In the scientific community, and particularly in psychology and health, there has been an active and ongoing debate on the relative merits of adopting either quantitative or qualitative methods, especially when researching into human behaviour (Bowling, 2009; Oakley, 2000; Smith, 1995a, 1995b; Smith, 1998). In part, this debate formed a component of the development in the 1970s of our thinking about science. Andrew Pickering has described this movement as the "sociology of scientific knowledge" (SSK), where our scientific understanding, developing scientific ‘products’ and 'know-how', became identified as forming components in a wider engagement with society’s environmental and social context (Pickering, 1992: 1). Since that time, the debate has continued so that today there is an increasing acceptance of the use of qualitative methods in the social sciences (Denzin & Lincoln, 2000; Morse, 1994; Punch, 2011; Robson, 2011) and health sciences (Bowling, 2009; Greenhalgh & Hurwitz, 1998; Murphy & Dingwall, 1998). The utility of qualitative methods has also been recognised in psychology. As Nollaig Frost (2011) observes, authors such as Carla Willig and Wendy Stainton Rogers consider qualitative psychology is much more accepted today and that it has moved from "the margins to the mainstream in psychology in the UK." (Willig & Stainton Rogers, 2008: 8). Nevertheless, in psychology, qualitative methodologies are still considered to be relatively 'new' (Banister, Bunn, Burman, et al., 2011; Hayes, 1998; Richardson, 1996) despite clear evidence to the contrary (see, for example, the discussion on this point by Rapport et al., 2005). Nicki Hayes observes, scanning the content of some early journals from the 1920s – 1930s that many of these more historical papers "discuss personal experiences as freely as statistical data" (Hayes, 1998, 1). This can be viewed as an early development of the case-study approach, now an accepted methodological approach in psychological, health care and medical research, where our knowledge about people is enhanced by our understanding of the individual 'case' (May & Perry, 2011; Radley & Chamberlain, 2001; Ragin, 2011; Smith, 1998)

Why do nations produce scientific research? This is a fundamental problem in the field of social studies of science. The paper confronts this question here by showing vital determinants of science to explain the sources of social power and wealth creation by nations. Firstly, this study suggests a new general definition of science and scientific research that synthetizes previous concepts and endeavors to extend them: Science discovers the root causes of phenomena to explain and predict them in a context of adaptation of life to new economic and social bases, whereas scientific research is a systematic process, applying methods of scientific inquiry, to solve consequential problems, to satisfy human wants, to take advantage of important opportunities and/or to cope with environmental threats. In particular, science and scientific research are driven by an organized social effort that inevitably reflect the concerns and interests of nations to achieve advances and discoveries that are spread to the rest of humankind. This study reveals that scientific research is produced for social and economic interests of nations (power, wealth creation, technological superiority, etc.), rather than philosophical inquiries. A main implication of this study is that the immense growth of science in modern society is not only due to activity of scientists but rather to general social efforts of nations to take advantage of important opportunities and/or to cope with environmental threats, such as war. Empirical evidence endeavors to support the sources of scientific research for nations, described here. Finally, relationships between R&D investment and productivity, and research policy implications are discussed.

ABSTRACT Recent concern about the impact of environmental preservation and the health of living beings has opened new avenues for scientific research. In this context, contemporary analytical chemistry has been marked by the development of green analytical methodologies, which aim to reduce the use of toxic reagents and minimize the environmental impact of analytical processes. Progress in this area involves the optimization of sample preparation techniques and the use of new functional materials, which contribute to a more sustainable and efficient analysis. Among these methodologies, miniaturized sample preparation techniques stand out, as they use smaller volumes of solvents and offer high sensitivity and selectivity. The use of advanced materials, such as molecularly imprinted polymers, MOFs, and conductive polymers, has driven innovation in analytical procedures regarding complex matrices, including environmental, food, and biological samples. These materials offer high selectivity and stability, improving efficiency in the extraction and detection of specific analytes. This review explores the integration of sustainable and green methodologies. It critically highlights applications and evaluates them using the Analytical Greenness Metric for Sample Preparation, based on publications from the past 6 years.

How do you perform a MANOVA? What is grounded theory? How do you draw up a repertory grid? These, and many other questions are addressed in this wide-ranging handbook of methods and analytic techniques which uniquely covers both quantitative and qualitative methods.Based on a broad survey of undergraduate curricula, the book takes curious readers through all of the methods that are taught on psychology courses worldwide, from advanced ANOVA statistics through regression models to test construction, qualitative research and other more unusual techniques such as Q methodology, meta-analysi

Context: Innovation thrives on scientific software, with useful code review feedback enhancing its correctness and impact. However, unlike general-purpose commercial and open-source software, the usefulness of code review feedback (CR comment) in scientific software remains largely unstudied. Objective: This paper aims to characterize the usefulness of CR comment in scientific opens ource software (Sci-OSS), leveraging existing research on useful CR comment. Method: To achieve this objective, we mine successful Sci-OSS from GitHub, analyze their CR comments with usefulness related features, and compare the findings from prior research on general-purpose commercial and open-source CR comments. Results: The investigation on the usefulness of CR comments in SciOSS confirms many characteristics that prior research identified in general-purpose software. For example, subjective or negative CR comments remain not useful for the Sci-OSS. We also find CR comments which receive negative emoji reactions have a very small correlation with not useful comments, whereas the positive emojis show mixed correlations. Importantly, 6-33% CR comments in Sci-OSS are not useful in our mined repositories. Conclusions: Our investigation into Sci-OSS extends findings from CR comments' usefulness research on general-purpose software, benefiting developers, scientists, and researchers in the Sci-OSS community.

Advanced oxidation processes (AOPs) by carbon‐based single‐atom catalysts (SACs) are recognized as an attractive scientific frontier for water treatment, with the outstanding benefits of ultra‐effective and anti‐interference capability. However, most of the research has paid more attention to the performance of SACs, while the in‐depth understanding of catalytic regulation by molecular interaction is relatively deficient. This critical review delves into deciphering the catalytic mechanism through a micro‐level, which makes it more convenient to interpret apparent catalytic phenomena. It first summarizes basic theories of quantum chemistry, which provide mechanism interpretation and prediction for molecular‐oxidation systems. Additionally, corresponding oxidation pathways of common oxidants are underscored. Following the oxidants, state‐of‐the‐art regulation methods are discussed with special attention to involved molecular interactions and pollutants. Particularly, the preliminary insights into the “oxidant‐catalyst‐pollutants” internal relationships are provided to help construct the SAC‐AOP system from a molecular standpoint. Meanwhile, some cutting‐edge laboratory devices and pilot‐scale engineering are presented to illustrate the ultimate purpose of scientific molecular exploration. Eventually, relative challenges of SACs‐AOPs upon the design of catalytic systems and investigation methods are provided. This review aims to promote the large‐scale potential of SACs‐based AOPs in practical water treatment by emphasizing the pivotal role of micro‐insights.

JAN Forum: your views and letters - Response to: Watson’s Guest Editorial ‘Scientific methods are the only credible way forward for nursing research’, Journal of Advanced Nursing 43, 219–220

Context: Scientific open-source software (SciOSS) plays a foundational role in research and engineering, yet its long-term sustainability has often been overlooked and remains a significant concern. Objective: This study investigates the long-term sustainability of SciOSS through code and test quality metrics. Method: We analyze CASS Software Portfolio projects, classifying them by sustainability and comparing their code structure, test coverage, and links between code quality and testing across the dataset. Results: Sustainable projects show higher, more consistent test coverage and clearer code-test correlations, while unsustainable ones show weaker patterns. Overall, test coverage is low in scientific software, and high complexity and coupling reduce testability. Conclusion: In this study, we present a practical, data-driven approach for assessing sustainability in scientific software, offering a foundation for evaluating long-term software health and supporting future efforts in quality assurance and sustainability monitoring.

