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The explosion of big data has resulted in both a dramatic increase in the volume of available data and the possibilities of how to use that data.
Major milestones include the automated control of multiple, mobile sensors for weeks using spatial coverage metrics and the transition from engineering a reliable data stream to managing the complexities of decision-making based on the data and the possibilities of timely feedback.
We need to take a similar approach to open access to data and the possibilities of multiple follow-on analyses and their potential for false-positive and false-positive anddings.
Use of literature data may occasionally be considered as an alternative source for quantifying background variation, but may present great difficulties both regarding the representativity of the data and the possibilities to discern the different components of variation.
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That means a loss of valuable research data and the possibility of more ship strikes.
We suggest a link between our data and the possibility of observing and modelling urban geometric structures using Lévy processes.
The present study indicates that the supervised LVQ1 architecture performs satisfactorily to identify the geochemical characters in the data and the possibility of mis-characterization is minimal.
The framework extends current approaches by explicitly considering temporal aspects of system responses to disruptions, volatility in system performance data, and the possibility of multiple disruption events.
Despite the observed differences, the emphasis on the individuals' control on their data and the possibility of sharing data have been seen as a key theme across these initiatives.
Since each method has its own strengths and weaknesses, the method used for a particular research question should be chosen depending on the object of research, the availability of data, and the possibility for conducting field experimentation.
It is important to note that the causal implications of these models are limited given the cross-sectional nature of the PIAAC data and the possibility of omitted variable bias.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com