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Visual inspection and the possibility for manual curation is important at all data abstraction levels, i.e. from RD to DD. Intuitive and responsive data visualizations are required as well as intelligent tools that allow to solve common tasks, such as the inspection of processing results or the navigation from an experiments overview to the spectrum of a single peak, in a few steps.
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Data at different abstraction levels (e.g., full images versus images where people's faces have been removed versus textual event descriptions) can be encrypted with different binding keys.
C-ME organizes data into a hierarchical structure that displays the organization of a data set at three abstraction levels: the Project (multiple) level, the Entity level, and the Annotation level.
The design steps that will be addressed include: system specification, simulation at various abstraction levels, data path synthesis, communication refinement and hardware/software co-design.
HotML is based on our prior work DPS that provides high-level data abstraction, lightweight task scheduling system, and SSP consistency.
Three grounding principles, namely autonomy, adaptability and emergence, support the co-construction of successive abstraction levels for data interpretation.
The extracted data is rerendered and multiple streams with different levels of data abstraction are created.
Terminology bindings can be vital in providing the different levels of data abstraction or granularity that guidelines often demand.
JK lead author, contributed to all elements of the study; specifically: study design; primary literature reviewer; responsible for executing the search for abstracts and grading for level of evidence; data abstraction, analysis and presentation; primary manuscript writer and responsible for all encompassing and all subsequent revisions throughout the editing process.
To enable us to implement the concrete level of our data abstraction, Python provides a compound structure called a tuple, which can be constructed by separating values by commas.
Although the majority of the reviews reported a two reviewer selection and data abstraction processes, only 35(5%) reported agreement levels among the reviewers.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com