Science Research Paradigm Research Articles

BC4LLM: A perspective of trusted artificial intelligence when blockchain meets large language models

In recent years, artificial intelligence (AI) and machine learning (ML) are reshaping society's production methods and productivity, and also changing the paradigm of scientific research. Among them, the AI language model represented by ChatGPT has made great progress. Such large language models (LLMs) serve people in the form of AI-generated content (AIGC) and are widely used in consulting, healthcare, and education. However, it is difficult to guarantee the authenticity and reliability of AIGC learning data. In addition, there are also hidden dangers of privacy disclosure in distributed AI training. Moreover, the content generated by LLMs is difficult to identify and trace, and it is difficult to cross-platform mutual recognition. The above information security issues in the coming era of AI powered by LLMs will be infinitely amplified and affect everyone's life. Therefore, we consider empowering LLMs using blockchain technology with superior security features to propose a vision for trusted AI. This survey mainly introduces the motivation and technical route of blockchain for LLM (BC4LLM), including reliable learning corpus, secure training process, and identifiable generated content. Meanwhile, this survey also reviews the potential applications and future challenges, especially in the frontier communication networks field, including network resource allocation, dynamic spectrum sharing, and semantic communication. Based on the above work combined with the prospect of blockchain and LLMs, it is expected to help the early realization of trusted AI and provide guidance for the academic community.

Exploring motivations for algorithm mention in the domain of natural language processing: A deep learning approach

With the formation of the fourth paradigm of scientific research, algorithms have become increasingly important in scientific research. In academic papers, algorithms may be mentioned by scholars with various motivations, using, comparing, or improving algorithms to solve complex research tasks. Identifying these motivations can help scholars discover the relationships between algorithms and further assess their roles and values. Therefore, taking the field of natural language processing (NLP) as an example, this article proposes a complete method to conduct the identification, distribution, and evolution of motivations for mentioning algorithms at the sentence level. Specifically, using manual annotation and machine learning methods, we identify algorithm entities and sentences in the full text of papers, classify motivations for mentioning algorithms by pre-training models and data augmentation techniques, and finally analyze the distribution and evolution of motivations. The results show that the deep learning models trained with the augmented data outperform the traditional machine learning models in the classification task. In academic papers, more than half of the sentences show the direct use of algorithms, while the lowest percentage of motivations are improving algorithms, and the diversity of motivations has been increasing with time. For specific algorithms, grammatical algorithms are mentioned more by the motivation of “description,” while more motivations of “use” are found in the machine learning algorithms category. As time passed, the “use” motivations gradually replaced the “description” motivations for different algorithms, and the number of motivation types decreased significantly. Our research explores the identification, distribution, and evolution of authors’ motivations for mentioning algorithm entities, which could provide a basis for future algorithm relationship identification and influence evaluation using motivations.

Future rescue management: Design specifications for a 5G-enabled emergency response information system

Today, access to real-time information from emergency scenes is still limited for emergency medical services, fire departments, and their professionals, also called first responders. Emergency Response Information Systems (ERISs) have recently been discussed in the literature as a potential solution to this problem. Using the Design Science Research (DSR) paradigm, we present a novel 5G-enabled ERIS (5G-ERIS) design that leverages 5G mobile network technologies to offer diverse real-time information. We provide a user-centered examination of design specifications for a 5G-ERIS based on a smart city digital twin. Based on literature and qualitative expert interviews with several first responders in Germany, we derive how emergency medical services and fire departments can improve their decision-making with this 5G-ERIS. Based on existing 5G application architectures, we structure our identified design specifications into four system layers. Our findings provide an essential knowledge base for the successful development, deployment, and long-term use of 5G-ERISs. We stimulate a broader discussion on the design objectives and specifications of 5G-ERISs in theory and practice.

