Data Organization Research Articles

Research on optimization of emergency supplies customs clearance strategy from the perspective of public health security based on Chinese trade data

BackgroundThis study is significant for improving the accuracy of Customs’ cross-border supervision of emergency supplies and ensuring the timely clearance of these essential goods.MethodsTo ensure both the convenience and security of Customs oversight regarding emergency supplies, this study first systematically collects and organizes representative data on the import and export trade of these supplies. Proposed an enhanced Recency Frequency Monetary (RFM) cluster analysis model, building on the principles of k-means clustering. Subsequently, the model is employed to cluster the import and export trade data of emergency supplies. Finally, the paper offers optimization suggestions for customs clearance supervision based on the analysis results.ResultsThe study primarily focuses on the collection and organization of import and export trade data across six major categories of representative emergency supplies. By employing K-means clustering techniques, the research develops an improved RFM cluster analysis model, referred to as TR-TF-TV, and subsequently proposes strategies for customs supervision of emergency supplies. By integrating K-means clustering techniques, this study develops an advanced RFM cluster analysis model, referred to as TR-TF-TV. It subsequently proposes customs supervision strategies for emergency supplies. These strategies include a clustering analysis of trade data to ensure safe and efficient customs clearance, the preservation of integrity and stability within the emergency supplies supply chain, the enhancement of the national emergency management system, and the improvement of response capabilities to public health emergencies.ConclusionThis analysis of trade data concerning the import and export of emergency supplies, based on the enhanced RFM clustering model, represents an exploratory initiative in original model design. Recognizing the inherent limitations associated with the model’s index design and data sample selection, we intend to refine these elements in future research, aiming to improve and validate the model to further optimize related countermeasures.

Prospective Harmonization, Common Data Elements, and Sharing Strategies for Multicenter Pre-Clinical Traumatic Brain Injury Research in the Translational Outcomes Project in Neurotrauma Consortium.

Effective team science requires procedural harmonization for rigor and reproducibility. Multicenter studies across experimental modalities (domains) can help accelerate translation. The Translational Outcomes Project in NeuroTrauma (TOP-NT) is a pre-clinical traumatic brain injury (TBI) consortium charged with establishing and validating noninvasive TBI assessment tools through team science. Here, we present practical approaches for harmonization of TBI research across five centers providing needed vocabulary and structure to achieve centralized data organization and use. This includes data sharing as an essential step that enables validating data between domains, evaluating reproducibility between sites, and performing multimodal analyses. As part of this process, TOP-NT (1) produced a library of TBI-relevant standard operating procedures to coordinate workflow, (2) aligned 481 pre-clinical and clinical common data elements (CDEs), and (3) generated 272 new pre-clinical TBI CDEs. This consortium then (4) connected diverse data types to validate assessments across domains and to allow multivariable TBI phenotyping. Lastly, TOP-NT (5) specified technical quality controls for pre-clinical studies. These harmonization tools can facilitate reproducibility in team science, help distinguish a wide injury spectrum from technical variability, apply quality-controls, and ease higher level data analyses. TOP-NT uses three rat TBI models across four sites. Each site collects primary outcome measures, including magnetic resonance imaging (MRI) protocols and blood biomarkers of neuronal and glial injury, validated by histopathology and behavioral outcomes. Collected data are organized using the 481 TOP-NT pre-clinical CDEs, covering surgical, behavioral, biomarker, MRI, and quantitative histopathological methods. We report data curation steps suited for data storage using the Open Data Commons for TBI as a centralized data repository, allowing unbiased cross-site analysis. This approach leads to introducing a higher level, syndromic understanding of TBI signatures. TOP-NT authors outline a semantic and structural framework suggesting strategies for robust pre-clinical research in multicenter trials to improve translatability for TBI assessments. [Figure: see text].

An ecosystem for producing and sharing metadata within the web of FAIR Data.

