dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments, Samples, and Data This work benefited from fruitful discussions with  Francoise Boudier ,  Cécile Prigent ,  Manuel Menzel  and  Emilien Oliot. We thank  Céline Martin  and  Léa Causse  for their assistance for trace element analyses at the  Géosciences Montpellier clean lab facility  and on the  AETE-ISO platform ("Analyses des Eléments en Trace dans l'Environnement et ISOtopes "; OREME observatory, University of Montpellier). We would like to thank also the associate editor, ,  Phil Janney  , as well as L. W. Diamond and an anonymous reviewer for their comments and suggestions on the manuscript. This research used samples and/or data provided by the Oman Drilling Project. The  Oman Drilling Project (OmanDP)  has been possible through co-mingled funds from the  International Continental Scientific Drilling Project  ( ICDP ;  Kelemen, Matter ,  Teagle Lead PIs) , the  Sloan Foundation -Deep Carbon Observatory  (Grant  2014-3-01 ,  Kelemen PI ), the  National Science Foundation  ( NSF-EAR-1516300 , Kelemen lead PI),  NASA -Astrobiology Institute  ( NNA15BB02A , Templeton PI), the  German Research Foundation  (DFG:  KO 1723/21-1 , Koepke PI), the  Japanese Society for the Promotion of Science  (JSPS no: 16H06347 , Michibayashi PI; and  KAKENHI 16H02742 ,  Takazawa PI ), the  European Research Council  (Adv: no.
669972 ; Jamveit PI), the  Swiss National Science Foundation  (SNF: 20FI21_163073 ,  Früh-Green  PI),  JAMSTEC , the  TAMU-JR Science Operator , and contributions from the  Sultanate of Oman Ministry of Regional Municipalities and Water Resources , the  Oman Public Authority of Mining, Sultan Qaboos University ,  CNRS-Univ. Montpellier, Columbia University of New York , and the  University of Southampton. This study was funded by Project  ANR-18-CE01-0014-01 LISZT. The dataset is available on the PANGAEA data archiving platform (https://doi. pangaea. de/10. 1594/PANGAEA. 937490 ).
Introduction Supporting information comprise a detailed description of the analytical techniques used to acquire the dataset presented in this article (Text S1 and Table  S1 ), the compiled composition shown for comparison in Figures  2  and 3  (Table  S2 ), and used for mass balance calculations (Table  S3 ) and the details of the method used for mass balance calculations (Text S2, Figure  S1 ). The lithology and depth of the studied samples and their major, trace and volatile element concentrations are reported as Supplementary Dataset S1.
After collecting data and proceeding to analyze them and draw conclusions from them, it was found that their ICIES'2020 geographical distribution is important, as it included six countries distributed on four continents, namely Yemen -Morocco -France -Egypt -Algeria -Turkey -the United States of America (this arrangement was based on the proportion of participants from largest to smallest). The ages of the participants included all groups over 20 years old, as the ages from 30 to 40 years participated by more than 80%, and ages between 20 and 30 years, participated by 10%, and the same percentage was for the age group of 40 years and over. It should also be noted that the percentage of participating doctors was the highest compared to PhD researchers, with three quarters of the participants.
Data analysis After collecting data and proceeding to analyze them and draw conclusions from them, it was found that their geographical distribution is important  [6] , as it included six countries distributed on four continents, namely Yemen -Morocco -France -Egypt -Algeria -Turkey and the United States of America (this arrangement was based on the proportion of participants from largest to smallest ). The ages of the participants included all groups over 20 years old, as the ages from 30 to 40 years participated by more than 80%, and ages between 20 and 30 years, participated by 10%, and the same percentage was for the age group of 40 years and over. It should also be noted that the percentage of participating doctors was the highest compared to researchers in the PhD, with three quarters of the participants   2 ), shows the percentage of males and females participating in this questionnaire, the percentage of males is 83.
Results of this model were applied to inpatient admission datasets for 44 countries to produce rates of hospital utilization for influenza and RSV respectively, and rates were compared to raw coded admissions for each disease. Results: For most age groups, these methods estimated a higher national admission rate than the rate of directly coded influenza or RSV admissions in the same inpatient sources. In many inpatient sources, International Classification of Disease (ICD) coding detail was insufficient to estimate RSV burden directly. The influenza inpatient burden estimates in older adults appear to be substantially underestimated using this method on primary diagnoses alone. Application of the mixed effects model reduced heterogeneity between countries in influenza and RSV which was biased by coding practices and between-country variation.
