Remediating toxic elements with sunflower, hemp, castor bean, & bamboo: an open dataset of harmonized variables.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Hyunji Ha, Ken G Sweat, Kendra D Conrow, Richard S Haney, Thomas M Cahill, David S LeBauer, Maxwell C K Leung
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引用次数: 0

Abstract

This dataset was compiled between August 1, 2022, and March 15, 2023, through a comprehensive literature review of 587 studies on the uptake of elements from the soil by plants (i.e., phytoremediation). As a proof of concept, we compiled research results on four commodity crops suitable for phytoremediation in semi-arid environments, namely sunflower, hemp, castor bean, and bamboo. Two hundred thirty-eight studies had data on soil types, elemental pollution, and plant components for calculating bioconcentration factors. Using a harmonized set of variables, we extracted data from these studies to create a database to organize results for interpretation and enable consistent and further literature analysis. This approach can help industry experts and environmental researchers select crops for their intended extraction applications, as well as provide insights into the bioaccumulation of toxic elements in plants.

用向日葵、大麻、蓖麻豆和竹子修复有毒元素:一个协调变量的开放数据集。
该数据集是在2022年8月1日至2023年3月15日期间编制的,通过对587项关于植物从土壤中吸收元素(即植物修复)的研究进行综合文献综述。作为概念验证,我们整理了四种适合半干旱环境植物修复的商品作物的研究成果,分别是向日葵、大麻、蓖麻和竹子。238项研究收集了土壤类型、元素污染和植物成分的数据,用于计算生物浓度因子。使用一组统一的变量,我们从这些研究中提取数据,创建一个数据库,以组织结果进行解释,并使文献分析一致和进一步。这种方法可以帮助工业专家和环境研究人员为他们的预期提取应用选择作物,并提供对植物中有毒元素的生物积累的见解。
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来源期刊
Scientific Data
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.
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