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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Arendt ClinEpidem2020 12.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Existing data sources in clinical epidemiology: Laboratory information system databases in Denmark|Existing data sources in clinical epidemiology: Laboratory information system databases in Denmark]]"'''
'''"[[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 />


Routine [[biomarker]] results from [[hospital]] [[laboratory information system]]s (LIS)—covering hospitals and general practitioners—in Denmark are available to researchers through access to the regional Clinical Laboratory Information System Research Database at Aarhus University and the nationwide Register of Laboratory Results for Research. This review describes these two data sources. The [[laboratory]] databases have different geographical and temporal coverage. They both include individual-level biomarker results that are electronically transferred from LISs. The biomarker results can be linked to all other Danish registries at the individual level using the unique identifier, the CPR number. ('''[[Journal:Existing data sources in clinical epidemiology: Laboratory information system databases in Denmark|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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