ARG and environmental parameters data collected at Proctor Creek, Atlanta, GA from 2015-2017. This dataset is associated with the following publication: Sowah, R., M. Molina, O. Georgacopoulos, B. Snyder, and M. Cyterski. Sources and Drivers of ARGs in Urban Streams in Atlanta, Georgia, USA. Microorganisms. MDPI, Basel, SWITZERLAND, 10(9): 1804, (2022).

In this study, the abundance and distribution of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs), as well as the concentrations of antibiotics present in a mixed-use watershed in Athens, GA, USA were examined, in order to enhance understanding of the existing state of AR in the freshwater environment. The current study has shown that antibiotic-related contaminants are prevalent in the freshwater environment, including commensal and pathogenic bacteria that are resistant to antibiotics used for human and veterinary purposes, medically important antibiotics, as well as the genes associated with resistance to these antibiotics. This dataset is not publicly accessible because: Data belong to coauthor at USDA ARS. It can be accessed through the following means: The data presented in this study are available on request from the corresponding author, Jonathan Frye at USDA. Format: Statistical analysis of data from surface water samples, see the journal article's Supplementary Materials for additional information: https://www.mdpi.com/article/10.3390/antibiotics12111586/s1. This dataset is associated with the following publication: Cho, S., L. Hiott, Q. Read, J. Damashek, J. Westrich, M. Edwards, R. Seim, D. Glinski, J. Bateman McDonald, E. Ottesen, E. Lipp, M. Henderson, C. Jackson, and J. Frye. Distribution of Antibiotic Resistance in a Mixed-Use Watershed and the Impact of Wastewater Treatment Plants on Antibiotic Resistance in Surface Water. The Journal of Antibiotics. Springer Nature, New York, NY, USA, 12(11): 1586, (2023).

ARG, intI1, fecal indicator bacteria PCR data. This dataset is associated with the following publication: Keely, S., N. Brinkman, E. Wheaton, M. Jahne, S. Siefring, M. Varma, R. Hill, S. Leibowitz, R. Martin, J. Garland, and R. Haugland. Geospatial Patterns of Antimicrobial Resistance Genes in the US EPA National Rivers and Streams Assessment Survey. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56(21): 14960–14971, (2022).

The relative abundance of antibiotic resistance genes measured from spatially and temporally distributed sampling in Kewaunee County, Wisconsin.

The data set includes relative abundances of antimicrobial resistance genes in urban karst groundwater systems in western Kentucky. This dataset is not publicly accessible because: This data was generated and is being stored with the first author, a non-EPA researcher. It can be accessed through the following means: This data can be accessed by emailing the first author, Rachel Kaiser at rakaiser42@tntech.edu. Format: Excel spreadsheets with columns for samples, sites, collection date and relative abundances. This dataset is associated with the following publication: Rachel , K., J. Polk, T. Datta, S. Keely, N. Brinkman, R. Parekh, and G. Agga. Occurrence and Prevalence of Antimicrobial Resistance in Urban Karst Groundwater Systems Based on Targeted Resistome Analysis. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 874: 162571, (2023).

,An Environmental Component of a "One Health" approach, the mission of the Agricultural Antibiotic Resistance (AgAR) project is to,ANTIBIOTIC DRUGS: Which drugs are the most relevant for each type of ag production system? At what level do excreted drugs continue to provide selective pressure in the environment?,RESISTANT BACTERIA: What is the relative contribution of specific bacteria to resistance in human clinical settings? Are some bacteria more likely than others to donate or receive resistance genes? What is the relative contribution of clonal spread of pathogens versus horizontal gene transfer?,RESISTANT GENES: How long do specific types of genes persist in agricultural samples? What conditions increase or decrease the likelihood of a successful transfer in manure, soil, water, and air? What is the role of the natural soil "resistome"?,AgAR Network Goals:,The AgAR network is composed of ARS scientists with an interest in understanding the ecology of antibiotic resistance in soil, water, air, insects, wildlife, and food. The network currently represents 4 national programs at 10 ARS locations across the United States, with over 200 peer-reviewed publications on AgAR topics, authored and co-authored by over 70 current and former ARS employees.,Activities:,Importance:,While there is broad agreement the use of antibiotics in food animals has the potential to adversely impact human clinical outcomes, the details of how this happens are unknown, and there is a critical need for information on antibiotic resistance (AR) in agricultural settings (AgAR). U.S. and international health organizations have taken the lead on identifying specific antibiotic drugs and resistant infections that are critical to human health. ARS is uniquely positioned to provide information on the "farm" side of the "farm to fork continuum". ARS scientists are able to address these questions in a practical way, by combining their experience (over 200 peer-reviewed ARS publications on antibiotic resistance) with their applied understanding of agricultural production systems.,ORGANIZATION: Scientists work on their own, individual research projects. The AgAR network provides resources to participants to encourage collaboration across program areas and geographical location.,MANAGEMENT: The AgAR network is operated using a wiki community approach. All participating scientists are encouraged to contribute to and share in the community resources. Currently, the group resources will be curated by the group coordinator, with input and guidance from a five person advisory panel.,RESOURCES: Bibliography of peer-reviewed AgAR papers by ARS authors • AgAR topic reference lists • information on meetings and conferences • "AR_in_environment" listserve • Community webinars,

