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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Nambiar BigDataCogComp22 6-4.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:An overview of data warehouse and data lake in modern enterprise data management|An overview of data warehouse and data lake in modern enterprise data management]]"'''
'''"[[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 />


Data is the lifeblood of any organization. In today’s world, organizations recognize the vital role of data in modern [[business intelligence]] systems for making meaningful decisions and staying competitive in the field. Efficient and optimal data analytics provides a competitive edge to its performance and services. Major organizations generate, collect, and process vast amounts of data, falling under the category of "big data." [[Information management|Managing]] and [[Data analysis|analyzing]] the sheer volume and variety of big data is a cumbersome process. At the same time, proper utilization of the vast collection of an organization’s [[information]] can generate meaningful insights into business tactics. In this regard, two of the more popular data management systems in the area of big data analytics—the [[data warehouse]] and [[data lake]]—act as platforms to accumulate the big data generated and used by organizations ... ('''[[Journal:An overview of data warehouse and data lake in modern enterprise data management|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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