Data Management Research Articles

Role of macroscopic on-site evaluation of endoscopic ultrasound-guided fine-needle aspiration/biopsy: Results of a multicentric prospective study

BACKGROUND The concept of macroscopic on-site evaluation (MOSE) was introduced in 2015 when the endoscopist observed better diagnostic yield when the macroscopically visible core on MOSE was superior to 4 mm. Recent studies suggest that MOSE by the endoscopist may be an excellent alternative to rapid on-site evaluation, and some classifications have been published. Few studies have assessed the adequacy of histologic cores in MOSE during endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/FNB). AIM To evaluate the performance of MOSE during EUS-FNA/FNB. METHODS This multicentric prospective study was conducted in 16 centers in 3 countries (Egypt, Iraq, and Morocco) and included 1108 patients with pancreatic, biliary, or gastrointestinal pathology who were referred for EUS examination. We prospectively analyzed the MOSE in 1008 patients with available histopathological reports according to 2 classifications to determine the adequacy of the histological core samples. Data management and analysis were performed using a Statistical Package for Social Sciences (SPSS) version 27. RESULTS A total of 1074 solid lesions were biopsied in 1008 patients with available cytopathological reports. Mean age was 59 years, and 509 patients (50.5%) were male. The mean lesion size was 38 mm. The most frequently utilized needles were FNB-Franseen (74.5%) and 22 G (93.4%), with a median of 2 passes. According to 2 classifications, 618 non-bloody cores (61.3%) and 964 good samples (95.6%) were adequate for histological evaluation. The overall diagnostic yield of cytopathology was 95.5%. The cytological examination confirmed the diagnosis of malignancy in 861 patients (85.4%), while 45 samples (4.5%) were inconclusive. Post-procedural adverse events occurred in 33 patients (3.3%). Statistical analysis showed a difference between needle types (P = 0.035) with a high sensitivity of FNB (97%). The analysis of the relationship between the MOSE-score and the final diagnosis showed a significant difference between the different scores of the MOSE (P < 0.001). CONCLUSION MOSE is a simple method that allows endoscopists to increase needle passes to improve sample quality. There is significantly higher FNB sensitivity and cytopathology diagnostic yield with good MOSE cores.

The “Key” to Bringing DNA Collections to the Next Level: A DiSSCo Flanders Working Group Product

The DiSSCo (Distributed System of Scientific Collections) Flanders consortium, with one of the set goals being "maturing" (i.e., optimizing the management of) and unlocking (i.e., publishing) their DNA collections, identified 1) the need for actively sharing best practices on the management of DNA collections; and 2) a need for guidance on how to bring theory into practice. During the DiSSCo Flanders project, a DNA collection working group was created. The working group is open to all biodiversity-related DNA collections associates in Belgium, including those in diverse roles such as researchers, lab technicians, collection managers and data managers. Around 50 people from 13 organizations are currently participating. Members can be passively (reading only) or actively (joining events) engaged. The strength, as well as one of the challenges, of the DiSSCo Flanders community is that the natural science collections are created and managed in different organizational contexts: universities, museum institutes and both governmental and non-governmental research organizations. This translates to a variety of collection management decisions and structures such as: decentralized or centralized; cold or room temperature storage; managed by an appointed curator or by a lab technician. The working group organizes meetings and workshops, tours of each other's collections, and shares a mailing list and an online document space. As its principal output, the group has co-created: “The key to bringing DNA collections to the next level” (Veltjen et al. 2024) with two main results: the "Challenges" and the “Key”. The "Challenges" is a list of 23 challenges applicable to DNA collection management. For example, challenge 8: "Select or customise collection management systems to meet the needs of DNA collections". They are intended to spark debate and give focus to the second output: the "Key." The "Key" lists seven yes/no questions: Do you have an up-to-date overview of all direct, internal stakeholders of the institute’s DNA collection and are you involving them in the (current) intent to “bring the DNA collection to the next level”? Is preserving a DNA collection within the scope of the institute? And is the DNA collection officially recognized within the institute? Do you have, on paper, a clear description of the scope of the DNA collection? Have you outlined the current overarching workflow of the DNA collection? Have you been able to establish your starting level on the "DNA collection maturation chart" and is the assessment properly logged? Level up, one level at a time, and log the process. Have you reached all of the goals in level 3 on the "DNA collection maturation chart"? Do you have a re-evaluation strategy for your DNA collection? Do you have an up-to-date overview of all direct, internal stakeholders of the institute’s DNA collection and are you involving them in the (current) intent to “bring the DNA collection to the next level”? Is preserving a DNA collection within the scope of the institute? And is the DNA collection officially recognized within the institute? Do you have, on paper, a clear description of the scope of the DNA collection? Have you outlined the current overarching workflow of the DNA collection? Have you been able to establish your starting level on the "DNA collection maturation chart" and is the assessment properly logged? Level up, one level at a time, and log the process. Have you reached all of the goals in level 3 on the "DNA collection maturation chart"? Do you have a re-evaluation strategy for your DNA collection? The "DNA collection maturation chart" has 11 categories (rows), three levels (columns) and 33 goals (see Table 1 in Veltjen et al. 2024). The Key provides 18 guidance chapters, which give in depth information, literature and user experiences (Suppl. material 2 in Veltjen et al. 2024). The Key is a specialized tool for DNA collections. It facilitates a standardized and holistic approach, allowing both a helicopter view of the maturation process and close-up view of specific goals. The working group aims to test the Key, whereby the process of "leveling up" is embedded in a community setting: sharing ambitions, setbacks, changes of plans and success stories. The output is ready in its first version. It is published as a reviewable publication, allowing post-publication peer review (Veltjen et al. 2024). The works are expected to evolve through time, depending on user feedback and user experiences. The working group and co-created output are positive examples of how a local community—sometimes managing smaller, or less conspicuous types of natural science collections—can work together and use their unique perspectives, experiences and needs to contribute to the international natural science collection and biobanking communities.

Blockchain in Health Information Systems: A Systematic Review

(1) Background: With the increasing digitalization of healthcare systems, data security and privacy have become crucial issues. In parallel, blockchain technology has gradually proven to be an innovative solution to address this challenge, as its ability to provide an immutable and secure record of transactions offers significant promise for healthcare information management. This systematic review aims to explore the applications of blockchain in health information systems, highlighting its advantages and challenges. (2) Methods: The publications chosen to compose this review were collected from six databases, resulting in the initial identification of 4864 studies. Of these, 73 were selected for in-depth analysis. (3) Results: The main results show that blockchain has been used mainly in electronic health records (63%). Furthermore, it was used in the Internet of Medical Things (8.2%) and for data sharing during the COVID-19 pandemic (6.8%). As advantages, greater security, privacy, and data integrity were identified, while the challenges point to the need for standardization and regulatory issues. (4) Conclusions: Despite the difficulties encountered, blockchain has significant potential to improve healthcare data management. However, more research and continued collaboration between those involved are needed to maximize its benefits.

Data managers and clinical research coordinators: a tool to collect the hourly activity

Background and Aims: the main purpose of this paper is analyzing the hourly activities performed by Data Managers (DMs) and Clinical Research Coordinators (CRCs) in a Clinical Trial Center (CTC) of an Italian Public Hospital in order to define and monitor the relevant job descriptions of these professionals Materials and Methods: the CTC of the Research and Innovation Departments (DAIRI) has created a tool to collect the hourly activity of DMs and CRCs. In the period between January 1 and December 31, 2022; the activities were collected and grouped in 24 macro areas. Results: the main activities conducted by staff are found in the macro areas "Study Management" (15.78%); "Briefing/Planning" (12.86%) and “Data Management/Data Entry” (12.42%). A difference was found between the activities carried out by CRCs and those conducted by DMs. Conclusions: the results of this monitoring, compared with data obtained at the national level, confirm the heterogeneity of the tasks performed by these professional figures. These differences increasingly underscore the need to obtain recognition of the figure by the National Health System (NHS) with the definition of a precise “job description”.

Understanding Multi-Cloud and Hybrid Cloud Architectures in Data Management

This comprehensive article explores the growing adoption of multi-cloud and hybrid cloud architectures in modern data management practices. It delves into the benefits, challenges, and best practices associated with these complex cloud strategies. The article presents key statistics and projections from industry leaders, highlighting the rapid growth and increasing importance of multi-cloud and hybrid cloud solutions. It discusses how these architectures enhance data redundancy, vendor flexibility, cost optimization, and performance while addressing critical issues such as data sovereignty, scalability, and legacy system integration. The piece also examines the challenges organizations face in implementing these strategies, including data integration, security, skill gaps, compliance, and cost management. Finally, it outlines best practices for effective implementation, emphasizing the need for comprehensive data governance, robust integration tools, cloud-agnostic technologies, and continuous optimization.

