Harmonizing heterogeneous multi-proxy data from lake systems
When performing spatial-temporal investigations of multiple lake systems, geoscientists face the challenge of dealing with complex and heterogeneous data of different types, structure, and format. To support comparability, it is necessary to transform such data into a uniform format that ensures syn...
| Published in: | Computers & Geosciences |
|---|---|
| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://epic.awi.de/id/eprint/55325/ https://epic.awi.de/id/eprint/55325/1/Pfalz_et_al_2021.pdf https://doi.org/10.1016/j.cageo.2021.104791 https://hdl.handle.net/10013/epic.8de95a70-abeb-4bb3-a7f5-ee5e15f39dea https://hdl.handle.net/ |
| Summary: | When performing spatial-temporal investigations of multiple lake systems, geoscientists face the challenge of dealing with complex and heterogeneous data of different types, structure, and format. To support comparability, it is necessary to transform such data into a uniform format that ensures syntactic and semantic comparability. This paper presents a data science approach for transforming research data from different lake sediment cores into a coherent framework. For this purpose, we collected published and unpublished data from paleolimnological investigations of Arctic lake systems. Our approach adapted methods from the database field, such as developing entity-relationship (ER) diagrams, to understand the conceptual structure of the data independently of the source. We demonstrated the feasibility of our approach by transforming our ER diagram into a database schema for PostgreSQL, a popular database management system (DBMS). We validated our approach by conducting a comparative analysis on a set of acquired data, hereby focusing on the comparison of total organic carbon and bromine content in eight selected sediment cores. Still, we encountered serious obstacles in the development of the ER model. Heterogeneous structures within collected data made an automatic data integration impossible. Additionally, we realized that missing error information hampers the development of a conceptual model. Despite the strong initial heterogeneity of the original data, our harmonized dataset leads to comparable datasets, enabling numerical inter-proxy and inter-lake comparison. |
|---|