,Groundwater was collected by dead-end ultrafiltration and small-volume grab sampling from 138 wells in southwest Wisconsin across Grant, Iowa, and Lafayette Counties. Samples were collected to assess occurrence of antibiotic resistance genes in private wells and investigate their association with microbial source tracking markers. For ultrafiltration samples, microbes were backflushed, desiccated beef extract was added to the eluate, and samples were concentrated by polyethylene glycol precipitation; concentrate was frozen at -80 degrees Celcius (C). Small-volume grab samples were concentrated on 0.45-micron mixed cellulose ester filters, filters were eluted, and eluate was frozen at -80 degrees C following addition of beef extract. Nucleic acids were extracted from both sample types using a QIAcube and QIAamp DNA mini kit with viral lysis buffer (AVL) and carrier RNA (Qiagen). Nucleic acids were extracted from 280 microliter (µL) of sample concentrate and eluted into 140 µL AE Buffer (Qiagen). Nucleic acids were analyzed in duplicate using quantitative polymerase chain reaction (qPCR) on a Roche LightCycler 480 II using hydrolysis probes. Inhibition was assessed for every sample using Sketa DNA as inhibition control and mitigated by dilution with AE buffer as necessary. No-template negative controls were performed for all analysis steps: secondary concentration, nucleic acid extraction, and qPCR. For each assay with amplification in negative controls, the cycle of quantification (Cq) in unknown samples must be below the censoring threshold to be accepted as positive. Censoring thresholds were calculated as the mean Cq of negative controls - 3 standard deviations; censoring thresholds for each assay and sample type are reported in a separate file (Censoring thresholds.csv). Positive controls (bovine herpes virus vaccine) for extraction were included with each analysis batch and evaluated qualitatively. Positive controls were run in duplicate reactions for all targets and had to be within 0.5 cycles of the expected Cq. Data are expressed as genomic copies per liter of groundwater sampled. Dataset consists of 1 spreadsheet file (qPCR data results.csv). Variables in this file are described in the included data dictionary.,

Surface water samples (n = 33) were collected in fall of 2023 at stream sites in Scott County Iowa, USA and were analyzed for microbial source tracking markers by quantitative polymerase chain reaction at the Laboratory for Infectious Disease and the Environment (LIDE). Microbial source tracking markers identify fecal sources of contamination by detecting microbes that are specific to certain animals. Cooperators include Partners of Scott County Watersheds, Prairie Rivers of Iowa, and U.S. Department of Agriculture-Agricultural Research Service.

Surface water samples (n = 33) were collected in fall of 2023 at stream sites in Scott County Iowa, USA and were analyzed for microbial source tracking markers by quantitative polymerase chain reaction at the Laboratory for Infectious Disease and the Environment (LIDE). Microbial source tracking markers identify fecal sources of contamination by detecting microbes that are specific to certain animals. Cooperators include Partners of Scott County Watersheds, Prairie Rivers of Iowa, and U.S. Department of Agriculture-Agricultural Research Service.

Vegetative buffer strips have been demonstrated to effectively reduce loads of sediment, nutrients, and herbicides in surface runoff, but their effectiveness for reducing veterinary antibiotic loads in runoff has not been well documented. Surface runoff simulation where performed to determine the effectiveness of Vegetative buffer strips vegetation and width on surface runoff loads of the veterinary antibiotics sulfamethazine and lincomycin. The three datasets in this release document 1. physical and chemical soil properties for soil located on experimental vegetated buffer strip plots prior to surface runoff simulations; 2. Rainfall simulator performance data and antecedent soil water content data for all vegetated buffer strip plots during the surface runoff simulations; and 3. Surface water hydrology data collected for determining veterinary antibiotic residue load concentrations and suspended sediment load concentrations in all vegetated buffer strip plots during rainfall simulations.