Timeliness refers to the availability of data in a timely manner and is not a dimension of data quality.

Data quality assessment aims to evaluate the fitness of data for its intended use, thereby improving its usability.

Data validation involves verifying the accuracy and consistency of data by comparing it against predefined rules or known values.

Data accuracy refers to the extent to which data is free from errors and represents the true value of the attribute it represents.

Data entry errors occur when data is incorrectly entered into a system, leading to inaccurate data.

Data cleansing involves identifying and correcting errors, inconsistencies, and missing values in data to improve its quality.

Data standardization involves converting data into a consistent format to ensure uniformity and comparability.

Data completeness refers to the extent to which data contains all the necessary information and is not missing any important values.

Data entry errors can lead to missing values or incorrect data, resulting in data incompleteness.

Data integration involves combining data from different sources into a single, consistent dataset to improve its usability and accessibility.

Data encryption involves converting data into a format that is difficult to understand without the appropriate key, ensuring data integrity.

Data consistency refers to the extent to which data is consistent with itself and with other related data, ensuring that it is reliable and trustworthy.

Data processing errors can lead to inconsistencies in data, such as incorrect calculations or missing values.

Data harmonization involves aligning data from different sources to ensure consistency and comparability, making it easier to combine and analyze.

Data heterogeneity, or the existence of data in different formats and structures, is a major challenge in ensuring data quality and accuracy in geographical data sharing.