dataesr/scientific-paragraphs-categorization huggingface dataset:
Going methodologically one step further, Yousefinaghani et al. (17)  used a real-time anomaly detection approach utilizing the Seasonal-Hybrid Extreme Studentized Deviate algorithm  (18)  to identify the onset and peak of COVID-19 waves in Google Trends and Twitter data from the US and Canada. This study also evaluated the correlation between tweets and Google trends data with official COVID-19 case numbers. Pearson correlation analysis demonstrated a strong correlation between officially reported infected cases and the relevant posts.
Machine learning models In addition to compartmental models, machine learning techniques, including neural networks, have become popular approaches for modeling and predicting disease spread. Examples include models for the disease spread in China  (43)  and worldwide  (47). Fong et al. (43)  tried to overcome the problem of a small dataset by using a polynomial neural network with corrective feedback, while Ibrahim et al. incorporated urban characteristics and NPIs via a variational Long Short-Term Memory (LSTM) encoder. In addition to neural networks, other machine learning techniques have been proposed as well: for example, Al-qaness et al. (42)  combined an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a flower pollination algorithm (FPA) using the salp swarm algorithm (SSA), creating the FPASSA-ANFIS model.
Social media and internet searches The epidemic spread has been shown to be correlated with search engine usage on the web in the past  (68). Nowadays people also share their opinion on social media networking sites such as Twitter, Reddit, and Facebook. These opinions can also be utilized to track epidemic disease spread. Masri et al. (69)  studied using tweets' time and geolocation data to improve the monitoring of the Zika virus (ZIKV) epidemic. The collected tweets were counted and compared with weekly data of the U. S. ZIKV cases, revealing a high Pearson correlation coefficient value of 0. 67 by applying a 1-week lag on tweets. Adding this 1-week-lag tweet data to the case counts in an auto-regression prediction model improved the coefficient of determination (R 2 ) from 0. 61 to 0. 74, which showed that tweet metadata is a significant predictor of future ZIKV cases.
Using deep learning methods (such as BERT and RoBERTa) that were trained on multiple available corpora (such as TwiMed, MedNorm, DS-NER), they achieved an F 1 -score of 0. 86 and 0. 75 on DailyStrength and Twitter datasets, respectively. They also applied their system on Twitter posts to collect COVID-19 symptoms. Users also share their opinions on COVID-19 measures on Twitter by supporting, refuting, or just commenting on them  (77). These opinions from German-speaking countries were manually labeled, and Beck et al. utilized predictions by transformer-based models. Jalil et al. (71)  performed sentiment analysis on tweets' text to classify them into positive, negative, and neutral. For the analysis, they used the COVIDSenti dataset  (78)  and reached the highest accuracy of 96. 66% with the proposed Multi-depth DistilBERT method.
Cell clustering has no effect on the incidence of apoptosis Following different pro-apoptotic treatments (Figure  1B ) and IncuCytebased live cell imaging, we obtained datasets of H2B-mCherry images (red images) and the corresponding SYTOX Green staining (green images) at different time points, for a kinetics analysis of 48 hours. We then designed two independent pipelines to segment H2B-mCherry-marked nuclei, one based on the CellProfiler software and the other written in Python, using standard image libraries to ensure a robust analysis (Figure  1A ). The analysis pipeline included the following steps. First, for a pair of red/green images the output is a segmented "red" image with a known red object centroid position (X i , Y i ) onto which a measurement of the green signal is superposed (I i , representing the maxima green signal intensity) (Figure  3A ).
Incucyte imager-based cell viability assay Cell viability was determined using an IncuCyte Zoom imaging system (Sartorius). Cells were seeded onto Imagelock 96-well plates in media containing 30 nM SYTOX Green (Life Technologies, S7020). Following different apoptotic treatments, the cells were imaged every 60 or 120 minutes and the analysis of the SYTOX Green-positive cells was performed using the available IncuCyte image analysis software (Essen Bioscience).
Acknowledgements Funding from  Institute Convergence PLAsCAN  ( ANR-17-CONV-0002 ),  LabEx DEVweCAN  ( University of Lyon ),  Agence Nationale de la Recherche (ANR)   Young Researchers Project  ( ANR-18-CE13-0005-01 ),  La Ligue Nationale Contre le Cancer and Fondation de France  supported this work. We thank  Brigitte Manship  for reviewing the manuscript.