Conclusions: This new method presents the opportunity of estimating hospital utilization rates for influenza and RSV using a wide range of clinical databases. Estimates generally seem promising for influenza and RSV associated hospitalization, but influenza estimates from primary diagnosis seem highly underestimated among older adults. Considerable heterogeneity remains between countries in ALRI coding (i. e. , primary vs non-primary cause), and in the age profile of proportion positive for influenza and RSV across studies. While this analysis is interesting because of its wide data utilization and applicability in locations without laboratory-confirmed admission data, understanding the sources of variability and data quality will be essential in future applications of these methods.
Background Despite the large burden of lower respiratory infections globally  [1] , it is difficult to estimate the proportion of the hospitalizations attributable to influenza and respiratory syncytial virus (RSV) across countries or over time. Heterogeneous coding practices in hospital records across countries limit the comparability of administrative datasets from different locations and pose a challenge to producing global hospitalization estimates using influenza and RSV-coded inpatient admissions alone. Without the addition of laboratory test result data, administrative data may not accurately estimate inpatient disease burden, further complicating efforts to model burden at the population level. Absent accurate population estimates of the burden of specific respiratory diseases, it will be challenging to conduct crosscountry comparison, a hallmark of linking health policies (e.
ALRI admissions calculation We extracted admission counts for ALRI from 29 inpatient all-cause admission datasets covering 44 countries and containing hospitalizations spanning the years 1990 to 2017, stratified by age in years or age groups depending on the source. These datasets included approximately 43 million admissions and represent all ICDcoded inpatient admission data used in the Global Burden of Disease Study, an international collaborative study led by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington and supported by over 4800 researchers in more than 140 Fig. 1  Flowchart of ALRI admission processing and meta-analysis modeling. Flowchart of data processing and analysis conducted under this study. This diagram describes processing of ALRI admissions from clinical administrative data as well as the modeling and processing performed on RSV and Influenza meta-analysis proportions countries  [1].
Additional detail on inpatient data from IHME is listed in Additional file 1. Because only 11 of the 44 datasets utilized in this study recorded secondary diagnoses, ALRI admissions were defined as those with a primary diagnosis code listed in Table  1  below. The majority of clinical datasets in this analysis contain a subset of the country's total inpatient utilization. For these non-comprehensive clinical sources, counts of ALRI admissions by age were divided by the total number of admissions in the dataset to produce age-specific proportions of inpatient utilization that have a primary ALRI diagnosis. This proportion is multiplied by IHME's total inpatient utilization envelope to approximate a comprehensive rate of ALRI utilization by age and country. The envelope is produced using a spatio-temporal Gaussian process regression that smooths over geographic distance and year of hospitalization and that models admission rate per capita by age using IHME's healthcare access quality indicator, supply of inpatient hospital beds, and all-cause mortality as predictive covariates.
More detail on the envelope estimation process, covariates used in the model, and results can be found in related Global Burden of Disease (GBD) publications  [1]. The UK Hospital Episode Statistics dataset [2] and Healthcare Cost and Utilization Project National Inpatient Sample (HCUP NIS)  [3]  are considered comprehensive datasets and the scaling described above was not applied to these sources. Instead, counts of admissions with a primary ALRI diagnosis in these sources were divided by the total population of that country to produce rates of ALRI admission by BIRD age group and year. Population estimates are produced as part of IHME's GBD study and detailed information on the methods to produce these estimates are available in related publications  [1]. Most clinical administrative data is provided in age in years or occasionally in various aggregated age bins.
The age groupings used for the BIRD analysis were at a higher level of aggregation than the majority of administrative sources used. Therefore, the final step in ALRI admission processing was to aggregate rate-space estimates to the BIRD analysis age groups, by summing both the numerator and denominator so that the rates of ALRI utilization are binned appropriately to match the rest of the analysis. While many of the data sources used in this analysis are also used in creating annual GBD estimates, there were some differences in data processing methods between the two projects that led to different estimates of rates of ALRI. GBD analysis adjusts inpatient data to Table  1  Acute lower respiratory infection ICD codes ICD Version account for readmissions, potential missingness of secondary inpatient diagnoses, unavailable outpatient data, and healthcare access and quality for every location.
Influenza and RSV proportion estimation Influenza and RSV admission rates were estimated by modeling the proportion of admissions for ALRI that were attributable to each cause respectively, and then estimating the proportion of total ALRI hospitalizations represented by these diseases, stratified by age, year, and country. The meta-analysis for this model included 156 independent studies on influenza-associated hospitalization rates covering 46 countries with data between 1979 and 2015 for influenza , and 204 studies on RSV admission rates covering 56 countries with data between 1982 and 2017  [4, 19, 73, 107, 133, 146,. Sample size of the study, age range, and location in study cohort, total admissions for ALRI, and admissions for influenza and RSV respectively were extracted from each study.
The proportion of ALRI admissions due to influenza and RSV were calculated for each location, age, and year present in the input study data. A Bayesian regularized trimmed meta-regression (MR-BRT) model was generated using ALRI admission metaanalysis data to produce estimates of the proportion of ALRI admissions due to each cause while accounting for within-study heterogeneity by age and location as well as error and bias between sources. Within the MR-BRT framework, the trend over age was modeled as a cubic spline with linear tails on the youngest and oldest age groups and an uninformative Gaussian prior. Linear tails on the age ends were used to smooth behavior of the age pattern at the poles in cases of sparse data, which can be highly unstable in MR-BRT modeling. Location was used as a covariate at the IHME Global Burden of Disease's super-region and regional levels, to account for potential geographic variation while informing estimations for locations with sparse data by the trend of those with a larger input evidence base.
Final admission rate estimation Admission counts and rates for influenza and RSV were calculated by multiplying the proportions from the influenza and RSV mixed effects attribution models to annual ALRI admission count estimates by age group and location. Seasonality was excluded from the scope of this analysis because seasonal information was not consistently available in influenza and RSV meta-analysis literature. Each location with clinical data received the attribution model fit for the corresponding GBD region, unless no input data for the model existed, in which case an average of the models within the GBD super-region was used. Uncertainty was quantified using the upper and lower uncertainty interval from the fit of the mixed effects model. Due to meta-analysis data sparsity in older ages for the RSV attribution mixed effects model, admission rates and counts for RSV were only calculated for children under five.
Influenza and RSV-coded primary admissions were extracted from a subset of clinical administrative datasets as illustrative scenarios in order to compare results of the BIRD analysis to direct ICD extraction with no adjustments. ICD codes used for this comparison can be found in Additional file 2. All locations used to illustrate the comparison contained at least 4-digit ICD detail, which was required to identify primary admissions for RSV. To assess the limitation of using primary diagnosis alone for ALRI admissions, we extracted non-primary diagnosis detail from the HCUP NIS data which was used to produce US estimates  [3]. Diagnosis levels available in HCUP NIS vary by state, but all available diagnosis detail up to the 30 th inpatient diagnosis was included for this analysis. We compared primary and nonprimary utilization for the year 2012 from this dataset, and applied influenza-attributable proportion estimates to the complete dataset in order to generate a comparison of influenza rates that include non-primary hospitalizations.
Results Figures 2 and 3 represent the number of sources of meta-analysis data for the proportion of ALRI admissions attributable to influenza and RSV, respectively. Meta-analysis sources varied in their age ranges and granularity, sample size, and the time range over which studies were conducted. All meta-analysis sources were used to inform the meta-regression analyses as described above. Metadata about each of IHME's inpatient data sources is available in Additional file 1. Only the inpatient sources that were ICD-9 or ICD-10 coded were used in this analysis. While all sources listed had sufficient ICD detail to extract ALRI utilization rates, not all locations with inpatient admission data have at least 4-digit ICD coding which is required to identify RSV cases by ICD diagnosis alone (see Additional file 2 for the list of 4-digit RSV codes).
5  and 6 , and tabulated in Tables  3  and 4. For almost all age groups, the methods as described in this paper estimated a higher national admission rate than the rate of directly coded influenza or RSV admissions in the same inpatient sources. Many inpatient data sources used at IHME are coded only to three or four digits, in which case it is less accurate or even not possible to estimate RSV admission rates. Detail on inpatient clinical sources and ICD granularity is listed in Additional file 1, and the ICD codes used to determine influenza and RSV inpatient admissions are listed in Additional file 2. The full dataset As non-primary diagnoses were not available for the majority of sources of inpatient admission data, only primary diagnosis was used to expand the number of useable sources and retain consistency across locations.
