Microplastics and Trash Cleaning and Harmonization (MaTCH): Semantic Data Ingestion and Harmonization Using Artificial Intelligence.

ABSTRACT: With the rapid expansion of microplastic research and reliance on semantic descriptors, there is an increasing need for plastic pollution data harmonization. Data standards have been developed but are seldom implemented across research sectors, geographic regions, environmental media, or size classes of plastic pollution. Harmonization of existing data is currently hindered by increasingly large datasets using thousands of different categorical variable descriptors, as well as various metrics used to describe particle abundance and differing size ranges studied across groups. For this study, we used manually developed relational databases to build an algorithm utilizing artificial intelligence capable of automatically curating harmonized, more usable datasets describing micro to macro plas

SUBMITTER: Hapich H 

PROVIDER: S-EPMC11580164 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature