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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Schröder JofBioSem22 13.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:Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation|Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation]]"'''
'''"[[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 />


[[Electronic laboratory notebook]]s (ELNs) are used to document experiments and investigations in the wet [[Laboratory|lab]]. Protocols in ELNs contain a detailed description of the conducted steps, including the necessary [[information]] to understand the procedure and the raised research data, as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create [[wikipedia:Semantics|semantic]] documentation of the provenance of research data by the use of [[Ontology (information science)|ontologies]] and linked data methodologies. Based on an ELN protocol of a biomedical wet lab experiment, a retrospective [[wikipedia:Provenance#Data provenance|provenance model]] of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment’s description—such as headings, tables, and links—to translate the ELN protocol into a semantic knowledge representation ... ('''[[Journal:Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation|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...)

Recently featured: