Standard Data Format Research Articles

Enhancing Compliance and Governance through Data Consistency and Rationalization for Effective Risk Mitigation in Health Care

In the era of big data, you absolutely must have consistency across your different data systems to make effective decision-making, be compliance, enhanced governance and security and also mitigation of risks,, and even have system integrity. The data consistency and how rationalization techniques help overcome challenges when working with heterogeneous data sources, is what this paper explores. Rationalization allows businesses to keep their data accurate and consistent by standardizing data formats, reducing duplications, measuring the data growth, categories the data sources, farmats, and aligning conflicting information. We compare the different methods of normalization, data deduplication, Master Data Management (MDM) and data integration framework. We also delve into the part that automation, artificial intelligence and machine learning play in optimizing these processes and provide scalable solutions that can potentially simplify the complexity of modern-day data environments. Practical implications from rationalization effort case studies are shown from sectors like, healthcare, finance, and e-commerce. Our specific findings show that long-term data consistency requires a systematic approach that employs technology and strong governance. In the end, this paper gives a blueprint for how to exploit rationalization methods to exploit the capabilities of data-driven insights fully.

From Data Integration to Precision Medicine: A Value-Based Healthcare Approach for Sarcoma Care.

The transformation of healthcare from a fee-for-service model to value-based care is particularly crucial in managing complex and rare diseases like sarcoma, where data fragmentation and variability present significant challenges. This manuscript reviews strategies for structured and harmonized data integration-a critical precursor to precision medicine in sarcoma care. We demonstrate how standardizing data formats, ontologies, and coding systems enable seamless integration of clinical, economic, and patient-reported outcomes across institutions, paving the way for comprehensive predictive analytics. By establishing robust value-based healthcare (VBHC) frameworks through digital transformation and predictive models, including digital twins, we create the foundation for personalized sarcoma treatment and real-world-time clinical decision-making. The manuscript also addresses practical challenges, including the need for system standardization, overcoming regulatory and privacy concerns, and managing high costs. We propose actionable strategies to overcome these barriers and discuss the role of advanced analytics and future research directions that further enhance VBHC and precision medicine. This work outlines the necessary steps to build a cohesive, data-driven approach that supports the transition to precision medicine, fundamentally improving outcomes for sarcoma patients.

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Data ecosystem using smart home technology to support healthy aging

Abstract Background Smart home technology has been widely used to mitigate the challenges that population aging brings. However, the lack of a centralized data ecosystem has prevented these technologies from realizing their full potential. This project aimed to construct a data ecosystem compatible with smart home sensors to monitor healthcare for seniors, improving their quality of life. Methods The infrastructure was constructed using a user-centered design approach, allowing smart devices such as motion, temperature sensors to operate independently and communicate through a Wi-Fi mesh network. API technology provides a reliable channel for real-time data collection. To ensure data privacy and security, extracted data is hosted in a cloud environment that provides comprehensive data protection, and cloud computing is utilized for high-performance computation tasks. Analytical models such as Machine Learning can be implemented to recognize indoor activities, achieving the goal of remote monitoring. Results This data ecosystem advances in privacy and security, interoperability, accessibility, and data-sharing. Unified authentication mechanism ensures data is transmitted among heterogeneous devices with minimal vulnerability. Additionally, the common communication protocol provides the foundation of interoperability between devices. With unobtrusive devices, inhabitants are not burdened with operation, ensuring their accessibility and improving their user experience. Lastly, the data is exchanged in a standardized JSON data format, enabling efficient data-sharing board data integration. Conclusions This project developed a data ecosystem based on existing smart home environments. The ecosystem is compatible with smart devices that families already have, effectively reducing additional healthcare costs. Implications of this study include helping health policy and decision makers to adopt more smart home ecosystems to keep older people healthy at home. Key messages • Study constructed a data ecosystem compatible with smart home sensors, achieving remote monitoring of indoor activities and improving the quality of life for seniors. • The developed ecosystem offers promotion in privacy and security, interoperability, accessibility, and efficient data-sharing, reducing healthcare costs.

