Publications

[1]
C. Jay et al., “Theory-Software Translation: Research Challenges and Future Directions,” arXiv:1910.09902 [cs], Oct. 2019 [Online]. Available: http://arxiv.org/abs/1910.09902. [Accessed: 23-Oct-2019]
[1]
E. A. Huerta et al., “Enabling real-time multi-messenger astrophysics discoveries with deep learning,” Nat Rev Phys, vol. 1, no. 10, pp. 600–608, Oct. 2019 [Online]. Available: https://www.nature.com/articles/s42254-019-0097-4. [Accessed: 03-Oct-2019]
[1]
A. E. Ahmed et al., “Managing genomic variant calling workflows with Swift/T,” PLOS ONE, vol. 14, no. 7, p. e0211608, Jul. 2019 [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0211608. [Accessed: 10-Jul-2019]
[1]
D. S. 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,” in 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science), 2019, pp. 17–24.
[1]
D. S. Katz, S. Druskat, R. Haines, C. Jay, and A. Struck, “The State of Sustainable Research Software: Learning from the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1),” Journal of Open Research Software, vol. 7, no. 1, p. 11, Apr. 2019 [Online]. Available: http://openresearchsoftware.metajnl.com/articles/10.5334/jors.242/. [Accessed: 02-Apr-2019]
[1]
E. A. Huerta, R. Haas, S. Jha, M. Neubauer, and D. S. Katz, “Supporting High-Performance and High-Throughput Computing for Experimental Science,” Comput Softw Big Sci, vol. 3, no. 1, p. 5, Feb. 2019 [Online]. Available: https://doi.org/10.1007/s41781-019-0022-7. [Accessed: 08-Feb-2019]
[1]
D. Sholler, K. Ram, C. Boettiger, and D. S. Katz, “Enforcing public data archiving policies in academic publishing: A study of ecology journals,” Big Data & Society, vol. 6, no. 1, p. 2053951719836258, Jan. 2019 [Online]. Available: https://doi.org/10.1177/2053951719836258. [Accessed: 01-Apr-2019]
[1]
M. Barker et al., “The global impact of science gateways, virtual research environments and virtual laboratories,” Future Generation Computer Systems, 2019 [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0167739X18314018. [Accessed: 04-Jan-2019]
[1]
Y. Babuji et al., “Scalable Parallel Programming in Python with Parsl,” in Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), New York, NY, USA, 2019, pp. 22:1–22:8 [Online]. Available: http://doi.acm.org/10.1145/3332186.3332231. [Accessed: 04-Sep-2019]
[1]
Y. Babuji et al., “Parsl: Pervasive Parallel Programming in Python,” in Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing, New York, NY, USA, 2019, pp. 25–36 [Online]. Available: http://doi.acm.org/10.1145/3307681.3325400. [Accessed: 13-Jul-2019]
[1]
D. S. Katz et al., “The principles of tomorrow’s university,” F1000Research, vol. 7, p. 1926, Dec. 2018 [Online]. Available: https://f1000research.com/articles/7-1926/v1. [Accessed: 04-Jan-2019]
[1]
M. D. Hildreth et al., “HEP Software Foundation Community White Paper Working Group - Data and Software Preservation to Enable Reuse,” arXiv:1810.01191 [physics], Oct. 2018 [Online]. Available: http://arxiv.org/abs/1810.01191. [Accessed: 09-Oct-2018]
[1]
S. Druskat and D. S. Katz, “Mapping the Research Software Sustainability Space,” in 2018 IEEE 14th International Conference on e-Science (e-Science), 2018, pp. 25–30.
[1]
J. Cohen, D. S. Katz, M. Barker, R. Haines, and N. C. Hong, “Building a Sustainable Structure for Research Software Engineering Activities,” in 2018 IEEE 14th International Conference on e-Science (e-Science), 2018, pp. 31–32.
