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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Alsaffar JMIRMedicalInfo2017 5-1.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:The state of open-source electronic health record projects: A software anthropology study|The state of open-source electronic health record projects: A software anthropology study]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''
[[Electronic health record]]s (EHR) are a key tool in managing and storing patients’ [[information]]. Currently, there are over 50 open-source EHR systems available. Functionality and usability are important factors for determining the success of any system. These factors are often a direct reflection of the domain knowledge and developers’ motivations. However, few published studies have focused on the characteristics of [[free and open-source software]] (F/OSS) EHR systems, and none to date have discussed the motivation, knowledge background, and demographic characteristics of the developers involved in open-source EHR projects.


This study analyzed the characteristics of prevailing F/OSS EHR systems and aimed to provide an understanding of the motivation, knowledge background, and characteristics of the developers. ('''[[The state of open-source electronic health record projects: A software anthropology study|Full article...]]''')<br />
[[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 />
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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...)

Recently featured: