Generally, python-ihm will consider two objects with the same information (e.g. two Datasets of the same type which point to the same accession) as identical, so as not to create duplicate rows in the output mmCIF tables. However, when reading in a file with duplicate rows, we should preserve that by forcing the resulting objects to not compare identical. The user may be intending to add additional information such as DOIs to these rows later, which would not be possible if we coalesce these into one row.
Generally, python-ihm will consider two objects with the same information (e.g. two Datasets of the same type which point to the same accession) as identical, so as not to create duplicate rows in the output mmCIF tables. However, when reading in a file with duplicate rows, we should preserve that by forcing the resulting objects to not compare identical. The user may be intending to add additional information such as DOIs to these rows later, which would not be possible if we coalesce these into one row.