Table showing number of interview respondents commenting on each item from the original framework, to gain an understanding of an end users view of the initial framework categories
| Category from initial framework | Dimension | Definition | Times mentioned in interviews |
| Description | Metadata completeness | Level of metadata completed | 2 |
| Metadata quality | Richness of metadata completion—including within required formats and quality of qualitative fields | 5 | |
| Characteristics and Service | Data source | The modality or source of data (eg, Electronic Health Record, study specific) | 14 |
| Data model | The data model or schema used by the dataset (eg, Observational Medical Outcomes Partnership (OMOP), Informatics for Integrating Biology and the Bedside (i2b2)) | 16 | |
| Data dictionary | Provided documented data dictionary and terminologies | 9 | |
| Provenance | The original source or jurisdiction of the dataset | ||
| Usage restrictions | The df to use the data for different purposes (eg, commercial licences, consent, expiry) | 6 | |
| Format | The technical presentation of the data format (eg, Digital Imaging and Communications in Medicine (DICOM) images vs Portable Graphics Format (PNG)) | 3 | |
| Timeliness | How quickly the data can be provided—in a useful timescale | 7 | |
| Fairness of the data | Extent to which the data are findable, accessible, interoperable and reusable | 0 | |
| Phenome | Extent and description of included patients/conditions (links with Phenome work re standards) | 2 | |
| Scale | Coverage | No of individuals, data points, lab tests, images, etc included in the dataset | 9 |
| Duration | Length of time to which the data relates | 3 | |
| Depth | Amount of information available per individual (eg, number of fields/records, types of data) | 3 | |
| Quality | Completeness | The proportion of data entries that should be populated are populated (and inverse—proportion that should not be populated are not) | 11 |
| Missing data handling | Description of missing value handling and default values | 4 | |
| Consistency/uniformity | Data are presented in the required format and a similar wayfor example, field types, date formats | 1 | |
| Uniqueness | Lack of duplication | 3 | |
| Validity | Data are valid based on acceptable ‘rules’ for example, age between 0 and 120, pregnancy in male patients, physiological readings within normal ranges | 7 | |
| Accuracy/verification | The extent to which the data reflects the ‘real-world’, for example, level of certainty that fields are accurate | 6 | |
| ‘Usefulness’ | Qualitative, subjective measure by user (eg, Net Promoter Score/star rating) | 12 | |
| Added value | Linkage/mapping | Ability to link with other datasets | 10 |
| Transformations/derivations | Level of derived data and descriptions, manual versus Natural Language Processing, etc | 1 | |
| Accuracy/verification | Level of manual verification/sampling | 0 | |
| Annotation | Additional fields added to provide further information, including phenotyping | 1 |