Standard Data Format Research Articles

Extended IFC-based information exchange for construction management of roller-compacted concrete dam

Building Information Modeling (BIM) plays a pivotal role in the construction management of dam engineering projects, which face challenges in data interoperability due to the absence of standardized data formats. While Industry Foundation Classes (IFC) offer a standardized digital representation of the built environment, it lacks the capability to accurately model the semantics of roller-compacted concrete dams. Therefore, this paper presents an extended IFC-based information exchange for the construction management of roller-compacted concrete dams, incorporating the development and validation of a prototype through a case study. The results indicate a significant enhancement in standardized information exchange for construction management compared to traditional practices. The approach facilitates BIM-assisted information exchange automation among stakeholders involved in roller-compacted concrete dam construction projects. The findings of this study provide valuable insights into dam construction management system prototyping and cross-platform collaboration.

A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

AbstractMicrophysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

A review of distributed ledger technologies application in medical systems interoperability

This study of the literature delves into the complex area of medical systems interoperability, focusing on mitigating variables that impact security and data transfer at the structural and semantic levels. In the era of digital healthcare, the secure sharing of medical data is crucial, and this study looks at how Distributed Ledger Technologies (DLTs) can play a major role in addressing these challenges. Complex interoperability issues that come from differences in communication protocols, data formats, established data structures, data models, and data meaning and codification methodologies face the healthcare industry. These problems typically impede the seamless transmission of electronic medical records between healthcare systems. Because of their decentralized structure and cryptographic foundation, DLTs offer a workable solution to these issues. By critically evaluating previous research and case studies, DLTs may be able to lessen these interoperability issues, as this literature review illustrates. Since DLTs provide an immutable and secure platform for the transmission of medical data, guaranteeing data integrity and confidentiality, they are a natural fit for the sensitive nature of healthcare data. Their importance in creating safe communication protocols, enhancing the meaning of data, and defining models and formats for data is emphasized in this review. A comprehensive architecture for DLT interoperability in healthcare is also recommended by the research. This framework encourages the development of DLT integration, shared data models, standardized data formats, and governance and policy. By implementing this strategy and strengthening secure medical data sharing, healthcare organizations and governments may increase the efficiency, precision, and speed of healthcare delivery. The crucial role that DLTs play in removing the structural and semantic barriers to safe medical systems interoperability is highlighted in the conclusion of this literature review. By adopting DLTs, the healthcare sector may usher in a new era of standardized, safe, and efficient medical data transmission, which will ultimately benefit both patients and healthcare providers. This study shows how distributed ledger technologies (DLTs) have the potential to revolutionize the healthcare industry by enabling the secure and meaningful exchange of medical data between different systems, thereby improving patient care and healthcare outcomes.

Visualizing set relationships: EVenn's comprehensive approach to Venn diagrams.

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step-by-step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user-friendly tool for the in-depth exploration of multiomics data.

Improving Healthcare Data Security and Privacy With Blockchain technology using SHA-256 Algorithm

Block chain technology has the potential to completely transform healthcare management systems by enhancing data security, interoperability, and integrity because to its decentralized and unchangeable structure. Sensitive patient information is more difficult for bad actors to breach thanks to the SHA-256 algorithm, which improves data security. Interoperability between diverse healthcare systems is facilitated by smart contracts and standardized data formats. However, issues with user adoption, scalability, and regulatory compliance also face block chain implementation in the healthcare industry. This study offers important insights into the changing environment of healthcare data management by highlighting the possible advantages and challenges of integrating block chain technology in healthcare management systems.

Ontology-based BIM-AMS integration in European Highways

BIM tools enable decision-making during the lifecycle of engineering structures, such as bridges, tunnels, and roads. National Road Authorities use Asset Management Systems (AMS) to manage and monitor operational information of assets from European Highways, including access to sensor and inspection data. Interoperability between BIM and AMS systems is vital for a timely and effective decision-making process during the operational phase of these assets. The European project Connected Data for Effective Collaboration (CoDEC) designed a framework to support the connections between AMS and BIM platforms, using linked data principles. The CoDEC Data Dictionary was developed to provide standard data formats for AMS used by European NRA. This paper presents the design and development of an Engineering Structures ontology used to encode the shared conceptualization provided by the CoDEC Data Dictionary. The ontology is evaluated, validated, and demonstrated as a base for data exchange between BIM and AMS.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics

BackgroundLiquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application. MethodsHere, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system. ResultsThe source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis. ConclusionsPhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

Mapping the Anatomy of the Human Lymphatic System.

While substantial anatomical study has been pursued throughout the human body, anatomical study of the human lymphatic system remains in its infancy. For microsurgeons specializing in lymphatic surgery, a better command of lymphatic anatomy is needed to further our ability to offer surgical interventions with precision. In an effort to facilitate the dissemination and advancement of human lymphatic anatomy knowledge, our teams worked together to create a map. The aim of this paper is to present our experience in mapping the anatomy of the human lymphatic system. Three steps were followed to develop a modern map of the human lymphatic system: (1) identifying our source material, which was "Anatomy of the human lymphatic system," published by Rouvière and Tobias (1938), (2) choosing a modern platform, the Miro Mind Map software, to integrate the source material, and (3) transitioning our modern platform into The Human BioMolecular Atlas Program (HuBMAP). The map of lymphatic anatomy based on the Rouvière textbook contained over 900 data points. Specifically, the map contained 404 channels, pathways, or trunks and 309 lymph node groups. Additionally, lymphatic drainage from 165 distinct anatomical regions were identified and integrated into the map. The map is being integrated into HuBMAP by creating a standard data format called an Anatomical Structures, Cell Types, plus Biomarkers table for the lymphatic vasculature, which is currently in the process of construction. Through a collaborative effort, we have developed a unified and centralized source for lymphatic anatomy knowledge available to the entire scientific community. We believe this resource will ultimately advance our knowledge of human lymphatic anatomy while simultaneously highlighting gaps for future research. Advancements in lymphatic anatomy knowledge will be critical for lymphatic surgeons to further refine surgical indications and operative approaches.

Imperative of Standards and Interoperability in Modern Medicine

In the rapidly evolving landscape of modern medicine, the pivotal role of standards and interoperability cannot be overstated. This research paper delves deep into the intricate web of healthcare systems and processes to show how standardized data formats, robust communication protocols, and interoperable systems can bring about transformative changes. Standardization streamlines data exchange and ensures consistency and accuracy in medical records, making them more reliable for healthcare professionals. It fosters interoperability, enabling different healthcare systems and devices to communicate seamlessly. This interoperability is a game-changer in ensuring patient information flows smoothly across various points of care, leading to improved healthcare quality and patient safety. Drawing from extensive literature reviews and empirical evidence, and this paper unveils the tangible benefits of adopting standardized practices and interoperable technologies in healthcare. However, it also acknowledges the implementation challenges, such as the need for updated infrastructure, privacy concerns, and resistance to change. Despite these challenges, the paper underscores the urgency of adopting a unified approach to standards and interoperability in healthcare. It argues that such an approach will revolutionize patient care and healthcare delivery, facilitating the development of innovative solutions, reducing errors, and ultimately improving patient outcomes.

