Life Cycle Data Research Articles (Page 6)

This study aimed to establish a set of guiding principles for data sharing and harmonization in critical care, focusing on the use of real-world data (RWD) and real-world evidence (RWE) to improve patient outcomes and research efficacy. The principles were developed through a systematic literature review and a modified Delphi process, with the goal of enhancing data accessibility, standardization, and interoperability across critical care settings. Data sources included a comprehensive search of peer-reviewed literature, specifically studies related to the use of RWD and RWE in healthcare, guidelines, best practices, and recommendations on data sharing and harmonization. A total of 8150 articles were initially identified through databases such as MEDLINE and Web of Science, with 257 studies meeting inclusion criteria. Inclusion criteria focused on publications discussing health-related informatics, recommendations for RWD/RWE usage, data sharing, and harmonization principles. Exclusion criteria ruled out non-human studies, case studies, conference abstracts, and articles published before 2013, as well as those not available in English. From the 257 selected studies, 322 statements were extracted. After removing irrelevant definitions and off-topic content, 232 statements underwent content validation and thematic analysis. These statements were then consolidated into 24 candidate guiding principles after rigorous review and consensus-building among the expert panel. A three-phase modified Delphi process was employed, involving a conceptualization group, a review group, and a Delphi group. In phase 1, experts identified key themes and search terms for the systematic review. Phase 2 involved validating and refining the prospective guiding principles, while phase 3 employed a Delphi panel to rate principles on acceptability, importance, and feasibility. This process resulted in 24 guiding principles, with high consensus achieved in rounds 2 and 3 on their relevance and applicability. The systematic review and Delphi process resulted in 24 guiding principles to improve data sharing and harmonization in critical care. These principles address challenges across the data lifecycle, including generation, storage, access, and usage of RWD and RWE. This framework is designed to promote more effective and equitable data practices, with relevance for the development of artificial intelligence-based decision support tools and clinical research. The principles are intended to guide the responsible use of data science in critical care, with emphasis on ethics and equity, while acknowledging the variability of resources across settings.

Accurate description of the condition of engineering structures is important for ensuring structural safety. Traditional analysis methods based on simplified physical mechanisms cannot accurately characterize the structural condition and neglect the value of the large amount of data generated during the construction process. This paper proposes a data-driven analysis framework that combines physical principles, dimensionality reduction techniques and ensemble learning models to trace back the deep-seated connections between data, achieving multi-factor analysis of structural defects. Using concrete structural cracks in a certain project as an example, the framework considers full life-cycle data, including material, environment, and construction processes, to construct an assessment model. The results show that by establishing a mapping relationship between construction data and structural condition, and integrating cumulative indicators from different construction stages, a reference for describing the structural safety condition can be provided to some extent, along with optimization suggestions, offering an analytical perspective for solving complex structural problems in engineering.

The role of cultural heritage collections within the research ecosystem is rapidly changing. From often-passive primary source or reference point for humanities research, cultural heritage collections are now becoming integral part of large-scale interdisciplinary inquiries using computational-driven methods and tools. This new status for cultural heritage collections, in the ‘collections-as-data’ era, would not be possible without foundational work that was and is still going on ‘behind the scenes’ in cultural heritage institutions through cataloguing, documentation and curation of cultural heritage records. This article assesses the landscape for cultural heritage collections data infrastructure in the UK through an empirical and critical perspective, presenting insights on the infrastructure that cultural heritage organisations use to record and manage their collections, exploring the range of systems being used, the levels of complexity or ease at which collections data can be accessed, and the shape of interactions between software suppliers, cultural heritage organisations, and third-party partners. The paper goes on to include a critical analysis of the findings based on the sector’s approach to ‘3s’, that is standards, skill sets and scale, and how that applies to different cultural heritage organisations throughout the data lifecycle, from data creation, stewardship to sharing and re-using.

Purpose: This study aimed to develop a comprehensive framework to enable the identification of risks pertaining to data security, privacy and confidentiality when using medical Internet of Things (IoT) devices. Design/methodology/approach: A qualitative, non-empirical study was undertaken to identify data-related risks when using medical IoT devices using a systematic literature review and two governance frameworks. Findings/results: Within the medical field, risks of using IoT are concentrated around data security, privacy and confidentiality throughout the data lifecycle prevalent within each layer of the IoT architecture. A comprehensive framework was developed to identify these risks at each layer within the architecture in order to facilitate sound information technology (IT) and data governance. Practical implications: This research documents evidence of the risks posed by IoT devices within the medical field particularly pertaining to IoT data. It provides those charged with governance with a tool to identify all significant risks in this field that is compliant with Health Insurance Portability and Accountability Act and Control Objectives for Information and related Technology 2019. Originality/value: This research provides a comprehensive framework that can be used by those in charge of governance including IT specialist for risk identification during implementation for sound IT and data governance of medical IoT devices using recognised benchmarks. The use of the benchmarks ensures that all significant risks are identified, compared to previous research that identified risks in an ad hoc manner.

In this paper, we report the results of a study examining 78 Research and Data Lifecycle (RDLC) models located in a review of the literature. Through synthesis-analysis and the nominal group technique, we investigated the RDLC models from the point of view of their disciplinary focus, use cases, model creators, as well as the specific stages and shapes. Our study revealed that the majority of the disciplinary focus for the models was generic, science, or multi-disciplinary. Models originating in the social sciences and humanities are less common. The use cases varied in a wide spectrum, with a total of 34 different scenarios. The creators and authors of the RDLC models came from more than 20 countries with the majority of the models created as a result of collaboration within or across different organizations. Our stage and shape analysis also outlined key characteristics of the RDLC models by showing the commonalities and variations of named stages and varying structures of the models. As one of the first empirical investigations examining the deep substance of the RDLC models, our study provides significant insights into the context and setting where the models were developed, as well as the details with regard to the stages and shapes, and thereby identified gaps that may impact the use and value of the models. As such, our study establishes a foundation for further studies on the practical utilization of the RDLC models in research data management practice and education.

The pharmaceutical industry is undergoing a significant transformation with the emergence of the Pharma 4.0 concept, driven by technological advancements such as the internet of things (IoT), artificial intelligence (AI), and robotic production systems (continuous manufacturing). One crucial aspect of this transformation is the need for strong data integrity (DI) to ensure the data reliability and security related to pharmaceutical product quality. In the era of Pharma 4.0, performance data from the pharmaceutical industry can be integrated to support real-time decision-making, the presence of DI is crucial to protect consumers and comply with industry regulations. The ALCOA+ principle is used to ensure the integrity of data throughout its lifecycle, including identification, sustainability, and availability of data. Quality behavior is needed to enhance self-awareness in the 4.0 transformation, especially with the increasing focus on cybersecurity and the rising number of data integrity cases, particularly in implementing the ALCOA+ principle. Quality behavior becomes crucial in addressing the challenges and opportunities presented by the Pharma 4.0 transformation and in maintaining data integrity in pharmaceutical industry.

The accurate prediction of lithium-ion battery capacity is crucial for the safe and efficient operation of battery systems. Although data-driven approaches have demonstrated effectiveness in lifetime prediction, the acquisition of lifecycle data for long-life lithium batteries remains a significant challenge, limiting prediction accuracy. Additionally, the varying degradation trends under different operating conditions further hinder the generalizability of existing methods. To address these challenges, we propose a Multi-feature Transfer Learning Framework (MF-TLF) for predicting battery capacity in small-sample scenarios across diverse operating conditions (different temperatures and C-rates). First, we introduce a multi-feature analysis method to extract comprehensive features that characterize battery aging. Second, we develop a transfer learning-based data-driven framework, which leverages pre-trained models trained on large datasets to achieve a strong prediction performance in data-scarce scenarios. Finally, the proposed method is validated using both experimental and open-access datasets. When trained on a small sample dataset, the predicted RMSE error consistently stays within 0.05 Ah. The experimental results highlight the effectiveness of MF-TLF in achieving high prediction accuracy, even with limited data.

Abstract Digital product passports (DPPs) are an emerging digital technology that advances the circular economy (CE) by facilitating inter‐organizational data sharing and life‐cycle management of products. This paper investigates DPP value ecosystems, articulated through the e3‐value modeling language, to address the research question: How to model the value ecosystem of digital product passports as boundary objects by identifying and analyzing the relevant actors, their needs, and the connections between them? Utilizing a systematic literature review, secondary data collection from active DPP instances, and gray literature, we construct a comprehensive e3‐value model. The model delineates the interactions and value flows among key stakeholders, including manufacturers, suppliers, consumers, end‐of‐life actors, and regulatory authorities. A specific application of the model is demonstrated through the battery passport, mandated by the EU Battery Regulation. The findings suggest that DPPs enhance transparency, regulatory compliance, and sustainable practices by providing detailed product lifecycle data. The study underlines the necessity for clear data‐sharing guidelines and highlights the multifaceted roles of DPPs in supporting a CE.

