FAIR-Compliant Database for Soil Erosion Studies: The Marganai Forest Experiment.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-04-02 DOI:10.1038/s41597-025-04797-0

Filippo Giadrossich, Ilenia Murgia, Enrico Guastini, Antonio Ganga, Simone Di Prima, Laura Chessa, Raffaella Lovreglio, Roberto Scotti

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Abstract

The '2018 Marganai Forest Soil Erosion Experiment Database' is a comprehensive collection of measures taken during scientific experiment trials designed to investigate the effects of forest canopy coverage on soil erosion under intense artificial rainfall, four years after coppicing. The investigation involved the establishment of eight paired plots with and without forest canopy coverage, subjected to artificial rainfall simulation aimed to measure the amount of sediment transported by runoff. The work represents a valuable resource for researchers interested in understanding the complex implications of forest management practices on soil erosion. The paper, produced using Quarto in a Gitlab-based RStudio project, is an example of 'reproducible research' documenting that the database provides detailed information on the experimental setup as well as on the range of different measurements that have been collected. The database, produced using NFS-DataDocumentationProcedure, is stored in an SQLite file, extensively exploiting the relational properties of the engine, enhancing data accessibility, interoperability and reusability.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.