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The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. SciTokens introduces a capabilities-based authorization infrastructure for distributed scientific computing, to help scientists manage their security credentials more reliably and securely. SciTokens uses IETF-standard OAuth JSON Web Tokens for capability-based secure access to remote scientific data. These access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems. In this extended abstract, we present the results over the past year of our open source implementation of the SciTokens model and its deployment in the Open Science Grid, including new OAuth support added in the HTCondor 8.8 release series.
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This content will become publicly available on January 1, 2026
Deep Ocean Early-Career Researchers Dive into Data Management Training
Open access to scientific data is increasingly recognized as critical to fostering scientific progress, trustworthy and reproducible science, global information equity, and evidence-based policymaking. It requires scientists to not only share their data, but to share in such a way that the data have high utility for later users. The FAIR data principles define a set of characteristics for making data “findable, accessible, interoperable, and reusable” (Wilkinson et al., 2016). Training scientists, particularly early-career scientists, on these principles can improve the volume and quality of open science data.
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- PAR ID:
- 10599688
- Publisher / Repository:
- The Oceanography Society
- Date Published:
- Journal Name:
- Oceanography
- ISSN:
- 1042-8275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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