dataesr/scientific-paragraphs-categorization huggingface dataset:
Examiner l'allure de la Dresser une représentation la VM en fonction du modèle suivi hist(x) Voir Annexe 1 normalité distribution des variables 8. S'interroger sur la graphique QQnorm(x) continues possibilité de valeurs S'assurer que la variable est manquantes non aléatoires Découper la variable en plusieurs binvar() unimodale (4a) (NMAR) catégories si elle s'avère 9. Pour distinguer les multimodale pour la traiter ensuite Effectuer une série de régressions glm(xDV ∼ xIV1 + xIV2, valeurs MAR des MCAR : en tant que variable catégorielle logistiques family = binomial(logit), Examiner les indices de créer une nouvelle variable Suspendre les analyses si la sd (x) data = Dataset) dispersion (4b) dichotomique variable ne varie pas ou trop peu Examiner les indices présence/absence de VM Envisager des transformations skewness (x) d'asymétrie (4c) pour chaque variable (qui si forte asymétrie positive log(x) présente des VM) puis si asymétrie positive modérée sqrt(x) tester la relation entre si asymétrie négative renverser les scores, puis cette variable et les autres log(x) ou sqrt(x) Gestion des valeurs 5.
5 (Figure  1 ). These relationships were particularly strong for retail/ recreation mobility and public transport mobility. In addition, cross-correlation functions for these datasets were computed, and revealed that correlations were maximal for a delay between 0 and 10 days, and this delay varied depending on the region (Figures  A2  and A3 , see online supplementary material). Earlier findings showed that mobility restriction can reduce the transmission of infection dramatically, and the present results confirmed that the reverse is also true; that is, increased mobility following the relaxation of lockdown rules can lead to an increase in transmission. This suggests the importance of tracking changes in mobility data at different spatial resolutions to anticipate future changes in the transmission of SARS-CoV-2 and guide public health policy.
En outre, les auteurs soulignent que l'augmentation du harcèlement xénophobe et du racisme envers les personnes d'origine chinoise et asiatique 3 pendant la pandémie constitue un autre facteur de frein dans le recours aux tests et aux soins. Les recours tardifs aux soins peuvent en effet contribuer aux taux élevés d'hospitalisation et de mortalité de ces patients d'origine asiatique. Au Brésil, une analyse s'appuyant sur 228196 cas de patients adultes hospitalisés atteints du Covid-19 (confirmés par test PACR -RT-qPCR) clôturés par un résultat médical définitif (sortie de l'hôpital ou décès) et enregistrés dans le système national de la Base de données de surveillance (SIVE-Gripe) entre le 16 février et le 8 août 2020, vise à expliquer les liens entre la race (ou la couleur) auto-déclarée (« Blanc », « Noir », « Brun », « Jaune », « Indigène » / « Branca », « Preta », « Parda », « Amarela », « Indígena ») et la mortalité hospitalière liée au Covid-19, selon un modèle de régression logistique après ajustement des caractéristiques cliniques et des comorbidités (Peres et al.
Enfin, l'approche intersectionnelle -articulant les divers rapports sociaux de race, de classe, de genre, d'âge, de handicap, etc. -s'avère particulièrement féconde, dans l'appréhension des disparités ethno-raciales dans les cas de morbidité et de mortalité liées au Covid-19. effetsurlesrésultatscliniques. Le premier article se fonde sur une lecture des centaines d'articles des bases de données EMBASE, MEDLINE, Cochrane Library, PROSPERO, NJEM, Lancet, BMJ, JAMA, MedRxiv, des protocoles d'essai cliniques, de la littérature grise, données de surveillance ; publié en anglais entre décembre 2019 et mai 2020(Pan et al. 2020). Et le second article s'appuie sur 50 études tirées des bases de données MEDLINE, EMBASE, PROSPERO, Cochrane Library, MedRxiv ; publiées aux États-Unis ou en Royaume-Uni entre décembre 2019 et août 2020(Sze et al.
