Data Interoperability Research Articles

Towards a Unified Identifier of Satellite Remote Sensing Images

The rapid growth of Earth observation technologies has resulted in over 2000 operational remote sensing satellites, collectively generating an exabyte-scale volume of data. However, despite the availability of large data-sharing platforms, global remote sensing imagery still faces challenges in seamless access, precise querying, and efficient retrieval. To address these limitations, this study introduces the concept of the “Digital Imagery Object” (DIO) and develops a unified identification framework for satellite remote sensing imagery. The proposed approach establishes a structured identification and parsing system based on core metadata, including data acquisition platforms and imaging timestamps. This enhances the consistency and standardization of multisource imagery encoding, enabling unified identification and interpretation under a common set of rules. The system’s feasibility and effectiveness were demonstrated through the integration and management of diverse global datasets, highlighting its ability to streamline multisource data workflows. By supporting standardized management and one-click parsing, this framework facilitates efficient imagery sharing and lays the foundation for its use as a tradable digital resource on the internet. The study offers a practical solution for addressing current challenges in remote sensing imagery management, paving the way for improved accessibility and interoperability of Earth observation data.

Digital Transformation and Location Data Interoperability Skills for Small and Medium Enterprises

In the dynamic landscape of digital transformation, data interoperability—particularly for location data—is a key enabler of operational efficiency, innovation, and collaboration for Small and Medium Enterprises (SMEs). Despite their strategic importance, SMEs face significant challenges in integrating and utilizing location data, which puts them at a disadvantage in the increasingly digital global market. As part of the European DIS4SME project, this study proposes a methodology to address these challenges, characterized by the rigorous development of a training curriculum aimed at upskilling and retraining SME owners and employees. The curriculum emphasizes practical learning through real business case studies and is aligned with European policies such as the INSPIRE Directive and the European Data Strategy. Accordingly, ten courses were designed, forming a modular and hierarchical curriculum that addresses SMEs’ diverse needs. Initial feedback from the first managers’ pilot implementation suggests that the structured training program effectively equips managers with strategic decision-making skills to address location data interoperability challenges.

Use of Semantic Web Technologies to Enhance the Integration and Interoperability of Environmental Geospatial Data: A Framework Based on Ontology-Based Data Access

Abstract: This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a common challenge within these domains due to data heterogeneity and semantic discrepancies. The proposed framework uses semantic web technologies to enhance data interoperability, accessibility, and usability. Several practical examples were demonstrated to validate its effectiveness. These examples were based in Lake Mjøsa, Norway, addressing both spatial and non-spatial scenarios to test the framework’s potential. By extending the GeoSPARQL ontology, the framework supports SPARQL queries to retrieve information based on user requirements. A web-based SPARQL Query Interface (SQI) was developed to execute queries and display the retrieved data in tabular and visual format. Utilizing free and open-source software (FOSS), the framework is easily replicable for stakeholders and researchers. Despite some limitations, the study concludes that the framework is able to enhance cross-domain data integration and semantic querying in various informed decision-making scenarios.

Access to longitudinal mental health data in Africa: Lessons from a landscape analysis by the INSPIRE network datahub

Background Data from longitudinal mental health research in Africa is critical to understanding the complexities of mental health disorders in the continent’s diverse contexts. To be useful, data need to adhere to the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Methods A literature search from 1970 to 2022 identified longitudinal studies on depression, anxiety, and psychosis in was done. Using Artificial Intelligence (AI) and Natural Language Processing techniques, the search found data from more studies. The search engaged stakeholders in understanding data sharing practices and barriers and categorizing methods and challenges for sharing data. Results The initial search yielded 18,019 articles, of which 284 were eligible for review, and 226 passed quality assessments. A significant effort to access data directly from researchers yielded positive responses for 100 articles with available data statements, from which datasets were requested through online repositories and direct correspondence. Analysis revealed significant disparities in the distribution of mental health research across countries, with a concentration of studies in specific areas and on certain conditions. The study also highlighted a varied adherence to FAIR principles, with only 17 (17%) datasets adhering to data-sharing practices. Conclusions Despite the challenges encountered in data accessibility and the manual adjustments required, the study’s findings irradiate the path toward a more equitable and effective mental health research ecosystem on the continent. By fostering collaboration and embracing advanced methodologies and technologies, this study advocates for a concerted effort to improve the accessibility, interoperability, and reusability of mental health data. Ultimately, the project aims to contribute to understanding data-sharing dynamics in Africa, paving the way for informed interventions and policies.