Descriptive metadata are vital for reporting, discovering, leveraging, and mobilizing research datasets. However, resolving metadata issues as part of a data management plan can be complex for data producers. To organize and document data, various descriptive metadata must be created. Furthermore, when sharing data, it is important to ensure metadata interoperability in line with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Given the practical nature of these challenges, there is a need for management tools that can assist data managers effectively. Additionally, these tools should meet the needs of data producers and be user-friendly, requiring minimal training. We developed Maggot (Metadata Aggregation on Data Storage), a web-based tool to locally manage a data catalog using high-level metadata. The main goal was to facilitate easy data dissemination and deposition in data repositories. With Maggot, users can easily generate and attach high-level metadata to datasets, allowing for seamless sharing in a collaborative environment. This approach aligns with many data management plans as it effectively addresses challenges related to data organization, documentation, storage, and the sharing of metadata based on FAIR principles within and beyond the collaborative group. Furthermore, Maggot enables metadata crosswalks (i.e., generated metadata can be converted to the schema used by a specific data repository or be exported using a format suitable for data collection by third-party applications). The primary purpose of Maggot is to streamline the collection of high-level metadata using carefully chosen schemas and standards. Additionally, it simplifies data accessibility via metadata, typically a requirement for publicly funded projects. As a result, Maggot can be utilized to promote effective local management with the goal of facilitating data sharing while adhering to the FAIR principles. Furthermore, it can contribute to the preparation of the future EOSC FAIR Web of Data within the European Open Science Cloud framework.

Decreased desire to have children: A qualitative study

Objective: To explain barriers to childbearing among working women from the perspective of working women and midwives. Methods: In this study, a qualitative study of content analysis method was employed. The study was conducted from December 2021 to July 2022. The population consisted of working women and midwives. Data collection was conducted using semi-structured interviews and continued until data saturation was achieved. Through purposeful sampling, 10 women and 11 midwives were selected as the sample. The data were analyzed by Graneheim and Lundman content analysis approach. For more rigorous, Nvivo 14 software was also used for data management, analysis, coding and organization of data. Results: This study included 21 women (10 women employees and 11 midwives) aged 25-43 years (for women employees) and 2756 years (for midwives) with 1 to 34 years of job experience. In examination of participant’ views about the barriers to childbearing, 1 124 codes were identified, of which we managed to extract six subthemes and two themes. Two themes emerged: “lack of social support and lack of family support”. Subthemes of “lack of social support” were “inadequate leave, long distance from workplace, and insufficient wages”. Subthemes of “lack of family support” were “lack of spouse support, lack of spouse's family support, and lack of their family support”. Conclusions: Different socioeconomic factors influence childbearing tendency in working women. Social and family supports play an important role in this regard. It is important to take these factors into account at the time of policymaking in the field of midwifery.

Build a Bot to Monitor the Dark Web for Mentions of Your Organization's Data or Employees

Build a Bot to Monitor the Dark Web for Mentions of Your Organization's Data or Employees

Ambulatory care utilization in the first 24 months' postpartum by rurality and pregnancy-related conditions: A prospective cohort study from Maine.

To estimate the rate of ambulatory care use among postpartum persons by rurality of residence and pregnancy-related conditions. We used Maine Health Data Organization's All Payer Claims Data for persons who delivered between 2007 and 2019 (N = 121,905). We estimated rates of ambulatory care (nonemergency department outpatient health care) utilization during the first 24 months' postpartum by level of rurality (urban, large rural, small rural, and isolated rural) and by pregnancy-related conditions (prenatal depression, hypertensive disorders of pregnancy, and gestational diabetes). To estimate rate ratios (RR), we used Poisson regression with an offset for population at risk, adjusting for potential confounders and restricting the analysis to those with continuous insurance (n = 70,431). The mean monthly rate per 100 deliveries of ambulatory care visits was 86.1; the median number of visits was 12 (interquartile range = 6, 25). Persons living in rural areas had lower monthly rates of visits than persons living in urban areas (adjusted RR ranged from 0.87 [95% CI: 0.85, 0.89] in isolated rural areas to 0.91 [95% CI: 0.90, 0.93] in large rural areas). Persons with prenatal depression (aRR = 2.07; 95% CI: 2.04, 2.11), hypertensive disorders of pregnancy (aRR = 1.07; 95% CI: 1.05, 1.10), and gestational diabetes (aRR = 1.11; 95% CI: 1.08, 1.14) had higher rates of visits than those without these conditions. New practices and policies may be needed to improve postpartum ambulatory care access and utilization in rural areas. Postpartum persons with pregnancy-related conditions are accessing ambulatory care at higher rates after delivery, which may reduce their need for acute health care use.

