Journal Description
Data
Data
is a peer-reviewed, open access journal on data in science, with the aim of enhancing data transparency and reusability. The journal publishes in two sections: a section on the collection, treatment and analysis methods of data in science; a section publishing descriptions of scientific and scholarly datasets (one dataset per paper). The journal is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, RePEc, and other databases.
- Journal Rank: CiteScore - Q2 (Information Systems and Management)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 22 days after submission; acceptance to publication is undertaken in 3.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.6 (2022);
5-Year Impact Factor:
3.0 (2022)
Latest Articles
Comparative Analysis of the Predictive Performance of an ANN and Logistic Regression for the Acceptability of Eco-Mobility Using the Belgrade Data Set
Data 2024, 9(5), 73; https://doi.org/10.3390/data9050073 - 19 May 2024
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To solve the problem of environmental pollution caused by road traffic, alternatives to vehicles with internal combustion engines are often proposed. As such, eco-mobility microvehicles have significant potential in the fight against environmental pollution, but only on the condition that they are widely
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To solve the problem of environmental pollution caused by road traffic, alternatives to vehicles with internal combustion engines are often proposed. As such, eco-mobility microvehicles have significant potential in the fight against environmental pollution, but only on the condition that they are widely accepted and that they replace the vehicles that predominantly pollute the environment. With this in mind, this study aims to elucidate the main variables that influence the acceptability of these vehicles, using prediction models based on binary logistic regression and a multilayer artificial neural network—a multilayer perceptron (ANN). The data of a random sample obtained via an online questionnaire, answered by 503 inhabitants of Belgrade (Serbia), were used for training and testing the model. A multilayer perceptron with 9 and 7 neurons in two hidden layers, a hyperbolic tangent activation function in the hidden layer, and an identity function in the output layer performed slightly better than the binary logistic regression model. With an accuracy of 85%, a precision of 79%, a recall of 81%, and an area under the ROC curve of 0.9, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that a respondent’s relationship to their current environmental pollution, the frequency of their use of modes of transport such as bicycles and motorcycles, their mileage for commuting, and their personal income have the greatest influence on the acceptability of using eco-mobility vehicles.
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Open AccessArticle
A Benchmark Data Set for Long-Term Monitoring in the eLTER Site Gesäuse-Johnsbachtal
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Florian Lippl, Alexander Maringer, Margit Kurka, Jakob Abermann, Wolfgang Schöner and Manuela Hirschmugl
Data 2024, 9(5), 72; https://doi.org/10.3390/data9050072 - 18 May 2024
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This paper gives an overview over all currently available data sets for the European Long-term Ecosystem Research (eLTER) monitoring site Gesäuse-Johnsbachtal. The site is part of the LTSER platform Eisenwurzen in the Alps of the province of Styria, Austria. It contains both protected
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This paper gives an overview over all currently available data sets for the European Long-term Ecosystem Research (eLTER) monitoring site Gesäuse-Johnsbachtal. The site is part of the LTSER platform Eisenwurzen in the Alps of the province of Styria, Austria. It contains both protected (National Park Gesäuse) and non-protected areas (Johnsbachtal). Although the main research focus of the eLTER monitoring site Gesäuse-Johnsbachtal is on inland surface running waters, forests and other wooded land, the eLTER whole system (WAILS) approach was followed in regard to the data selection, systematically screening all available data in regard to its suitability as eLTER’s Standard Observations (SOs). Thus, data from all system strata was included, incorporating Geosphere, Atmosphere, Hydrosphere, Biosphere and Sociosphere. In the WAILS approach these SOs are key data for a whole system approach towards long term ecosystem research. Altogether, 54 data sets have been collected for the eLTER monitoring site Gesäuse-Johnsbachtal and included in the Dynamical Ecological Information Management System – Site and Data Registry (DEIMS-SDR), which is the eLTER data platform. The presented work provides all these data sets through dedicated data repositories for FAIR use. This paper gives an overview on all compiled data sets and their main properties. Additionally, the available data are evaluated in a concluding gap analysis with regard to the needed observation data according to WAILS, followed by an outlook on how to fill these gaps.
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Open AccessArticle
Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study
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Rodrigo Cuéllar Hidalgo, Raúl Pinto Elías, Juan-Manuel Torres-Moreno, Osslan Osiris Vergara Villegas , Gerardo Reyes Salgado and Andrea Magadán Salazar
Data 2024, 9(5), 71; https://doi.org/10.3390/data9050071 - 18 May 2024
Abstract
In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation
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In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM + CRF), and Transformer Encoder with CRF (Transformer + CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM + CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM + CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.
