dataesr/scientific-paragraphs-categorization huggingface dataset:
The software developers also provide free access to user manuals and demo projects. Interested readers are referred to the tool manuals for detailed step-by-step description of the analyses. We used VOSviewer for conducting different analyses, namely, term co-occurrence analysis (using the 'full counting' counting method and setting 'all keywords' as the unit of analysis), citation (setting 'documents' as the unit of analysis), co-citation (using the 'full counting' counting method, and setting 'cited references', 'cited sources', and 'cited authors', as units of analysis), and bibliographic coupling (using the 'full counting' counting method, and setting 'organizations' and 'countries' as units of analysis)  [57]. Term co-occurrence analysis presents frequently occurred terms and the way they are connected to one another.
This can be used to highlight major thematic areas. As a term may have different variants, before conducting the analysis, a thesaurus file was developed and added to the VOSviewer database to avoid separate counting of synonyms (e. g. , Internet of Things and IoT). As can be seen in Section 3. 2, results of the term co-occurrence analysis (and also other analyses done by VOSviewer) are presented as a network graph of nodes and links. The node size is proportional to the occurrence frequency, and the link width is proportional to the strength of connections between two nodes. Terms that co-occur more frequently form clusters that show different thematic areas. To understand thematic transition over time, we divided the study period into two sub-periods  (1991-2015 and 2016-2021). The beginning year was set as 1991 as this is the publication date of the first paper indexed in the WoS.
Co-citation indicates the link between two documents that are both cited simultaneously by a third document  [57]. In other words, it considers not only the documents in the database, but also their cited references. In contrast, citation analysis was conducted to understand highly cited papers in the database. To understand which countries and institutions have contributed more to this field of research, we used the bibliographic coupling analysis. "A bibliographic coupling link is a link between two items that both cite the same document"  [57]. Finally, the citation burst function of CiteSpace was used to better understand which subjects have received more attention at specific times during the study period. This can complement the findings of the term co-occurrence analysis and allow a better understanding of thematic transition and intellectual turning points over time  [58].
The results show that there is an overall growth in the number of publications per year. It is clear that this is still a young field, as most papers have been published over the past 10 years or so. In fact, only 17 papers have been published between 1991-2010, accounting for less than 1% of the documents in the database. In the following 5 years, 429 papers have been published, indicating that 'smart cities' has become a mainstream research topic since 2010. From 2015, however, a rapid growth pattern can be observed. In particular, the growth pattern has been exponential since 2018. Interestingly, the number of articles published over the past three years is greater than the cumulative number of articles published between 1991 and 2018. As explained in the Introduction, this is a clear indication of the increasing recognition of the significance of smart cities for dealing with multiple challenges that cities around the world are facing and have been highlighted in major policy documents such as the New Urban Agenda and the Sustainable Development Goals (SDGs).
Based on this, an upward trend in publications on smart cities is expected for the coming years. The results show that there is an overall growth in the number of publications per year. It is clear that this is still a young field, as most papers have been published over the past 10 years or so. In fact, only 17 papers have been published between 1991-2010, accounting for less than 1% of the documents in the database. In the following 5 years, 429 papers have been published, indicating that 'smart cities' has become a mainstream research topic since 2010. From 2015, however, a rapid growth pattern can be observed. In particular, the growth pattern has been exponential since 2018. Interestingly, the number of articles published over the past three years is greater than the cumulative number of articles published between 1991 and 2018.
Thematic Focus Areas and Their Transition 3. 2. 1. Thematic Clusters The output of the term co-occurrence analysis for the entire dataset is shown in Figure  2. Three major clusters can be identified from this figure. These are: (1) the smart city concept, depicted by the color red, (2) big data analytics, represented by the color blue, and (3) the technological aspects, especially in relation to Internet of Things, depicted by the color green. The link's thickness between nodes indicates strength of connection, while the size of the node is directly proportional to the term frequency. Therefore, as is clearly depicted in the diagram, the most dominant clusters were the smart city and the IoT. The other cluster that is mainly focused on the application of big data analytics and other smart solutions (e.
Thematic Focus Transition over Time To explore the thematic focus transition, we divided the dataset into two sub-periods and conducted separate term co-occurrence analyses for each period. First Period  Although the concept of 'smart cities' can be noted as early as the 1980s, it did not gain considerable attention until after the fourth revolution (early 2000). This is evident in the co-occurrence analysis map (Figure  3 ) for the period between 1991 and 2015, where the frequency of many terms relevant to the smart city concept is seen to be low. Additionally, the list of keywords with citation bursts (Figure  4 ) shows that during this period, topics related to precedent concepts such as 'intelligent city' have still been dominant. This indicates that, though there have been some interests by researchers, the level of understanding of smart cities was still in its infancy stage.
