Life Cycle Data Research Articles

Data governance in metaverse: Addressing security threats and countermeasures across the data lifecycle

Enterprise data modernization initiatives have been revolutionized by end-to-end observability capabilities, offering unprecedented visibility into complex data ecosystems. This article traces the evolution from rudimentary monitoring to sophisticated observability, establishing a theoretical framework that encompasses layered visibility across complete data lifecycles. By examining banking and insurance sector implementations, the article showcases tangible operational advantages, including automated incident remediation, regulatory compliance verification, and significant operational stability improvements. Technical deployment strategies are detailed, highlighting instrumentation methodologies, performance measurement frameworks, notification design philosophies, and context-enriched analytics. The article concludes with an exploration of emerging directions, examining intelligence-augmented observability platforms, organizational hurdles in adopting visibility-centric cultures, standardization challenges, and practical guidance for organizations undertaking data infrastructure renewal projects.

ABSTRACTThe accurate estimation of steel spring fatigue life is essential to the safe operation of railway systems. However, the absence of full life cycle data makes it difficult to determine the failure threshold for remaining life estimation. A data‐driven framework integrating finite element (FE) simulation with a transformer‐based health indicator (HI) is proposed to predict fatigue life under limited data conditions. Stress and failure‐stress data are obtained from the FE model. The sensitive features are selected using monotonicity and trend metrics for HI construction. To validate the result of the Transformer‐HI method, fatigue damage is calculated using the nominal stress method. The result is compared with the Transformer‐HI prediction. The nominal stress‐based method estimates 4.86 × 106 km, while Transformer‐HI yields 4.54 × 106 km, confirming the reliability of the Transformer‐HI framework in predicting the fatigue life of steel springs.

RDMkit: A research data management toolkit for life sciences

In the context of the digital transformation of metrology, ensuring the trustworthiness and integrity of measurement data during its generation, transmission, and storage—i.e., trustworthy detection of measurement data—has become a critical challenge. Data traces are residual marks left during the data processing, which help identify malicious activities targeting measurement data. These traces are especially important when the trust and integrity of potential data evidence are under threat. To this end, this article systematically reviews relevant core techniques and analyzes various detection methods across the different stages of the data lifecycle, evaluating their applicability and limitations in identifying data tampering, unauthorized access, and anomalous operations. The findings suggest that trace detection technologies can enhance the traceability and transparency of metrological data, thereby providing technical support for building a trustworthy digital metrology system. This review lays the theoretical foundation for future research on developing automated anomaly detection models, improving forensic techniques for data tampering in measurement devices, and constructing multi-modal, full-lifecycle traceability frameworks for measurement data. Subsequent studies should focus on aligning these technologies with metrological standards and verifying their deployment in real-world measurement instruments.

Pomegranate production in Siab (Lorestan), Iran, faces significant challenges related to high energy consumption and environmental degradation, particularly due to inefficient use of agricultural inputs such as fertilizers, water and machinery. These inefficiencies contribute to increased greenhouse gas emissions and higher production costs, making optimization efforts essential for sustainable development. This study investigated the optimization of energy consumption and the reduction of environmental impacts in pomegranate production using a combination of Data Envelopment Analysis (DEA) and Life Cycle Assessment (LCA). Data were collected through interviews with farmers and agricultural experts in the region, supported by structured questionnaires. The research evaluated several energy indicators, including an energy ratio of 2.14, which indicates that every unit of energy input yields more than double in output-comparable to other fruit crops like apple or citrus, which typically range between 1.5 and 3.0. Energy productivity was found to be 1.12 kgMJ-1, meaning 1.12 kilograms of pomegranate are produced per megajoule of energy consumed, while specific energy was calculated at 0.89 MJkg ⁻ ¹, showing relatively efficient energy use compared to similar horticultural crops. Net energy gain was 17,142.33 MJha ⁻ ¹, with total energy consumption at 15,211.04 MJha ⁻ ¹ and an energy output of 32,353.38 MJha ⁻ ¹. Economic analysis revealed a gross value of 9,081.64 USDha ⁻ ¹, fixed costs of 204.44 USDha ⁻ ¹, and gross revenue of 8,059.42 USDha ⁻ ¹, resulting in a benefit-to-cost ratio of 0.83. LCA results showed that optimized practices significantly reduced environmental impacts across most of the 15 intermediate environmental indicators analyzed. For instance, global warming potential was reduced from 40.563 kg CO₂ eq per ton of pomegranate under conventional methods to 35.975 kg CO₂ eq with optimized practices. DEA under the Variable Returns to Scale (VRS) model revealed that 66.68% of the surveyed orchards operated at 100% technical efficiency. The average technical efficiency across all units was estimated at 98.96%. The remaining 33.32% of orchards were identified as technically inefficient. Scale efficiency averaged at 99.39%, suggesting that most farms operate near optimal size.