A review of editorial policies of leading journals and of research relevant to scientific journals revealed conflicts between 'science' and 'scientists.” Owing to these conflicts, papers are often weak on objectivity and replicability. Furthermore, papers often fall short on importance, competence, intelligibility, or efficiency. Suggestions were made for editorial policies such as: (1) structured guidelines for referees, (2) open peer review, (3) blind reviews, and (4) full disclosure of data and method. Of major importance, an author's “Note to Referees” (describing the hypotheses and design, but not the results) was suggested to improve the objectivity of the ratings of importance and competence. Also, recommendations are made to authors for improving contributions to science (such as the use of multiple hypotheses) and for promoting their careers (such as using complex methods and obtuse writing).scientific journals, authors, research, editors

Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that require reasoning, problem-solving, and decision-making. Their exceptional capabilities suggest their potential as scientific research assistants but also highlight the need for holistic, rigorous, and domain-specific evaluation to assess effectiveness in real-world scientific applications. This paper describes a multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory. This methodology incorporates four primary classes of evaluations. 1) Multiple Choice Questions to assess factual recall; 2) Open Response to evaluate advanced reasoning and problem-solving skills; 3) Lab-Style Experiments involving detailed analysis of capabilities as research assistants in controlled environments; and 4) Field-Style Experiments to capture researcher-LLM interactions at scale in a wide range of scientific domains and applications. These complementary methods enable a comprehensive analysis of LLM strengths and weaknesses with respect to their scientific knowledge, reasoning abilities, and adaptability. Recognizing the rapid pace of LLM advancements, we designed the methodology to evolve and adapt so as to ensure its continued relevance and applicability. This paper describes the methodology state at the end of February 2025. Although developed within a subset of scientific domains, the methodology is designed to be generalizable to a wide range of scientific domains.

Green design and manufacturing strategies are necessary to cope with the current resource, energy, and environmental problems of the manufacturing industry. To meet various enterprises’ needs for green design and manufacturing, colleges and universities should integrate these concepts into their curricula. This study discusses the application of green strategies in the mechanical engineering field from the viewpoints of teaching, scientific research, and practical education. Based on its development and a basic connotation analysis, this study highlights the challenges and urgency of incorporating green concepts into teaching and research modules of mechanical engineering, as well as methods and implementation strategies to incorporate them in professional curricula using teaching method reform and the teaching and research integration method. An experimental training course of advanced manufacturing processes at the authors’ institution was amended to emphasize the integrated application of green design and manufacturing. This integration not only enriches the field of mechanical engineering but also provides scientific research directions and methods to educators, along with new ideas to imbibe students with mechanical talents for their technical development. These efforts lay the foundation for the sustainable development of China’s manufacturing industry.

Advection of trace species (tracers), also called tracer transport, in models of the atmosphere and other physical domains is an important and potentially computationally expensive part of a model's dynamical core (dycore). Semi-Lagrangian (SL) advection methods are efficient because they permit a time step much larger than the advective stability limit for explicit Eulerian methods. Thus, to reduce the computational expense of tracer transport, dycores often use SL methods to advect passive tracers. The class of interpolation semi-Lagrangian (ISL) methods contains potentially extremely efficient SL methods. We describe a set of ISL bases for element-based transport, such as for use with atmosphere models discretized using the spectral element (SE) method. An ISL method that uses the natural polynomial interpolant on Gauss-Legendre-Lobatto (GLL) SE nodes of degree at least three is unstable on the test problem of periodic translational flow on a uniform element grid. We derive new alternative bases of up to order of accuracy nine that are stable on this test problem; we call these the Islet bases. Then we describe an atmosphere tracer transport method, the Islet method, that uses three grids that share an element grid: a dynamics grid supporting, for example, the GLL basis of degree three; a physics grid with a configurable number of finite-volume subcells per element; and a tracer grid supporting use of our Islet bases, with particular basis again configurable. This method provides extremely accurate tracer transport and excellent diagnostic values in a number of validation problems. We conclude with performance results that use up to 27,600 NVIDIA V100 GPUs on the Summit supercomputer.

