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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Liscouski DirectLabSysOnePerPersp21.png|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[LII:Directions in Laboratory Systems: One Person's Perspective|Directions in Laboratory Systems: One Person's Perspective]]"'''
'''"[[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 purpose of this work is to provide one person's perspective on planning for the use of computer systems in the [[laboratory]], and with it a means of developing a direction for the future. Rather than concentrating on “science first, support systems second,” it reverses that order, recommending the construction of a solid support structure before populating the lab with systems and processes that produce knowledge, information, and data (K/I/D). This material is intended for those working in laboratories of all types. The biggest benefit will come to those working in startup labs since they have a clean slate to work with, as well as those freshly entering into scientific work as it will help them understand the roles of various systems. Those working in existing labs will also benefit by seeing a different perspective than they may be used to, giving them an alternative path for evaluating their current structure and how they might adjust it to improve operations ... ('''[[LII:Directions in Laboratory Systems: One Person's Perspective|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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