Exploring Jalisco's water quality: A comprehensive web tool for limnological and phytoplankton data

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Cristofer Camarena-Orozco, Eduardo Juárez Carrillo, Martha Alicia Lara González, Edlin Guerra-Castro
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引用次数: 0

Abstract

This study presents a comprehensive dataset of hydrological information gathered from five key eastern basins in Jalisco, Mexico. The dataset encompasses approximately 50 limnological variables and phytoplankton counts specifically for one of these basins. Water-quality data were collected by the State Water Commission of Jalisco, adhering to the methods outlined in the Official Mexican Norm ‘NOM-127’. Monthly samplings were conducted to assess environmental variables such as pH, temperature, oxygen, nutrients and heavy metals. Monitoring has been ongoing for three basins since 2009, while the remaining two basins have been monitored since 2015 and 2020. Phytoplankton data were obtained from monthly samples taken by the University of Guadalajara between 2014 and 2019 in Lake Cajititlán. The original data were cleaned and organized using tidy data principles, with codes accessible on GitHub. To facilitate data exploration and visualization, we developed a user-friendly web application with the Shiny package in R. This application enables users to explore the dataset through summary statistics tables, time series plots and phytoplankton community analysis. The dataset is accessible on Zenodo. The presented data hold significance for environmental and water-quality assessment and applications in machine learning, neural network models, community ecology and broader environmental research. Notably, the raw data, publicly accessible from the State Water Commission of Jalisco, have been previously utilized for these purposes. This dataset offers value due to its diverse limnological and phytoplankton variables, an extended time frame of availability, a curated and streamlined accessibility process and the inclusion of a web application for intuitive exploration and visualization.

探索哈利斯科州的水质:湖泊学和浮游植物数据综合网络工具
本研究介绍了从墨西哥哈利斯科州东部五个主要盆地收集的综合水文信息数据集。该数据集包括约 50 个湖泊学变量和其中一个流域的浮游植物计数。水质数据由哈利斯科州水务委员会按照墨西哥官方规范 "NOM-127 "中规定的方法收集。每月进行一次采样,以评估 pH 值、温度、氧气、营养物质和重金属等环境变量。自 2009 年以来,一直对三个流域进行监测,其余两个流域则分别自 2015 年和 2020 年开始监测。浮游植物数据来自瓜达拉哈拉大学 2014 年至 2019 年期间在卡希特兰湖采集的月度样本。原始数据采用整洁数据原则进行了清理和整理,其代码可在 GitHub 上访问。为便于数据探索和可视化,我们使用 R 中的 Shiny 软件包开发了一个用户友好型网络应用程序。该应用程序使用户能够通过汇总统计表、时间序列图和浮游植物群落分析来探索数据集。该数据集可在 Zenodo 上访问。所提供的数据对环境和水质评估以及机器学习、神经网络模型、群落生态学和更广泛的环境研究中的应用具有重要意义。值得注意的是,哈利斯科州水务委员会公开提供的原始数据此前已用于上述目的。该数据集的价值在于其多样的湖泊学和浮游植物变量、较长的可用性时间框架、精心策划和简化的访问流程,以及包含一个用于直观探索和可视化的网络应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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