Research gap and study objectives The current concept of the Metaverse is based on Generation Z's social values that the online self is no different from the ideal self  (Duan et al. , 2021). In other words, it is assumed that online digital identities are a reflection and representation of the real identities of the offline physical worlds. With the growth and influence of Generation Z in the Metaverse, it is now different from the Metaverse before, and, thus, it is argued that there is a need for a new definition  (Park & Kim, 2022a , 2022b). Additionally, the rapid advance of mobile technology and deep learning have facilitated access to the Metaverse in anytime at anywhere compared to the early versions of the Metaverse, and improved the accuracy of vision and language recognition, resulting in more immersive environments  (Park & Kim, 2022a , 2022b).
Method This study combines quantitative and qualitative synthesis approaches to review the Metaverse in education studies published in the literature. A traditional systematic review is an important step before carrying out any study, however, outcome reporting bias may be introduced, and the interpretation of results is prone to be subjective in a manual review  (He et al. , 2017). Therefore, a mixed-methods systematic review that combines bibliometric analysis and content analysis is needed to scientifically identify the knowledge base and evolution of a topic  (Tlili et al. , 2022). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to produce this systematic review  (Moher et al. , 2010). To deal with this complex topic, an extensive search for research papers was conducted using the following search strings in both Web of Science and Scopus databases: Metaverse and education (Topic) OR Metaverse and learning (Topic) OR Metaverse and teaching  (Topic).
The data in this study include academic studies published until 31 December 2021. A study was excluded if it: (1) discusses Metaverse in general and not in education; (2) is not in English; and (3) is not accessible online. As a result, a total of 47 studies in the Web of Science database and 34 studies in the Scopus database were identified. As part of the analysis, content analysis and bibliometric analysis were used. The data were analyzed and interpreted through these approaches for the purpose of data triangulation in order to gain a multi-dimensional perspective and increase the validity of the research. For the bibliometric analysis and synthesis, VOSviewer software was used to make distance-based co-occurrence maps: terms retrieved from keywords, titles, and abstracts were clustered and mapped according to their relatedness in a similarity matrix  (Van Eck & Waltman, 2010).
Trends of Metaverse in education by publication year, document type, country, keywords and research method As shown in Fig. 2 , research on the use of Metaverse in education first started in 2007. The number of studies in the WOS database increased after 2008, with a peak of five studies per year in 2009, 2010, and 2013. There is a sharp decrease after 2013, and only one study was found in 2014. The maximum number of studies reached its peak with seven academic studies in 2015. After 2015, there was a decrease in the number of research papers until 2019, and it was seen that no research was conducted in 2019. Four studies were conducted each year, with a spike in 2020 and 2021. When the polynomial regression trend line is examined, it is seen that the studies on the Metaverse in the WOS database showed a fluctuating trend from 2007 to 2021 but tended to increase in recent years.
When the trend line of the Scopus database is examined, it is seen that the studies on the Metaverse show a similar trend to the WOS database over the years. A  In terms of the distribution of Metaverse in education research by document type, Fig. 3  shows that most publications were conference proceedings. It is seen that the number of studies in the WOS database in the article type is two times more than the Scopus database. In the book chapter genre, there are eight studies in WOS and two in Scopus. In editorial material, there are only two works in WOS. Books and review article studies have the same rate in both databases. The common feature of both databases is that there are more tendencies towards conference papers in Metaverse studies. The interest in conference papers can be explained by the faster publication processes of proceedings and the effort by academia to quickly understand the promises and potentials of Metaverse technologies.
6. The size of the labels and circles depends on the number of co-occurrences. Lines identify major links between terms, and their thickness and the distance between the terms represent the association strength. For example, in Fig. 6 , the terms "metaverse" and "e-learning" have a short distance between them, which means that they occurred together several times. Additionally, Fig. 6  shows that the cluster distribution of keywords belonging to both databases is almost the same (green, blue, red, and yellow clusters). The green cluster in both databases is centralized around "Second Life" and "e-learning" showing the trend of using second life as a learning environment. The blue cluster shows research trends toward communication and social interaction through the Metaverse. The red cluster represents the Metaverse technology, covering terms like virtual reality, augmented reality, avatars, and interactive computer graphics.
, 2021) , while fewer studies used the Augmented Reality Metaverse type; and even fewer used Lifelogging and Mirror Worlds types. Although the articles either used or explained virtual environments through implementing 3D technologies. However, according to the Metaverse roadmap, the articles in this review did not exploit nor explain the technology to a high level of complexity. For example, none of the articles reached to the level of communication and collaboration with AI characters such as the description of VW Metaverse. Furthermore, it seems that Lifelogging and Mirror Worlds types, even though were coded so, did not fully exploit the explicit technology of those two Metaverse types to a high level in educational settings as described by  (Kye et al. , 2021). For instance, Lifelogging can generate valuable new kinds of data, such as digital language syntax and image/ video sharing by students, which can be analysed to explore new areas of integrating educational technology and psychology.
Besides, The Metaverse in education is combined with virtual learning laboratory, HotPotatoes, MOOC, Moodle, Institutional learning management system (LMS), Teleduc, Eduquito, Sloodle. For example, Massive Open Online Courses (MOOCs) provide students with a social network through Web 2. 0 and AVAS  (Wagner et al. , 2013). The combination of the Metaverse and MOOC can provide subject resources to a large number of students for free, and online courses allow students to broaden their knowledge  (Wagner et al. , 2013). A virtual learning laboratory is usually used in natural science, mathematics, and engineering courses. Virtual Learning Laboratory (VLL) usually combines software derived from Second Life and OpenSim is widely used in the virtual world environment, providing students with a collaborative, interactive and dynamic learning environment, thereby improving students' learning motivation and learning quality  (Tarouco et al.
, 2011). The main feature of OpenSim is that universities can easily customise their development, design management systems, and integrate with LMS databases to create personalized content  (González Crespo et al. , 2013). The virtual reality platform was developed by OpenSim, with the help of Sketchup Modulator and Autodesk 3DsMax, to provide students with a visualization platform and allow student avatars to walk and interact in the virtual environment  (Wagner et al. , 2013). In Fig. 12 , mobile technologies are also the dominant ones in the Metaverse, including mobile devices and geospatial mobility, because of its ability to create a connection between the medium and the student, thereby enhancing authenticity in the virtual world. In a study by  Estudante and Dietrich (2020) , a smartphone application (iOS and Android) was used to create a virtual world augmented reality for the Metaverse.
Sensors and wearable devices are one of the categories of technologies, including Microsoft HoloLens2 smart glasses and eye blinking, which enable teachers to monitor student dynamics by analyzing student behaviour  (Barry et al. , 2015). When students wear HoloLens 2 smart glasses, they are able to interact with content and execute commands in the virtual space  (Siyaev & Jo, 2021b ). The Blinking system is also a common tool in the Metaverse, mainly recording students' blinking times through specialized software  (Barry et al. , 2015). When students are emotionally unstable, the number of blinks in the blinking system increases, so teachers can better analyze students' responses  (Barry et al. , 2015). In the artificial intelligence category, the application of neuro-symbolic AI, convolutional neural network, machine learning, and semantic database technology help students to better process learning-related data.
Machine learning and semantic database modeling are often combined with web 3. 0 to allow users to access virtual worlds  (González Crespo et al. , 2013). Through a web-based architecture, machine learning and semantic database modeling can link with external scientific data sources, search for knowledge, and solve practical problems  (González Crespo et al. , 2013). Moreover, the potentials of AI in Metaverse enable new roles of intelligent Non-player Character (NPC) tutors, peers, and tutees  (Hwang & Chien, 2022). Therefore, there are future research opportunities to leverage AI technologies to analyse students' behaviour and interaction patterns with their performance levels in the Metaverse and coming up with new roles. As discussed above, several technology categories have been used in Metaverse in education to create a balanced eco-system.
, 2021). AR is the third most common technology, and it is mainly used by Gen Z and Gen Alpha. There was only one case where Augmented Reality, along with multiple technologies, was involved with Gen Y and Z  (Sourin, 2017). Furthermore, it is seen  that Artificial Intelligence, such as the application of Convolutional Neural Network (CNN) and Natural Language Processing (NLP), is more implemented with Gen Z, reflecting that the Metaverse in education environments are getting smarter from one generation to another. There are two points to consider from this analysis; to begin with, Second Life as an immersive technological platform seems to be more common among older generations (Gen Y, see Table  3 ). This raises the question of how new immersive changes of Second Life would be useful to deliver education to Gen Z and Gen Alpha? Secondly, Augmented Reality seemed to be more common with Gen Z and Gen Alpha, with only one with Gen Y.
, 2013). These features enable the Metaverse to have a large space for implementation in the field of education. Metaverse offers aircraft maintenance students an online alternative to flying, and allows students to socialize and perform virtual aircraft maintenance in a virtual space, thereby reducing unnecessary expenses  (Siyaev & Jo, 2021b). The Metaverse can also bring positive feedback to language courses, it allows the integration of the language grid system with Second Life, which enables virtual discussions between students from different countries, and the translation system will also enable students to have more concrete exchanges  (Kanematsu et al. , 2010). In the STEM field, the Metaverse is implemented to get students excited about avatars and virtual three-dimensional spaces that students are eager to continue learning  (Barry et al.
