Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
270
datasets available to search
ShareScore release 0.9.0
Dataset results
270 results for “antimicrobial resistance”
DRIAMS: Database of Resistance Information on Antimicrobials and MALDI-TOF Mass Spectra
Open the record for dataset details and reuse information.
Aquaculture at the crossroads of global warming and antimicrobial resistance
Open the record for dataset details and reuse information.
Antibiotic Resistance Microbiology Dataset (ARMD): A resource for antimicrobial resistance from EHRs
Open the record for dataset details and reuse information.
Dataset for: Social dilemma in the excess use of antimicrobials incurring antimicrobial resistance
Open the record for dataset details and reuse information.
Genomic epidemiology of Escherichia coli: antimicrobial resistance through a One Health lens in sympatric humans, livestock and peri-domestic wildlife in Nairobi, Kenya
Open the record for dataset details and reuse information.
Co-occurrence of antimicrobials and metals in swine manure as potential drivers of antimicrobial resistance in the environment
Open the record for dataset details and reuse information.
Genomic analysis of the diversity, antimicrobial resistance and virulence potential of Campylobacter jejuni and Campylobacter coli strains from a private health care center in Central Chile
<p>Supplementary Dataset for the work entitled "Genomic analysis of the diversity, antimicrobial resistance and virulence potential of Campylobacter jejuni and Campylobacter coli strains from a private health care center in Central Chile".</p> <p>This dataset includes bacterial draft genome sequence reannotations of 69 C. jejuni and 12 C. coli strains, the fasta file containing the nucleotide sequence of <em>Campylobacter</em> pathogenicity genes screened for the virulome analysis and the fasta file for the in-house database for plasmid screening analysis in <em>Campylobacter </em>genomes.</p>
Neisseria gonorrhoeae clustering to reveal major European WGS-based genogroups in association with antimicrobial resistance (cgMLST and MScgMLST schemas, allelic profile matrices and GrapeTree input file)
<p>This dataset refers to the gene-by-gene analysis of 3791 <em>Neisseria gonorrhoeae</em> genomes from 21 European countries and includes the used cgMLST and MScgMLST loci schemas prepared for the chewBBACA core suite, as well as the associated allelic profile matrices for all genomes. Additionally a <em>.json</em> file is made available for direct input in the GrapTree vizualization software for data/metadata exploration. </p> <p>All novel raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB36482). Additional raw sequence read data used were retrieved from the following ENA BioProjects: PRJEB14933; PRJEB2124; PRJEB23008; PRJEB26560; PRJEB9227; PRJNA275092; PRJNA348107; PRJNA473385; PRJNA315363. </p>
Annexes to the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2017/2018
<p><strong>Version 2 - changes as compared to version 1</strong></p> <p>The following revisions were applied to the file EUSR AMR2018_Annexes A-F.docx in the<em> section Annex F</em>:</p> <p>- in Table 6B, pages 119-120, the footnotes (e) to (p) were re-attributed in the table, so as to match the table footnotes;</p> <p>- in Table 31 on page 123, 'LA' was replaced with 'HA' for Belgium data in the column titled 'LA, CA or HA'.</p> <p>Light formatting has been applied on pages 121 and 122, Tables 7a and 7b, changed from portrait to landscape orientation, as well as to footers and headers that were made consistent throughout the document.</p> <p>---------</p> <p><strong>Version 1 </strong></p> <p>All tables produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2018 :</p> <p>- Campylobacter</p> <p>- E. coli</p> <p>- MRSA</p> <p>-Salmonella</p> <p>- ESBL</p> <p>Annexes A to F are also included.</p> <p> </p>
Horizontal gene transfer is the main driver of antimicrobial resistance in broiler chicks infected with Salmonella enterica serovar Heidelberg
