SciOps: Achieving Productivity and Reliability in Data-Intensive Research

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, re...

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Bibliographic Details
Main Authors:	Johnson, Erik C, Nguyen, Thinh T, Dichter, Benjamin K, Zappulla, Frank, Kosma, Montgomery, Gunalan, Kabilar, Halchenko, Yaroslav O, Neufeld, Shay Q, Ratan, Kristen, Edwards, Nicholas J, Ressl, Susanne, Heilbronner, Sarah R, Schirner, Michael, Ritter, Petra, Wester, Brock, Ghosh, Satrajit, Martone, Maryann E, Pestilli, Franco, Yatsenko, Dimitri
Format:	Journal Article
Language:	English
Published:	29-12-2023
Subjects:	Computer Science - Computers and Society Quantitative Biology - Neurons and Cognition
Online Access:	Get full text
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Summary:	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.
DOI:	10.48550/arxiv.2401.00077