Data-driven Research Research Articles

Time-lapse Full Waveform Inversion with a new gradient-weighting strategy applied to marine seismic data

Time-lapse full waveform inversion (FWI) has the potential to reveal high-resolution reservoir production-related velocity changes. However, time-lapse FWI can introduce inversion artifacts, which might mask the true time-lapse signature within the reservoir zone, especially for the inversion of field data examples. We implement sequential bootstrap time-lapse FWI on marine seismic data from North Australia and observe noticeable inversion artifacts. To better understand these time-lapse FWI artifacts, we implement time-lapse FWI using the seismic data with different frequency bands and observe the distribution of the potential true time-lapse signature and inversion artifacts.On the inverted δVp (P wave velocity change) models using data with different frequency bands, the distribution of inversion artifacts exhibits inconsistencies, while the inverted δVp at the reservoir remains consistent. In order to comprehend these observations, we analyze the origin of these time-lapse artifacts from a theoretical perspective and find that a portion of these artifacts may arise from inversion null space and differences in data residuals between baseline and monitoring inversions. Building on these insights, we develop a novel time-lapse FWI method to suppress these inversion artifacts. We use the energy of the inverted δVp utilizing data with a lower dominant frequency band as a gradient-weighting term for time-lapse FWI using data with a higher dominant frequency band. The test of the novel time-lapse FWI method on marine data demonstrates its capability to suppress inversion artifacts effectively. The inverted δVp primarily reflects the 4D response, which is interpreted to be the softening of the reservoir caused by gas coming out of the solution within the oil column due to a pressure drop within the reservoir. To validate this data-driven research solution, we conduct tests on the SEG Advanced Modeling 4D (SEAM4D) model and synthetic 4D data where we know the true underlying earth models and time-lapse reservoir changes.

Enabling coastal analytics at planetary scale

Coastal science has entered a new era of data-driven research, facilitated by satellite data and cloud compute. Despite its potential, the coastal community has yet to fully capitalize on these advancements due to a lack of tailored data, tools, and models. This paper demonstrates how cloud technology can advance coastal analytics at scale. We introduce GCTS, a novel foundational dataset comprising over 11 million coastal transects at 100-meter resolution. Our experiments highlight the importance of cloud-optimized data formats, geospatial sorting, and metadata-driven data retrieval. By leveraging cloud technology, we achieve up to 700 times faster performance for tasks like coastal waterline mapping. A case study reveals that 33% of the world’s first kilometer of coast is below 5 meters, with the entire analysis completed in a few hours. Our findings make a compelling case for the coastal community to start producing data, tools, and models suitable for scalable coastal analytics.

The Synergistic Impact of Human-AI Collaboration: A Multi-Domain Analysis

This comprehensive article explores the transformative impact of human-AI collaboration across healthcare, manufacturing, and scientific research. The article examines how the synergistic integration of artificial intelligence with human expertise is revolutionizing key industries, enhancing efficiency, accuracy, and innovation. In healthcare, we discuss the implementation and impact of AI-assisted diagnostics and AI-augmented surgery, highlighting improvements in diagnostic accuracy and surgical outcomes. The manufacturing sector is analyzed through the lens of AI-driven quality control systems and the introduction of collaborative robots (cobots), demonstrating significant gains in productivity and worker safety. In scientific research, we explore AI's role in data-driven research and hypothesis testing, particularly in areas such as drug discovery and genomics. The article also provides a critical analysis of the complementary strengths of humans and AI, addresses ethical considerations in AI implementation, and speculates on the future potential of this collaboration. Drawing on recent research and case studies, this article presents a holistic view of how human-AI synergy is driving progress across multiple domains, while also considering the challenges and ethical implications of this technological integration. The findings suggest that the future of human-AI collaboration holds immense potential for accelerating scientific discovery, improving healthcare outcomes, and revolutionizing manufacturing processes, ultimately benefiting society as a whole.