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(This article belongs to the Special Issue Advances in Text Mining Techniques and Applications for Knowledge Discovery)
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Open AccessData Descriptor
Continuous Wave Measurements Collected in Intermediate Depth throughout the North Sea Storm Season during the RealDune/REFLEX Experiments
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Jantien Rutten, Marion Tissier, Paul van Wiechen, Xinyi Zhang, Sierd de Vries, Ad Reniers and Jan-Willem Mol
Data 2024, 9(5), 70; https://doi.org/10.3390/data9050070 - 17 May 2024
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High-resolution wave measurements at intermediate water depth are required to improve coastal impact modeling. Specifically, such data sets are desired to calibrate and validate models, and broaden the insight on the boundary conditions that force models. Here, we present a wave data set
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High-resolution wave measurements at intermediate water depth are required to improve coastal impact modeling. Specifically, such data sets are desired to calibrate and validate models, and broaden the insight on the boundary conditions that force models. Here, we present a wave data set collected in the North Sea at three stations in intermediate water depth (6–14 m) during the 2021/2022 storm season as part of the RealDune/REFLEX experiments. Continuous measurements of synchronized surface elevation, velocity and pressure were recorded at 2–4 Hz by Acoustic Doppler Profilers and an Acoustic Doppler Velocimeter for a 5-month duration. Time series were quality-controlled, directional-frequency energy spectra were calculated and common bulk parameters were derived. Measured wave conditions vary from calm to energetic with 0.1–5.0 m sea-swell wave height, 5–16 s mean wave period and W-NNW direction. Nine storms, i.e., wave height beyond 2.5 m for at least six hours, were recorded including the triple storms Dudley, Eunice and Franklin. This unique data set can be used to investigate wave transformation, wave nonlinearity and wave directionality for higher and lower frequencies (e.g., sea-swell and infragravity waves) to compare with theoretical and empirical descriptions. Furthermore, the data can serve to force, calibrate and validate models during storm conditions.
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Open AccessReview
Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery
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Ietezaz Ul Hassan, Krishna Panduru and Joseph Walsh
Data 2024, 9(5), 69; https://doi.org/10.3390/data9050069 - 15 May 2024
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Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns
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Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns and maintaining operational safety. In this review, we look at different methods of vibration data processing in the context of vibration-based condition monitoring for heavy machinery. We divided primary approaches related to vibration data processing into three categories–signal processing methods, preprocessing-based techniques and artificial intelligence-based methods. We highlight the importance of these methods in improving the reliability and effectiveness of heavy machinery condition monitoring systems, highlighting the importance of precise and automated fault detection systems. To improve machinery performance and operational efficiency, this review aims to provide information on current developments and future directions in vibration-based condition monitoring by addressing issues like imbalanced data and integrating cutting-edge techniques like anomaly detection algorithms.
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Open AccessData Descriptor
EEG and Physiological Signals Dataset from Participants during Traditional and Partially Immersive Learning Experiences in Humanities
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Rebeca Romo-De León, Mei Li L. Cham-Pérez, Verónica Andrea Elizondo-Villegas, Alejandro Villarreal-Villarreal, Alexandro Antonio Ortiz-Espinoza, Carol Stefany Vélez-Saboyá, Jorge de Jesús Lozoya-Santos, Manuel Cebral-Loureda and Mauricio A. Ramírez-Moreno
Data 2024, 9(5), 68; https://doi.org/10.3390/data9050068 - 15 May 2024
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The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12
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The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 participants in each group) engaging in simulated learning scenarios, traditional learning, and partially immersive learning experiences. Data recordings from each participant contain recordings of physiological signals and psychometric data collected from applied questionnaires. Physiological signals include electroencephalography, real-time engagement and emotion recognition calculation by a Python EEG acquisition code, head acceleration, electrodermal activity, blood volume pressure, inter-beat interval, and temperature. Before the acquisition of physiological signals, participants were asked to fill out the General Health Questionnaire and Trait Meta-Mood Scale. In between recording sessions, participants were asked to fill out Likert-scale questionnaires regarding their experience and a Self-Assessment Manikin. At the end of the recording session, participants filled out the ITC Sense of Presence Inventory questionnaire for user experience. The dataset can be used to explore differences in physiological patterns observed between different learning modalities in the Humanities.