<p>Overuse and misuse of antibiotics in clinical settings and in food production have been linked to the increased prevalence and spread of antimicrobial resistance (AR). Consequently, public health and consumer concerns have resulted in a remarkable reduction in antibiotics used for food animal production. However, there are no data on the effectiveness of antibiotic removal in reducing AR shared through horizontal gene transfer (HGT). In this study, we used neonatal broiler chicks and Salmonella enterica serovar Heidelberg (SH), a model food pathogen, to test if chicks raised antibiotic-free harbor transferable AR. We challenged chicks with an antibiotic susceptible SH strain using various routes of inoculation and determined if SH isolates recovered carried plasmids conferring AR. We used antimicrobial susceptibility testing and whole genome sequencing (WGS) to show that chicks grown without antibiotics harbored antimicrobial resistant SH population 14 days after challenge and chicks challenged orally acquired AR at a higher rate than chicks inoculated via the cloaca. Using 16S rRNA gene sequencing we found that SH infection perturbed the microbiota of broiler chicks and used metagenomics and WGS to confirm commensal Escherichia coli population as the main reservoir of IncI1 plasmid acquired by SH. The carriage of this IncI1 plasmid posed no fitness cost to SH but increased its fitness when exposed to acidic pH in vitro. These results suggest that HGT of plasmids carrying AR shaped the evolution of SH and that antibiotic use reduction alone is insufficient to limit antibiotic resistance transfer from commensal bacteria to Salmonella.</p>
Supplementary Material : Antimicrobial Resistance and Faecal Sterols in Marine Sediments: An Evidence of AMR away from point sources - Kuwait's Example
<p>Supplementary data for <strong><span>Antimicrobial Resistance and Faecal Sterols in Marine Sediments: An Evidence of AMR away from point sources - <span> </span>Kuwait’s Example</span></strong></p>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 2 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 2/2 of a dataset used for running and training the ML model.</p> <p>Part 1 is available at <a href="https://doi.org/10.5281/zenodo.14513933">10.5281/zenodo.14513933</a></p> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 1 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 1/2 of a dataset used for running and training the ML model.</p> <div> <div>Part 2 is available at <a href="https://doi.org/10.5281/zenodo.14524613">10.5281/zenodo.14524613</a></div> <div> </div> </div> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
Millennial-Scale Microbiome Analysis Reveals Ancient Antimicrobial Resistance Conserved Despite Modern Selection Pressures
<p>This dataset contains metadata and profiled antibiotic resistance genes from ancient metagenome permafrost.</p>
States of genome assembly supporting data for complete genome assembly of clinical multidrug resistant Bacteroides fragilis isolates enables comprehensive identification of antimicrobial resistance genes and plasmids.
<p>Assemblies for each isolate and assembly stage is in .gfa and .fasta format.</p> <p>the best SPAdes assembly is also included in the .zip files.</p> <p>1) Unicycler with illumina data and Nanopore data from the first sequencing run, filtered with FiltLong.<br> 2) Unicycler with illumina data and Nanopore data from the first sequencing run, filtered with FiltLong and error corrected with Canu<br> 3) Unicycler with illumina data and Nanopore data from the first and second sequencing run, filtered with FiltLong.<br> 4) manual finshing of assembly 3. <br> Methods are described in the paper and at the github repository (https://github.com/thsyd/bfassembly)</p> <p> </p>
Creating a 7,000 strains genotype-phenotype dataset of E. coli and antimicrobial resistance phenotypes
<h1>Description</h1> <p>This Zenodo repository contains the data (except for the input fastq files available on SRA and intermediary files generated during the variant calling process) and code to recapitulate the study from <a href="https://doi.org/10.57844/arcadia-d2cf-ebe5">https://doi.org/10.57844/arcadia-d2cf-ebe5</a> and the associated GitHub repository, where the code, pipelines, and analysis are described in more detail.</p> <p> </p> <h1>Work summary</h1> <p>In this work, we established a framework for compiling large genotype-phenotype datasets and produced a large-scale dataset of more than 7,000 <em>E. coli</em> strains and antimicrobial resistance phenotypes.