Data Access Research Articles

OpenPVSignal Knowledge Graph: Pharmacovigilance Signal Reports in a Computationally Exploitable FAIR Representation.

Pharmacovigilance signal report (PVSR) documents contain valuable condensed information published by drug monitoring organizations, typically in a free-text format. They provide initial insights into potential links between drugs and harmful effects. Still, their unstructured format prevents this valuable information from being integrated into data-processing pipelines (e.g., to support either the investigation of drug safety signals or decision-making in the clinical context). OpenPVSignal is a data model designed specifically to publish PVSRs via a computationally exploitable format, compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles to promote ease of access and reusability of these valuable data. This paper outlines the procedure for converting pharmacovigilance signals published by the World Health Organization Uppsala Monitoring Centre (WHO-UMC) into the OpenPVSignal data model, resulting in a Knowledge Graph (KG). It details each step of the process, including the technical validation by KG engineers and the qualitative verification by medical and pharmacovigilance experts, leading to the finalized KG. A total of 101 PVSRs from 2011 to 2019 were incorporated into the openly available KG. The presented KG could be useful in various data-processing pipelines, including systems that support drug safety activities.

Challenges and Optimization Strategies of AI Applications in Supply Chain Management

As globalization accelerates and market dynamics grow increasingly complex, supply chain management has emerged as a pivotal factor in determining corporate competitiveness. Concurrently, the rapid advancement of artificial intelligence (AI) technology has positioned its application as a critical solution for addressing supply chain optimization challenges. This study reviews the challenges faced by AI applications in supply chain management, proposes feasible optimization solutions and suggestions, and aims to help enterprises correctly apply AI technology to achieve cost reduction and efficiency improvement. Research has found that AI applications currently face challenges such as high initial investment, data security, and talent shortages. To address these issues, the study focuses on three critical industriesautomotive, medical, and information technologyanalyzing their unique challenges and exploring targeted solutions. This article suggests that enterprises reduce investment risks, improve data access mechanisms, and strengthen talent cultivation and introduction, to promote the broader integration of artificial intelligence within supply chain operations.

Exploring physiological beta-hydroxybutyrate level in children treated with the classical ketogenic diet for drug-resistant epilepsy

BackgroundThe ketogenic diet (KD) therapy is a primary treatment for drug-resistant epilepsy, and beta-hydroxybutyrate (BHB) is the main ketone produced during KD. However, the pattern of increase in BHB levels is not well understood, and the reference range for BHB need to be defined. The aim of this study was to evaluate the BHB levels in the first three months, especially one week, after KD initiation, and to explore the physiological reference range for BHB.MethodsIn our study, a fasting initiation strategy was used for the majority of patients (252/300, 84%) who underwent fasting for 24–48 h, the rest fasted for at least 12 h. The concentration of blood BHB was measured four times a day during the first week, at one month and three months. Seizure frequency was recorded at one week, one month and three months. Responders were defined as those with a seizure reduction 50% or more compared to baseline. BHB levels were compared between responders and non-responders. The BHB levels of responders were used to calculate the reference range.ResultsA total of 300 patients were recruited, of whom 172 (57%) had accessible BHB data. BHB levels rapidly rose to 2.0 mmol/L at 19 h, peaked at 4.2 mmol/L at 43 h of therapy, and stabilized by three months. The reference range for BHB was 1.1 to 4.9 mmol/L.ConclusionsBHB levels increased rapidly following fasting, reaching the peak at day 2, stabilizing from the end of the first week through three months. The lower reference limit for BHB to ensure KD efficacy should be set at 1.1 mmol/L.

WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis

This paper introduces the Welsh Advanced Neuroimaging Database (WAND), a multi-scale, multi-modal imaging dataset comprising in vivo brain data from 170 healthy volunteers (aged 18–63 years), including 3 Tesla (3 T) magnetic resonance imaging (MRI) with ultra-strong (300 mT/m) magnetic field gradients, structural and functional MRI and nuclear magnetic resonance spectroscopy at 3 T and 7 T, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS), together with trait questionnaire and cognitive data. Data are organised using the Brain Imaging Data Structure (BIDS). In addition to raw data, we provide brain-extracted T1-weighted images, and quality reports for diffusion, T1- and T2-weighted structural data, and blood-oxygen level dependent functional tasks. Reasons for participant exclusion are also included. Data are available for download through our GIN repository, a data access management system designed to reduce storage requirements. Users can interact with and retrieve data as needed, without downloading the complete dataset. Given the depth of neuroimaging phenotyping, leveraging ultra-high-gradient, high-field MRI, MEG and TMS, this dataset will facilitate multi-scale and multi-modal investigations of the healthy human brain.