Studying high-energy nuclear physics with machine learning

The research paradigm in physics has evolved through three distinct phases: empirical observation and induction, theoretical modeling and deduction and computational numerical analysis and simulation. We are now situated within a novel epoch wherein the scientific research paradigm is increasingly shaped by the preeminence of large-scale data and artificial intelligence, particularly within the realm of AI for science applications. The advent of high-energy colliders coupled with Monte Carlo simulations has given rise to an unprecedented accumulation of data. Nested within this transformative research paradigm, machine learning and artificial intelligence technologies have been extensively harnessed for the analysis of these vast data sets. Within the domain of high-energy nuclear physics, two prevalent machine learning techniques have emerged: Bayesian analysis and deep learning. The former employs comprehensive fitting methodologies that compare extensive data sets against theoretical models, enabling the extraction of critical information pertaining to the initial nuclear structure, parton distributions, the equation of state governing hot and dense nuclear matter, and the transport coefficients of the quark–gluon plasma, among other parameters. Conversely, the latter capitalizes on the unparalleled pattern recognition capabilities of deep learning to discern robust features from high-dimensional raw data, specifically targeting individual physical parameters. This paper elucidates the fundamental principles of machine learning and delineates its potential to augment high-energy nuclear physics research endeavors.

Understanding science data literacy: a conceptual framework and assessment tool for college students majoring in STEM

BackgroundIn the era defined by the fourth paradigm of science research, the burgeoning volume of science data poses a formidable challenge. The established data-related requisites within science literacy now fall short of addressing the evolving needs of researchers and STEM students. Consequently, the emergence of science data literacy becomes imperative. However, notwithstanding the escalating importance of science data literacy, a comprehensive definition and conceptual framework are still elusive, posing challenges to effective assessment.ResultsThis study analyzes the science literacy frameworks of six international organizations and countries, including the OECD, and reviews 16 data literacy-related literature sources identified using the PRISMA process. We also consider the characteristics of science data. Based on these sources, we clarify the connotation of science data literacy and construct a tailored conceptual framework for college students majoring in STEM. The framework undergoes two rounds of Delphi method refinement to enhance its applicability. Subsequently, we created and piloted a set of questions using physics, astronomy, geography, and other STEM subjects as examples to assess science data literacy. The revised assessment tool was then used in a formal test with 198 university students, employing Rasch modeling to evaluate its effectiveness. The tool’s validity in assessing science data literacy was confirmed.ConclusionsThis study offers a systematic and comprehensive conceptual framework for science data literacy tailored to STEM undergraduates. Endorsed by experts, the framework outlines essential literacies for STEM students in handling science data. The developed assessment tool enables educators to measure students’ science data literacy levels and serves as a scientific guide to enhance their competencies in this area.

Geo-STEM learning ecosystems: A transdisciplinary theory-based framework to establish and engage communities in the geosciences

The geological sciences, technology, engineering, and mathematics (geo-STEM) have the capacity to investigate and address geological and environmental challenges. Many of these challenges (e.g., pollution, flooding, and slope failure) unjustly impact lower socio-economic communities in urban and rural settings where there is limited access to geo-STEM expertise. Geo-STEM learning ecosystems (GLEs) integrate geo-STEM and social science research paradigms to broaden civic engagement in the geosciences. GLEs are communities of practice consisting of geo-STEM professionals, educators, and learners who address local issues through place-based STEM research and education. The purpose of this study was to connect the concept of GLEs with the extant literature on learning ecosystems. We conducted a thematic review of the literature evaluating how the term “learning ecosystem” is used in psychology, educational technology, and STEM education research. We categorized the learning ecosystem components described in the literature in terms of inputs (e.g., motivation, software, learning environment), processes (e.g., cognition, device development, curriculum), and outputs (e.g., creativity, innovation, engagement). This approach revealed themes aligned with GLE-relevant research on communities of practice, and place-based and problem-based education, connecting GLEs to a century of existing research. To demonstrate how to use a GLE framework, we analyze the attributes of three existing programs through the GLE lens. Based on our findings, we recommend that to build GLEs geo-STEM researchers should: engage in transdisciplinary collaborations to develop local geo-STEM solutions, use the GLE framework to assess geoscience education and outreach programs, evaluate the role of place in efforts to engage diverse communities, and leverage other fields of inquiry to broaden our theoretical understanding of GLEs.

Addressing Knowledge Gaps in ITSM Practice with “Learning Digital Commons”: A Case Study

AbstractInformation Technology Service Management (ITSM) constitutes a suite of specialized organizational capabilities aimed at optimizing the value derived from IT services. The practice of ITSM encompasses a range of complementary frameworks. However, the practitioner community lacks a comprehensive, holistic understanding of the knowledge embedded within these frameworks.Addressing this gap, we developed Service-Symphony, an instance of a Learning Digital Commons (LEDICO) designed to empower the ITSM community with a holistic knowledge-learning experience, with design principles based on epistemic logic, cognitive constructivist learning theory and cognitive schema theory. Leveraging the Design Science Research (DSR) paradigm, we systematically constructed and assessed Service-Symphony. Evaluation tools included surveys and web analytics. Survey responses revealed a consensus among participants, with the majority expressing alignment with the four design principles underpinning Service-Symphony. Web analytics data further indicated significant engagement, with 148,796 users accessing Service-Symphony between April 2019 and September 2022.This paper contributes both to theory and practice. Theoretical contributions include the establishment of a conceptual model and a multi-grounded design theory based on cognitive constructivist learning, cognitive schema and epistemic logic. The practical contributions include e the deployment of a public domain ITSM digital commons tailored to the specific needs of the ITSM community, which is also used as supplementary learning resource for ITSM students.

New roles of research data infrastructure in research paradigm evolution

Abstract Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.

Design principles for sustainable community currency projects

AbstractCommunity currencies (CCs) are alternative forms of money usually issued and managed by citizens, NGOs and companies as well as local public administrations. Used to trade goods and services in limited territories or among a certain community, CCs are composed of symbolic, cultural, and social dimensions and fully immersed in particular economic and institutional contexts. Despite the increasing adoption of CCs, the discussion on guiding principles and values to address their purposes, particularly in relation to environmental sustainability issues is still scarce in the literature. Given this gap, this paper relies on the following research question: what are the design principles to be considered for community currency projects oriented to sustainable development goals? We follow the design science research paradigms to describe the design principles of a CC project. We analyze the institutional and economic context in which they are implemented, the goals for what they were created, the processes and mechanisms to rely upon their operation, and how they are evaluated according to their specific goals and to their implications to the sustainable development goals. It is important to emphasize that in this article we focus on improving the understanding of CC projects and what should be considered in their evaluation, however not to the extent on how they should be implemented. The design principles framework presented in this paper specifies guidelines to discuss principles to be considered within a range of different possibilities of diversified CC implementation strategies.

Understanding the AIE phenomenon of nonconjugated rhodamine derivatives via aggregation-induced molecular conformation change

The bottom-up molecular science research paradigm has greatly propelled the advancement of materials science. However, some organic molecules can exhibit markedly different properties upon aggregation. Understanding the emergence of these properties and structure-property relationship has become a new research hotspot. In this work, by taking the unique closed-form rhodamines-based aggregation-induced emission (AIE) system as model compounds, we investigated their luminescent properties and the underlying mechanism deeply from a top-down viewpoint. Interestingly, the closed-form rhodamine-based AIE system did not display the expected emission behavior under high-viscosity or low-temperature conditions. Alternatively, we finally found that the molecular conformation change upon aggregation induced intramolecular charge transfer emission and played a significant role for the AIE phenomenon of these closed-form rhodamine derivatives. The application of these closed-form rhodamine-based AIE probe in food spoilage detection was also explored.

Computational modeling for medical data: From data collection to knowledge discovery

<p>Biomedical data encompasses images, texts, physiological signals, and molecular omics data. As the costs of various data acquisition methods, such as genomic sequencing, continue to decrease, the availability of biomedical data is increasing. However, this data often exhibits high dimensionality, heterogeneity, and multimodal characteristics, necessitating the use of advanced computational modeling. Transforming raw data into meaningful biological insights is a critical aspect of computational modeling, which plays an increasingly important role in biomedical research in the era of big data. This review outlines the collection of various types of biomedical data and the challenges faced in data modeling, including high dimensionality, standardization, and privacy protection. Additionally, it addresses the complexity and interpretability of models used to guide knowledge discoveries. The review also discusses computational architectures such as parallel computing, cloud computing, and edge computing, which are essential to meet the demands of large-scale computation. Furthermore, it highlights the driving force of computational modeling in advancing medical research. With the foundation of big data, big models, and big computation, biomedical research is transitioning from experimental observation to theoretical deduction and data-driven approaches, profoundly impacting scientific research methodologies and paradigms. The development of biomedical computational modeling is steering medical research toward intelligent medicine, redefining the scientific research paradigm in biomedicine.</p>

Caring for Epistemic Bases in the 21st Century: Paradigms of Scientific Research

The general objective of this systematic review article was to describe the care of epistemic bases in the twenty-first century: paradigms of scientific research. Method: The study used an in-depth literature review of the latest research related to the thematic axis of care-care-research in nursing, which included 23 publications in various databases, which went through a process of inclusion and exclusion criteria. Results: The act of caring has evolved considerably, transforming into a multidimensional practice that goes beyond simple physical attention and is immersed in a deep understanding of the emotional and social needs of individuals. In this context, the epistemic bases that support care in the 21st century find their foundation in the paradigms of scientific research in nursing. Conclusion: In the 21st century, nursing care is nourished by an epistemic foundation that recognizes the complexity and individuality of the health experience. Scientific research paradigms in nursing provide the conceptual framework to address care holistically, considering not only the physical, but also the emotional, social, and cultural aspects that influence people's health and well-being. This approach reflects the evolution of nursing towards a more inclusive and human-centred discipline.

中国湖泊科学数据共享的路径和方法

The reuse of scientific data can foster the sharing and collaboration of research processes, facilitating a more efficient allocation of data resources. Lake scientific data, as an important fundamental strategic resources, play a crucial role in driving national scientific and technological innovation, fostering economic and social development, and advancing the construction of a national ecological civilization. The sharing of scientific data pertaining to lakes has gradually emerged as an important part of China's national scientific and technological innovation system construction. This research synthesizes the primary channels for lake data production, offering a comprehensive overview of the main data types and formats of lake data from the perspective of lake discipline as an interdisciplinary discipline, and summarizes the current situation of sharing scientific data on lakes both domestically and internationally. Furthermore, this paper also proposed strategies and methodologies for facilitating sharing scientific data related to lakes based on standardized systems, data classification, service platforms, and hierarchical sharing services. In this study, we concluded the institutional measures for lake data sharing, and provided insights for the further development of lake science data sharing under new circumstances. This research is undertaken with the aim of fostering the transformation of lake science data sharing modes under the new paradigm of lake science research.

The Impact of Resource Allocation on the Machine Learning Lifecycle

AbstractAn organization’s ability to develop Machine Learning (ML) applications depends on its available resource base. Without awareness and understanding of all relevant resources as well as their impact on the ML lifecycle, we risk inefficient allocations as well as missing monopolization tendencies. To counteract these risks, the study develops a framework that interweaves the relevant resources with the procedural and technical dependencies within the ML lifecycle. To rigorously develop and evaluate this framework the paper follows the Design Science Research paradigm and builds on a literature review and an interview study. In doing so, it bridges the gap between the software engineering and management perspective to advance the ML management discourse. The results extend the literature by introducing not yet discussed but relevant resources, describing six direct and indirect effects of resources on the ML lifecycle, and revealing the resources’ contextual properties. Furthermore, the framework is useful in practice to support organizational decision-making and contextualize monopolization tendencies.