Precise hexagonal pixel modeling and an easy-sharing storage scheme for remote sensing images based on discrete global grid system

ABSTRACT Discrete Global Grid Systems (DGGSs) are an emerging Earth reference model that support the integration and analysis of remote sensing (RS) data. Grid modeling, sampling, quantization, and storage are the key points and difficulties of DGGS. The Icosahedral Snyder Equal-area Aperture-4 Hexagonal DGGS was introduced as the basic framework to improve the geospace sampling efficiency. We presented an approach for precise hexagonal pixel modeling and an easy-sharing storage scheme compatible with open-standard formats for RS images based on the DGGS. Firstly, the proposed interpolation is computed by overlaps between quadrilateral and hexagonal pixels. The hexagonal grids were then mapped onto the icosahedral surface, and a strict correspondence between hexagonal and rectangular pixels is established. The open-standard formats were used to accurately store the hexagonal attribute values and metadata. Finally, a multiscale hexagonal aggregation algorithm based on the dataset was designed. Experiments showed that the proposed modeling methodology had a higher accuracy and smaller errors. The hexagonal data can be stored as regular rectangles with a fixed pattern in any standard format. This storage scheme was more conducive to data processing and sharing compared to SMOS, which was expected to promote hexagonal DGGS in RS data organization, processing and sharing.

STRENGTHS, WEAKNESSES, OPPORTUNITIES AND THREATS (SWOT MATRIX) OF TRADITIONAL COMMUNITIES UNDER THE PERSPECTIVE OF ECOLOGICAL AND SOCIOCULTURAL RESILIENCE IN THE PANTANAL BIOSPHERE RESERVE, MATO GROSSO, BRAZIL

This study analyzed how the traditional communities of Sesmaria Boa Ventura and Água Branca see and respond to socio-environmental changes and public policies. These communities are located in the municipality of Santo Antônio de Leverger, with a population of 15 and 12 families, respectively, between the rivers Cupim and Água Branca in the inland region of Morrarias São Vicente, with the mouth in the Chacororé–Sinhá Mariana lake system in the Pantanal of the state of Mato Grosso, Brazil. The SWOT method (S - strengths, W - weaknesses, O - opportunities, T - threats) served as a guide for the semistructured interviews and data organization, and the Snowball method was used to indicate participants. The participants were born and always lived in the region, had a mean age of 71 years and 73% were male. The communities practice subsistence family farming, their residents have a low education level and social habits. The 15 participants mentioned 19 strengths, 16 weaknesses, 10 opportunities, and 16 threats. The most mentioned strengths were public security (15.48%), and peace and nature (11.90%), The weaknesses were water (18.87%) and food insecurity (15.09%) and deficient health (11.32%). The opportunities were electric power for everyone and the construction of roads (25.71%), construction of schools (17.14%), and health centers (11.43%). The threats were environmental (27.59%) and economic insecurity (13.79%), use of pesticides, and lack of rain (10.34%). The communities responded to weaknesses and threats with adaptations such as building and expanding wells (18.42%), changing the agricultural calendar (15.79%), and using agricultural input (10.53%). Unused traditional ecological knowledge was reincorporated as cultural adaptations in situations of high vulnerability, highlighting oral transmission as a resilience mechanism.

Driving National R&D: Methodological Insights into Developing a Classifier for the Ukrainian National CRIS System by the State Scientific and Technical Library of Ukraine

Objective. The objective of this study is to develop and characterize a comprehensive classifier for research and development (R&D), which plays a crucial role in the effective implementation of a National Current Research Information System (CRIS). The study aims to address the challenges and methodologies involved in creating a system that categorizes diverse R&D initiatives while ensuring interoperability with existing systems and adaptability to evolving scientific fields. Methods. The development of the classifier involved a multi-step process, including consultation with domain experts and reviewing existing classification systems. The study focused on identifying key research areas, ensuring compatibility with international standards, and developing a flexible taxonomy to cover both established and emerging fields. A diagnostic study on CRIS systems in Latin America and insights from similar systems, such as in Croatia and Portugal, were examined to refine the classifier's design for Ukraine. Results. The study successfully developed a classifier that addresses the specific needs of the Ukrainian research landscape, particularly within the Ukrainian Information System for Current Research (URIS). The classifier's structure aligns with international standards and supports interoperability with global databases. Furthermore, the dynamic nature of the classifier allows for continuous updates, making it adaptable to new research fields. The classification system was also tailored to accommodate Ukraine’s unique research ecosystem and infrastructure. Conclusions. The development of this R&D classifier represents a strategic advancement for Ukraine’s research infrastructure, enhancing data organization, accessibility, and collaboration. By addressing both technical and contextual challenges, the classifier provides a flexible, scalable solution that supports long-term scientific innovation. This study highlights the importance of context-driven approaches in creating effective research management tools, positioning the classifier as a robust framework for future developments in CRIS systems. The State Scientific Technical Library of Ukraine has played a pivotal role in developing this classifier, ensuring it meets the specific needs of the Ukrainian research community.

