Open-access Dataset Research Articles

An open access dataset for strong-motion data (PGA, PGV, and Site VS) of 2023 M6.2 Jishishan, Gansu, China earthquake

An open access dataset for strong-motion data (PGA, PGV, and Site VS) of 2023 M6.2 Jishishan, Gansu, China earthquake

The negative impact of the COVID-19 pandemic on breast cancer tackle in Brazil’s public and private healthcare system: time series study between 2014 and 2022

BackgroundThe COVID-19 pandemic has overwhelmed the healthcare systems of many countries and negatively impacted the care of other diseases.ObjectiveTo evaluate the trend of screening mammograms, oncological breast surgeries, and breast cancer hospitalizations in Brazil’s public and private healthcare system between 2014 and 2022.MethodThis ecological time series study uses the inflection point regression model and semester percentage change (SPC). We use the open-access dataset of the different healthcare systems in Brazil. We analyzed the trend of the variables in the pre-pandemic and the effect of the pandemic on the total time series.ResultsIn 2020, compared to 2019, the decrease in screening mammograms, oncological breast surgeries, and breast cancer hospitalization was − 41.44%, -23.13%, and − 10.52% (public health system) and − 29.49%, -18.96%, and − 15.35% (private healthcare system). In the public healthcare system, the pandemic has enhanced the decreasing trend of mammograms (SPC − 1.6% before and − 3.4% after), has reverted the stationary trend of oncological breast surgeries to decreasing (SPC − 1.0%), has slowed the increasing trend of breast cancer hospitalization (SPC 1.8% before and 0.9% after). In the private healthcare system, the pandemic has reverted the stationary trend of mammograms to decreasing (SPC − 1.0%), has slowed the increasing trend of breast cancer surgeries (SPC 2.3% before and 0.8% after), has reverted the growing trend of breast cancer hospitalization (SPC 3.9%) to stationary.ConclusionDuring the COVID-19 pandemic, there was an increase in inequalities between healthcare systems, especially in breast cancer screening.

Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available.Scientific contributionOur research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.Graphical

Including soil spatial neighbor information for digital soil mapping

Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.

Impact of sleep deprivation on aperiodic activity: a resting-state EEG study.

Sleep deprivation (SD) has been shown to have a negative impact on alertness, as evidenced by behavioral and electroencephalographic studies. Nevertheless, conventional fixed-bandwidth spectral analysis confused the aperiodic and periodic components, and ignored some important periodic parameters (i.e., center frequency, bandwidth). Here, based on a large open access dataset of SD, we employed a standardized process for multiple electrode analysis and group inference. We found that, compared to SC, the aperiodic offset shifted overall after SD, primarily in the occipital region. And this shift was associated with a reduction in subjective alertness. For periodic component, we did not found any power change in alpha rhythm, but there is an increase in bandwidth of alpha. And the increased regions distributed in the occipital and temporal lobes. These findings highlight the potential significance and value of aperiodic parameters in behavioral and electrophysiological researches.

Linking housing, socio-demographic, environmental and mental health data at scale

Mental disorders are a growing problem worldwide, putting pressure on healthcare systems and wider society. Anxiety and depression are estimated to cost the global economy US$1 trillion per year, yet only 2% of global median government healthcare expenditure goes towards mental health. There is growing evidence linking housing, socio-economic status and local environmental conditions with mental health inequalities. The aim of this paper is to link several open-access datasets at the local area level (N = 32,844) for England to clinical mental health metrics and describe initial statistical findings. Two mental health metrics were used: Small Area Mental Health Index (SAMHI) and diagnosed depression prevalence. To demonstrate the utility of the longitudinal mental health data, changes in depression prevalence were investigated over two study periods (2011–19, i.e. austerity; and 2019–22, i.e. COVID-19). These data were linked to housing data (energy efficiency, floor area, year built, type and tenure) from Energy Performance Certificates (EPCs); socio-demographic data (age, sex, income and education deprivation, household size) from administrative records; and local environment data (winter temperature, air pollution and access to green space). The linked dataset provides a useful resource with which to investigate the social and environmental determinants of mental health. Practice relevance Initial observations of the data revealed a non-linear relationship between home energy efficiency (EPC band) and the mental health metrics, with depression prevalence higher in local areas where the mode EPC bands were C and D, compared with B and E. Researchers can further investigate this relationship using the dataset through robust statistical analysis, adjusting for confounding variables. National and local governments may use the dataset to help allocate resources to prevent and treat mental health conditions. Practitioners can map and interrogate the data to describe their local areas and make preliminary conclusions about the relationships between the built environment and mental health. This preliminary analysis of the data demonstrated a gradient in SAMHI and depression prevalence with income and employment deprivation at the local area level.

