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Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data., (2024 The Author(s). Nursing Inquiry published by John Wiley & Sons Ltd.)

Non-native species are spreading at an unprecedented rate over large spatial scales, with global environmental change and growth in commerce providing novel opportunities for range expansion. Assessing the pattern and rate of spread is key to the development of strategies for safeguarding against future invasions and efficiently managing existing ones. Such assessments often depend on spatial distribution data from online repositories, which can be spatially biased, imprecise, and lacking in quantity. Here, the influence of disparities between occurrence records from online data repositories and what is known of the invasion history from peer-reviewed published literature on non-native species range expansion was evaluated using 6693 records of the Pacific oyster, Magallana gigas (Thunberg, 1793), spanning 56 years of its invasion in Europe. Two measures of spread were calculated: maximum rate of spread (distance from introduction site over time) and accumulated area (spatial expansion). Results suggest that despite discrepancies between online and peer-reviewed data sources, including a paucity of records from the early invasion history in online repositories, the use of either source does not result in significantly different estimates of spread. Our study significantly improves our understanding of the European distribution of M. gigas and suggests that a combination of short- and long-range dispersal drives range expansions. More widely, our approach provides a framework for comparison of online occurrence records and invasion histories as documented in the peer-reviewed literature, allowing critical evaluation of both data sources and improving our understanding of invasion dynamics significantly., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Abstract Background The advent of Personalized Medicine (PM) holds significant promise in revolutionizing healthcare by tailoring treatments to individual patients based on their data. However, its successful implementation requires the seamless integration of innovative technologies and presents formidable challenges in terms of sustainability. To tackle these challenges head-on, the International Consortium for Personalized Medicine (ICPerMed) was established, and the IC2PerMed project, as part of this consortium, seeks to foster collaboration between the European Union (EU) and China in the field of Personalized Medicine. Based on the results collected by the project, the objective of this study is to discern the key priorities for the implementation of Personalised Medicine concerning Information and Communication Technologies (ICT) and Big Data and digital solutions, with a particular emphasis on data management and protection. Methods A Delphi survey was conducted to gather expert’s consensus on the main priorities for actions on Information and Communication Technologies (ICT) and Big Data and digital solutions in the field of Personalized Medicine. Results The survey identified seven priorities in the area of Big Data and digital solutions, including data interoperability, standards, security measures, and international partnerships. Additionally, twelve priorities were identified for the innovation-to-market process, emphasizing cost-effectiveness, need assessment, and value definition in resource allocation. Conclusions The effective implementation of new technologies in Personalized Medicine research and practice is essential for the advancement of healthcare systems in both the European and Chinese contexts. The identified priorities play a pivotal role in promoting the sustainability of health systems and driving innovation in the implementation of Personalized Medicine. Addressing challenges related to data interoperability, standards, security, international collaboration, cost-effectiveness, and value assessment is of utmost importance in order to propel the progress of Personalized Medicine in healthcare systems.

In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]

In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain of scientific paper analysis. Our method employs a novel neural network embedding technique that leverages textual components, such as keywords, titles, abstracts, and full texts, to represent papers in a vector space. By integrating recent advancements in text representation and unsupervised feature aggregation, SPBERT offers a sophisticated approach to encode essential information implicitly, thereby enhancing paper classification and literature retrieval tasks. We applied our method to several real-world academic datasets, demonstrating notable improvements over existing methods. The findings suggest that SPBERT not only provides a more effective representation of scientific papers but also facilitates a deeper understanding of large-scale academic data, paving the way for more informed and accurate scholarly analysis. [ABSTRACT FROM AUTHOR]

Artificial Intelligence in higher education (AIED) is becoming a more important research area with increasing developments and application of AI within the wider society. However, as yet AI based tools have not been widely adopted in higher education. As a result there is a lack of sound evidence available on the pedagogical impact of AI for learning and teaching. This conceptual paper thus seeks to bridge the gap and addresses the following question: is artificial intelligence really the new big thing that will revolutionise learning and teaching in higher education? Adopting the technological pedagogical content knowledge (TPACK) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical foundations, we argue that Artificial Intelligence (AI) technologies, at least in their current state of development, do not afford any real new advances for pedagogy in higher education. This is mainly because there does not seem to be valid evidence as to how the use of AI technologies and applications has helped students improve learning, and/or helped tutors make effective pedagogical changes. In addition, the pedagogical affordances of AI have not yet been clearly defined. The challenges that the higher education sector is currently experiencing relating to AI adoption are discussed at three hierarchical levels, namely national, institutional and personal levels. The paper ends with recommendations with regard to accelerating AI use in universities. This includes developing dedicated AI adoption strategies at the institutional level, updating the existing technology infrastructure and upskilling academic tutors for AI. [ABSTRACT FROM AUTHOR]

