dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to the mandatory SARS-CoV-2 vaccination for HCWs in France, we have no missing data regarding this variable. Moreover, the declaration of SARS-CoV-2 infection is compulsory for all staff to obtain daily allowances without loss of salary during the imposed quarantine. The use and analysis of data from the occupational health medical file were authorized after a regulatory declaration to the National Commission for Information Technology and Civil Liberties according to the reference methodology (declaration MR004 number 20-121 of April 30 th 2020). Population included in the immune response investigations Eighty-eight naive HCWs for COVID-19 and vaccinated with BNT and/or ChAd and/or mRNA-1273 vaccines were included in a prospective longitudinal cohort study conducted at the Hospices Civils de Lyon.
Indeed, in the Lyon University hospital, the pattern of contacts (frequency and duration) between nurses and patients and within nurses is superior than for other professionals  [18] , and this pattern could result in a higher exposure to pathogens, as previously reported in a study of nosocomial influenza spreading at the hospital  [19]. Obviously, this higher exposure of middle-aged women is specific to the HCW community and may not apply to the general population. Some limitations of the present study should be acknowledged. First, these results were obtained from observational data and not from a randomized clinical trial. As a result there were some inherent differences between the compared groups. For example subjects vaccinated with a heterologous schedule were on average younger than those vaccinated with a homologous schedule.
Ethical statements_ For the Covid-Ser study, clinical data were recorded by a trained clinical research associate using Clinsight software (v. Csonline 7. 5. 720. 1). Written informed consent was obtained from all participants. Ethics approval was obtained from the national review board for biomedical research in AprilM2020 (Comité de Protection des Personnes Sud Méditerranée I, Marseille, France; ID RCB 2020-A00932-37), and the study was registered at ClinicalTrials. gov (NCT04341142). Figure legends ns Homologous Heterologous Figure_4.
compare the risk of SARS-CoV-2 infection following a second or third dose of COVID-19 mRNA vaccine (the Pfizer-BioNTech mRNA vaccine, BNT162b2, or the Moderna mRNA vaccine, mRNA-1273) in subjects who received a priming dose of ChAdOx1-S or BNT162b2 vaccine, we extracted data from the occupational medicine database of the University Hospital of Lyon (Hospices Civils de Lyon, HCL), France. We focused on individuals not previously infected with SARS-CoV-2 (PCR diagnosis) before vaccination to avoid any bias linked to hybrid immunity (Figure1).
subjects in each group. The different viral strains that were used were sequenced and deposited at GISAID (https://www. gisaid. org/) (accession numbers EPI_ISL_1707038 19A (B. 38 lineage); EPI_ ISL_1904989 Delta (B. 1. 617. 2 lineage); and EPI_ISL_7608613 Omicron (B. 1. 1. 529 lineage)).
Significance How and why do human systems of marriage and reproduction differ from comparable systems in other mammals? To answer these questions, we use data from 90 human societies and 49 mammalian species. We demonstrate that humans exhibit lower average sex differences in reproductive inequality than do most other mammals, while nevertheless falling within the mammalian range. We attribute these small sex differences in reproductive skew to institutions supporting monogamy, to a limited intensity of polygyny in those groups practicing it, and especially to heavy dependence of children on care from both parents in our species. Such mammal-wide comparisons reveal the extent of, and possible reasons for, human exceptionalism. polygyny threshold model  (19, 20). We use the model to identify the parameter space where high reproductive skew is expected to emerge in each sex.
The theoretical model grounds a suite of predictions about why humans might differ from nonhuman mammals in terms of reproductive inequality. We then introduce a large-scale comparative database of reproductive outcomes. The database contains individual-level reproductive records from 80,223 human individuals (male and female) from 90 extant and historical human societies-including foragers, horticulturalists, and pastoralists, as well as market-integrated rural communities-and comparable data from 49 species of (free-ranging) nonhuman mammals. We use a measure of skew-the multinomial index, M -that is not biased by mean reproductive success rate, age variation, or sample size  (23) , to robustly measure reproductive inequality in each dataset (SI Appendix, S1 for details). We then use Bayesian meta-analysis models to examine how reproductive skew in humans compares with that of other mammals generally-and nonhuman primates specifically.
Prediction 1. Fig. 2A  visualizes the distribution of male and female reproductive skew values for all of the mammal species included in our dataset. Monogamous nonhuman mammals cluster in the upper right of the plot, where male and female reproductive skew values are similar, and reproduction is highly unequal. In contrast, polygynous nonhuman mammals show more variation, with some species showing high male skew and low female skew, and other species showing only slightly elevated male relative to female skew. In contrast to the wide distribution of skew values in nonhuman mammals, skew in human populations has a quite limited range. In particular, sex differences in reproductive skew are small among humans, and there is comparatively little variation across human populations, despite the fact that the populations in our sample differ markedly in terms of subsistence mode, mating system, and market integration.
here extend past comparative work unpacking the components of sex differences in skew and demonstrating the substantial role that variation in female reproductive success plays in driving sex differences in skew in both humans and nonhuman mammals. We also note that variation in reproductive success across human populations is much more limited than variation across species. There is, however, appreciable betweenpopulation variation in both male and female reproductive skew in humans (e. g. , as a function of the marriage system), supporting many of Brown et al. 's (80) inferences, but now using a much more comprehensive database. Our data also suggest that between-population variation in reproductive skew is not well predicted by subsistence mode. While this finding appears to stand at odds with expectations derived from a mating market framework  (19, 20, 33, 34) , we find it quite plausible that there is substantial variation in social and material inequality within subsistence modes.
Our model demonstrates the potential importance of complementarity in generating monogamy, but both the model and data presented here show that monogamy does not necessarily result in low skew for either sex. As such, future empirical work, integrating individual-level data on reproduction, marriage, and resource holdings is needed to provide finer-scale tests of the model. Conclusions. The multinomial index, M , provides a metric for directly comparing skew across datasets with substantial differences in mean reproductive success, group/sample size, and age structure  (23). The analytic relationship between M and the binomial index, opportunity for selection, coefficient of variation, standard deviation, and variance allowed us to compile comparable data from a large number of published skew values in nonhuman mammals.
Using this index, we analyzed a carefully constructed dataset to test whether reproductive inequality in a large sample of human societies differs from that observed in other mammals, generally, and nonhuman primates, specifically. We find that humans exhibit lower skew among males, and smaller sex differences in skew, than most other mammals, while nevertheless falling within the mammalian range. These low values can be attributed in part to the prevalence of monogamous marriage in humans, compared to the predominance of polygyny in nonhuman mammals, and in part to the limited intensity of polygyny where it is practiced.
Materials and Methods Data Inclusion. We compiled data on reproductive outcomes in human populations by reviewing the published literature on variation in human reproductive success and inviting anthropologists who had published on the topic to submit individual-level data (derived from a random sampling or census methodology) for this analysis. Additional data were gleaned from genealogical datasets published by KinSources  (107). SI Appendix, S6, for a complete list of populations, sources, and citations. SI Appendix, S10, for details on research permissions, ethics reviews, and informed consent procedures. To classify the marriage system of each population, we used standardized categories:normativemonogamy,sociallytolerated(buttypicallyrare)polygyny, or culturally normative polygyny (e.
SI Appendix, S8 shows that our qualitative findings hold. We compiled data on nonhuman mammal populations by performing a systematic search in Google Scholar and reviewing papers that included the term "reproductive skew. " Additionally, we reviewed all empirical articles on mammals that cite Nonacs  (73). We also located data cited by Lukas and Clutton-Brock (100) and referred to the original data cited therein. Finally, we invited field researchers to contribute unpublished individual-level data on reproductive outcomes. SI Appendix, S6, for sources and citations. We attempted to restrict our nonhuman mammal dataset to high-quality estimates (e. g. , samples for which paired molecular data on male and female reproduction were available). Data based simply on observed rates of copulation were deemed of limited value for assessing reproductive skew and therefore never integrated into the dataset or analyses.
