Publications

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. [Online]. Available: https://ieeexplore.ieee.org/document/10064469/authors#authors

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. [Online]. Available: https://ieeexplore.ieee.org/document/10075674

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. [Online]. Available: https://openresearchsoftware.metajnl.com/

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. [Online]. Available: https://www.nature.com/articles/s41597-022-01710-x

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.

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. [Online]. Available: https://ieeexplore.ieee.org/document/9899739/authors#authors

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.

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.

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, Jun. 2022, vol. Article 10, pp. 1–11, doi: https://doi.org/10.1145/3539781.3539795 [Online]. Available: https://doi.org/10.1145/3539781.3539795

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. [Online]. Available: https://peerj.com/articles/cs-963/

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. [Online]. Available: https://eos.org/opinions/credit-where-credit-is-due

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, Mar. 2022, doi: https://doi.org/10.1007/978-3-030-96188-6_5 [Online]. Available: https://meetings.aps.org/Meeting/MAR22/Session/T08.7

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.

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 [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-83418-0_10

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.

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.

D. S. Katz, F. E. Psomopoulos, and L. J. Castro, “Working Towards Understanding the Role of FAIR for Machine Learning,” DaMaLOS 2021, Oct. 2021.

K. McHenry, “EarthCube GeoCODES - Data Plus X Towards a Geoscience Scientific Gateway,” Gateways 2021, Oct. 2021.

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.

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.

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, Oct. 2021, doi: https://doi.org/10.5281/zenodo.5569811.

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?,” Virtual, Oct. 2021.

T. Nicholson et al., “Creating a Permafrost Discovery Gateway - Providing Researchers and the Public withaccess to arctic data,” Gateways 2021, Oct. 2021.

D. S. Katz, K. McHenry, and J. Lee, “Senior  level  RSE  career  paths,” Virtual, Sep. 27, 2021 [Online]. Available: https://danielskatzblog.wordpress.com/2021/09/27/senior-rse-paths

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.

D. S. Katz, K. McHenry, and J. Lee, “Senior  level  RSE  career  paths,” Virtual, Sep. 2021 [Online]. Available: ttps://septembrse.society-rse.org

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.

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. [Online]. Available: https://ieeexplore.ieee.org/document/9486501

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.

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.

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.

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 [Online]. Available: http://library.ucsd.edu/dc/object/bb8676950x/_1.pdf

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.

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.

Y. Babuji et al., “Federated Function as a Service for eScience,” eScience, 2021, doi: https://doi.org/10.1109/eScience51609.2021.00046.

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.

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.

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 [Online]. Available: http://library.ucsd.edu/dc/object/bb7106971q/_1.pdf

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. [Online]. Available: https://dl.acm.org/doi/10.1145/3463478.3463486

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. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S2212420920312772

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.

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., vol. 1, CRC Press, 2020 [Online]. 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

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.

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

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

J. Zwart et al., “Reproducible Forecasting Workflows,” Ecological Forecasting Intiative Blog, 2020, 2020 [Online]. Available: https://ecoforecast.org/introducing-efi-task-views

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

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

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

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