Decentralizing Healthcare and Securing with Blockchain Technology

It has been shown in recent years that the safe interchange of medical data improves people's care and treatment, thereby enhancing their quality of life. Blockchain technology has the power to revolutionize healthcare management systems by improving data security, interoperability, and integrity, thanks to its decentralized and unchangeable structure. As an emerging option for achieving balance in the healthcare ecosystem, blockchain technology facilitates interoperability between diverse healthcare systems through the use of smart contracts and standardized data formats. Despite its potential advantages, interoperability remains an ongoing challenge in the healthcare environment. From an architectural perspective, trade-offs must be considered in the design of a blockchain-based system. However, obstacles such as user acceptance, scalability issues, and regulatory compliance impede the implementation of blockchain in the healthcare industry. This study provides valuable insights into the evolving landscape of healthcare data management by highlighting the potential advantages and challenges associated with integrating blockchain technology into healthcare management systems. Additionally, it suggests a Blockchain-based electronic healthcare management (EHR) model that could address some of the limitations of the existing systems. Keywords: Decentralization, Security, Blockchain, Electronic Health Record, Health Care Management

OPO-CVI: design and implementation of an ocean profiling observation system for wave-powered vertical profiler following an ISO standard

This paper presents the design and implementation of an ISO-compliant ocean profiling observation system for wave-powered vertical profiler. This system aims to provide a comprehensive, scalable, and interoperable solution for high-resolution, real-time oceanic observation. As a part of this system, we introduce a wave-powered vertical profiler, known as “Wave Master,” designed to offer enhanced stability and reliability for long-term oceanic data collection. The core of the paper focuses on the Ocean Profiling Observation Complex Virtual Instrument (OPO-CVI), a comprehensive system developed in alignment with ISO 21851 standard. OPO-CVI seamlessly integrates data collection, transmission, storage, and visualization. Specifically, OPO-CVI addresses the challenges of information isolation, system rigidity, and lack of modularity in traditional ocean profiling methods by standardizing data formats and transmission protocols, allowing for seamless integration of new observation elements, and employing a modular architecture for enhanced scalability and reusability. By offering detailed technical insights into the OPO-CVI architecture and its compliance with ISO 21851 standard, this paper aims to contribute significantly to the advancement of standardized, efficient, and reliable oceanic observation systems.

Big data and electronic health records for glaucoma research.

The digitization of health records through electronic health records (EHRs) has transformed the landscape of ophthalmic research, particularly in the study of glaucoma. EHRs offer a wealth of structured and unstructured data, allowing for comprehensive analyses of patient characteristics, treatment histories, and outcomes. This review comprehensively discusses different EHR data sources, their strengths, limitations, and applicability towards glaucoma research. Institutional EHR repositories provide detailed multimodal clinical data, enabling in-depth investigations into conditions such as glaucoma and facilitating the development of artificial intelligence applications. Multicenter initiatives such as the Sight Outcomes Research Collaborative and the Intelligent Research In Sight registry offer larger, more diverse datasets, enhancing the generalizability of findings and supporting large-scale studies on glaucoma epidemiology, treatment outcomes, and practice patterns. The All of Us Research Program, with a special emphasis on diversity and inclusivity, presents a unique opportunity for glaucoma research by including underrepresented populations and offering comprehensive health data even beyond the EHR. Challenges persist, such as data access restrictions and standardization issues, but may be addressed through continued collaborative efforts between researchers, institutions, and regulatory bodies. Standardized data formats and improved data linkage methods, especially for ophthalmic imaging and testing, would further enhance the utility of EHR datasets for ophthalmic research, ultimately advancing our understanding and treatment of glaucoma and other ocular diseases on a global scale.

AI and machine learning in climate change research: A review of predictive models and environmental impact