Statistical Modeling. Methodological and mathematical details are provided in SI Appendix, S7. Model code and data are provided as supplementary files. The full workflow from raw data (cleaned of missing cases and stripped of identifying information) to estimation of M, meta-analysis, model diagnostics, and visualization of results is available to anyone wishing to reproduce or extend the analyses: https://github. com/ctross/reproductive_skew (108). Because M values are very high for some species, we visualize and analyze the data using the signed square root transform: M * = sign(M) |M|, which yields more normally distributed data. All data processing is handled using the R software environment  (109). All statistical models are coded in the Stan (110) language and are fit using Hamiltonian Monte Carlo  (111).
The Stan C++ Library is accessed using the rstan package. Data, Materials, and Software Availability. All study data are included in the article and/or supporting information. ACKNOWLEDGMENTS. Thanks to Geoff Wild, Christoph Hauert, Woodrow Denham, Martin Surbeck, Kevin Langergraber, Joseph Henrich, Robert Boyd, Kay E. Holekamp, Russell Van Horn, Eli Swanson, Dieter Lukas, Peter Nonacs, and Jane Lancaster for helpful input and feedback. This work was conducted as a part of the "Emergence of Hierarchy and Leadership in Mammalian Societies" group at the National Institute for Mathematical and Biological Synthesis, supported by NSF Award DBI-1300426 and the University of Tennessee, Knoxville. It was supported by NSF awards SMA-1329089 and SMA-1743019, and the Santa Fe Institute, as well as the Max Planck Institute for Evolutionary Anthropology, Department of Human Behavior, Ecology and Culture.
S. G. was supported by the US Army Research Office grants W911NF-14-1-0637, W911NF-17-1-0150, and the Office of Naval Research grant W911NF-18-1-0138. Additional funding for data collection was provided by the Wenner-Gren Foundation for Anthropological Research awards: 8913 and 7970, by NSF awards: BCS-0924630, BCS-0925910, BCS-0848360, BCS-0514559, BCS-0613226, BCS-0827277, SES-9870429, and DDRIG-1357209, by the National Geographic Society awards: HJ-099R-17, 20113909, 8671-09, and 7968-06, by the Kone Foundation awards: 086809, 088423, and 088423, and by the Jacobs Foundation, the UCSB Broom Center for Demography, and the UCSB Department of Anthropology. We also express our gratitude to all of the people who provided data, to individuals who assisted in data collection and management, and to the scholars who made their data and results freely available.
Chinese scholars' performance in international academic community and research on foreign archaeology has brought hot discussion about the internationalization of Chinese archaeology. Based on the databases of the China National Knowledge Infrastructure (CNKI) and the Web of Science core collection (WoS), this paper collected archaeology-related papers published by Chinese scholars in Chinese and world core journals (CCJs and WCJs for short), then filtered translated and original articles about foreign archaeology in CCJs, as well as all original archaeological articles in WCJs. Using Excel, CiteSpace and VOSviewer visualization software, we analyzed these data to give a bird's-eye view of how archaeology research in Mainland China has become internationalized. Chinese archaeology has seen activeinterrupt-active phases characterized by learning from foreign academics in the last century.
As a widely accepted technique, the bibliometric method-particularly mathematical and statistical approaches-has been applied to all types of scholarly communication in the natural and social sciences  (Pritchard 1969). It has also been proven effective in describing and evaluating the history and performance of individual disciplines, institutions, and even countries in terms of scholarly production and knowledge diffusion  (Potter 1988). At present, the increasing application and proliferation of big data in e-resource databasesincluding the China National Knowledge Infrastructure (CNKI), published by Tsinghua University, and the Web of Science core collection (WoS), published by the American Institute for Scientific Information (ISI) -have further facilitated the use of quantitative assessment indicators and tools developed based on bibliometrics to evaluate the performance of academic disciplines, institutions, and countries in a more visual way.
