Data in Brief最新文献

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.

There has been a global surge in the need for commercially accessible plant conditioners that are derived from natural ingredients and are therefore environmentally benign. Currently, sustainable agriculture and minimizing the ecological impact are of great importance. Preparations that contain commonly used humic acids and/or natural amino acids are ideal for meeting these criteria. An investigation was conducted to examine the impact of three plant foliar fertilizers containing humic acid and one fertilizer containing a combination of humic and amino acids on maize crops. By employing the shallow mRNA sequencing technique, we acquired datasets that, once processed, are ideal for investigating the impacts of the foliar fertilizers examined in the study. Five SRA datasets were uploaded to NCBI. These datasets include the TSA (Transcriptome Shotgun Assembly), the contigs that were blasted, mapped, and annotated from the pre-processed datasets, as well as the count table obtained from the RNA-seq read quantification. All of these data are included in the Mendeley database. In the future, the databases will enable the investigation of alterations in plant biochemical processes at the gene expression level.

The creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management. This dataset enables the development of accurate machine learning models for early disease detection, reducing manual inspections and facilitating timely interventions. It serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. This leads to targeted interventions, reduced chemical use, and improved crop management. Global collaboration is fostered, contributing to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. Field surveys conducted from October 2023 to January 2024 ensured meticulous image capture under diverse conditions. The images are categorized into eight classes, representing specific disease manifestations, pests, or environmental stress in cotton plants. The dataset comprises 2137 original images and 7000 augmented images, enhancing deep learning model training. The Inception V3 model demonstrated high performance, with an overall accuracy of 96.03 %. This underscores the dataset's potential in advancing automated disease detection in cotton agriculture.

This data article presents a comprehensive buccal dental microwear raw database, accompanied by all corresponding archaeological sample micrographs acquired through a ZEISS Axioscope A1 optical microscopy (OM). The dataset includes teeth specimens from 88 adult individuals, representing eight distinct groups spanning the Middle-Late Neolithic to the Middle Bronze Age from the northeastern Iberian Peninsula. These groups include Cova de l'Avi, Cova de Can Sadurní, Cova de la Guineu, Cova Foradada, Cova del Trader, Roc de les Orenetes, Cova del Gegant, and Cova dels Galls Carboners.

The data collection process was based on the use of optical microscopy to obtain dental microwear patterns, with a specific focus on the buccal surface of the teeth. To facilitate future comparative studies, we have also included all the micrographs obtained with the optical microscopy and the processed images with the counted striations. The presentation of this extensive dataset sets a base for future research on dental microwear patterns and dietary variations across various prehistoric periods.

Naphthol Green B (NGB) is a synthetic azo dye widely used in various industries, including textiles and leathers. NGB poses significant environmental and ecological concerns when released into natural water systems. This paper investigates the decolorization of NGB using UV/sulfite system. The % decolorization of NGB was optimized using 3² Full Factorial Design (FFD), and the ANOVA results show that the model has a good fit for the data (R² = 99.54 %, R²_(adj) = 98.76 %) and the significant factors contributing to the % decolorization are A, B, A², and B² where A = mM sulfite and B = pH. The model predicted ≥100 % decolorization with the optimum conditions 12 mM sulfite and pH 10. An actual experiment was conducted to verify the response, resulting in 96.2 % decolorization which is in good agreement with the model.

Pythium species are distributed globally, with certain members playing significant roles as plant and animal pathogens. Pythium cedri Chen 4 has been identified as a pathogenic isolate responsible for causing root rot on Cedrus deodara. Here, a comprehensive genome-wide sequence of P. cedri strain Chen 4 utilizing the Illumina NovaSeq sequencing platform and a Pacific Biosciences Sequel sequencing platform is presented. The genome of P. cedri strain Chen 4 was assembled into 150 contigs containing a combined size of 41.25 Mb, N50 value of 1,717,859 bp and N90 value of 431,829 bp. Genome annotation revealed 14,077 protein-encoding genes and 364 of the 1016 predicted proteins were putative effectors. The present work enriches the genetic resources of P. cedri for studying its evolution and can contribute to a better understanding of P. cedri–host interaction.