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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Martin LIBER2017 27-1.jpg|240px]]</div>
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'''"[[Journal:Data management: New tools, new organization, and new skills in a French research institute|Data management: New tools, new organization, and new skills in a French research institute]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


In the context of e-science and open access, visibility and impact of scientific results and data have become important aspects for spreading [[information]] to users and to the society in general. The objective of this general trend of the economy is to feed the innovation process and create economic value. In our institute, the French National Research Institute of Science and Technology for Environment and Agriculture, Irstea, the department in charge of scientific and technical information, with the help of other professionals (scientists, IT professionals, ethics advisors, etc.), has recently developed suitable services for researchers and their data management needs in order to answer European recommendations for open data. This situation has demanded a review of the different workflows between databases, questioning the organizational aspects among skills, occupations, and departments in the institute. In fact, data management involves all professionals and researchers assessing their workflows together. ('''[[Journal:Data management: New tools, new organization, and new skills in a French research institute|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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