REMERCIEMENTS Paul-Émile Paradan et Moulay Tahar Benameur sont les premiers que je remercie pour m'avoir choisi pour le thème de recherche traité dans cette thèse. Un très très grand merci pour tous vos accueils, vos conseils, que ce soit en géométrie, en topologie, dans la rédaction d'un manuscrit et bien d'autres. Vous m'avez permis d'apprendre des connaissances dans tous les domaines que m'étaient étrangers auparavant. Paul-Émile pour les discussions régulières en toutes semaines, pour votre panorama de ce thème de recherche, Moulay pour votre soutien continu et pour les critiques posées sur mes résultats obtenus. Je vous remercie pour les multiples relectures attentives de ce manuscrit. Je tiens ensuite à exprimer ma gratitude à Xiaonan MA et Jean-Louis TU pour avoir accepté sans hésitation de rapporter ma thèse, aussi à Gilbert HECTOR et Eric LEICHTNAM pour avoir accepté sans hésitation d'examiner ma thèse.
Acknowledgements I thank  Y. André ,  C-L. Chai ,  N. Katz ,  C. Simpson  and  C. Voisin  for their comments on my note [5], and  A. Pillay  for the numerous exchanges we had on the definitions of Manin maps. André's report [2] was of great help for the present version, as well as his remarks on Coleman's paper. Finally, I thank  Hiroshi Umemura  for the inspiration he brought me, sharing not only his expertise, but also his broad views on our mutual cultures.
Data resource basics Information about pandemic dynamics is crucial to understand the potential impacts on populations, design mitigation strategies and evaluate the efficacy of their implementation. Centralization, standardization and harmonization of data are critical to enable comparisons of the demographic impact of COVID-19 which take into account differences in the age and sex compositions of confirmed infections and deaths. The international data landscape must keep pace with the global march of the pandemic, and researchers must work to triangulate the available data to create comparable measures to monitor and predict its demographic impacts. COVerAGE-DB aims to provide global coverage of key demographic aspects of the COVID-19 pandemic as it unfolds in an up-to-date, transparent and open-access format.
COVerAGE-DB offers data with standardized count measures by sex and harmonized age groups, which is a necessary but not sufficient condition to allow comparisons between populations at national and subnational scales. The database is currently under expansion through both the increase in coverage of national and subnational populations and the inclusion of more recent periods as the pandemic continues. At the time of writing, the database contains daily counts of COVID-19 cases, deaths and tests performed, by age and sex, for 108 national and 371 subnational populations around the world, depending on the available data for each source. The date range available for each country or subpopulation varies. In several country series, the database includes the earliest confirmed cases in January 2020.
Data collected Official counts of COVID-19 cases, deaths, and tests are extracted from reports published by official governmental institutions, such as health ministries and statistical offices. Depending on the source, data are collected in a variety of formats, including machine-readable files, pdf tables, html tables, interactive dashboards, press releases, official announcements via Twitter, and in a few instances, from digitized graphics. A full list of data sources is available in a dashboard view [https://bit. ly/2Qg1MxL]. Generally, COVID-19 cases, deaths and tests in age groups are reported as counts, but some sources report data in other metrics (fractions, percentages, ratios) or as summary indicators such as case fatality ratios (CFRs) by age. Reported age intervals vary by source, ranging from single ages to 30-year or greater age bands, and sometimes reported age intervals change over time within sources.
Data production All source data are entered into standard spreadsheet templates hosted in a central folder on Google Drive. Data entry into the templates is either manual or automatic, depending on the source. R programs collect data from the source templates and compile the merged input database. The merged input file is then subject to a series of automatic validity checks. Initial checks are carried out by the individual responsible for data collection and entry, using an interactive application [https://mpidr. shinyapps. io/cleaning_tracker/]. Data are then harmonized to standard metrics (counts), measures (cases, deaths, tests) and age bands (5-and 10-year age intervals). Harmonization procedures include rescaling to ensure coherence between age distributions and reported total counts.