STRENGTHS, WEAKNESSES, OPPORTUNITIES AND THREATS (SWOT MATRIX) OF TRADITIONAL COMMUNITIES UNDER THE PERSPECTIVE OF ECOLOGICAL AND SOCIOCULTURAL RESILIENCE IN THE PANTANAL BIOSPHERE RESERVE, MATO GROSSO, BRAZIL

This study analyzed how the traditional communities of Sesmaria Boa Ventura and Água Branca see and respond to socio-environmental changes and public policies. These communities are located in the municipality of Santo Antônio de Leverger, with a population of 15 and 12 families, respectively, between the rivers Cupim and Água Branca in the inland region of Morrarias São Vicente, with the mouth in the Chacororé–Sinhá Mariana lake system in the Pantanal of the state of Mato Grosso, Brazil. The SWOT method (S - strengths, W - weaknesses, O - opportunities, T - threats) served as a guide for the semistructured interviews and data organization, and the Snowball method was used to indicate participants. The participants were born and always lived in the region, had a mean age of 71 years and 73% were male. The communities practice subsistence family farming, their residents have a low education level and social habits. The 15 participants mentioned 19 strengths, 16 weaknesses, 10 opportunities, and 16 threats. The most mentioned strengths were public security (15.48%), and peace and nature (11.90%), The weaknesses were water (18.87%) and food insecurity (15.09%) and deficient health (11.32%). The opportunities were electric power for everyone and the construction of roads (25.71%), construction of schools (17.14%), and health centers (11.43%). The threats were environmental (27.59%) and economic insecurity (13.79%), use of pesticides, and lack of rain (10.34%). The communities responded to weaknesses and threats with adaptations such as building and expanding wells (18.42%), changing the agricultural calendar (15.79%), and using agricultural input (10.53%). Unused traditional ecological knowledge was reincorporated as cultural adaptations in situations of high vulnerability, highlighting oral transmission as a resilience mechanism.

Accuracy Evaluation Method for Vector Data Based on Hexagonal Discrete Global Grid

With the continuous advancement of technology for obtaining geographic spatial data, the accumulated volume of such data has been increasing, thus imposing higher demands on the storage, organization, and management of such data. As a new form of data management, the Discrete Global Grid System (DGGS) provides standardized descriptions and the exchange of geographic information on a global scale, enabling the efficient storage and application of large-scale global spatial data. Constituting a traditional type of GIS spatial data, vector data have advantages such as clear positions, implicit attributes, and suitability for map output. The representation of vector data in the global discrete grid network based on an equal-area projection, such as the hexagonal grid, fundamentally solves problems such as data redundancy, geometric deformation, and data discontinuity that arise when representing multiple vector data in a gridded format. This paper proposes different gridding methods for various types of vector data, and a quantifiable accuracy evaluation system is established from the perspectives of geographical deviation, geometric features, and topological relationships, to evaluate the accuracy of the gridded vector data, covering all types of gridded vector data based on the hexagonal grid. The evaluation method is generally applicable to all hexagonal-grid-based gridded vector data, and can be generalized based on application scenarios, for evaluating the usability of hexagonal grid vector data.

An online approach to teaching graduate nursing students to write integrative literature reviews: Examples from two published cases.

To empower nursing graduate students, in master's or doctoral programs, through distance-accessible methods for conducting integrative reviews, enhancing their ability to transition from clinical to publication-oriented writing. Mastering literature review methods is vital for advancing evidence-based practice. Integrative reviews, inclusive of multiple research methodologies, offer a comprehensive approach suited for nursing students. However, transitioning from clinical to publication-oriented writing poses challenges, necessitating innovative solutions. This discussion presents strategic writing methods, including technologies for distance-accessible collaboration and data organization, framework use and critical analysis skills to support nursing graduate students in conducting integrative reviews. Approaches for preparing and developing an integrative review are outlined, including integrating online tools and collaborative platforms and structured frameworks to guide data organization and critical analysis. Two published integrative reviews exemplifying these approaches are presented. Distance-accessible and strategic approaches have significantly improved the quality of integrative literature reviews conducted by nursing graduate students. These innovations have equipped students with essential skills for navigating the contemporary academic landscape. Embracing innovative approaches and staying informed about technological advancements empowers nursing graduate students to excel in their research pursuits. This contributes to evidence-based practice and nursing scholarship in the evolving healthcare landscape.