Review of the Seasonal Wastewater Challenges in Baltic Coastal Tourist Areas: Insights from the NURSECOAST-II Project

The NURSECOAST-II project addresses the challenge of managing wastewater in near-coastal tourist destinations around the Baltic Sea, particularly from small treatment plants (<2000 PE) that experience fluctuating flows due to seasonal tourism. These fluctuations make it difficult to meet environmental standards, potentially harming both the environment and tourism. The project has created a GIS-based inventory of small wastewater treatment plants within 100 km of the coast. This inventory includes crucial operational data like flow rates, pollutant levels, and treatment technologies. Initial findings reveal significant discrepancies in data management, regulations, and treatment standards across the Baltic Sea region countries, as EU legislation does not uniformly cover plants under 2000 PE. Key findings highlight that small treatment plants are often undocumented, their environmental impact underestimated, and regulations vary widely. Small plants can significantly contribute to nutrient pollution, affecting the Baltic Sea, particularly in local areas. The data gathered will support local authorities in identifying gaps and improving management strategies. This study stresses the need for harmonized data collection and reporting methods across countries and suggests establishing a unified database accessible to both specialists and the public. The status of the collected data depending on the type of data and country was as follows: 38.11% from Denmark, 46.14% from Estonia, 26.36% from Finland, 15.56% from Germany, 23.47% from Latvia, 34.77% from Lithuania, 14.51% from Poland, and 45.40% from Sweden. Ultimately, this project aims to enhance wastewater management, protect the environment, and improve tourist satisfaction in coastal regions.

The EURISCO-EVA Information System, an innovative approach to the data management of multi-site crop evaluation data

This paper introduces EURISCO-EVA, an extension of the European Search Catalogue for Plant Genetic Resources (EURISCO), facilitating standardized data collection, sharing, and analysis for plant genetic resources for food and agriculture (PGRFA) characterization and evaluation. In the framework of the European Evaluation Network (EVA), a private-public partnership aiming at the evaluation of crop accessions conserved in European genebanks, this information system addresses the need for a standardized data repository for multi-site evaluations of different crops. Through centralizing metadata maintenance, EURISCO-EVA ensures uniformity in trait definitions, experimental designs, and passport data, promoting efficient exchange of observed phenotypic data. EURISCO-EVA currently stores more than half a million phenotypic data points for 4,845 PGRFA accessions from six genera and 17 species, collected through 382 phenotypic experiments conducted at 115 experimental locations across 33 countries, involving 89 project partners. This platform offers a user-friendly web interface, empowering its users with features such as map-based filtering of trial locations, statistical overviews, and customizable reports. EURISCO-EVA’s robust administrative functionalities, coupled with standardization efforts, enhance data quality and harmonization, providing a robust tool for storage of and access to crop evaluation data that could be further enhanced by adding analysis modules. EURISCO-EVA also formed the basis for the data management of two research projects (AGENT and INCREASE) under the Horizon 2020 funding program, providing the background organization of complex datasets used to address future challenges in European agriculture.

Natural Language Processing for Electronic Health Record Optimization in Android Applications

This paper aims to demonstrate the development of an Android application designed to improve healthcare data management using Natural Language Processing (NLP) technologies. It develops an Android application that enables healthcare professionals to access patient records anytime, enhancing decision-making. The system uses Optical Character Recognition (OCR) to extract patient data from documents, simplifying data entry for diagnoses and treatments. It also incorporates Quick Response (QR) code generation for accurate data retrieval. By leveraging NLP, the mobile application will enhance efficiency, accessibility, and accuracy in healthcare, particularly benefiting developing countries where digital health records are uncommon..

DiabeticChain: a novel blockchain approach for patient-centric diabetic data management

DiabeticChain: a novel blockchain approach for patient-centric diabetic data management

SLR ABOUT THE COSTS OF PRIVATE BLOCKCHAIN TECHNOLOGY FOR BUSINESSES

Since the appearance of Bitcoin (2009), blockchain technology has been used in many fields. Companies use it for financial services, goods tracking, data authentication and secure storage, among other things, in order to make their operations more transparent and efficient. This technology has revolutionized the data management and transaction processing, but the associated costs are also significant and relevant to understand. The costs of blockchains vary and depend on the type of blockchain and how it is used. In this article, we examine the costs of using blockchain technology based on international literature, studies and reports with systematic literature review. In the results the range of possibilities offered by technology is presented, as well as the technology related cost types, and expenditures what companies have to deal with.

