A roundtable discussion: Defining urban data science

Electrochemical processes underlie the functioning of electrochemical devices for energy storage and conversion. In this paper, electrochemoinformatics is defined as a scientific discipline, a part of computational electrochemistry, dealing with the application of information technologies, specifically data science, machine learning (ML), and artificial intelligence, to solve problems related to electrochemical processes, including the development of materials and electrochemical devices. Electrochemoinformatics is closely related to chemoinformatics and materials informatics and relies on the technologies they use. The definitions and the basics of chemoinformatics and materials informatics are reviewed with an emphasis on the role of data science, data representation, the use of ML methods, and molecular and material descriptors. The third constituent part of electrochemoinformatics is full‐cell informatics, which deals with the performance and design of whole electrochemical devices. The article focuses on the use of electrochemoinformatics in battery science and technology (battery informatics), including the development of electrolyte and electrode materials, battery performance monitoring, modeling, and design. The scope and the main challenges facing electrochemoinformatics are discussed.

During geotechnical and geophysical site characterisation for large infrastructure projects, significant data volumes are being collected which need to be processed and interpreted. Due to the limited budgets available for site characterisation and the various sources of uncertainty, the interpretation relies on a combination of data from various sources (e.g. in-situ and laboratory tests), the use of parameter correlations from the literature and expert judgement. In recent years, modern data science techniques have become increasingly accessible to practicing engineers and researchers and they offer the possibility to improve several aspects of the site characterisation and parameter selection process. Machine learning models can be trained on high-quality datasets and expert judgement can also be internalised in the model formulations. In this contribution, the role of data science and machine learning for geotechnical site characterisation is discussed based on several example applications using datasets from offshore wind farm projects. The role of data coverage and data quality is discussed as well as the role of geophysical data for interpolating geotechnical point measurements in a quantitative way. Supervised and unsupervised machine learning techniques are explained and illustrated on the provided datasets. Finally, a perspective is given on the role of the emerging Large Language Models (LLM) for geotechnical site characterisation applications.

Scientific research processes are accompanied by burgeoning amounts of research data that serves as findings of the research and provides a platform for the results to be verifiable. Scientific researchers are under pressure to make their data openly available to other researchers for use. Access to scientific data and its curation has become a dominant theme in scientific policy discussions. Besides the funder requirements, researchers are making their data openly available for a number of reasons that include, raising the profiles of their research outputs, to increase their citations, to validate their research outputs and for their authenticity and integrity as researchers. However, not all researchers possess the skills to curate their own data and make it openly available for use in an open environment that is FAIR (Findable, Accessible, Interoperable and Reusable). The primary objective of this paper is to raise awareness of the strategic role of data scientists in the scientific research process. The study seeks to contribute towards a growing and emergent discourse of data science in HEIs. It also seeks to contribute towards an educational model for data scientists in HEIs in South Africa. In-order to realise these objectives the paper asks the following research questions:How are the emergent roles of data scientists conceptualised in HEIs in South Africa?What is the strategic role of a data scientist in the scientific research process?.This study analysed data science programs offered by universities in South Africa to understand how the role of data scientists is conceptualised and to delineate the scope of this emergent field. A qualitative content analysis survey method is used to analyse courses offered by HEIs in South Africa. Preliminary findings from the research revealed that data scientists are drawn from fields like mathematics, statistics, information science, and computer science. The practical jobs of data scientists as conceptualised by the majority of universities employs theories drawn from the information science, computer science and statistics. The data scientists are expected to work closely with the researchers where the research is done. Most universities are offering data science programs at post-graduate level as professional courses for learners coming in from diverse backgrounds. The study recommends that the next generation of data scientists in a South African context should possess a cocktail of skills that enable them to conduct data analysis and enquiry to enable others to work with digital data. Presentation at the UCT E-Research Conference 2019: DataScience4D: Explore the practice, principles and opportunities of data science for development15 – 18 April 2019 held at the University of Cape Town

