Antimicrobial resistance dataset for pattern recognition in machine learning application

IF 1 Q3 MULTIDISCIPLINARY SCIENCES Data in Brief Pub Date : 2024-09-10 DOI:10.1016/j.dib.2024.110922
Bukola O. Atobatele , Segun Adebayo , Odunola O. Olaniran , Abimbola A. Owoseni
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引用次数: 0

Abstract

This study presents a dataset of bacterial isolates collected from abattoirs in Osun State, Nigeria, designed to support research on antimicrobial resistance (AMR). The environment plays a critical role in the development and spread of AMR, posing a growing threat to global health. This dataset aims to address challenges in antibiotic selection by enabling the prediction of effective drugs for specific bacterial infections.

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用于机器学习应用模式识别的抗菌药耐药性数据集
本研究介绍了从尼日利亚奥逊州屠宰场收集的细菌分离物数据集,旨在支持抗菌药耐药性(AMR)研究。环境在 AMR 的发展和传播中起着至关重要的作用,对全球健康构成日益严重的威胁。该数据集旨在通过预测针对特定细菌感染的有效药物来应对抗生素选择方面的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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