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In the example of collecting data on a patient’s 'sex', it might be unclear if it means ‘biological sex at birth' or ‘gender’.
In the below spreadsheet we can see what the issues are with our current metadata and suggested improvements in order to make the meaning of them clearer
Metadata Field | Value | Issue | Suggested Value | Suggested description |
|---|---|---|---|---|
Dataset Name | Health Data | Generic and not descriptive. | Patient Health Records 2023 | Comprehensive dataset containing health records of patients from Hospital A in the year 2023. |
Date of Upload | 01/02/2023 | Ambiguous format (MM/DD/YY or DD/MM/YY). | 2023-01-02 | Date when the dataset was uploaded, in ISO 8601 format. |
Keywords | BP, HR, Conditions | Abbreviations used without context. | Blood Pressure, Heart Rate, Medical Conditions, Hypertension, Diabetes | Keywords describing the main topics covered by the dataset. |
Creator | Dr. Smith | Generic name without additional identifying information. | Dr. John Smith, Hospital A | Full name and affiliation of the dataset creator. |
Description | Patient health data including BP and HR | Lacks detail. | Detailed patient health records including measurements of blood pressure (BP) and heart rate (HR), along with diagnosed medical conditions and prescribed medications. | Extended description providing context and details about the dataset. |
Format | CSV | Broad category, can be more detailed. | CSV, version 1.0 | Data format and version. |
Source | Hospital A | Lacks detail, too generic. | Hospital A, Department of Cardiology | Specific department and institution where the data was sourced. |
Rights | Open | Too broad. | CC BY 4.0 | Licensing terms specifying the rights for data usage. |
Step 3
Compile information about the relationships between data elements. For example, if the dataset is in a relational database, the relational schema provides information about the dataset structure, the types involved (the field names), cardinality, etc.
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