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(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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Open AccessArticle
Unveiling University Groupings: A Clustering Analysis for Academic Rankings
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George Matlis, Nikos Dimokas and Petros Karvelis
Data 2024, 9(5), 67; https://doi.org/10.3390/data9050067 - 11 May 2024
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The evaluation and ranking of educational institutions are of paramount importance to a wide range of stakeholders, including students, faculty members, funding organizations, and the institutions themselves. Traditional ranking systems, such as those provided by QS, ARWU, and THE, have offered valuable insights
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The evaluation and ranking of educational institutions are of paramount importance to a wide range of stakeholders, including students, faculty members, funding organizations, and the institutions themselves. Traditional ranking systems, such as those provided by QS, ARWU, and THE, have offered valuable insights into university performance by employing a variety of indicators to reflect institutional excellence across research, teaching, international outlook, and more. However, these linear rankings may not fully capture the multifaceted nature of university performance. This study introduces a novel clustering analysis that complements existing rankings by grouping universities with similar characteristics, providing a multidimensional perspective on global higher education landscapes. Utilizing a range of clustering algorithms—K-Means, GMM, Agglomerative, and Fuzzy C-Means—and incorporating both traditional and unique indicators, our approach seeks to highlight the commonalities and shared strengths within clusters of universities. This analysis does not aim to supplant existing ranking systems but to augment them by offering stakeholders an alternative lens through which to view and assess university performance. By focusing on group similarities rather than ordinal positions, our method encourages a more nuanced understanding of institutional excellence and facilitates peer learning among universities with similar profiles. While acknowledging the limitations inherent in any methodological approach, including the selection of indicators and clustering algorithms, this study underscores the value of complementary analyses in enriching our understanding of higher educational institutions’ performance.
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Open AccessData Descriptor
A Series Production Data Set for Five-Axis CNC Milling
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Anna-Maria Schmitt and Bastian Engelmann
Data 2024, 9(5), 66; https://doi.org/10.3390/data9050066 - 30 Apr 2024
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The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different
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The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.
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Open AccessArticle
Spectral Library of Plant Species from Montesinho Natural Park in Portugal
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Isabel Pôças, Cátia Rodrigues de Almeida, Salvador Arenas-Castro, João C. Campos, Nuno Garcia, João Alírio, Neftalí Sillero and Ana C. Teodoro
Data 2024, 9(5), 65; https://doi.org/10.3390/data9050065 - 30 Apr 2024
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In this work, we present and describe a spectral library (SL) with 15 vascular plant species from Montesinho Natural Park (MNP), a protected area in Northeast Portugal. We selected species from the vascular plants that are characteristic of the habitats in the MNP,
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In this work, we present and describe a spectral library (SL) with 15 vascular plant species from Montesinho Natural Park (MNP), a protected area in Northeast Portugal. We selected species from the vascular plants that are characteristic of the habitats in the MNP, based on their prevalence, and also included one invasive species: Alnus glutinosa (L.) Gaertn, Castanea sativa Mill., Cistus ladanifer L., Crataegus monogyna Jacq., Frangula alnus Mill., Fraxinus angustifolia Vahl, Quercus pyrenaica Willd., Quercus rotundifolia Lam., Trifolium repens L., Arbutus unedo L., Dactylis glomerata L., Genista falcata Brot., Cytisus multiflorus (L’Hér.) Sweet, Erica arborea L., and Acacia dealbata Link. We collected spectra (300–2500 nm) from five records per leaf and leaf side, which resulted in 538 spectra compiled in the SL. Additionally, we computed five vegetation indices from spectral data and analysed them to highlight specific characteristics and differences among the sampled species. We detail the data repository information and its organisation for a better understanding of the data and to facilitate its use. The SL structure can add valuable information about the selected plant species in MNP, contributing to conservation purposes. This plant species SL is publicly available in Zenodo platform.
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Open AccessData Descriptor
A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain
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Kanak Agarwal, Vedant Vijaykrishnan, Dyutit Mohanty and Manikandan Murugaiah
Data 2024, 9(5), 64; https://doi.org/10.3390/data9050064 - 30 Apr 2024
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This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients
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This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients were determined at a Reynolds number of 105. Considering the limited availability of data on low Reynolds number airfoils, this dataset is invaluable for a wide range of applications, including unmanned aerial vehicles (UAVs) and wind turbines. Additionally, the study offers a method for automating CFD simulations that could be applied to obtain aerodynamic coefficients at higher Reynolds numbers. The breadth of this dataset also supports the enhancement and creation of machine learning (ML) models, further advancing research into the aerodynamics of airfoils and lifting surfaces.