Facilitating cooperation of smallholders in developing countries: design principles for a cooperative-oriented decentralized autonomous organization

AbstractClimate change and an increasing food demand due to a growing world population pose significant challenges for agriculture. Smallholders play a decisive role in establishing a sustainable and efficient future agricultural system since they already provide up to 80% of food in developing countries. However, they often face severe obstacles, especially in developing countries, hampering effective and efficient cooperation and productivity. Even though organizations in the form of cooperatives could help overcome some of the challenges of facilitating smallholders’ cooperation, they still suffer from structural problems. Further, in many countries, a lack of formal mechanisms to enforce contractual agreements exists. Given such challenges, decentralized autonomous organizations (DAOs) have already proven to provide alternative forms of governance independent of formal contracts or intermediaries. Therefore, this study follows the design science research paradigm to design, develop, and evaluate a decentralized autonomous organization in the agricultural sector that makes use of cooperative principles. This cooperative-oriented DAO is governed by smart contracts and technically enabled by blockchain technology as the underlying infrastructure. Through our developed and evaluated artifact, the AgriDAO, we guide researchers and practitioners on how such a cooperative-oriented DAO could look to solve existing problems related to smallholders and cooperatives. Additionally, we present eight design principles that will guide the development of cooperative-oriented DAOs. Finally, our research shall initiate lively discussion and extensive exploration of this new form of organization.

Citation content in literature review sections of research articles: A cross-paradigm comparison of design science and interpretivist research in information systems

Despite a long history of citation research, its focus has been on the linguistic and rhetorical characteristics of citations rather than their semantic nature. Using a move-based approach grounded in the CARS model (Swales, 1990), this paper studies the sorts of source ideas cited in different parts of literature reviews (LR) of Information Systems research articles following the design science research (DSR) and interpretivist research (IR) paradigms. Findings reveal three types of epistemically-oriented semantic content – theoretical, research, and methodological – and one type that is non-epistemically-framed, which vary quantitatively and qualitatively between the DSR and IR LRs. For example, both types of LRs cite more often in Move 1 Establishing a territory than in Moves 2 and 3 Establishing a niche and Occupying the niche, with theoretical terminologies, definitions, propositions being referred to more frequently in the IR writing than in the DSR writing. On the other hand, citations in the DSR writing often display prescriptive propositions, design concepts, artefacts, and methods which are absent from the IR writing. These findings not only help us better understand how citation is shaped and constrained by rhetorical moves and research paradigms but also provide implications for the teaching of citation content.

Getting Around to It: How Design Science Researchers Set Future Work Agendas

Abstract Background: There is a long tradition of writing about future work in research papers, and information systems design science research (IS DSR) is no exception. However, there is a lack of studies on (1) how IS DSR authors currently envision the next steps for their work and (2) guidelines to improve the communication of opportunities to accumulate knowledge. Method: This paper contributes to this topic, building on a systematic literature review of 123 IS DSR papers published between 2018 and 2022. Results: Design-oriented research requires the research team to decide which tasks to carry out immediately in building the future and which to postpone as research debt. The paper's contribution is threefold. First, we propose a research debt lifecycle to support (1) project stakeholders, (2) IS DSR community, and (3) societies looking for better futures. Second, we discuss the anatomy of future work in recent IS DSR. Finally, we suggest guidelines to manage and report the next research steps. Conclusion: This paper presents a pioneering assessment of future work suggestions in the IS field, focusing on the design science research paradigm. Future work directions emerge from researchers' choices during the IS DSR process that must be continuously managed.

Every information context is a CRiTical Race information Theory opportunity: informatic considerations for the information industrial complex

PurposeThis paper is an extension of a panel presentation delivered in response to a joint call for panels by the Social Informatics and Information Ethics and Policy Special Interest Groups for the 2022 Association for Information Science and Technology conference. The purpose is to introduce critical race frameworks and tenets as a lens to develop, assess and analyze the social informatics (SI) within information science (IS) research, professional discourse, praxis and pedagogical paradigms. This paper spotlights one of the presentations from that panel, an iteration of Critical Race Theory (CRT) designed specifically for information studies: CRiTical Race information Theory (CRiT).Design/methodology/approachJust as importantly, using SI as part of the context, the paper also includes a discussion that illustrates research and theory building possibilities as both counter and complement to the technocratic advances that permeate society at every level (macro, mezzo and micro), which can also be reasonably framed as the information industrial complex. Thus, CRiT joins other forms of critical discourse and praxis grappling with deconstructing, decolonizing, demarginalizing and demystifying the influence and impact of information technologies. While CRiT has global intentions and implications, this specific discussion has an extensive American focus.FindingsIf we consider the rapid pace in which techno-determinism is moving toward the vise grip of techno-fatalism controlled by frameworks generated from the information industrial complex, we can reasonably consider that humanity on a global basis is living within a meta-large technocratic crisis moment. This crisis moment is both acute and chronic. That is, the technocratic crisis is continuously moving quickly while simultaneously worsening over an extended period of time with no remedies and few responses to substantively address the crisis.Research limitations/implicationsPart of the nature of information and data is measurability. Thus, identifying compatible nomenclature connecting the descriptiveness of intersectionality (a seminal CRT tool) as a qualitative research method to the measurability of data connected to quantitative research, a mixed method approach moves from possible to plausible. Additionally, within IS, there are often opportunities to measure human engagement, such as social media content, search engine use, assessing practices of categorizations, and multiple forms of surveillance data as a short list. Hence, the descriptiveness of intersectional qualitative research “mixed” with the measurability of quantitative research within information settings implies exponential methodological possibilities.Practical implicationsCRiT is multilayered, on the one hand, with the intention of being a discipline-specific, information-specific form of CRT. On the other hand, CRiT theory building is interdisciplinary and transdisciplinary based on information as omnipresent phenomena. An ongoing challenge for CRiT theory building is identifying and working within a balance between, practitioners who typically throw anything and everything at practical problems, while scholars often slice problems into such small segments that practical understanding is severely limited. Embracing and integrating the dynamic interplay between developing ideas and using them is the key to evolving CRiT within the social sciences.Social implicationsThere is plenty of room as well as a need for additional narrative discussing or challenging the use or appropriation of information from a technocratic approach, a counter to the information industrial complex.Originality/valueCRiT is emerging and cutting edge in discussion that addresses the technocratic determinism found in most scholarly discourses.

The Design of Citizen-Centric Green IS in Sustainable Smart Districts

Green information systems are often praised for their potential to foster sustainability in citizens’ daily lives and meet their needs. With this focus on citizens, districts that use smart technologies provide a litmus test, the results of which will indicate how to design smart green information systems that better meet the needs and desires of citizens. To date, however, guidelines on how to design such green information systems in urban areas or actively involve citizens in this process are few and far between. In recognition and remedy of this shortage, the study draws on the design science research paradigm to develop seven design principles for citizen-centric green information systems that can be used in sustainable smart districts. These principles are evaluated in 15 semi-structured interviews and a prototype of a mobile district app for a citizen-centric green information system is instantiated. By taking this citizen-centric perspective, the paper fosters the active involvement of humans in the design of sustainable urban environments.

Blockchain-Based Business Process Management (BPM) for Finance: The Case of Credit and Claim Requests

Because of the competitive economy, organizations today seek to rationalize, innovate, and adapt to changing environments and circumstances as part of business process improvement efforts. The strength of blockchain technology lies in its usage as an apt technology to enhance the efficiency and effectiveness of business processes; furthermore, it prevents the use of erroneous or obsolete data and allows sharing of confidential data securely. The use of superior technology in the execution and automation of business processes brings opportunities to rethink the specific process itself as well. Business processes modeling and verification are essential to control and assure organizational evolution, therefore, the aim of this paper is three-fold: firstly, to provide business process management patterns in finance, based on blockchain, specifically for the loan-application process in the banking industry and claim process in the insurance industry that could be used and customized by companies; secondly, to critically analyze challenges and opportunities from the introduction of such approach for companies, and thirdly, to outline how companies can implement the loan business process as a web service. Partner companies (a bank and an insurance company) formulated the potential requirements for M2P along with the application of blockchain technology. An experimental design framework was established that gave the necessary services to model the requirements, check the models, and operationalize the models. The applied research methodologies are as follows: design science research paradigm and software case study, model-to-programming (M2P) of business processes, and utilization of patterns of workflow and blockchain.