Background: The success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research.
Objective: This review aims to synthesize the evidence on public involvement and engagement in big data research.
Methods: This scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review.
Results: A total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research.
Conclusions: This review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research.
International Registered Report Identifier (irrid): RR2-https://doi.org/10.1136/bmjopen-2021-050167.
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http://dx.doi.org/10.2196/56673 | DOI Listing |
Mayo Clin Proc
January 2025
Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department and Institute of Physiology, National Yang Ming Chiao Tung University, Taipei, Taiwan; Center For Intelligent Drug Systems and Smart Bio-devices (IDS(2)B) National Yang Ming Chiao Tung University, Hsinchu, Taiwan. Electronic address:
Objective: To investigate how estimated glomerular filtration rate (eGFR) decline following sodium-glucose cotransporter-2 inhibitors (SGLT2i) initiation predicts long-term cardiorenal outcomes.
Methods: From 2016 to 2020, a longitudinal cohort of 4942 diabetic patients treated with SGLT2i were enrolled and followed until December 2021. Patients were categorized into mild (≤30%), moderate (>30%∼≤40%) and severe (>40%) decline groups by the maximal eGFR change between 2 to 12 weeks after SGLT2i treatment.
Clin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Orthopaedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea.
: Legg-Calvé-Perthes disease (LCPD) is characterized by idiopathic avascular necrosis of the femoral head in children. There are several hypotheses regarding the cause of LCPD; however, the exact cause remains unclear. Studies on comorbidities can provide better insight into the disease.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images.
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