Improve Data Availability Research Articles

Protection beyond park boundaries: On the impact of systematic patrolling in Divisional Forest Office, Sarpang, Bhutan

AbstractProtected areas and biological corridors play a crucial role in conserving biodiversity and promoting connectivity between habitat patches. Bhutan, known as a stronghold for tigers (Panthera tigris), has dedicated a significant portion of its land to a network of protected areas and biological corridors. However, these areas face risks from illegal activities, posing challenges for their management. To address this, the Department of Forests & Park Services in Bhutan deployed an adaptive management framework, informed by the Spatial Monitoring and Reporting Tool in 2016 in the Divisional Forest Office, Sarpang, an important corridor area outside of official protected area boundaries. This study analyses 5 years of data (2017–2021) recorded through a monitoring tool to assess changes in ranger activities and on‐ground observations. The results show improved data availability, increased patrol effort, enhanced monitoring of wildlife, and a reduction in threat detection over time. Furthermore, camera trap surveys targeting tigers reveal a potential increase in the number of individual tigers utilizing the corridor since the adoption of the tool and other management actions. These findings highlight the effectiveness of these efforts in a critical wildlife corridor.

WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics data

Open Data Processing Services are used to solve the bottleneck of big data storage and operation. At the same time, massive trajectory data is generated, and the basic information of users’ spatio-temporal historical data is provided, including points of interest and movement patterns. Improving the availability of published trajectory statistics data without compromising user privacy is critical. Differential privacy technology is a standard technology to realize the secure release of trajectory statistics data. Several research efforts have focused on secure publication of trajectory statistics data in a central environment by adding noise to a trusted third-party server. However, this central approach is vulnerable to privacy breaches, where adversaries can access real data by locking down the third-party server. The local differential privacy, based on a distributed architecture, overcomes this form of attack by allowing users to scramble personal data records before they are sent to third-party server. However, the existing distributed privacy protection schemes still have the balance problem of poor availability of data when ensuring privacy, as well as the problem of excessive operation cost. Therefore, a local differential privacy mechanism based on water-filling for secure release of trajectory statistics data (WF-LDPSR) is proposed in this paper. Firstly, in order to protect user privacy individually, a user automatic personalized segmentation method is proposed to determine the effective user sensitivity level automatically. Secondly, a distributed privacy protection model based on local differential privacy is designed to resist the attacks on the third-party server. Finally, in order to achieve the optimal allocation of privacy budget, the water-filling theorem in the field of communication is introduced. An adaptive privacy budget allocation algorithm based on water-filling theorem is proposed to realize the adaptive privacy budget allocation. In addition, to further improve data availability, a group processing idea based on user set sampling is proposed, which divides users into multiple disjoint subsets randomly, thus reducing the differential privacy noise effectively. Experiments prove that compared with other advanced mechanisms, the WF-LDPSR mechanism can improve the availability of published data by 84.92% while protecting user privacy.

Paper-and-Pencil vs. Electronic Patient Records: Analyzing Time Efficiency, Personnel Requirements, and Usability Impacts on Healthcare Administration.

Background: This study investigates the impact of transitioning from paper and pencil (P&P) methods to electronic patient records (EPR) on workflow and usability in surgical ward rounds. Methods: Surgical ward rounds were audited by two independent observers to evaluate the effects of transitioning from P&P to EPR. Key observations included the number of medical personnel and five critical workflow aspects before and after EPR implementation. Additionally, usability was assessed using the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ). Results: A total of 192 P&P and 160 EPR observations were analyzed. Physicians experienced increased administrative workload with EPR, while nurses adapted more easily. Ward teams typically consisted of two physicians and three or four nurses. Usability scores rated the system as "Not Acceptable" across all professional groups. Conclusions: The EPR system introduced usability challenges, particularly for physicians, despite potential benefits like improved data access. Usability flaws hindered system acceptance, highlighting the need for better workflow integration. Addressing these issues could improve efficiency and reduce administrative strain. As artificial intelligence becomes more integrated into clinical practice, healthcare professionals must critically assess AI-driven tools to ensure safe and effective patient care.

Research and Enlightenment of Purchasing Power Parity Test

Purchasing Power Parity (PPP) is the cornerstone of many theoretical models in international finance. The PPP test is of great importance to market participants, central banks and policy makers. This paper reviews the relevant literature on the validity test of PPP in recent years: involving micro and macro directions, which are embodied in four aspects: empirical, model, exchange rate and evaluation. Although the research and application of PPP test have made great progress, there are still some problems and challenges. With the continuous development of the global economy and the improvement of data availability, the future research on PPP test will be more in-depth and comprehensive.

Data caching technologies in modern microprocessors

Objective. The study presented in the paper is aimed at studying the methods for improving the efficiency of software in modern computing systems with a hierarchical memory structure. Method. The study is based on data caching technologies in microprocessors. Result. The article presents the results of the analysis of various approaches to the development of efficient software taking into account the characteristics of the memory subsystem of the computing system, which made it possible to prove the importance of cache memory in improving the performance and interaction of computer components. Conclusion. Cache memory is a critical element in the architecture of microprocessors, playing a key role in determining the performance of the computing system. Optimizing the use of cache can significantly improve data access time and, as a result, overall system performance. Software developers need to pay special attention to the characteristics of the memory subsystem when designing and implementing solutions.

Visualization acceptance among the data journalists in the United Arab Emirates: A structural equation modeling-based study

New trends and practices in journalism and news-making contribute to data journalism’s increasing adoption and use. This study highlights and examines data journalism as a significant practice among journalists in the United Arab Emirates. Theoretically supported by social cognitive theory, data from 309 journals is analyzed using structural equation modelling. The results show a strong preference among Emirati journalists for using data journalism professionally. These journalists encode data using various visualization approaches to improve data availability and transparency for readers. Also, they prioritize assuring easy and understandable data decoding among audiences, potentially promoting critical thinking. Thus, the study concludes that Emirati journalists are assertive about adopting and using data journalism approaches to enhance their skills and provide transparent data to readers. Also, data journalism’s preference reflects technology’s integration into traditional journalism, transforming communication into a two-way process. Finally, the research discusses the study implications, limitations, and recommendations accordingly.

Data Commons Platforms: Addressing Challenges and Opportunities in Large-Scale Biomedical Data Integration and Analysis

Abstract: Data Commons Platforms are emerging as transformative tools in the landscape of medical research, offering a centralized, accessible, and scalable infrastructure for managing, analyzing, and sharing vast amounts of biomedical data. This article provides a comprehensive examination of these platforms, exploring their core functionalities, benefits, and challenges in the context of advancing medical discovery. We discuss how Data Commons Platforms accelerate research by facilitating datadriven hypothesis generation, enhancing collaboration among researchers, improving data accessibility, and ensuring data interoperability. The article also presents a reference architecture for these platforms, detailing the key components that enable their powerful capabilities. Furthermore, we address critical challenges such as data privacy, quality standardization, and ethical considerations, while also highlighting opportunities for future development. Through case studies and an analysis of emerging trends, this work demonstrates the potential of Data Commons Platforms to revolutionize biomedical research, paving the way for more efficient, collaborative, and impactful scientific discoveries in the era of big data and precision medicine.

Monitoring river discharge from space: An optimization approach with uncertainty quantification for small ungauged rivers

The number of in-situ stations measuring river discharge, one of the Essential Climate Variables (ECV), is declining steadily, and numerous basins have never been gauged. With the aim of improving data availability worldwide, we propose an easily applicable and transferable approach to estimate reach-scale discharge solely using remote sensing data that is suitable for filling gaps in the in-situ network. We combine 20 years of satellite altimetry observations with high-resolution satellite imagery via a hypsometric function to observe large portions of the reach-scale bathymetry. The high-resolution satellite images, which are classified using deep learning image segmentation, allow for detecting small rivers (narrower than 100m) and can capture small width variations. The unobserved part of the bathymetry is estimated using an empirical width-to-depth function. Combined with precise satellite-derived slope measurements, river discharge is calculated at multiple consecutive cross-sections within the reach. The unknown roughness coefficient is optimized by minimizing the discharge differences between the cross-sections. The approach requires minimal input and approximate boundary conditions based on expert knowledge but is not dependent on calibration. We provide realistic uncertainties, which are crucial for data assimilation, by accounting for errors and uncertainties in the different input quantities. The approach is applied globally to 27 river sections with a median normalized root mean square error of 12% and a Nash–Sutcliffe model efficiency of 0.560. On average, the 90% uncertainty range includes 91% of the in-situ measurements.

Precision Metrics: A Narrative Review on Unlocking the Power of KPIs in Radiology for Enhanced Precision Medicine.

(Background) Over the years, there has been increasing interest in adopting a quality approach in radiology, leading to the strategic pursuit of specific and key performance indicators (KPIs). These indicators in radiology can have significant impacts ranging from radiation protection to integration into digital healthcare. (Purpose) This study aimed to conduct a narrative review on the integration of key performance indicators (KPIs) in radiology with specific key questions. (Methods) This review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar using a combination of keywords related to radiology and KPIs, with Boolean logic to refine results. From an initial yield of 211 studies, 127 were excluded due to a lack of focus on KPIs. The remaining 84 studies were assessed for clarity, design, and methodology, with 26 ultimately selected for detailed review. The evaluation process involved multiple assessors to minimize bias and ensure a rigorous analysis. (Results and Discussion) This overview highlights the following: KPIs are crucial for advancing radiology by supporting the evolution of imaging technologies (e.g., CT, MRI) and integrating emerging technologies like AI and AR/VR. They ensure high standards in diagnostic accuracy, image quality, and operational efficiency, enhancing diagnostic capabilities and streamlining workflows. KPIs are vital for radiological safety, measuring adherence to protocols that minimize radiation exposure and protect patients. The effective integration of KPIs into healthcare systems requires systematic development, validation, and standardization, supported by national and international initiatives. Addressing challenges like CAD-CAM technology and home-based radiology is essential. Developing specialized KPIs for new technologies will be key to continuous improvement in patient care and radiological practices. (Conclusions) In conclusion, KPIs are essential for advancing radiology, while future research should focus on improving data access and developing specialized KPIs to address emerging challenges. Future research should focus on expanding documentation sources, improving web search methods, and establishing direct connections with scientific associations.

Data as infrastructure: Systematic data curation addressing fundamental data content differences across the UK

Objective and ApproachHealth Data Research UK, the UK national institute for health data science, is coordinating efforts alongside national academic partners to streamline data curation at disease, population, and data structure level to enhance data offerings and provide networked data infrastructure supporting whole-UK research. Due to clinical, coding, and system differences across the constituent countries of the UK, data is often not standardised for whole-UK analyses, creating burden on research teams and leading to long data preparation times in order to run even distributed analyses. The approach to solve this is multi-faceted, including deploying data curation and cohort creation algorithms into health data providers’ environments, and through the novel integration of federated analytics solutions (such as those piloted through recent national infrastructure programmes) improving data access and research deployment efficiency. ResultsStandardising data through clinical and structural data curation directly deployed to health data providers creates a framework for whole-UK studies to be readily achievable, and provide the data infrastructure base to integrate new technical federated analytics solutions to deploy and reproduce analytics without unnecessary large scale data migration. Conclusions and ImplicationsSystematic curation of health data within national data providing organisations provides flexibility and choice for researchers in terms of the data they will apply for to answer vital research questions affecting the UK populace. Such advances will improve the quality and efficiency of research for all corners of the UK, and create a community of practice in terms of developing data from health systems to research environments.

SynD: Australian synthetic health data community of practice

ObjectivesThe current workflow for health data research in Australia is inefficient. After funding is secured, researchers often face delays of months or years to access the necessary data. Synthetic data could significantly improve the pace and impact of health data research but lacks foundational infrastructure. We aim to develop this infrastructure and support the use of synthetic data to improve data access and research quality across Australia. ApproachWe held two workshops with Australian groups working on synthetic data. The format included participant updates and invited talks on international approaches to synthetic data and health data research. Workshops collected use cases and stimulated discussion on national collaboration. A facilitator then led thematic analysis to draft a consensus roadmap and terms of reference towards national synthetic data infrastructure. ResultsWe recruited 18 participants. Participants were cross sectoral: universities (9), research funding bodies (5), state health departments (4). Represented six states and territories: Queensland (6), New South Wales (3), Victoria (3), Western Australia (3), Australian Capital Territory (2), South Australia (1). Gender: women (11), men (7). The roadmap includes stakeholder engagement, a governance framework, and training events. ConclusionSynD is an Australian community of practice for synthetic health data. Our mission is to unlock the value of health information through synthetic data to advance research, education, innovation and service delivery within the health and care sector. This collaborative effort should ensure a harmonised approach to the safe and effective utilisation of synthetic data to enhance health outcomes across Australia.

User experience with HIV molecular epidemiology in research, surveillance, and cluster detection and response: a needs assessment

Objective HIV molecular epidemiology (HIV ME) is a tool that aims to improve HIV research, surveillance, and cluster detection and response. HIV ME is a core pillar of the U.S. initiative to End the HIV Epidemic but faces some challenges and criticisms from stakeholders. We sought to assess user experience to identify the current needs for HIV ME. Methods Users of HIV ME, including researchers and public health practitioners, were engaged via a structured survey. Needs were assessed via open-ended questions about HIV ME. Data were analyzed using reflexive thematic analysis; the concordance of results was assessed semi-quantitatively. Results Of 90 possible HIV-ME end-users, 57 completed the survey (response rate = 63%), which included users engaged in research (n = 29) and public health (n = 28). Respondents identified current imperatives, challenges, and strategies to improve HIV ME. Imperatives included characterization of the virus, identification of HIV hotspots, and tailoring of HIV interventions. Challenges encompassed technological issues, ethical concerns, and implementation difficulties. Strategies to improve HIV ME involved improving data access and analysis, enhancing implementation guidance and resources, and fostering community engagement and support. Researchers and public health practitioners prioritized different imperatives, but similarly emphasized the ethical concerns with HIV ME. Conclusion The imperatives identified by users underscore the necessity of HIV ME, while the challenges highlight the hurdles to be overcome, including ethical concerns which emerged as a shared emphasis across user groups. The strategies outlined offer a roadmap for overcoming these challenges. These insights, drawn from user experience, present a valuable opportunity to inform the development of guidelines for the ethical application of HIV ME in research, surveillance, and cluster detection and response.

Crowdsourcing the digital parliament

ABSTRACT Parliaments will eventually not evade the digital evolution of every institution to become data-driven organisations. This study examines the Hellenic Optical Character Recognition (OCR) Team, a pioneering crowdsourcing initiative aimed at processing and analysing parliamentary data. Under certain conditions, crowdsourcing, in other words, the power of the people, can be appropriately channelled and exploited to support representative institutions and their societal stakeholders in managing their change processes. Based on survey findings, this research highlights the profiles and motivations of participants, identifies best practices for crowdsourcing in parliamentary contexts, and demonstrates the initiative's impact on improving data accessibility and transparency. The results suggest that the Hellenic OCR Team significantly contributes to the digital evolution of parliamentary functions and promotes greater public involvement in the legislative process.

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

A Prototype Model of Zero Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done for immutability, privacy and security, mutual consensus, transparency, accountability, tracking and tracing, and temperature‒humidity control parameters.

A Comprehensive Review on : Aquaponic Farming Water Quality Prediction

Aquaponic farming, which combines aquaculture and hydroponics, depends strongly on maintaining optimal water quality to guarantee the health and productivity of both fish and plants. This review paper explores the latest developments in IoT-based automated water monitoring systems, focusing on their role in predicting and managing water quality in aquaponic systems. Regardless of significant progress there are several research gaps. Recent studies highlight challenges such as inconsistent sensor selection, calibration issues, insufficient publicly available data, and inadequate data cleaning and preprocessing. Also, the issues of imbalanced datasets, limited long-term data, and underdeveloped IoT and AI integration prevent the development of accurate predictive models. The scalability and maintenance of systems, understanding microbial dynamics, and nutrient management are also critical areas needing further exploration. This review also identifies the need for deeper case studies and advanced feature extraction methods to enhance prediction accuracy. By addressing these gaps, including system scalability and nutrient management, future research can improve data availability and quality, enabling more robust predictions and contributing to more efficient and sustainable aquaponic systems.