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2020.2020.11920EDITORIA

Topic discovery in scientific literature provides valuable insights for researchers to identify emerging trends and explore new avenues for investigation, facilitating easier scientific information retrieval. Many machine learning methods, particularly deep embedding techniques, have been applied to discover research topics. However, most existing topic discovery methods rely on word embedding to capture the semantics and lack a comprehensive understanding of scientific publications, struggling with complex, high-dimensional text relationships. Inspired by the exceptional comprehension of textual information by large language models (LLMs), we propose an advanced topic discovery method enhanced by LLMs to improve scientific topic identification, namely SciTopic. Specifically, we first build a textual encoder to capture the content from scientific publications, including metadata, title, and abstract. Next, we construct a space optimization module that integrates entropy-based sampling and triplet tasks guided by LLMs, enhancing the focus on thematic relevance and contextual intricacies between ambiguous instances. Then, we propose to fine-tune the textual encoder based on the guidance from the LLMs by optimizing the contrastive loss of the triplets, forcing the text encoder to better discriminate instances of different topics. Finally, extensive experiments conducted on three real-world datasets of scientific publications demonstrate that SciTopic outperforms the state-of-the-art (SOTA) scientific topic discovery methods, enabling researchers to gain deeper and faster insights.

METHODS IN SCIENTIFIC RESEARCH : Address delivered by Sir E. J. RUSSELL, D.Sc., F.R.S. Director, Rothamsted Experimental Station, on the occasion of the first award of the ]oy Kissen Mookerjee Medal to him, on March, 13th, 1937

Novel research ideas play a critical role in advancing scientific inquiries. Recent advancements in Large Language Models (LLMs) have demonstrated their potential to generate novel research ideas by leveraging large-scale scientific literature. However, previous work in research ideation has primarily relied on simplistic methods, such as keyword co-occurrence or semantic similarity. These approaches focus on identifying statistical associations in the literature but overlook the complex, contextual relationships between scientific concepts, which are essential to effectively leverage knowledge embedded in human literature. For instance, papers that simultaneously mention "keyword A" and "keyword B" often present research ideas that integrate both concepts. Additionally, some LLM-driven methods propose and refine research ideas using the model's internal knowledge, but they fail to effectively utilize the scientific concept network, limiting the grounding of ideas in established research. To address these challenges, we propose the Deep Ideation framework to address these challenges, integrating a scientific network that captures keyword co-occurrence and contextual relationships, enriching LLM-driven ideation. The framework introduces an explore-expand-evolve workflow to iteratively refine research ideas, using an Idea Stack to track progress. A critic engine, trained on real-world reviewer feedback, guides the process by providing continuous feedback on the novelty and feasibility of ideas. Our experiments show that our approach improves the quality of generated ideas by 10.67% compared to other methods, with ideas surpassing top conference acceptance levels. Human evaluation highlights their practical value in scientific research, and ablation studies confirm the effectiveness of each component in the workflow. Code repo is available at https://github.com/kyZhao-1/Deep-Ideation.

The integration of smart grid technologies in interconnected power system networks presents multiple challenges for the power industry and the scientific community. To address these challenges, researchers are creating new methods for the validation of: control, interoperability, reliability of Internet of Things systems, distributed energy resources, modern power equipment for applications covering power system stability, operation, control, and cybersecurity. Novel methods for laboratory testing of electrical power systems incorporate novel simulation techniques spanning real-time simulation, Power Hardware-in-the-Loop, Controller Hardware-in-the-Loop, Power System-in-the-Loop, and co-simulation technologies. These methods directly support the acceleration of electrical systems and power electronics component research by validating technological solutions in high-fidelity environments. In this paper, members of the Survey of Smart Grid International Research Facility Network task on Advanced Laboratory Testing Methods present a review of methods, test procedures, studies, and experiences employing advanced laboratory techniques for validation of range of research and development prototypes and novel power system solutions.

A pressing issue facing health psychology research, and science in general, is ensuring precision and confidence in findings of empirical studies. With concerns surrounding the replicability of findings in psychology more broadly (namely, the ‘replication crisis’; Maxwell, Lau, & Howard, 2015; Pashler & Wagenmakers, 2012), questions and criticisms have been raised over the way researchers conduct and analyse studies. In response, researchers need to make substantial changes to the way studies are conducted to ensure research integrity (Asendorpf et al., 2013; Cumming, 2014; Munafò et al., 2017) and that findings stand up to replication (Earp & Trafimow, 2015). Researchers must use the best methods and data analytic practices feasible to conduct tests of research questions that stand up to scientific scrutiny and the rigours of replication. Applying contemporary statistical methods to analyse data is paramount to ensure rigour and confidence in the findings of research in health psychology (Cumming, 2014). Researchers therefore have a responsibility and an obligation to draw on advances and progress in statistical methods when analysing data and drawing inferences, and need to avail themselves of the emerging advanced statistical tools to avoid selection and other inappropriate data analytic practices.No Full Tex

Identifying and predicting the factors that contribute to the success of interdisciplinary research is crucial for advancing scientific discovery. However, there is a lack of methods to quantify the integration of new ideas and technological advancements in astronomical research and how these new technologies drive further scientific breakthroughs. Large language models, with their ability to extract key concepts from vast literature beyond keyword searches, provide a new tool to quantify such processes. In this study, we extracted concepts in astronomical research from 297,807 publications between 1993 and 2024 using large language models, resulting in a set of 24,939 concepts. These concepts were then used to form a knowledge graph, where the link strength between any two concepts was determined by their relevance through the citation-reference relationships. By calculating this relevance across different time periods, we quantified the impact of numerical simulations and machine learning on astronomical research. The knowledge graph demonstrates two phases of development: a phase where the technology was integrated and another where the technology was explored in scientific discovery. The knowledge graph reveals that despite machine learning has made much inroad in astronomy, there is currently a lack of new concept development at the intersection of AI and Astronomy, which may be the current bottleneck preventing machine learning from further transforming the field of astronomy.

The exponential growth of scientific literature emphasizes the need for employing advanced techniques for effective text summarization, which can significantly speed up the research process. This study tackles the challenge by advancing scientific text summarization through AI and deep learning methods. We delve into the integration and fine-tuning of cutting-edge models, including LED_Large, Pegasus variants, and BART, aiming to refine the summarization process. Unique combinations, such as SciBERT with LED_Large, were investigated to ensure the capture of critical details frequently missed by traditional methods. This novel approach led to notable improvements in summarization effectiveness. Our findings indicate that models like LED_Large excel in quickly adapting to training data, achieving impressive semantic understanding with fewer training epochs, evidenced by achieving a FRES score of 28.5852 and ROUGE scores, including a ROUGE-l F1-Score of 0.4991. However, while extensively trained models like BART _large and Pegasus displayed strong semantic capabilities, they also pointed to the necessity for refinements in readability and higher-order n-gram overlap in the produced summaries.