National biodiversity data infrastructures: ten essential functions for science, policy, and practice

Abstract Today, at the international level, powerful data portals are available to biodiversity researchers and policymakers, offering increasingly robust computing and network capacities and capable data services for internationally agreed-on standards. These accelerate individual and complex workflows to map data-driven research processes or even to make them possible for the first time. At the national level, however, and alongside these international developments, national infrastructures are needed to take on tasks that cannot be easily funded or addressed internationally. To avoid gaps, as well as redundancies in the research landscape, national tasks and responsibilities must be clearly defined to align efforts with core priorities. In the present article, we outline 10 essential functions of national biodiversity data infrastructures. They serve as key providers, facilitators, mediators, and platforms for effective biodiversity data management, integration, and analysis that require national efforts to foster biodiversity science, policy, and practice.

Taking Law Seriously: The Challenges of Law as Research Data in Socio-legal Scholarship

‘Law-as-data’ is often understood in terms of computational data. This article reframes it around a research-centred notion of data, broadening the methodological possibilities available to empirical legal research. This approach transcends the qualitative/quantitative divide, identifying common challenges that arise when law is treated as research data. Methodological tools from the social sciences and examples from recently published socio-legal scholarship are used to demonstrate how law presents unique challenges at the stages of conceptualisation, operationalisation and coding. These challenges stem from factors such as the non-commensurability of legal texts, the fluidity and contested meaning of legal notions, the difficulty of systematically gathering legal data and the risk of bias towards data-driven research rather than law- or reality-centred inquiry. However, strategies and best practices can be developed to address or mitigate these risks. Legal academics are particularly well suited to this task, given the high level of expertise required to effectively leverage law as research data. The article argues that a true methodological naturalisation of law as research data, which embraces the unique characteristics and limitations of law as a data source, would greatly benefit legal studies by diversifying its methodological toolbox while preserving its specificities.

What is the real value of omics data? Enhancing research outcomes and securing long-term data excellence.

A wealth of high-throughput biological data, of which omics constitute a significant fraction, has been made publicly available in repositories over the past decades. These data come in various formats and cover a range of species and research areas providing insights into the complexities of biological systems; the public repositories hosting these data serve as multifaceted resources. The potentially greater value of these data lies in their secondary utilization as the deployment of data science and artificial intelligence in biology advances. Here, we critically evaluate challenges in secondary data use, focusing on omics data of human embryonic kidney cell lines available in public repositories. The emerging issues are obstacles faced by secondary data users across diverse domains as they concern platforms and repositories, which accept deposition of data irrespective of their species type. The evolving landscape of data-driven research in biology prompts re-evaluation of open access data curation and submission procedures to ensure that these challenges do not impede novel research opportunities through data exploitation. This paper aims to draw attention to widespread issues with data reporting and encourages data owners to meticulously curate submissions to maximize not only their immediate research impact but also the long-term legacy of datasets.

Mediating Role of Sustainability Reporting Quality on the Relationship Between Green Banking and Firm Value

Abstract The mounting environmental concerns have become a pressing issue across industries. Nevertheless, the banking sector has a distinct influence in shaping economic growth and development. This study sought to evaluate the impact of green banking in strengthening corporate value through its level of sustainability reporting in order to address these concerns. A research framework was developed based on theoretical support. The sampled data was collected from banks listed on the Indonesian Stock Exchange from 2018-2021. An empirical analysis was performed through hierarchical regression. The study’s findings indicated that green banking positively and significantly impacts firm value. Furthermore, there is a mediating effect between green banking and business value due to the quality of sustainability reporting. The empirical test revealed that the quality of sustainability reporting has a mediating effect to some extent. The results also showed that there is an interaction between business size (assets) and correlations between firm value and green banking. By undertaking a data-driven research that explains the impact of green banking on business value, this study aims to fill a significant gap in the body of knowledge on green banking and sustainability reporting.

A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges.

