This contribution reviews recent concepts for large-scale SAR analysis including numerical functions to characterize global and local SAR information content of compound data sets, alternative activity landscape representations and data mining strategies.
We convened workshops that included stakeholders and experts in toxicology, epidemiology, children's health, exposure assessment, and public health surveillance to discuss conceptual approaches, topics to be addressed, data sources, metrics, graphical representations, and data gaps.
Three key issues regarding the engineering data access system are discussed, including data modeling, data representation, and data retrieval.
The integration is based on the introduction of a conceptual level, linking together a subconceptual, behavioral, level, and a linguistic level, encompassing symbolic representation and data processing.
Since standards and protocols, such as those respecting descriptive metadata and knowledge representation, and data sets constitute additional key resources for legal informatics research, links to many of them have been collected on the Legal Information Systems site.
While the initial workshops focused on the implementation of chemical databases, other topics, such as structure elucidation, structure representation and data mining, gained importance over the following years.
As concluded above, the incorporation of this analysis in combinatorial PV research provides a substantial addition to the investigation cycle for device physics and material studies via data representation and data mining.
Concept analysis is a method of conceptual knowledge representation and data analysis that can be used to clarify meanings and develop operational definitions, through considering evidence from multiple disciplines and sources.
These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others.
The quality data representation is divided into measured data representation, non-measured data representation, and summarized data representation.
The measured raw data and averaged data are represented as "measured data representation" and "summarized data representation," respectively, on the basis of the ontology.
