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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Riley JOfBioEng2017 11.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory|Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


[[Electronic laboratory notebook]]s (ELNs) are better equipped than paper [[laboratory notebook]]s (PLNs) to handle present-day life science and engineering experiments that generate large data sets and require high levels of data integrity. But limited training and a lack of workforce with ELN knowledge have restricted the use of ELN in academic and industry research [[Laboratory|laboratories]], which still rely on cumbersome PLNs for record keeping. We used [[LabArchives, LLC|LabArchives]], a cloud-based ELN in our bioprocess engineering lab course to train students in electronic record keeping, good documentation practices (GDPs), and [[data integrity]].
[[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 />


Implementation of ELN in the bioprocess engineering lab course, an analysis of user experiences, and our development actions to improve ELN training are presented here. ELN improved pedagogy and learning outcomes of the lab course through streamlined workflow, quick data recording and archiving, and enhanced data sharing and collaboration. It also enabled superior data integrity, simplified information exchange, and allowed real-time and remote monitoring of experiments. ('''[[Journal:Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory|Full article...]]''')<br />
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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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