Pattern Recognition in Older Adults’ Activities of Daily Living

Monitoring daily activities and behaviors is essential for improving quality of life in elderly care, where early detection of behavioral anomalies can lead to timely interventions and enhanced well-being. However, monitoring systems often struggle with scalability, high rates of false positives and negatives, and lack of interpretability in understanding anomalies within collected data. Addressing these limitations requires an adaptable, accurate solution to detect patterns and reliably identify outliers in elderly behavior data. This work aims to design a scalable monitoring system that identifies patterns and anomalies in elderly activity data while prioritizing interpretability to make well-informed decisions. The proposed system employs pattern recognition to detect and analyze outliers in behavior analysis, incorporating a service worker generated with Crontab Guru for automated data gathering and organization. Validation is conducted through statistical measures such as accumulated values, percentiles, and probability analyses to minimize false detections and ensure reliable performance. Experimental results indicate the system achieves high accuracy, with an occupancy probability across compartments and fewer outliers detected. The system demonstrates effective scalability and robust anomaly detection. By combining pattern recognition with a focus on interpretability, the proposed system provides actionable insights into elderly activity patterns and behaviors. This approach enhances the well-being of older adults, offering caregivers reliable information to support timely interventions and improve overall quality of life.

The MetBase database has been merged into Astromat

AbstractMetBase has been the world's largest database for meteorite compositions, but has now passed this torch on to the Astromaterials Data System (Astromat), into which MetBase has recently been merged. This merger had been planned for some time and took almost 1 year to complete. Not only differences in the structure of the databases, in the content and organization of data and metadata, and in the terminology used but also incorporation of new data needed to be resolved to combine the data holdings of MetBase with the Astromat synthesis database. Astromat is NASA's primary archive for laboratory analyses of astromaterial samples and funded by NASA to provide services for the preservation and open access of data from astromaterials, including meteorites, in alignment with the FAIR principles. After merging MetBase into Astromat's synthesis database, this now provides the cosmochemical community the largest compilation of cosmochemical analytical data by far: over 2 million analytical data points. Astromat is also part of a bigger ecosystem of geo‐ and cosmochemcial databases, as its foundation is aligned with other large geochemical databases such as EarthChem and GEOROC. The visualization tools and the teaching tool from MetBase will be further developed and now exist as independent tools. We provide a brief history of the two databases and their journeys, an outlook toward the future, as well as lessons learned from this merger. We recommend that other cosmochemical databases try whenever possible to adopt the Astromat database schema as early as possible, or get in contact for alternative options. We believe MetBase now being a part of Astromat is a match made in heaven and hope Astromat will become a reliable and trusted service within the community.

The Skills of Information Professionals for the Analysis and Organization of Data. Conceptual Proposal and Application of OpenAI in Job Advertisements on the Web

This study explores the evolving roles of library and information professionals (IPs) in data analysis and organization, particularly within interdisciplinary teams. It builds on the premise that data is pivotal for strategic business and institutional management, with IPs contributing through their expertise in information and communication technologies (ICTs), mediation with users, and mastery of taxonomies and semantic organizational structures (SOCs). The study examines job advertisements to determine the skills, experience, and training employers seek in IPs for data-centric roles. A total of 84 international job postings from platforms such as ALAJobLIST, LinkedIn, and Glassdoor were analyzed. The postings were XML-tagged to identify descriptions, competencies, and requirements. Using OpenAI tools, keywords were extracted, standardized, and grouped into thematic clusters and macro-clusters for analysis. The findings reveal that required skills span eight macro-clusters: business management; data management; information organization; data analysis and statistics; programming; web technologies; analytical creativity; and domain-specific expertise. IPs are valued for their technical and technological skills, particularly in navigating and applying standards for data description, visualization, and communication. Conclusions highlight that while IPs possess strong foundational skills, additional training in statistics and analysis platforms is critical to fully meet employer demands. Employers trust IPs for their ability to handle data-centric roles in competitive environments, emphasizing the importance of communication, leadership, and customer-oriented skills. The study underscores the need to enhance LIS curricula to better integrate advanced statistical training and entrepreneurial competencies. This research emphasizes the growing recognition of IPs’ contributions to data analysis and organization, underscoring their essential role in addressing the complexities of interdisciplinary data-driven projects