Age group harmonization is done using the penalized composite link model for ungrouping  1  which was designed for splitting histograms of count data. Output data also include a file containing selected diagnostics of data quality, such as completeness of age reporting, for each source and date. The complete details on all steps of production are available in the COVerAGE-DB Method Protocol, which is publicly available on the web. 2  A table listing which adjustments are applied to each population is available on the project website [https://bit. ly/2E61BSV]. The merged input database, the harmonized output and the data quality files are uploaded daily as zipped csv files to an Open Science Framework repository (OSF) [https://osf. io/mpwjq/]. A GitHub repository [https://bit. ly/2YbtPCJ], which is linked to OSF, contains all R scripts used in the complete production pipeline, including compilation, diagnostics and harmonization.
Data resource use Since collection efforts began for COVerAGE-DB in late March 2020, we are aware of 15 studies using the data, many of which provide R code online and are fully reproducible. Broadly, these studies aim to measure the influence of demographic factors on mortality from COVID-19,  3, 4  assess the pandemic impact on health and mortality within  5, 6  and across populations,  [7] [8] [9] [10] [11] [12]  analyse COVID-19 data availability and quality,  13  propose methodological innovations that allow comparisons of CFRs  14  and the development of indirect methods to estimate infections in the population. 15, 16 The database is also used to monitor COVID-19 impacts in particular age ranges. For instance, UNICEF has used the database for monitoring the burden of the pandemic on children around the world  17  and the UN Department of Economic and Social Affairs has used it similarly to focus on older age groups.
3 M). Because individuals may be tested more than once, comparison between these two units is not straightforward. Testing performance measures, such as positive rates (e. g. 30% in Colombia and 45% in Mexico  26  ), are essential for interpreting differences in cases and deaths across populations, because they help to assess the extent of infection under-reporting. 27 However, differences in test definitions pose serious challenges for direct comparisons. Dates in both sources are comparable, corresponding to the occurrence of events. Since information from both sources relies on individual-level databases, delays in diagnosis and death registration are retrospectively adjusted. Differences in testing capacity and strategy between countries are also key determinants for infection diagnosis.
Strengths and weaknesses Since the beginning of the pandemic, it has been evident that population characteristics are key to understanding the prevalence, spread and fatality of COVID-19 across countries. However, data on cases, deaths and tests disaggregated by age and sex are not easily comparable across countries, and sometimes not even accessible. The main strength of COVerAGE-DB is to provide a centralized, open-access and fully reproducible repository of age-and sex-specific case, death and test counts from COVID-19, collected from official sources and harmonized to standard output formats. The data harmonization process is transparent, following a strict protocol. 2 The initial input data are provided alongside the harmonized counts, as well as the code used to harmonize the different input measures, metrics and age groups into comparable granular output metrics.
All scripts are written in the open-source R programming language. 30 he data sources and limitations are documented for each country in a standard metadata framework. A limitation of the COVerAGE-DB is the heterogeneous and difficult-to-evaluate quality of the underlying data. No single data source can currently claim accurate estimates of COVID-19 incidence or fatalities. Age-specific case counts are highly dependent upon the testing capacity,  31  testing strategy  32  and differences in the definition of cases across sources and over time. Recorded cases underestimate infections everywhere, with underestimation expected to vary by age, given the relationship between age and case severity. 33 he accuracy of diagnostic RT-PCR tests used to confirm infections is also known to vary. 34 Furthermore, at any given date, cumulative counts are underestimated because of the lag between infection and a positive test result.
35 eath counts from COVID-19 are also likely underestimated for similar reasons and also due to various kinds of delays in death registration. Media reports have circulated about intentional data manipulation in some of the official data covered in the database. 36 8] [39] [40]  Although some of these deaths likely are from postponing or foregoing treatment from non-COVID-19-related causes, the magnitude of this excess is suggestive that numerous COVID-19-related deaths are classified under different causes. Populations also differ in whether deaths of suspected COVID-19 cases are included in official statistics and in post-mortem practices when an infection is suspected. 41 Some populations only report deaths occurring in hospitals, neglecting a potentially sizeable proportion of deaths occurring in institutional settings and at home.
