Data, Volume 9, Issue 4 (April 2024) – 14 articles

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. Full article

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. Full article

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). Full article

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. Full article

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. Full article

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. Full article

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. Full article

Wearable devices, such as headsets and activity trackers, rely heavily on the Bluetooth and/or the Bluetooth Low Energy wireless communication standard to exchange data with smartphones or other peripherals. Since these devices collect personal health and activity data, ensuring the privacy and security of the transmitted data is crucial. Therefore, we present a dataset that captures complete Bluetooth communications—including advertising, connection, data exchange, and disconnection—in an RF isolated environment using software-defined radio. We were able to successfully decode the captured Bluetooth packets using existing tools. This dataset provides researchers with the ability to fully analyze Bluetooth traffic and gain insight into communication patterns and potential security vulnerabilities. Full article

Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects intensifying competition between technology giants and research institutions. This research aims to synthesize key patterns and developments in NLP innovation revealed through patent data analysis, highlighting implications for firms and policymakers. A detailed understanding of NLP patenting activity can inform intellectual property strategy and technology investment decisions in this burgeoning AI domain. Full article

The present study explored how sharing verbal status updates on Facebook and receiving Likes, as a form of positive social feedback, correlate with current and perceived changes in Quality of Life (QoL). Utilizing the Facebook Graph API, we collected a longitudinal dataset comprising status updates and Likes received by 1577 adult Facebook users over a 12-month period. Two monthly indicators were calculated: the percentage of verbal status updates and the average number of Likes per post. Participants were administered a survey to assess current and perceived changes in QoL. Confirmatory Factor Analysis (CFA) and the Auto-Regressive Latent Trajectory Model with Structured Residuals (ALT-SRs) were used to model longitudinal patterns emerging from the objective recordings of Facebook activity and explore their correlation with QoL measures. Findings indicated a positive correlation between the percentage of verbal status updated on Facebook and current QoL. Online positive social feedback, measured through received Likes, was associated with both current QoL and perceived improvements in QoL. Of note, perceived improvements in QoL correlated with an increase in received Likes over time. Results highlight the relevance of collecting and modeling longitudinal Facebook data for the investigation of the association between activity on social media and individual well-being. Full article

Sadhukarn, a sacred music composition performed ritually to salute and invite divine powers to open a ceremony or feast, is played in Thailand, Cambodia, and Laos. Different countries have unique versions, arranged based on musicians’ skills and en vogue styles. This study presents the results of multivariate statistical analyses of 26 different versions of Sadhukarn main melodies using non-metric multidimensional scaling (NMDS) and cluster analysis. The objective was to identify the optimal number of parameters for identifying the origin and relationships among Sadhukarn versions, including rhyme structures, pillar tone, rhythmic and melodic patterns, intervals, pitches, and combinations of these parameters. The data were analyzed using both full and normalized datasets (32 phrases) to avoid biases due to differences in phrases among versions. Overall, the combination of six parameters is the best approach for data analysis in both full and normalized datasets. The analysis of the ‘full version’ shows the separation of Sadhukarn versions from different countries of origin, while the analysis of the ‘normalized version’ reveals the rhyme structure, rhythmic structure, and pitch as crucial parameters for identifying Sadhukarn versions. We conclude that multivariate statistics are powerful tools for identifying relationships among different versions of Sadhukarn compositions from Thailand, Laos, and Cambodia and within the same countries of origin. Full article

In academic environments, students take exams simultaneously in campus examination classrooms. Due to recent advancements in technology, examination rooms are flooded with Bluetooth data traffic generated by personal devices (smartphones, smartwatches, etc.). The work presented in this article proposes a method for collecting Bluetooth traffic in an academic examination setting. The desired data were collected during several examination sessions using an Ubertooth One device, and then an in-depth post-processing analysis was performed on the collected dataset. The devices generating traffic were precisely located within the examination room, and areas with heightened data traffic were highlighted. Additionally, another goal of the current research was to provide a unique type of dataset to the academic community, facilitating its utilization in further research endeavors. Full article

The concept of luxury, considering it a rare and exclusive attribute, is evolving due to technological advances and the increasing influence of consumers in the market. Luxury cars have always symbolized wealth, social status, and sophistication. Recently, as technology progresses, the ability and interest to gather, store, and analyze data from these elegant vehicles has also increased. In recent years, the analysis of luxury car data has emerged as a significant area of research, highlighting researchers’ exploration of various aspects that may differentiate luxury cars from ordinary ones. For instance, researchers study factors such as economic impact, technological advancements, customer preferences and demographics, environmental implications, brand reputation, security, and performance. Although the percentage of individuals purchasing luxury cars is lower than that of ordinary cars, the significance of analyzing luxury car data lies in its impact on various aspects of the automotive industry and society. This literature review aims to provide an overview of the current state of the art in luxury car data analysis. Full article

This dataset was acquired during collaboration and competition tasks performed by sixteen subject pairs (N = 32) of one female and one male under different (face-to-face and online) modalities. The collaborative task corresponds to cooperating to put together a 100-piece puzzle, while the competition task refers to playing against each other in a one-on-one classic 28-piece dominoes game. In the face-to-face modality, all interactions between the pair occurred in person. On the other hand, in the online modality, participants were physically separated, and interaction was only allowed through Zoom software with an active microphone and camera. Electroencephalography data of the two subjects were acquired simultaneously while performing the tasks. This article describes the experimental setup, the process of the data streams acquired during the tasks, and the assessment of data quality. Full article