Conclusion, implications, and future research This study notes that despite the solid ground that it provides about the Metaverse in education, it still has some limitations that should be acknowledged. For instance, the obtained findings are limited by the databases and keywords used in this systematic review. Additionally, this current study did not review papers about the Metaverse in education which were not in English. These papers might report some interesting findings that could not be covered here. Furthermore, not too many published studies were identified from the top journals in educational technology, and this might be due to the infancy of the research topic. Therefore, this current study provides a step forward to researchers and practitioners about the potential research directions to investigate while exploring this research topic, namely Metaverse in education.
Although AI has not yet achieved self-awareness-that is, the ability to autonomously operate-it is able to support lower-order routine and repetitive cognitive tasks normally handled by humans. Moreover, many of these systems can "learn" over time, increasing and improving their accuracy, speed, and fidelity. AlphaZero, an AI-based program developed by Deepmind, recently defeated the world's best chess engine over the course of 100 matches by teaching itself how to improve. Recent consumer advancements, demonstrated in products such as Google's new Assistant, illustrate the potential of text-to-speech, deep-learning, and natural language processing-all elements of AI and ML.
SAM will soon be embedded throughout the university portal, allowing students to engage with the service on-demand. Meanwhile, Penn State University is leveraging ML algorithms to predict a student's grade performance-even before courses begin. Using more than 8. 5 million records culled from 2005 through 2016, the university developed a model to leverage data from the SIS, including transcript data and information found on admission applications. This predictive algorithm assists university administration in identifying students who might present with higher-than-average academic risks, allowing intervention strategies to be developed in advance. In another ambitious project, the Online Computer Library Center (OCLC), in coordination with seventy librarians and specialists from various organizations, developed Responsible Operations, a product that leverages ML and AI to track and chart engagement with various library services.
Responsible Operations explores patronage engagement along seven domains, including workforce development and data science services. To track the growing database of AI projects, OU has developed the Projects in Artificial Intelligence Registry (PAIR), which supports cross-institutional collaboration and locates and tracks grants in the field of AI. PAIR serves as a global directory of active and archival AI projects and research and might eventually serve as a hub for various initiatives. Many of these emergent projects realize a significant return on their initial investment. For example, although developing a chatbot can involve a significant time and resource investment that requires specialized development, that investment might yield returns in the form of extended hours and operation of the university to meet the needs of a 24/7/365 audience.
The following examples highlight a few attempts by institutions to work directly with their data and develop analytics-based applications that support student success. With increased pressure on advising staff to address student outcomes, the use of analytics as a tool for early alerts and proactive outreach is becoming essential. The Berkeley Online Advising project at the University of California at Berkeley and COMPASS, a project at the University of California, Irvine, are examples of learning analytics tools designed for academic advisors. These tools provide advisors with information that allows for proactive outreach and intervention when critical student outcomes are not met. Both tools represent solutions created internally on campus that are able to maintain data integrity and allow institutions to create solutions in response to unique student needs.
Berkeley Online Advising Berkeley Online Advising is a platform developed at the University of California, Berkeley for academic advisors that synthesizes student data from multiple campus services, generates and displays alerts about student academic progress, and provides advisors with new user-focused tools to record data about their in-person and virtual interactions with students. reflect the changing resources available to advisors but also the need for solutions that can use unique cross-functional data that complicate learning analytics when using vendor-based products. Applications that give students access to learning analytics are also becoming more common. Enabling students to access and track their individual data through insightful and easy-to-understand visualizations provides a model for giving students greater agency in their own success.
Emerging from the digital learning space, the field of learning engineering has garnered attention from higher education and industry. Learning engineering is "an evolving field that focuses on how engineering methodologies can inform and improve learning technologies and related architectures. " It has brought a new systems-thinking approach and better tools to measure how, where, and to what extent learning is happening in digital spaces. The Simon Initiative at Carnegie Mellon University provides an example of how learning engineering is harnessing a cross-disciplinary learning engineering ecosystem. The initiative's goal is to improve student learning outcomes through a continuous feedback cycle of data creation, the application of learning theory, and the design of technology to support learning.
OER is rapidly expanding far beyond the traditional textbook boundaries, as well. Stanford University's Center for Health Education developed a mobile application that seeks to promote global health awareness and reduce infant mortality. The Digital MEdIC app includes free public health courses and worldwide medical education. The University of Victoria Libraries in British Columbia has made available an entire series of workshop curriculum, including 3D printing and scanning, video editing, and data visualization using RStudio and Tableau. SUNY Empire State College has developed a free Thesis Generator that guides students in developing a thesis statement for essays. This effort wonderfully illustrates that OER is not only about textbook replacements but also includes online utilities. Finally, Alquimétricos, an international consortium based in South America, has developed a collection of open source didactic toys and building blocks that teach the fundamentals of math, architecture, engineering, physics, chemistry, and other disciplines.