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.

Optimization of environmental and energy performance of egg production using data envelopment analysis (DEA) and life cycle assessment (LCA).

For a large amount of tasks classical structured programming approach is preferred to object-oriented one. These preferences are typical for deterministic world and in machine-representation-oriented systems. A modular Oberon programming language was oriented on such tasks. It demonstrate minimalistic way of reliability, which differs from vast majority of program systems maximizing amount of features supported. Usage of instrumental approach instead of OOP was proposed before for solving the problems of deterministic world. The data-code separation principle assumes that data lifecycle is independently controlled, and lifetime duration is longer then code lifetime. The areal data types proposed by author are aimed for implementation within instrumental approach. Areal data types provide orthogonal persistency and are integrated with codes, defined in types hierarchy for instruments. Areal data types are embodied in MultiOberon system compilers. Reference to address conversion methods are based on runtime system metadata. Areal types integration resulted in developing additional test in MultiOberon. MultiOberon restrictive semantics makes an opportunity to turn off pointer usage permissions and switch on areal types usage. Areal is fixed for specifically marked data type. Areal references are implemented as persistent ones in areal array. Due to such paradigm the problem of persistence reference during software restarts was solved. Novelty in work is using areal references which gains in index type and pointer type advantages. Such approach implements principles of generic programming without creating dependencies of types extensions and template specifications. An example of generic sorting algorithm is provided for areal types. A new data type differs with compactness and simplicity in comparison to dynamic structures. It demonstrates advantages for systems with complex technological objects data structures in relatively static bounds.

Digital twin-driven operational CycleGAN-based multiple virtual-physical mappings for remaining useful life prediction under limited life cycle data.

Robust data governance frameworks are becoming increasingly necessary for enterprises to make effective decisions. This study looks at how important data governance is in promoting better decision-making by guaranteeing the accuracy, security, and integrity of organizational data. It demonstrates how businesses may strategically create and implement data governance structures, policies, and procedures by referencing accepted data governance principles and examining pertinent case studies. In addition to protecting data assets, these frameworks give stakeholders access to dependable and consistent data that is necessary for making well-informed decisions. The research shows that organizational agility and competitiveness are greatly enhanced by well-designed data governance frameworks through a synthesis of theoretical ideas and real-world examples. Additionally, it highlights the importance of proactive data management at every stage of the data lifecycle, from collection and storage to analysis. It also highlights the need for proactive data management from collection and storage to analysis and distribution. In the end, the results emphasize how data governance may significantly improve an organization's capacity for making decisions, and they support the methodical implementation of data governance as a fundamental component of contemporary business strategy.

Several characteristics of data open up many new avenues for exploitation, and as such, new privacy and security models are required to address these emerging challenges. The magnitude of datasets generated make it near impossible for data managers to capture all the contextual semantics relevant to a unit of data, and this causes novel difficulties in privacy and security of these data systems using BLAKE2 Algorithm. At the micro-level, a datum in any software system transitions between several phases, from inception to deletion; this is known as the data life cycle, which provides a concrete guide for tracking the many states of a unit of data. The usefulness of such a model only increases with the complexity of the system that it represents, so it is useful to examine privacy and security from this perspective, as it provides a precise framework for discussion. This paper provides practical privacy and security recommendations for every step of the data life cycle, examining prominent infrastructures and their features that relate to their data management policies using BLAKE2 Algorithm with 98.97% secure and reliable all the time. BLAKE2 is a cryptographic hash function faster than MD5, SHA-1, SHA-2, and SHA-256. The most glaring issue with SHA is that its data storage system is completely unencrypted by default. Like MD5, SHA1, and many encryption algorithms were designed to be run within a trusted environment. In the case of unencrypted data, this means that malicious parties that have physical or virtual access to the file system can extract information as they please. Fortunately, BLAKE2 is largely reliant on the client code for encryption of any sensitive information before it saves to the database. Some mitigation techniques for this scheme include implementing proper file system permissions and file system level encryption.

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI . The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI .

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

Database storage is paramount in mission-critical applications, significantly influencing system performance metrics. In the contemporary technological landscape, the financial burden of procuring servers equipped with high- capacity storage solutions is considerable. To mitigate these expenses, organizations should evaluate alternative avenues, such as NetApp, a remarkably economical option. NetApp has many features, including data cloning, deduplication, and advanced snapshot management, which optimize the storage architecture where databases are housed. This discourse will delve into best practices for integrating NetApp storage with an array of database management systems, emphasizing algorithms that enhance data retrieval efficiency and storage optimization. The strategies proposed herein will furnish organizations with pragmatic methodologies to curtail storage expenditures while ensuring that critical performance measures and scalability are upheld. This approach guarantees that databases operate at their pinnacle of efficiency, all while remaining within predetermined budgetary parameters. Keywords: NetApp storage, cost optimization, database management, deduplication, tiering, cloud integration, data lifecycle management.

Retrieval-Augmented Generation (RAG) has significantly enhanced the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate external knowledge sources, thereby improving response accuracy and relevance. However, the security of RAG pipelines remains a paramount concern as these systems become integral to various critical applications. This paper introduces a comprehensive framework designed to secure RAG pipelines through the integration of advanced encryption techniques, zero-trust architecture, and structured guardrails. The framework employs symmetric and asymmetric encryption to protect data at rest and in transit, ensuring confidentiality and integrity throughout the data lifecycle. Adopting zero-trust principles, the framework mandates continuous verification of all entities within the data flow, effectively mitigating unauthorized access and lateral movement risks. Additionally, the implementation of guardrails, such as immutable system prompts and salted sequence tagging, fortifies the system against prompt injection and other malicious attacks. A detailed lifecycle security continuum is presented, illustrating the application of these security measures from data ingestion to decommissioning. Case studies across healthcare, finance, retail, and education sectors demonstrate the framework’s effectiveness in maintaining high performance and scalability without compromising security. This work provides a foundational model for future research and practical implementation, emphasizing the necessity of robust security protocols in the deployment of RAG-based applications.

Taking a sewage treatment plant in Suzhou City, Jiangsu Province, as an example, the greenhouse gas （GHG） emissions generated in the sewage treatment system were calculated using the carbon balance method and the emission factor method. The environmental impacts and economic aspects of different treatment units in wastewater treatment plants were analyzed using life cycle assessment, cost-benefit analysis, and data envelopment analysis models, and emission reduction pathways were proposed. The results indicated that the total GHG emissions （in terms of CO2） from a certain municipal wastewater treatment plant in Suzhou were 6 653.08 kg·（104 m3）-1, with direct and indirect GHG emissions accounting for 29.22% and 74%, respectively. The reuse of treated effluent achieved a reduction of 3.3% in emissions. The biological treatment phase and the sludge treatment phase were the main impact stages for GHG emissions at a certain wastewater treatment plant in Suzhou, where the high-power equipment, specifically the blowers used in the biological treatment phase, and the use of polymeric ferric sulfate agents in the sludge treatment phase were the primary factors contributing to GHG emissions. Life cycle assessment analysis revealed that electricity consumption, direct CO2 emissions, pollutant concentration in the effluent, and the use of chemical agents at wastewater treatment plants had negative impacts on global warming, atmospheric acidification, and eutrophication of water bodies. Calculations indicated that for every 10 000 m3 of wastewater treated, the sewage treatment plant achieved a net benefit of 13 630 RMB. However, from April to May 2023, the scale efficiency of the sewage treatment plant was less than 1. This indicates that during this period, the proportion of output increase was less than that of input increase, demonstrating an irrational structure of input-output. After June, through enhancing the overall operational load, advancing technical improvements, and management efforts, the optimization of scale efficiency was achieved. A sewage treatment plant in Suzhou could achieve the goal of being "green and low-carbon" by installing high-efficiency pumps and fans, utilizing solar photovoltaic and water source heat pump systems, and making process improvements.

Abstract Participation is a prevalent topic in many areas, and data-driven projects are no exception. While the term generally has positive connotations, ambiguities in participatory approaches between facilitators and participants are often noted. However, how facilitators can handle these ambiguities has been less studied. In this paper, we conduct a systematic literature review of participatory data-driven projects. We analyse 27 cases regarding their openness for participation and where participation most often occurs in the data life cycle. From our analysis, we describe three typical project structures of participatory data-driven projects, combining a focus on labour and resource participation and/or rule- and decision-making participation with the general set-up of the project as participatory-informed or participatory-at-core. From these combinations, different ambiguities arise. We discuss mitigations for these ambiguities through project policies and procedures for each type of project. Mitigating and clarifying ambiguities can support a more transparent and problem-oriented application of participatory processes in data-driven projects.