Biodiversity futures: digital approaches to knowledge and conservation of biological diversity

Abstract. Biodiversity, encompassing species diversity, genetic resources, and ecosystems, is essential for human well-being and quality of life. However, the scale of human activities has significantly impacted the planet's biodiversity, with many species facing extinction in the coming decades with unknown consequences. Global commitments, such as the Aichi Biodiversity Targets and the United Nations (UN) Sustainable Development Goals, are not delivering consistent results, and progress on conservation has been frustratingly slow. With a short time frame to meet the 2030 targets of the Kunming-Montreal Global Biodiversity Framework, urgent action is needed to address the crisis. Digital technologies emerge as indispensable tools in understanding, monitoring, and conserving biodiversity. They offer multiple solutions, from remote sensing to citizens involvement mediated by science apps, providing unprecedented volumes of data and innovative tools for conservation efforts. Despite their immense potential, digital solutions raise concerns about technology and data accessibility, environmental impacts, and technical limitations, as well as the need for specialized human resources, robust collaboration networks, and effective communication strategies. This paper, drawn from discussions at the Digital with Purpose Global Summit in 2023 and 2024, held in Portugal, and complemented by expert opinion and literature, reflects on existing biodiversity-related digital technologies, identifies challenges and opportunities, and proposes steps to strengthen the nexus between technology and the biodiversity agenda. By providing science and technology stakeholders with recommendations on accelerating the role of digital technologies in biodiversity knowledge and conservation, it aims to catalyse impactful change in this critical field of devising brighter futures for biodiversity and humanity.

Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Role of primary and secondary care data in atrial fibrillation ascertainment: impact on risk factor associations, patient management, and mortality in UK Biobank.

Electronic healthcare records (EHR) are at the forefront of advances in epidemiological research emerging from large-scale population biobanks and clinical studies. Hospital admissions, diagnoses, and procedures (HADP) data are often used to identify disease cases. However, this may result in incomplete ascertainment of chronic conditions such as atrial fibrillation (AF), which are principally managed in primary care (PC). We examined the relevance of EHR sources for AF ascertainment, and the implications for risk factor associations, patient management, and outcomes in UK Biobank. UK Biobank is a prospective study, with HADP and PC records available for 230 000 participants (to 2016). AF cases were ascertained in three groups: from PC records only (PC-only), HADP only (HADP-only), or both (PC + HADP). Conventional statistical methods were used to describe differences between groups in terms of characteristics, risk factor associations, ascertainment timing, rates of anticoagulation, and post-AF stroke and death. A total of 7136 incident AF cases were identified during 7 years median follow-up (PC-only: 22%, PC + HADP: 49%, HADP-only: 29%). There was a median lag of 1.3 years between cases ascertained in PC and subsequently in HADP. AF cases in each of the ascertainment groups had comparable baseline demographic characteristics. However, AF cases identified in hospital data alone had a higher prevalence of cardiometabolic comorbidities and lower rates of subsequent anticoagulation (PC-only: 44%, PC + HADP: 48%, HADP-only: 10%, P < 0.0001) than other groups. HADP-only cases also had higher rates of death [PC-only: 9.3 (6.8, 12.7), PC + HADP: 23.4 (20.5, 26.6), HADP-only: 81.2 (73.8, 89.2) events per 1000 person-years, P < 0.0001] compared to other groups. Integration of data from primary care with that from hospital records has a substantial impact on AF ascertainment, identifying a third more cases than hospital records alone. However, about a third of AF cases recorded in hospital were not present in the primary care records, and these cases had lower rates of anticoagulation, as well as higher mortality from both cardiovascular and non-cardiovascular causes. Initiatives aimed at enhancing information exchange of clinically confirmed AF between healthcare settings have the potential to benefit patient management and AF-related outcomes at an individual and population level. This research underscores the importance of access and integration of de-identified comprehensive EHR data for a definitive understanding of patient trajectories, and for robust epidemiological and translational research into AF.

