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That's the reason that metadata is often more intrusive.
Metadata is often formatted as XML, and there are metadata standards, such as Dublin Core.
Secondly, the lack of metadata is often used as an excuse for being unable to identify the source of an image.
It is the hardest part to gather, to make correct, and to maintain; not surprisingly, metadata is often inaccurate and incomplete.
However, in existing large-scale publication databases – such as Thomson Reuter's Web of Science (WoS) – metadata is often sparse, especially for older publications.
Similar(55)
But much of the new data created is metadata – data about people and their communication patterns – and metadata alone is often enough to create a real breach of privacy.
It is the untargeted collection of metadata – which is often mislabelled as innocuous because it's not the direct content of messages – that poses the greater threat to journalism as a whole.
The metadata model is often related to the standard formats used.
Some of these systems have proved to be useful in large-scale projects, and the metadata model is often related to the standard format used [ 1].
The metadata in public databases is often confusing; a test case with Zea mays mRNA seq data reveals a high proportion of missing, misleading or incomplete metadata.
While markup language-based formats are very powerful tools to store metadata relationships, their use is often impractical for laboratories in the life sciences lacking bioinformatics support.
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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