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https://hdl.handle.net/10356/179972
Title: | The digital lab manager: automating research support | Authors: | Rihm, Simon D. Tan, Yong Ren Ang, Wilson Hofmeister, Markus Deng, Xinhong Laksana, Michael Teguh Quek, Hou Yee Bai, Jiaru Pascazio, Laura Siong, Sim Chun Akroyd, Jethro Mosbach, Sebastian Kraft, Markus |
Keywords: | Other | Issue Date: | 2024 | Source: | Rihm, S. D., Tan, Y. R., Ang, W., Hofmeister, M., Deng, X., Laksana, M. T., Quek, H. Y., Bai, J., Pascazio, L., Siong, S. C., Akroyd, J., Mosbach, S. & Kraft, M. (2024). The digital lab manager: automating research support. SLAS Technology, 29(3), 100135-. https://dx.doi.org/10.1016/j.slast.2024.100135 | Project: | CREATE | Journal: | SLAS Technology | Abstract: | Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph. This Digital Laboratory Framework serves as a flexible tool, enabling users to efficiently leverage data from diverse systems and formats without being confined to a specific software or protocol. Establishing dedicated ontologies and agents and combining them with technologies such as QR codes, RFID tags, and mobile apps, enabled us to develop modular applications that tackle some key challenges related to lab management. Here, we showcase an automated tracking and intervention system for explosive chemicals as well as an easy-to-use mobile application for asset management and information retrieval. Implementing these, we have achieved semantic linking of BIM and BMS data with laboratory inventory and chemical knowledge. Our approach can capture the crucial data points and reduce inventory processing time. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability. | URI: | https://hdl.handle.net/10356/179972 | ISSN: | 2472-6303 | DOI: | 10.1016/j.slast.2024.100135 | Schools: | School of Chemical and Biomedical Engineering | Organisations: | Cambridge Centre for Advanced Research and Education in Singapore | Rights: | © 2024 The Author(s). Published by Elsevier Inc. on behalf of Society for Laboratory Automation and Screening. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCBE Journal Articles |
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PIIS2472630324000177.pdf | 2.69 MB | Adobe PDF | View/Open |
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