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Disagreements between the two observers related to the data were solved by a third researcher, unaware of the answers given by each observer.
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Further, the data association problem between the tracker and the data is solved at each node separately and the association with maximum likelihood is transmitted along with data to other nodes.
Even if the data are solved in the correct space group and unit cell, the data quality will be poor.
The reported data were solved through intrinsic phasing (SHELXTL XT-2014) and refined by least-squares refinement on F SHELXL-20144) (Sheldrick, 2008 ▶).
To maximize data quality, all observed random or potentially systematic inconsistencies within resulting data were solved.
A test example with all the needed data is solved to shows the features of the presented approach.
The efficient allocation problem for the multi-stage process for a given historical data is solved by the general linear model approach, and then the predicted responses are ordered to choose the subsequently optimal equipment paths.
Any disagreement in the process of selecting, assessing and collecting data was solved by a third researcher (GJ).
The problem of extracting pore size distributions from characterization data is solved here with particular reference to adsorption.
Conway weighed in: "Big data is solving big problems and we need a home to foster the next generation of this unique breed of scientist.
Two investigators (FS and FC) collected the data, and disagreements were solved by consensus and by the opinion of a third author (AC), if necessary.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com