In the field of archaeology, bibliometric methods have also been used to review the discipline or a subfield's development  (Lin 2016; Mays 2010; Palomar et al. 2009; Pena-Poza et al. 2011)  or to discuss some special topics  (Bardolph and Vanderwarker 2016; Beaudry and White 1994; Beck et al. 2021; Heath-Stout (2020) ;  Hutson 2002 Hutson , 2006;; Sterud 1978). Particularly, this method has recently been applied to make a macroscopic review of the general characteristics and trends of Chinese archaeology on the basis of the papers indexed in CNKI and WoS datasets during the past century  (Wei et al. 2023). By adding new data published in 2021 into the dataset and using new software, i. e. Citespace and VOSviewer, in addition to Microsoft Excel, this paper focuses on how archaeology research in China has become internationalized and how is the current level of internationalization of Chinese archaeology, which means a more focused and in-depth bibliometric study.
2020). So as a first attempt, the current study focuses on research content, international issues, international collaboration, and journals of publication, based on translated articles and original articles on foreign archaeological research in Chinese core journals (CCJs) and all articles published by Chinese archaeologists in World core journals  (WCJs). We collected all archaeology-related articles in WCJs published by scholars of mainland China, Hong Kong, Macao, and Taiwan, but in the analysis, if not specifically stated, we focus on Mainland China while presenting and analyzing the data from Hong Kong, Macao, and Taiwan separately. The following research questions are of significant concern in this study: 1. How well were Chinese archaeologists informed about the international archaeological community in the last seven decades? 2.
Data and method Data source. We collected all archaeology-related papers from the two largest databases of scientific literature accessed via the library of Wuhan University: CNKI in China and the global WoS core collection. In CCJs, we collected papers based on the CNKI e-resources database. Several citation indexes for sciences, social sciences and humanities were established and largely accepted in China; they are Chinese Sciences Citation Database (CSCD for short), Chinese Social Sciences Citation Index (CSSCI for short), and Chinese Core Periodicals of PKU (CCP-PKU for short). So, all the journals indexed by these datasets (according to CSCD 2020, CSSCI 2019-2020 and CCP-PKU 2020) were firstly included in the analysis. In view of the interdisciplinary nature of archaeology, we not only collected all the journals belonging to the "Archaeology" catalog but also filtered the journals belonging to the "History" and "Geology" catalog.
In sum, we collected 27 Chinese Core Journals in total (see Table  1  for details). It should be noted that some journals such as Archaeological Collectanea (Kaoguxue Jikan), A Collection of Studies on Archaeology (Kaoguxue Yanjiu), East Asia Archaeology (Dongfang Kaogu), Beijing Cultural Relics and Museums (Beijing Wenbo), etc. are also related to archaeology, but they are not included in the three indexes mentioned above, so they are not included in our data source. The papers in more than 500 WCJs were collected from the Web of Science (WoS) Core Collection, including the Science Citation Index (SCI), the Social Sciences Citation Index (SSCI), and the Arts & Humanities Citation Index (A&HCI) (see Table  2  for details). We used a series of keywords for the search, including "archaeolog*," "Paleoanthropolog*," "Prehistor*," "Paleolithic*," "Neolithic*," and selecting "Peoples R China," "China," "Hong Kong," "Macao" or "Taiwan" for "Affiliation" and "Article" for "Document Type".
The publication is included in our data set for analysis if the institutional address of at least one of the authors is linked to China, Hong Kong, Macao, or Taiwan. Notably, the data analyzed in this paper only included papers directly related to archaeology sensu stricto, not museums, cultural heritage, cultural relics protection and management, or ancient architecture. The deadline for data collection was December 31, 2021 (articles in press were also included). Data processing and methods. According to the research content and aims of this work, we organized the data source and processing procedures, as indicated in Fig. 1. The raw data were examined and calculated by using various software, including Excel (Microsoft 365), VOSviewer (version 1. 6), and CiteSpace (version 6. 1 R6), each having its own strengths.