</p> <p>We leveraged the genetic information and antimicrobial resistance (AMR) phenotype data available for the bacterium <em>Escherichia coli</em> to construct our dataset and took advantage of the existing knowledge about genetic variations and AMR phenotypes to validate our approach and dataset. We performed variant calling and compiled a genotype-phenotype dataset for more than 7,000 <em>E. coli</em> strains. Briefly, variant calling consists of identifying all genetic variations and their associated genotypes in a population compared to a reference genome. This is performed by aligning sequencing reads for each strain of the population against a reference genome, then identifying polymorphic regions in the population, and finally characterizing variants and their genotypes at each of these polymorphic regions.</p> <p>We have generated a dataset that successfully revealed significant genetic diversity and identified 2.4 million variants. By focusing on non-silent variants within genes associated with AMR, we confirmed the dataset's accuracy. </p> <p>We hope this study is a foundational resource for conducting large-scale genotype-phenotype studies that will offer valuable insights for genetics investigations, informing the development of treatments and prevention strategies for AMR. This resource is invaluable for microbiologists and epidemiologists seeking to understand AMR mechanisms and improve genotype-phenotype predictions in pathogenic <em>E. coli</em> outbreaks. Additionally, it's of particular interest to geneticists and evolutionary biologists, providing a dataset to develop strategies for studying genetic interactions and broader applications in phenotype-phenotype predictions and phylogenetic research.</p> <h1>Data organization</h1> <p>Data are organized in the compressed folder. Briefly, they’re divided into two main folders.</p> <p>The first folder, <strong>dataset_generation</strong>, includes the code and information necessary to build the genotype dataset and perform the variant calling. It covers major steps like the generation of the reference pangenome used for variant calling, the variant calling pipeline applied to each of the 7,000 strains, the filtering of false positive variants, and the annotation of the variants. </p> <p>The second section, <strong>dataset_analysis</strong>, includes the code and information used to process and analyze the dataset and generate figures for the Pub (https://doi.org/10.57844/arcadia-d2cf-ebe5). It includes the preliminary analysis of AMR phenotypes within the population and the analysis of variants regarding known AMR phenotypes.</p> <p> </p> <h1>Files description</h1> <p>The following table provides a list and description of the different files and their locations.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Location</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>variant_calling_pipeline</td> <td>dataset_generation/scripts/</td> <td>Snakefile: performs variant calling from raw paired-end sequencing files and generate one vcf.gz file per sample</td> </tr> <tr> <td>snakemake_ECOR72_annotation</td> <td>Snakefile: performs Prokka annotation on inputs whole genome fastq files</td> </tr> <tr> <td>ECOR72_and_DP_threshold_analysis.Rmd</td> <td>R markdown: analyses the coverage of known present and absebt loci in the ECOR population</td> </tr> <tr> <td>average_coverage_41.csv</td> <td>dataset_generation/data/dp_threshold/</td> <td>Pangenome loci read coverage information for 40 ECOR strains</td> </tr> <tr> <td>average_coverage_last32.csv</td> <td>Pangenome loci read coverage information for 32 ECOR strains</td> </tr> <tr> <td>whole_pan_ecor_presence_absence.csv</td> <td>Reformated pangenome loci presence-absence in ECOR strains</td> </tr> <tr> <td>pangenome_genomes_SRA_GCA.csv</td> <td>Correspondance table between ECOR72 strains genome names and raw sequencing files SRA accession number</td> </tr> <tr> <td>index_loci_pangenome_good.txt</td> <td>List of indexed positions in the pangenome</td> </tr> <tr> <td>list_ecor_txtfiles.txt</td> <td>List of txt files (containing the DP information per nucleotide) to use - This corresponds to the files for each 72 ECOR strains</td> </tr> <tr> <td>ECOR72_SRA_and_assembly_accessions.csv</td> <td>dataset_generation/data/</td> <td>List of Genome accession number and the SRA accession number of the associated sequencing files for the 72 ECOR strains</td> </tr> <tr> <td>sample_list_SRA.csv</td> <td>List of SRA accession numbers of the E. coli strains used for variant calling</td> </tr> <tr> <td>gene_presence_absence.csv</td> <td>dataset_generation/results/pangenome_cds/</td> <td>Roary