Therefore, considerations to issues of security, especially in regard to data and surveillance, is noted to be prevalent in the civic realm. In addition, it is evident from the analysis that most technologies were being directly focused on the energy sector, though the number of publications in regard to the same are still limited, and key words such as 'smart grid' and 'renewable energy' were not attracting much attention from academia. Another area of noted interest during this period is the Internet of Things. This topic was very relevant during this period, as most of the emerging technologies that could help actualize the smart city concept were hinged on it. For this reason, most of the publications on IoT are seen to have focused on other sub-cluster terms such as 'big data analytics', 'internet', 'management', and 'design'.
The IoT cluster has seemingly gained more attention in this period. Attention in this cluster is seen to have focused especially on the sub-cluster 'internet', which is seen to have attracted further research in new terms such as 'challenges, 'protocol', and 'cloud'. These terminologies may have emerged with the realization that IoT enabled devices need to communicate in a standardized protocol to aid in the seamless collection of data, and further, relaying back the insights after analysis of data to relevant parties for better urban management  [95]. During this period, it is observable that the term 'energy' is still a key term that is strongly connected to the term 'optimization'. This may indicate the increasing use of smart solutions for enhancing operational efficiency of energy systems.
Attention to energy is not surprising, given that energy sector plays an essential role in addressing key challenges highlighted in policy documents such as SDGs and the New Urban Agenda. In addition, several other key terms such as 'machine learning' and 'deep learning' have appeared in this cluster and gained even a stronger position than 'energy'. This may indicate that, though energy-related issues are dominant in the global agenda, they have been overtaken by other research areas in respect to IoT technologies, leading to the understanding that research interests and global conversations moved towards how to utilize the concept across an array of other fields, hence leading to the need for the development and deployment of the data and technology infrastructure. on IoT are seen to have focused on other sub-cluster terms such as 'big data analytics', 'internet', 'management', and 'design'.
Attention to energy is not surprising, given that energy sector plays an essential role in addressing key challenges highlighted in policy documents such as SDGs and the New Urban Agenda. In addition, several other key terms such as 'machine learning' and 'deep learning' have appeared in this cluster and gained even a stronger position than 'energy'. This may indicate that, though energy-related issues are dominant in the global agenda, they have been overtaken by other research areas in respect to IoT technologies, leading to the understanding that research interests and global conversations moved towards how to utilize the concept across an array of other fields, hence leading to the need for the development and deployment of the data and technology infrastructure. In respect to data management, it is clear that during the second period (Figure  5 ), there has been increased attention on areas such as big data  [35, 105, 106]  and computation, especially in relation to climate change  [107, 108]  and others become apparent.
Data management and big data analytics are expected to gain further momentum in the post-COVID era. In fact, there are arguments that the recent pandemic has increased interest in big data analytics and smart city development  [110]. In respect to the smart city cluster, this second period is observed to have experienced numerous publications, not only touching on cities, but also on the relationship between cities, data, and IoT. However, in respect to cities, and with the growing number of smart cities  [111] [112] [113] , it is evident that researchers were and continue to be interested in areas such as sustainability, governance, technology, policies, and the impacts of the same. Those areas of interest are some of the new emerging frontiers that the literature has gained during this timeline.
The second important cluster (blue) includes the authors that have expertise in big data and have worked on issues related to the implementation and the future of smart cities (e. g. , Rob Kitchin, Anthony Townsend, Robert Hollands, and Alberto Vanolo). This cluster also associates with the big data analytics and smart city applications cluster identified in thematic analyses (blue cluster in Figure  2 ) and blue cluster identified in previous section (influential documents in Figure  9 ). The third cluster (green) includes authors whose expertise revolve around the Internet of Things and the underlying technical issues related to smart cities. Key authors in this cluster are Andrea Zanella and Luigi Atzori. This cluster is also in close connections with the green cluster (Internet of Things) identified in Figure  2  and the green cluster in the most influential documents (Figure  9 ).
For this purpose, we relied on bibliometric analysis and science mapping tools (VOSviewer and CiteSpace) that allow analyzing performance and visualizing knowledge domain. The second important cluster (blue) includes the authors that have expertise in big data and have worked on issues related to the implementation and the future of smart cities (e. g. , Rob Kitchin, Anthony Townsend, Robert Hollands, and Alberto Vanolo). This cluster also associates with the big data analytics and smart city applications cluster identified in thematic analyses (blue cluster in Figure  2 ) and blue cluster identified in previous section (influential documents in Figure  9 ). The third cluster (green) includes authors whose expertise revolve around the Internet of Things and the underlying technical issues related to smart cities.
Overall, this study has provided a better understanding of the overall structure of the smart cities literature. However, given the rapid pace of publications, regular updates are needed to keep up with the tremendous knowledge explosion on smart cities. Additionally, more specific analyses on each of the three clusters identified in this study is recommended, as it may provide more detailed information on how they have evolved over years. Finally, it is essential to mention that the approach taken in this study is not without limitations. The main limitation is that the existing software tools for bibliometric analysis can only process data related to publications indexed in scientific databases such as the WoS and Scopus. Accordingly, they cannot be used to analyze potentially influential documents not indexed in such databases (i.
Figure1displays the distribution of the 5722 publications over the study period. The results show that there is an overall growth in the number of publications per year. It is clear that this is still a young field, as most papers have been published over the past 10 years or so. In fact, only 17 papers have been published between 1991-2010, accounting for less than 1% of the documents in the database. In the following 5 years, 429 papers have been published, indicating that 'smart cities' has become a mainstream research topic since 2010. From 2015, however, a rapid growth pattern can be observed. In particular, the growth pattern has been exponential since 2018. Interestingly, the number of articles published over the past three years is greater than the cumulative number of articles published between 1991 and 2018.
However, by following the time availability approach, we will investigate the following hypotheses: Hypothesis 1a. We expect to find a more egalitarian division of domestic work in households in which men have more available time than women, and vice versa. Hypothesis 1b. This association between time and domestic work may not hold if men and women use their time very differently or if women engage in compensatory behaviors. Hypothesis 2. Regarding specific work arrangements, we expect to find a more egalitarian division of domestic work when men are unemployed than when they are working from home. Our analysis contributes to the literature by focusing on the division of domestic work within couples and taking into account different employment configurations; in addition, we used a dataset that is representative of the French population.
Data and Variables We used data from ELIPSS, 1 a longitudinal panel that is representative of the French population. ELIPSS panelists are randomly selected and answer a monthly online survey on different topics. There is only one respondent per household. During the lockdown period, a research project (CoCo 2 ) was funded by the French research agency (ANR, Appel Flash Covid 19) to use the ELIPSS panel to research different sociological dimensions of the COVID-19 pandemic. During the third wave of this project, which was conducted in May 2020, the survey included questions on domestic work  (Recchi et al. 2020b ). Out of the 1404 panelists invited to respond to this particular survey, 1076 completed it, which represents a response rate of 76. 4 percent. The survey included information on the sociodemographic characteristics of respondents (e.
g. , sex, age, level of education, and marital status) and their household (e. g. , income and number of children and adults). Because we are interested in the gendered division of domestic work, we only analyzed the responses of participants who were in a cohabiting or married relationship. The survey did not collect data on the sex of the partner; therefore, we imputed it by assuming that all couples were heterosexual. This is, of course, a biased assumption, but the most recent data from the Institut National de la Statistique et des Etudes Économiques indicates that same-sex couples amount to 0. 9 percent of all French couples, so we do not expect our assumption to significantly bias the results. We also only selected couples in which at least one of partners was economically active. After cleaning the data, we had a sample of 406 households.
In a context of fierce competition for scarce positions, Daniela and Aline thus considered, as soon as their PhD studies ended, that following institutional requirements would still leave them with little to no hope of reaching stable employment in academia. Mobility as a spectre for women? Considerations in disentangling (im)mobility and gender The cases of Daniela, who worked 7000 km away from her husband, and Aline, a longdistance commuter, among other cases from the POCARIM and GARCIA research projects, illustrate the need to go beyond the common representations of women's mobility. In accordance with the scarce parallel data tracking of men's and women's mobility  (European Commission, 2016; Goastellec, 2016) , the POCARIM and GARCIA figures enable one to rethink the supposed "women's immobility" within the Swiss context as the persistence of an essentialist epistemology overlapping a "mobile duality" (to be or not to be "mobile") and a gendered one (to be a man or a woman).
Acknowledgements This paper was originally presented in a symposium at the Meeting of the Society for Research in Higher Education (SRHE, Newport, 2017). I thank  Charikleia Tzanakou  and  Emily Henderson , the convenors of the symposium and editors of this special issue for their inspiration, feedback and support. The qualitative data analysed in this article were collected within the GARCIA and the  POCARIM EU  projects. I am particularly grateful to  Nicky Le Feuvre , leader of the Swiss section of the  GARCIA  project, for her continuous support, and also wish to thank  Dominique Vinck , co-leader of the Swiss section of the  POCARIM  project, who granted me access to the data. Special thanks to  Pierre Bataille ,  Maria del Rio Carral ,  Farinaz Fassa ,  Gaële Goastellec ,  Sabine Kradolfer ,  Soraya Ksontini ,  Michael Posse ,  Mélody Pralong , members of the GARCIA team, as well as  Lucie Haye , for her precious and meticulous editorial work.
