Grids use a form of distributed computing to tackle complex computational and data processing problems scientists are presented with today.
Above sections have indicated that the similarity between the computational digital data processing and plant decision-making processes by defining the actions (states and transition functions) of the natural automata in plant systems.
However, analysis of fluorescence spectra requires the application of robust statistical and computational data pre-processing and analysis tools.
This is instrumental in enhancing the computational understanding and data processing of human mobility, urban dynamic processes, and urban operational functioning, a process that entails analysis, interpretation, modeling, and evaluation of big data for enhanced decision-making and deep insights.
The intensive use of ensemble data requires computational resources for data processing and storage, management of long-run simulations and transfer of huge datasets.
With the amount of data growing continuously, it is becoming increasingly daunting for small laboratories or even large institutions to establish and maintain computational infrastructures for data processing.
In future editions of GigaScience, we will be working with authors to make the computational tools and data processing pipelines described in their papers available and, where possible, executable on an informatics platform.
These findings were made possible not only by advances in experimental techniques but also because of the development of statistical and computational methods for data processing, filtering, normalisation and visualisation [ 19, 28– 33], and currently there is considerable work on developing new statistical methodologies for analysing this type of data [ 34, 35].
ANNs are computational modeling tools defined by structures comprising interconnected adaptive processing elements that perform parallel computations for data processing and interpretation [ 24].