The Martijn Trilogy Manuscripts: An Open Dataset for Analyzing Scribal Variation

Abstract The authors present a dataset containing transcriptions of manuscripts of the Middle Dutch strophic poem Martijn Trilogy by the Flemish poet Jacob van Maerlant. Of his very large oeuvre, Maerlant’s Strophic Poems had the longest tradition: originally written in the thirteenth century, copyists and printers continued to disseminate them until about 1500. These ten shorter poems on social, religious, and ethical issues stand out for their unusual and complex stanza form. The Martijn Trilogy was his most successful strophic poem: 17 text witnesses are extant, and the trilogy was imitated and even translated into French and Latin. This dataset contains hyperdiplomatic transcriptions of all witnesses, amounting to a total of 15,814 verses or 79,337 tokens. This open-access dataset abides by the fair principles, is licensed under a cc-by-sa license, and is made available in multiple, complementary file formats. Since these transcriptions are strictly diplomatic, this corpus offers valuable possibilities for research on scribal attributions (scribal profiling), abbreviations, stemmatology, textual stability and more.

Towards Improving E-Commerce Customer Review Analysis for Arabic Language Opinion Mining

In recent years, the rapid development of Internet-related technologies has facilitated the widespread adoption of online purchasing as a convenient means of satisfying consumer needs. Conducting sentiment analysis on user reviews on e-commerce platforms can substantially improve customer satisfaction. In order to resolve this issue, we propose a novel model for sentiment analysis that employs hybrid deep learning ensembles, combining RNN and TreeLSTM with AraBERT as the word embedding. Our research concentrates on creating a hybrid deep-learning model to predict Arabic sentiment accurately. We employ deep learning models with various word embeddings, such as RNN, LSTM, BiLSTM, TreeLSTM, RNN-LSTM, RNN-BiLSTM, and RNN-TreeLSTM. Multiple open-access datasets are used to evaluate the efficacy of the proposed model, including the BRAD dataset, the ARD dataset, and merged datasets containing 610,600 items. The experimental findings indicate that our proposed model is well-suited for evaluating the sentiments expressed in Arabic texts. Our strategy starts with extracting features using the Arabert model, followed by developing and training five hybrid deep-learning models. We attained a significant accuracy improvement of 0.9409 when comparing our method to traditional and hybrid deep learning techniques. This demonstrates that our proposed model precisely analyzes sentiment. These findings are important for enhancing the comprehension of emotions conveyed in Arabic text and have practical implications for various applications, especially e-commerce. By accurately assessing sentiment, businesses can better comprehend customer preferences and enhance consumer satisfaction by enhancing their offerings.

EEG Dataset for the Recognition of Different Emotions Induced in Voice-User Interaction

Electroencephalography (EEG)-based open-access datasets are available for emotion recognition studies, where external auditory/visual stimuli are used to artificially evoke pre-defined emotions. In this study, we provide a novel EEG dataset containing the emotional information induced during a realistic human-computer interaction (HCI) using a voice user interface system that mimics natural human-to-human communication. To validate our dataset via neurophysiological investigation and binary emotion classification, we applied a series of signal processing and machine learning methods to the EEG data. The maximum classification accuracy ranged from 43.3% to 90.8% over 38 subjects and classification features could be interpreted neurophysiologically. Our EEG data could be used to develop a reliable HCI system because they were acquired in a natural HCI environment. In addition, auxiliary physiological data measured simultaneously with the EEG data also showed plausible results, i.e., electrocardiogram, photoplethysmogram, galvanic skin response, and facial images, which could be utilized for automatic emotion discrimination independently from, as well as together with the EEG data via the fusion of multi-modal physiological datasets.

CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.

Functional near-infrared spectroscopy (fNIRS), an optical neuroimaging technique, has been widely used in the field of brain activity recognition and brain-computer interface. Existing works have proposed deep learning-based algorithms for the fNIRS classification problem. In this paper, a novel approach based on convolutional neural network and Transformer, named CT-Net, is established to guide the deep modeling for the classification of mental arithmetic (MA) tasks. We explore the effect of data representations, and design a temporal-level combination of two raw chromophore signals to improve the data utilization and enrich the feature learning of the model. We evaluate our model on two open-access datasets and achieve the classification accuracy of 98.05% and 77.61%, respectively. Moreover, we explain our model by the gradient-weighted class activation mapping, which presents a high consistent between the contributing value of features learned by the model and the mapping of brain activity in the MA task. The results suggest the feasibility and interpretability of CT-Net for decoding MA tasks.