[1]
M. L. Mondelli et al., “BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments,” PeerJ, vol. 6, p. e5551, Aug. 2018 [Online]. Available: https://peerj.com/articles/5551. [Accessed: 29-Aug-2018]
[1]
D. S. Katz and N. P. C. Hong, “Software Citation in Theory and Practice,” in Mathematical Software – ICMS 2018, 2018, pp. 289–296 [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-96418-8_34. [Accessed: 21-Jul-2018]
[1]
H. S. Foundation et al., “HEP Software Foundation Community White Paper Working Group - Training, Staffing and Careers,” arXiv:1807.02875 [hep-ex, physics:physics], Jul. 2018 [Online]. Available: http://arxiv.org/abs/1807.02875. [Accessed: 10-Jul-2018]
[1]
S. Druskat and D. S. Katz, “Mapping the research software sustainability space,” arXiv:1807.01772 [cs], Jul. 2018 [Online]. Available: http://arxiv.org/abs/1807.01772. [Accessed: 10-Jul-2018]
[1]
D. S. Katz et al., “Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4),” Journal of Open Research Software, vol. 6, no. 1, Feb. 2018 [Online]. Available: http://openresearchsoftware.metajnl.com/articles/10.5334/jors.184/. [Accessed: 15-Feb-2018]
[1]
A. M. Smith et al., “Journal of Open Source Software (JOSS): design and first-year review,” PeerJ Comput. Sci., vol. 4, p. e147, Feb. 2018 [Online]. Available: https://peerj.com/articles/cs-147. [Accessed: 13-Feb-2018]
[1]
D. S. Katz, K. E. Niemeyer, and A. M. Smith, “Publish Your Software: Introducing the Journal of Open Source Software (JOSS),” Computing in Science & Engineering, vol. 20, no. 3, pp. 84–88, 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8358035/. [Accessed: 21-May-2018]
[1]
J. C. Carver, S. Gesing, D. S. Katz, K. Ram, and N. Weber, “Conceptualization of a US Research Software Sustainability Institute (URSSI),” Computing in Science & Engineering, vol. 20, no. 3, pp. 4–9, 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8358036/. [Accessed: 21-May-2018]
[1]
Y. Babuji et al., “Parsl: Scalable Parallel Scripting in Python,” in 10th International Workshop on Science Gateways (IWSG 2018), Edinburgh, Scotland, 2018 [Online]. Available: http://ceur-ws.org/Vol-2357/paper11.pdf
[1]
D. S. Katz et al., “Community Organizations: Changing the Culture in Which Research Software Is Developed and Sustained,” Computing in Science Engineering, pp. 1–1, 2018.
[1]
D. S. Katz, “Software Citations and the ACAT Community,” J. Phys.: Conf. Ser., vol. 1085, no. 2, p. 022010, 2018 [Online]. Available: http://stacks.iop.org/1742-6596/1085/i=2/a=022010. [Accessed: 30-Oct-2018]
[1]
B. Couturier et al., “HEP Software Foundation Community White Paper Working Group - Software Development, Deployment and Validation,” arXiv:1712.07959 [hep-ex, physics:physics], Dec. 2017 [Online]. Available: http://arxiv.org/abs/1712.07959
[1]
A. A. Alves Jr et al., “A Roadmap for HEP Software and Computing R&D for the 2020s,” arXiv:1712.06982 [hep-ex, physics:physics], Dec. 2017 [Online]. Available: http://arxiv.org/abs/1712.06982
[1]
J. P. Tennant et al., “A multi-disciplinary perspective on emergent and future innovations in peer review,” F1000Research, vol. 6, p. 1151, Nov. 2017 [Online]. Available: https://f1000research.com/articles/6-1151/v3
[1]
U. Nangia and D. S. Katz, “Track 1 Paper: Surveying the U.S. National Postdoctoral Association Regarding Software Use and Training in Research.” 30-Aug-2017 [Online]. Available: https://figshare.com/articles/Track_1_Paper_Surveying_the_U_S_National_Postdoctoral_Association_Regarding_Software_Use_and_Training_in_Research/5328442
[1]
A. M. Smith et al., “Journal of Open Source Software (JOSS): design and first-year review,” arXiv:1707.02264 [cs], Jul. 2017 [Online]. Available: http://arxiv.org/abs/1707.02264
[1]
R. C. Jiménez et al., “Four simple recommendations to encourage best practices in research software,” F1000Research, vol. 6, p. 876, Jun. 2017 [Online]. Available: https://f1000research.com/articles/6-876/v1
[1]
D. S. Katz et al., “Report on the Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4),” arXiv:1705.02607 [cs], May 2017 [Online]. Available: http://arxiv.org/abs/1705.02607
[1]
