The phrase "filter out data" is correct and usable in written English.
It can be used when discussing the process of removing unwanted or irrelevant information from a dataset.
Example: "To improve the accuracy of our analysis, we need to filter out data that does not meet our criteria."
Alternatives: "exclude data" or "sift through data."
The motion removal approach acted as a pre-processing stage to filter out data that were associated with moving objects.
It acts as a pre-processing stage to filter out data that are associated with moving objects.
Quality assurance and quality control methods from the National Oceanic and Atmospheric Administration NOAA Meteorological Assimilation Data Ingest SystemeMADISDIS) were used to filter out data that may have resulted from sensor drift or spiking (http://madis.noaa.gov/madis_qc.html).noaa.gov/madis_qc.html
This was performed to filter out data that did not relate to health systems and to organise the substantial amount of extracted data for ease of analysis.
The development and implementation of the described QC steps meets a need to be able to add quality flags to records and to filter out data based on user needs, taking into account the fitness for purpose of the available records.
They used fuzzy curve with surface analysis to identify information-rich well logs and filter out data dependencies.
Instead of filtering out data signals deemed to be irrelevant according to companies' current analysis patterns, as older warehouse solutions have done, data lakes allow complete storage of unstructured data sets so that when organizations mature and analysis models evolve, all of the information they need will still be accessible.
Checking "other QA" included filtering out data with clouds (adjacent cloud, mixed clouds, and possible shadow), aerosols (high and climatology aerosols), and possible shadow.
After filtering out data from weak signals as described above, the remaining signals do not appear to show intensity-dependent biases (blue spots in Figure 2A).
We carried out a profile preprocessing step on the samples, which included filtering out data with more than 5% missing values, combining probes of the same gene, and then filling missing values using the K-neighbors algorithm.
The ratios based on the 33 surveyed clients were similar to those based on the full data set, and were only modestly affected by filtering out data for clients performing testing in-house.