PCDP-CRLPPM: a classified regional location privacy-protection model based on personalized clustering with differential privacy in data management

Abstract Location data management plays a crucial role in facilitating data collection and supporting location-based services. However, the escalating volume of transportation big data has given rise to increased concerns regarding privacy and security issues in data management, potentially posing threats to the lives and property of users. At present, there are two possible attacks in data management, namely Reverse-clustering Inference Attack and Mobile-spatiotemporal Feature Inference Attack. Additionally, the dynamic allocation of privacy budgets emerges as an NP-hard problem. To protect data privacy and maintain utility in data management, a novel protection model for location privacy information in data management, Classified Regional Location Privacy-Protection Model based on Personalized Clustering with Differential Privacy (PCDP-CRLPPM), is proposed. Firstly, a twice-clustering algorithm combined with gridding is proposed, which divides continuous locations into different clusters based on the different privacy protection needs of different users. Subsequently, these clusters are categorized into different spatiotemporal feature regions. Then, a Sensitive-priority algorithm is proposed to allocate privacy budgets adaptively for each region. Finally, a Regional-fuzzy algorithm is presented to introduce Laplacian noise into the centroids of the regions, thereby safeguarding users’ location privacy. The experimental results demonstrate that, compared to other models, PCDP-CRLPPM exhibits superior resistance against two specific attack models and achieves high levels of data utility while preserving privacy effectively.

Big Data Analytics And Predictive Analysis In Enhancing Customer Relationship Management (CRM): A Systematic Review Of Techniques And Tools

This systematic review examines the transformative role of big data analytics and predictive techniques in Customer Relationship Management (CRM), focusing on how these advancements enhance customer engagement, satisfaction, and retention strategies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzed a final selection of 100 articles, exploring CRM applications such as predictive modeling, real-time data processing, data integration, and Natural Language Processing (NLP). Findings indicate that predictive analytics enables CRM systems to anticipate customer behaviors and personalize interactions, while real-time data processing supports immediate responses to customer needs, essential in high-demand sectors. Data integration helps overcome silos, creating a cohesive customer view that enhances personalized engagement across all touchpoints. NLP applications, including sentiment analysis and chatbots, further improve CRM by fostering empathy and responsiveness in customer interactions. Despite these advancements, challenges related to data privacy and regulatory compliance remain central, necessitating strict data protection and ethical handling practices. This review underscores that while CRM technology is evolving to meet modern demands, effective implementation depends on a balance between technical innovation and adherence to ethical standards in data management, ultimately fostering meaningful and sustainable customer relationships.

Enhancing data management and real‐time decision making with IoT, cloud, and fog computing

AbstractThe convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real‐time decision‐making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real‐time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud‐only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real‐time decision‐making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real‐world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large‐scale IoT deployments. Future research will focus on real‐world validation and the development of enhanced dynamic resource management techniques.

Linked Data in Library Workflows

As the library community continues to explore linked data for cataloging and data management, there are a variety of new opportunities as well as challenges to ensure that data is interoperable and manageable across bibliographic datasets. Data model interoperability is critical to ensure we can continue to manage bibliographic materials at scale across the wide range of material types and metadata providers and consumers. Additionally, the interoperability of the linked data that connects our bibliographic materials together—traditionally seen as authorities—must be evaluated and managed to improve the way users find and interact with library materials. To meet the challenges, OCLC, in partnership with others, is building a robust and interoperable set of linked data that can be used in future-facing data management workflows and discovery interfaces.

Is This the End of Anthropology as We Know It? Some Implication of FAIR Principles on Tales in Ethnological and Anthropological Qualitative Research

Open science, accessibility and knowledge sharing, especially of articles and monographs stemming from publicly funded research, seem to be moving in quite a positive direction toward scientific development and have received almost unanimous approval from the scientific community. However, when it comes to data sharing, the existing practice reveals a different picture, and not exclusively a discipline-dependent one. FAIR principles are developed and promoted as guiding tools for creating contextualized standards. The fact that data obtained by a qualitative methodology deserve special attention and treatment regarding the accessibility principle is recognized. Although FAIR principles provide ways to anonymize the data and interlocutors, individuals coming from smaller communities or even communities of practice can sometimes be easily recognized by members of the same community if data are openly accessed. Sometimes the interlocutors might agree with these terms, but sometimes they do not. According to the disciplinary code of ethics, a researcher is obliged to thoroughly describe the ways of the raw data management and usage, and in the case of mandatory raw data sharing (e.g., for receiving funding), it can inevitably impact the narratives collected. The prerogative to make all data open inevitably leads to autocensorship among interlocutors, i.e., resulting in a kind of FAIRy tale being collected. The article discusses the results obtained from the survey carried out among Croatian ethnologists and cultural anthropologists about the currently practiced data sharing, their attitudes about data sharing and their perceived behavior in hypothetical situations connected with data sharing. The results show mixed opinions about data sharing and a desire to follow the disciplinary code of ethics first, i.e., to follow the interlocutors’ wishes in the case of data management and usage.

