dataesr/scientific-paragraphs-categorization huggingface dataset:
The current study: how is COVID-19 conceptualized, and what is its semantic relation with other disease concepts? In the present study, our aim was to investigate how people conceptualize a relatively new concept, i. e. , COVID-19, and whether and how the introduction of this concept shaped the semantic associations of other concepts in the disease domain. Importantly, we recruited participants from Italy, a country severely hit by COVID-19, and the data were collected in the first wave of the pandemic, during lockdown-the first national lockdown in the world. So, the timing of the data collection allowed us to capture the emerging meaning of COVID-19 in its initial phase. To tackle these questions, here we apply a method typically used to investigate conceptual representations, i. e. , a semantic fluency task.
Results Correlation between demographic information, perception of risk, and level of information. Before moving to the analysis of free-listing data, we addressed whether participants had a different perception of COVID-19 related risks (tested with a continuous scale from 1 to 7) depending on three main parameters: their regional provenience (North of Italy; Center of Italy; South of Italy), the frequency with which they received information and news about COVID-19 (never; rarely; sometimes; often; very often), and the number of cases positively tested for COVID-19 within regions at the time of participation-as reported by the National website https:// github. com/ pcm-dpc/ COVID-19. Data were analyzed using R (version 3. 6. 3  37 ) and RStudio (version 1. 4. 1100  38 ). All data, scripts, and analyses are available at https:// osf.
The resulting network comprises 15 nodes (i. e. , associates) and 15 edges (i. e. , links). Before applying any clustering algorithms, we calculated the modularity of the network. Modularity values greater than 0 indicate non-random clustering. The modularity of COVID-19 was 0. 38. We used Louvain's algorithm for community detection to detect representative clusters of associates  47. Communities are groups of nodes in a network that are more densely connected to one another than to other nodes. We found five different communities in our dataset. To visualize the network, we used the Fruchterman-Reingold force-directed layout algorithm. Figure  1  shows the semantic network for COVID-19; associates that were listed in succession (i. e. , bigrams) are connected by links. Thicker links represent bigrams that were most frequently linked together.
Compared to other concepts, COVID-19 evokes fewer possibilities of cure and healing, probably because it is less known. It has to be borne in mind that these data were collected during the first COVID-19 outbreak, when the possibility of a vaccine was still far from its current development. For example, with the concept TUMOR, the word fear is also the most frequent word, but also cure and hospital are highly frequent. With the concept DISEASE, and even more with FEVER and FLU, the focus is not only on fear but on the concrete possibilities to deal with the illness: in fact, hospital, medicines, doctors are highly frequent words. To further investigate commonalities across concepts, we inspected the ANEW database  48   ). We also found anxious (instead of anxiety), valence M = 2. 16 (SD = 1.
Originally we contacted 166 people, but 90 of them did not complete the questionnaire, likely because it required a long time (see below) and we allowed participants to interrupt and continue it later within a 3-days-time (n = 88). A small percentage of participants (n = 2, 1. 2%) instead completed the task, but typed answers not congruent with what we were asking (e. g. , swear words, symbols), so their responses were not considered for the analyses. From the remaining 76 participants, we excluded data from participants who indicated that their nationality was other than Italian (n = 2, 2. 63% of the sample), as we were specifically interested in testing people sharing a common cultural milieu. The final sample is therefore composed of 74 participants (50 females, M age = 37. 46; SD = 12.
searching for the seven terms shared across the six target concepts. ANEW provides affective norms for over 1000 Italian words, measuring on 9-point Likert scales Valence (1 = very unpleasant; 9 = very pleasant); Arousal (1 = very calm; 9 = very aroused); Dominance (1 = very submissive; 9 = very dominant) and Concreteness (1 = abstract; 9 = concrete), among other psycholinguistic variables. We found 4 of the seven words in the database, i. e. , fear, valence M = 2. 21 (SD = 1. 45); arousal M = 6. 94 (SD = 2. 25); dominance M = 3. 52 (SD = 2. 08); concreteness M = 5. 00 (SD = 2. 96), medicine, valence M = 4. 93 (SD = 2. 47); arousal M = 5. 59 (SD = 2. 09); dominance M = 4. 41 (SD = 2. 07); concreteness M = 6. 30 (SD = 1. 87), hospital, valence M = 2. 83 (SD = 1. 84); arousal M = 6. 71 (SD = 1.
, 2019) , and Earth System model CNRM-ESM2-1  (Séférian et al. , 2019). The following work uses the single-column model (SCM) version of  ARPEGE-Climat (e. g. , Abdel-Lathif et al. , 2018) , in the context of the GABLS4 framework (see Section 3. 1). The model physical package is fully described in  Roehrig et al. (2020)  and therefore we only insist hereafter on the model features relevant for the present study. ARPEGE-Climat 6. 3 standard vertical grid consists of 91 vertical levels, following the progressive hybrid σ-pressure discretization of  Simmons and Burridge (1981). The altitude of the first 5 model levels is approximately 8. 5, 29, 55, 91, and 132 m. The model timestep is 15 min. A version of ARPEGE-Climat 6. 3 with higher vertical resolution (2 m up to 400 m) is also used in Section 5.
