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The diversity and advance of information, communication, and analytical technologies and their increasing adoption to assist instruction and learning give rise to various technology-driven conferences (e.g., artificial intelligence in education) in educational technology. Previous reviews on educational technology commonly focused on journal articles while seldom including mainstream conference papers which also contribute to an important part of scientific output in computer science and emerging disciplines like educational technology and are equally and even more important than articles in knowledge transmission. Hence, conference papers should also be included in bibliometric studies to produce a complete and precise picture of scientific production concerning educational technology. This study, therefore, uses bibliometrics and topic modeling to analyze papers from mainstream conferences, including Artificial Intelligence in Education, Learning Analytics and Knowledge, Educational Data Mining, Intelligent Tutoring System, and Learning at Scale, focusing on contributors, collaborations, and particularly research topics and topic evolutions to inform relevant stakeholders about educational technology's development and its future. Results indicate promising areas like affective computing and behavior mining for adaptive instruction, recommender systems in personalized learning recommendations, eye-tracking for cognitive process diagnosis, videos for feedback provision, and natural language processing in discourse analysis and language education.

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users' interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students demonstrated the significance of this approach using modified collaborative filtering techniques. However, two key issues remain: (1) How would the modified filtering perform when target users are inexperienced undergraduate students who have a different pedagogical background and contextual information-seeking goals, such as task- and course-related goals, from those of graduate students?; (2) Should we combine graduates and undergraduates in the same pool, or should we separate them? We conducted two studies aimed at addressing these issues and they showed that (1) the system can be effectively used for inexperienced learners; (2) recommendations are less effective for different learning groups (with different pedagogical features and learning goals) than they are for the same learning groups. Based on the results obtained from these studies, we suggest several context-aware filtering techniques for different learning scenarios. (Contains 7 tables, 4 figures, and 1 footnote.)

The general purpose of this study is to conduct a content study on 'computer' and 'educational technologies' research and trends between 2016 and 2020. The topics were evaluated according to years, universities of the authors, citations, keywords, document type, source, sponsors and publication languages. The articles examined in the research include keywords related to 'computer' and 'instructional technologies' between 2016 and 2020; 1,798 articles obtained by scanning the Scopus database according to the title, keywords and summary of the articles were examined. When the results of the study were examined, it was concluded that English, which is the universal language, is very common; the researches are mostly published as 'conference papers' and the most used keyword in the study is 'Computer Science'. In addition, it has been concluded that computer science is the basis of educational technologies in recent years. Similar content analysis studies may be recommended for other software used in computer training.

Every word signifies multiple senses. Many studies using comprehension-based measures suggest that polysemes' senses (e.g., "paper" as in "printer paper" or "term paper") share lexical representations, whereas homophones' meanings (e.g., "pen" as in "ballpoint pen" or "pig pen") correspond to distinct lexical representations. Less is known about the lexical representations of polysemes compared to homophones in language production. In this study, speakers named pictures after reading sentence fragments that primed polysemes and homophones either as direct competitors to pictures (i.e., semantic-competitors), or as indirect-competitors to pictures (e.g., polysemous senses of semantic competitors, or homophonous meanings of semantic competitors). Polysemes (e.g., "paper") elicited equal numbers of intrusions to picture names (e.g., "cardboard") compared to in control conditions whether primed as direct competitors ("printer paper") or as indirect-competitors ("term paper"). This contrasted with the finding that homophones (e.g., "pen") elicited more intrusions to picture names (e.g., crayon) compared to in control conditions when primed as direct competitors ("ballpoint pen") than when primed as indirect-competitors ("pig pen"). These results suggest that polysemes, unlike homophones, are stored and retrieved as unified lexical representations.

Using CiteSpace to draw a keyword co-occurrence knowledge map for 1,048 research papers on the quality of higher education from 2000 to 2014 in the Chinese Social Sciences Citation Index database, we found that over the past 15 years, research on the quality of Chinese higher education was clearly oriented toward policies, and a good interactive relationship formed between research and policy. Looking at research topics, apart from macrodiscussions of the spirit of several educational reforms, the relevant topics are mainly focused on higher education quality issues in the massification stage, issues of higher education academic management, issues of student cultivation, and issues of ensuring and evaluating higher education quality. Of these, issues of ensuring and evaluating higher education quality have continually been the mainstream of research on Chinese higher education quality, and there has been an increasing trend of emphasizing ensuring internal quality within schools, emphasizing student participation, and evaluating the effectiveness of student learning. Scholars had a renewed focus on the topics of higher education academic management and student cultivation after 2010, and truly improving the quality of higher education will become a future hot topic of study, however there is an urgent need for further study.

