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'''"[[Journal:A pocket guide to electronic laboratory notebooks in the academic life sciences|A pocket guide to electronic laboratory notebooks in the academic life sciences]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Every professional doing active research in the life sciences is required to keep a [[laboratory notebook]]. However, while science has changed dramatically over the last centuries, laboratory notebooks have remained essentially unchanged since pre-modern science. We argue that the implementation of [[electronic laboratory notebook]]s (ELN) in academic research is overdue, and we provide researchers and their institutions with the background and practical knowledge to select and initiate the implementation of an ELN in their laboratories. In addition, we present data from surveying biomedical researchers and technicians regarding which hypothetical features and functionalities they hope to see implemented in an ELN, and which ones they regard as less important. We also present data on acceptance and satisfaction of those who have recently switched from paper laboratory notebook to an ELN. We thus provide answers to the following questions: What does an electronic laboratory notebook afford a biomedical researcher, what does it require, and how should one go about implementing it? ('''[[Journal:A pocket guide to electronic laboratory notebooks in the academic life sciences|Full article...]]''')<br />
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />
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Latest revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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