The Role of Wearable Health Technology in Monitoring and Managing Chronic Conditions

Wearable health technology has emerged as a transformative tool in healthcare, offering continuous monitoring and management of chronic conditions. These devices, ranging from fitness trackers to advanced biosensors, collect real-time data on parameters such as heart rate, glucose levels, and physical activity. This paper investigates the potential of wearable health technology to enhance self-management, improve clinical outcomes, and reduce healthcare disparities. Key case studies demonstrate their efficacy in chronic condition management, while challenges such as data privacy, accessibility, and integration with existing healthcare systems are addressed. Future advancements, including AI integration and improved usability, could further revolutionize chronic disease management and patient care. Keywords: Wearable health technology, chronic disease management, biosensors, patient monitoring, digital health.

Sharing of cmeRABC alleles between C. coli and C. jejuni associated with extensive drug resistance in Campylobacter isolates from infants and poultry in the Peruvian Amazon.

Campylobacter is a serious health threat because of the rapid progressive evolution of antimicrobial resistance and efficient transmission from zoonotic as well as human sources. Resistance to fluoroquinolones and macrolides is particularly concerning as this compromises the two most effective oral antibiotic agents currently available for human campylobacteriosis. Here, we report on the prevalence and worldwide distribution of the operon cmeRABC, which encodes an efflux pump conferring high levels of combined resistance to fluoroquinolones and macrolides in Campylobacter strains isolated from poultry (n = 75) and children (n = 177). These mutations were found to be highly prevalent in isolates from poultry (62.7%) and children (29.4%) in Iquitos, Peru. We investigated the population structure of genes in the cmeRABC operon and identified a potential genetic bottleneck for the cmeA and cmeB genes. While most cmeB alleles segregate by species, alleles associated with high resistance to fluoroquinolones and macrolides were found in both Campylobacter jejuni and Campylobacter coli. We inferred that the likely ancestry of these alleles was from C. jejuni and was later acquired by C. coli through recombination. Publicly accessible global genomic data from 16,120 Campylobacter genomes identified these mutations in approximately 6% of C. jejuni and C. coli isolates globally, with higher prevalence in samples from poultry in many countries, including Peru. Our findings suggest that these extensively drug-resistant Campylobacter strains originated from C. jejuni in poultry.IMPORTANCEAntimicrobial resistance in Campylobacter is a growing public health concern, driven by the rapid evolution and zoonotic transmission of resistant strains. This study focuses on mutations in the cmeABC efflux pump, which confer high resistance to fluoroquinolones and macrolides, the two most effective oral antibiotics for human campylobacteriosis. By analyzing genomes from poultry and children in Iquitos, Peru, as well as global genomic data sets, we identified a significant prevalence of these resistance-associated mutations, particularly in poultry and children. Our findings suggest that these mutations originated in Campylobacter jejuni and spread to C. coli through recombination. Globally, these mutations are found in approximately 6% of isolates, with higher prevalence in poultry in multiple countries. This research underscores the critical role of genomic epidemiology in understanding the origins, evolution, and dissemination of antimicrobial resistance and highlights the need to address poultry as a reservoir for resistant Campylobacter.

Understanding the Effect of a Changing Climate on the Re-Emergence of Mosquito-Borne Diseases in Vulnerable Small Island Nations: A Systematic Review.

