Publications

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J. M. Duarte et al., “FAIR AI Models in High Energy Physics,” Machine Learning: Science and Technology/IOP Science, Dec. 2023, doi: 10.1088/2632-2153/ad12e3. Available: https://iopscience.iop.org/article/10.1088/2632-2153/ad12e3
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E. A. Huerta et al., “FAIR for AI: An interdisciplinary and international community building perspective,” Scientific Journal, no. 19, Jul. 2023, doi: https://doi.org/10.1038/s41597-023-02298-6
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E. McKiernan et al., “Policy recommendations to ensure that research software is openly accessible and reusable,” Plos Biology, Jul. 2023, doi: https://doi.org/10.1371/journal.pbio.3002204
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D. S. Katz, B. Clifford, Y. Babuji, K. H. Kesling, A. Woodard, and K. Chard, “The Changing Role of RSEs over the Lifetime of Parsl,” presented at the PEARC 2023, Portland, OR: arXiv, Jul. 2023. doi: https://doi.org/10.48550/arXiv.2307.11060
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S. Grayson, R. Milewicz, J. Teves, D. S. Katz, and D. Marinov, “Wanted: standards for automatic reproducibility of computational experiments,” presented at the PEARC 2023, in Computer Science Software Engingeering, vol. 11383. Portland, OR: arXiv, Jul. 2023. doi: https://doi.org/10.48550/arXiv.2307.11383
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S. Grayson, D. Marinov, D. S. Katz, and R. MIlewicz, “Automatic Reproduction of Workflows in the Snakemake Workflow Catalog and nf-core Registries,” in Proceedings of the 2023 ACM Conference on Reproducibility and Replicability, Santa Cruz, California: ACM, Jun. 2023, pp. 74–84. doi: 10.1145/3589806.3600037
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W. F. Godoy et al., “Giving RSEs a Larger Stage through the Better Scientific Software Fellowship,” Computing in Science & Engineering (IEEE), pp. 1–10, Mar. 2023, doi: 10.1109/MCSE.2023.3253847. Available: https://ieeexplore.ieee.org/document/10064469/authors#authors
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I. A. Cosden, K. McHenry, and D. S. Katz, “Research Software Engineers: Career Entry Points and Training Gaps,” Computing in Science & Engineering, pp. 1–9, Mar. 2023, doi: 10.1109/MCSE.2023.3258630. Available: https://ieeexplore.ieee.org/document/10075674
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C. , Martinez-Ortiz et al., “What Do We (Not) Know About Research Software Engineering,” Journal of Open Research Software, vol. 10:11, Dec. 2022, doi: https://doi.org/10.5334/jors.384. Available: https://openresearchsoftware.metajnl.com/
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T. Gamblin and D. S. Katz, “Overcoming Challenges to Continuous Integration in HPC,” Computing in Science and Engineering, vol. 24, no. 6, pp. 54–59, Dec. 2022, doi: 10.1109/MCSE.2023.3263458
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M. Barker et al., “Introducing the FAIR Principles for research software,” Scientific Data, vol. 622, Oct. 2022, doi: https://doi.org/10.1038/s41597-022-01710-x t. Available: https://www.nature.com/articles/s41597-022-01710-x
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E. A. Huerta et al., “FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective,” ArXiv, vol. arXiv:2210.08973v1, p. 10, Sep. 2022, doi: https://doi.org/10.48550/arXiv.2210.08973
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Z. Li et al., “func X: Federated Function as a Service for Science,” IEEE Transactions on Parallel and Distributed Systems, pp. 1–16, Sep. 2022, doi: 10.1109/TPDS.2022.3208767. Available: https://ieeexplore.ieee.org/document/9899739/authors#authors
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M. Solis, C. Pasquier, S. Nunez-Corrales, G. Madrigal-Redondo, and A. Gatica-Arias, “Estimating the performance of mass testing strategies for COVID-19: a case study for Costa Rica,” MedRxiv, Sep. 2022, doi: https://doi.org/10.1101/2022.09.05.22279618
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S. Puthanveetil Satheesan, B. Bhavya, A. Davies, A. Craig, Y. Zhang, and C. Zhai, “Toward a Big Data Analysis System for Historical Newspaper Collections Research,” presented at the Platform for Advanced Scientific Computing (PASC) Conference, Basel, Switzerland, Jun. 2022.
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S. Puthanveetil Satheesan, B. Bhavya, A. Davies, A. B. Craig, Y. Zhang, and C. Zhai, “Toward a big data analysis system for historical newspaper collections research,” presented at the Platform for Advanced Scientific Computing, Basel, Switzerland: Proceedings PASC 2022, Jun. 2022, pp. 1–11. doi: https://doi.org/10.1145/3539781.3539795. Available: https://doi.org/10.1145/3539781.3539795
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J. C. Carver, N. Weber, K. Ram, S. Gesing, and D. S. Katz, “A survey of the state of the practice for research software in the United States,” PeerJ Computer Science, May 2022, doi: https://doi.org/10.7717/peerj-cs.963. Available: https://peerj.com/articles/cs-963/
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M. A. Parsons, D. S. Katz, M. Langseth, H. Ramapriyan, and S. Ramdeen, “Credit Where Credit Is Due,” Eos Science News by AGU, May 2022, doi: https://doi.org/10.1029/2022EO220239. Available: https://eos.org/opinions/credit-where-credit-is-due
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S. P. Mudigonda, S. Nunez-Corrales, R. Venkatachalapathy, and J. Graham, “Scheduler Dependencies in Agent-Based Models: A Case-Study Using a Contagion Model,” in Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas, Chicago: Springer, Mar. 2022. doi: https://doi.org/10.1007/978-3-030-96188-6_5. Available: https://meetings.aps.org/Meeting/MAR22/Session/T08.7
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L. Marini et al., “Applications and Roadmap of the Clowder Open Source Data Management Framework,” presented at the Australasian Computer Science Week, Brisbane, Australia (Hybrid), Feb. 2022.
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S. Nunez-Corrales, M. Friesen, S. Mudigonda, R. Venkatachalapathy, and J. Graham, “In-Silico Models With Greater Fidelity to Social Processes: Towards ABM Platforms With Realistic Concurrency,” in Proceedings of the 2020 Conference of The Computational Social Science Society of the Americas, Virtual, November 2021, Jan. 2022, pp. 155–169. doi: 10.1007/978-3-030-83418-0_10. Available: https://link.springer.com/chapter/10.1007/978-3-030-83418-0_10
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D. S. Katz and S. Stall, “The 2021 NISO Plus Conference: Global connections and global conversations,” Information Services & Use, vol. 41, no. 1–2, pp. 39–42, Dec. 2021, doi: 10.3233/ISU-210108
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C. Jay, R. Haines, and D. S. Katz, “Software Must be Recognised as an Important Output of Scholarly Research,” International Journal of Digital Curation, vol. 16, no. 1, Dec. 2021, doi: DOI: https://doi.org/10.2218/ijdc.v16i1.745
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D. S. Katz, F. E. Psomopoulos, and L. J. Castro, “Working Towards Understanding the Role of FAIR for Machine Learning,” DaMaLOS 2021, Oct. 2021.
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K. McHenry, “EarthCube GeoCODES - Data Plus X Towards a Geoscience Scientific Gateway,” Gateways 2021, Oct. 2021.
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S. Malik et al., “Software Training in HEP,” Computing and Software for Big Science, vol. 22, Oct. 2021, doi: https://doi.org/10.1007/s41781-021-00069-9
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J. Salamanca and S. Nunez-Corrales, “Social Viscosity, Fluidity, and Turbulence in Collective Perceptions of Color: An Agent-Based Model of Color Scale Convergence,” Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas, pp. 191–212, Oct. 2021, doi: 10.1007/978-3-030-77517-9_13
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T. Nicholson et al., “Creating a Permafrost Discovery Gateway - Providing Researchers and the Public with access to arctic data,” presented at the Science Gateways 2022, San Diego, CA: Science Gateways, Oct. 2021. doi: https://doi.org/10.5281/zenodo.5569811
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D. LeBauer, M. Burnette, N. Fahlgren, R. Kooper, K. McHenry, and A. Stylianou, “What Does TERRA-REF’s High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer VisionCommunity?,” presented at the Computer Vision in Plant Phenotyping and Agriculture (CVPPA), 2021., Virtual, Oct. 2021.
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T. Nicholson et al., “Creating a Permafrost Discovery Gateway - Providing Researchers and the Public withaccess to arctic data,” Gateways 2021, Oct. 2021.