Purpose Mega transport projects (MTPs) present intricate challenges throughout the processes of planning, appraising and managing, with environmental, social and economic impacts that often extend beyond the immediate project boundaries. If these challenges are overlooked, then they can undermine the very sustainable development goals that such projects aim to achieve. However, current research has not extensively examined the influence of these challenges on sustainable development principles (SDPs). The purpose of this study is to address this gap by identifying key challenges in MTPs and examining their influence on SDPs. Design/methodology/approach A systematic literature review of 163 papers was first conducted to identify challenges in MTPs, which were then validated through a two-round Delphi survey with industry experts. Following a pilot test to refine the questionnaire, a structured survey was administered to professionals actively engaged in transport megaprojects in India, yielding 127 completed responses that were analysed using structural equation modelling. Findings The results of structural equation modelling analysis show best fitted measurement model with 18 MTP challenges acting as indicators of six latent variables that impact sustainability principles, including challenges with project appraisal, social injustice, collaborative decision-making, environmental and occupational concerns, environmental impact assessment process and capacity building. The structural model shows that “transparency issues in decision-making”, “pollutants emission” and “monitoring and auditing deficiency” indicators are the leading indicators impacting SDPs with path coefficient values of 0.94, 0.93 and 0.91, respectively. Originality/value This study contributes to the academic discussion on megaprojects sustainability by identifying key challenges and calling for a shift towards socio-ecological resilience and equity. Importantly, this study offers practical guidance for policymakers and practitioners to overcome these challenges through inclusive appraisals capturing socio-ecological costs, equitable resettlement and stakeholder engagement, improved environmental impact assessment via life cycle and big data tools, green construction practices and capacity building through incentives and training.

Electronic health record (EHR) data are widely used in public health research, including in HIV-related studies, but are limited by potential bias due to incomplete and inaccurate information, lack of generalizability, and lack of representativeness. This study explores how workflow processes among HIV health care providers (HCPs), data scientists, and state health department professionals may potentially introduce or minimize bias within EHR data. One focus group with 3 health department professionals working in HIV surveillance and 16 in-depth interviews (ie, 5 people with HIV, 5 HCPs, 5 data scientists, and 1 health department professional providing retention-in-care services) were conducted with participants purposively sampled in South Carolina from August 2023 to April 2024. All interviews were transcribed verbatim and analyzed using a constructivist grounded theory approach, where transcripts were first coded and then focused, axial, and theoretically coded. The EHR data lifecycle originates with people with HIV and HCPs in the clinical setting. Data scientists then curate EHR data and health department professionals manage and use the data for surveillance and policy decision-making. Throughout this lifecycle, the three primary stakeholders (ie, HCPs, data scientists, and health department professionals) identified challenges with EHR processes and provided their recommendations and accommodations in addressing the related challenges. HCPs reported the influence of socio-structural biases on their inquiry, interpretation, and documentation of social determinants of health (SDOH) information of people living with HIV, the influence of which is proposed to be mitigated through people living with HIV access to their EHRs. Data scientists identified limited data availability and representativeness as biasing the data they manage. Health department professionals face challenges with delayed and incomplete data, which may be addressed statistically but require consideration of the data's limitations. Overall, bias within the EHR data lifecycle persists because workflows are not intentionally structured to minimize bias and there is a diffusion of responsibility for data quality between the various stakeholders. From the perspective of various stakeholders, this study describes the EHR data lifecycle and its associated challenges as well as stakeholders' accommodations and recommendations for mitigating and eliminating bias in EHR data. Based upon these findings, studies reliant on EHR data should adequately consider its challenges and limitations. Throughout the EHR data lifecycle, bias could be reduced through an inclusive, supportive health care environment, people living with HIV verification of SDOH information, the customization of data collection systems, and EHR data inspection for completeness, accuracy, and timeliness. Future research is needed to further identify instances where bias is introduced and how it can best be mitigated and eliminated across the EHR data lifecycle. Systematic changes are necessary to reduce instances of bias between data workflows and stakeholders.