The burgeoning threat of climate change has spurred an increased reliance on advanced technologies to comprehend and mitigate its far-reaching consequences. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as indispensable tools in climate change research, offering unprecedented capabilities for predictive modeling and assessing environmental impact. This review synthesizes the current state of AI and ML applications in climate change research, emphasizing their role in predictive modeling and understanding environmental repercussions. Predictive models leveraging AI and ML algorithms have demonstrated remarkable efficacy in forecasting climate patterns, extreme weather events, and sea-level rise. These models incorporate vast datasets encompassing meteorological, geospatial, and oceanic information, enabling more accurate predictions of future climate scenarios. Moreover, AI-driven models excel in recognizing intricate patterns and non-linear relationships within climate data, enhancing their capacity to simulate complex environmental systems. Environmental impact assessment stands as a critical facet of climate change research, and AI and ML techniques are proving instrumental in this regard. These technologies facilitate the analysis of diverse ecological parameters, including deforestation rates, biodiversity loss, and carbon sequestration dynamics. By discerning nuanced patterns within immense datasets, AI systems contribute to a more nuanced understanding of the direct and indirect consequences of climate change on ecosystems. Despite these advancements, challenges persist, such as the need for standardized data formats, model interpretability, and ethical considerations. Additionally, the integration of AI and ML findings into policy frameworks remains a crucial frontier. As the intersection of AI, ML, and climate change research evolves, continuous interdisciplinary collaboration is essential to harness the full potential of these technologies in safeguarding our planet's future. This review illuminates the current landscape of AI and ML applications, providing insights into their efficacy, challenges, and potential contributions to advancing climate change research and environmental sustainability.

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes and edges within specific applications, such as text, entities, and relations, to create graph structures. Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today. It also discusses the message-passing mechanism employed by GNN models and examines the strengths and limitations of these models in different domains. Furthermore, the paper explores the diverse applications of GNNs, the datasets commonly used with them, and the Python libraries that support GNN models. It offers an extensive overview of the landscape of GNN research and its practical implementations.

Enhancing IoT systems through Cloud-Fog-Edge architectures challenges and opportunities

This article examines the deployment and implications of Cloud- Fog-Edge architectures in Internet of Things (IoT) systems, highlighting their significance in enhancing data management and system security across diverse sectors. As IoT ecosystems expand, the necessity for architectures that efficiently handle large volumes of data and ensure real-time processing capabilities becomes paramount. The Cloud-Fog-Edge architecture addresses these needs by distributing computing resources across three layers—cloud, fog, and edge—each optimized for specific tasks within the IoT workflow. We discuss the challenges and solutions associated with interoperability in such multi-layered systems, emphasizing the need for standardized communication protocols and data formats to facilitate seamless interactions between heterogeneous devices and platforms. Furthermore, the article delves into the critical aspects of security within these architectures, outlining strategies for robust data encryption, access management, regular security updates, and comprehensive network activity monitoring to safeguard against unauthorized access and cyber threats. The integration of Cloud-Fog-Edge architectures not only promises enhanced operational efficiency and scalability but also significantly boosts the adaptability of IoT systems to meet evolving technological and operational demands. By providing a detailed analysis of the functionalities, integration challenges, and security practices associated with each architectural layer, this article contributes to a deeper understanding of how Cloud-Fog-Edge frameworks can be optimized to bolster the reliability, efficiency, and security of modern IoT environments.

Digital child health: opportunities and obstacles. A joint statement of European Academy of Paediatrics and European Confederation of Primary Care Paediatricians.

The advancement of technology and the increasing digitisation of healthcare systems have opened new opportunities to transform the delivery of child health services. The importance of interoperable electronic health data in enhancing healthcare systems and improving child health care is evident. Interoperability ensures seamless data exchange and communication among healthcare entities, providers, institutions, household and systems. Using standardised data formats, coding systems, and terminologies is crucial in achieving interoperability and overcoming the barriers of different systems, formats, and locations. Paediatricians and other child health stakeholders can effectively address data structure, coding, and terminology inconsistencies by promoting interoperability and improving data quality and accuracy of children and youth, according to guidelines of the World Health Organisation. Thus, ensure comprehensive health assessments and screenings for children, including timely follow-up and communication of results. And implement effective vaccination schedules and strategies, ensuring timely administration of vaccines and prompt response to any concerns or adverse events. Developmental milestones can be continuously monitored. This can improve care coordination, enhance decision-making, and optimise health outcomes for children. In conclusion, using interoperable electronic child health data holds great promise in advancing international child healthcare systems and enhancing the child's care and well-being. By promoting standardised data exchange, interoperability enables timely health assessments, accurate vaccination schedules, continuous monitoring of developmental milestones, coordination of care, and collaboration among child healthcare professionals and the individual or their caregiver. Embracing interoperability is essential for creating a person-centric and data-driven healthcare ecosystem where the potential of digitalisation and innovation can be fully realized.