Aplikasi Manajemen Kegiatan Ekstrakurikuler Berbasis Web di SMK Negeri 6 Padang

Extracurricular activities, being non-academic pursuits conducted outside of regular school hours, are a common feature in many schools, including SMK Negeri 6 Padang. To streamline the registration and management of these activities, this application was developed. Employing a systematic waterfall methodology, the development process encompassed needs analysis, design, implementation, testing, and maintenance, culminating in a well-structured and reliable system. The application was built using PHP, MySQL, and UML, and its functionality and usability were assessed through black-box testing. This web application has been meticulously developed to facilitate the management of extracurricular data at SMK Negeri 6 Padang. Equipped with a comprehensive suite of features, the system enables users to effortlessly perform a wide range of administrative tasks.

The Role of Productization in End-To-End Traceability

End-to-end traceability offers significant opportunities for product lifecycle visibility, sustainability enhancement, and regulatory compliance in product management. However, it faces challenges in data integration and management, supplier collaboration, cost and complexity, and the sharing of information across the supply chain. Productization refers to the representation of a product and connects commercial and technical aspects to the systemic perspective of product management. This includes a focus on the engineering lifecycle with inherent linkages to product data. The product management perspective, specifically in relation to the connection between end-to-end traceability and the productization concept, has not been extensively studied. This study explores the role of both productization and traceability in the context of end-to-end traceability. It combines an extensive literature review and an empirical example of applying productization logic across company borders to support end-to-end traceability. The key findings indicate that productization logic with a product structure focus can support end-to-end traceability in product management by providing consistency and a foundation for tracking both technical and operational data across the engineering lifecycle of a product. By focusing on productization, companies can overcome traceability challenges and unlock the benefits of end-to-end traceability.

Embracing digital transformation for sustainable development: Barriers to adopting digital twin in asset management within Singapore's energy and chemicals industry

AbstractIn the era of Industry 4.0, digital twin has the potential to revolutionize asset management and contribute to the sustainable development of the energy and chemicals industry. This study aims to investigate barriers to adopting digital twin in asset management within Singapore's energy and chemicals industry through a literature review and interviews. It identified five categories of barriers: resource constraints, data management issues, technical challenges, lack of government and industry support, and strategic and cultural barriers. The five categories of barriers presented unique implications, mutually reinforcing their negative impact on digital twin adoption. The analysis of interviews revealed that the most prevalent barriers, as mentioned by the interviewees, were lack of successful use cases (78.6%), high investment (71.4%), and lack of effective data analysis techniques (64.3%). Lack of successful use cases hinders return‐on‐investment justification, high investment requirements compete with other operational priorities, and data analysis challenges impede the realization of digital twin's full potential. The findings provide organizations with a comprehensive understanding of early stage barriers to formulate targeted strategies, driving the industry towards increased competitiveness and sustainable growth.

Navigating the Pandemic Through Technology: Colombian NGOs Promoting Peace During the COVID-19 Era

This article examines how Colombian NGOs use Information and Communication Technologies (ICTs) for peacebuilding attempts amid the COVID-19 pandemic. Drawing from 17 interviews with NGO members, the study underscores the pivotal role of digital peacebuilding in sustaining and expanding peace attempts, effective data management, and a broader engagement of target groups beyond in-person activities often associated with safety and financial concerns. The findings also identify prevailing challenges of incorporating ICTs in NGO's peacebuilding activities, ranging from privacy-related concerns to connectivity issues. The article points out the potential for NGOs to enhance interactivity, knowledge transfer, and diversity in their activities with ICTs, including IT security training and awareness campaigns on hate speech and propaganda. It also emphasizes the importance of developing risk reduction strategies tailored to the specific needs of different target groups.

Real-Time Industrial Data Analysis and Visualization

In the era of Industry 4.0, real-time data monitoring and analysis have become essential for enhancing operational efficiency and decision-making in industrial environments. This project focuses on the development of a comprehensive system for visualizing and analyzing real-time industrial data using a DHT11 sensor, which measures temperature and humidity. The collected data is transmitted and visualized through a dynamic web interface built on MyPHPAdmin, enabling users to access and interpret data seamlessly. The visualization aspect of the project aims to present the data in an intuitive format, allowing stakeholders to monitor environmental conditions effectively. Following the visualization phase, the data will undergo thorough analysis using advanced tools like Tableau and Excel, providing insights into patterns, trends, and anomalies in the industrial setting. This project not only demonstrates the integration of hardware and software technologies for real-time data management but also emphasizes the importance of data visualization and analysis in optimizing industrial processes. Ultimately, it serves as a foundation for further research and development in the field of industrial automation and data-driven decision-making.