<strong> A Panel from the 2019 American Library Association Annual Conference in Washington D.C. </strong> <em> Monica Ihli, Librarian and Liaison to Oak Ridge National Laboratory, Data Science, and Energy Science at the University of Tennessee, summarizes the discussion </em> A speaker panel assembled on Saturday, June 22nd 2019 at the 2019 American Library Association Annual Conference, to address and promote dialogue about the barriers to

We thank the discussants for their perspectives on our presentation (Hero et al., 2023) of emerging challenges in cybersecurity and the role of data science in addressing these challenges.We enjoyed reading the discussants' comments, as they amplify and reinforce our principal point: that statistical methods of data science will have an increasingly important impact on cybersecurity solutions.Each of the discussants' narratives stands on its own and we are in agreement with most of their points.The comments in the contribution of discussants Sanna Passino et al. ( 2023) are highly relevant, both from a foundations standpoint and from an applications perspective.We thank them for their remarks on challenges, data structures, and future directions in statistical cybersecurity.The proposal by Sanna Passino et al. ( 2023) to segment multimodal data into a few well-defined data structures and design fusion approaches for anomaly analysis over multiclass data is timely and could potentially enable a systematic approach to cybersecurity analysis of complex systems.We heartily agree that data structures, data fusion, and streaming methods will be important components to developing domain-specific data-driven cybersecurity solutions.Concerning data structures, Sanna Passino et al. ( 2023) give three common examples: graphs, point processes, and textual data.We note that data can either come directly in these forms or, more often, they are derived from more complex data types, such as images and video; for example, respectively as spatial correspondence graphs, event-marking point processes, or text captions.In the latter case, it may be beneficial to consider these data types as unstructured and in context, rather than solely considering the extracted data structures.Below we add to the discussant's comments on the importance of each of the three data structures to cybersecurity.

Financial market efficiency is significantly influenced by the availability and quality of information, with information asymmetry posing a major barrier to optimal market functioning. This article reviews the role of data science in mitigating information asymmetry and enhancing market efficiency, comparing traditional approaches with modern data-driven methods (e.g., machine learning, NLP, and blockchain). It systematically evaluates traditional approaches used to measure and mitigate information asymmetry and highlights their limitations in accurately capturing complex market dynamics. Traditional approaches such as statistical testing, price behavior analysis, and asset pricing models provide fundamental insights but often fail to capture complex, non-linear market dynamics, such as adverse selection, moral hazard, and asset mispricing, due to their reliance on historical data and linear assumptions. In contrast, data science has revolutionized financial market analysis by combining machine learning, natural language processing (NLP), big data analytics, and blockchain technology to solve information imbalances. It enables real-time analysis of unstructured data, improves predictive modeling, and enhances transparency through sentiment analysis, algorithmic trading, and decentralized ledgers. It concludes that integrating data science with traditional finance theory significantly reduces information gaps, offering policymakers and investors tools to foster fairer, more efficient markets. This bridges theoretical finance with computational innovations, demonstrating how data science addresses longstanding limitations in measuring and improving market efficiency.

Continental Physiography, Climate, and the Global Distribution of Human Population

The digital revolution made available vast amounts of data both in industry and in the research landscape. The ability to manipulate and extract knowledge and value from this data represents a new profession called the Data Scientist: expected to be the most visible job in future years. The EDISON project has been established in order to support universities, research centers, industry and research infrastructure organisations to cope with the potential shortfall of Data Scientists, to define the framework of competences as well as the body of knowledge for this profession. In this paper the EDISON team describes how it intends to nurture the profession of Data Scientist to cope with the expected increase in demand. The strategy proposed is based on both the analysis of the demand side (industries, research centers and research infrastructure organisations) and the supply side (Universities and training centers) bridging between the providers and employers by cooperating on the establishment of a Competence Framework and a Body of Knowledge for the Data Scientist Professional. The project will exploit piloting initiatives in cooperation with pioneer universities and also involve external experts as evangelists.

This paper explores the importance of workflow automation in unlocking the business value of data science in Nigeria's oil and gas industry, specifically in the midstream and downstream sectors. It provides an in-depth analysis of the current state of data science and workflow automation in the industry and presents a case study to demonstrate the benefits of implementing these technologies. The paper also proposes a framework for achieving better outcomes on data science projects and offers recommendations for leveraging workflow automation to enhance and deliver optimal value creation through data science.

Wintershall Dea is developing together with partners a digital system to monitor and optimize electrical submersible pump (ESP) performance based on the data from Mittelplate oil field. This tool is using machine learning (ML) models which are fed by historic data and will notify engineers and operators when operating conditions are trending beyond the operating envelope, which enables an operator to mitigate upcoming performance problems. In addition to traditional engineering methods, such a system will capture knowledge by continuous improvement based on ML. With this approach the engineer has a system at hand to support the day-to-day work. Manual monitoring and on demand investigations are now backed up by an intelligent system which permanently monitors the equipment. In order to create such a system, a proof of concept (PoC) study has been initiated with industry partners and data scientists to evaluate historic events, which are used to train the ML-systems. This phase aims to better understand the capabilities of machine learning and data science in the subsurface domain as well as to build up trust for the engineers with such systems. The concept evaluation has shown that the intensive collaboration between engineers and data scientist is essential. A continuous and structured exchange between engineering and data science resulted in a mutual developed product, which fits the engineer's needs based on the technical capabilities and limits set by ML-models. To organize such a development, new project management elements like agile working methods, sprints and scrum methods were utilized. During the development Wintershall Dea has partnered with two organizations. One has a pure data science background and the other one was the data science team of the ESP manufacturer. After the PoC period the following conclusions can be derived: (1) data quality and format is key to success; (2) detailed knowledge of the equipment speeds up the development and the quality of the results; (3) high model accuracy requires a high number of events in the training dataset. The overall conclusion of this PoC is that the collaboration between engineers and data scientists, fostered by the agile project management toolkit and suitable datasets, leads to a successful development. Even when the limits of the ML-algorithms are hit, the model forecast, in combination with traditional engineering methods, adds significant value to the ESP performance. The novelty of such a system is that the production engineer will be supported by trusted ML-models and digital systems. This system in combination with the traditional engineering tools improves monitoring of the equipment and taking decisions leading to increased equipment performance.

The European Commission requested EFSA to review the former EFSA/BIOHAZ opinion on microbiological risks in infant formulae and follow-on formulae. Particularly it was requested to provide a scientific opinion on the possible correlation between Enterobacteriaceae and Enterobacter sakazakii and Enterobacteriaceae and Salmonella in infant formulae as well as on the correlation between Enterobacteriaceae and Salmonella in follow-on formulae, in the light of new scientific data. The usefulness of Enterobacteriaceae as indicators for E. sakazakiiand Salmonella in powdered infant formulae and follow-on formulae was also requested if available scientific data do not indicate a clear correlation. The products of concern are powdered infant formulae and powdered follow-on formulae. Consideration of microbiological criteria is outside of the remit of this opinion. Enterobacter sakazakii and Salmonella, both in the family Enterobacteriaceae, are hazards that are occasionally present in powdered infant formulae and powdered follow-on formulae. Given that Salmonella and E. sakazakii are both members of the family Enterobacteriaceae, there is some relationship between the presence of E. sakazakii and Salmonella and the presence of Enterobacteriaceae. If E. sakazakii or Salmonella are present, Enterobacteriaceae should be found provided that the methods are equally sensitive. In some instances levels are so low that if E. sakazakii or Salmonella is detected, Enterobacteriaceae are not detected, just due to “chance”. However, the reverse is not inevitably true since the presence of Enterobacteriaceae does not necessarily mean the presence of Salmonella or E. sakazakii. Industry data from different companies showed that E. sakazakii / Enterobacteriaceae ratios range from 9 to 30%. A correlation between the prevalences of Enterobacteriaceae and that of E. sakazakii was observed in one plant but sufficient specific information was not available for the other plants. A correlation might be apparent according to the ecology of industrial units, with each unit having a characteristic ratio. The BIOHAZ Panel concluded that there is a relationship between the presence of Enterobacteriaceae and E. sakazakii in powdered infant formulae. No universal correlation can be established. There are indications that correlations could be established at individual plant level. It is not possible to establish a correlation between Enterobacteriaceae and Salmonella in infant formulae and in follow-on formulae and, because Salmonella is so rarely present, suitable data are not available. Industrial experience indicates that monitoring Enterobacteriaceae in the processing environment and in the product can be used to confirm the application of GMP/GHP and HACCP and would be expected to reduce the prevalence of E. sakazakii. The BIOHAZ Panel recommended that documented data ideally using standardized methods be accumulated (by industry, regulators, academia) testing for Enterobacteriaceae, Salmonella, and E. sakazakii in the same samples (ingredients, end-product and environmental) across the industry to explore correlations and their relevance in setting control measures

