Some studies (e.g. ISARIC CORE) contain multiple datasets from the same study, but with some subset of the data (e.g. follow-up fields) missing. Rather than creating an entirely new parser in this case, where the only difference is that some fields are missing compared to the base case, it would be preferable to be able to tag certain fields, or field patterns (flw_* for example) as able to be 'skipped' - i.e., if the field is not present in the data, pass over it rather than throwing an error.
Some studies (e.g. ISARIC CORE) contain multiple datasets from the same study, but with some subset of the data (e.g. follow-up fields) missing. Rather than creating an entirely new parser in this case, where the only difference is that some fields are missing compared to the base case, it would be preferable to be able to tag certain fields, or field patterns (flw_* for example) as able to be 'skipped' - i.e., if the field is not present in the data, pass over it rather than throwing an error.