Abstract Academic libraries play an increasingly crucial role in providing services, information, education, and infrastructure support related to research data management (RDM). This systematic review aims to provide a comprehensive and critical analysis of the state of RDM services offered by academic libraries worldwide. Utilizing the systematic review methodology, the paper examines 89 empirical studies to answer four research questions: (1) the types of RDM services implemented by academic libraries; (2) what are the infrastructure, workflow, and resources used to support these services; (3) what are the reasons for implementing these RDM services; and (4) the effectiveness of these RDM services in supporting the research data life cycle, if any. This review highlights the critical reasons academic libraries provide RDM services and how they implemented these services through partnerships, infrastructure, and systems, and adapting to new workflows within the library. These findings also examine the balance between institutional contexts, researchers' needs, and library resources required to provide these RDM services. By investigating these questions, the results will provide recommendations and guidance for academic libraries interested in implementing RDM services in their own library and institutional contexts.

Privacy preservation techniques through data lifecycle: A comprehensive literature survey

6G Network Architecture: QoS Paradigms and Data Lifecycle Management for Next-Generation Networks

This paper looks at the concept of sustainable digitalization using the interests of information access and preserving information. With the on going transformation of the digital technologies,current developments and the related need to be able to maintain fair access and long-term preservation of data are an important part of long-term sustainable development. The study focuses on the problem of the digital divide, data lifecycle and the issue of the policy and infrastructure in the hope of achieving sustainability in digital practices. Through the mixed-methods approach, the paper outlines some of the major barriers and provides the policy and practice insights in order to foster inclusive, accessible, and sustainable digital environments.

The growing global population presents a dual challenge: increasing crop production while minimizing environmental impact. To overcome this challenge, the eco-efficiency of crops must be improved. Measuring eco-efficiency—defined as the ratio of desirable output (crop production) and undesirable output (environmental degradation) to resource use (inputs)—is crucial for sustainable agriculture. This study assesses the eco-efficiency of crop production in Madhya Pradesh, India, using life cycle assessment (LCA) and data envelopment analysis–directional distance function (DEA–DDF). We obtained the input data for crops from multiple packages of practices from government sources for the 2021–2022 agricultural year. LCA quantified the environmental impact of crop production, while DEA–DDF evaluated efficiency by considering both economic output and environmental degradation. Our results indicate that rainfed wheat, maize, sorghum, and soybean exhibit production inefficiencies, with an average inefficiency of 0.22, suggesting a 22% potential for improvement. Inefficient decision-making units can enhance efficiency by optimizing input use, reducing environmental degradation, and increasing crop and residue output. The study also determines target values for input reduction and output improvement to guide sustainable agriculture. It helps optimise crop eco-efficiency by emphasizing resource-efficient and environmentally sustainable agricultural practices, thereby supporting long-term food security.

Abstract This systematic review examines serverless security mechanisms proposed between 2018 and 2024, categorizing them into a layered security model comprising runtime, network, function, orchestration, and data. The proposed model provides a structured framework to analyze serverless-specific threats and protective measures that can be used in future works to better contextualize the threat scope of new protection techniques. Our findings reveal notable advancements in serverless security but highlight persistent gaps, such as function-level observability or data lifecycle protection. In addition to cataloging existing mechanisms, we identify key research directions and share all review data to facilitate future studies. This work advances the understanding of serverless security and offers a foundation for developing more robust protective measures.

Abstract. This paper proposes a framework for dynamic depreciation estimation by integrating Building Information Modelling (BIM), cost estimation datasets and maintenance data from Computerized Maintenance Management Systems (CMMS) or Facility Management Systems (FMS). The framework operates within a Common Data Environment (CDE), a centralized platform that consolidates and synchronizes data across various systems. By leveraging interoperability standards such as Industry Foundation Classes (IFC) and Construction-Operations Building Information Exchange (COBie), the framework facilitates seamless data exchange and integration. Maintenance records, asset conditions, lifecycle data and costs of building work items are systematically tracked within the CDE, enabling real-time updates and comprehensive visibility into the state of building components. This integration provides a robust foundation for accurate and condition-based depreciation estimation, enhancing property valuation practices by reflecting the actual wear, usage and maintenance history of building assets.

