Data Gaps

This option applies to time series data only

Fill missing/bad data with NaNs (Not a Number)Do not fill gaps

Fill missing/bad data with NaNs (Not Number)

This option will, as it says, fill in data gaps with 'NaN' values in the data products. For CSV files, the text 'NaN' is inserted, while MAT files have a built-in of the same name. Data gaps occur when the time difference between subsequent readings is greater than 1.9 times the sample period (otherwise known as the data rating).

This option will also keep any existing NaNs in the data. These are most often caused by quality control (QAQC) automatic or manual tests. The metadata report accompanying the data product will eloborate on the QAQC test that was applied.

This is the default option.

Do not fill gaps

This option will not take action to fill in data gaps.

This option will cause action to be taken to remove any existing NaNs in the data. Therefore, if the clean option had been selected, data that had been replaced by NaNs will now be removed entirely. There are exceptions to this unfortunately. For device-level time series scalar data, if one sensor at a given time stamp has valid data, the entire time stamp cannot be removed, so the remaining sensors will be left as NaNs. For example in a CSV file: