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We've taken a look at the dataset and pulled out some interesting tables which you can find below.
Of more significance, however, are the trends visible when one looks at the dataset for citations per faculty, a measure of global research impact.
RS2 estimated uncertainty at the dataset and country level using a Monte Carlo approach.
An initial look at the dataset demonstrates the localisation of the individual types of equipment in the particular self-governing regions in Fig.1.
The false negatives might be further investigated by looking at the dataset from the plain transcriptome-mapping data (see present/absent regions further below).
The reviewer suggests we look at the dataset for serine‐starved E. coli.
It has been tested and fine-tuned for several years in our laboratory and its use leads to significant time savings at the dataset preparation and analysis stages.
Below we further study the high-scoring co-training matches (both at the dataset and at the array levels) and show how they can be used to derive biological insights about processes and diseases.
The Authority Effect would thus appear to hold, at least for the datasets and similarity measures used here.
This allowed us to look at how the datasets cluster – whether by platform, laboratory, experiment or otherwise.
Those models, like most early ones, are not based on our understanding of the biological process but are instead mathematical constructs aimed at fitting the datasets empirically.
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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