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Open AccessArticle
Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform
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Junlei Zhao, Chong Xu and Xinwu Huang
Data 2024, 9(5), 63; https://doi.org/10.3390/data9050063 - 29 Apr 2024
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Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The
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Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The occurrence of landslide disasters often causes a large number of casualties along with economic losses in the local area, seriously restricting local economic development. Although risk assessment and deformation mechanism analysis for single landslides have been performed for landslide disasters in the Hancheng area, this area lacks a landslide traces database. A complete landslide database comprises the basic data required for the study of landslide disasters and is an important requirement for subsequent landslide-related research. Therefore, this study used multi-temporal high-resolution optical images and human-computer interaction visual interpretation methods of the Google Earth platform to construct a landslide traces database in Hancheng County. The results showed that at least 6785 landslides had occurred in the study area. The total area of the landslides was about 95.38 km2, accounting for 5.88% of the study area. The average landslide area was 1406.04 m2, the largest landslide area was 377,841 m2, and the smallest landslide area was 202.96 m2. The results of this study provides an important basis for understanding the spatial distribution of landslides in Hancheng County, the evaluation of landslide susceptibility, and local disaster prevention and mitigation work.
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(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)
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Open AccessData Descriptor
Stimulated Microcontroller Dataset for New IoT Device Identification Schemes through On-Chip Sensor Monitoring
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Alberto Ramos, Honorio Martín, Carmen Cámara and Pedro Peris-Lopez
Data 2024, 9(5), 62; https://doi.org/10.3390/data9050062 - 28 Apr 2024
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Legitimate identification of devices is crucial to ensure the security of present and future IoT ecosystems. In this regard, AI-based systems that exploit intrinsic hardware variations have gained notable relevance. Within this context, on-chip sensors included for monitoring purposes in a wide range
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Legitimate identification of devices is crucial to ensure the security of present and future IoT ecosystems. In this regard, AI-based systems that exploit intrinsic hardware variations have gained notable relevance. Within this context, on-chip sensors included for monitoring purposes in a wide range of SoCs remain almost unexplored, despite their potential as a valuable source of both information and variability. In this work, we introduce and release a dataset comprising data collected from the on-chip temperature and voltage sensors of 20 microcontroller-based boards from the STM32L family. These boards were stimulated with five different algorithms, as workloads to elicit diverse responses. The dataset consists of five acquisitions (1.3 billion readouts) that are spaced over time and were obtained under different configurations using an automated platform. The raw dataset is publicly available, along with metadata and scripts developed to generate pre-processed T–V sequence sets. Finally, a proof of concept consisting of training a simple model is presented to demonstrate the feasibility of the identification system based on these data.
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Open AccessData Descriptor
Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from Electroencephalography Time Series
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Christian Riccio, Angelo Martone, Gaetano Zazzaro and Luigi Pavone
Data 2024, 9(5), 61; https://doi.org/10.3390/data9050061 - 26 Apr 2024
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We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological disorder which has
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We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological disorder which has seizures as its hallmark. Electroencephalography plays a crucial role in epilepsy assessment, offering insights into the brain’s electrical activity and advancing our understanding of seizures. The availability of tagged training sets covering all seizure phases—inter-ictal, pre-ictal, ictal, and post-ictal—is crucial for data-driven epilepsy analyses. Methods: Using the sliding window technique with a two-second window length and a one-second time slip, we extract multiple features from the preprocessed EEG time series of 20 patients from the Freiburg Seizure Prediction Database. In addition, we assign a class label to each instance to specify its corresponding seizure phase. All these operations are made through a software application we developed, which is named Training Builder. Results: The 20 tagged training datasets each contain 1080 univariate and bivariate features, and are openly and publicly available. Conclusions: The datasets support the training of data-driven models for seizure detection, prediction, and clustering, based on features engineering.
