Cyberinfrastructure Center of Excellence
CI CoE provides expertise and active support to cyberinfrastructure practitioners at NSF Major Facilities in order to accelerate the data lifecycle and ensure the integrity and effectiveness of the cyberinfrastructure upon which research and discovery depends.
Platform for knowledge sharing and community building
Grounded in re-use of dependable CI tools and solutions
Key partner for the establishment and improvement of Major Facilities with advanced CI architecture designs
Forum for discussions about CI sustainability and workforce development and training
Overall, the CI CoE was very proactive in enabling and advancing constructive conversations during the development of the NCAR-NEON cyberinfrastructure technical plan and in providing feedback that improved the quality of the plan. The CI CoE facilitated weekly telecons among NCAR, NEON, and the CI CoE to discuss the proposed project and to provide guidance. Discussions during the telecom were mostly high level, helping to identify needs and priorities for the cyberinfrastructure collaborations, but also identifying gaps in the technical plan as well as providing overviews of various alternative implementation strategies. The discussions were always constructive, collaborative, and respectful of all participants. The CI CoE also undertook written feedback on the technical plan at several stages of development. Again, the comments were constructive and improved the quality of the final document. The CI CoE’s engagement was critical to preparing the final technical plan, in part because of the CI CoE’s familiarity with NEON’s data cyberinfrastructure, but also as external computer science experts familiar with the computational needs of the modeling (NCAR) and the data (NEON) and serving to bridge the two different areas of expertise.– Gordon Bonan, National Center for Atmospheric Research (NCAR)
The CI CoE had four types of profound influence on NEON developers. First, as we transitioned from construction to operations, our developers benefited from greater awareness of the wider NSF CI community practices. Second, deep engagement with CI CoE experts produced three major technologies insertions into NEON CI, remarkably within 6 months. Third, open dialog and prototyping with CI CoE experts affirmed our workflow-based sensor message handling strategy and built our confidence to invest in this novel method. NEON's CI and Data Sciences team mission includes advancing methods and ecological science; interaction with CI CoE nudged our efforts ahead significantly through community workgroup involvement, presentations and publications.– Tom Gulbransen, NEON
CICoE has been a valuable resource for the Ocean Observatories Initiative CI function. Their topics are relevant to our needs and have provided beneficial input to help us reach our goals. I look forward to working with them in the future to continue to meet the unique challenges facing research computing.– Jeff Glatstein, OOI
During the 2020 U.S. Academic Research Fleet Identity Management (IdM) Engagement with CI CoE Pilot, I participated from a customer perspective looking for help solving a complex and custom Identity Management problem on behalf of the fleet. The CI CoE Pilot was able to provide access to subject matters expertise for consulting and testing. During the engagement we were able to successfully assess the problem scope, draft a recommended solution, and proof of concept test several components of that solution. Following the engagement we have been able to leverage the recommended solution and newly established relationships from the engagement to write a proposal that would implement a Federated Identity Management solution for the U.S. Academic Research Fleet. Our engagement with CI CoE Pilot has been instrumental in accelerating efforts to realize this much needed service.– John Haverlack, College of Fisheries and Ocean Science, University of Alaska Fairbanks, U.S. Academic Research Fleet
The CICoE Pilot project developed a data lifecycle (DLC) model that identifies specific stages of the data flow within MFs – from scientific data collection to ultimate dissemination and use of the data by scientists. The DLC model also captures the specific functions and services offered at each DLC stage, the CI supporting each stage, and identifies CI and services that span and impact multiple DLC stages. The DLC stages are composed of 1) data capture/collection, 2) data processing near the instrument(s), 3) data processing at a central location (data center), 4) data storage, archiving, and curation, 5) data access, dissemination, and visualization.