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The use of the database enabled us to identify different cell populations among cell mixtures.
The extremely large-scale gait database enabled us to evaluate recognition performance under cooperative and uncooperative settings, the impact of the training data size, the recognition difficulty level of the CS labels, and the possibility of the classification of CS labels.
This database enabled us to visualize and compare the metabolism of these two C. pseudotuberculosis strains (Figure 2).
Finally, our rich tumour database enabled us to simultaneously assess pathologic and tumoral molecular correlates and control for confounding by a number of tumoral molecular alterations.
Comparison with the spolDB3.0 database enabled us to compare spoligotypes generated in a New Delhi laboratory with different laboratories around the world.
The comparison with the NR database enabled us to assign putative functions to or find homologues from other organisms for 857 (83%) identified proteins.
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These 2 databases enabled us to use CRC to estimate the true number of Ebola cases in Montserrado County.
Nevertheless, the use of a population database enables us to study a large contemporary sample of patients and make significant statistical conclusions relevant to the general population, which is not possible in single-institution studies.
The GUC100 database enables us to evaluate various performances and interoperability settings by taking into account different influencing factors such as fingerprint scanner and image quality.
The HELFO file was linked with the RGP database, enabling us to identify doctors who were RGPs.
We had access to the Norwegian Prescription Database, enabling us to record whether an antibiotic prescription was dispensed at a pharmacy, provided that all variables needed for matching the data were available.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com