Research Software Sustainability: Lessons Learned at NCSA

This paper discusses why research software is important, and what sustainability means in this context. It then talks about how research software sustainability can be achieved, and what our experiences at NCSA have been using specific examples, what we have learned from this, and how we think these lessons can

[1]  Joe Futrelle,et al.  Medici : A Scalable Multimedia Environment for Research , 2011 .

[2]  Konrad Hinsen,et al.  Dealing With Software Collapse , 2019, Computing in Science & Engineering.

[3]  Daniel S. Katz,et al.  Research Software Development & Management in Universities: Case Studies from Manchester's RSDS Group, Illinois' NCSA, and Notre Dame's CRC , 2019, 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science).

[4]  Ian Foster,et al.  funcX: A Federated Function Serving Fabric for Science , 2020, HPDC.

[5]  Yan Zhao,et al.  Clowder: Open Source Data Management for Long Tail Data , 2018, PEARC.

[6]  Ian Foster,et al.  Parsl: Pervasive Parallel Programming in Python , 2019, HPDC.

[7]  Daniel S. Katz,et al.  Swift: A language for distributed parallel scripting , 2011, Parallel Comput..

[8]  Daniel S. Katz,et al.  Understanding Software in Research: Initial Results from Examining Nature and a Call for Collaboration , 2017, 2017 IEEE 13th International Conference on e-Science (e-Science).

[9]  J. Cohen,et al.  The Four Pillars of Research Software Engineering , 2021, IEEE Software.

[10]  Les Carr,et al.  UK Research Software Survey 2014 , 2014 .

[11]  Luigi Marini,et al.  Medici 2: a scalable content management system for cultural heritage datasets , 2017 .

[12]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[13]  Hussam Mahmoud,et al.  Hindcasting community-level building damage for the 2011 Joplin EF5 tornado , 2018, Natural Hazards.

[15]  Alfonso Valencia,et al.  Towards FAIR principles for research software , 2020, Data Sci..

[16]  James D. Myers,et al.  MAEviz: an earthquake risk assessment system , 2008, GIS '08.

[17]  Douglas Thain,et al.  Work Queue + Python: A Framework For Scalable Scientific Ensemble Applications , 2011 .

[18]  Peter Bajcsy,et al.  Towards a Universal Viewer for Digital Content , 2011, ICCS.