We utilized cross tabulation statistics, word frequency counts, and content analysis of research output to conduct a bibliometric study, and used CiteSpace software to depict a knowledge map for research on entrepreneurship education in China from 2004 to 2013. The study shows that, in this duration, the study of Chinese entrepreneurship education experienced a progression through three stages, an "exploratory stage of learning from foreign models," a "developmental stage geared toward employment problems," and a "transformative stage promoting cultivation of student abilities," and featured three primary characteristics, "multidisciplinary fusion," "imbalanced regional distribution," and "policy orientation." In the future, popular fields in the study of entrepreneurship education are to be concentrated in the three areas, namely "entrepreneurship education and talent cultivation," "entrepreneurship education and entrepreneurial environments," and "entrepreneurship education and innovation education."

The present study aims to identify the most productive countries, journals, authors, institutions and the most used keywords in the field of special education during 2011-2020, based on the WoS database. The widespread effects of the papers and how they are related were analyzed with the bibliometric analysis method. The findings of the study showed that the USA is inarguably the most productive country, followed by England and Australia. On the other hand, there was a very strong positive correlation (r = 0.929) between the number of papers published by countries and their h-index, a similar finding was also found to be present between the countries' h-index and GDP per capita (r = 0.790). Moreover, it was found that the journals with the highest quartile (Q1 and Q2) in the field of special education published significantly more papers than the journals with the lowest quartile (Q3 and Q4). Matson, JL (USA), Sigafoos, J (New Zealand) and Lancioni, GE (Italy) were determined as the most prolific authors, respectively. Autism, intellectual disability, and Down syndrome were the phrases most frequently used as keywords. Our findings provide key information regarding the developments that the research direction of special education field has recently taken. This study also serves a potential roadmap for future studies.

As humanity reaches the 5.0 industrial revolution, education plays a critical role in boosting the quality of human resources. This paper reports bibliometric research on emerging TiP during 1993-2022 in the educational field to analyse its development on any level of education during the last three decades. This study employed a Scopus database. The findings are that the trend of TiP publication in educational fields has tended to increase every year during the past three decades and conference paper became the most published document type, the USA is the country which produces the most publications; "Students" being the most occurrences keyword and total link strength. The publication of the TiP is ranked to the Quartile 1, which implies that a publication with the cited performance is a publication with credibility because the publisher has a good reputation. Researchers can find the topics most relevant to other metadata sources such as Web of Science, Publish, and Perish.

In the recent years, the academic paper influence analysis has been widely studied due to its potential applications in the multiple areas of science information metric and retrieval. By identifying the academic influence of papers, authors, etc., we can directly support researchers to easily reach academic papers. These recommended candidate papers are not only highly relevant with their desired research topics but also highly-attended by the research community within these topics. For very recent years, the rapid developments of academic networks, like Google Scholar, Research Gate, CiteSeerX, etc., have significantly boosted the number of new published papers annually. It also helps to strengthen the borderless cooperation between researchers who are interested on the same research topics. However, these current academic networks still lack the capabilities of provisioning researchers deeper into most-influenced papers. They also largely ignore quite/irrelevant papers, which are not fully related with their current interest topics. Moreover, the distributions of topics within these academic papers are considered as varying and it is difficult to extract the main concentrated topics in these papers. Thus, it leads to challenges for researchers to find their appropriated/high-qualified reference resources while doing researches. To overcome this limitation, in this paper, we proposed a novel approach of paper influence analysis through their content-based and citation relationship-based analyses within the biographical network. In order to effectively extract the topic-based relevance from papers, we apply the integrated graph-based citation relationship analysis with topic modeling approach to automatically learn the distributions of keyword-based labeled topics in forms of unsupervised learning approach, named as TopCite. Then, we base on the constructed graph-based paper–topic structure to identify their relevancy levels. Upon the identified relevancy levels between papers, we can support for improving the accuracy performance of other bibliographic network mining tasks, such as paper similarity measurement, recommendation, etc. Extensive experiments in real-world AMiner bibliographic dataset demonstrate the effectiveness of our proposed ideas in this paper. [ABSTRACT FROM AUTHOR]