In light of artificial intelligence and new technologies, organizations have to follow new ways of working with different skill sets to achieve strategic goals. Therefore, human resource analytics is the scientific solution that enables organizations to make important decisions related to human capital and strategic business and thus gain a competitive advantage. Through new technologies comes the role of big data, as it works to establish the reputation of human resources as a strategic business partner that makes decisions driven by analytics. Evidence-based decisions, therefore, all equal a significant competitive advantage. This conceptual paper aims to understand the relationship between human resource analytics and achieving a competitive advantage in the presence of big data. This study will use quantitative data through a survey list that will be distributed to middle managers and human resources employees of the telecommunications company sector in Egypt. [ABSTRACT FROM AUTHOR]

Doula services represent an underutilized maternal and child health intervention with the potential to improve outcomes through the provision of physical, emotional, and informational support. However, there is limited evidence of the infant health effects of doulas despite well-established connections between maternal and infant health. Moreover, because the availability of doulas is limited and often not covered by insurers, existing evidence leaves unclear if or how doula services should be allocated to achieve the greatest improvements in outcomes. We use unique data and machine learning to develop accurate predictive models of infant health and doula service participation. We then combine these predictive models within the double machine learning method to estimate the effects of doula services. We show that while doula services reduce risk on average, the benefits of doula services increase as the risk of negative infant health outcomes increases. We compare these benefits to the costs of doula services under alternative allocation schemes and show that leveraging the risk predictions dramatically increases the cost effectiveness of doula services. Our results show the potential of big data and novel analytic methods to provide cost-effective support to those at greatest risk of poor outcomes., (© 2024 John Wiley & Sons Ltd.)

Traces of mercury ions in environmental water can harm humans and animals. Paper-based visual detection methods have been widely developed for the rapid detection of mercury ions; however, existing methods are not sensitive enough to be used in real environments. Here, we developed a novel, simple and effective visual fluorescent sensing paper-based chip for the ultrasensitive detection of mercury ions in environmental water. CdTe-quantum-dots-modified silica nanospheres were firmly absorbed by and anchored to the fiber interspaces on the paper's surface to effectively avoid the unevenness caused by liquid evaporation. The fluorescence of quantum dots emitted at 525 nm can be selectively and efficiently quenched with mercury ions, and the ultrasensitive visual fluorescence sensing results attained using this principle can be captured using a smartphone camera. This method has a detection limit of 2.83 µg/L and a fast response time (90 s). We successfully achieved the trace spiking detection of seawater (from three regions), lake water, river water and tap water with recoveries in the range of 96.8–105.4% using this method. This method is effective, low-cost, user-friendly and has good prospects for commercial application. Additionally, the work is expected to be utilized in the automated big data collection of large numbers of environmental samples. [ABSTRACT FROM AUTHOR]

Big data analytics in medicine is driving significant change, as it offers vital information for improving functions, developing cutting-edge solutions and overcoming inefficiencies. With the right archiving and analysis tools, all players in the healthcare system, from hospitals to patients and from medical personnel to the pharmaceutical industry, can yield numerous benefits. Therefore, to analyze and interpret these analytics effectively, so that they can be useful for the advancement of scientific knowledge, we require information sharing, specific skills, training, integration between all system players, unique infrastructures and security. All these characteristics will make it possible to establish and harmonize real big data biobanks, for which it will be appropriate to consider new forms of governance compared to those traditionally conceived for large-sample biobanks.

Background: The advent of Personalized Medicine (PM) holds significant promise in revolutionizing healthcare by tailoring treatments to individual patients based on their data. However, its successful implementation requires the seamless integration of innovative technologies and presents formidable challenges in terms of sustainability. To tackle these challenges head-on, the International Consortium for Personalized Medicine (ICPerMed) was established, and the IC2PerMed project, as part of this consortium, seeks to foster collaboration between the European Union (EU) and China in the field of Personalized Medicine. Based on the results collected by the project, the objective of this study is to discern the key priorities for the implementation of Personalised Medicine concerning Information and Communication Technologies (ICT) and Big Data and digital solutions, with a particular emphasis on data management and protection. Methods: A Delphi survey was conducted to gather expert's consensus on the main priorities for actions on Information and Communication Technologies (ICT) and Big Data and digital solutions in the field of Personalized Medicine. Results: The survey identified seven priorities in the area of Big Data and digital solutions, including data interoperability, standards, security measures, and international partnerships. Additionally, twelve priorities were identified for the innovation-to-market process, emphasizing cost-effectiveness, need assessment, and value definition in resource allocation. Conclusions: The effective implementation of new technologies in Personalized Medicine research and practice is essential for the advancement of healthcare systems in both the European and Chinese contexts. The identified priorities play a pivotal role in promoting the sustainability of health systems and driving innovation in the implementation of Personalized Medicine. Addressing challenges related to data interoperability, standards, security, international collaboration, cost-effectiveness, and value assessment is of utmost importance in order to propel the progress of Personalized Medicine in healthcare systems. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) algorithm has been widely used in various optimization problems. Although PSO has been successful in many fields, solving optimization problems in big data applications often requires processing of massive amounts of data, which cannot be handled by traditional PSO on a single machine. There have been several parallel PSO based on Spark, however they are almost proposed for solving numerical optimization problems, and few for big data optimization problems. In this paper, we propose a new Spark-based parallel PSO algorithm to predict the co-authorship of academic papers, which we formulate as an optimization problem from massive academic data. Experimental results show that the proposed parallel PSO can achieve good prediction accuracy.

