This readme file was generated on [2026-10-05] by [Yanyan Zhou]

GENERAL INFORMATION

Title of Dataset: Data of paper titled Inadequate waste management drives the spread of antimicrobial resistance across the watershed of a large African city

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Author/Principal Investigator Information
Name: David W Graham
ORCID: 0000-0002-9753-496X
Institution: Department of Biosciences, Durham University, United Kingdom
Address: Department of Biosciences, Durham University, United Kingdom
Email: david.w.graham@durham.ac.uk

Author/Associate or Co-investigator Information
Name: Jian-Qiang Su
ORCID: 0000-0003-1875-249X
Institution: Institute of Urban Environment,Chinese Academy of Sciences
Address: 1799 Jimei Road, Xiamen 361021 China
Email: jqsu@iue.ac.cn


Date of data collection: 2023-12

Geographic location of data collection: Akaki River watershed, Addis Ababa, Ethiopia

Information about funding sources that supported the collection of the data: This research was supported by the CGIAR One Health initiative and science program on Sustainable Animal and Aquatic Food Systems (CGIAR Trust Fund); the China National Science and Technology Major Project (Grant No. 2025ZD1204205); the National Natural Science Foundation of China (NSFC) (Grant Nos. 42407572, 42507178); the JPIAMR project 'TEXAS' (Towards next-generation AMR surveillance); and the UK Medical Research Council (Grant No. MR/Z000017/1).


SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: CC BY 4.0 (Creative Commons Attribution 4.0 International)

Links to publications that cite or use the data: https://www.sciencedirect.com/science/article/pii/S016041202600512X?via%3Dihub

Links to other publicly accessible locations of the data: N/A

Links/relationships to ancillary data sets: N/A

Was data derived from another source? 
No
If yes, list source(s): 

Recommended citation for this dataset: 
Zhou, Y., Haile, A. T., Kibret, M., Li, H., Haileselassie, M. M., Bekele, T. W., Kassa, J. M., Demissie, K., Wyrsch, E. R., Djordjevic, S. P., Jobling, K., Quintela-Baluja, M., Su, J.-Q., Mateo-Sagasta, J., & Graham, D. W. (2026): Inadequate waste management drives the spread of antimicrobial resistance across the watershed of a large African city.  Durham University.  (dataset).  
DOI:  https://doi.org/10.15128/r26d56zw722


DATA & FILE OVERVIEW

File List: 
EI_01_AllGenesRelativeAbundance.csv: Contains the relative abundance data of antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and taxon-specific markers across 38 sampling sites in the Akaki River watershed. Data is normalized to 16S rRNA gene copies. Used for generating manuscript figures.

EI_02_AllGenesAbsoluteAbundance.xlsx: Contains the absolute abundance data for the targeted genes and 16S rRNA across the 38 sampling sites. Used for generating manuscript figures. The unit is copies/L.

Relationship between files, if important: 
The absolute abundance data for all target genes (in EI_02) was calculated by multiplying their relative abundances (from EI_01) by the absolute abundance of the 16S rRNA gene for each corresponding sample. Only the 16S rRNA gene absolute abundance was directly quantified using a standard curve.

Additional related data collected that was not included in the current data package: 

Are there multiple versions of the dataset?
NO
If yes, name of file(s) that was updated: 
N/A
Why was the file updated? 
N/A
When was the file updated? 
N/A

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data: Genes detected in at least two of three technical replicates with a cycle threshold (Ct) ≤ 31 were considered positive. Gene copy number was estimated as 10^((31−Ct)/(10/3)). Relative abundance was calculated by dividing each target gene copy number by the 16S rRNA gene copy number within the same sample.

Methods for processing the data: 

Instrument- or software-specific information needed to interpret the data: 
SmartChip Real-Time PCR system (Takara Bio). Software for downstream analysis: R project, utilizing packages including vegan, ggplot2, randomForest, and ComplexHeatmap.

Standards and calibration information, if appropriate: 
Standard curves for 16S absolute quantification were generated using ten-fold serial dilutions of a standard plasmid ranging from 10⁴ to 10⁸ copies/μL. The amplification efficiency was 95%, and the correlation coefficient (R²) was consistently > 0.99.

Environmental/experimental conditions: Sampling was conducted exclusively during the dry season (December 2023) under low-flow conditions to capture peak pollutant concentrations and maximum resident exposure risks.

Describe any quality-assurance procedures performed on the data: Each HT-qPCR reaction was performed with three technical replicates. A strict detection threshold was applied (Ct ≤ 31 and detected in at least two replicates) to eliminate false positives. DNA quality and quantity were verified using a Qubit 4 Fluorometer prior to amplification.

People involved with sample collection, processing, analysis and/or submission: Yanyan Zhou, Alemseged Tamiru Haile, Hu Li, Mihret Mersha Haileselassie, Tilaye Worku Bekele, Jemila Mohammed Kassa, Kirubel Demissie, Jian-Qiang Su, and David W. Graham.


DATA-SPECIFIC INFORMATION FOR: [EI_01_AllGenesRelativeAbundance.csv]

Number of variables: 367

Number of cases/rows: 38

Variable List: Name of the targeted ARG, MGE, or taxon-specific marker, and ID of sample.

Missing data codes: 0 

Specialized formats or other abbreviations used: N/A


DATA-SPECIFIC INFORMATION FOR: [EI_02_AllGenesAbsoluteAbundance.xlsx]

Number of variables:
Varies by sheet (typically includes 1 or 2 identifier columns followed by 38 site-specific columns).

Number of cases/rows:
Varies by sheet (e.g., the ARG sheet contains all individual resistance genes, while DrugClass and ResistanceMechanism contain aggregated categories).

Variable List:
General structure across all sheets: The first column identifies the specific target (Gene/Class/Mechanism). The subsequent 38 columns (S26u, S25u, S21c... S71d) represent the absolute abundance of that target at specific sampling locations. All numerical abundance values are expressed in copies/L.

Specific Sheet Descriptions:

ARG: Absolute abundance of individual antibiotic resistance genes.

DrugClass: Absolute abundance of ARGs aggregated by antibiotic drug class (calculated based on the ARG sheet).

ResistanceMechanism: Absolute abundance of ARGs aggregated by resistance mechanism (calculated based on the ARG sheet).

MGE: Absolute abundance of individual mobile genetic elements.

HeavyMetal: Absolute abundance of heavy metal resistance genes.

Taxo: Absolute abundance of taxon-specific marker genes.

16S: Absolute abundance of the bacterial 16S rRNA gene.

Missing data codes:
0

Specialized formats or other abbreviations used: NO
