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Abstract
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 For this purpose, we collected published and unpublished data from paleolimnological investigations of Arctic lake systems.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.
Highlights
On-going global warming impacts Arctic landscapes through the “Arctic amplification” effect, where temperatures in the Arctic exceed the average Northern Hemisphere surface air temperature change (Bis kaborn et al, 2019b; IPCC, 2014; Miller et al, 2010)
We present a conceptual integration approach to enable a comprehensive comparison of datasets of varying quality from labo ratory analysis of lake sediment
For the cleansing and integration process presented in this paper, we used a collection of published and unpublished laboratory data and corresponding metadata from lake sediment cores
Summary
On-going global warming impacts Arctic landscapes through the “Arctic amplification” effect, where temperatures in the Arctic exceed the average Northern Hemisphere surface air temperature change (Bis kaborn et al, 2019b; IPCC, 2014; Miller et al, 2010). While scientists continue to collect new data from lake systems each year, thorough data handling of already existing datasets might help to fill remaining knowledge gaps of past changes The quality of these older datasets varies depending on different factors, such as date of creation, individual project goals, available laboratory resources, and personnel bias (Cai and Zhu, 2015; Heidorn, 2008; Wang et al, 2001). When integrating these existing datasets into a coherent framework and reporting standard, we can work with higher reliability and reproduc ibility enabling large-scale synthesis studies
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