Research Computing and Data (RCD) Capabilities Model

The Research Computing and Data (RCD) Capabilities Model allows institutions to assess their support for computationally- and data-intensive research and education, to identify potential areas for improvement, and to understand how institutions across the broader community offer capabilities. The Capabilities Model was developed by a diverse group of institutions with a range of support models, in a collaboration among Internet2, CaRCC, and EDUCAUSE with support from the National Science Foundation. The newest incarnation of the Assessment Tool is designed for use by a range of roles at each institution, from front-line support through campus leadership, and is intended to be inclusive across small and large, and public and private institutions.

Want a quick summary and intro for your team? See our 1-page overview.

New online assessment, engagement guide, and community data viewer are now available!

We are excited to announce that the CaRCC Capabilities Model online Assessment Tool is available as of November 2024, streamlining the submission process. This new web-based version offers a much better user experience.

We are no longer accepting submissions through the Google Sheets-based tool. The new Assessment Tool will accept submissions any time of year. 

Our new Community Data Viewer is also up and running! It offers new functionality to explore and visualize the community dataset of assessment data across participating institutions. Users can also benchmark their own institutional capabilities coverage relative to the community of contributors.

And if you’re looking to understand how to begin discussing RCD Capabilities assessment at your institution, especially if you’re a smaller campus or just starting to work toward coordinated RCD capabilities, check out our Engagement Guide and Script.

How will my institution benefit?

The Capabilities Model can help you answer these questions: 

  • How well is my institution supporting computationally- and data-intensive research, and how can we get a comprehensive view of our support? 
  • What is my institution not thinking about or missing that the community has identified as significant? 
  • How can my institution (and my group) identify potential areas for improvement? 

Some common uses for the Capabilities Model include: 

  • To identify and understand areas of strength and weakness in an institution’s support to aid in strategic planning and prioritization.
  • To benchmark your institution’s support against peers – often when making an argument for increased funding to remain competitive on faculty recruitment and retention. (See the list of contributors).
  • To compare local institutional approaches to a common community model (i.e., a shared vocabulary), to facilitate communications and collaboration. 

Need help or have questions? 

Just getting started with research computing and data (RCD) and looking for a simpler approach? 

You’re not alone, and we’ve started a new Focused Tools effort to create tools that are geared at smaller and emerging programs. Check out that work here!

Want to get involved?

Join the working group and help shape the future enhancements of the tools, or contribute to the analysis of the community dataset! If you’re interested, write to us at: capsmodel-help@carcc.org or contact the chairs: Patrick Schmitz (Semper Cogito) and Dana Brunson (Internet2).

2021 Capabilities Model Community Data report now available

The second Research Computing and Data (RCD) Capabilities Model Community Dataset report is now available through CaRCC’s RCD Nexus as part of a project funded by the National Science Foundation. The report aggregates assessments contributed by 51 higher education institutions, providing insight into the current state of support for RCD. See also the related post.

2020 Capabilities Model Community Dataset report

The report describes the first Research Computing and Data (RCD) Capabilities Model Community Dataset, aggregating the assessments of 41 Higher Education Institutions. Read more about the Community Dataset.

Thank you to our contributing institutions!

These institutions contributed assessment data in 2020, 2021, and/or 2022 (a number have completed and contributed updated assessments)*.

Arizona State UniversityRutgers UniversityUniversity of Guam
Boston UniversitySouthern Methodist UniversityUniversity of Hawaii at Hilo
Carleton CollegeTennessee Tech UniversityUniversity of Hawaii System
Chaminade University of HonoluluTexas A & M UniversityUniversity of Hawaii West Oahu
Cornell UniversityThe Jackson LaboratoryUniversity of Kentucky
George Mason UniversityThe Ohio State UniversityUniversity of Maryland, College Park
Harvard School of Engineering and Applied SciencesUniversity of AlbertaUniversity of Massachusetts Boston
Kennesaw State UniversityUniversity of ArkansasUniversity of Nevada Reno
Louisiana State UniversityUniversity of British ColumbiaUniversity of New Mexico
Michigan State UniversityUniversity of California BerkeleyUniversity of Rhode Island
Montana State UniversityUniversity of California Los AngelesUniversity of South Carolina
North Carolina State UniversityUniversity of California MercedUniversity of South Dakota
Northwestern UniversityUniversity of California San DiegoUniversity of Texas Health Science Center at San Antonio
Pennsylvania State UniversityUniversity of California Santa BarbaraUniversity of Utah
Princeton UniversityUniversity of CincinnatiUniversity of Washington
Purdue UniversityUniversity of DelawareYale University
Rochester Institute of TechnologyUniversity of Florida
*Note that we only include those institutions that have agreed to be listed. If your institution contributed assessment data and would like to be included, let us know!

Read More about the Capabilities Model

This work has been supported in part by an RCN grant from the National Science Foundation (OAC-1620695, PI: Alex Feltus, “RCN: Advancing Research and Education through a national network of campus research computing infrastructures – The CaRC Consortium”), and by an NSF Cyberinfrastructure Centers of Excellence (CI CoE) pilot award (OAC-2100003, PI Dana Brunson, “Advancing Research Computing and Data: Strategic Tools, Practices, and Professional Development”).

Related posts:

Supported by the National Science Foundation

This work has been supported in part by an RCN grant from the National Science Foundation (OAC-1620695, PI: Alex Feltus, “RCN: Advancing Research and Education through a national network of campus research computing infrastructures – The CaRC Consortium”), and by an NSF Cyberinfrastructure Centers of Excellence (CI CoE) pilot award (OAC-2100003, PI Dana Brunson, “Advancing Research Computing and Data: Strategic Tools, Practices, and Professional Development”).