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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Xu BMCMedEd23 23.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Evaluating the effectiveness of a new student-centred laboratory training strategy in clinical biochemistry teaching|Evaluating the effectiveness of a new student-centred laboratory training strategy in clinical biochemistry teaching]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|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 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 />


The error-proneness in the pre-analytical and post-analytical stages is higher than that in the analytical stage of the total [[laboratory]] testing process. However, pre-analytical and post-analytical [[Quality (business)|quality]] management has not received enough attention in [[Clinical laboratory|medical laboratory]] education and tests in clinical [[biochemistry]] courses. Clinical biochemistry teaching programs aim to improve students’ awareness of and ability to use quality management practices according to the [[International Organization for Standardization]]'s [[ISO 15189]] requirements ... ('''[[Journal:Evaluating the effectiveness of a new student-centred laboratory training strategy in clinical biochemistry teaching|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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