This data set list the distribution of microbial taxa from three sets of sampling campaigns from unit operations in two large desalination facilities in the US conducted between March and May 2021. The desalination plants include the Claude "Bud" Lewis Carlsbad Desalination Plant in Carlsbad, California and the Seater Desalination facility in Tampa Bay, Florida.

The data associated with the following data release were collected between 2016 and 2017 at three locations on Lake Michigan: Racine, WI; Chicago, IL; and East Chicago, IN. Individual water samples were collected one day a week for ten weeks between June and August. Samples were collected from eight specific sites made up of two river and six shoreline type environments. Sampling was completed at sites where various morphology (embayment, sand and sediment characteristics, size and shape) and hydrologic conditions (currents and waves) were present. Then samples were analyzed using microbial communities (metagenomic analysis), markers of contamination (microbial source tracking), and fecal indicator bacteria (E. coli).

The data associated with the following data release were collected between 2016 and 2017 at three locations on Lake Michigan: Racine, WI; Chicago, IL; and East Chicago, IN. Individual water samples were collected one day a week for ten weeks between June and August. Samples were collected from eight specific sites made up of two river and six shoreline type environments. Sampling was completed at sites where various morphology (embayment, sand and sediment characteristics, size and shape) and hydrologic conditions (currents and waves) were present. Then samples were analyzed using microbial communities (metagenomic analysis), markers of contamination (microbial source tracking), and fecal indicator bacteria (E. coli).

The data were gathered as a preliminary assessment of soil microbiology and conditions in selected urban stormwater best management practices (BMPs) in Clarksburg, MD. Four bioretention facilities (BF), four dry ponds (DP), and four surface sand filters (SSF) were selected. Three samples were taken from each BMP (a single sample from one dry swale (DS) was also collected). BMPs were selected based on their position along various stormwater treatment trains. Soil samples were taken after precipitation events in the summer of 2015 and analyzed for various soil chemistry parameters and microbial taxonomic profiling

The data were gathered as a preliminary assessment of soil microbiology and conditions in selected urban stormwater best management practices (BMPs) in Clarksburg, MD. Four bioretention facilities (BF), four dry ponds (DP), and four surface sand filters (SSF) were selected. Three samples were taken from each BMP (a single sample from one dry swale (DS) was also collected). BMPs were selected based on their position along various stormwater treatment trains. Soil samples were taken after precipitation events in the summer of 2015 and analyzed for various soil chemistry parameters and microbial taxonomic profiling

This data collection consists of microbial count data from seawater microbiomes based on both flow cytometry and 16S rRNA copy numbers from droplet digital PCR (ddPCR) that were used to validate a framework for quantifying absolute abundance of microbial communities for use in downstream ecotoxicology modelling. Seawater was collected from the AIMS jetty (19°16′38.4”S, 147°03′32.1”E) and the relationship between cell counts and absolute 16S rRNA gene copy numbers was determined by preparing varying proportions of natural seawater and artificial seawater suspensions (natural seawater proportions of 100, 80, 60, 40, 20, and 0% of a total 500 mL sample, with ASW making up the remaining proportion). For full methodological details please refer to the full research publication, Thomas et al. 2025 (https://doi.org/10.1186/s40793-025-00741-2) its supplementary materials and the associated GitHub repository (GitHub - MarieCThomas/Quantitative-PMA-16S-rRNA-Gene-Sequencing-Workflow). Additionally, raw sequencing data can be found in the NCBI Sequence Read Archives under BioProject number PRJNA1176196.

The data set consists of fecal indicator bacteria and microbial source tracking marker concentrations in water and sediments across multiple seasons. Samples were collected during baseflow from a variety of watershed classifications. Portions of this dataset are inaccessible because: Researchers are still working on manuscripts out of this data set and can't be released at this time. They can be accessed through the following means: Contacting corresponding author for product. Format: The data set will contain environmental parameters, fecal indicator bacteria concentrations and DNA source tracking markers. This dataset is associated with the following publication: Bradshaw, J.K., B. Snyder, A. Oladeinde, D. Spidle, M. Berrang, R. Meinersmann, B. Oakley, R. Sidle, K. Sullivan , and M. Molina. Characterizing relationships among fecal indicator bacteria, microbial source tracking markers, and associated waterborne pathogen occurrence in stream water and sediments in a mixed land use watershed. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 101: 498-509, (2016).

Private wells (n = 138) were sampled by large- and small-volume sampling methods in Grant, Iowa, and Lafayette Counties, Wisconsin, USA in 2019 and 2020. Well water samples were analyzed for microorganisms by quantitative polymerase chain reaction at the Laboratory for Infectious Disease and the Environment (LIDE). Gene targets for viruses, bacteria, and protozoa were analyzed, including pathogens and microbial source tracking markers. Data were collected to characterize microbial contamination of private well water to better understand water quality and potential causes of contamination. Collaborators include U.S. Department of Agriculture-Agricultural Research Service; Wisconsin Geological and Natural History Survey; and Grant, Iowa, and Lafayette Counties, Wisconsin.

Private wells (n = 138) were sampled by large- and small-volume sampling methods in Grant, Iowa, and Lafayette Counties, Wisconsin, USA in 2019 and 2020. Well water samples were analyzed for microorganisms by quantitative polymerase chain reaction at the Laboratory for Infectious Disease and the Environment (LIDE). Gene targets for viruses, bacteria, and protozoa were analyzed, including pathogens and microbial source tracking markers. Data were collected to characterize microbial contamination of private well water to better understand water quality and potential causes of contamination. Collaborators include U.S. Department of Agriculture-Agricultural Research Service; Wisconsin Geological and Natural History Survey; and Grant, Iowa, and Lafayette Counties, Wisconsin.

Data were collected in August and September 2015 for analysis of bacteria communities of the Grand Calumet River and associated shorelines. Water samples were collected on three occasions corresponding to one rain-related (wet) events and two non-rain (dry) events. Water samples were collected in the Grand Calumet River, at the mouth of the river, at offshore locations around the peninsular impoundment and at shoreline locations: Jeorse Park (East Chicago, Indiana), Whihala (Whiting, Indiana), and 63rd Street (Chicago, Illinois) beaches. Samples were collected in triplicate, and water was filtered at the USGS Lake Michigan Ecological Research Station. After DNA extraction, samples were analyzed using 16S rRNA sequencing using Illumina sequencing. Taxonomic identification was assigned for communities in each sample, at multiple taxonomic levels (Kingdom, Phylum, Class, Order, Family, Genus). Water was also analyzed for E. coli bacteria and turbidity, at the USGS laboratory. Hydrological conditions corresponding to the days of sample collection were obtained from publicly available USGS information (NWIS-National Water Information System). The coordinate file includes information regarding sampling locations and their corresponding latitude and longitudes. The Data file include E. coli densities, laboratory turbidity measurements, and NWIS hydrological data. The raw metagenomic data can be accessed at the NCBI repository under the biproject accession PRJNA541325: https://www.ncbi.nlm.nih.gov/sra/PRJNA541325