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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.18408 |
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| _version_ | 1866915303210876928 |
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| author | Hayot-Sasson, Valérie Stevens, Abby Collier, Nicholson Sridhar, Sudershan Conroy, Kyle Pauloski, J. Gregory Babuji, Yadu Gonthier, Maxime Hudson, Nathaniel Sanchez-Gallegos, Dante D. Foster, Ian Ozik, Jonathan Chard, Kyle |
| author_facet | Hayot-Sasson, Valérie Stevens, Abby Collier, Nicholson Sridhar, Sudershan Conroy, Kyle Pauloski, J. Gregory Babuji, Yadu Gonthier, Maxime Hudson, Nathaniel Sanchez-Gallegos, Dante D. Foster, Ian Ozik, Jonathan Chard, Kyle |
| contents | The COVID-19 pandemic highlighted the need for new data infrastructure, as epidemiologists and public health workers raced to harness rapidly evolving data, analytics, and infrastructure in support of cross-sector investigations. To meet this need, we developed AERO, an automated research and data sharing platform for continuous, distributed, and multi-disciplinary collaboration. In this paper, we describe the AERO design and how it supports the automatic ingestion, validation, and transformation of monitored data into a form suitable for analysis; the automated execution of analyses on this data; and the sharing of data among different entities. We also describe how our AERO implementation leverages capabilities provided by the Globus platform and GitHub for automation, distributed execution, data sharing, and authentication. We present results obtained with an instance of AERO running two public health surveillance applications and demonstrate benchmarking results with a synthetic application, all of which are publicly available for testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AERO: An autonomous platform for continuous research Hayot-Sasson, Valérie Stevens, Abby Collier, Nicholson Sridhar, Sudershan Conroy, Kyle Pauloski, J. Gregory Babuji, Yadu Gonthier, Maxime Hudson, Nathaniel Sanchez-Gallegos, Dante D. Foster, Ian Ozik, Jonathan Chard, Kyle Computational Engineering, Finance, and Science The COVID-19 pandemic highlighted the need for new data infrastructure, as epidemiologists and public health workers raced to harness rapidly evolving data, analytics, and infrastructure in support of cross-sector investigations. To meet this need, we developed AERO, an automated research and data sharing platform for continuous, distributed, and multi-disciplinary collaboration. In this paper, we describe the AERO design and how it supports the automatic ingestion, validation, and transformation of monitored data into a form suitable for analysis; the automated execution of analyses on this data; and the sharing of data among different entities. We also describe how our AERO implementation leverages capabilities provided by the Globus platform and GitHub for automation, distributed execution, data sharing, and authentication. We present results obtained with an instance of AERO running two public health surveillance applications and demonstrate benchmarking results with a synthetic application, all of which are publicly available for testing. |
| title | AERO: An autonomous platform for continuous research |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2505.18408 |