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Objectives The rapid growth of machine learning methods has led to an increase in the demand for data. For bearing fault diagnosis, the data acquisition is time-consuming with complicated processes. Existing datasets are only focused on only one type of bearing, which limits real-world applications. Therefore, the objective of this work is to propose a diverse dataset for ball bearing fault diagnosis based on vibration. Data description In this work, we introduce a practical dataset named HUST bearing , which provides a large set of vibration data on different ball bearings. This dataset contains 99 raw vibration signals of 6 types of defects (inner crack, outer crack, ball crack, and their 2-combinations) on 5 types of bearing (6204, 6205, 6206, 6207, and 6208) at 3 working conditions (0 W, 200 W, and 400 W). Each vibration signal is sampled at a rate of 51,200 samples per second for 10 s. The data acquisition system is elaborately designed with high reliability.

Objectives The ongoing Coronavirus disease 2019 (COVID-19) pandemic has drastically impacted the global health and economy. Computed tomography (CT) is the prime imaging modality for diagnosis of lung infections in COVID-19 patients. Data-driven and Artificial intelligence (AI)-powered solutions for automatic processing of CT images predominantly rely on large-scale, heterogeneous datasets. Owing to privacy and data availability issues, open-access and publicly available COVID-19 CT datasets are difficult to obtain, thus limiting the development of AI-enabled automatic diagnostic solutions. To tackle this problem, large CT image datasets encompassing diverse patterns of lung infections are in high demand. Data description In the present study, we provide an open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists. CT images were acquired from two main general university hospitals in Mashhad, Iran from March 2020 until January 2021. COVID-19 infections were ratified with matching tests including Reverse transcription polymerase chain reaction (RT-PCR) and accompanying clinical symptoms. All data are 16-bit grayscale images composed of 512 × 512 pixels and are stored in DICOM standard. Patient privacy is preserved by removing all patient-specific information from image headers. Subsequently, all images corresponding to each patient are compressed and stored in RAR format.

Objectives The genomes to fields (G2F) 2024 Maize Genotype by Environment (GxE) Prediction Competition challenged participants to develop and submit their best performing models to predict grain yield for the 2024 maize GxE project field trials, using G2F data collected from 2014 to 2023 and other publicly available data. Data description The G2F Maize GxE Project is a collaborative effort, with all generated data made publicly available. The resource presented here includes the training and test datasets used for the G2F 2024 Maize GxE Prediction Competition. Specifically, data collected from 2014 to 2023 served as the training set to predict grain yield in the 2024 test set. The dataset comprises phenotypic, genotypic, soil, weather, and environmental covariate data, along with metadata describing environments (year-location combinations). It has been curated and lightly filtered for quality control and to ensure consistent naming across years. Competitors also had access to readme files that describe the structure and content of the datasets.

Objectives The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. Data description This resource used data from the Maize GxE project within the G2F Initiative [ 1 ]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [ 2 ]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

Objectives This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. Data description The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Objectives Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F’s genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [ 1 ] while 2016 and 2017 datasets are newly available to the public.

Soundscape is the perception of an acoustic environment. This perception includes thoughts and feelings of the human being owing to the environment interaction, and physical properties of such environment. Soundscape recordings involve the human perception usually captured by questionnaires. However, human perception is inherently subjective, and questionnaires are tools to gather information with several bias (e.g., design, response, sampling, event recall, and management). To move towards the study of the soundscape effects in terms of not only involving questionnaire-based information, but also the neurophysiological reaction, this works aims to provide a database of 30 individuals, who experienced four types of soundscapes in Monterrey, N. L., Mexico (ecological park, riverwalk, music avenue, and traffic) in two audio formats (stereo and binaural). The sense of being in the auditory environment was gathered by applying the Usoh and Steed questionnaire and recording the electroencephalographic activity (brain electrical reaction) of the individuals. This database could be useful to study stress level and cognitive load induced by traffic, to measure whether ecological park or riverwalk reduce stress, to evaluate the engagement and excitedness level arisen in music venues, and to improve user-experiences in immersive environments such virtual reality and augmented reality.

Objectives Publicly available datasets are essential for the development, evaluation, and benchmarking of fall detection and human activity recognition algorithms. Although numerous datasets include falls and activities of daily living (ADLs), prayer movements—despite exhibiting motion patterns that may resemble falls—remain largely underrepresented. The objective of this study is to present a publicly available IMU-based dataset that explicitly includes prayer movements alongside falls and ADLs, thereby addressing an important gap in existing datasets and supporting methodological research on activity classification and false-positive reduction. Data description The dataset comprises motion recordings of 11 types of fall movements, 13 types of activities of daily living (ADLs), and 5 types of prayer movements. Data were collected from 17 healthy young adult participants using two wearable IMU sensors placed on the forehead and forearm. Each activity was performed three times by each participant. Tri-axial accelerometer, gyroscope, and magnetometer signals were recorded at a sampling frequency of 200 Hz. All recordings were manually labeled by direct observation during data acquisition. The dataset is publicly available and systematically organized to support algorithm development, benchmarking, and reproducible research in fall detection and human activity recognition. Although data were collected from young adults, the dataset is intended as a controlled reference resource, and applicability to other populations requires further validation.

Objectives This study presents the genome sequencing of the MC3 strain of Fusarium oxysporum f. sp. cubense (Foc), isolated from damaged banana ( Musa sp.) tissue from La Peca, Bagua city, Amazonas, Peru. The aim was to characterize the genome to understand the resistance and virulence potential of the isolate. Data description The genomic DNA of the MC3 strain of Foc was sequenced using the Illumina platform, generating high-quality reads with an average genome coverage of 150 bp PE. Quality control was performed with FastQC, and the reads were preprocessed with Trimmomatic. De novo assembly was carried out with MaSuRCA, resulting in a draft genome of 45,518,950 bp assembled into 493 contigs with a GC content of 47.71%. Genome quality analysis revealed 99.2% completeness.

Objectives Slag detection in steel manufacturing is essential for ensuring high product quality and process efficiency. The purpose of the accelerometer-based data is to allow for accurate monitoring and differentiation between slag and molten metal flow. This is vital to prevent equipment damage, maintain steel quality, and enhance operational effectiveness. The data is collected specifically to support the development of machine learning models for real-time monitoring in the steel production process, addressing the critical need for precise slag detection. Data description The Steel Slag Flow Dataset (SSFD) offers a comprehensive set of data obtained from a triaxial accelerometer during various stages of steel production. By leveraging this dataset, researchers can effectively analyze and classify the flow of slag versus molten metal. The dataset allows for data-driven approaches so that machine learning researchers can optimize steel manufacturing processes, ensuring high-quality steel production and minimizing the risks associated with slag contamination. The SSFD provides a valuable resource for researchers seeking to enhance predictive maintenance and monitoring in industrial applications.