About

Daniel S. Katz is a Research Professor at the Siebel School of Computing and Data Science and the School of Information Sciences at the University of Illinois Urbana-Champaign. He holds a PhD in Electrical Engineering from Northwestern University and has a background in electrical engineering, with additional academic positions including Chief Scientist at the National Center for Supercomputing Applications (NCSA). His research interests focus on sustainable and open science, particularly in the areas of research software engineering, software citation and publication, parallel and distributed computing, and resilience and fault-tolerance in computing systems. Katz has contributed extensively to the understanding and advancement of research software funding models, scientific workflow systems, and open-source research software for proteomics. His work emphasizes the importance of software sustainability, open science practices, and the development of resilient computational infrastructures. He has held various professional roles, including senior fellowships at the Computation Institute at Argonne National Laboratory and the University of Chicago, and has been involved in numerous research projects and publications related to scientific computing, software engineering, and data systems.

Research topics

• Computer Science
• Artificial Intelligence
• Data science
• World Wide Web
• Political Science
• Software engineering
• Engineering
• Machine Learning
• Knowledge management
• Programming language
• Parallel computing
• Engineering ethics
• Computational science
• Psychology
• Engineering management
• Database

Selected publications

• PRO4RS WG Recommendations

  Research Data Alliance · 2026-04-16

  articleOpen access
  PublisherDOI

• DIRECT competencies framework for digital Research Technical Professionals

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-09

  otherOpen accessSenior author

  The DIgital REsearch CompeTencies (DIRECT) framework helps classify and describe the wide range of technical and non-technical skills used across various digital research roles. These include researchers, research software engineers (RSEs), data specialists, group leads, principal investigators (PIs), archivists, bioinformaticians, and many more. It brings together skills (abilities to perform tasks or behaviours we possess) together with technology tools, methodologies and languages that demonstrate knowledge and proficiency, alongside learning resources to support skill development. The framework provides a shared language for recognising expertise, planning training, and mapping career pathways.

  PublisherDOI

• Technology Research Software: An Often Overlooked Category of Research Software

  Computing in Science & Engineering · 2026-01-01

  articleOpen access

  Research software has been categorized for various goals. One fundamental dimension of such categorizations is the role that the software plays in the research process. Recently, a new role category has emerged: technology research software, which covers research software developed in technology research. Until now, this category of technology research software has often been overlooked and neglected within the research software engineering community. In this article, we explain technology research software and its primary subroles. Technology readiness levels are an established method of estimating the maturity of technologies, including software systems. For technology research software, these readiness levels define secondary subroles. To illustrate the concept of technology research software and to make it more tangible, we present examples of research software that, depending on its specific use within or outside of research, take on the role of technology research software as well as that of another research software category.

  PublisherOA PDFDOI

• Brazil at the forefront of future construction

  ZKG International · 2026-01-01

  article1st authorCorresponding
  PublisherDOI

• DIRECT competencies framework for digital Research Technical Professionals

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-16

  otherOpen accessSenior author

  The DIgital REsearch CompeTencies (DIRECT) framework helps classify and describe the wide range of technical and non-technical skills used across various digital research roles. These include researchers, research software engineers (RSEs), data specialists, group leads, principal investigators (PIs), archivists, bioinformaticians, and many more. It brings together skills (abilities to perform tasks or behaviours we possess) together with technology tools, methodologies and languages that demonstrate knowledge and proficiency, alongside learning resources to support skill development. The framework provides a shared language for recognising expertise, planning training, and mapping career pathways.

  PublisherDOI

• DIRECT competencies framework for digital Research Technical Professionals

  Open MIND · 2026-04-16

  otherOpen accessSenior author

  The DIgital REsearch CompeTencies (DIRECT) framework helps classify and describe the wide range of technical and non-technical skills used across various digital research roles. These include researchers, research software engineers (RSEs), data specialists, group leads, principal investigators (PIs), archivists, bioinformaticians, and many more. It brings together skills (abilities to perform tasks or behaviours we possess) together with technology tools, methodologies and languages that demonstrate knowledge and proficiency, alongside learning resources to support skill development. The framework provides a shared language for recognising expertise, planning training, and mapping career pathways.

  PublisherDOI

• A Terminology for Scientific Workflow Systems

  Research Explorer (The University of Manchester) · 2025-06-09

  preprintOpen access

  The term scientific workflow has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow management systems (WMSs) have been developed to manage and run these workflows. However, no turnkey solution has emerged to address the diversity of scientific processes and the infrastructure on which they are implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new WMS. A direct consequence is that many existing WMSs share some salient features, offer similar functionalities, and can manage the same categories of workflows but also have some distinct capabilities. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. This terminology is composed of fives axes: workflow characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.

  PublisherDOI

• Understanding and advancing research software grant funding models

  Open Research Europe · 2025-07-25 · 1 citations

  articleOpen access1st authorCorresponding

  Research software funding currently operates across a disconnected landscape of public and private grant-making organizations, leading to inefficiencies for software projects and the broader research community. The lack of coordination forces projects to pursue multiple, often overlapping opportunities, and forces funders to independently evaluate projects and proposals, resulting in duplicated effort and suboptimal resource distribution. By examining existing collaboration models, including centralized and distributed approaches, we highlight how joint decision-making mechanisms could improve sustainability for reusable software resources. An international set of examples illustrates how cross-organization cooperation for research software funding can be structured. Such collaborations can optimize grant disbursement and align priorities. Increased collaboration could allow funders to better address the ongoing maintenance and evolution of research software, lowering barriers that hamper discovery across multiple research domains. Encouraging both bottom-up user-driven and top-down coordination mechanisms ultimately supports more robust, widely accessible research software, improving global research outcomes.

  PublisherDOI

• A terminology for scientific workflow systems

  Future Generation Computer Systems · 2025-06-24 · 12 citations

  articleOpen access
  PublisherOA PDFDOI

• 318. CHANGES IN DECISION MAKING BEHAVIOR BETWEEN PREGNANCY AND POSTPARTUM

  The International Journal of Neuropsychopharmacology · 2025-08-01

  articleOpen access

  Abstract Background Pregnancy causes major physiological changes that are thought to impact decision making and reward processing. It is poorly understood if these alterations remain or change in the postpartum period. Aims & Objectives We aim to characterize decision making with repeated measures in pregnancy and postpartum periods using a validated cognitive gambling task and one of the largest sample sizes to date. Method Participants completed the Gambling Task in the Cambridge Neuropsychological Test Automated Battery (CANTAB) in the third trimester of pregnancy and three months post-partum (N=101). Quality of decision making, delay aversion, risk adjustment and risk taking scores from the CANTAB gambling task were compared between pregnancy and postpartum using a two-way repeated measures ANOVA with Tukey HSD post-hoc analysis. Results Risk taking, the mean proportion of gambled points, was significantly higher (F(1, 99)=48.732, p < 0.0001) in the postpartum period (M=0.60, SE=0.02) compared to pregnancy (M=0.52, SE=0.02). Risk adjustment, or risk taking while accounting for new information, was significantly lower (F(1, 99)=3.977, p=0.049) in postpartum period (M=1.35, SE=0.11) compared to pregnancy (M=1.55, SE=0.11). Differences in decision making and delay aversion were not statistically significant. Discussion & Conclusions Higher risk taking and lower risk adjustment in the postpartum period suggests there may be changes in risk decision making behavior between pregnancy and postpartum. One explanation for these findings is that they may be influenced by heightened impulsivity and diminished cognitive flexibility during the postpartum period. These results are clinically relevant because they suggest a potential shift in cognitive or emotional states, which could inform healthcare providers in tailoring support and interventions to address decision-making behaviors and promote maternal well-being during this critical period.

  PublisherOA PDFDOI

Recent grants

• REU Site: INCLUSION - Incubating a New Community of Leaders Using Software, Inclusion, Innovation, Interdisciplinary and OpeN-Science

  NSF · $360k · 2017–2022

• Promoting Action to Build Research Communities in the Age of Open Science

  NSF · $50k · 2016–2018

• Collaborative Research: OAC Core: Small: Efficient and Policy-driven Burst Buffer Sharing

  NSF · $207k · 2020–2023

• Collaborative Research: Frameworks: funcX: A Function Execution Service for Portability and Performance

  NSF · $481k · 2020–2025

Frequent coauthors

• İlkay Altıntaş

  261 shared

• Sayeed Cho -Kisti

  University of California, San Diego

  256 shared

• Won Kum

  Ludwig-Maximilians-Universität München

  256 shared

• Peng Chen

  256 shared

• Saurabh Bagchi

  164 shared

• Deborah Silver

  164 shared

• Cyril Onwubiko

  163 shared

• Grace A. Lewis

  Software Engineering Institute

  162 shared

Education

• Ph.D., Computer Science

  University of Illinois at Urbana-Champaign

  1996

• M.S., Computer Science

  University of Illinois at Urbana-Champaign

  1992

• B.S., Computer Science

  University of Illinois at Urbana-Champaign

  1990

Awards & honors

• Celebration of Excellence 2026
• Celebration of Excellence 2025
• Celebration of Excellence 2024
• Celebration of Excellence 2023
• Celebration of Excellence 2022