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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Kempler DataScienceJournal2017 16.png|240px]]</div>
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'''"[[Journal:Earth science data analytics: Definitions, techniques and skills|Earth science data analytics: Definitions, techniques and skills]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


The continuous evolution of data management systems affords great opportunities for the enhancement of knowledge and advancement of science research. To capitalize on these opportunities, it is essential to understand and develop methods that enable data relationships to be examined and [[information]] to be manipulated. Earth science data analytics (ESDA) comprises the techniques and skills needed to holistically extract information and knowledge from all sources of available, often heterogeneous, data sets. This paper reports on the ground-breaking efforts of the Earth Science Information Partners' (ESIP) ESDA Cluster in defining ESDA and identifying ESDA methodologies. As a result of the void of earth science data analytics in the literature, the ESIP ESDA definition and goals serve as an initial framework for a common understanding of techniques and skills that are available, as well as those still needed to support ESDA. Through the acquisition of earth science research use cases and categorization of ESDA result oriented research goals, ESDA techniques/skills have been assembled. The resulting ESDA techniques/skills provide the community with a definition for ESDA that is useful in articulating data management and research needs, as well as a working list of techniques and skills relevant to the different types of ESDA. ('''[[Journal:Earth science data analytics: Definitions, techniques and skills|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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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...)

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