]
Z. Ivezic, S. Kahn, and J. A. Tyson, “LSST: From Science Drivers to Reference Design and Anticipated Data Products,” The Astrophysical Journal, March 2019, no. 873, 2019, doi: 10.3847/1538-4357/ab042c. [Online]. Available: LSST: From Science Drivers to Reference Design and Anticipated Data Products
[1]
B. Galewsky et al., “ServiceX - A Distributed, Caching, Columnar Data Delivery Service,” 2019 [Online]. Available: https://indico.cern.ch/event/773049/contributions/3474438/
[1]
B. Zhang and T. Kosar, “SMURF: Efficient and Scalable Metadata Access for Distributed Applications from Edge to the Cloud,” IEEE International Conference on Edge Computing (EDGE), 2019 [Online]. Available: https://ieeexplore.ieee.org/document/8812196
[1]
S. P. Satheesan et al., “Extensible Framework for Analysis of Farm Practices and Programs,” PEARC, 2019.
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S. Puthanveetil, A. Craig, and Y. Zhang, “A Historical Big Data Analysis to Understand the Social Construction of Juvenile Delinquency in the United States,” eScience, 2019.
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C. Wang et al., “Social Media Intelligence and Learning Environment: an Open Source Framework for Social Media Data Collection, Analysis and Curation,” eScience, 2019.
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C. Willis, M. Lambert, R. Kalyanam, K. Coakley, K. McHenry, and C. Kirkpatrick, “Labs Workbench: A Scalable Platform for Research Data Access, Education, and Training,” SCGI, 2019.
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P. Kumar, L. Marini, M. Pitcel, L. Keefer, and K. McHenry, “Design of an Observatory Data System and Open Source Sharing with Active Curation and Machine Learning,” GSA, 2019.
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K. Chard et al., “Implementing Computational Reproducibility in the Whole Tale Environment,” P-RECS’19, 2019.
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D. Katz, K. McHenry, C. Reinking, and R. Haines, “Research Software Development & Management in Universities: Case Studies from Manchester’s RSDS Group, Illinois’ NCSA, and Notre Dame’s CRC,” ACM/IEEE International Conference on Software Engineering, International Workshop on Software Engineering for Science (SE4Science), 2019.
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P. Nguyen et al., “Bracelet: Edge-Cloud Microservice Infrastructure for Aging Scientific Instruments,” International Conference on Computing, Networking and Communications (ICNC), 2019.
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T. Jenness, F. Economou, W. O’Mullane, B. van Klaveren, and S. Pietrowicz, “LSST data management software development practices and tools,” Software and Cyberinfrastructure for Astronomy V, July 2018, 2018, doi: 10.1117/12.2312157. [Online]. Available: https://docushare.lsst.org/docushare/dsweb/Get/Document-28452
[1]
N. Attary et al., “Hindcasting community-level building damage for the 2011 Joplin EF5 tornado,” Natural Hazards, 2018, vol. 93, pp. 1295–1316, 2018 [Online]. Available: https://link.springer.com/article/10.1007/s11069-018-3353-5
[1]
R. Kooper et al., “Developing and Adapting Data Management Services Across Multiple Virtual Observatories,” AGU, 2018.
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L. Marini et al., “Using Docker to Ease the Sharing and Running of Code in the PEcAn Project,” AGU, 2018.
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M. Ruiz et al., “A Prediction System for Vector-Borne Diseases : a Use Case for Weekly Estimation of West Nile Virus Risk,” AGU, 2018.
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W. Thomas et al., “Petabytes in Practice: Working with Collections as Data at Scale,” Workshop on Cyberinfrastructure and Machine Learning for Digital Libraries and Archives, JCDL, 2018.
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S. Satheesan et al., “Brown Dog: Making the Digital World a Better Place, a Few Files at a Time,” PEARC, 2018.
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L. Marini et al., “Clowder: Open Source Data Management for Long Tail Data,” PEARC, 2018.
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M. Burnette et al., “TERRA-REF Data Processing Infrastructure,” PEARC, 2018.
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M. Simeone, C. Morris, K. McHenry, and R. Markley, “The Canoe and the Superpixel: Image Analysis of the Changing Shorelines on Historical Maps of the Great Lakes M. Simeone, C. Morris, K. McHenry, and R. Markley,” Journal18, 2018 [Online]. Available: http://www.journal18.org/issue5/the-canoe-and-the-superpixel-image-analysis-of-the-changing-shorelines-on-historical-maps-of-the-great-lakes/
[1]
D. Li, B. Deal, X. Zhou, M. Slavenas, and W. Sullivan, “Moving Beyond the Neighborhood: Daily Exposure to Nature and Adolescents’ Mood,” Landscape and Urban Planning, 2018 [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0169204618300318
[1]
P. Suppakittpaisarn, B. Jiang, M. Slavenas, and W. Sullivan, “Does Density of Green Infrastructure Predict Preference?,” Urban Forestry & Urban Greening, 2018 [Online]. Available: https://www.researchgate.net/publication/323514146_DOES_DENSITY_OF_GREEN_INFRASTRUCTURE_PREDICT_PREFERENCE
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R. Kooper, M. Burnette, J. Maloney, and D. LeBauer, “Data Flow for the TERRA-REF Project,” presented at the AGU, 2017.
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S. Satheesan et al., “Brown Dog: A Data Transformation Ecosystem for Research - Advancing from Beta to 1.0,” presented at the AGU, 2017.
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I. Gutierrez-Polo et al., “Monitoring Water Quality in the Great Lakes leveraging Geo-Temporal Cyberinfrastructure,” presented at the IEEE eScience, 2017.
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A. Langmead, P. Rodriguez, S. Puthanveetil Satheesan, and A. Craig, “Extracting Meaningful Data from Decomposing Bodies,” presented at the PEARC, 2017.
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A. Langmead, P. Rodriguez, S. Puthanveetil Satheesan, and A. Craig, “Extracting Meaningful Data from Decomposing Bodies,” in Workshop on Computer Vision in Digital Humanities, Digital Humanities, 2017.