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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:CarrierCloud.png|180px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''[[Carrier cloud]]''' is class of [[cloud computing]] service that merges the high-performance capabilities and reliability of a communications service provider's network with the lower costs and flexibility provided by traditional public cloud services. The carrier cloud attempts to remove the data bottleneck and security issues that often occur in and to the virtualized data center due to lack of control of data flow over the public Internet.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Carrier cloud service is similar to public cloud service in that infrastructures are converged into a single, optimized computing package, and services are shared across a group or organization. Carrier cloud service, however, utilizes the existing and upgraded network structures of the communication service provider (CSP) to provide end-to-end services over their own network. Since the CSP more readily controls the data flow through its content delivery networks and/or dedicated virtual private networks, it can better manage issues with bandwidth, latency, and jitter. Additional "last mile" carrier-grade services already provided by CSPs in cities also "offset the latencies associated with cross-country or inter-continental backhaul." ('''[[Carrier cloud|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...)

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