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D. S. Katz, K. McHenry, and J. Lee, “Senior  level  RSE  career  paths,” presented at the Dan Katz Blog, Virtual, Sep. 27, 2021. Available: https://danielskatzblog.wordpress.com/2021/09/27/senior-rse-paths
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L. Marini, T. Nicholson, and K. McHenry, “Leveraging Open Source Technologies to Support Arctic Permafrost Science,” presented at the Polar Data Forum, Virtual, Sep. 2021.
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D. S. Katz, K. McHenry, and J. Lee, “Senior  level  RSE  career  paths,” presented at the SeptembRSE - The Virtual Conference, Virtual, Sep. 2021. Available: ttps://septembrse.society-rse.org
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S. Nunez-Corrales and E. Jakobsson, “Entropic boundary conditions towards safe artificial superintelligence,” Journal of Experimental & Theoretical Artificial Intelligence, Jul. 2021, doi: https://doi.org/10.1080/0952813X.2021.1952653
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V. Sagan et al., “Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data,” IEEE Transactions on Geoscience and Remote Sensing, Jul. 2021, doi: 10.1109/TGRS.2021.3091409. Available: https://ieeexplore.ieee.org/document/9486501
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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
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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.
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C. Kirkpatrick et al., “National and International Trends in Research Storage at Scale.” San Diego Super Computer Center, University of California, San Diego, Mar. 12, 2021. Available: http://library.ucsd.edu/dc/object/bb8676950x/_1.pdf
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C. B. Bushell, P. Escalante, R. Bailey, R. Zhu, and C. Blatti, “Risk Assessment of Latent Tuberculosis Infection through a Multiplexed Cytokine Biosensor Assay and Machine Learning Feature Selection,” Nature Scientific Reports, 2021.
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S. L. Brantley et al., “The future low-temperature geo-chemical data-scape as envisioned by the U.S. geochemical community,” Computers & Geosciences,2021., 2021.
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Y. Babuji et al., “Federated Function as a Service for eScience,” eScience, 2021, doi: https://doi.org/10.1109/eScience51609.2021.00046
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A. Villarreal, Y. Babuji, T. Uram, D. S. Katz, K. Chard, and K. Heitmann, “Extreme Scale Survey Simulation with Python Workflows,” eScience, 2021, doi: https://doi.org/10.1109/eScience51609.2021.00031
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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.
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C. Kirkpatrick, K. Coakley, M. Cragin, J. Glasgow, and J. Goodhue, “Research Drivers and Capabilities.” San Diego Supercomputer Center, University of California, San Diego, Dec. 07, 2020. Available: http://library.ucsd.edu/dc/object/bb7106971q/_1.pdf
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K. Chard et al., “Extended Abstract: Productive Parallel Programming with Parsl,” ACM SIGAda Ada Letters, vol. 40, no. 2, pp. 73–75, Dec. 2020, doi: https://doi.org/10.1145/3463478.3463486. Available: https://dl.acm.org/doi/10.1145/3463478.3463486
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N. Makhoul, C. Navarro, J. S. Lee, and P. Gueguen, “A comparative study of buried pipeline fragilities using the seismic damage to the Byblos wastewater network.,” International Journal of Disaster Rick Reduction, vol. 51, Dec. 2020, doi: https://doi.org/10.1016/j.ijdrr.2020.101775. Available: https://www.sciencedirect.com/science/article/abs/pii/S2212420920312772
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N. Makhoul, C. Navarro, and J. Sung Lee, “Seismic estimation of casualties and direct economic loss to Byblos city: a contribution to the ‘100 resilient cities’ strategy.,” Sustainable and Resilient Infrastructure, Apr. 2020, doi: https://doi.org/10.1080/23789689.2020.1745531
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L. Wang et al., “Community resilience assessment of an EF-5 tornado using the IN-CORE modeling environment,” in Life-Cycle Civil Engineering: Innovation, Theory and Practice, 1st ed.CRC Press, 2020. Available: https://www.taylorfrancis.com/chapters/edit/10.1201/9780429343292-49/community-resilience-assessment-ef-5-tornado-using-core-modeling-environment-wang-van-de-lindt-cutler-rosenheim-koliou-lee-calderon
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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