Source code is essential for researchers to reproduce the methods and replicate the results of artificial intelligence (AI) papers. Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community. However, manual collection is a labor-intensive and time-consuming task. To address this issue, we propose a method to automatically identify papers with available source code and extract their source code repository URLs. With this method, we find that 20.5% of regular papers of 10 top AI conferences published from 2010 to 2019 are identified as papers with available source code and that 8.1% of these source code repositories are no longer accessible. We also create the XMU NLP Lab README Dataset, the largest dataset of labeled README files for source code document research. Through this dataset, we have discovered that quite a few README files have no installation instructions or usage tutorials provided. Further, a large-scale comprehensive statistical analysis is made for a general picture of the source code of AI conference papers. The proposed solution can also go beyond AI conference papers to analyze other scientific papers from both journals and conferences to shed light on more domains. [ABSTRACT FROM AUTHOR]

Discusses high recall search strategies for undergraduates and how to overcome information overload that results. Highlights include word-based versus visual-based schemes; five summarization and visualization schemes for presenting information retrieval citation output; and results of a study that recommend visualization schemes geared toward mapping and associative thinking. (Contains 63 references.) (Author/LRW)

Death is an inevitable part of life and highly relevant to information management: its approach often requires preparation, and its occurrence often demands a response. Many works in information science have acknowledged so much, and yet death is rarely a focused topic, appearing instead sporadically and disconnected across research. As a result there is no introduction to, overview of, or synthesis across studies on death and information. We therefore conducted an extensive literature search and reviewed nearly 300 scholarly publications at the intersection of death and information (and data) management. Covering seven topics in total, we review two groups of work directly engaging information management in relation to death (digital possessions, inheritance, and legacy; information behavior, needs, and practices around death), three engaging death and technology that require information and its management (death and the Internet, thanatosensitive design and technology‐augmented death practices, and the digital afterlife and digital immortality), and two reflecting the ethical and legal dimensions unique to death and information. We then integrate the collective findings to summarize the landscape of death‐related information research, outline remaining challenges for individuals, families, institutions, and society, and identify promising directions for future information science research. [ABSTRACT FROM AUTHOR]

Reports on a use study of citations from college freshman composition papers at Western Illinois University (Illinois) that indicated the need for library instruction and library collection building. Tables and graphs indicate the number of citations per bibliography; the number and types of format sources; publication dates; and distribution of topics. (10 references)

Objectives: Information professionals have supported medical providers, administrators and decision-makers, and guideline creators in the COVID-19 response. Searching COVID-19 literature presented new challenges, including the volume and heterogeneity of literature and the proliferation of new information sources, and exposed existing issues in metadata and publishing. An expert panel developed best practices, including recommendations, elaborations, and examples, for searching during public health emergencies. Methods: Project directors and advisors developed core elements from experience and literature. Experts, identified by affiliation with evidence synthesis groups, COVID-19 search experience, and nomination, responded to an online survey to reach consensus on core elements. Expert participants provided written responses to guiding questions. A synthesis of responses provided the foundation for focus group discussions. A writing group then drafted the best practices into a statement. Experts reviewed the statement prior to dissemination. Results: Twelve information professionals contributed to best practice recommendations on six elements: core resources, search strategies, publication types, transparency and reproducibility, collaboration, and conducting research. Underlying principles across recommendations include timeliness, openness, balance, preparedness, and responsiveness. Conclusions: The authors and experts anticipate the recommendations for searching for evidence during public health emergencies will help information specialists, librarians, evidence synthesis groups, researchers, and decision-makers respond to future public health emergencies, including but not limited to disease outbreaks. The recommendations complement existing guidance by addressing concerns specific to emergency response. The statement is intended as a living document. Future revisions should solicit input from a broader community and reflect conclusions of meta-research on COVID-19 and health emergencies.

This article examines issues faced by scholarly societies that are developing and sustaining online paper repositories. It is based on the AERA Conference on Online Paper Repositories, which focused on fundamental issues of policy and procedure important to the operations of online working paper repositories. The report and recommendations address a range of issues and offer recommendations relevant to disseminating scholarly knowledge online. It is meant to serve as useful guidance for scholarly societies that might undertake such an endeavor and for researchers and other readers interested in wider access and use of scholarly work. It also aims to be informative to authors who might consider placing their working papers in an online paper repository.

Describes how students can exploit the power and flexibility of computers to find, organize, and store information gathered while conducting research. Describes how students can use computers to compose, revise, and edit their research papers. (RS)

The restricted time allocated to courses and the immediate need to read literature in English necessitates the teaching of selective reading in many English as a foreign language contexts. This paper reports on one element of an expeditious (quick, effective, efficient and selective) reading course for learners in an English for Academic Purposes (EAP) context. It details how postgraduate students of chemistry in a Tunisian university are taught to read titles of empirical research papers, as this becomes as important as reading abstracts for fast-tracking relevant data. Analysis of the literature in the field of technical writing identifies four types of titles. Based on this classification, the course component aims at raising the learners' awareness of the linguistic exponents that typify titles, and making them appreciate the importance of titles and their classification. The paper details how this single strategy fits into an expeditious approach to the teaching of reading.

Maturity models have been introduced over the last five decades as guides and references for information system (IS) management in organizations from different sectors. In the education sector, maturity models have also been used to evaluate higher education institutions (HEIs) across several dimensions, such as information and communication technology (ICT), management, process management, course curricula, course/HEI accreditation, e-/m-learning, online courses and pedagogical strategies. In fact, HEIs are faced with enormous complexity due to the specificity and quantity of their processes. Based on the guidelines concerning a methodology for a systematic literature review, the maturity models of different subareas of education are identified and categorized in this paper. For each maturity model associated with HEIs' ISs, the appropriate methodology for development and validation is identified, as well as the scope, stages and their dimensional characteristics. This study highlights the need to develop an IS maturity model that supports HEIs in their core business activities and management.

Addresses pitfalls in using the Internet for undergraduate research, including: increased ability to commit plagiarism and academic fraud; difficulties sorting valid, useful information from inaccurate or misleading sources; difficulties searching the Internet; growing confusion between reality and virtual reality; and lack of context for much information on the Internet. (DSK)

Monotonic logic requires reexamination of the entire logic string when there is a contradiction. Nonmonotonic logic allows the user to withdraw conclusions in the face of contradiction without harm to the logic string, which has considerable application to the field of information searching. Artificial intelligence models and neural networks based on nonmonotonic logic have the potential for more robust findings. (Author/AEF)

Authors, publishers, libraries, and individual consumers have different, sometimes conflicting, needs for scientific and technical information that are met in varying degrees by different publishing media. A number of print and nonprint formats (full-text journals, synopsis journals, microform, CD-ROM, online, etc.) are compared, and combinations of formats for access are proposed. (23 references) (LAE)

Describes how an interactive text retrieval system with natural language queries is used in information studies education. The benefits of programing methods involving encapsulation and inheritance are explained in terms of reusability and extendibility, and future plans to produce a library of reusable objects for information retrieval are discussed. (11 references) (EAM)

Exam period can be a stressful time for students, and having examples of past papers to help prepare for the tests can be extremely helpful. It is possible that past exams are already shared on your campus--by professors in their specific courses, via student unions or groups, or between individual students. In this article, we will go over the workflows and infrastructure to support the systematic collection, provision of access to, and repository management of past exam papers. We will discuss platform-agnostic considerations of opt-in versus opt-out submission, access restriction, discovery, retention schedules, and more. Finally, we will share the University of Toronto setup, including a dedicated instance of DSpace, batch metadata creation and ingest scripts, and our submission and retention workflows that take into account the varying needs of stakeholders across our three campuses. [ABSTRACT FROM AUTHOR]

The Spatial Search specialist meeting in Santa Barbara (December 2014) brought together 35 academic and industry representatives from computational, geospatial, and cognitive sciences with interest in focused discussions on the development of an interdisciplinary research agenda to advance spatial search from scientific and engineering viewpoints. The position papers from participants represent the shared expertise that guided discussions and the formulation of research questions about processes of spatial search and about the conceptual ideas, infrasturctures, and tools needed to enhance the search experience in both physical and virtual spaces. The call for position papers is included.

The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification. [ABSTRACT FROM AUTHOR]

This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.

Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India. Design/methodology/approach: Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions. Findings: The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance. Research limitations/implications: This study is limited to course students from one institute and does not consider external factors. Practical implications: Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions. Originality/value: 1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as "prerequisite relations" (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation.

Automatic Term Extraction (ATE) systems have been studied for many decades as, among other things, one of the most important tools for tasks such as information retrieval, sentiment analysis, named entity recognition, and others. The interest in this topic has even increased in recent years given the support and improvement of the new neural approaches. In this article, we present a follow-up on the discussions about the pipeline that allows extracting key terms from medical reports, presented at MDTT 2022, and analyze the very last papers about ATE in a systematic review fashion. We analyzed the journal and conference papers published in 2022 (and partially in 2023) about ATE and cluster them into subtopics according to the focus of the papers for a better presentation. [ABSTRACT FROM AUTHOR]

Briefly reviews problems in the cataloging of conference proceedings and their treatment by major cataloging codes, and discusses results of examination of citations to 50 conference papers in field of chemistry delivered in 1970 to ascertain implications of current citation practices for the cataloging of conference proceedings. (17 references) (EJS)

Businesses and higher education leaders are looking for students with the ability to evaluate and analyze information and to use this information to solve real-world problems. These are the information literacy skills students need for the 21st century. However, several recent studies on the ability of college freshmen to handle the rigor of college courses and research indicate that high school students are not being adequately prepared to apply these skills. The authors provide a case study of a collaborative effort between an English teacher and the high school librarian to better prepare high school seniors on how to locate reliable information, analyze the information and then determine how it can be applied to solving a real world issue or problem.

