# Metadata Quality for Biobanks
## 6 Metadata Quality Characteristics and Metrics  
In this section, we describe specific metadata quality characteristics and corresponding metrics in detail. For this, we selected the following nine characteristics: _metadata accuracy_,completeness, coverage, consistency, timeliness, provenance, reliability, conformance to expectations, and accessibility. Intrinsic and Relative Metadata Quality Characteristics and Metrics  
In [10], following [PERSON] et al. [20], we distinguished between intrinsic and relative data item quality metrics. Similarly, this paper distinguishes between _intrinsic and relative metadata quality metrics_:  
1. Metadata quality assessment based on the _intrinsic metadata quality metric_ must rely only on the existing biobank metadata values, not on human opinions, standard data, or metadata schema. Such metrics are objective.
2. Metadata quality assessment based on the _relative metadata quality metric_ may rely on human opinions, standard data, or the metadata schema. Such metrics are subjective.  
It is also possible to define intrinsic and relative metadata quality characteristics: for such a characteristic, all possible metrics are, respectively, intrinsic or relative. In addition, mixed metadata quality characteristics may possess intrinsic and relative metrics at the same time. Metadata Accuracy  
This characteristic reflects the need for the metadata to be accurate. It contributes to addressing the metadata goals as follows:  
1. high-accuracy metadata allows for a better description of the external data, being more likely to provide the correct description of the data, adequately representing it for the prospective users;
2. high-accuracy metadata supports the search better as the data item values corresponding to the found artifacts after searching within accurate metadata values are more likely to correspond to the provided search criteria;
3. high-accuracy metadata supports decisions based on the found artifacts better as these decisions are more accurately based on the data item values corresponding to the artifacts. Related Work on Metadata Accuracy  
The following authors address metadata accuracy in their papers:  
1. [PERSON] et al. [19] defined metadata accuracy as accuracy in values, correspondence to factual information;
2. [PERSON] et al. [18] defined metadata accuracy as semantic correctness in describing relations;
3. [PERSON] et al. [17] defined metadata accuracy with respect to data sources and with respect to the real world (the accuracy of labeling, the accuracy of classification), also they defined non-spuriousness as contributing to accuracy;
4. [PERSON] et al. [22] also defined accuracy with respect to the real world;
5. [PERSON] et al. [25] calculated accuracy values for the metadata records using a specific formula;
6. [PERSON] et al. [16] defined _semantic accuracy_ (in representing the real-world facts) and _syntactic accuracy_ (correspondence of metadata values to a specific domain);
7. [PERSON] and [PERSON] [26] defined accuracy as a distance between the information to be taken from the metadata description and from the data object itself; they proposed to calculate the accuracy metrics (defined only for text-based metadata) based on the semantic distance between data attribute and metadata attribute texts;
8. [PERSON] et al. [37] defined accuracy as a quality characteristic for open data portals understood as correctness in representing the resource; [PERSON] et al. [38] proposed similar characteristic to access the accuracy of metadata for open government data. Furthermore, these researchers proposed to define conformance as a syntactic accuracy in correspondence to the domains.  
In the survey of opinions by metadata practitioners regarding the relative importance of metadata quality characteristics conducted by [PERSON] et al. (2019), accuracy was put in second place after consistency.