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Lake water temperature (LWT) is an important indicator of physical processes within a lake, but traditional process‐based and data‐driven models are limited in their ability to estimate long‐term changes in LWT because of simplified physical laws, insufficient onsite measurements and high computational demands. To overcome these limitations, this study proposes a hybrid multi‐parameter scientific knowledge‐guided neural network (MP‐KgNN) for solving 1‐D lake temperature governing equation trained using both simulations of the WRF‐Lake model and onsite LWT measurements based on a novel training framework called physics‐informed deep learning (PIDL) framework and simulates the thermodynamics in a large deep reservoir located in eastern China from 1960 to 2021. The results revealed that the MP‐KgNN can estimate the dynamic changes in LWT with satisfactory accuracy (mean absolute error [MAE] = 1.14 K, root mean square error [RMSE] = 1.49 K). Moreover, it outperformed the pre‐trained MP‐KgNN trained with only the WRF‐Lake model (MAE = 2.43 K, RMSE = 2.77 K), which indicates its successful prediction of the thermal structure of the lake. The prediction derived by MP‐KgNN showed an increasing trend (0.04 K decade−1) of LWT in the Lake Qiandaohu. Specifically, the LWT was experienced to increase at a rate of 0.10 K decade−1 near the lake surface. These changes resulted in an extension and deepening of lake thermal stratification, as indicated by a 0.58 m increase in metalimnion thickness and a 20.46 kJ increase in Schmidt stability. The proposed MP‐KgNN is expected to become a powerful tool for estimating long‐term variations in the thermodynamics of lake ecosystems.

This review traces the history of research on the teaching and learning of nature of scientific knowledge (NOSK), and its implications for curriculum and instruction. Initially, the complex rubric of NOSK is clearly conceptualized, while recognizing that there is no singularly accepted definition. As part of this conceptualization NOSK is distinguished from the body of scientific knowledge and science practices/inquiry, the latter of which is often conflated with NOSK. The empirical research on NOSK related to teaching, learning, and assessment is briefly reviewed, followed by a discussion of the challenges that teachers face and a delineation of research foci that can help alleviate teachers’ challenges. Finally, a variety of important questions yet to be answered are delineated and explained.

Epistemic relativism rests on the existence of a plurality of epistemic systems. There is, however, no consensus on what epistemic systems actually are. Critics argue that epistemic relativism fails because its proponents cannot convincingly show the possibility of two mutually exclusive epistemic systems. Their accounts of epistemic systems are, however, highly idealized, conceptualizing them as sets of epistemic principles exclusively. But epistemic systems are necessarily inhabited by epistemic agents who negotiate these principles. Focusing on epistemic principles exclusively thus might abstract away too much from the actual dynamics within epistemic systems. Drawing from the sociology of scientific knowledge and the distinction between sociolect and idiolect in the philosophy of language, I aim to provide a richer account of epistemic systems and show that current arguments against epistemic relativism fail because they rest on an unrealistic conceptualization of epistemic systems.

Detecting texts that contain semantic-level new information is not straightforward. The problem becomes more challenging for research articles. Over the years, many datasets and techniques have been developed to attempt automatic novelty detection. However, the majority of the existing textual novelty detection investigations are targeted toward general domains like newswire. A comprehensive dataset for scientific novelty detection is not available in the literature. In this paper, we present a new triplet-based corpus (SciND) for scientific novelty detection from research articles via knowledge graphs. The proposed dataset consists of three types of triples (i) triplet for the knowledge graph, (ii) novel triplets, and (iii) non-novel triplets. We build a scientific knowledge graph for research articles using triplets across several natural language processing (NLP) domains and extract novel triplets from the paper published in the year 2021. For the non-novel articles, we use blog post summaries of the research articles. Our knowledge graph is domain-specific. We build the knowledge graph for seven NLP domains. We further use a feature-based novelty detection scheme from the research articles as a baseline. Moreover, we show the applicability of our proposed dataset using our baseline novelty detection algorithm. Our algorithm yields a baseline F1 score of 72%. We show analysis and discuss the future scope using our proposed dataset. To the best of our knowledge, this is the very first dataset for scientific novelty detection via a knowledge graph. We make our codes and dataset publicly available at https://github.com/92Komal/Scientific_Novelty_Detection.

For populating Scientific Knowledge Graphs (SciKGs), research publications pose a central information source. However, typical forms of research publications like traditional papers do not provide means of integrating contributions into SciKGs. Furthermore, they do not support making direct use of the rich information SciKGs provide. To tackle this, the present paper proposes RDFtex, a framework enabling (1) the import of contributions represented in SciKGs to facilitate the preparation of -based research publications and (2) the export of original contributions from papers to facilitate their integration into SciKGs. The framework’s functionality is demonstrated using the present paper itself since it was prepared with our proof-of-concept implementation of RDFtex. The runtime of the implementation’s preprocessor was evaluated based on three projects with different numbers of imports and exports. A small user study (N=10) was conducted to obtain initial user feedback. The concept and the process of preparing a -based research publication using RDFtex are discussed thoroughly. RDFtex’s import functionality takes considerably more time than its export functionality. Nevertheless, the entire preprocessing takes only a fraction of the time required to compile the PDF. The users were able to solve all predefined tasks but preferred the import functionality over the export functionality because of its general simplicity. RDFtex is a promising approach to facilitate the move toward knowledge graph augmented research since it only introduces minor differences compared to the preparation of traditional -based publications while narrowing the gap between papers and SciKGs.

