bdc : A toolkit for standardizing, integrating and cleaning biodiversity data
bdc : A toolkit for standardizing, integrating and cleaning biodiversity data
复制标题
bdc：用于标准化、整合和清理生物多样性数据的工具包
DOI：
10.1111/2041-210x.13868
复制
发表时间：
2022
影响因子：
6.6
通讯作者：
Loyola, Rafael
中科院分区：
文献类型：
--
作者：
Ribeiro, Bruno R.;Velazco, Santiago José;Guidoni‐Martins, Karlo;Tessarolo, Geiziane;Jardim, Lucas;Bachman, Steven P.;Loyola, Rafael
在线和可公开获取的生物多样性数据库的增加为支持研究和政策提供了大量和宝贵的资源。然而，在没有仔细检查的情况下，初级物种发生数据中的错误可能会导致错误的结果和误导性的信息。在这里，我们介绍生物多样性数据清洗(BDC)，这是一个R包，用于解决质量问题和提高生物多样性数据集的适用性。Bdc包将生物多样性数据清理的几个方面集中在一个地方。它按与不同生物多样性层面有关的专题模块组织，其中包括：(A)合并数据集：不同数据集的标准化和合并；(B)预过滤：标记和删除无效或无法解释的信息，然后修改数据；(C)分类学：通过应用精确和部分匹配算法，对照当地存储的分类数据库清理、解析和统一来自若干分类组的学名；(D)空间：标记错误、可疑和低精度的地理坐标；(E)时间：标记和尽可能纠正不一致的收集日期。此外，该程序包还包含可视化、记录和报告数据质量的功能--这对于使数据质量评估透明和可重现至关重要。所示的模块和其中的功能被链接起来，形成了一个拟议的可重复工作流，该工作流还可以集成其他R包的功能。我们演示了bdc包在清理巴西3000多万种陆地植物物种的发生记录中的适用性。我们发现，大约五分之一的原始数据集满足标准的质量要求。与其他可用的R包相比，bdc包的主要优点是它将可用工具-以及一系列新工具-整合到一个单一且灵活的工具包中，以评估不同维度的生物多样性数据的质量。这些功能可以应用于许多分类组、数据集(包括区域或本地存储库)、国家或世界范围内。我们希望bdc包能够促进数据清理过程，并促进改进，以便明智和有效地使用初级生物多样性数据。
The increase in online and openly accessible biodiversity databases provides a vast and invaluable resource to support research and policy. However, without scrutiny, errors in primary species occurrence data can lead to erroneous results and misleading information.Here, we introduce the Biodiversity Data Cleaning (bdc), an R package to address quality issues and improve the fitness‐for‐use of biodiversity datasets. Thebdcpackage brings together several aspects of biodiversity data cleaning in one place. It is organized in thematic modules related to different biodiversity dimensions, including (a) Merge datasets: standardization and integration of different datasets; (b) Pre‐filter: flagging and removal of invalid or non‐interpretable information, followed by data amendments; (c) Taxonomy: cleaning, parsing and harmonization of scientific names from several taxonomic groups against taxonomic databases locally stored through the application of exact and partial matching algorithms; (d) Space: flagging of erroneous, suspect and low‐precision geographic coordinates; and (e) Time: flagging and, whenever possible, correction of inconsistent collection date. In addition, the package contains features to visualize, document and report data quality—which is essential for making data quality assessment transparent and reproducible. The modules illustrated, and functions within, were linked to form a proposed reproducible workflow that can also integrate functions from other R packages.We demonstrated thebdcpackage's applicability in cleaning more than 30 million occurrence records for terrestrial plant species in Brazil. We found that around one‐fifth of the original datasets hold the standard quality requirements.Compared to other available R packages, the main strengths of thebdcpackage are that it brings together available tools—and a series of new ones—to assess the quality of different dimensions of biodiversity data into a single and flexible toolkit. The functions can be applied to many taxonomic groups, datasets (including regional or local repositories), countries, or world‐wide. We hope thebdcpackage can facilitate the data cleaning process and catalyse improvements to allow the wise and efficient use of primary biodiversity data.