Scaling Data Infrastructure for High-Volume Manufacturing: Challenges and Solutions in Big Data Engineering

Abstract—Scaling data infrastructure for high- volume manufacturing presents significant challenges owing to the rapid growth, diversity, and complexity of the data generated by modern production processes. This review explores the key challenges and solutions in big-data engineering to enable efficient, scalable, and reliable data management in manufacturing environments. The primary challenges include handling the volume, velocity, and variety of data; ensuring real-time processing and analysis; managing data storage and retrieval at scale; and maintaining data quality and consistency. To address these challenges, various big data engineering solutions have been discussed, including distributed computing frameworks, cloud-based storage and computing resources, data lakes, data governance and metadata management, stream processing technologies, machine learning, and AI for predictive analytics. This review also examines the role of data architecture and infrastructure in building scalable systems, highlighting the importance of microservices, containerization, orchestration, NoSQL databases, and data security and privacy. Performance optimization techniques, such as query optimization, data partitioning, sharding, caching, and data compression, have been explored to ensure efficient operation of large-scale data systems. The review includes case studies of successful implementations and discusses emerging trends, such as edge computing, and the growing importance of data interoperability and standardization. Future research directions were identified, emphasizing the need for ongoing development in this field to meet the ever- growing demand for high-volume manufacturing. Keywords—high-volume manufacturing, big data, data infrastructure, distributed computing frameworks, cloud computing, data lakes, microservices, containerization, orchestration, query optimization, data partitioning, sharding, caching, data compression

A Framework of Life-Cycle Infrastructure Digitalization for Highway Asset Management

With increased highway mileage, various types and quantities of infrastructure are equipped on the roadside to improve traffic safety and efficiency but also encounter difficulty in asset management. The collected data are separately stored with diverse formats, granularity and quality, causing repeated acquisitions and islands of information coherence. The life-cycle interoperability of infrastructure data are required to support life-cycle application scenarios in sustainable development. This paper analyzes 459 papers and 538 survey questionnaires to obtain the literature and practical digital requirements, including unified classification and standardized formats, linkage from separated data sources, support for data analysis across different scenarios, etc. To satisfy these requirements, an infrastructure digitalization framework is proposed, including road infrastructure and other data, data governance, life-cycle data integration, application scenarios, regulations and standards, and performance assessment. The application scenarios involve four categories—design and construction, maintenance, operation, and highway administration—each of which contains four or five scenarios. Then, the data integration approach is first developed with master data identification and determination of data elements for data interoperation between different application scenarios, using a modified data–process matrix, correlation matrix, and evaluation factors. A data relationship model is adopted to present static and dynamic correlations from the multi-source data. Numerical experiments are implemented with two practical highway administration and maintenance systems to demonstrate the effectiveness of the data integration approach. Master data identification and data element determination are applied to guide life-cycle data interoperation.

BIG DATA AND MACHINE LEARNING FOR HEALTHCARE RESOURCE ALLOCATION AND OPTIMIZATION

Healthcare systems face increasing pressure to allocate limited resources effectively due to growing populations, rising healthcare costs, and the increasing complexity of medical needs. The advent of big data and machine learning (ML) technologies offers transformative potential for addressing these challenges. This paper explores the integration of big data and ML in healthcare resource allocation and optimization, focusing on how these technologies enable data-driven decision-making, improve operational efficiency, and enhance patient outcomes. We discuss applications such as predictive modeling for patient admissions, optimization of staffing, inventory management, and strategic planning. Additionally, challenges such as data privacy, interoperability, and algorithmic bias are analyzed, and potential solutions are proposed. This paper concludes with insights into future directions for research and practice in leveraging big data and ML to create more efficient and equitable healthcare systems.

