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In Linked data section, we describe briefly the Bio2RDF project and its application to retrieve meaningful information from various, heterogeneous life sciences datasets for practical use cases.
The challenges stemming from the integration of disparate, heterogeneous life sciences datasets on the web has led to the adoption of a new set of technologies, based on the Semantic Web and Linked Data concepts, by the biomedical researchers (11).
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It is part of the EarthServer project which builds infrastructure for online access and analysis of huge Earth Science datasets.
Thus, it would be helpful to develop methods that integrate citizen-science datasets with models that account for animal movement.
Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight.
This system offers an automatic way to link and query over a rapidly growing number of semantic web friendly life science datasets.
The final social science dataset consists of 1866 in-depth interviews, most with matching CD, CRF and applicator count data, from 725 women.
This problem is not jus with Ensembl, there are several other such popular life sciences applications and datasets that are available in public domain but there are hosted and managed internally by most organizations resulting in redundancies.
In that regard, Big Data is gaining meaning into social science from quantitative datasets side, which differs from traditional social science where collecting data has always been hard, time consuming, and resource intensive.
We have every confidence in the science and the various datasets we use.
Face recognition is a long-standing challenging topic in computer science, especially on insufficient datasets.
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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