Conjecture about the weak replicability in social sciences has made scholars eager to quantify the scale and scope of replication failure for a discipline. Yet small-scale manual replication methods alone are ill-suited to deal with this big data problem. Here, we conduct a discipline-wide replication census in science. Our sample (N= 14,126 papers) covers nearly all papers published in the six top-tier Psychology journals over the past 20 y. Using a validated machine learning model that estimates a paper's likelihood of replication, we found evidence that both supports and refutes speculations drawn from a relatively small sample of manual replications. First, we find that a single overall replication rate of Psychology poorly captures the varying degree of replicability among subfields. Second, we find that replication rates are strongly correlated with research methods in all subfields. Experiments replicate at a significantly lower rate than do non-experimental studies. Third, we find that authors' cumulative publication number and citation impact are positively related to the likelihood of replication, while other proxies of research quality and rigor, such as an author's university prestige and a paper's citations, are unrelated to replicability. Finally, contrary to the ideal that media attention should cover replicable research, we find that media attention is positively related to the likelihood of replication failure. Our assessments of the scale and scope of replicability are important next steps toward broadly resolving issues of replicability. [ABSTRACT FROM AUTHOR]

The characteristics of big data, including high volume, increased variety, and velocity, pose special challenges for data analysis. As these characteristics generally preclude manual data inspection and processing, researchers must often use computational methodologies to deal with this type of data; techniques that may be unfamiliar to nonspecialists, including behavioral scientists. However, previous data analytics methodologies within the field of computer science, developed to handle the generic tasks of data collection, preprocessing, and analysis, can be appropriated for use in other disciplines. These methodologies involve a sequential pipeline of quality checks to prepare data sets for analysis and application. Building upon these methodologies, this paper describes the Big Data Quality & Statistical Assurance (BDQSA) model, applicable for researchers in the behavioral sciences. It involves a series of data preprocessing tasks, to achieve data understanding, as well as data screening, cleaning, and transformation. These are followed by a statistical quality phase, which includes extraction of the relevant data subset, type conversions, ensuring sample representativeness when appropriate, and assessing statistical assumptions. The resulting model thereby provides methodological guidance for the preprocessing of behavioral science big data, aimed at ensuring acceptable data quality before analysis is undertaken. Sample R code snippets demonstrating the application of this model are provided throughout the paper., (© 2022. The Psychonomic Society, Inc.)

Data science is a rapidly growing technology in the technical world that fulfills the requirements for data and various data aspects. The core of all emerging technologies is data science, which includes machine learning, artificial intelligence, robotics, edge computing, and blockchain technology. In this review paper, we consider the detailed concept on data science, such as where the data is generated, the skills to handle data, its growth, how it works, and the impact of data science on other technologies. The basic aim of this review paper is to provide a basic summary of data science that everyone easily understands. [ABSTRACT FROM AUTHOR]

With the prevalence of artificial intelligence technologies, big data has been utilized to higher extent in many cross-domain fields. This paper concentrates on the digital assessment of vocational education level in some specific areas, and proposes a graph neural network-based assessment model for this purpose. Assume that all vocational colleges inside a specific region are with a social graph, in which each college is a node and the relations among them are the edges. The graph neural network (GNN) model is formulated to capture global structured features of all the nodes together. The GNN is then employed for the sequential modeling pattern, and the evolving characteristics of all the colleges can be captured. Some experiments are also conducted to evaluate the performance of the proposed GNN-VEL. It is compared with two typical forecasting methods under evaluation of two metrics. The results show that it performs better than other two methods. [ABSTRACT FROM AUTHOR]

This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings. [ABSTRACT FROM AUTHOR]

Background: The clinical effects of smartphone‐based interventions for bipolar disorder (BD) have yet to be established. Objectives: To examine the efficacy of smartphone‐based interventions in BD and how the included studies reported user‐engagement indicators. Methods: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random‐effects meta‐analysis to calculate the standardized difference (Hedges' g) in pre‐post change scores between smartphone intervention and control conditions. The study was pre‐registered with PROSPERO (CRD42021226668). Results: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta‐analyses comparing the pre‐post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta‐analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user‐engagement indicators. Conclusion: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field. [ABSTRACT FROM AUTHOR]