Acknowledgments The authors are grateful for helpful comments and suggestions from two anonymous reviewers and from the associate editor  T. Mauritsen. This work received funding from grant  HIGH-TUNE ANR-16-CE01-0010. It was supported by the  DEPHY2  project, funded by the  French national program LEFE/ INSU  and the  GDR-DEPHY. Daniel Williamson  was funded by  NERC  grant:  NE/N018486/1  and by the  Alan Turing Institute  project " Uncertainty Quantification of multi-scale and multiphysics computer models: applications to hazard and climate models " as part of the grant  EP/N510129/1  made to the  Alan Turing Institute  by  EPSRC. The LESs of the GABLS4-stage3-10 h have been kindly provided by  G. Matheou ,  B. Maronga ,  C. Van Heerwaarden , and  J. Edwards.
Data Availability Statement The High-Tune Explorer (htexplo) tool used here for history matching with iterative refocusing is available through the open source version control system "subversion" (svn) at https://svn. lmd. jussieu. fr/HighTune. A snapshot version of the tool used here is also available at http://doi. org/10. 14768/70efa07b-afe3-43a4-8334-050354f9deac. LES and SCM data archiving is underway at https://doi. org/10. 5281/zenodo. 4350522.
Climate proxy data are required for improved understanding of climate variability and change in the pre-instrumental period. We present the first international initiative to compile and share information on pro pluvia rogation ceremonies, which is a well-studied proxy of agricultural drought. Currently, the database has more than 3500 dates of celebration of rogation ceremonies, providing information for 153 locations across 11 countries spanning the period from 1333 to 1949. This product provides data for better understanding of the pre-instrumental drought variability, validating natural proxies and model simulations, and multi-proxy rainfall reconstructions, amongst other climatic exercises. the database is freely available and can be easily accessed and visualized via http://inpro. unizar.
Due to the demonstrated utility of this proxy, as well as the significant effort made by many researchers to rescue rogation ceremonies, a specific international repository for this proxy may be of particular importance for the research community. This repository can assure data quality and the reuse of this invaluable climatic information for different applications. For this reason, this work presents the INternational Pro pluvia ROgation database (INPRO), which incorporates information from more than 3500 PPRs spanning 153 different locations across the globe. The availability of this dataset for the research community will increase the reuse of PPRs in different climatic or social studies. Moreover, this initiative is open to any contributions of new research and we encourage any researcher to share their PPRs through the IMPRO initiative.
Methods A wide range of documentary sources can be used to retrieve information about rogations (Fig. 1 ). Each documentary source has different characteristics, especially in terms of consultation accessibility, time span, and dating precision. Here, we provide a brief description of the different types of documentary sources used. As illustrated, data were extracted from both primary and secondary sources. While primary sources refer mainly to those created when an event was reported from first-hand information, secondary sources describe events based on other sources. Herein, it is noteworthy to indicate that secondary sources must be handled with more care, given that they may contain errors originating from the transcription or interpretation of the documentary sources on which they are based.
Data Records We retrieved 3536 dates of rogation ceremonies from 153 locations spanning 11 countries (Mexico, Guatemala, Ecuador, Peru, Chile, Argentina, Portugal, Spain, France, Italy, Philippines) (Fig. 2 ). The earliest records were collected from Spain, dating back to the 14 th century. The available records from France, Portugal, and Italy started in the 16 th century, while those located in Latin-America had data from the early 17 th century. The latest records are from the first half of the 20 th century, referring mainly to rural areas whose celebrations still have an important significance  38. The database is in figshare repository  39. It is a unique. csv file. Each row provides information about the celebration of one PPR, including the date of the celebration, location (with the geographic coordinates), and documentary source from which it was retrieved.
Date of celebration: When possible, we provide the exact date of celebration of each rogation on a daily scale. The date recorded in the database is the date when PPR was celebrated. This is not a trivial question, because we had a bureaucratic period of around one week for each rogation (i. e. from the first time that famers requested celebration to the exact date of celebration). Some liturgical acts lasted some days (e. g. novenas (nine days) and triduo (three days)). In such cases, we provide the date of the first day. In our database, there are four columns related to the date: "year", "season", "month", and "day". We filled in these columns depending on the information provided by the document. We found that 48% of the rogation records had daily resolution, compared to 44% (seasonal) and 4% for monthly, and annual scales.
technical Validation The validation of our dataset depends largely on the reliability of the documentary sources. For this reason, we calculated a reliability classification, with values ranging from low (1) to high (3) for each record: 1. For secondary sources that do not cite properly their primary documentary source or were directly based on secondary sources. 2. For secondary sources based on primary sources, which were cited correctly. These sources were produced by specialized researchers, with a possibility to verify the information from their primary source. 3. Only for primary sources with direct evidence. Our findings reveal that 16%, 68%, and 16% of the PPRs had high, moderate, and low reliability, respectively. However, it should be noted that the level of reliability can be improved, following the consultation of new documents.