People diagnosed with cancer and their formal and informal caregivers are increasingly faced with a deluge of complex information, thanks to rapid advancements in the type and volume of diagnostic, prognostic, and treatment data. This commentary discusses the opportunities and challenges that the society faces as we integrate large volumes of data into regular cancer care.

Data-Driven Clinical Pharmacy Research: Utilizing Machine Learning and Medical Big Data.

To conduct clinical pharmacy research, we often face the limitations of conventional statistical methods and single-center observational study. To overcome these issues, we have conducted data-driven research using machine learning methods and medical big data. Decision tree analysis, one of the typical machine learning methods, has a flowchart-like structure that allows users to easily and quantitatively evaluate the occurrence percentage of events due to the combination of multiple factors by answering related questions with Yes or No. Using this feature, we first developed a risk prediction model for acute kidney injury caused by vancomycin, a condition we frequently encounter in clinical practice. Additionally, by replacing the prediction target from a binary variable (i.e., presence or absence of adverse drug reactions) to a continuous variable (i.e., drug dosage), we built a model to estimate the initial dose of vancomycin required to reach the optimal blood level recommended by guidelines. We found its accuracy to be better than that of conventional dose-setting algorithms. Moreover, employing Japanese medical big data such as the claims database helped us overcome the major limitations of conventional clinical pharmacy research such as institutional bias caused by single-center studies. We demonstrated that the combined use of machine learning and medical big data could generate high-quality evidence leveraging the strengths of each approach. Data-driven clinical pharmacy research using machine learning and medical big data has enabled researchers to surpass the limitations of conventional research and produce clinically valuable findings.

THE IMPORTANCE OF TEACHING NATURAL SCIENCES WITH THE HELP OF INFORMATION AND COMMUNICATION TECHNOLOGIES

In today's digital age, integrating Information and Communication Technologies (ICT) in natural sciences education has become essential for fostering scientific literacy, enhancing student engagement, and preparing learners for a technology-driven world. This article explores the multifaceted benefits of ICT in teaching natural sciences, focusing on its role in improving conceptual understanding, promoting interactive learning experiences, and supporting data-driven research skills. By leveraging tools such as simulations, interactive models, and virtual labs, educators can create dynamic environments that encourage critical thinking and practical application of scientific theories. Additionally, ICT facilitates personalized learning by adapting to individual student needs and learning styles, thereby supporting differentiated instruction. This article discusses the challenges and strategies in implementing ICT effectively in natural science classrooms and offers insights into future trends in technology-enhanced science education. Emphasizing the importance of teacher training, infrastructure support, and curriculum alignment, the article argues that ICT is a supplementary tool and a transformative force in modern science education.

Bibliometric Assessment of Data-Driven Marketing Research Trends in the Last Two Decades

This study presents a bibliometric analysis of data-driven marketing research trends over the last two decades, with a focus on its intersection with innovation, decision-making, and related topics. Using VOSviewer for network visualization, we analyze co-authorship, keyword co-occurrence, publication frequency, and country collaboration to uncover key themes and research developments. The findings indicate a significant rise in data-driven marketing research, particularly from 2015 onwards, driven by advancements in big data, machine learning, and artificial intelligence. Co-authorship networks reveal strong interdisciplinary collaboration, while keyword co-occurrence maps highlight the growing role of innovation, decision-making, and machine learning in data-driven marketing. Additionally, country collaboration networks show the United States, China, the United Kingdom, and India as central contributors to global research. The keyword density heatmap emphasizes the increasing focus on data-driven innovation and product development. These insights offer valuable implications for academics and practitioners seeking to understand and apply data-driven marketing in a rapidly evolving digital landscape.