S. Jha, D. S. Katz, A. Luckow, N. Chue Hong, O. Rana, and Y. Simmhan, “Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure,” Concurrency Computat.: Pract. Exper., vol. 29, no. 8, p. n/a-n/a, Apr. 2017 [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cpe.4032/abstract
[1]
T. Clark, H. Cousijn, D. S. Katz, and M. Fenner, “Open Science strategies for NIH data management, sharing, and citation,” PeerJ Preprints, e2836v1, Mar. 2017 [Online]. Available: https://peerj.com/preprints/2836. [Accessed: 12-Jul-2017]
[1]
K. Chard, S. Caton, K. Kugler, O. Rana, and D. S. Katz, “A social content delivery network for e-Science,” Concurrency Computat.: Pract. Exper., vol. 29, no. 4, p. n/a-n/a, Feb. 2017 [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cpe.3854/abstract
[1]
J. M. Wozniak, J. Ozik, D. S. Katz, and M. Wilde, “Streaming supercomputing needs workflow-enabled programming-in-the-large,” arXiv:1702.07425 [cs], Feb. 2017 [Online]. Available: http://arxiv.org/abs/1702.07425
[1]
K. Maheshwari, D. Katz, S. D. Olabarriaga, J. Wozniak, and D. Thain, “Report on the first workshop on negative and null results in eScience,” Concurrency Computat.: Pract. Exper., vol. 29, no. 2, p. n/a-n/a, Jan. 2017 [Online]. Available: http://onlinelibrary.wiley.com/doi/10.1002/cpe.3908/abstract
[1]
U. Nangia and D. S. Katz, “Understanding Software in Research: Initial Results from Examining Nature and a Call for Collaboration,” 2017, pp. 486–487 [Online]. Available: http://ieeexplore.ieee.org/document/8109183/. [Accessed: 05-Dec-2017]
[1]
M. Turilli et al., “Evaluating Distributed Execution of Workloads,” 2017, pp. 276–285 [Online]. Available: http://ieeexplore.ieee.org/document/8109146/. [Accessed: 05-Dec-2017]
[1]
E. A. Huerta et al., “BOSS-LDG: A Novel Computational Framework That Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery,” 2017, pp. 335–344 [Online]. Available: http://ieeexplore.ieee.org/document/8109152/. [Accessed: 05-Dec-2017]
[1]
A. Allen et al., “Engineering Academic Software (Dagstuhl Perspectives Workshop 16252),” Dagstuhl Manifestos, vol. 6, no. 1, pp. 1–20, 2017 [Online]. Available: http://drops.dagstuhl.de/opus/volltexte/2017/7146
[1]
A. Marshall-Colon et al., “Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform,” Front. Plant Sci., vol. 8, 2017 [Online]. Available: http://journal.frontiersin.org/article/10.3389/fpls.2017.00786/full. [Accessed: 03-Jun-2017]
[1]
D. S. Katz, K. E. Niemeyer, and A. M. Smith, “Strategies for biomedical software management, sharing, and citation,” PeerJ Preprints, e2640v1, Dec. 2016 [Online]. Available: https://peerj.com/preprints/2640. [Accessed: 12-Jul-2017]
[1]
D. Katz et al., “Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3),” Journal of Open Research Software, vol. 4, no. 1, Oct. 2016 [Online]. Available: http://openresearchsoftware.metajnl.com/article/10.5334/jors.118/. [Accessed: 04-Jun-2017]
[1]
K. E. Niemeyer, A. M. Smith, and D. S. Katz, “The Challenge and Promise of Software Citation for Credit, Identification, Discovery, and Reuse,” J. Data and Information Quality, vol. 7, no. 4, pp. 16:1–16:5, Oct. 2016 [Online]. Available: http://doi.acm.org/10.1145/2968452
[1]
A. M. Smith, D. S. Katz, and K. E. Niemeyer, “Software citation principles,” PeerJ Comput. Sci., vol. 2, p. e86, Sep. 2016 [Online]. Available: https://peerj.com/articles/cs-86. [Accessed: 04-Jun-2017]
[1]
M. Gamell et al., “Evaluating Online Global Recovery with Fenix Using Application-Aware In-Memory Checkpointing Techniques,” in 2016 45th International Conference on Parallel Processing Workshops (ICPPW), 2016, pp. 346–355 [Online]. Available: https://doi.org/10.1109/ICPPW.2016.56
[1]
Y. Perez-Riverol et al., “Ten Simple Rules for Taking Advantage of Git and GitHub,” PLOS Computational Biology, vol. 12, no. 7, p. e1004947, Jul. 2016 [Online]. Available: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004947. [Accessed: 04-Jun-2017]
[1]
Z. Zhang, D. S. Katz, A. Merzky, M. Turilli, S. Jha, and Y. Nand, “Application Skeleton: Generating Synthetic Applications for Infrastructure Research,” The Journal of Open Source Software, vol. 1, no. 1, May 2016 [Online]. Available: http://joss.theoj.org/papers/10.21105/joss.00017
[1]