output of presence-absence of the pangenome cds loci in the ECOR72 strains</td> </tr> <tr> <td>genes.gff</td> <td>Annotation file of the pangenome cds sequences (Prokka output)</td> </tr> <tr> <td>pangenome_cds.fa</td> <td>Roary output cds_pangenome sequencing file</td> </tr> <tr> <td>summary_statistics.txt</td> <td>Roary statistics output of creations of the cds pangenome</td> </tr> <tr> <td>roary_output</td> <td>Roary output folder</td> </tr> <tr> <td>IGR_presence_absence.csv</td> <td>dataset_generation/results/pangenome_igr/</td> <td>Piggy output of presence-absence of the pangenome igr loci in the ECOR72 strains</td> </tr> <tr> <td>pangenome_igr.fasta</td> <td>Piggy output igr_pangenome sequencing file</td> </tr> <tr> <td>piggy_output</td> <td>Piggy output folder</td> </tr> <tr> <td>whole_pangenome.fasta</td> <td>dataset_generation/results/pangenome_whole/</td> <td>whole pangenome sequences</td> </tr> <tr> <td>annot_summary_filtered.html</td> <td>dataset_generation/results/vcf/</td> <td>Summary of snpEff annotations</td> </tr> <tr> <td>annotated_output.vcf.gz</td> <td>snpEff annotated vcf file</td> </tr> <tr> <td>annotated_output.vcf.gz.csi</td> <td>indexed annotated vcf file</td> </tr> <tr> <td>filtered_output.vcf.gz</td> <td>filtered vcf file (removed low coveraged and low quality variants)</td> </tr> <tr> <td>filtered_output.vcf.gz.csi</td> <td>indexed filtered vcf files</td> </tr> <tr> <td>output.non_silent.vcf.gz</td> <td>vcf file containing only the nonsilent variants in the pangenome cds loci</td> </tr> <tr> <td>merged_output_listN.vcg.gz</td> <td>intermediary vcf files of 1000 merged strains vcf - these intermediary merged files are numbered from 1 to 7</td> </tr> <tr> <td>merged_output_all.vcf.gz</td> <td>final vcf.gz files of all merged vcf files in this study</td> </tr> <tr> <td>List_N_merging.txt</td> <td>dataset_generation/data/vcf_merging/</td> <td>List of the 1000 vcf.gz files to be merged together. There are 7 lists, numbered from 1 to 7</td> </tr> <tr> <td>ecor72_array.txt</td> <td>dataset_generation/results/ecor72_DP/</td> <td>Consolidate DP information per nucleotide for each ECOR strain</td> </tr> <tr> <td>variants_pos.tsv</td> <td>dataset_analysis/data/variant_analysis/</td> <td>List of all the variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_freqs.txt</td> <td>Variant frequency informations</td> </tr> <tr> <td>variants_non_silent_pos.tsv</td> <td>List of all the non-silent variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_non_silent_freqs.txt</td> <td>Non-silent variant frequency informations</td> </tr> <tr> <td>cds_eggNog.tsv</td> <td>eggNog output file of the pangenome annotation</td> </tr> <tr> <td>COG_functional_categories.csv</td> <td>Correspondance between COG functional categories and higher-order annotation</td> </tr> <tr> <td>BVBRC_genome_May31.csv</td> <td>dataset_analysis/data/dataset_analysis</td> <td>List of E. coli with available genomes as reported in BCBRV database</td> </tr> <tr> <td>BVBRC_genome_amr_May31.csv</td> <td>E. coli antimicrobial resistance information available in BCBRV database</td> </tr> <tr> <td>antibiotic_class.csv</td> <td>Antibiotic name and Antibiotic class information</td> </tr> <tr> <td>resistance_output.non_silent.vcf.gz</td> <td>dataset_analysis/data/antimicrobial_resistance_analysis</td> <td>vcf.gz file of the loci expected to be associated with antimicrobial resistance</td> </tr> <tr> <td>antibiotic_resistance_freq.csv</td> <td>Frequency information for the non-silent variant in the selected antimicrobial genes</td> </tr> <tr> <td>SRA_to_genome_name.csv</td> <td>correspondence between strain SRA accession number and genome name (as reported in BVBRC)</td> </tr> <tr> <td>Dataset_metainfo_AMR_analysis.Rmd</td> <td>dataset_analysis/scripts</td> <td>R markdown: conducts the characterization of the population and analysis of the AMR phenotype distribution</td> </tr> <tr> <td>Variant_population_analysis.Rmd</td> <td>R markdown: conducts the analysis and investigation of identified variants in the population</td> </tr> <tr> <td>Antimicrobial_resistance_investigation.Rmd</td> <td>R markdown: conducts the antimicrobial resistance investigation </td> </tr> </tbody> </table> <p> </p> <p> </p>
INVITED REVIEW: Antimicrobial Resistance Genes in Milk: a 10-year-systematic review and critical comment
Open the record for dataset details and reuse information.