Community Partnerships in Digital Cultural Heritage: A Systematic Review

ABSTRACTCommunity partnerships with libraries, archives, and museums are increasingly recognized as essential to developing and stewarding complete, accurate, and equitable digital cultural records. While research and practice in this area has burgeoned, there is limited empirical research characterizing the state of the field. This study offers preliminary findings of a systematic, state‐of‐the‐art review of community‐institutional partnerships in digital cultural heritage collections and initiatives. This review systematically identifies wide‐ranging, active community partnerships among relevant institutions and their high‐level objectives, and a correlated array of approaches taken in pursuit of those objectives. The resulting conceptual framework and open access dataset form the base of a toolkit supporting community‐institutional partnerships for a more sustainable, community‐centered cultural record.

An open dataset for individual tree detection in UAV LiDAR point clouds and RGB orthophotos in dense mixed forests

We present an open access dataset for development, evaluation, and comparison of algorithms for individual tree detection in dense mixed forests. The dataset consists of a detailed field inventory and overlapping UAV LiDAR and RGB orthophoto, which make it possible to develop algorithms that fuse multimodal data to improve detection results. Along with the dataset, we describe and implement a basic local maxima filtering baseline and an algorithm for automatically matching detection results to the ground truth trees for detection algorithm evaluation.

Efficient Detection of Apparent Defects in Subway Tunnel Linings Based on Deep Learning Methods

High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the foundational architecture due to its comprehensive performance. The coordinate attention module and Bottleneck Transformer 3 were then integrated into the model’s backbone to enhance the focus on defect-prone areas and improve the learning of feature relationships between defects and other infrastructure. Subsequently, a high-resolution detection layer was added to the model’s head to further improve sensitivity to subtle defects. Additionally, a low-quality crack dataset was created using an open access dataset, and transfer learning combined with Real-ESRGAN was employed to enhance the detail and resolution of fine cracks. The results of the field experiments demonstrate that the proposed model significantly improves detection accuracy in high-incidence areas and for small-scale defects, achieving a mean average precision (mAP) of 87% in detecting cracks, leakage, exfoliation, and related infrastructure defects. Furthermore, the crack enhancement techniques substantially improve the representation of fine-crack details, increasing feature extraction accuracy by a factor of four. The findings of this paper could provide crucial technical support for the automated operation and maintenance of metro tunnels.

TOOL CONDITION MONITORING IN MACHINING USING ROBUST RESIDUAL NEURAL NETWORKS

Tool condition monitoring (TCM) aims to improve process efficiency, quality and tool maintenance costs by monitoring critical variables such as tool wear. This study proposes a deep learning (DL) architecture based on process-informed robust residual networks (Robust-ResNet) to predict tool wear in milling processes using time series of internal computer numerical control (CNC) signals. The Robust-ResNet architecture uses skip connections to move through multiple convolutional layers, avoiding the vanishing gradient problem of other neural network algorithms. The study includes an evaluation of the binding of process information as input to the architecture and an attention mechanism between skips to make more robust predictions. The proposed architecture has been trained and optimised using an open access data set of face milling time series. In this particular case, AC and DC signals have been used together with the corresponding tool wear values. The results of this study demonstrate the benefits of using deep learning techniques in the prediction of tool wear using internal signals provided by the CNC itself. The implementation of the proposed architecture is expected to help reduce maintenance costs, improve product quality and increase production efficiency in milling manufacturing processes.

The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts.

White matter (WM) tract-related strains are increasingly used to quantify brain mechanical responses, but their dynamics in live human brains during invivo impact conditions remain largely unknown. Existing research primarily looked into the normal strain along the WM fiber tracts (i.e., tract-oriented normal strain), but it is rarely the case that the fiber tract only endures tract-oriented normal strain during impacts. In this study, we aim to extend the invivo measurement of WM fiber deformation by quantifying the normal strain perpendicular to the fiber tract (i.e., tract-perpendicular normal strain) and the shear strain along and perpendicular to the fiber tract (i.e., tract-oriented shear strain and tract-perpendicular shear strain, respectively). To achieve this, we combine the three-dimensional strain tensor from the tagged magnetic resonance imaging with the diffusion tensor imaging (DTI) from an open-access dataset, including 44 volunteer impacts under two head loading modes, i.e., neck rotations (N = 30) and neck extensions (N = 14). The strain tensor is rotated to the coordinate system with one axis aligned with DTI-revealed fiber orientation, and then four tract-related strain measures are calculated. The results show that tract-perpendicular normal strain peaks are the largest among the four strain types (p < 0.05, Friedman's test). The distribution of tract-related strains is affected by the head loading mode, of which laterally symmetric patterns with respect to the midsagittal plane are noted under neck extensions, but not under neck rotations. Our study presents a comprehensive invivo strain quantification toward a multifaceted understanding of WM dynamics. We find that the WM fiber tract deforms most in the perpendicular direction, illuminating new fundamentals of brain mechanics. The reported strain images can be used to evaluate the fidelity of computational head models, especially those intended to predict fiber deformation under noninjurious conditions.

QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation

The QUANT study represents the most extensive open-access evaluation of commercial air quality sensor systems to date. This comprehensive study assessed 49 systems from 14 manufacturers across three urban sites in the UK over a three-year period. The resulting open-access dataset captures high time-resolution measurements of a variety of gasses (NO, NO2, O3, CO, CO2), particulate matter (PM1, PM2.5, PM10), and key meteorological parameters (humidity, temperature, atmospheric pressure). The quality and scope of the dataset is enhanced by reference monitors’ data and calibrated products from sensor manufacturers across the three sites. This publicly accessible dataset serves as a robust and transparent resource that details the methods used for data collection and procedures to ensure dataset integrity. It provides a valuable tool for a wide range of stakeholders to analyze the performance of air quality sensors in real-world settings. Policymakers can leverage this data to refine sensor deployment guidelines and develop standardized protocols, while manufacturers can utilize it as a benchmark for technological innovation and product certification. Moreover, the dataset has supported the development of a UK code of practice, and the certification of one of the participating companies, underscoring the dataset’s utility and reliability.

A web-based mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity

The recent ‘Mpox’ outbreak, formerly known as ‘Monkeypox’, has been a significant public health concern and spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods excel at learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the “Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)” as a follow-up to the previously released open-access dataset, one of the first to contain mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. We leverage transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images. To reduce the impact of racial bias, we employ a color space data augmentation method to increase skin color variability during training and present a rigorous assessment of the technique’s effectiveness in skin lesion classification; we achieved the best overall accuracy of 83.59±2.11%. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient. We believe that the presented tool can be of significant impact in screening of mpox in case of a future outbreak.

A comprehensive review of retinal disease diagnosis and open access datasets: Fundus and OCT images

The rapid advancements in deep learning algorithms and the availability of large, open-access databases of fundus and OCT (optical coherence tomography) images have contributed greatly to advancements in computer-assisted diagnostics and the localization of various disorders affecting the retina. This study offers a comprehensive examination of retinal diseases and various recent applications of deep learning strategies for categorising key retinal conditions, such as diabetic retinopathy, glaucoma, age-related macular degeneration, choroidal neovascularization, retinal detachment, media haze, myopia, and dry eyes. Open-access datasets continue to play a critical role in the advancement of digital health research and innovation within the field of ophthalmology. Thirty open-access databases containing fundus and OCT (optical coherence tomography) pictures, which are often utilised by researchers, were carefully examined in this work. A summary of these datasets was created, which includes the number of images, dataset size, and supplementary items in the dataset, as well as information on eye disease and country of origin. We also discussed challenges and limitations of novel deep learning models. Finally, in conclusion, we discussed some important insights and provided directions for future research opportunities.

Measuring wellbeing in the Global Flourishing Study: insights from a cross-national analysis of cognitive interviews from 22 countries

AbstractPrior research suggests that cross-national cognitive interviewing can provide preliminarily insights into the extent to which survey items that will be employed in large-scale global research might be understood similarly or differently across countries. Against the backdrop of the recently launched Global Flourishing Study, we used multinational cognitive interview data from 116 individuals (M = 41.4 years, SD = 14.9, female = 50.9%) in 22 countries to explore similarities and differences in item difficulty and comprehension of five of the Global Flourishing Study survey items that are related to personal wellbeing. Interviewer observations indicated that most participants (≥ 90%) did not experience a lot of difficulty responding to each of the items. Focusing on a specific comprehension probe that was common across the five items (i.e., “In your own words, what is this question asking?”), we applied the constant comparative analytic method to generate an overarching theme for each item that captured the common core or essence of how participants across the countries interpreted the items. However, there was some variability—both between and within countries—in how responses to the probe in the different countries related to the overarching theme for each item. Given the richness of the Global Flourishing Study as a possible data resource, including its broad representativeness of the global population and availability as an open access dataset, these findings will be useful to researchers who are interested in using data from the Global Flourishing Study to study human wellbeing in different parts of the world.

Improving Soybean Gross Primary Productivity Modeling Using Solar-Induced Chlorophyll Fluorescence and the Photochemical Reflectance Index by Accounting for the Clearness Index

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μmol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μmol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.