"unmatched data" is a correct and usable phrase in written English.
It is typically used in a technical or scientific context to refer to data that does not fit into the expected categories or format. For example, "The analysis of the experiment revealed several cases of unmatched data which required further investigation."
The result is unmatched data on sea ice movement and new insights into changes in the environment.
In the Arctic, for example, oceanographer and Lamont associate research professor Christopher Zappa is gathering unmatched data by redesigning instruments typically found aboard research ships or aircrafts and fitting them into drones that measure sea ice topography and movement.
The result is unmatched data on sea ice topography and movement and new insights into how sea ice breaks up and how the atmosphere and ocean affect one another.
The INTERHEART study was originally designed as a "matched" case-control study but was unmatched in the analysis of nine modifiable risk factors [5] to minimize the loss of cases and controls for whom matching was not possible, given that there was general agreement for key results among the matched and unmatched data analyses.
See Additional file 1: Table S1 for a summary of balance for matched and unmatched data.
Most of these specimens were deemed infections (91%% in both the matched and unmatched data sets) according to NHSN definitions.
It was also surprising that the increased risk of sexual debut amongst female double orphans did not seem to translate into an increased risk of HIV infection, especially given that having started sex was strongly associated with HIV infection amongst female adolescents in our large, unmatched data-set (results not shown).
The RFd reflected the number of bipartitions that were unmatched within the data set.
Since the CA and the RAI-HC are targeted at different populations of home care clients, we sampled a sub-population of the RAI-HC clients in such a way that matched their general characteristics to the population receiving the CA. To investigate the effect of this matching exercise, we repeated the LASSO part of our analysis using the original (unmatched) RAI-HC data.
That is the reason why it outperforms 5 and 9 where DNN-HMMs were trained using the unmatched training data.
The unmatched reference data are calculated as false negative (FN). Figure 3 Matched extraction (a) and matched reference (b).
