Easier said than done: unexpected hurdles to preparing ∼1,000 cranial CT scans for data collection from an online digital repository

Although the discrepancy was straightforward to describe, resolving it required over 250 person-hours across 8 months. Accessing physical specimens, conducting repeated measurements, and cross-validating with multiple tools made the process labor-intensive. Nonetheless, this effort avoided a 3-5% measurement bias in nearly 20% of our sample and allowed inclusion of these scans in downstream semi-automated data collection. We urge researchers to thoroughly understand the digital datasets they work with and resist the temptation to ignore apparent errors during import. We also recommend that funding bodies provide support for the extensive time needed to validate and process digital imagery, both for data generators and users. Finally, we highlight the need for public repositories to implement stronger quality control. If a data import check similar to 3D Slicer's had been applied during data submission, the inconsistency between manually entered metadata and embedded DICOM information might have been caught and corrected at the time of upload.