Data in Brief最新文献

In modern complex mechanical systems, machine faults typically occur in multiple components simultaneously, and the domain of collected sensor data changes continuously due to variations in operating conditions. Deep learning-based fault diagnosis approaches have recently been enhanced to address these real-world industrial challenges. Comprehensive labeled data covering compound fault scenarios and multi-domain conditions are crucial for exploring these issues. However, existing multi-domain datasets focus on a limited range of operating conditions, such as motor rotating speeds and loads. This limits their applicability to real-world industrial scenarios. To bridge this gap, we present a novel multi-domain dataset that incorporates these basic conditions and extends to various bearing types and compound machine faults. The deep groove ball bearing, the cylindrical roller bearing, and the tapered roller bearing were utilized to provide data that reflect diverse mechanical interactions between the shaft and the bearing. Vibration data were collected using a USB digital accelerometer at two sampling rates and six rotating speeds, encompassing three single bearing faults, seven single rotating component faults, and 21 compound faults of the bearing and rotating component. Additionally, the dataset provides spectrograms of vibration data using short-time Fourier transform (STFT) for data-driven analysis with a 2-D input. This dataset encompasses more complex compound fault and domain shift problems than those presented in conventional public vibration datasets, thereby aiding researchers in studying intelligent fault diagnosis methods based on deep learning.

This CIDACC dataset was created to determine the cell population of Chlorella vulgaris microalga during cultivation. Chlorella vulgaris has diverse applications, including use as food supplement, biofuel production, and pollutant removal. High resolution images were collected using a microscope and annotated, focusing on computer vision and machine learning models creation for automatic Chlorella cell detection, counting, size and geometry estimation. The dataset comprises 628 images, organized into hierarchical folders for easy access. Detailed segmentation masks and bounding boxes were generated using external tools enhancing the dataset's utility. The dataset's efficacy was demonstrated through preliminary experiments using deep learning architecture such as object detection and localization algorithms, as well as image segmentation algorithms, achieving high precision and accuracy. This dataset is a valuable tool for advancing computer vision applications in microalgae research and other related fields. The dataset is particularly challenging due to its dynamic nature and the complex correlations it presents across various application domains, including cell analysis in medical research. Its intricacies not only push the boundaries of current computer vision algorithms but also offer significant potential for advancements in diverse fields such as biomedical imaging, environmental monitoring, and biotechnological innovations.

A synthetic population is a distribution of synthetic agents that replicates the demographic distribution of a real-world population based on census records. This paper presents an end-to-end model to generate a synthetic population of residents in Gothenburg, Sweden, along with activity schedules and mobility patterns for present and past populations. Using a stochastic modelling approach, we describe the model and present its corresponding dataset. The model is designed for applications in neighbourhood planning and includes detailed replicas of people in different neighbourhoods of Gothenburg organised as persons, households, houses, buildings, and daily activity chains. While the persons, households, and houses are synthetic replicas, they are connected to existing buildings. The model considers the allocation of primary and secondary locations based on a gravity model, realistic routing for active, public, and private motorised modes of transportation and allows users to introduce new buildings and amenities if needed. The model aims to impute national-level mobility patterns from a household travel survey and apply them locally to capture the nuances of a neighbourhood's built environment and demographic composition.

This dataset is the output of a long term multi-resolution calibrated hydrodynamic model of Bass Strait waters in south-eastern Australia. The model is 3 dimensional with 16 sigma layers. It is forced by tides, wind, non-tidal sea level variability as well as salinity and temperature through a nudging scheme. The model was calibrated against existing data from previous fixed location instrument deployments and hull mounted ADCP data. While the model has limitations, it performs well against measured data and provides a useful tool for describing spatially varying currents throughout East Victorian waters.

A draft genome sequence of an isolate of Arthrobacter koreensis BSB from Santiniketan soil is being published. A. koreensis BSB produces lignocellulases, which are crucial in plant biomass degradation. It is a potential source of enzymes of digestive importance, especially lignocellulases. Genomic DNA was isolated from a single bacterial colony using a QIAgen Blood and Tissue kit (QIAgen Inc., Canada). Illumina HiSeq X performed the DNA sequence, employing 2 × 150 paired-end chemistry, and 8,725,587 reads were obtained, corresponding to a sequence coverage of 755X. The draft genome assembly formed 15 contigs > 200 base pairs in length (N50 value= 446, 958 and L50= 3). The genome size is 3,466,004 base pairs with an average GC percentage of 65.94 %. Annotation and prediction of genes were carried out with Prokka v.1.14.6, and 3,172 CDS, 3236 genes, 58 tRNA genes, 4 rRNA genes, and 2 tmRNA genes were identified.