Publications

D. S. Katz, J. Carver, N. Chue Hong, M. Mundt, and C. Venters, “Research Software Engineering: A SWEBOK Specialism?,” in Research Software Engineering: A SWEBOK Specialism?, Ottawa, Ontario, Canada: Zenodo, Mar. 2025. doi: https://doi.org/10.5281/zenodo.14950265

P. Diehl, C. Soneson, R. C. Kurchin, R. Mounce, and D. S. Katz, “The Journal of Open Source Software (JOSS): Bringing Open-Source Software Practices to the Scholarly Publishing Communityfor Authors, Reviewers, Editors, and Publishers,” JLSC, vol. 12, no. 2, Feb. 2025, doi: https://doi.org/10.31274/jlsc.18285

J. T. Feng, S. Puthanveetil Satheesan, S. Kong, T. Donders, and S. Punyasena, “Addressing the open world: detecting and segmenting pollen on palynological slides with deep learning,” BioRxiv - Preprint, Jan. 2025, Available: https://www.biorxiv.org/content/10.1101/2025.01.05.631390v1

E. Nyenah, P. Doll, D. S. Katz, and R. Reinecke, “Software sustainability of global impact models,” Geoscientific Model Development, vol. 17, no. 23, pp. 8593–8611, Dec. 2024, doi: https://doi.org/10.5194/gmd-17-8593-2024. Available: https://gmd.copernicus.org/articles/17/8593/2024/

E. A. Jensen and D. S. Katz, “Strategic priorities and challenges in research software funding: Results from an international survey,” Research on Research, Policy & Culture, Nov. 2024, doi: https://doi.org/10.12688/f1000research.155879.1. Available: https://f1000research.com/articles/13-1447/v1

S. Druskat, L. Grunske, C. Jay, and D. S. Katz, “Research Software Engineering: Bridging Knowledge Gaps,” Dagstuhl Reports, vol. 14, no. 4, pp. 42–53, Oct. 2024, doi: https://doi.org/10.4230/DagRep.14.4.42

F. Psomopoulos et al., “Building the future of research software as a first-class citizen in science; a global perspective,” SciLifeLab, Uppsala, Sweden, Sep. 2024. Available: https://zenodo.org/records/13915755

R. Ananthakrishnan et al., “Enabling Remote Management of FaaS Endpoints with Globus Compute Multi-User Endpoints,” presented at the PEARC 2024, Jul. 2024, pp. 1–5. doi: https://dl.acm.org/doi/10.1145/3626203.3670612

D. S. Katz and J. C. Carver, “Thoughts on Learning Human and Programming Languages,” Computing in Science & Engineering, vol. 26, no. 1, pp. 77–80, Jul. 2024, doi: 10.1109/MCSE.2024.3398949

R. Van Nieuwpoort and D. S. Katz, “Defining the roles of research software (Version 2),” Upstream, Jul. 2024, doi: https://doi.org/10.54900/xdh2x-kj281

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

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

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.

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

M. Hosseini, A. O. Holcombe, M. Kovacs, H. Zwart, D. S. Katz, and K. Holmes, “Group authorship, an excellent opportunity laced with ethical, legal and technical challenges,” Accountability in Research, no. Ethics, Integrity and Policy, Mar. 2024, doi: https://doi.org/10.1080/08989621.2024.2322557

R. Ananthakrishnan et al., “Establishing a High-Performance and Productive Ecosystem for Distributed Execution of Python Functions Using Globus Compute,” in HUST-24: 11th International Workshop on HPC User Support Tools, Atlanta, GA, 2024. doi: https://doi.org/10.1109/SCW63240.2024.00083

N. Karle, B. Clifford, Y. Babuji, R. Chard, D. S. Katz, and K. Chard, “Parsl+CWL: Experiences Combining the Python and CWL Ecosystems,” in 19th Workshop on Workflows in Support of Large-Scale Science (WORKS 2024), Atlanta, GA, 2024. doi: https://doi.org/10.1109/SCW63240.2024.00255

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

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

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

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

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

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

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

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

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

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/

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

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

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. 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: Proceedings PASC 2022, Jun. 2022, pp. 1–11. doi: https://doi.org/10.1145/3539781.3539795. 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. 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. 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: 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

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. 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: Science Gateways, 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?,” presented at the Computer Vision in Plant Phenotyping and Agriculture (CVPPA), 2021., 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,” presented at the Dan Katz Blog, Virtual, Sep. 27, 2021. 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.