Sentence examples for machine readability and from inspiring English sources

(10) The specifications of the International Civil Aviation Organisation ICAOO) Document 9303 (seventh edition, 2015) on machine-readable documents which ensure global interoperability including for machine readability and using visual inspection should be taken into account for the purpose of this Regulation.

(10) The specifications of the International Civil Aviation Organisation ICAOO) Document 9303 (seventh edition, 2015) on machine-readable documents which ensure global interoperability including for machine readability and using visual inspection should be taken into account by the Commission when drawing up additional technical specifications.

This change will be supported by changes to the underlying structure of our content to promote machine readability and reuse of links between scholarly articles and datasets.

Thus, availability on the web under an open license, machine readability and use of non-proprietary formats form the core exposure requirements for public big data resources.

The road map engine described in this report demonstrates the sufficiency of the first 3-stars in (Berners-Lee, 2007) (availability on the web under an open license, machine readability and non-proprietary format) as a minimal foundation, on which the community of users of the data may freely build the remaining elements (RDF representations, and linking annotations of that RDF).

But when the reporting structure is normalized and enforced, machine readability is automatic and painless.

But when the reporting structure is normalized and enforced, machine readability is automatic and painless.

This culture shift requires a recognition of the primacy of machine readability: database maintainers and journals must do their best to ensure that digital content makes sense (e.g. chemical structures can be resolved to a distinct molecular formula, properties have units, etc).

For example, digital makes it possible to publish associated datasets and to disambiguate authors, funders, and institutions and digital has led to new, complex, standards for things like content capture and metadata to improve discoverability and machine readability.

In this paper, aiming at novel documents, we propose a topic modeling based approach to extractive automatic summarization, so as to achieve a good balance among compression ratio, summarization quality and machine readability.

The attention that the Directory pays to both the social aspect of annotation and its machine readability reflects the trend towards the development of communities of interest [ 73] and towards the formalization of the research process, which is explored in [ 74].