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Open AccessArticle
Predicting Academic Success of College Students Using Machine Learning Techniques
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Jorge Humberto Guanin-Fajardo, Javier Guaña-Moya and Jorge Casillas
Data 2024, 9(4), 60; https://doi.org/10.3390/data9040060 - 22 Apr 2024
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College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been
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College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been studied extensively in recent work; however, there is little literature on predicting academic success using educational machine learning. For this reason, CRISP-DM methodology was applied to extract relevant knowledge and features from the data. The dataset examined consists of 6690 records and 21 variables with academic and socioeconomic information. Preprocessing techniques and classification algorithms were analyzed. The area under the curve was used to measure the effectiveness of the algorithm; XGBoost had an AUC = 87.75% and correctly classified eight out of ten cases, while the decision tree improved interpretation with ten rules in seven out of ten cases. Recognizing the gaps in the study and that on-time completion of college consolidates college self-efficacy, creating intervention and support strategies to retain students is a priority for decision makers. Assessing the fairness and discrimination of the algorithms was the main limitation of this work. In the future, we intend to apply the extracted knowledge and learn about its influence of on university management.
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(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-Learning and Education—2nd Edition)
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Open AccessReview
Mapping of Data-Sharing Repositories for Paediatric Clinical Research—A Rapid Review
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Mariagrazia Felisi, Fedele Bonifazi, Maddalena Toma, Claudia Pansieri, Rebecca Leary, Victoria Hedley, Ronald Cornet, Giorgio Reggiardo, Annalisa Landi, Annunziata D’Ercole, Salma Malik, Sinéad Nally, Anando Sen, Avril Palmeri, Donato Bonifazi and Adriana Ceci
Data 2024, 9(4), 59; https://doi.org/10.3390/data9040059 - 20 Apr 2024
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The reuse of paediatric individual patient data (IPD) from clinical trials (CTs) is essential to overcome specific ethical, regulatory, methodological, and economic issues that hinder the progress of paediatric research. Sharing data through repositories enables the aggregation and dissemination of clinical information, fosters
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The reuse of paediatric individual patient data (IPD) from clinical trials (CTs) is essential to overcome specific ethical, regulatory, methodological, and economic issues that hinder the progress of paediatric research. Sharing data through repositories enables the aggregation and dissemination of clinical information, fosters collaboration between researchers, and promotes transparency. This work aims to identify and describe existing data-sharing repositories (DSRs) developed to store, share, and reuse paediatric IPD from CTs. A rapid review of platforms providing access to electronic DSRs was conducted. A two-stage process was used to characterize DSRs: a first step of identification, followed by a second step of analysis using a set of eight purpose-built indicators. From an initial set of forty-five publicly available DSRs, twenty-one DSRs were identified as meeting the eligibility criteria. Only two DSRs were found to be totally focused on the paediatric population. Despite an increased awareness of the importance of data sharing, the results of this study show that paediatrics remains an area in which targeted efforts are still needed. Promoting initiatives to raise awareness of these DSRs and creating ad hoc measures and common standards for the sharing of paediatric CT data could help to bridge this gap in paediatric research.
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Open AccessTutorial
Introduction to Reproducible Geospatial Analysis and Figures in R: A Tutorial Article
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Philippe Maesen and Edouard Salingros
Data 2024, 9(4), 58; https://doi.org/10.3390/data9040058 - 20 Apr 2024
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The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data,
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The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading, transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries).
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(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration)
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Open AccessData Descriptor
Experimental Data on Maximum Swelling Pressure of Clayey Soils and Related Soil Properties
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Reza Taherdangkoo, Muntasir Shehab, Thomas Nagel, Faramarz Doulati Ardejani and Christoph Butscher
Data 2024, 9(4), 57; https://doi.org/10.3390/data9040057 - 16 Apr 2024
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Clayey soils exhibit significant volumetric changes in response to variations in water content. The swelling pressure of clayey soils is a critical parameter for evaluating the stability and performance of structures built on them, facilitating the development of appropriate design methodologies and mitigation
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Clayey soils exhibit significant volumetric changes in response to variations in water content. The swelling pressure of clayey soils is a critical parameter for evaluating the stability and performance of structures built on them, facilitating the development of appropriate design methodologies and mitigation strategies to ensure their long-term integrity and safety. We present a dataset comprising maximum swelling pressure values from 759 compacted soil samples, compiled from 16 articles published between 1994 and 2022. The dataset is classified into two main groups: 463 samples of natural clays and 296 samples of bentonite and bentonite mixtures, providing data on various types of soils and their properties. Different swelling test methods, including zero swelling, swell consolidation, restrained swell, double oedometer, free swelling, constant volume oedometer, UPC isochoric cell, isochoric oedometer and consolidometer, were employed to measure the maximum swelling pressure. The comprehensive nature of the dataset enhances its applicability for geotechnical projects. The dataset is a valuable resource for understanding the complex interactions between soil properties and swelling behavior, contributing to advancements in soil mechanics and geotechnical engineering.