Another important question in the quality of the dataset is record duplication. When the same PPR date was recorded in more than one source, the record with a higher value in the reliability index was retained, while the other was deleted. When both records had the same reliability index, we merged the records, providing details of both sources for the PPR record. Duplicated records were defined as two PPRs, belonging to the same location, with less than three days of difference. However, this definition extended also to identical dates provided at different temporal resolutions (e. g. a PPR recorded in 1798, winter 1798, February 1798 and 21st February of 1798). Figure  3  depicts a flowchart summarizing our procedure to assure data accuracy in terms of date, reliability, and location. This is a fundamental issue in database development, given that the frequency of celebration in a particular location during a period is used as an indicator of dryness during this period.
Usage Notes In addition to the figshare repository  39  and for an easy consultation of the dataset, we have created a public web viewer, available at http://inpro. unizar. es/. This viewer allows direct inquiries for any time span between 1333 and 1949. For each location, we provided information about the total number of rogations for the selected period (centre of the circle) and the percentage of rogations in the different seasons: spring (MAM, green), summer (JJA, red), fall (SON, blue), winter (DJF, yellow), and no seasonal detail (black). When clicking on a location, information about the rogations for that location during the selected time period (such as location, rogation dates, and documentary sources) is displayed. Also, the complete database can be downloaded. The viewer is a useful tool for a quick screening of drought conditions in a particular location or period.
The grain size distribution over the whole disc is then normalized following a power law d() ∝  -3. 5 d, and a dust-to-gas ratio of 100. The dust optical properties are determined using the Mie theory. Following  Benisty et al. (2018) , we convolve the IR image with an angular resolution of ∼ 0. 037". We then perform synthetic observations of the sub-mm flux, using the software CASA  (McMullin et al. 2007 ). We use an integration time of 35 minutes and a beam size of ∼52 × 45 mas consistent with the observations, using the corresponding preset configuration. We measure an RMS noise of ∼6. 2 10 -2 mJy beam -1.
Sub-mm dust emission Finally, we compute the dust temperature structure using MCFOST and produce a continuum image at 1. 3 mm (240 GHz). In contrast to the IR scattered light observations, much of the disc is optically thin in mm continuum emission, so these observations primarily trace the disc mid-plane. The lower left panel of Figure  5  shows a synthetic Band 6 ALMA map, calculated using the CASA software  (McMullin et al. 2007). The 10 M Jup embedded planet at 32 au is massive enough to open a gap in the dust. The large annular dust gap is detected in the sub-mm observations and can also be seen in our simulations (see lower right panel of Figure  5 ). We additionally compute a Band 6 synthetic observation of a snapshot from the hydrodynamic simulation at about 1300 yr, using an integration time of 5 hours and removing any noise.
ACKNOWLEDGMENTS We thank the anonymous referee for their constructive review that improved the clarity of the paper. We also thank  Rebecca Martin  for fruitful discussions. GB acknowledges support from the  University of Leicester  through a  College of Science  and Engineering PhD studentship. GB is also supported by a  Royal Society Dorothy Hodgkin Fellowship. RN acknowledges support from  UKRI/EPSRC  through a  Stephen Hawking Fellowship  ( EP/T017287/1 ). RDA has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  681601 ). NC has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  210021.
The vaccine has now been licensed in 21 countries and the EU. A phase 2 trial in 2012 (ClinicalTrials. gov number NCT00842530)  [5]  reported moderate efficacy of 30. 2% (-13•4% to 56•6%) and showed the vaccine to be well tolerated and largely safe. Two large scale phase 3 trials followed: the CYD14 trial (ClinicalTrials. gov number NCT01373281) in South East Asia of 10,275 children aged 2-14  [6] , and the CYD15 trial (ClinicalTrials. gov number NCT01374516) in Latin America of 20,869 children aged 9-16  [7]. After stratifying by age, participants were randomly assigned to vaccine or control arms in a 2:1 ratio, and vaccine doses were given at baseline then 6 and 12 months later. For a subset of participants (approximately 20% for CYD14 and 10% for CYD15), immunogenicity and prior dengue exposure was determined using baseline sera.
