SciOps: Achieving Productivity and Reliability in Data-Intensive Research
Abstract
Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals, leading to new bursts of discovery. Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, reproducibility, and automation. Drawing inspiration from advancements in the software industry, we present a roadmap to enhance the reliability and scalability of scientific operations for diverse research teams tackling large and complex projects. We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations in projects ranging from small-scale exploratory studies to large-scale, multi-disciplinary research endeavors. Achieving higher levels of operational maturity necessitates the adoption of new, technology-enabled methodologies, which we refer to as SciOps. This concept is derived from the DevOps methodologies that have revolutionized the software industry. SciOps involves digital research environments that seamlessly integrate computational, automation, and AI-driven efforts throughout the research cycle-from experimental design and data collection to analysis and dissemination, ultimately leading to closed-loop discovery. This maturity model offers a framework for assessing and improving operational practices in multidisciplinary research teams, guiding them towards greater efficiency and effectiveness in scientific inquiry.
Cite
@article{arxiv.2401.00077,
title = {SciOps: Achieving Productivity and Reliability in Data-Intensive Research},
author = {Erik C. Johnson and Thinh T. Nguyen and Benjamin K. Dichter and Frank Zappulla and Montgomery Kosma and Kabilar Gunalan and Yaroslav O. Halchenko and Shay Q. Neufeld and Kristen Ratan and Nicholas J. Edwards and Susanne Ressl and Sarah R. Heilbronner and Michael Schirner and Petra Ritter and Brock Wester and Satrajit Ghosh and Maryann E. Martone and Franco Pestilli and Dimitri Yatsenko},
journal= {arXiv preprint arXiv:2401.00077},
year = {2024}
}
Comments
16 pages, two figures