A Data Model for Unrecognised Historical Neighbourhood (Case study: Telukbetung, Indonesia)

Abstract. Unrecognised Historical places, which are locations with history but lacking official recognition, particularly prevalent in Southeast Asia, often face a dearth of accessible historical information. To tackle this challenge, there is an urgent need to commence the collection and documentation of historical data concerning Unrecognised Historical places. This initiative aims to prevent the loss of history, given that such sites are frequently subjected to rapid development and commercialization. To date, City Geography Markup Language (CityGML), an Open Geospatial Consortium (OGC) standard 3D data format, has been developed for presenting city models along with semantic data and is often used in the heritage domain for preservation. It aids in digital infrastructure development and the conservation of urban elements. Furthermore, it provides a mechanism to extend the standard data model to meet the requirements of various purposes. While several CityGML extensions tailored to the documentation of Cultural Heritage already exist, there is currently no extension specifically designed to address the requirements of Unrecognised Historical places. The aim of this paper is to identify a suitable approach for developing a new standardised data model that integrates geometric and semantic information, which is demonstrated for the modelling of Telukbetung, an old city in Bandar Lampung through time.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Twenty years of volcano data at GeoNet—collection, custodianship, and evolution of open data on New Zealand’s volcanoes

The GeoNet programme at GNS Science has monitored and managed data for volcanoes, earthquakes, landslides, and tsunami in Aotearoa New Zealand since 2001. Volcano monitoring data are collected from seismometers, acoustic sensors, GNSS receivers, webcams, remote gas monitoring sensors, and a range of environmental sensors, as well as manually during visits to volcanoes. The primary user of volcano data is the internal cross-specialised Volcano Monitoring Group (VMG), which fulfils the role of the national volcano observatory. GeoNet concentrates on automatic data collection and analysis, while supporting members of the VMG with manual data collection and interpretation. The application of open-data principles to both data and metadata has always been a core aspect of GeoNet; responses have been overwhelmingly positive, despite concerns regarding some high value, manually collected datasets. The website www.geonet.org.nz represents the primary data access portal. Data analysis and delivery applications are organised by data type rather than hazard, with no volcano-specific data applications. Most datasets have web-based and API delivery application options; both provide standard data formats from a cloud-based archive. One of the challenges for volcano data collection and management has been shifting from a reliance on manually collected data to automatic collection. Additionally, awareness of important questions related to Indigenous Māori data governance is increasing, although the associated impact is not yet understood. Overall, the current centralised, cooperative volcano monitoring and data collection and management system, which benefits from improved efficiency, interoperability, and data quality, has proved effective in Aotearoa New Zealand. Ongoing work aims to ensure optimal data collection and management for volcano monitoring and downstream activities.

Data depth functions for non-standard data by use of formal concept analysis

In this article we introduce a notion of depth functions for data types that are not given in standard statistical data formats. We focus on data that cannot be represented by one specific data structure, such as normed vector spaces. This covers a wide range of different data types, which we refer to as non-standard data. Depth functions have been studied intensively for normed vector spaces. However, a discussion of depth functions for non-standard data is lacking. In this article, we address this gap by using formal concept analysis to obtain a unified data representation. Building on this representation, we then define depth functions for non-standard data. Furthermore, we provide a systematic basis by introducing structural properties using the data representation provided by formal concept analysis. Finally, we embed the generalised Tukey depth into our concept of data depth and analyse it using the introduced structural properties. Thus, this article presents the mathematical formalisation of centrality and outlyingness for non-standard data and increases the number of spaces in which centrality can be discussed. In particular, we provide a basis for defining further depth functions and statistical inference methods for non-standard data.

Common core variables for childhood cancer data integration

IntroductionData-driven research has improved paediatric cancer outcomes for children. However, challenges in sharing data between institutions prevent the use of artificial intelligence (AI) to address substantial unmet needs in children diagnosed with cancer. Harmonising collected data can enable the application of AI for a greater understanding of paediatric cancers. The main goal of the paper was to analyse the currently used childhood cancer databases to identify a core of variables able to capture the most relevant data on the diagnosis and treatment of children and adolescents with cancer. MethodsWe arbitrarily identified different types of existing databases dedicated to collecting data of patients with solid tumours, Umbrella, FAR-RMS; PARTNER; ERN PAEDCAN Registry; INSTRUCT and INRG; the common data elements for Rare Disease by Joint Research Centre. The different elements of the CRFs were analysed and ranked “essential” and “good to have”. Domains that included a group of variables structurally connected were identified. Each variable was defined by name, data type, description, and permissible values. ResultsWe identified six structural domains: Patient registration, Personal information, Disease History, Diagnosis, Treatment, and Follow-up and Events. For each of them, “essential” and “good to have” variables were defined. DiscussionData harmonisation is essential for enhancing integration and comparability in research. By standardizing data formats and variables, researchers can facilitate data sharing, collaboration, and analysis across multiple studies and datasets. Embracing data harmonization practices will advance application of AI, scientific knowledge, improve research reproducibility, and contribute to evidence-based decision-making in various fields.

CRYPTOCURRENCY PRICE PREDICTION USING LSTM

Cryptocurrency, a digital form of currency, has emerged as a prominent asset class with unique characteristics such as decentralization, security, and global accessibility. As the adoption of cryptocurrencies continues to grow, the need for accurate price prediction using machine learning (ML) algorithms becomes crucial for various stakeholders in the financial ecosystem. This paper presents a comprehensive approach to cryptocurrency price prediction, focusing on the following key aspects: The data for this study is collected from the Binance platform, a leading cryptocurrency exchange known for its extensive market data and liquidity. The dataset includes historical price data across different time frames, including 1-hour, 4-hour, and daily intervals. Prior to analysis, the collected data undergoes thorough preprocessing steps to ensure data quality and consistency. This process includes handling missing values, removing outliers, and standardizing data formats for further analysis. Long Short-Term machine learning model algorithm is employed for price prediction. This model is chosen for its ability to capture complex patterns and dynamics in cryptocurrency price movements. The prediction phase involves training and testing the ML model using the preprocessed data. Performance evaluation metrics such as R-squared, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean absolute percentage error (MAPE) are utilized to assess the accuracy and robustness of the prediction model across different time frames. Accurate cryptocurrency price prediction is essential for various stakeholders, including investors, traders, businesses, and regulators. It facilitates informed decision- making, risk management, market analysis, trading strategy development, business planning, and regulatory compliance in the dynamic cryptocurrency market. By addressing these key elements, this study aims to contribute to the advancement of cryptocurrency price prediction methodologies using ML techniques, thereby enhancing decision-making processes and fostering a more efficient and transparent digital asset market ecosystem.

Blockchain-Enhanced Traceability Framework for Smart Farming with Integrated Ontology-Based Data Standardization

This paper introduces a new conceptual framework to enhance crop traceability within smart farming environments. The framework merges blockchain technology with ontology-based data standardization to address persistent challenges in traditional traceability systems, such as data fragmentation and inconsistencies. The primary goal is establishing a transparent, reliable, and efficient traceability process from farm to table. The methodology employed in this research follows the Design Science Research Methodology (DSRM), involving iterative cycles of designing, building, and evaluating the proposed framework to ensure its effectiveness in real-world applications. The framework offers a promising solution to improve food safety, quality, regulatory compliance, and data-driven agricultural decision-making by capitalizing on blockchain's immutable and decentralized attributes and leveraging standardized data formats facilitated by ontologies. The proposed framework comprises three key components: ontology development, system integration, and blockchain implementation. Ontology development addresses data fragmentation and inconsistencies by providing a common language and structure for data exchange. Meanwhile, system integration ensures seamless data exchange among stakeholders, and blockchain implementation secures and verifies traceability records to maintain data integrity and trust. This integrated system is expected to foster trust and collaboration among all participants in the agricultural supply chain, thereby advancing the efficiency and sustainability of smart farming practices. Through continuous refinement and validation, this research aims to significantly contribute to the practical implementation of advanced traceability solutions in the agricultural sector, ensuring that these systems are robust, scalable, and adaptable to various farming contexts.

Observational study examining the feasibility of generating real-world evidence (RWE) for new drug applications from the clinical trials database (DB; RELIASE study).

