Research use is part of validation. Automation of the workflow would in principle allow also the analysis of the robustness of this approach to varying technical choices in the data harmonization, although such analysis falls beyond the scope in this manuscript. Future development could take increasing advantage of machine learning, and borrow further methods from ecology and related fields that have well established methods for spatio-temporal data analysis. Machine learning and articial intelligence (AI) could help to significantly improve the scalability and accuracy of data harmonization and verification. For instance, the raw page count fields have systematic structure, and instead of a lengthy algorithm construction process, adaptive machine learning algorithms could be trained with a limited set of well chosen training examples, and the accuracy of the conversions into page counts could be easily monitored and exactly quantified until a satisfactory accuracy and coverage is reached.
Mitä merkitystä tällä työllä ja näillä julkaisuilla suhteessa jo julkaistuihin on -- myös projektio koskien muita vastaavia katalogeja. Tämä arvokas osuus paperissa itsessään - Joo tätä pitäs avata vielä lisää / LLSystematic data harmonization, where the original raw entries are polished, disambiguated, mapped to controlled vocabularies, and verified by internal and external cross-checking of the correspondence between available data sources.