Difference between revisions of "Template:Article of the week"

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text.)
(Updated article of the week text)
 
(72 intermediate revisions by the same user not shown)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 AbuHalimeh FrontBigData2022 5.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:Improving data quality in clinical research informatics tools|Improving data quality in clinical research informatics tools]]"'''
'''"[[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 />


Maintaining [[data quality]] is a fundamental requirement for any successful and long-term [[Information management|data management]] project. Providing high-quality, reliable, and statistically sound data is a primary goal for [[wikipedia:Clinical research|clinical research]] [[Informatics (academic field)|informatics]]. In addition, effective data governance and management are essential to ensuring accurate data counts, reports, and validation. As a crucial step of the clinical research process, it is important to establish and maintain organization-wide standards for data quality management to ensure consistency across all systems designed primarily for cohort identification ... ('''[[Journal:Improving data quality in clinical research informatics tools|Full article...]]''')<br />
<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:Electronic tools in clinical laboratory diagnostics: Key examples, limitations, and value in laboratory medicine|Electronic tools in clinical laboratory diagnostics: Key examples, limitations, and value in laboratory medicine]]
* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
* [[Journal:Anatomic pathology quality assurance: Developing an LIS-based tracking and documentation module for intradepartmental consultations|Anatomic pathology quality assurance: Developing an LIS-based tracking and documentation module for intradepartmental consultations]]
* [[Journal:Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study|Critical analysis of the impact of AI on the patient–physician relationship: A multi-stakeholder qualitative study]]
* [[Journal:Using knowledge graph structures for semantic interoperability in electronic health records data exchanges|Using knowledge graph structures for semantic interoperability in electronic health records data exchanges]]
* [[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]
}}
}}

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...)

Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=64398"