42 ost populations currently report all deaths to confirmed SARS-CoV-2 infections as COVID-19 deaths for this database, but the underlying cause of death eventually reported on the death certificate may differ in patients with severe comorbidities. To mitigate biases and misinterpretations due to different practices and definitions, such information is constantly updated and documented in the metadata of the database which are freely accessible to users. Further, a supplementary data quality metrics file contains a suite of data quality indicators that is easily merged with the main output data. Quality metrics include age-reporting completeness, some indicators on how aggressive age harmonization is, and two positivity measures from Our World in Data database on COVID-19 testing. 26 ll of these issues compromise the comparability of the data contained within the COVerAGE-DB, both across populations at any given time and within populations over time.
That is, the database enables direct calculation of agespecific CFRs, but one must be careful when making comparisons. Care must also be taken not to interpret calculated CFRs as infection fatality ratios, the latter of which include both detected and undetected SARS-CoV-2 infections in the denominator. Proper estimation of incidence and fatality, and of total demographic impacts, will likely require triangulating data across numerous sources as these become available. To this end, the COVerAGE-DB was designed to be easily merged with other databases such as the Our World In Data testing or excess mortality data,  26  the COVID-19 dashboard of Johns Hopkins,  43  the World Population Prospects database  44  and the Short Term Mortality Fluctuations database. 40 Moreover, given that we have near-complete time series capturing the whole pandemic curve in some places, careful modelling of lag structures might allow some of these data-driven biases to be estimated.
io/mpwjq/wiki/home/] and the Method Protocol. 2 Files are shared in csv format to be as universally accessible as • The database also documents variations in definitions of all input data and indicators of reporting completeness across sources and over time. • An international team, composed of more than 60 researchers, contributed to the collection of data and metadata in COVerAGE-DB from governmental institutions, as well as to the design and implementation of the data processing and validation pipeline. We encourage researchers interested in supporting this project to send a message to the email: [coverage-db@demogr. mpg. de]. possible. A guide to getting started using the data in R is also provided [https://bit. ly/3g8nIVU], to merge COVerAGE-DB with other databases, and tips for other statistical packages may also be added.
Ce mouvement inattendu montre le chemin parcouru en cinq ans depuis la COP 21, et est très encourageant, car il pourrait catalyser l'action des autres États et nous placer finalement sur la trajectoire fixée par l'Accord de Paris. Il ne doit donc pas être sous-estimé, même si la neutralité carbone ne veut pas forcément dire la même chose pour tous, et même si les États ont du mal à définir des trajectoires de court et moyen terme en cohérence avec cet objectif de neutralité, trajectoires sans lesquelles ce dernier restera un voeu pieu. C'est ici que notre troisième exemple, celui des procès climatiques, est intéressant. En effet, ces contentieux qui se développent et font tache d'huile sur la planète (ils seraient plus de 1579 aux US, 434 dans le monde selon la base de données du Sabin Center) sont une occasion de « boucler la boucle » comme le montrent trois affaires.
L'évaluation des bilans s'est concentrée principalement sur les premières phases du bilan partagé de médication: l'entretien de recueil en présence du patient et l'analyse du traitement réalisée par les étudiants. Le traitement des données, uniquement descriptif, a été effectué à l'aide du logiciel Microsoft Excel ® pour Office ® 365, version 1904.
Our model considered three urban centers, taken from the Greater Sydney Commission's (2018) current metropolitan plan: A Metropolis of Three Cities. This plan envisions Sydney as having three major urban centers -the existing CBD in the east, Parramatta close to the geographical center, and the second international airport under construction in the west. Considering that one major goal of the metropolitan plan is to increase the proportion of the population living within 30 minutes of one of these urban centers by road, this figure (referred to as accessibility throughout this paper) was also tracked by the model throughout each of the scenarios. When it came to the specific software chosen for the modeling, a number of factors came into play. Like Raumulus, our model makes use of CA and MAS; both of which have been shown to be appropriate for simulating urban growth  (Chong & Jianquan, 2007).