Blockchain-Based Electronic Health Record System

The EHR healthcare system currently stores patients’ medical records in a way that allows general access, compromising confidentiality. Since medical records contain sensitive information, ensuring that only authorized individuals have access is essential. Most EHR data sharing still relies on email due to a lack of secure and reliable channels for health data exchange, which poses a significant issue in the current system. Blockchain addresses these problems, making patients the center of the healthcare system, yet improving data security and interoperability. This technology uses the Hyperledger Fabric: where a permissioned blockchain system solution for storing and exchanging medical records can be found. This provides an integrative framework for ensuring security via blockchain while simultaneously granting and revoking access to patient medical records to the appropriate doctor. Also, each transaction is organized into separate blocks that form a secure, unalterable chain.

Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery

Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures.

Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative

The MaterialDigital initiative represents a major driver toward the digitalization of material science. Next to providing a prototypical infrastructure required for building a shared data space and working on semantic interoperability of data, a core focus area of the Platform MaterialDigital (PMD) is the utilization of workflows to encapsulate data processing and simulation steps in accordance with findable, accessible, interoperable, and reusable principles. In collaboration with the funded projects of the initiative, the workflow working group strives to establish shared standards, enhancing the interoperability and reusability of scientific data processing steps. Central to this effort is the Workflow Store, a pivotal tool for disseminating workflows with the community, facilitating the exchange and replication of scientific methodologies. This article discusses the inherent challenges of adapting workflow concepts, providing the perspective on developing and using workflows in the respective domain of the various funded projects. Additionally, it introduces the Workflow Store's role within the initiative and outlines a future roadmap for the PMD workflow group, aiming to further refine and expand the role of scientific workflows as a means to advance digital transformation and foster collaborative research within material science.

Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs

The Homo sapiens Chromosomal Location Ontology (HSCLO) is designed to facilitate the integration of human genomic features into biomedical knowledge graphs from releases GRCh37 and GRCh38 at multiple resolutions. HSCLO comprises two distinct versions, HSCLO37 and HSCLO38, each tailored to its respective human genome release. This ontology supports the efficient integration and analysis of human genomic data across scales ranging from entire chromosomes to individual base pairs, thereby enhancing data retrieval and interoperability within large-scale biomedical datasets. Unlike existing ontologies that primarily focus on genomic feature identification or annotation, HSCLO is specifically engineered to optimize the interoperability and scalability of genomic data within biomedical knowledge graphs. The utility and performance of HSCLO are demonstrated through a case study involving the integration of high-resolution chromatin interaction data, which reveals significant improvements in query efficiency and data linkage. HSCLO represents a valuable resource for advancing research in disease genetics, personalized medicine, and other domains that require complex genomic data integration.

Application of Google Earth Engine to Monitor Greenhouse Gases: A Review

Google Earth Engine (GEE) is a cloud-based platform revolutionizing geospatial analysis by providing access to vast satellite datasets and computational capabilities for monitoring environmental and societal issues. It incorporates machine learning (ML) techniques and algorithms as part of its tools for analyzing and processing large geospatial data. This review explores the diverse applications of GEE in monitoring and mitigating greenhouse gas emissions and uptakes. GEE is a cloud-based platform built on Google’s infrastructure for analyzing and visualizing large-scale geospatial datasets. It offers large datasets for monitoring greenhouse gas (GHG) emissions and understanding their environmental impact. By leveraging GEE’s capabilities, researchers have developed tools and algorithms to analyze remotely sensed data and accurately quantify GHG emissions and uptakes. This review examines progress and trends in GEE applications, focusing on monitoring carbon dioxide (CO2), methane (CH4), and nitrous oxide/nitrogen dioxide (N2O/NO2) emissions. It discusses the integration of GEE with different machine learning methods and the challenges and opportunities in optimizing algorithms and ensuring data interoperability. Furthermore, it highlights GEE’s role in pinpointing emission hotspots, as demonstrated in studies monitoring uptakes. By providing insights into GEE’s capabilities for precise monitoring and mapping of GHGs, this review aims to advance environmental research and decision-making processes in mitigating climate change.

