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As research projects grow more complex and researchers use a mix of tools - command-line scripts, science gateways, and Jupyter notebooks - it becomes increasingly difficult to track exactly how a final result was produced. Each tool often keeps its own logs, making it hard to reconstruct the full sequence of computational steps. This lack of end-to-end visibility poses a serious challenge for scientific reproducibility. Yet advanced computing remains a critical part of nearly every field of academic research, and researchers continue to rely on a wide range of interfaces to run their scientific software. To address this challenge, the Advanced Computing Interfaces group at the Texas Advanced Computing Center (TACC) created a system that collates logs from multiple sources - science gateways, Jupyter notebooks, and the Tapis platform - into one unified “audit trail.” The TACC Research Audit and Integration of Logs (TRAIL) system allows researchers and staff to follow the complete path a dataset or file took: from the moment it was first uploaded to TACC, through every step of computation, to the final result. This kind of tracking helps ensure scientific results can be reproduced and gives advanced computing services better insight into how data and resources are being used. 

Abstract Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.