“Sustaining Research Software: an SC18 Panel.” [Online]. Available: https://arxiv.org/abs/1902.08942. [Accessed: 27-Feb-2019]

ISDA

[1]
S. Grayson, F. Aguilar, R. Milewicz, D. S. Katz, and D. Marinov, “A benchmark suite and performance analysis of user-space provenance collectors,” presented at the ACM REP ’24: Proceedings of the 2nd ACM Conference on Reproducibility and Replicability, Rennes, France: Association for Computing Machinery, Jun. 2024, pp. 85–95. doi: https://doi.org/10.1145/3641525.3663627
[1]
E. Nyenah, P. Döll, D. S. Katz, and R. Reinecke, “Software sustainability of global impact models,” Geoscientific Model Development, Jun. 2024, doi: https://doi.org/10.5194/gmd-2024-97
[1]
H. Gruson, L. Wolansky, A. Kiragga, and D. S. Katz, “FAIR-USE4OS: Guidelines for creating impactful open-source software,” PLOS Computational Biology, vol. 20, no. 5, May 2024.
[1]
D. S. Katz and N. P. C. Hong, “Special issue on software citation, indexing, and discoverability,” PeerJ Computer Science, Mar. 2024, doi: https://doi.org/10.7717/peerj-cs.1951
[1]
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
[1]
D. S. Katz, V. Kindratenko, O. Kindratenko, and P. Mazumdar, “Training Next-Generation Artificial Intelligence Users and Developers at NCSA,” Computing in Science & Engineering, vol. 25, no. 6, pp. 28–32, Nov. 2023, doi: 10.1109/MCSE.2024.3375572
[1]
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
[1]
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
[1]
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
[1]
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
[1]
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
[1]
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
[1]
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
[1]
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/
[1]
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
[1]
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
[1]
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
[1]
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
[1]
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
[1]
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.
[1]
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
[1]
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/
[1]
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
[1]
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
[1]
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.
[1]
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
[1]
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
[1]
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
[1]
D. S. Katz, F. E. Psomopoulos, and L. J. Castro, “Working Towards Understanding the Role of FAIR for Machine Learning,” DaMaLOS 2021, Oct. 2021.
[1]
K. McHenry, “EarthCube GeoCODES - Data Plus X Towards a Geoscience Scientific Gateway,” Gateways 2021, Oct. 2021.
[1]
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
[1]
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
[1]
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
[1]
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.
[1]
T. Nicholson et al., “Creating a Permafrost Discovery Gateway - Providing Researchers and the Public withaccess to arctic data,” Gateways 2021, Oct. 2021.
[1]
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
[1]
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.
[1]
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
[1]
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
[1]
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
[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
[1]
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, Mar. 12, 2021. Available: http://library.ucsd.edu/dc/object/bb8676950x/_1.pdf
[1]
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.
[1]
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.
[1]
Y. Babuji et al., “Federated Function as a Service for eScience,” eScience, 2021, doi: https://doi.org/10.1109/eScience51609.2021.00046
[1]
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
[1]
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]
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