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A '''[[scientific data management system]]''' (SDMS) is a piece or package of software that acts as a document management system (DMS), capturing, cataloging, and archiving data generated by [[laboratory]] instruments ([[HPLC]], [[mass spectrometry]]) and applications ([[LIMS]], analytical applications, [[electronic laboratory notebook]]s) in a compliant manner. The SDMS also acts as a gatekeeper, serving platform-independent data to informatics applications and/or other consumers.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


As with many other [[laboratory informatics]] tools, the lines between a [[LIMS]], [[ELN]], and an SDMS are at times blurred. However, there are some essential qualities that an SDMS owns that distinguishes it from other informatics systems. It's built to handle unstructured, mostly heterogeneous data; it typically acts as a seamless "wrapper" for other data systems like LIMS and ELN in the laboratory; and it is designed primarily for data consolidation, knowledge management, and knowledge asset realization. An SDMS also must be focused on increasing research productivity without sacrificing data sharing and collaboration efforts. ('''[[Scientific data management system|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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