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'''[[Software as a service]]''' ('''SaaS''') — sometimes referred to as "on-demand software" — is a software delivery model in which software and its associated data are hosted centrally (on the [[Cloud computing|cloud]], for example) and are typically accessed by users using a thin client, normally using a web browser over the Internet. The customer subscribes to this "service" rather than requiring a software license, and the software doesn't require an implementation on customer premises.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


A SaaS solution is typically a "multi-tenant solution," meaning more than one entity is sharing the server and database resource(s) hosted by the vendor, though in the process potentially limiting customer customization. With this model, a single version of the application with a single configuration (hardware, network, operating system, etc.) is used for all customers. To support scalability, the application is installed on multiple machines. In some cases, a second version of the application may be set up to offer a select group of customers a separate instance of the software environment, better enabling customers to customize their configuration. (This could be accomplished with platform as a service (PaaS), for example. This is contrasted with traditional software, where multiple physical copies of the software — each potentially of a different version, with a potentially different configuration, and often customized — are installed across various customer sites. ('''[[Software as a service |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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