CLINIC MANAGEMENT SYSTEM

This study explores the implementation and benefits of a Clinic Management System (CMS) in healthcare settings. It focuses on the objectives of the CMS, including eliminating errors in form writing, establishing standardized processes and data formats, and generating reports for doctor scheduling and patient disease data storage. The study discusses the challenges faced by clinics in traditional manual-based systems, such as errors in form writing, lack of standardized processes, and the absence of robust reporting mechanisms. It highlights the significance of transitioning to a digital platform for minimizing errors in form writing and emphasizes the importance of intelligent form validation and auto-correction features.

A digital solution for determining the severity of paroxysmal sympathetic hyperactivity syndrome in patients with brain injury

Paroxysmal sympathetic hyperactivity syndrome is a frequent complication of acute severe brain injury, so with severe traumatic brain injury, it develops in 30 % of patients. Despite a large number of publications, the issues of diagnosis, prevention and treatment of this syndrome remain unresolved. To date, the assessment of the syndrome of paroxysmal sympathetic hyperactivity takes, with proper training of the doctor, from 8 to 15 minutes, taking into account the calculation of points using a calculator. With the digitalization of this process, a single assessment will take about 2–3 minutes to enter the indicators. In this case, the calculation will occur automatically. Subsequently, when using «wearable gadgets for measurement», the process of filling in indicators can also occur automatically. The application allows to register and store basic data about users who can have three roles: patients, specialist doctors, administrators. The creation of a database of patients with sympathetic hyperactivity syndrome will significantly improve diagnostic methods and assess the adequacy of therapy. Medical data is only useful if it can be turned into meaningful information. This requires high-quality data sets, uninterrupted communication between IT systems, and standard data formats that can be processed by humans and machines.

Role of digital economic development in environmental transition of renewable energy sector of China.

This research looks at how China's expanding digital economy influences the country's efforts to green its renewable energy sector. Regression tests, endogeneity tests, correlation analysis, and robustness analysis are used to investigate the relationship between digital economic development and environmental consequences in the renewable energy sector. The results of the research show a significant positive association between the expansion of the digital economy and the transition to a more sustainable energy model in the clean energy sector. Regression studies reveal that the growth of the digital economy is positively correlated with a number of environmental indicators, including the generation of renewable energy, energy efficiency, and the decrease of carbon emissions. Endogeneity analyses have shown a causal relationship between digitization and environmental benefits in the renewable energy sector. The adoption of different digital technologies is strongly correlated. Robustness study, which ensures the validity and reliability of the collected results, further supports the importance of digital economic development in driving the environmental transformation of the renewable energy sector. The study's findings have significant implications for public policy. Funding digital infrastructure, fostering partnerships between the public and private sectors, standardizing data formats and interoperability, strengthening cybersecurity safeguards, and developing pro-digital policies and regulations are all important steps that policymakers can take to promote the widespread adoption of digital technologies.

RNA interaction format: a general data format for RNA interactions.

RNA molecules play crucial roles in various biological processes. They mediate their function mainly by interacting with other RNAs or proteins. At present, information about these interactions is distributed over different resources, often providing the data in simple tab-delimited formats that differ between the databases. There is no standardized data format that can capture the nature of all these different interactions in detail. Here, we propose the RNA interaction format (RIF) for the detailed representation of RNA-RNA and RNA-Protein interactions and provide reference implementations in C/C++, Python, and JavaScript. RIF is released under licence GNU General Public License version 3 (GNU GPLv3) and is available on https://github.com/RNABioInfo/rna-interaction-format.