ArMoR Cluster: 7 research projects fight Antimicrobial Resistance in livestock farming (updated version)
<p>Supported by the European Commission, Horizon Dissemination Booster (HRB) contributes to an effective transfer of research and innovation project results to policy makers, industry and society by offering various services as dissemination, exploitation strategy and business plan development to projects. Within Horizon Results Booster programme (HRB), 7 research projects AMRILS, AVANT, BM-FARM, FARMCARE, DISARM, HealthyLivestock and ROADMAP have formed the "ArMoR Cluster" to develop a conceptual framework to improve understanding of AMR in livestock systems.</p> <p>The video is available on YouTube: <a href="https://www.youtube.com/watch?v=ACbnyu3PhOY">https://www.youtube.com/watch?v=ACbnyu3PhOY</a></p> <p>For any further questions please contact us at:</p> <ul> <li><strong><a href="https://zenodo.org/record/avant@rtds-group.com">avant@rtds-group.com</a></strong> (project AVANT)</li> </ul>
Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
<p><strong>Introduction</strong>: The lack of standardized methods for detecting biofilms continues to pose a challenge to microbiological diagnostics since biofilm-mediated infections induce persistent and recurrent infections in humans that often defy treatment with common antibiotics. This study aimed to evaluate diagnostic parameters of four in vitro phenotypic biofilm detection assays in relation to antimicrobial resistance in aerobic clinical bacterial isolates.</p> <p><strong>Methods</strong>: In this cross-sectional study, bacterial strains from clinical samples were isolated and identified following the standard microbiological guidelines. The antibiotic resistance profile was assessed through the Kirby-Bauer disc diffusion method. Biofilm formation was detected by gold standard tissue culture plate method (TCPM), tube method (TM), Congo red agar (CRA), and modified Congo red agar (MCRA). Statistical analyses were performed using SPSS version 17.0, with a significant association considered at p<0.05.</p> <p><strong>Result</strong>: Among the total isolates (n=226), TCPM detected 140 (61.95%) biofilm producers, with CoNS (9/9) (p<0.001) as the predominant biofilm former. When compared to TCPM, TM (n=119) (p<0.001) showed 90.8% sensitivity and 70.1% specificity, CRA (n=88) (p=0.123) showed 68.2% sensitivity and 42% specificity, and MCRA (n=86) (p=0.442) showed 65.1% sensitivity and 40% specificity. Juxtaposed to CRA, colonies formed on MCRA developed more intense black pigmentation from 24 to 96 hours. There were 77 multi-drug-resistant (MDR)-biofilm formers and 39 extensively drug-resistant (XDR)-biofilm formers, with 100% resistance to ampicillin and ceftazidime, respectively.</p> <p><strong>Conclusion</strong>: It is suggested that TM be used for biofilm detection. Unlike MCRA, black pigmentation in colonies formed on CRA declined with time. MDR- and XDR-biofilm formers were frequent among the clinical isolates.</p>
Investigating Point-of-care Diagnostics for Sexually Transmitted Infections and Antimicrobial Resistance in Primary Care in Zimbabwe
ClinicalTrials.gov study NCT05541081. IPD Sharing: YES. Countries: 1. Publications: 3.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.