The latter two are visualization mapping software and are frequently used in current bibliometric analysis. On a technical level, VOSviewer pays special attention to the co-occurrence of keywords, authors, or institutions in a graphical representation of bibliometric maps, which are especially useful for displaying data in an easy-tointerpret way  (Eck and Waltman 2010) ; CiteSpace is slightly better to generate interactive visualizations of structural and temporal patterns and trends of a scientific field by mapping timeline and time-zone views  (Chen 2006). Note, we only collected the papers in which the first or corresponding author's affiliation is in Mainland China because the order in which the authors' names appear generally reflects the contribution that each author made to the article  (Egghe et al.
Screening of original articles on foreign archaeology in CCJs and WCJs published by Mainland Chinese scholars. We screened the original articles on foreign archaeology (AFAs for short, i. e. , the articles about research on foreign sites or remains, not including translated articles and abstracts) published by Chinese scholars in CCJs and WCJs. We then tabulated the data by the number of annual publications and the countries concerned. At the same time, we highlighted the articles directly related to Sino-foreign archaeological projects that Chinese teams conducted in foreign countries. We also analyzed all original articles on archaeology published by Mainland Chinese scholars in WCJs. The high-frequency occurring keywords of these articles were examined with the software CiteSpace. We chose "keyword" in the "Node types", "from 1980 to 2021" in "Time Slicing", set "#Years Per Slice" to "5" and others to "default value".
To better understand the relationship between hot topics of research among Chinese archaeologists and the international archaeological community, we compared the high-frequency occurring keywords between domestic and international scholar publications in WCJs. All archaeology-related publications searched from WoS (not refined by countries, 103,840 papers in total) were loaded into CiteSpace for the keywords analysis, with the parameters set as above; we then compared these keywords with those in articles published by Mainland Chinese researchers. Then the international influence of Chinese authors was analyzed by using the software CiteSpace, from the two sections of highly published and highly cited authors. To analyze the highly published authors, we chose "Author" in the "Node Types", and set other parameters as above, while in the analysis of highly cited authors, "Cited Author" is chosen in the "Node Types".
The analysis of keywords' evolution in different stages was conducted by using the software CiteSpace, providing the results shown in Fig. 6. The nodes and typefaces' size represent the keywords' weight. From left to right, this image shows the change of keywords from 1980 to 2021, and the year in which the keywords are located is the average year of their appearance. Figure  6  indicated that before 2000 the number of keywords was small but the occurrence frequency was high. The number of keywords increased dramatically after 2000, especially from 2002  to 2010; many top 20 high-frequency keywords appeared in this period. Some new high-frequency keywords have emerged since 2018, though the weight is not so important since it just started.
Since 2020 some new keywords have occurred, like "food globalization", "genetic history", "population history" and "human-environment interactions", which represent to some extent the current research frontiers. The top 20 keywords in articles published in WCJs by Chinese scholars (left) and World scholars (right) in terms of frequency of occurrence are generated by the software CiteSpace, as shown in Table  4. Eight of these keywords were shared, including "archaeological site", "climate change", "origin(s)", "evolution", "late pleistocene", "site", "ancient dna" and "history". In addition,  some of the keywords in the left and right columns look different, but they are all closely related to specific field, such as "environmental change" (left) and "climate" "vegetation" (right), "human activity" "modern humans" (left) and "population" (right).
These results suggested that Chinese scholars were highly coupled with hotspots in international academic communities. Chinese archaeologists in international academic community. High-impact scholars include not only the founders of disciplines, schools or classic theories in a field, but also researchers who are currently active in the frontline of scientific research  (Qiu and Ma 2006). The statistical analysis of high output and highly cited authors can help us to understand the core scholars in a field and the trend of research practice in a discipline  (Hu 2018). So with the software CiteSpace we analyzed articles number and citation frequency of Chinese scholars in WCJs in order to see the influence of Chinese archaeologists in international academic community. The results are shown in Fig.