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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Pillai FrontBioengBiotech2022 10.jpg|120px]]</div>
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'''"[[Journal:Practical considerations for laboratories: Implementing a holistic quality management system|Practical considerations for laboratories: Implementing a holistic quality management system]]"'''
'''"[[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 />


A [[quality management system]] (QMS) is an essential element for the effective operation of [[research]], clinical, testing, or production/manufacturing [[Laboratory|laboratories]]. As technology continues to rapidly advance and new challenges arise, laboratories worldwide have responded with innovation and process changes to meet the continued demand. It is critical for laboratories to maintain a robust QMS that accommodates laboratory activities (e.g., basic and applied research; regulatory, clinical, or proficiency testing), records management, and a path for [[Continual improvement process|continuous improvement]] to ensure that results and data are reliable, accurate, timely, and reproducible. A robust, suitable QMS provides a framework to address gaps and risks throughout the laboratory's [[workflow]] that could potentially lead to a critical error, thus compromising the integrity and credibility of the institution. While there are many QMS frameworks (e.g., a model such as a consensus standard, guideline, or regulation) that may apply to laboratories, ensuring that the appropriate framework is adopted based on the type of work performed and that key implementation steps are taken is important for the long-term success of the QMS and for the advancement of science ... ('''[[Journal:Practical considerations for laboratories: Implementing a holistic quality management system|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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