Bridging evidence gaps in attributing loss and damage, and measures to minimize impacts

Losses and damages from climate change have been increasing as global temperatures continue to rise above pre-industrial levels. Low-income, climate vulnerable countries bear a disproportionate share of these losses and damages. After decades of international negotiations, the Loss and Damage Fund was established in late 2022, aiming at addressing both economic and non-economic losses arising from slow- and sudden-onset climate change events. Recognizing the complex nature of climate-related events, the establishment of the Loss and Damage Fund underscores an urgent need for precise attribution of these events to climate change, highlighting the fund’s reliance on scientific evidence to guide its efforts. Attribution science, which decouples specific causes of changes in climate hazards and impacts, can support loss and damage negotiations. Low-income countries, which have contributed the least to climate change, are experiencing more severe impacts. However, data quality and coverage required for scientific studies to attribute loss and damage to climate change remain limited in these developing countries. In this paper, we highlight the challenges to attribute losses and damages to climate change in developing countries and underscore strategies to overcome those challenges using examples from the agrifood sector. These strategies have implications for the operationalizing of the Loss and Damage Fund. We emphasize how improving data availability and quality can lead to rigorous scientific conclusions, supporting evidence-based, inclusive, and effective interventions. We also indicated measures that enable strengthening climate resilience to avoid and minimize losses and damages.

The status of MRI databases across the world focused on psychiatric and neurological disorders.

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

Healthcare workers’ views on decentralized primary health care management in Lesotho: a qualitative study

BackgroundLesotho experienced high rates of maternal (566/100,000 live births) and under-five mortality (72.9/1000 live births). A 2013 national assessment found centralized healthcare management in Ministry of Health led to fragmented, ineffective district health team management. Launched in 2014 through collaboration between the Ministry of Health and Partners In Health, Lesotho’s Primary Health Care Reform (LPHCR) aimed to improve service quality and quantity by decentralizing healthcare management to the district level. We conducted a qualitative study to explore health workers’ perceptions regarding the effectiveness of LPHCR in enhancing the primary health care system.MethodsWe conducted 21 semi-structured key informant interviews (KII) with healthcare workers and Ministry of Health officials purposively sampled from various levels of Lesotho’s health system, including the central Ministry of Health, district health management teams, health centers, and community health worker programs in four pilot districts of the LPHCR initiative. The World Health Organization’s health systems building blocks framework was used to guide data collection and analysis. Interviews assessed health care workers’ perspectives on the impact of the LPHCR initiative on the six-health system building blocks: service delivery, health information systems, access to essential medicines, health workforce, financing, and leadership/governance. Data were analyzed using directed content analysis.ResultsParticipants described benefits of decentralization, including improved efficiency in service delivery, enhanced accountability and responsiveness, increased community participation, improved data availability, and better resource allocation. Participants highlighted how the reform resulted in more efficient procurement and distribution processes and increased recognition and status in part due to the empowerment of district health management teams. However, participants also identified limited decentralization of financial decision-making and encountered barriers to successful implementation, such as staff shortages, inadequate management of the village health worker program, and a lack of clear communication regarding autonomy in utilizing and mobilizing donor funds.ConclusionOur study findings indicate that the implementation of decentralized primary health care management in Lesotho was associated a positive impact on health system building blocks related to primary health care. However, it is crucial to address the implementation challenges identified by healthcare workers to optimize the benefits of decentralized healthcare management.

Findings from a National Survey of Canadian perspectives on predicting river channel migration and river bank erosion

AbstractRiver bank erosion and river channel migration are geomorphic processes that can result in significant hazards when there are impacts to humans or infrastructure. Unlike flooding, there are limited national guidelines in Canada that provide recommendations on how to best assess riverine erosion hazards. Instead regional and local jurisdictions rely on techniques based on varying levels of policy maturity. The current study presents findings of a nationwide survey on Canadian perspectives on predicting river channel migration and river bank erosion which received more than 40 responses from across Canada. Results showed that predictions were used for a variety of purposes, but that confidence intervals were rarely reported. Aerial imagery and survey‐based methods were the well‐known and widely‐used techniques for predicting river channel migration and river bank erosion. A majority of respondents identified both technical and financial challenges to improving accuracy including client willingness to pay, data quality/cost issues, and hydrologic changes due to land use and climate change. Several recommendations for improving best‐practices are provided, with a focus on the development of erosion datasets, improving data access, and providing additional training opportunities.