A typology of scientific knowledge classifiers is constructed according to form of ownership and on a regional basis. An incremental model of the life cycle of scientific knowledge classifiers is proposed. It is shown that the social effect of introducing standard knowledge classifiers is incommensurable with the economic effects of its private knowledge counterpart. It is proposed to use different methods of evaluation of economic benefits from selling scientific knowledge classifiers as part of information products and from their implementation in corporate knowledge management systems or from their adoption as national standards. The business models used in or potentially applicable to the commercialization of scientific knowledge classifiers are considered. The cost items that form the classifier value are listed. Special attention is paid to the costs of methodological support. It is noted that, although scientific knowledge classifiers are not a public good in market economies, the development of open science requires ensuring the maximally free access to them.

The metropolitan areas act as incubators of new knowledge, and play a central role in the process of scientific knowledge production. On the basis of highly cited papers data, this paper adopts spatial scientometrics and social network analysis to investigate the geography, position and link of science cities between 2007 and 2017. The results are demonstrated below: (1) The two seemingly paradoxical trends, the regional concentration and global spread, coexist in the process of knowledge production, which are rapidly reshaping the global pattern of science. (2) The whole knowledge collaboration network has been dominated by the Global North cities, while the rise of the Global South cities has an increasing influence in the network, both driving the evolution of the world order. (3) The number of scientific collaborations between cities has increased dramatically, while domestic collaborations have higher strength than international collaborations. Finally, we discuss the limitations of this study and set out three directions in the future research agenda of knowledge production.

In April 1922 Charlotte Wheeler-Cuffe was elected a Fellow of the Royal Geographical Society. This honour was in recognition of her contribution to plant hunting and exploration, botanical illustration and anthropological knowledge accumulated about Burma during the quarter of a century (1897-1922) she spent there with her husband as part of the colonial service. While historical geographers have acknowledged that the colonies, in particular, often afforded women the space for practising science, the work of female naturalists in the field has received limited detailed scholarly attention. For Charlotte Wheeler-Cuffe, her plant-hunting expeditions across Burma allow us to extend the epistemic reach of a spatial perspective developed by geographers and to demonstrate how the web of connections she developed in the colonies enabled her to circulate scientific knowledge across the globe. By focusing on a major expedition to Mount Victoria undertaken by Wheeler-Cuffe, this paper unravels the complexity of the practice of natural history within a global imperial framework through an examination of the private correspondence and pictorial archive maintained during her time in Burma.

A number of different approaches have been used to explain the successes and failures of biodiversity conservation strategies in developing countries. However, to date, little attention has been paid toward assessing the influence of knowledge transfer between science, policy, and conservation practices in the implementation of these strategies. Vietnam’s Pu Luong Cuc Phuong Conservation Area is a globally important ecosystem, situated within a limestone landscape and inhabited by hundreds of local communities. Biodiversity conservation has become an important part of sustainable development in this area. This study analyzes three conservation strategies employed in the Pu Luong Cuc Phuong Conservation Area by applying the Research–Integration–Utilization (RIU) model of scientific knowledge transfer. Our analyses reveal weaknesses in scientific knowledge transfer arising from low-quality research and poor integration strategies. Based on our results, we developed recommendations to improve research and integration in an effort to enhance science-based policy support.

This study critically appraises the Japanese government’s high-level radioactive disposal policy by drawing on three sociological perspectives: risk society, sociology of scientific knowledge, and social acceptance. The risk society theory emphasizes that the Government of Japan and scientists under its control are pursuing nuclear power policy and repository siting within the conventional paradigm of the first modernity, which no longer aligns with the current reality of nuclear power utilization and its public awareness in Japan. Thus, a reflexive response from the policy side is essential to address the demands of a risk society. The sociology of scientific knowledge supports this view by demonstrating that, while scientists under governmental control attempt to convince the public of the safety of their geological disposal methods and the scientific validity of their siting procedures, these claims are largely a social construction of knowledge riddled with uncertainty and ambiguity about inherent environmental risks. The social acceptance standpoint also reveals a substantial bias in government measures toward ensuring distributive, procedural, and interpersonal fairness. Specifically, it critiques the heavy official reliance on monetary compensation to the host community, limited consideration of the allocation of intergenerational decision-making rights based on the reversibility principle, and the implementing agency’s one-way asymmetrical risk communication for public deliberation.