Behavioural psychology is to connect our minds and activities. Our intentions and actions must be such that they do not affect others. Education is changing to digital education so we force to act with good behaviour. Nowadays, text mining is to be challenged for educationists. This paper addresses the ethical behaviour of avoiding text mining in digital education in the twenty-first century such as have explained how to act with ethical behaviour in others' text data in the current situation. This article advises following ethical behaviour in an educational text message, information, materials, etc. Further, this article explains what text mining is and how to use it with ethical behaviour. We will hope that text mining protection will get to everyone in future. Beginning with an overview of text data mining, this paper concludes with how to play with good behaviour.

This summary is about a special issue of the International Journal on Digital Libraries (IJDL) that features selected papers from the 23rd International Conference on Asia-Pacific Digital Libraries (ICADL 2021). ICADL is an annual interdisciplinary conference that connects digital libraries, computer science, and library and information science communities. The special issue includes five papers that have undergone a rigorous review process and cover topics such as improving scientific publication retrieval, multi-label document classification, extracting leaderboards from research papers, and AI-powered peer review systems. The articles offer diverse perspectives and high-quality research within these fields. [Extracted from the article]

Since 1950, through 70 volumes, I Libri: International Journal of Libraries and Information Studies i has been a leader among scholarly journals in the international library and information science world. This competition supports I Libri i 's goal of publishing the best articles from the next generation of library and information science professionals. Footnotes 1 Exception: Senior information scholars returning to school for additional degrees outside the field of library and information science are not eligible for this award. [Extracted from the article]

Objectives: Information professionals have supported medical providers, administrators and decision-makers, and guideline creators in the COVID-19 response. Searching COVID-19 literature presented new challenges, including the volume and heterogeneity of literature and the proliferation of new information sources, and exposed existing issues in metadata and publishing. An expert panel developed best practices, including recommendations, elaborations, and examples, for searching during public health emergencies. Methods: Project directors and advisors developed core elements from experience and literature. Experts, identified by affiliation with evidence synthesis groups, COVID-19 search experience, and nomination, responded to an online survey to reach consensus on core elements. Expert participants provided written responses to guiding questions. A synthesis of responses provided the foundation for focus group discussions. A writing group then drafted the best practices into a statement. Experts reviewed the statement prior to dissemination. Results: Twelve information professionals contributed to best practice recommendations on six elements: core resources, search strategies, publication types, transparency and reproducibility, collaboration, and conducting research. Underlying principles across recommendations include timeliness, openness, balance, preparedness, and responsiveness. Conclusions: The authors and experts anticipate the recommendations for searching for evidence during public health emergencies will help information specialists, librarians, evidence synthesis groups, researchers, and decision-makers respond to future public health emergencies, including but not limited to disease outbreaks. The recommendations complement existing guidance by addressing concerns specific to emergency response. The statement is intended as a living document. Future revisions should solicit input from a broader community and reflect conclusions of meta-research on COVID-19 and health emergencies. [ABSTRACT FROM AUTHOR]

Searching for relevant material on the internet has become a big difficulty with the growing number of internet users and the large volume of data available on the internet. Traditional search engines return a lot of irrelevant results in response to a user's search query, wasting a lot of time and effort because the context and semantics of the user's request are not examined. This emphasizes the requirement for an embedded approach in the search engine, allowing semantic web search. Keyword based search engines fetch online pages by comparing the tokens in the user query to the tokens in the web documents. This strategy has several flaws. To overcome these obstacles, the suggested system employs semantic search using a Sentence transformer model. The system that has been proposed can speed up data retrieval and improve data searching to provide relevant outcomes to the user. The proposed model uses the arXiv dataset which contains an archive of research papers and using BERT and FAISS, it gives relevant research paper titles as the output to the query. For semantic retrieval of data, the model behaves as a vector-based search engine. This vector-based model will save time and give efficient results to the user's query. [ABSTRACT FROM AUTHOR]