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Discover LudwigThe phrase "and high levels data" is not correct in written English.
It seems to be an incomplete expression and should likely be "high-level data" or "high levels of data" depending on the intended meaning.
Example: "The analysis requires high-level data to draw accurate conclusions."
Alternatives: "high-quality data" or "elevated data levels."
Exact(1)
Log-rank tests identified significant differences between survival curves of all pairs of PQ treatment groups in this assay except between the control and low levels and the medium and high levels (data not shown).
Similar(59)
The large size and complexity of planetary data acquired by spacecraft during the last two decades create a demand within the planetary community for access to the archives of raw and high level data and for the tools necessary to analyze these data.
In DCMS, we envision two types of user programs: primary queries and high-level data analytics.
Recently, there have been a large number of attempts to solve diagnosis problems by mixing low-level and high-level data.
Workflow systems can be used to build virtual systems for image acquisition and can perform feature extraction and high-level data analysis without writing complex scripts.
With the increasing need in sophisticated processing, image analysis, and high-level data interpretation, open-source workflow systems are gaining popularity.
The central objective in Systems Biology is to fuse and analyze the diverse molecular, cellular, tissue-level and higher level data sources to deduce how sub systems and whole organisms work from these network of interactions.
However, most display clinical quality measures rather than patient safety measures and provide high level data to executives rather than individual level specific data for performance management [ 9].
Healthbank focuses specifically on the availability of individualised control and high levels of data security.
The population within this region is relatively homogenous, Caucasian and stable in terms of movement into and out of the region, ensuring good follow-up and high levels of data completeness.
The drawbacks of the PCA are well known and include the lack of sparseness, i.e., activations are linear combinations of the input data, difficulty to interpret the results in terms of high level data shapes, and the upper limit on the number of achievable basis vectors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com