Special Considerations for Data Analysis

As ACDEA is a prototype for integrating existing data sources and is still in a stage that requires further cleansing, there are some special considerations to be aware of when conducting data analysis.

1. Ambiguous Concepts: When working with ACDEA, it is important to keep in mind that some of the concepts may have different interpretations across cultural data in different domains.
   
   

   For example, the entity project in DAQA may have a different definition from work in DAAO, despite being integrated into the same entity Work. Similarly, AusStage’s event, which primarily corresponds to production in performing arts, may be defined differently from the general event concept seen across other data sources.
   
   

   Hence, it is important to understand the specific concept being used in each data source. Additional efforts in data manipulation might be necessary to construct the specific entities that one wishes to analyse.
   
   

2. Duplicated Records: Another important consideration when working with the ACDEA is dealing with duplicated records. Due to the nature of integrating data from multiple sources, it is possible for some records to appear in multiple places. As de-duplication progress is still under experimentation at this stage, it is crucial to identify and handle duplicated records appropriately to avoid skewing analysis results.
   
   

3. Empty Values: When working with current datasets, it is important to note that some records may contain missing or empty values for certain attributes. These empty expressions could be represented as null, "", None, or other variations. This could be due to incomplete or inconsistent data in the original data sources, or due to errors during the integration process.
   
   

   To avoid skewing the analysis results, it is important to identify and handle these missing values appropriately. Depending on the analysis being conducted, empty values may need to be imputed or removed. Additionally, it is important to carefully consider the impact of missing data on the validity of the results and to acknowledge any limitations or uncertainties caused by missing values.