Sample and Sources The data used for our comparative overview of Baggara Arabic come from different sources. First, we refer to a heterogeneous literature that provides linguistic information on different Baggara dialects of Chad and Sudan. These bibliographical sources have been completed by new first-hand data gathered during fieldwork in the White Nile region (Sudan) in 2018. The data and the sources can be summarized as follows: -Nigerian Baggara Arabic (NA) Shuwa tribe  (Carbou 1913; Lethem 1920; Owens 1993a Owens , 1993b)) ; -Baguirmi Baggara Arabic (BgA) Walād 'Ali tribe  (Zeltner and Tourneux 1986; Decobert 1985) ; -Bath. a Baggara Arabic (BaA), Walād Rashid tribe  (Decobert 1985) ; -Kordofanian Baggara Arabic (KA), H. awāzma and Missiriye tribes  (Manfredi 2010 (Manfredi , 2013)) ; and -White Nile Baggara Arabic (WA), Bānu Sulēym tribe (own fieldwork data).
Sample and Sources The data used for our comparative overview of Baggara Arabic come from differe sources. First, we refer to a heterogeneous literature that provides linguistic informati on different Baggara dialects of Chad and Sudan. These bibliographical sources have be completed by new first-hand data gathered during fieldwork in the White Nile regi (Sudan) in 2018. The data and the sources can be summarized as follows: -Nigerian Baggara Arabic (NA) Shuwa tribe  (Carbou 1913; Lethem 1920; Owe 1993a Owe , 1993b)) ; -Baguirmi Baggara Arabic (BgA) Walād 'Ali tribe  (Zeltner and Tourneux 198 Decobert 1985) ; -Batḥa Baggara Arabic (BaA), Walād Rashid tribe  (Decobert 1985) ; -Kordofanian Baggara Arabic (KA), Ḥawāzma and Missiriye tribes  (Manfredi 201 2013) ; and -White Nile Baggara Arabic (WA), Bānu Sulēym tribe (own fieldwork data).
Data were collected face-to-face, by randomly approaching possible participants, and about 20 min were needed to complete each survey. The Sardinian sample is composed of 45% of male respondents (figures in the population are 49% from the 2011 census, so our sample underestimates the male population). The average participant is 46. 4 years old (44 is the average age in population, census 2011). Secondly, the use of interviews in the North Moroccan study was two-fold. They served to introduce the problem of risk management in the region and to construct the questionnaire. Besides, they are an additional source of information to be crossed with the responses from the questionnaires. There were twenty-five interviews conducted, and the interviewees were grouped into three profiles: a) the elected or appointed administrators (eight), b) the scientists and technicians that work in the risk research or management (eight), and c) the members of civil society, i.
Quantitative part of the study: Survey content and variables The Moroccan and Sardinian questionnaires consisted of 39 and 30 questions, respectively, and were divided into five sections. Not all questions from the questionnaires were used in this analysis. The common part of the questionnaire were the sections that dealt with risk awareness, personal experience, formal and informal education on risks; in addition to questions regarding institutional trust, environmental identity and place attachment; and finally questions about the belief in climate change, adopting precautionary measures and investing money. Those questions were close-ended (dichotomous and Likert rating scale). The full questionnaires (in French and Italian) are available upon request. The data were described in Excel and SPSS, the latter used for correlations and regression model.
Acknowledgements These research activities have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement  No713750. Also, they have been carried out with the financial support of the  Regional Council of Provence-Alpes-Côte d'Azur  and with the financial support of the A*MIDEX (n•  ANR-11-IDEX-0001-02 ). This work has also been funded by the  RiskMED  project , Labex OT-Med  ( ANR-11-LABE-0061 ) supported by the  Investissements d'Avenir, French Government  project of the  French Agence National pour la Recherche (ANR)  through the A* MIDEX  project ( ANR-1-1E-0001-02 ). The authors are grateful to the local population and other local stakeholders that provided useful information for this study.
Case data sources The cases presented in this study come from two sources. Three cases were drawn from the World Health Organization (WHO) Community Engagement Package database. This database serves as a repository of experiences of community engagement practices in different settings, contexts and groups. It showcases good practices, lessons learned, challenges, and innovative approaches to working with communities in WHO regions. It is also used for developing learning resources on community engagement. We selected three cases from among 52 in the health emergencies and disaster management category from this database, based on the following inclusion criteria: (i) Case studies documenting good health promotion principles and strategies, with innovations and potential to adapt and scale up across regions in emergency context; (ii) Cases representing different emergency settings; (iii) Cases reflecting COVID-19 and other health emergencies; and (iv) Cases published within the last 10 years.