Abstract

AbstractThis 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.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.