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Open AccessData Descriptor
A Dataset for Studying the Relationship between Human and Smart Devices
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Francesco Lelli and Heidi Toivonen
Data 2024, 9(4), 56; https://doi.org/10.3390/data9040056 - 11 Apr 2024
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This dataset reports the responses to a survey designed for investigating the relationship that humans have with their smart devices. The dataset was collected between May and July 2020 and is a sample of over 500 respondents of various ethnicities and backgrounds. These
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This dataset reports the responses to a survey designed for investigating the relationship that humans have with their smart devices. The dataset was collected between May and July 2020 and is a sample of over 500 respondents of various ethnicities and backgrounds. These data were used for modeling the ways that people relate to their devices using the notion of agency. However, the data can be used for complementing any study that intends to investigate a tool-mediated communication from the perspective of users, applying a variety of beliefs, attitudes, and expectations that users have in relation to their devices and themselves. This article presents the survey items as well as some preliminary data insights. The collected data were in English and the responses were anonymized to ensure GDPR compliance. The data were stored in a .csv file containing the respondents’ answers to the questions.
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Open AccessArticle
Learning from conect4children: A Collaborative Approach towards Standardisation of Disease-Specific Paediatric Research Data
by
Anando Sen, Victoria Hedley, Eva Degraeuwe, Steven Hirschfeld, Ronald Cornet, Ramona Walls, John Owen, Peter N. Robinson, Edward G. Neilan, Thomas Liener, Giovanni Nisato, Neena Modi, Simon Woodworth, Avril Palmeri, Ricarda Gaentzsch, Melissa Walsh, Teresa Berkery, Joanne Lee, Laura Persijn, Kasey Baker, Kristina An Haack, Sonia Segovia Simon, Julius O. B. Jacobsen, Giorgio Reggiardo, Melissa A. Kirwin, Jessie Trueman, Claudia Pansieri, Donato Bonifazi, Sinéad Nally, Fedele Bonifazi, Rebecca Leary and Volker Straubadd
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Data 2024, 9(4), 55; https://doi.org/10.3390/data9040055 - 8 Apr 2024
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The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is
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The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials.
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Illumina 16S rRNA Gene Sequencing Dataset of Bacterial Communities of Soil Associated with Ironwood Trees (Casuarina equisetifolia) in Guam
by
Tao Jin, Robert L. Schlub and Claudia Husseneder
Data 2024, 9(4), 54; https://doi.org/10.3390/data9040054 - 7 Apr 2024
Abstract
Ironwood trees, which are of great importance for the economy and environment of tropical areas, were first discovered to suffer from a slow progressive dieback in Guam in 2002, later referred to as ironwood tree decline (IWTD). A variety of biotic factors have
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Ironwood trees, which are of great importance for the economy and environment of tropical areas, were first discovered to suffer from a slow progressive dieback in Guam in 2002, later referred to as ironwood tree decline (IWTD). A variety of biotic factors have been shown to be associated with IWTD, including putative bacterial pathogens Ralstonia solanacearum and Klebsiella species (K. variicola and K. oxytoca), the fungus Ganoderma australe, and termites. Due to the soilborne nature of these pathogens, soil microbiomes have been suggested to be a significant factor influencing tree health. In this project, we sequenced the microbiome in the soil collected from the root region of healthy ironwood trees and those showing signs of IWTD to evaluate the association between the bacterial community in soil and IWTD. This dataset contains 4,782,728 raw sequencing reads present in soil samples collected from thirty-nine ironwood trees with varying scales of decline severity in Guam obtained via sequencing the V1–V3 region of the 16S rRNA gene on the Illumina NovaSeq (2 × 250 bp) platform. Sequences were taxonomically assigned in QIIME2 using the SILVA 132 database. Firmicutes and Actinobacteria were the most dominant phyla in soil. Differences in soil microbiomes were detected between limestone and sand soil parent materials. No putative plant pathogens of the genera Ralstonia or Klebsiella were found in the samples. Bacterial diversity was not linked to parameters of IWTD. The dataset has been made publicly available through NCBI GenBank under BioProject ID PRJNA883256. This dataset can be used to compare the bacterial taxa present in soil associated with ironwood trees in Guam to bacteria communities of other geographical locations to identify microbial signatures of IWTD. In addition, this dataset can also be used to investigate the relationship between soil microbiomes and the microbiomes of ironwood trees as well as those of the termites which attack ironwood trees.
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(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)
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