Data reuse in agricultural genomics research: challenges and recommendations.

The scientific community has long benefited from the opportunities provided by data reuse. Recognizing the need to identify the challenges and bottlenecks to reuse in the agricultural research community and propose solutions for them, the data reuse working group was started within the AgBioData consortium framework. Here, we identify the limitations of data standards, metadata deficiencies, data interoperability, data ownership, data availability, user skill level, resource availability, and equity issues, with a specific focus on agricultural genomics research. We propose possible solutions stakeholders could implement to mitigate and overcome these challenges and provide an optimistic perspective on the future of genomics and transcriptomics data reuse.

Revolutionizing Healthcare Information Systems through Blockchain Technology

Integrating blockchain technology in healthcare represents a transformative approach to addressing critical data security, interoperability, and patient privacy challenges. This comprehensive article examines the implementation of blockchain solutions across various healthcare domains, including clinical trials, pharmaceutical supply chains, and personalized medicine. The technology's distributed ledger architecture enables secure patient data sharing, enhances clinical trial management, and revolutionizes pharmaceutical supply chain oversight. Blockchain demonstrates significant potential in improving healthcare delivery efficiency through smart contracts and consensus mechanisms while maintaining regulatory compliance. The article explores the transformative benefits and implementation challenges, highlighting the technology's role in advancing patient-centric care and medical research while addressing concerns regarding scalability, system integration, and regulatory requirements.

The influence of e-government integration and data interoperability policies on poverty reduction

Abstract This research analyzed the influence of e-government integration policies and data interoperability on poverty alleviation in Tasikmalaya City. The research was motivated by the importance of creating sustainable government governance, especially in realizing reduced poverty rates in Tasikmalaya City. One of the most appropriate approaches was service integration policy intervention. The research used a quantitative approach with survey research through data collection in questionnaires to 330 respondents using random sampling clusters spread throughout the sub-districts of Tasikmalaya City. The independent variables used were e-government policy and data interoperability. Apart from that, the dependent variable was poverty reduction. The data interoperability and e-government influenced poverty alleviation by 54.07%. On the other hand, some evidence suggested a connection between data interoperability and poverty reduction to a certain extent. This emphasized the importance of integration between agencies, which could be accessed through a collection of inter-agency activity programs, especially in the distribution of poverty assistance programs. Therefore, the Tasikmalaya City Government needed to immediately implement a data interoperability policy as a valuable effort to design economic empowerment programs and create an integrated public service online portal that provided information ranging from social assistance to workforce training programs. This service would make it easier for poor target groups to obtain accurate information as part of poverty reduction services. Some programs run by the Tasikmalaya City Government included distributing aid programs to the poor, providing information on job opportunities, and increasing information on various departmental collaboration programs.

Applying Front-end Analysis Insights in the Development of a Data Management Course for Biomedical Researchers

In this article, we describe how our design team used front-end analysis to identify the training needs, gather design precedents, conduct a learner analysis study, and apply the insights in designing a data management course for biomedical researchers. In-person and online versions of the course were developed and implemented with cohorts of a research training and mentorship program for biomedical researchers. The course development project adds to the emerging literature base of design precedents for instruction on research data management guided by the FAIR principles of findability, accessibility, interoperability, and reusability of research data.

Development of a standardized data collection and intelligent fabric quality prediction system for the weaving department

The diversity of weaving equipment has led to inconsistencies in communication protocols, impeding data collection and interoperability between devices, and ultimately reducing production efficiency. Additionally, fabric defects significantly impact product quality, while current visual inspection technologies are primarily reactive and traditional quality prediction methods often exhibit considerable errors. This study leverages the standardization and interoperability features of open platform communications unified architecture technology to facilitate data acquisition within the weaving department, establishing a reliable Internet of Things framework that supports subsequent fabric quality prediction, and optimizing the back propagation neural network through the K-means clustering algorithm and particle swarm optimization to predict the type and number of fabric defects. A comparative analysis with traditional BP and PSO-BP prediction models was conducted, ultimately verifying the feasibility of using OPC UA to transmit weaving data for fabric quality prediction. The research results demonstrate that using OPC UA technology enables the unified transmission of weaving equipment data, addressing the issue of heterogeneity in weaving department equipment. The K-means-PSO-BP model can effectively predict defects such as double weft, hundred feet, and broken warp with minimal error, achieving a root mean square error of less than 0.15.