Data We use the individual-level trial data from the CYD14 and CYD15 trials, in both the active phase (25 months post first dose) and the 1 st year of passive phase hospital follow up. In the active phase, all symptomatic dengue disease is detected, but in the passive phase only hospitalisations were detected. All cases refer to virologically confirmed dengue. Infecting serotype is known for almost all cases (97. 6% for CYD14, 95. 8% for CYD15, 96. 7% overall). Baseline serostatus is known for only a minority of subjects (19. 3% for CYD14, 9. 6% for CYD15, 12. 7% overall). Model variants (including our main model) that consider serotype-specific effects omit all cases of unknown serotype. We right-censor after date of first case for each patient, and so do not consider multiple cases per patient.
Model variants and fitting Model fitting was performed using the Metropolis-Hastings algorithm for parameter   S1 ], and proposal distributions were normal. Each model variant was run for 1,100,000 iterations with a burn in period of 100,000, storing 1 in every 100 iterations as posterior samples. Convergence was assessed visually. The model was coded in C++ using OpenMP  [17] , and results were analysed in R 3. 6. 1  [18]. All model code is available at https://github. com/dlaydon/DengVaxSurvival. We consider alternative model variants that do not incorporate explicit serotype or age effects [Figure  2B -2D, Table  1 ], and also a variant without vaccine-induced immune priming. Model fit is assessed both visually, and using the Bayesian Information Criterion (BIC)  [19] , and the Widely Applicable Bayesian Information Criterion (WBIC)  [20].
Acknowledgements DJL, GNG,  NMF  acknowledge grant funding from the  Bill and Melinda Gates Foundation. DJL, ID, WRH, GNG,  NMF acknowledge joint Centre  funding from the  UK Medical Research Council and Department for International Development  (grant  MR/R015600/1 ). DJL, GNG, NMF acknowledge funding from  Vaccine Efficacy Evaluation for Priority Emerging Diseases (VEEPED)  grant, (ref. NIHR:  PR-OD-1017-20002 ) from the  National Institute for Health Research. Views expressed do not necessarily represent those of the funders. DJL, ID and  NMF have advised Sanofi Pasteur Ltd. , without payment, on the implications that this work has on the use of their vaccine. We thank  Nick Jackson  and  Jean-Sebastien Persico  at  Sanofi Pasteur , and  Ben Lambert ,  Anne Cori  and  Natsuko Imai  at  Imperial College London.
In this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure-metabolites and pathways-to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties.
The rise of sequencing and high-throughput technologies has opened a new era of information and characterisation of previously unknown species. Different techniques have emerged alongside it, to make sense of this new knowledge. Through the use of large metabolic databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG)  1  , it is possible to have access to genomic, enzymatic, and metabolic information, and deduce some complex interactions happening inside the organisms. In this context, metabolic networks have been used to study the set of all chemical reactions of organisms in a holistic manner, providing insights into its underlying structure and the different adaptations of these organisms to their environment. Networks or graphs are mathematical structures linking objects-nodes-with edges.
Generally, graph embeddings in computational biology are based on different strategies (random walk based, deep learning based and factorization based)  12  , and are rarely biologically interpretable  12, 13. In contrast, the scope can be considered as an interpretable graph embedding, since the nodes in the scope can still be understood from a metabolic viewpoint, and are mechanistically related. In this study we analyse the scopes of a very large dataset of 5610 prokaryotic metabolic networks that we have derived from KEGG. Crossing with other databases, we assess the relationship between the scope and several environment features. In order to detect this relationship, we use machine learning approaches to predict different evolutionary and environmental conditions (taxonomy, growth temperature, habitat, and oxygen tolerance) from their scope.
Results Scope of metabolic networks. The metabolism of living organisms can be described with metabolic networks. In this study, the directed metabolic networks of 301 archaea and 5309 bacteria were derived from KEGG database, with nodes as metabolites and reactions, and edges linking substrates to reaction and reaction to products (see "Methods"). In the field of systems biology, it is necessary to focus on the complex interactions within an organism and with its environment to be able to understand the complete system. Previous works have shown the strong associations between evolutionary and environmental variables and the underlying topology of the metabolic networks of different organisms  [7] [8] [9]. However, these approaches do not provide any information on the affected metabolic functions.
These results display how the number of important compounds and reactions needed to distinguish the temperature classes is fairly lower than 50, with the rest of nodes being rather inconsistent. We developed and tested another approach, based on sliding masks, that leads to similar results (Supplementary Figure  S1  in the supplementary data). For each subset of 50 most predictive nodes, we also considered in which metabolic pathways these nodes appeared, by consulting the KEGG database 1. In the table on Fig. 3d , we show these metabolic pathways, along with the average number of nodes from these 50 most important nodes, across the cross-validation. We observe that several pathways refer to amino acid metabolism and other basic metabolic pathways (pyruvate metabolism, glutathione metabolism, etc.