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Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.
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Scientific Discovery at the Press of a Button: Navigating Emerging Cloud Laboratory Technology
Abstract The “cloud lab,” an automated laboratory that allows researchers to program and conduct physical experiments remotely, represents a paradigm shift in scientific practice. This shift from wet‐lab research as a primarily manual enterprise to one more akin to programming bears incredible promise by democratizing a completely new level of automation and its advantages to the scientific community. Moreover, they provide a foundation on which automated science driven by artificial intelligence (A.I.) can be built upon and thereby resolve limitations in scope and accessibility that current systems face. With a focus on DNA nanotechnology, the authors have had the opportunity to explore and apply the cloud lab to active research. This perspective delves into the future potential of cloud labs in accelerating scientific research and broadening access to automation. The challenges associated with the technology in its current state are further explored, including difficulties in experimental troubleshooting, the limited applicability of its parallelization in an academic setting, as well as the potential reduction in experimental flexibility associated with the approach.
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- Award ID(s):
- 1944130
- PAR ID:
- 10573763
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Advanced Materials Technologies
- Volume:
- 9
- Issue:
- 16
- ISSN:
- 2365-709X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/admt.202400084