Data-Driven Research on Belt Conveyors Energy Efficiency Classification

Data-Driven Research on Belt Conveyors Energy Efficiency Classification

A scenario-customizable and visual-rendering simulator for on-vehicle vibration energy harvesting

The rising demand for renewable energy supply in standalone computing devices has led to the emergence of vibration energy harvesting (VEH) to overcome technical and environmental challenges. For instance, VEH is desirable in IoT scenarios where maintaining a battery supply is non-sustainable or impractical due to many devices or remote circumstances. VEH can be environmentally friendly given that it reduces the reliance on traditional battery production and usage, thus reducing the carbon footprint and chemical waste in disposable batteries. However, a significant hurdle in VEH adoption is the lack of effective simulation tools for generating various application scenarios to describe, validate, or predict the efficacy of the VEH-based devices. It is necessary for designing and implementing a VEH simulator for a variety of realistic application scenarios. Being the first of its kind, this study presents a scenario-customizable and visual-rendering VEH simulation system based on the Unity3D Engine. The proposed simulator features a modular design that consists of several key functional components including vibration scenarios’ creation and manipulation, VEH model specification, Unity-Python Co-computing mechanism, and 3D visualization. This paper also presents two AI-based case studies leveraging the functionality and data provided by the simulator to demonstrate its potential for data-driven research and applications.

Data-Driven Materials Research and Development for Functional Coatings.

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time-consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data-driven approaches are gaining traction in materials science. In this paper, recent advances in data-driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data-driven processes. It is demonstrated how data-driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data-driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data-driven materials R&D for functional coatings, shedding light on its potential and future developments.

Predicting effective thermal conductivity of HGM composite using ML

Data-driven materials research can be used as an effective tool to supplement conventional approaches in designing new materials. Hollow glass microsphere (HGM) composites are widely used for high-temperature thermal insulation applications, and their synthesis is traditionally done by varying parameters of the constituents on a trial-and-error basis. In this work, a prediction tool based on a supervised machine learning (ML) model is developed to predict the effective thermal conductivity (ETC) of an HGM composite by tailoring the composition and parameters of the constituents to reduce the time and cost involved. A comprehensive database containing the various input features is generated from previous experimental investigations conducted on various HGM composites. ML models, namely random forest regression (RF), K-nearest neighbor (KNN), support vector regression (SVR), and artificial neural network (ANN), are used to predict the ETC of HGM composites. Feature importance analysis showed that the matrix material’s thermal conductivity and the composite’s bulk density have the highest impact on the ETC of the HGM composite, followed by porosity and average microsphere size. ANN emerged as the best model to predict HGM composite ETC with the lowest root mean square error and highest R2 value for predictions. Moreover, a systematic approach for key feature selection using ANN shows that adding or omitting additional features beyond an optimal combination degrades the model’s predictive accuracy.

A comprehensive data network for data-driven study of battery materials

ABSTRACT Data-driven material research for property prediction and material design using machine learning methods requires a large quantity, wide variety, and high-quality materials data. For battery materials, which are commonly polycrystalline, ceramics, and composites, multiscale data on substances, materials, and batteries are required. In this work, we develop a data network composed of three interlinked databases, from which we can obtain comprehensive data on substances such as crystal structures and electronic structures, data on materials such as chemical composition, structure, and properties, and data on batteries such as battery composition, operation conditions, and capacity. The data are extracted from research papers on solid electrolytes and cathode materials, selected by screening more than 330 thousand papers using natural language processing tools. Data extraction and curation are carried out by editors specialized in material science and trained in data standardization.