TPS100 Background: The extraction of the data that contributes to regulatory approval from real-world data (RWD) are difficult due to the lack of standardized data format as well as the extraction methodology. In addition, when RWE is used as an external control group, the similarity between internal and external control data has not been evaluated. Methods: In our ‘REALISE’ study, we will investigate the data extraction methodology as external control data of rare molecular subtype. We also elucidate the 'quality' and 'reliability' of RWD/RWE necessary for regulatory submissions. In addition, as most databases are not designed for regulatory use in the creation phase, we will investigate the retrospective methodologies to ensure reliability of RWD/RWE. We compare ‘data quality’ and ‘data reliability’ of ARCAD global DB (N=45,224), SCRUM-Japan registry (N=546), SCRUM-Japan observational study (N=14,325), and the Flatiron health RWD in Japan (N=650) and statistically analyze the differences and similarities among 4 databases. All analyses will be summarized descriptively. Similarity of patient characteristics and efficacy endpoints such as response rate, progression-free survival, overall survival, and all available endpoints will be evaluated by cancer type, treatment line, and standard treatment. For colorectal cancer, data extracted from the SCRUM-Japan registry, SCRUM-Japan observational study, and the Flatiron health RWD will be compared with data extracted from randomized controlled trials in the ARCAD global DB to examine the availability as an external control of RWD. The Kaplan-Meier method will be applied to estimate the survival distribution of time-to-event data. No imputation method for missing data will be used, and the frequency of missing data will also be summarized. We will also examine the methodology for extracting data with sufficient quality from the SCRUM-Japan observational study. In addition, if the reliability of RWD/RWE does not reach the required level, we will also examine the methodologies to retrospectively assure the reliability of the SCRUM-Japan observational study for regulatory submissions. [Table: see text]

A Cloud-Based System for Automated AI Image Analysis and Reporting.

Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to facilitate the deployment and use of AI tools in a large multi-site university healthcare system and used it to conduct opportunistic screening for hepatic steatosis. During the 60-day study period, 991 abdominal CTs were processed at multiple different physical locations with an average turnaround time of 2.8min. Quality control images and AI results were fully integrated into the existing clinical workflow. All input into and output from the server was in standardized data formats. The authors describe the methodology in detail; this framework can be adapted to integrate any clinical AI algorithm.

Integrated usage of artificial intelligence, blockchain and the internet of things in logistics for decarbonization through paradox lens

The objective of this study was to identify, assess, and prioritize resolution strategies to address paradoxes associated with the integrated use of artificial intelligence (AI), blockchain, and the Internet of Things (IoT) in logistics for decarbonization. To achieve these objectives, a four-phase mixed-methods approach was employed, involving the identification, prioritization, and verification of resolution strategies for their significance in managing paradoxes. A panel of 10 experts with experience in adopting AI, blockchain, and IoT in logistics participated in the study. The study identified ten key resolution strategies: education & training, coordination & collaboration, government regulations & support, standardized data format and protocol, modular architecture, interoperability standard, process optimization, security auditing, management participation, and man–machine interactions. Using an integrated multicriteria decision-making approach, the study presented compromised rank of these strategies based on their effectiveness in addressing paradoxes. The most significant resolution strategy identified was modular architecture. This was followed by a second compromise solution set of interoperability standards, process optimization, management participation, coordination & collaboration, and man–machine interactions, and a third compromise solution set including education & training, government regulation & support, standardized data format, and security auditing. The derived solutions were then verified by logistics domain experts. This study provided the first empirical investigation of paradox theory related to the adoption of AI, blockchain, and IoT for decarbonization in logistics. Overall, the study enhanced the understanding of competing demands, tensions, and complexities involved in adopting digital technologies for decarbonization using paradox theory.

Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia.

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

Effect of different treatment protocols on in vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) outcomes in adenomyosis women: a systematic review and meta-analysis

ObjectivesPregnancy outcomes of different ovarian stimulation protocols for in vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) in patients with adenomyosis are not explicit. This meta-analysis aimed to systematically evaluate the effects of...