[EN] The more people shop online, the more consumer returns e-tailers face. In order to plan the returns management process capacity adequately, it is necessary to forecast the expected amount of returned parcels. Big data analytics provides a vast number of methods to perform such tasks. However, it should be noted that particularly small- and medium-sized e-tailers lack the capabilities and resources to employ such complex techniques. Against this background, this paper analyses the performance of several data analysis methods that differ in application complexitiy using real data from an apparel e-tailer. On the one hand, we find that –as expected– complex methods outperform simple ones. On the other hand, and from a practitioner’s perspective probably even more interesting, we also conclude that a binary logistical regression as the simplest analyzed method may already provide satisfactory results. The findings indicate that the use of big data analytics is of great value to effectively and efficiently manage consumer returns – even if not the most sophisticated state-of-the-art method is used.Asdecker, B.; Karl, D. (2018). Big data analytics in returns management – Are complex techniques necessary to forecast consumer returns properly?. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 39-46. https://doi.org/10.4995/CARMA2018.2018.8303OCS394

Scientific researchers need intensive information about datasets to effectively evaluate and develop theories and methodologies. The information needs regarding datasets are implicitly embedded in particular research tasks, rather than explicitly expressed in search queries. However, existing scientific retrieval and question-answering (QA) datasets typically address straightforward questions, which do not align with the distribution of real-world research inquiries. To bridge this gap, we developed ScIRGen, a dataset generation framework for scientific QA \& retrieval that more accurately reflects the information needs of professional science researchers, and uses it to create a large-scale scientific retrieval-augmented generation (RAG) dataset with realistic queries, datasets and papers. Technically, we designed a dataset-oriented information extraction method that leverages academic papers to augment the dataset representation. We then proposed a question generation framework by employing cognitive taxonomy to ensure the quality of synthesized questions. We also design a method to automatically filter synthetic answers based on the perplexity shift of LLMs, which is highly aligned with human judgment of answers' validity. Collectively, these methodologies culminated in the creation of the 61k QA dataset, ScIRGen-Geo. We benchmarked representative methods on the ScIRGen-Geo dataset for their question-answering and retrieval capabilities, finding out that current methods still suffer from reasoning from complex questions. This work advances the development of more sophisticated tools to support the intricate information needs of the scientific community.

Fluorescence spectroscopy serves as a vital technique for studying the interaction between light and fluorescent molecules. It encompasses a range of methods, each presenting unique advantages and applications. This technique finds utility in various chemical studies. This review discusses Fluorescence spectroscopy, its branches such as Time-Resolved Fluorescence Spectroscopy (TRFS) and Fluorescence Lifetime Imaging Microscopy (FLIM), and their integration with other spectroscopic methods, including Raman, Infrared (IR), and Circular Dichroism (CD) spectroscopies. By delving into these methods, we aim to provide a comprehensive understanding of the capabilities and significance of fluorescence spectroscopy in scientific research, highlighting its diverse applications and the enhanced understanding it brings when combined with other spectroscopic methods. This review looks at each technique's unique features and applications. It discusses the prospects of their combined use in advancing scientific understanding and applications across various domains.

Advanced Research Methods for Applied Psychology provides a comprehensive discussion of 21 key topics for the completion of an applied psychology (or similar) research thesis/project. The book provides a one-stop shop for the current issues and discussions of key research methods and common statistical analysis techniques, but avoids being a step-by-step instruction guide. The book is divided into four sections, representing the stages of thesis completion: getting started, data collection, data analysis, and reporting research. Each chapter presents a detailed scholarly discussion on a topic and represents the most up-to-date reference for that topic. The Chapters also provide key references for further detailed readings and guides. The chapters are authored by leading researchers from all around the world. This book discusses both emerging and traditional research methods commonly utilised within applied psychology research projects and directly assists early researchers in providing an informed discussion of their decisions relating to their choice of, for example, research sampling, the use of diary studies, appropriate survey time-lags, conducting systematic reviews, and the macro and micro process issues involved with conducting organisational interventions. This book is an important reference text for applied psychology research thesis/project students and researchers, including both undergraduate and postgraduate students. It will be of interest to applied psychology researchers in all fields (clinical, organisational, developmental, forensic, etc.) and to those in other disciplines. The book provides coverage of advanced research methods and statistical topics and is suitable for adoption for these courses in honours/post-graduate levels of study within applied psychology and related fields.No Full Tex

Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (MLLMs) for AI-assisted research, existing multimodal reasoning benchmarks only target up to college-level difficulty, while research-level benchmarks emphasize lower-level perception, falling short of the complex multimodal reasoning needed for scientific discovery. To bridge this gap, we introduce MicroVQA, a visual-question answering (VQA) benchmark designed to assess three reasoning capabilities vital in research workflows: expert image understanding, hypothesis generation, and experiment proposal. MicroVQA consists of 1,042 multiple-choice questions (MCQs) curated by biology experts across diverse microscopy modalities, ensuring VQA samples represent real scientific practice. In constructing the benchmark, we find that standard MCQ generation methods induce language shortcuts, motivating a new two-stage pipeline: an optimized LLM prompt structures question-answer pairs into MCQs; then, an agent-based `RefineBot' updates them to remove shortcuts. Benchmarking on state-of-the-art MLLMs reveal a peak performance of 53\%; models with smaller LLMs only slightly underperform top models, suggesting that language-based reasoning is less challenging than multimodal reasoning; and tuning with scientific articles enhances performance. Expert analysis of chain-of-thought responses shows that perception errors are the most frequent, followed by knowledge errors and then overgeneralization errors. These insights highlight the challenges in multimodal scientific reasoning, showing MicroVQA is a valuable resource advancing AI-driven biomedical research. MicroVQA is available at https://huggingface.co/datasets/jmhb/microvqa, and project page at https://jmhb0.github.io/microvqa.

Large Language Models (LLMs), such as ChatGPT (OpenAI, CA, US), have revolutionized scientific writing and research processes across academic disciplines, providing comprehensive support throughout the entire research lifecycle. Generative artificial intelligence (GAI) tools enhance every aspect of scientific writing, from hypothesis generation and methodology design to data analysis and manuscript preparation. This review examines the applications of LLMs in hematological research, with particular emphasis on advanced techniques, including prompt engineering and retrieval augmented generation (RAG) frameworks. Prompt engineering methods, including zero-shot and few-shot learning along with a chain-of-thought approach, enable researchers to generate more precise context-specific content, especially in scientific writing. Integrating RAG frameworks with the current medical literature and clinical guidelines significantly reduces the risk of misinformation while ensuring alignment with contemporary medical standards. Even though these GAI tools offer remarkable potential for streamlining research writing and enhancing documentation quality, the study also addresses the critical importance of maintaining scientific integrity, ethical considerations, and privacy concerns in hematological research.

This report presents the research priorities for the EPSRC funded Digitally Enhanced Advanced Services (DEAS) NetworkPlus in the manufacturing sector. Recent developments in digital technologies make it easy for manufacturers to monitor the use of their products in the field and then offer services based on the capability of their product. These outcome-based services are known as Digitally Enhanced Advanced Services and these have the potential to deliver sustained value for the UK economy

Introduction. Advances in large language models (LLMs) offer a chance to act as scientific assistants, helping people grasp complex research areas. This study examines how LLMs evolve in healthcare disparities research, with attention to public access to relevant information. Methods. We studied three well-known LLMs: ChatGPT, Copilot, and Gemini. Each week, we asked them a consistent prompt about research themes in healthcare disparities and tracked how their answers changed over a one-month period. Analysis. The themes produced by the LLMs were categorized and cross-checked against H-index values from the Web of Science to verify relevance. This dual approach shows how the outputs of LLMs develop over time and how such progress could help researchers navigate trends. Results. The outputs aligned with actual scientific impact and trends in the field, indicating that LLMs can help people understand the healthcare disparities landscape. Time-series comparisons showed differences among the models in how broadly and deeply they identified and classified themes. Conclusion. The study offers a framework that uses the evolution of multiple LLMs to illuminate AI tools for studying healthcare disparities, informing future research and public engagement strategies.