Drastic changes in meteorological variables due to climate change will likely have an implication on the proliferation of vectors such as mosquitoes. Extreme weather events may therefore promote the emergence/re-emergence of mosquito-borne diseases (MBDs) and potentiate the risk of endemicity, particularly, in small island nations. A systematic review was chosen to methodically ascertain the knowledge gaps that exist in determining the influence of the changing climate on MBDs in small islands with vulnerable public health systems. This review was conducted using the PRISMA guidelines. Following extraction of 600 articles from the databases, 16 studies were determined to meet the selection criteria. The majority of these research papers were from Sri Lanka (n = 9) while the remaining articles were distributed between islands in the Pacific and Atlantic Ocean. Several of these studies used regression modelling techniques to discuss the effect of multiple meteorological variables on the incidence of MBDs. A positive relationship was observed between temperature and the relative risk of MBDs in 72% of the papers. Rainfall enhanced dengue transmission in 84% of the studies included. All the articles discussing the effect of humidity illustrated a similar trend while wind speed was the only climatic variable demonstrating a negative relationship with MBDs. Considering the intricate nature of the non-linear exposure-response link is crucial when estimating the lagged effect of the changing climate on MBDs transmission. Other challenges associated with bias and confounders in the selected studies as well as meteorological data accessibility, were highlighted. Therefore, it was not possible to conclusively establish that the changing climatic variables do influence the spread of MBDs which accentuated the need for conducting further studies to illustrate the effect of changing weather variables on the incidence of MBDs, with an emphasis on vulnerable small island nations.

Children's digital privacy on fast-food and dine-in restaurant mobile applications.

Children are targeted by unhealthy food marketing on digital media, influencing their food preferences, intakes and non-communicable disease risk. Restaurant mobile applications are powerful platforms for collecting users' data and are popular among children. This study aimed to provide insight into the privacy policies of top dine-in and fast-food mobile apps in Canada and data collected on child users. Privacy policies of the top 30 fast-food and dine-in restaurants in Canada were reviewed. A convenience sample of 11 English-speaking Canadian residents aged 9-12 years with fast-food apps on their mobile phones were recruited to use ≥1 fast-food restaurant mobile app(s). Children used the app(s) for 5-10 minutes and placed food orders. Parents submitted a Data Access Request (DAR) on their child's behalf to the food company. Descriptive analysis and a flexible deductive approach to content analysis evaluated data collected through DARs. Overall, 26 privacy policies were analyzed. The intended age of app users was indicated by 12 (46%) food companies, 10 (39%) of which specified it as ≥13 years. No company had a compulsory age verification process. Twenty-four (92%) companies disclosed the data collected on app users: 23 (89%) did not distinguish between information pertaining to children or adults, and 21 (81%) described a protocol for action if they inadvertently collected data on children. Twenty-four DARs were sent to companies; 11 (45.8%) of which were fulfilled by companies, and 4 (16.7%) resulted in the receipt of children's data. All responding food companies were found to collect sociodemographic information on child participants (e.g., name, email). Some collected other information, such as order details and available promotional offers. This study demonstrates current fast-food and dine-in restaurant privacy policies are insufficient and provides insight into data collected on children via fast-food apps. Policies must be strengthened to ensure children's privacy and protection online.

TrusO- A Secured Approach Data Transmission from IOT to Cloud

IoT is the intercommunication between electronic devices that acts as an intelligent sensing system. The IoT is connected to the internet to share data among various electronic devices to work intelligently and efficiently. Massive amounts of data are shared among the devices transferred through the insecure internet for storage. Cloud computing is a tremendous emerging technology to store large amounts of data related to the IoT. Data privacy is a significant issue in cloud computing. The data, which should be highly confidential, are transferred by the IoT devices, and will be stored in the cloud. The data in the cloud are highly vulnerable to various attacks namely Man-in-The-Middle attack, Impersonation attack, spoofing attack, Password Guessing attack etc. To avoid particular situations, this article proposes an access control Attribute-Based Encryption (ABE) method to store the data in the cloud using a Trusted Organizer (TrusO) in NTRU encryption. With this approach, the data are proved to be confidential during the data transmission and while stored in the cloud infrastructure. The data stored in the cloud shall be accessed by trusted parties, and TrusO will rectify unwanted data leakage. So it ensures data privacy, confidentiality, and access control to the data in the cloud that are transferred by the IoT devices.