The Tropical Andes Biodiversity Hotspot: A Comprehensive Dataset for the Mira-Mataje Binational Basins

We present a flora and fauna dataset for the Mira-Mataje binational basins. This is an area shared between southwestern Colombia and northwestern Ecuador, where both the Chocó and Tropical Andes biodiversity hotspots converge. We systematized data from 120 sources in the Darwin Core Archive (DwC-A) standard and geospatial vector data format for geographic information systems (GIS) (shapefiles). Sources included natural history museums, published literature, and citizen science repositories across 13 countries. The resulting database has 33,460 records from 6,821 species, of which 540 have been recorded as endemic, and 612 as threatened. The diversity represented in the dataset is equivalent to 10% of the total plant species and 26% of the total terrestrial vertebrate species in both hotspots. The dataset can be used to estimate and compare biodiversity patterns with environmental parameters and provide value to ecosystems, ecoregions, and protected areas. The dataset is a baseline for future assessments of biodiversity in the face of environmental degradation, climate change, and accelerated extinction processes.

Advanced Acoustic Emission-based SHM for Concrete Structures: Real-Time, High-Precision Imaging of Crack Geometry and Damage Source-type Using Moment Tensor Inversion

Acoustic Emission (AE) monitoring is a powerful technique for continuously inspecting the health of structures, thereby aiding in the prevention of potential failures. AE consists of elastic waves that are produced and emitted during fracture processes and are captured by sensors. Utilizing quantitative geophysics-based methods, these recorded signals can be processed to monitor and delineate the spatiotemporal progression of fractures in opaque materials, such as concrete, in real-time. Moreover, the classification of damage source types, namely shear versus tensile, by processing AE waveform features, is crucial for appropriate structural treatment. Owing to the complex nature of the recorded elastic signals and the heterogeneity of the media, data are typically processed manually, covering both AE localization and source type classification. This, coupled with the high processing costs of extensive datasets, often exceeding terabytes, has limited the method's practical applications. In this paper, we introduce a novel automated and high-precision AE monitoring algorithm and software (Momeni et al., 2021a, b) suitable for SHM. It is designed to work with various standard data formats and is capable of handling both trigger-based and continuous data, and medium heterogeneity. We present results from implementing this software in the AE monitoring of two 4.88-meter-long concrete beams in a laboratory setting, comparing it with manually processed AE data (Mhamdi, 2015). Our approach enabled the identification of over three times more AE sources than manual processing, achieving higher precision. By processing waveform features in both time and frequency domains, we successfully classified the damage sources into three categories: tensile, shear, and mixed-mode, at different stages of the experiment. With adequate processing units, the software can operate in parallel, facilitating real-time SHM with exceptional precision in imaging both crack geometry and source types. This provides invaluable information to decision-makers regarding the nature of the data captured.

Artificial intelligence in cardiology: Present state and prospective directions

Artificial intelligence (AI) is a fast-growing technology that has great potential for advancements in cardiovascular medicine. Cardiovascular diseases (CVDs) are currently diagnosed through a comprehensive patient evaluation that includes a review of the patient's medical history, biomarker analysis, physical examinations, and specialized testing. The clinical expertise and experience of medical practitioners are required for the interpretation of test results, and the findings are used to devise treatment plans for patients. However, the efficacy of this strategy has been brought into question owing to variations in the way healthcare professionals perform the treatment and the likelihood of errors. By integrating AI into the healthcare domain, workflow efficiency, cost-effectiveness, and decision-making across various scientific disciplines, including medicine, can be significantly enhanced. Although the potential advantages of AI in cardiovascular care are substantial, it is essential to recognize and tackle the existing constraints. AI models necessitate access to superior and varied datasets, which might pose difficulties in acquisition due to data accessibility, privacy considerations, and the requirement for standardized data formats. Moreover, the comprehensibility of AI algorithms is crucial in order to establish confidence and promote cooperation between AI systems and healthcare practitioners. This article presents an overview of AI in the context of cardiovascular care, discussing its potential applications, current limitations, and future directions.

A global four‐dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles

AbstractLack of a long‐term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC‐Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC‐Argo has shorter time series coverage and fewer profiles compared to Core‐Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core‐Argo and BGC‐Argo profiles and then developing a spatial, temporal and depth‐specific gridded DOC dataset, named G4D‐DOC. Validation results demonstrate that G4D‐DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D‐DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D‐DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D‐DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D‐DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.