Statistical Measures of Center: A Focus on Mean, Median, and Mode

In statistics, systematic data collection, organization, analysis, interpretation, and presentation are the main topics of study. Understanding variability, seeing trends, and coming to wise conclusions in a variety of fields all depend on it. In general, statistics can be separated into: Descriptive statistics: These statistics include the description and summarisation of datasets. Inferential statistics: Inferential statistics is the capacity to infer and forecast information about a population from a sample. Through methods like regression analysis, probability, hypothesis testing, and data visualisation, statistics— which are extensively employed in fields including business, healthcare, social sciences, and science—assist in decision-making. It transforms unstructured data into meaningful knowledge that supports problem-solving and strategic planning. Key Words: Mean, Median, Mode, Statistics, discrete maths, maths, Descriptive statistics

Is Football Unpredictable? Predicting Matches Using Neural Networks

The growing sports betting market works on the premise that sports are unpredictable, making it more likely to be wrong than right, as the user has to choose between win, draw, or lose. So could football, the world’s most popular sport, be predictable? This article studies this question using deep neural networks to predict the outcome of football matches using publicly available data. Data from 24,760 matches from 13 leagues over 2 to 10 years were used as input for the neural network and to generate a state-of-the-art validated feature, the pi-rating, and the parameters proposed in this work, such as relative attack, defence, and mid power. The data were pre-processed to improve the network’s interpretation and deal with missing or inconsistent data. With the validated pi-rating, data organisation methods were evaluated to find the most fitting option for this prediction system. The final network has four layers with 100, 80, 5, and 3 neurons, respectively, applying the dropout technique to reduce overfitting errors. The results showed that the most influential features are the proposed relative defending, playmaking, and midfield power, and the home team goal expectancy features, surpassing the pi-rating. Finally, the proposed model obtained an accuracy of 52.8% in 2589 matches, reaching 80.3% in specific situations. These results prove that football can be predictable and that some leagues are more predictable than others.

BDLT-IoMT—a novel architecture: SVM machine learning for robust and secure data processing in Internet of Medical Things with blockchain cybersecurity

The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.

Managing Data Sovereignty: An Organizational Competence for Successful Open Value Creation

ABSTRACTManaging big data benefits organizations via efficient and effective data‐driven decision making, productivity gains, customization potential, or innovation opportunities. Data‐driven innovation opportunities are particularly valuable in open value creation (OVC), that is, the collaboration of independent stakeholders who open their boundaries to share data and knowledge to jointly create value they could not have achieved in isolation. At the same time, however, incumbents often struggle with the implementation of data sharing in the broader stakeholder ecosystem. In this exploratory study, based on an abductive qualitative study with 27 expert interviews and three industry roundtables with 19 experts, we explore how the concept of organizational data sovereignty could serve as an organizational competency to address the data challenges for OVC. Using the strategy‐as‐practice framework, we define organizational data sovereignty as the self‐determined and intentional exercise of control over an organization's data assets and show which data sovereignty practices, in line with practitioner and praxis conditions, support inter‐organizational sharing and management of big data in OVC. Specifically, we identify key practices for implementing data sovereignty, such as constituting the special value of data and establishing a consistent value assessment for data. When designed and applied in the right organizational framework that allows for openness, agility, and interdisciplinarity, they enable industrial enterprises to realize the potential of OVC.

Data Modeling Essentials: Techniques, Best Practices, and Future Trends

Data modeling, in essence, is based on the process of data structuring and organization to effectively handle, analyze, and make informed decisions across organizations. This paper reviews the major concepts and methods significant for modern data architecture in "Data Modeling Essentials: Techniques, Best Practices, and Future Trends.". We start by considering the base techniques, namely Entity-Relationship and Dimensional modeling, and look into their applications in operational and analytical data systems. The paper also covers more advanced topics that include Data Vault modeling for agile data warehousing and NoSQL modeling to accommodate unstructured and semi-structured data, increasingly important in today's big data era. The paper also gives an overview of best practices in developing scalable, flexible data models: stakeholder communication, documentation, and the use of automation tools in accelerating model development and consistency. Data quality, adherence to regulations, and alignment of models with business objectives are among the practices identified as crucial in today's fast-moving data environments. New trends are taking precedence, and data modeling is continuously evolving. We will review the rise of AI- assisted data modeling, graph database structures for highly interconnected data, and model-driven development to accelerate system deployments. This paper examines actionable insights for data professionals, IT teams, and business leaders on how to design robust, agile models that improve the quality of data, enhance decision-making, and foster innovation in an ever-changing data ecosystem. Keywords Data Modeling, Entity-Relationship (ER) Modeling, Dimensional Modeling, Data Quality, Scalable Data Models, Model Driven Development (MDD), Data Management, Structured & Unstructured Data, Future Trends in Data Modeling.