The standard implementation of the Internet of Things (IoT) has renovated numerous sectors, supporting agriculture with modern technological development. Termed Agriculture-Internet of Things (Agri-IoT), this combination has helped in Smart Farming (SF) using wireless sensors that record real-time data improvement sustainable agriculture practices like irrigation, pest control, and overall field operations. So far, Agri-IoT research faces challenges, mainly focusing on data security and management, which are vulnerabilities in existing centralized solutions. Enter Blockchain Technology (BCT): a decentralized, transparent, and perfect mechanism that improves data security and access control and paves the technique for efficient transactions. This research work introduces a novel multi-tiered BCT personalized for Agri-IoT. The model comprises Edge, Fog, and Cloud levels, employing discrete 'Data Handlers' for each tier, confirming an efficient data lifecycle. Central to this model is the proposed Quantum Neural Network + Bayesian Optimization (QNN + BO), a practiced algorithm that, when combined with methods like the Elliptic Curve Cryptography (ECC) and Coyote Optimization Algorithm (COA), guarantees secure data flow, processing, and storage. The proposed QNN + BO model, evaluated using the ToN_IoT dataset, validates significant performance enhancements - reducing encryption and decryption times by up to 46.7% and 54.6%, and improving prediction accuracy with a 19.3% Mean Absolute Percentage Error (MAPE), outperforming baseline models. Additionally, it consumes up to 33% less memory, supporting its suitability for resource-constrained agricultural environments. This integrative model proposes a complete solution to connect Agri-IoT's potential while addressing its challenges.

Abstract Climate change and resource scarcity have underscored the need for sustainable product design strategies. This study introduces the Carbon Reduction Engineering Framework, a systematic approach that integrates carbon footprint reduction into product design while maintaining functionality, manufacturability, and lifecycle performance. It consists of four sequential phases: product digitalization, diagnostic analysis, product redesign and carbon footprint recalculation, and definition and selection of carbon reduction scenarios. The framework was demonstrated using a tricycle case study, achieving a 9.3% reduction in carbon footprint for a combined redesign scenario. Key modifications included geometry optimization, material substitution, and joint redesign, targeting high-impact components such as rims and mainframe. The results highlight the proposed approach to prioritize high-impact areas and balance environmental benefits with technical feasibility. Moreover, the proposed framework supports modularity and circularity principles, facilitating repair, remanufacturing, and recycling. In addition, it offers a robust tool for integrating sustainability into diverse design processes. Future work should explore dynamic lifecycle data integration, advanced manufacturing technologies, and broader economic implications.

This study advocates a “Sustainable-by-Design” approach that embeds sustainability throughout the data lifecycle—from collection to disposal—within digital transformation (DT). Based on a systematic literature review of 55 peer-reviewed articles (2006–March 2025), and guided by Dynamic Capabilities Theory, RBV/NRBV, and Institutional Theory, the paper identifies seven key enablers: Sustainable Data Analytics and AI, Strategy Alignment, Organizational Capabilities, Infrastructure, ESG Governance, Data Design, and Institutional Pressures. These are synthesized into a three-stage maturity model—Foundational, Integrated, and Transformative. Findings highlight a focus on internal capabilities while institutional forces remain underexplored. The study contributes a multi-theoretical framework and offers practical guidance for embedding ESG principles into data governance, promoting responsible and regenerative DT.

Open research data in human movement analysis: Swiss-initiated-guidelines for broader scientific communities

The world’s largest artificial intelligence models now draw more electricity than some mid-sized nations and evaporate billions of liters of freshwater each year, yet neither international climate law nor the classical transboundary harm doctrine has fully absorbed their impact. This article conducts a systematic review of empirical footprint studies with a comparative legal analysis of the no harm principle, emerging corporate due diligence statutes, and transparency rules of the Paris Agreement. Lifecycle data show that training a single GPT 3 class model consumes about 1.3 GWh of power and 5.4 million L of water, while global inference loads could withdraw 22 billion L annually by 2027—concentrated in already stressed basins. Because affordable mitigation tools (carbon-aware routing, liquid cooling, and a mixture of expert architectures) can reduce these impacts by 40–60 percent, failure to deploy them breaches the due diligence standard embedded in Trail Smelter and its progeny. This study proposes a hybrid allocation framework that attributes operational footprints to host states but assigns embodied and service-based impacts to consumer states, enabling parties to integrate Scope 3 emissions and virtual water transfers into Biennial Transparency Reports without amending treaty text. Embedding dual carbon and water baselines into Article 6 crediting schemes would channel finance toward low-impact data centers and close a rapidly widening governance gap.