Mapping materials, drawing Europe: Sample and data quality and accessibility in the biobanking infrastructure BBMRI-ERIC

The European biobanking infrastructure BBMRI-ERIC was established in the context of European Union science policy to facilitate collaboration between European repositories of biological samples and associated data, or so-called biobanks. To allow the exchange of research materials, the infrastructure has created several platforms for quality management and making materials visible and accessible. In this article, I develop the metaphor of mapping to explore the workings of these platforms. I consider maps as devices for giving directions, as representations of science, and as (symbolic) outlines of the territory of Europe to explore how BBMRI-ERIC's activities in quality management and IT-platforms for making materials visible and accessible (aim to) facilitate circulation. Across the different platforms, efforts to harmonize and integrate European biobanking activities confront both scientific and European tendencies toward fragmentation. The map of a European biobanking community drawn by BBMRI-ERIC consequently both makes and unmakes Europe sketching a Europe of both harmonization and fragmentation. In laying bare these paradoxical effects of the construction of a European biobanking infrastructure, using mapping as a lens shows how, rather than realizing its much-cited role as a “facilitator” for research, BBMRI-ERIC embodies key tensions at the center of efforts to advance “data-based” (big) science.

Smart Tags as Enablers for Digital Product Passports in Circular Electronics Value Chains

Abstract The electronics industry is expected to adopt more sustainable and circular product concepts and operations. Since the electronics value chains are complex, digital product passports (DPPs) that provide value chain transparency and traceability can be seen as one key enabler for shifting towards circular economy. Data carriers that are physical identifiers attached to products provide access to product data stored in the cloud and databases. Smart tags that combine item-level identification with condition monitoring are proposed to enable access also to dynamic lifecycle data of products to improve decision-making at end-of-life based on conditions that the product has been exposed to during its lifecycle. This dynamic information could be effectively used together with product data to decide on which circular economy strategy to adopt: reuse, remanufacture, repair, recycle etc. This paper analyses the data requirements of electronics value chain for DPPs, specifically focusing on which conditions to monitor with the help of smart tags. The data for this analysis was collected from ten developmental value chains aiming for sustainability and circularity with a questionnaire related to data needs, data access, data gaps, and data availability. The responses highlighted the need for data exchange and tools to monitor performance of components during storage and use. A printed visual humidity sensor is developed and analyzed as an experimental case study to help the value chains to dynamically monitor lifecycle conditions of products. This smart tag principal was functional with a visible colour change over time at different humidities between 33–72%RH, while not reacting at 0%RH. The relevance of different smart tag concepts is discussed and other important aspects, such as sustainability and durability of the smart tags, are included in the discussion.

Machine Learning for Prediction of Drug Concentrations: Application and Challenges.

With the advancements in algorithms and increased accessibility of multi-source data, machine learning in pharmacokinetics is gaining interest. This review summarizes studies on machine learning-based pharmacokinetics analysis up to September 2024, identified from the PubMed and IEEE Xplore databases. The main focus of this review is on the use of machine learning in predicting drug concentration. This review provides a comprehensive summary of the advances in the machine learning algorithms for pharmacokinetics analysis. Specifically, we describe the common practices in data preprocessing, the application scenarios of various algorithms, and the critical challenges that require attention. Most machine learning models show comparable performance to those of population pharmacokinetics models. Tree-based algorithms and neural networks have the most applications. Furthermore, the use of ensemble modeling techniques can improve the accuracy of these models' predictions of drug concentrations, especially the ensembles of machine learning and pharmacometrics.

Learning Analytics in Mathematics Teacher Education At The Ceará State University

Objective: This research sought to develop a statistical model that helps to predict students at risk of not being successful in the subjects of the Undergraduate Mathematics Teacher Education Program, in distance education modality, at the Open University System of Brazil / Ceará State University (UAB/UECE). Theoretical Framework: Teacher education in Brazil faces several problems highlighted by studies. Low graduation rates require urgent measures to change this situation with quality. Learning analytics emerges as an option to improve student learning, reducing the risk of academic failure. Method: A statistical method was used for this research. It was decided to use data from the students who entered the University in 2009, in poles of two cities. The students’ access and interaction data in the Moodle environment and final performance grades in the subjects were collected. In data analysis, binary logistic regression was used. Results and Discussion: The regression model created has a high accuracy, with statistically significant predictor variables and high success percentages to predict failure and non-failure. Research Implications: This research serves as a basis for the creation of tools for real-time application of learning analysis, favoring the observation of the performance of the predictive model for the program studied. Originality/Value: Learning analytics is a new, but very promising field of research. This research is one of the pioneers on this topic in Brazil, carried out by researchers in the education area.

