Contributions of Pakistani authors to Library Philosophy and Practice from 2008 to 2017

: library philosophy and practice is a well-known online free access and peer reviewed journal in the field of library and information sciences. The online journal covers all aspect of librarianship and library and information sciences. The present study has carried out to analyze the contribution of Pakistani authors to library philosophy and practice from 2009 to 2017. The 86 total numbers of articles has been retrieved from LPP in chosen period of study. The recovered data has been categorized in number of bibliometric aspects like year wise publications, author’s collaboration, papers length, most prolific authors, most productive institutions and foreign contributions

Trends of Publications in Peshawar Islamicus Research Journal: A Bibliometric Study from 2010- 2020

This paper presents a bibliometric analysis of “Peshawar Islamicus” a research journal published by the Department of Islamiyat, University of Peshawar, Pakistan. The analysis covers authorship pattern, gender-wise distribution, number of articles, paper length, most prolific authors, number of references and department wise distribution etc. The data was collected from different issues of the journal which was analyzed by using MS Access and MS Excel 2016. The study found that 290 articles were published from 2010 to 2020 and highest numbers of articles (34) were published in 2019. The trend of male and two author was dominant, the volume of 2018 had maximum number of citations and most of papers were published in length 11-15 pages. The province of Khyber Pakhtunkhwa produced maximum number of articles and the university of Peshawar was the most productive institution. Dr. Miraj ul Islam was the most productive author with 14 articles and maximum number of articles 103(46.18%) were published in Urdu language

On the role and assessment of research at European Universities of Applied Sciences

The relevance of the Universities of Applied Sciences (UASs) is growing in a world where practice-based learning and research are essential to meet societal development goals. Public policies for science, technology, and innovation must, therefore, account for an increasingly complex context where higher education institutions of varied shapes co-evolve. On the other hand, the increasing influence of international ranking systems and of bibliometric indicators calls for a critical revision of their adequacy to account for UASs’ key characteristics such as the importance of interdisciplinary and transdisciplinary approaches when tackling context-based challenges. This paper examines the roles of UASs in contemporary knowledge-intensive societies and critically addresses dominant research evaluation approaches. It is argued that i) qualitative approaches based on the visualization of bibliometric data can provide useful perspectives on interdisciplinarity, ii) the assessment of stakeholder engagement should be incorporated in rankings such as U-Multirank, and iii) knowledge dissemination at large could be assessed using Altmetrics indicators.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Effect of Research Impact on Emerging Camel Husbandry, Welfare and Social-Related Awareness

The lack of applied scientific research on camels, despite them being recognized as production animals, compels the reorganization of emerging camel breeding systems with the aim of achieving successful camel welfare management strategies all over the world. Relevant and properly-framed research widely impacts dissemination of scientific contents and drives public willingness to enhance ethically acceptable conditions for domestic animals. Consumer perception of this livestock industry will improve and high-quality products will be obtained. This paper draws on bibliometric indicators as promoting factors for camel-related research advances, tracing historical scientific publications indexed in ScienceDirect directory from 1880–2019. Camel as a species did not affect Journal Citation Reports (JCR) impact (p > 0.05) despite the journal, author number, corresponding author origin, discipline and publication year affecting it (p < 0.001). Countries with traditionally well-established camel farming are also responsible for the papers with the highest academic impact. However, camel research advances may have only locally and partially influenced welfare related laws, so intentional harming acts and basic needs neglect may persist in these species. A sustainable camel industry requires those involved in camel research to influence business stakeholders and animal welfare advocacies by highlighting the benefits of camel wellbeing promotion, co-innovation partnership establishment and urgent enhancement of policy reform

Effect of research impact on emerging camel husbandry, welfare and social-related awareness

The lack of applied scientific research on camels, despite them being recognized as production animals, compels the reorganization of emerging camel breeding systems with the aim of achieving successful camel welfare management strategies all over the world. Relevant and properly-framed research widely impacts dissemination of scientific contents and drives public willingness to enhance ethically acceptable conditions for domestic animals. Consumer perception of this livestock industry will improve and high-quality products will be obtained. This paper draws on bibliometric indicators as promoting factors for camel-related research advances, tracing historical scientific publications indexed in ScienceDirect directory from 1880–2019. Camel as a species did not affect Journal Citation Reports (JCR) impact (p &gt; 0.05) despite the journal, author number, corresponding author origin, discipline and publication year affecting it (p &lt; 0.001). Countries with traditionally well-established camel farming are also responsible for the papers with the highest academic impact. However, camel research advances may have only locally and partially influenced welfare related laws, so intentional harming acts and basic needs neglect may persist in these species. A sustainable camel industry requires those involved in camel research to influence business stakeholders and animal welfare advocacies by highlighting the benefits of camel wellbeing promotion, co-innovation partnership establishment and urgent enhancement of policy reform

On the Second Mission of Higher Education Institutions: The Case of Polytechnics in Europe

The mission of Higher Education Institutions (HEIs) has progressively developed from an educational role to knowledge production, innovation generation and engagement with society at large. At the same time, the HEIs space has been enriched with the evolution of ‘non-university’ institutions, and a clear drift between the university and ‘non-university’ sectors can be observed all over the world. This challenges current higher education (HE) non-unitary systems and questions the validity of criteria currently used to typify HEIs. This study was initiated to clarify the status of the HE divide in European countries, by assessing the research characteristics and performance of several HEIs. To this end, HEIs in France, Italy and Portugal were analyzed using a multi-dimensional set of quantitative and qualitative indicators. It can be observed that in Portugal the HE drift is both policy and practice driven. The evidence and conclusions of this study can be considered by policy makers when developing public strategies and policies aimed at the ‘non-university’ sector in contemporary research and innovation ecosystems.The author is grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2021)info:eu-repo/semantics/publishedVersio

Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model

Early identification of emergent topics is of eminent importance due to their potential impacts on society. There are many methods for detecting emerging terms and topics, all with advantages and drawbacks. However, there is no consensus about the attributes and indicators of emergence. In this study, we evaluate emerging topic detection in the field of artificial intelligence using a new method to evaluate emergence. We also introduce two new attributes of collaboration and technological impact which can help us use both paper and patent information simultaneously. Our results confirm that the proposed new method can successfully identify the emerging topics in the period of the study. Moreover, this new method can provide us with the score of each attribute and a final emergence score, which enable us to rank the emerging topics with their emergence scores and each attribute score

Automatic Recognition and Classification of Future Work Sentences from Academic Articles in a Specific Domain

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts