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We have developed RImmPort that prepares NIAID-funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies.
The neutron monitor data of a specific station depend on the modelling used for the particular monitor as well as the fact they are at ground level.
The examined optimization problem uses data of a specific site and is solved in the single and two-objective modes, considering, together with economic, some additional objectives, as maximization of the produced energy and the best exploitation of the water stream potential.
Therefore, if a profile indicates about transmitting a message it means that all the vendor devices will transmit it, hence we can perform in general similar operations to the data of a specific type of all the sensors that meet a particular profile.
The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes.
We inspected three major issues in the process: 1) indoor temperature data of a specific room collected at a second interval; 2) such a data set can be varied by subdivided spots of interest using multiple toolkits; 3) as a result, the collection of data is regarded as one type of parameter for visualization on top of the room's floor plan, e.g. a sudden change of sequential numbers.
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We focused the machine learning analysis of averaged data on a specific subset of voxels that showed a consistent activation pattern across the six trials of each target object32,33.
Nevertheless, every empirical method faces this issue due to the unavoidable dependency of data on a specific context.
Focus group discussions (FGD) serve as a useful research methodology to gather qualitative data on a specific topic of interest.
"If consent has been given, it is for the use of data for a specific purpose.
The pseudonymisation process permits linkage of data for a specific patient registered in different departments and guarantees data confidentiality, because pseudonymisation prevents identification of the patient.
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