MPI Derived Data Types Support in VIRTUS

2000 ◽  
pp. 84-99 ◽  
Author(s):  
Rosario Cristaldi ◽  
Giulio Iannello
Keyword(s):  
Data Types ◽  
Derived Data

Advanced type parameter features

2018 ◽  
Author(s):  
Michael Metcalf ◽  
John Reid ◽  
Malcolm Cohen
Keyword(s):  
Data Types ◽  
Derived Data ◽  
Type Parameter

The advanced type parameter features consist of type parameter enquiry and the ability to parameterize derived-data types.

scholarly journals Geostatistical merging of ground-based and satellite-derived data of surface solar radiation

2011 ◽  
Vol 6 (1) ◽  
pp. 1-5 ◽  
Author(s):  
M. Journée ◽  
C. Bertrand
Keyword(s):  
Solar Radiation ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Data Types ◽  
Surface Solar Radiation ◽  
Geostatistical Methods ◽  
Radiation Data ◽  
Single Use ◽  
Derived Data

Abstract. In this paper, we demonstrate the benefit of using observations from Meteosat Second Generation (MSG) satellites in addition to in-situ measurements to improve the spatial resolution of solar radiation data over Belgium. This objective has been reached thanks to geostatistical methods able to merge heterogeneous data types. Two geostatistical merging methods are evaluated against the interpolation of ground-data only and the single use of satellite-derived information. It results from our analysis that merging both data sources provides the most accurate mapping of surface solar radiation over Belgium.

scholarly journals A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

2019 ◽  
Author(s):  
Antoine Bodein ◽  
Olivier Chapleur ◽  
Arnaud Droit ◽  
Kim-Anh Lê Cao
Keyword(s):  
Microbial Communities ◽  
Time Course ◽  
Molecular Mechanisms ◽  
Smoothing Splines ◽  
Individual Variability ◽  
Integrated Analysis ◽  
Data Types ◽  
Phenotypic Data ◽  
Different Types ◽  
Derived Data

AbstractSimultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g. sparsity, compositional).We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial communities data, and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modelling, to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

Expressions and assignments

2018 ◽  
Author(s):  
Michael Metcalf ◽  
John Reid ◽  
Malcolm Cohen
Keyword(s):  
Data Types ◽  
Derived Data

A description of numerical, relational, and logical expressions and assignments, also involving objects of derived data types and pointers. Both scalar and array variables are considered.

Language elements

2018 ◽  
Author(s):  
Michael Metcalf ◽  
John Reid ◽  
Malcolm Cohen
Keyword(s):  
Data Types ◽  
Character Set ◽  
Source Form ◽  
Derived Data

An introduction to the character set, the source form, types, constants, scalar variables, derived data types, arrays, the concepts of objects and subobjects, and allocatable and pointer objects.

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

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