Remote sensing For the purposes of this study, it was necessary to obtain maps that provide a snapshot of the land use within Sydney's geographical bounds for the years 1971, 1986 and 2014. Armed with these snapshots, a model could be calibrated to allow for simulations and consequently a spatio-temporal analysis of the land use changes over this period. Unfortunately, while there were several sources containing land use data for 2014, there were no viable existing options for 1971 or 1986. There was one series of land use maps that were sufficiently close to these two dates, however they were not sufficiently detailed and the author provided no details for how they had been produced and thus these were overlooked. Instead, we used remote sensing to produce our own land use maps utilizing satellite images from the United States Geological Survey's (USGS) LANDSAT-1, -5 and -8 satellites.
Unfortunately, the first satellite images under the LANDSAT program only became available in 1972, and thus this date was used in place of 1971. As a result, appropriate satellite images from 1972, 1986 and 2014 were downloaded and processed using the method set forth by  Andrieu & Mering (2009) , relying upon a nested string of k-means classifications (see Table  1  for a more detailed description). 1972, 1986 and 2014  were sourced and downloaded. 2.
, 2021). Most of the contributions on the topic have focused on the stranding of fossil reserves  (McGlade and Ekins, 2015; Mercure et al. , 2018) , on knock-on financial effects  (Battiston et al. , 2017)  or on the role of policies and institutions in mitigating climate-related risks  (Campiglio et al. , 2018). Sectoral dependencies have been incorporated in a limited number of works (e. g. Vermeulen et al. , 2018; Allen et al. , 2020)  but without considering physical capital stocks. As a result, we currently do not have methods to study how a decrease in fossil fuel inputs would strand capital stocks in the rest of the productive system. We contribute to filling this research gap by providing a systemic perspective on the supplyside risk of physical capital stranding. We do so by developing a simple methodology rooted in input-output analysis and applying it to a multi-regional production network database.
Data Our main source of data is the World Input-Output Database (WIOD) ( respective sector's element in the fossil mining column of the κ(B l ) T matrix. Thus, the network simply depicts subprocesses of the matrix multiplication of the power series. Setting n equal to the total number of sectors means that all possible stranding channels originating in the fossil sector (i. e. all sub-processes of the matrix multiplication leading to the fossil column of the transposed power series matrices) are considered. By defining the parameter n for filtering the top direct stranding linkages of each sector in each layer, we simply extract the most pronounced stranding channels. 7 A few other MRIO datasets exist, such as EORA  (Lenzen, 2011)  or EXIOBASE  (Stadler et al. , 2018). However, to the best of our knowledge, WIOD is the only one offering sector-specific values for physical capital stocks, a necessary component of our analysis.
The WIOD database is available at http://www. wiod. org. The 56 productive sectors present in WIOD are classified using NACE level 2 categories  (Eurostat, 2008). Table  2  in the Appendix lists all sectors included in the analysis. We create new sector codes to make our results easier to understand. The first three upper-case letters of each sector code reflect the NACE level 1 category (e. g. MAN for manufacturing), while the following three lower-case letters reflect the NACE level 2 category (e. g. MANche for manufactured chemical products). When discussing a NACE level 1 sector, or in the case of NACE level 1 sectors for which no further disaggregation is available, we use a + sign at the end of the code, to signify that several sub-activities are included there (e. g. MAN+ is the equivalent of the entire NACE C level 1 sector).
The most important sector for our analysis is denominated MINfos, as it records the activities of mining and extraction of fossil fuels, themselves a part of the larger mining sector. WIOD offers values for the mining sector as a whole (NACE sector B). This is where fossil fuels are extracted, hence at the core of our analysis. However, other materials are also included in the B sector, such as metal ores, stone, sand, clay and numerous other minerals. Hence, using the whole B sector as the core of our analysis will not accurately represent a supply shock in fossil fuels, and thus bias the results. A more detailed disaggregation of the mining sector can be found in the OECD Inter-Country Input-Output (ICIO) database  (OECD, 2018). ICIO covers 66 countries (plus a Rest of the World region) and 36 sectors 10.