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符合公平信息规范的土壤侵蚀研究数据库:马尔加奈森林实验

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

作者信息

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

机构信息

Nuoro Forestry School, Dipartmento di Agraria, University of Sassari, Viale Italia, 39a, Sassari, 07100, Italy.

Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, University of Florence, Via San Bonaventura, 13, Florence, 50145, Italy.

出版信息

Sci Data. 2025 Apr 2;12(1):561. doi: 10.1038/s41597-025-04797-0.

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.

摘要

“2018年马尔加奈森林土壤侵蚀实验数据库”全面收集了在科学实验中采取的各项措施,这些实验旨在研究在皆伐四年后,森林冠层覆盖对强人工降雨条件下土壤侵蚀的影响。调查涉及设置八对分别有和没有森林冠层覆盖的样地,通过人工降雨模拟来测量径流携带的沉积物量。这项工作对于有兴趣了解森林管理实践对土壤侵蚀复杂影响的研究人员来说是一份宝贵的资源。该论文使用Quarto在基于Gitlab的RStudio项目中生成,是“可重复研究”的一个范例,记录了该数据库提供了关于实验设置以及所收集的不同测量范围的详细信息。该数据库使用NFS - 数据文档程序生成,存储在一个SQLite文件中,充分利用了该引擎的关系属性,增强了数据的可访问性、互操作性和可重用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04f9/11965339/97c54b6c8819/41597_2025_4797_Fig1_HTML.jpg

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