The term “Industry 4.0” signifies a pivotal stage in industrial development, representing the current wave of transformation characterized by the integration of digital technologies into manufacturing and business processes. This concept spans multiple disciplines, including industrial engineering, data science, marketing, and strategic management. While a considerable volume of research has been conducted under the umbrella of Industry 4.0, there remains a lack of a comprehensive synthesis that captures the full scope and evolution of the field. The objective of this study is to systematically analyze, classify, and evaluate published research related to Industry 4.0, while identifying key gaps and forecasting potential future research directions. To accomplish this, a total of 10,696 journal articles indexed in the SCOPUS database from 2013 to 2023 were analyzed. The Latent Dirichlet Allocation (LDA) algorithm—a widely used probabilistic topic modeling technique in text mining—was applied to uncover latent themes within the literature. The analysis revealed 41 major topics, which were subsequently organized into five overarching categories: Sustainability, Manufacturing, Technology, Process, and Management. Findings indicate a notable shift in research emphasis over the past decade—from predominantly technical and engineering-focused issues toward broader concerns involving process optimization and organizational management. This transition highlights the growing importance of interdisciplinary approaches in addressing the complex challenges posed by Industry 4.0. Ultimately, this study not only maps the intellectual structure of existing research but also serves as a valuable resource for scholars, helping to navigate emerging trends and target underexplored areas in future investigations. This comprehensive thematic overview supports academics in pinpointing neglected subject areas, aligning future investigations with emerging priorities, and refining the strategic focus of Industry 4.0 research agendas.

The IT landscape is continuously changing based on the needs of large enterprises and also the needs of individuals who use technology for their personal undertakings. The change derives from the fact that people want to do things faster and in a more reliable way, so they can achieve better results more rapidly, thus IT developers, be it on the software or hardware field, are always in search for developing and providing better tools. Data Science is probably the most important development in the field of Data Analytics, which in return, is the most important aspect of a successful enterprise. To make things more qualitatively, we are entering a new age of computer hardware such as Quantum and DNA computers. Our paper will deal particularly with the importance of these developments in this age of IT, by bringing forward examples of how these technologies can help in the betterment of data management. Keywords: Quantum, DNA Computers, data science, data management.

In recent days, there has been a huge increase in data due to emerging new technologies. This huge data is created for different reasons, like markets, IoT sensors, email mining, information privacy, stock data, and so on. This data can be gathered and analyzed to provide useful information related to human life. This has generated a prospect for analyzing and deriving meaningful information from massive datasets. So, big data and data science are recent, emerging technology fields that are a combination of different activities, like data analysis and data mining. The various techniques, such as programming, pattern recognition and learning, statistics, and data technology, visual image, and high-performance computing are used. This chapter presents a review of how data science is applied to address various critical problems that exist in the field of big data analytics. It also explains the opportunities and challenges that come with the increase in new computational technologies.

Data science is revolutionizing sustainable agriculture by enabling precision farming, optimizing resource utilization, and improving crop yield predictions. By leveraging big data analytics, machine learning (ML), and the Internet of Things (IoT), farmers can enhance soil health, reduce water consumption, and minimize environmental impact. This paper explores the role of data science in sustainable agriculture, covering key applications such as AI-driven crop monitoring, climate-responsive farming, and predictive analytics for pest control. Additionally, challenges such as data accessibility, technological adoption in rural areas, and ethical concerns related to data ownership are discussed, along with emerging trends in AI-powered smart farming