TY - JOUR
T1 - Building a vision for reproducibility in the cyberinfrastructure ecosystem
T2 - Leveraging community efforts
AU - Chapp, Dylan
AU - Stodden, Victoria
AU - Taufer, Michela
N1 - This work was performed with the support of the National Science Foundation awards: OAC #1941443 EAGER: Reproducibility and Cyberinfrastructure for Computational and Data-Enabled Science; OAC #1841399 Collaborative: EAGER: Exploring and Advancing the State of the Art in Robust Science in Gravitational Wave Physics; OAC #1839010: EAGER: Preserve/Destroy Decisions for Simulation Data in Computational Physics and Beyond; and OAC #1541450: CC*DNI DIBBS: Merging Science and Cyberinfrastructure Pathways: The Whole Tale.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The scientific computing community has long taken a leadership role in understanding and assessing the relationship of reproducibility to cyberinfrastructure, ensuring that computational results-such as those from simulations-are "reproducible", that is, the same results are obtained when one re-uses the same input data, methods, software and analysis conditions. Starting almost a decade ago, the community has regularly published and advocated for advances in this area. In this article we trace this thinking and relate it to current national efforts, including the 2019 National Academies of Science, Engineering, and Medicine report on "Reproducibility and Replication in Science". To this end, this work considers high performance computing workflows that emphasize workflows combining traditional simulations (e.g. Molecular Dynamics simulations) with in situ analytics. We leverage an analysis of such workflows to (a) contextualize the 2019 National Academies of Science, Engineering, and Medicine report's recommendations in the HPC setting and (b) envision a path forward in the tradition of community driven approaches to reproducibility and the acceleration of science and discovery. The work also articulates avenues for future research at the intersection of transparency, reproducibility, and computational infrastructure that supports scientific discovery.
AB - The scientific computing community has long taken a leadership role in understanding and assessing the relationship of reproducibility to cyberinfrastructure, ensuring that computational results-such as those from simulations-are "reproducible", that is, the same results are obtained when one re-uses the same input data, methods, software and analysis conditions. Starting almost a decade ago, the community has regularly published and advocated for advances in this area. In this article we trace this thinking and relate it to current national efforts, including the 2019 National Academies of Science, Engineering, and Medicine report on "Reproducibility and Replication in Science". To this end, this work considers high performance computing workflows that emphasize workflows combining traditional simulations (e.g. Molecular Dynamics simulations) with in situ analytics. We leverage an analysis of such workflows to (a) contextualize the 2019 National Academies of Science, Engineering, and Medicine report's recommendations in the HPC setting and (b) envision a path forward in the tradition of community driven approaches to reproducibility and the acceleration of science and discovery. The work also articulates avenues for future research at the intersection of transparency, reproducibility, and computational infrastructure that supports scientific discovery.
KW - High-performance computing
KW - In situ analytics
KW - Molecular dynamics
KW - Replicability
KW - Reproducibility
KW - Transparency
UR - http://www.scopus.com/inward/record.url?scp=85086357155&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086357155&partnerID=8YFLogxK
U2 - 10.14529/js200106
DO - 10.14529/js200106
M3 - Article
AN - SCOPUS:85086357155
SN - 2409-6008
VL - 7
SP - 112
EP - 119
JO - Supercomputing Frontiers and Innovations
JF - Supercomputing Frontiers and Innovations
IS - 1
ER -