Empowering water utilities: crafting an end-user-friendly reliability ranking for evaluating satellite remote sensing advances through literature insights

ABSTRACT Satellite remote sensing provides extensive data for water management, enabling the measurement of hydro-meteorological and environmental variables. It aids in assessing trends, and hydrological conditions and guiding appropriate management actions. Satellite remote sensing serves as a cost-effective supplement to ground-based monitoring infrastructure. Over the past decade, satellite Earth observation technologies have advanced significantly, offering new opportunities for water utilities and agencies. These developments include improved satellite capabilities, enhanced data access, private sector involvement, and advancements in data processing and analytics. However, an end-user-friendly reliability ranking tool for evaluating numerous satellite remote sensing options is needed for operational decision-making purposes. Here, we summarise recent trends and the literature on satellite remote sensing for water management, evaluating its capabilities and available tools, focusing on the routine but essential operation of utilities. A novel assessment of satellite potential implementation to water-related applications using an end-user-friendly reliability ranking process is presented. The study focuses on selected application areas, including catchment monitoring, water demand estimation, flood monitoring, water quality monitoring, farm dam monitoring, urbanization trends, drought forecasting, fire spotting, and post-fire water quality impacts. This paper outlines the operational advantages/limitations of satellite remote sensing and provides recommendations for its adoption.

Database models for storing big data

The issues related to the formation of large data collections are not fully resolved. The amount of information in the world is constantly increasing, which has resulted in the problem of its storage. The term "big data" created to define this data includes the following characteristics such as quantity, processing speed, variety, reliability, variability and value. This type of information includes environmental characteristics; the data determine the distribution of relevant indicators on the Earth and make it possible to make a forecast for the future regarding their changes in time and space, which is important for economic management and sustainable development of humanity. However, there is not enough information on the effective organisation of the storage and processing of such data, and further research is needed. Thus, the object of the study is the data obtained at environmental monitoring stations. The subject of the study is the storage of data obtained as a result of environmental monitoring. The purpose of the study is to develop criteria for evaluating and comparing different types of data repositories, taking into account specific requirements for their storage; to determine the types of information to be stored in the database; to create an ER-diagram of a particular database. The received data are classified according to the state of the environment, its location and pollution. As the data is obtained from an extended system of observations, it passes in stages from the place of its registration through the city, regional, state and global network to the place of its storage. Accordingly, the following criteria for the information received must be provided such as the ability to store data of various types, quick access and processing, and scalability. There are two main models of data bases such as relational and non-relational, each of them has its advantages and disadvantages. For example, relational (SQL) data storage systems have rigid schemes that ensure the reliability of information storage, but are inefficient for processing a large number of queries and have no significant scalability. Non-relational (NoSQL) systems store data in an unstructured type, are easily scalable, and provide high speed of query processing. Conclusions. The research has shown that non-relational databases are more appropriate for storing data obtained from environmental monitoring stations. A scheme for processing the data was created. The groups of parameters that will be stored in the database are outlined. The main criteria for data storage were developed, allowing for more efficient data organisation. An ER diagram for the database was implemented.

Design and Implementation of a ROLAP Cube in Scalable Distributed Data Structure

The Scalable Distributed Data Structure (SDDS) is a data model specifically designed for distributed environments. An SDDS file comprises records that are dynamically distributed across servers using an SDDS algorithm. A notable feature of SDDS is the removal of a centralized addressing component, simplifying client-server communication and reducing both the message count and data access time in distributed systems. This work also explores a Data Warehouse (DW) within a decision support system, where multidimensional data are represented as a cube and managed through Relational Online Analytical Processing (ROLAP). Although extensive research has been conducted in both the data warehousing and SDDS fields, no prior studies have combined these two areas. This paper introduces a novel approach to implementing a ROLAP cube within an SDDS using the Linear Hashing algorithm (LH*), which eliminates centralized addressing, enabling direct client-server communication and improving performance by reducing inter-site message exchanges. This work demonstrates the feasibility of this method and its positive impact on data processing efficiency in distributed systems.

Co-producing a safe mobility and falls informatics platform to drive meaningful quality improvement in the hospital setting: a mixed-methods protocol for the insightFall study

IntroductionManual investigation of falls incidents for quality improvement is time-consuming for clinical staff. Routine care delivery generates a large volume of relevant data in disparate systems, yet these data are...