Bringing data to life: A model for involving lived experience in data-driven research

ObjectiveIt is generally accepted that involving experts by experience is necessary to deliver research that is relevant to community need and to progress the data democratisation agenda. We have piloted and evaluated a model of co-production for data-driven research with seldom heard groups. ApproachIn partnership with the charity Voice of Young People in Care (VOYPIC) and a group of care experienced young people, the Administrative Data Research Centre Northern Ireland (ADRC NI) piloted and implemented a model of co-production in data-driven research. To evaluate the model, interviews were conducted with the young people, staff at VOYPIC, and ADRC NI researchers and public engagement team members. ResultsThematic analysis of interview data identified numerous benefits and barriers to co-producing data-driven research with experts by experience. We offer reflections from three perspectives (the young people, VOYPIC and ADRC NI), as well as a model that can be adapted with different lived experience groups in other data-driven research programmes. Conclusion Experts by experience can be equal partners in most stages of the data research cycle, and there are tangible benefits for all partners and the research itself. However, aligning the aspirational principles of co-production with the realities of delivering data research is challenging and has implications for achieving the democratisation of data. ImplicationsThis session will share real world perspectives on the co-production of data research with seldom heard groups.

Democratising data within a data-driven research centre: Examining the role of public engagement and involvement

Over the last decade there has been an increasing focus on utilising government-held data for large-scale data linkage research projects, with an emphasis on public benefit. Most, if not all, administrative data research initiatives will recognise public involvement and engagement (PI&E) as a cornerstone of research and emphasise that administrative data is essentially public data, and, therefore, that publics must have a say in how it is used. Much of this focuses on engaging with communities and their representative organisations. What is less explicitly discussed is the role that data-driven research plays within a broader public realm that is increasingly driven by data, or datafied. This paper will examine the ways in which PI&E both challenges and contributes to the datafication of society, through the work of the Administrative Data Research Centre Northern Ireland (ADRC NI), part of ADR UK. It will explore if – and how – PI&E in administrative data research can play a role in democratising this datafied society. The paper will use the Northern Ireland Public Data Panel (NIPDP) and the Voices of Young People in Care (VOYPIC) initiative as case studies to explore the ways in which ADRC NI successfully or unsuccessfully create sites of democratisation within the data ecosystem. Finally, the paper will consider how to amplify the democratic benefits of PI&E in data-driven research while minimising any potential harms, in the form of a potential framework for data justice within PI&E in data-driven research.

GDPR-anonymous population overlap estimation

The feasibility of many collaborative, data-driven research projects in Public Health and Social Sciences depends on the size of the overlap between the populations of contributing data partners, that is, the number of individuals for whom all parties can provide data. Where the identity of respective population members is subject to privacy protections, the overlap is traditionally estimated using de-identified cryptographic tokens to represent individuals. This does however not meet the strict standard of anonymity under GDPR and potentially runs counter to emerging privacy laws of US States, and may thus impose significant regulatory and governance burdens on research institutions, even at the early scoping stage. We present a novel method for overlap estimation, where two data partners construct a single, anonymous Bloom filter for their populations from which the overlap can be estimated. Two new techniques -- (a) the randomization of the contribution of individual population members to the Bloom filter and (b) an interactive bit-flipping protocol between data partners -- are presented which ensure that the Bloom filters are safe against dictionary attack by one party against the other party. We discuss the security aspects of this method, present the required mathematical formulas for calculating the overlap estimate, and report on the practical performance in terms of computational effort and resulting accuracy. The goal of the method is to facilitate new research by reducing the cost and complexity of initial planning and by making the overlap estimate GDPR-anonymous, and thus safer and subject to less onerous compliance requirements.

Implementing geographic information systems in academic paper search: A framework and case study: GISAPS

The Geographic Information Systems Academic Publication Search (GISAPS) project pioneers the integration of spatial data into academic research through a novel online platform. Utilizing Web Map Service (WMS) and advanced search functionalities, GISAPS enables efficient access and analysis of georeferenced academic publications. This paper outlines the development and capabilities of GISAPS, including experimental WMS layer integration for enhanced data visualization. Key contributions include facilitating interdisciplinary research, improving spatial data accessibility, and promoting open science through its open-source framework. GISAPS's implications for academia and the research community are significant, offering new avenues for data-driven research and education. Future enhancements aim to incorporate real-time data feeds, advanced spatial analysis tools, and mobile accessibility, ensuring GISAPS remains at the forefront of GIS technology in academic settings.