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Data sharing practices in high-impact rehabilitation journals
Background
The demand for rehabilitation services is rising due to the aging population and increasing number of chronic conditions. High-quality research is essential to address these challenges, with recent mandates emphasizing the importance of data sharing for transparency and reproducibility. However, data sharing remains limited across clinical research. Data sharing statements (DSS) have been proposed to improve accessibility, but their implementation and effectiveness in rehabilitation research remain unclear. We aim to identify barriers and guide future policies for standardizing data sharing in the field of rehabilitation.
Methods
On June 6th, 2024, a PubMed/MEDLINE search identified clinical studies from five top rehabilitation journals based on impact factor. We extracted DSS and general characteristics in a duplicated and masked fashion to identify influential factors on DSS inclusion and then used a hierarchical logistic regression and thematic analysis. Email requests were sent to authors to verify their willingness to share data.
Results
Of 1,278 studies that underwent data extraction, 25.5% of studies in our sample featured a DSS; however, this figure was significantly influenced by one journal with a 99% inclusion rate, while the other four journals collectively had only a 5% rate. Further analysis of 314 DSS revealed the majority designated a gatekeeper role for handling data requests. After emailing authors to verify their commitment to the reported DSS, only 22.7% adhered to them.
Conclusions
Our study found substantial variation in DSS inclusion across rehabilitation journals, reflecting inconsistencies in how data sharing policies are implemented. We also identified a significant gap between stated data sharing intentions and actual author follow-through. These findings highlight the need for stronger accountability mechanisms. We recommend adopting the Transparency and Openness Promotion (TOP) to provide a framework for data sharing in the field of rehabilitation. Further standardization of DSS is needed, as alternative methods like data repositories have been shown to improve transparency and reproducibility.
Trial registration
Clinical trial number: not applicable.
Journal Article
The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals
Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13 th , 2023, to September 1 st , 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.
Journal Article
Past, present, and future of smart learning: a topic-based bibliometric analysis
Innovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example, Interactive and multimedia learning, STEM (science, technology, engineering, and mathematics) education, Attendance and attention recognition, Blended learning for smart learning, and Affective and biometric computing. Furthermore, several emerging topics were identified, for example, Smart learning analytics, Software engineering for e-learning systems, IoT (Internet of things) and cloud computing, and STEM education. Additionally, potential inter-topic directions were highlighted, for instance, Attendance and attention recognition and IoT and cloud computing, Semantics and ontology and Mobile learning, Feedback and assessment and MOOCs (massive open online courses) and course content management, as well as Blended learning for smart learning and Ecosystem and ambient intelligence.
Journal Article
The Business Value of DB2 for z/OS
Celebrating the 30th anniversary of the first release of DB2, this book highlights the important milestones, capabilities, and impacts of the database management software for IBM's mainframe operating system. Special focus is given to IBM DB2 Analytics Accelerator, covering the key design and operational aspects that enable IBM DB2 for z/OS clients to benefit from faster performance, reduced CPU usage, and lower costs. The second half of the book discusses performance enhancements and cost-saving measures in the version 10 release and is rich with hints and tips for a successful upgrade. A special section on query performance and IBM DB2 Optimizer illustrates how DB2 10 addresses customer issues such as reducing total cost of ownership while maintaining stability and reliability. The final section is a collection of case studies in which DB2 10 for z/OS customers share their migration experiences and articulate the business benefits they are seeing since upgrading to the new release.
eBook
An automated approach for developing geohazard inventories using news: integrating natural language processing (NLP), machine learning, and mapping
Spatiotemporal inventories of geohazards are essential for comprehending the building of resilient societies; yet, restricted access to global inventories hinders the advancement of mitigation strategies. Consequently, we developed an approach that enhances the potential of using online newspapers in the creation of geohazard inventories by utilizing web scraping, natural language processing (NLP), clustering, and geolocation of textual data. Here, we use online newspapers from 1997–2023 in Türkiye to employ our approach. In the first stage, we retrieved 15 569 news articles by using our tr-news-scraper tool, considering wildfire-, flood-, landslide-, and sinkhole-related geohazard news. Further, we utilized NLP preprocessing approaches to refine the raw texts obtained from newspaper sources, which were subsequently clustered into four geohazard groups, resulting in 3928 news articles. In the final stage of the approach, we developed a method that geolocates the news using the OpenStreetMap (OSM) Nominatim tool, ending up with a total of 13 940 geohazard incidents derived from news comprising multiple incidents across various locations. As a result, we mapped 9609 floods, 1834 wildfires, 1843 landslides, and 654 sinkhole formation incidents from online newspaper sources, showing a spatiotemporally consistent distribution with the existing literature. Consequently, we illustrated the potential of using online newspaper articles in the development of geohazard inventories with our approach, which draws text data from web sources to generate maps by leveraging the capabilities of web scraping, NLP, and mapping techniques.
Journal Article
Use of parameters in equations and systems of linear equations: A proposal to boost variational thinking
Students often instrumentally use variables and unknowns without considering the variational thinking behind them. Using parameters to modify the coefficients or unknowns in equations or systems of linear equations (without altering their structure) involves consciously incorporating variational thinking into problem-solving. We will test the scope of this approach to undergraduate students by using contextual problems modelled with systems of linear equations that have one solution, infinitely many solutions, or none. In this context, knowing the solution was not enough to decide; instead, modifications to the system were necessary, and incorporating parameters proved to be very useful for this purpose. The goal is for students, in addition to seeing variational thinking as a valuable strategy for determining the validity of a solution, to develop the ability to distinguish between unknowns, variables, and parameters.
Journal Article
Wide-Open: Accelerating public data release by automating detection of overdue datasets
Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.
Journal Article
Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media
The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors’ predictions to improve the fake news detection system’s overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.
Journal Article