THE ETHICAL DILEMMAS OF THE BLOCKCHAIN TECHNOLOGY USE IN e-HEALTCARE SYSTEMS

Blockchain technology is increasingly being recognized as a game-changer in healthcare, offering solutions for improved data security, transparency, and interoperability. However, its integration into healthcare systems brings with it a range of ethical challenges that need to be addressed. This paper delves into some of the most pressing issues, such as protecting patient privacy, defining data ownership, obtaining informed consent, and ensuring equitable access. It explores the delicate balance between the transparency that blockchain provides and the potential risks of exposing sensitive health information. The paper also considers the ethical concerns surrounding decentralized governance and the possibility of deepening inequalities due to the digital divide. By addressing these dilemmas, the goal is to offer a thoughtful analysis that can guide policymakers, healthcare professionals, and technologists in leveraging blockchain’s potential while upholding ethical principles in healthcare.

Scenario-oriented data interoperability: maximising the connection between data and users in collaboration environments

ABSTRACT In large-scale collaborative environments, data interoperability faces challenges due to varying data standards, differences in time and space, and rising demands for data services. Traditional methods focus on integrating resources but often miss the need for interoperability between users and data. This research introduces a new approach to data interoperability, which emphasises scenario-based strategies. We use topic analysis and context fusion to handle industry-specific terminology, making it easier for users to understand cross-speciality data. Our approach includes a concept-extended metamodel to address business logic differences and connect data with user scenarios. We tested our model in a railway company and found it effective and useful based on survey feedback.

The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data

Background Medical narratives are fundamental to the correct identification of a patient’s health condition. This is not only because it describes the patient’s situation. It also contains relevant information about the patient’s context and health state evolution. Narratives are usually vague and cannot be categorized easily. On the other hand, once the patient’s situation is correctly identified based on a narrative, it is then possible to map the patient’s situation into precise classification schemas and ontologies that are machine-readable. To this end, language models can be trained to read and extract elements from these narratives. However, the main problem is the lack of data for model identification and model training in languages other than English. First, gold standard annotations are usually not available due to the high level of data protection for patient data. Second, gold standard annotations (if available) are difficult to access. Alternative available data, like MIMIC (Sci Data 3:1, 2016) is written in English and for specific patient conditions like intensive care. Thus, when model training is required for other types of patients, like oncology (and not intensive care), this could lead to bias. To facilitate clinical narrative model training, a method for creating high-quality synthetic narratives is needed.MethodWe devised workflows based on generative AI methods to synthesize narratives in the German language to avoid the disclosure of patient’s health data. Since we required highly realistic narratives, we generated prompts, written with high-quality medical terminology, asking for clinical narratives containing both a main and co-disease. The frequency of distribution of both the main and co-disease was extracted from the hospital’s structured data, such that the synthetic narratives reflect the disease distribution among the patient’s cohort. In order to validate the quality of the synthetic narratives, we annotated them to train a Named Entity Recognition (NER) algorithm. According to our assumptions, the validation of this system implies that the synthesized data used for its training are of acceptable quality.ResultWe report precision, recall and F1 score for the NER model while also considering metrics that take into account both exact and partial entity matches. Trained models are cautious, with a precision up to 0.8 for Entity Type match metric and a F1 score of 0.3.ConclusionDespite its inherent limitations, this technology has the potential to allow data interoperability by using encoded diseases across languages and regions without compromising data safety. Additionally, it facilitates the synthesis of unstructured patient data. In this way, the identification and training of models can be accelerated. We believe that this method may be able to generate discharge letters for any combination of main and co-diseases, which will significantly reduce the amount of time spent writing these letters by healthcare professionals.