For the USA and Sweden at age 65+, the simple extracted ICD-coded admission rate from administrative datasets surpasses the rate produced by this study. The inclusion of nonprimary diagnoses did increase estimates for influenza in the USA by more than 50%. Nonetheless, these rates are still lower than those produced by comparable studies in the oldest age group. Previous studies estimate that anywhere between 39. 5 and 96. 6% of all admissions across all ages for influenza have a primary diagnosis related to influenza, and the relative proportion of burden as a primary diagnosis in this analysis fall within that range  [359] [360] [361] [362]. While using only the primary diagnosis allowed us to maintain consistency with the 33 sources containing only primary diagnostic detail, future iterations of this method should consider inclusion of non-primary diagnoses for more comprehensive utilization estimation, if at the expense of geographic coverage.
Availability of data and materials Inpatient admissions datasets and influenza meta-analysis data as used in this analysis are available via IHME's Global Health Data Exchange (GHDx), http:// ghdx. healthdata. org/ [2,  3,. RSV meta-analysis data is available through the Edinburgh Datashare, https:// datashare. is. ed. ac. uk/handle/10283/3611  [365].
Authors state that, along with auxiliary spatial information, such as drone-based local data gathering procedures, the EO dataset would be useful to identify the spatial foci for Covid-19 control measurements. In the study done by Seale et al. [35] , adherence to preventive measures against the novel coronavirus in Australia, such as social distancing, was associated with factors, such as greater trust in the government and authorities and a greater perception that the recommendations made by them are efficient. In the study conducted by Huynh  [31] , it was found that the mobility of the population during the Covid-19 pandemic was lower in nations in which population tends to have a higher perception of risk in relation to Covid-19, considering a group of 58 countries. In Reference  [36] , using data for the city of Kansas, in the Midwest of the United States of America, authors found the same relationship between risk perception of the novel coronavirus and adherence to measures of social distancing.
Studies for other outbreaks also point out that the greater the level of knowledge of individuals regarding the disease, the greater the perception of risks, and also the adherence to preventive measures, such as social distancing  [37, 38]. The present work established two hypotheses for the mobility trend of the population in ten South American countries: Countries that have adopted stricter social distancing measures have a lower level of movement of people during the pandemic (H1), and mobility is occurring randomly in space during the pandemic (H2). To evaluate these hypotheses, we used Google Community Mobility Reports dataset to construct a mobility index that summarises the Google categories into a single one  [39]. In order to compare the level of population mobility with the strictness degree of social distancing measures in each country, we used the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT).
Google Community Mobility Reports Dataset Since April 2020, Google has made available in its Google Community Mobility Reports  [40]  information about places visited, captured by mobile devices for 132 countries. For the visited places to be registered, the user needs to activate the Location History of his account, which by default is disabled. The information is made available as historical series in Comma-Separated Value format files (anonymous and aggregated records) for each country and has the following categories of physical spaces: (a) Retail & recreation (restaurants, cafes, shopping centres, theme parks, museums, libraries, and movie theatres); (b) Grocery & pharmacy (grocery markets, food warehouses, farmers markets, speciality food shops, drug stores, and pharmacies); (c) Parks (national parks, public beaches, marinas, dog parks, plazas, and public gardens); (d) Transit stations (public transport hubs, such as subway, bus, and train stations); and (e) Workplaces; and Residential spaces.
South American Countries and Covid Stringency Index For the analysis of mobility trends in the countries in South America, nations in which data was available for the period from 15 February to 27 October 2020 were selected. Based on this criterion, ten South American countries were included: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, and Venezuela. This same group of countries and period were used in the comparative analysis between the people movement and the strictness degree of the social distancing measures adopted, according to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT)  [41]. It is a summary indicator of nine dimensions related to the policies employed by countries during the pandemic to contain the circulation of people: school closing, workplace closing, cancel public events, restrictions on gathering size, close public transport, stay at home requirements, restrictions on internal movement, restrictions on international travel, and public information campaign.
In terms of income inequality (measured by the Gini Index), Chile  (44. 4)  and Argentina (41. 4) shared practically the same high level of inequality in 2018, while Brazil presented a higher level of inequality (53. 9), pointing to a most economically unequal society. With regard to life expectancy at birth, which can be interpreted as a measure of quality of life, Argentina, Brazil, and Chile had similar levels. The highest value was presented by Chile (80 years), followed by Argentina (76. 5 years) and Brazil (75. 7 years), given estimated values for 2018, for all cases  [53]. For Argentina, Brazil, and Chile, mobility information was also disaggregated for smaller units of analysis; in this case, provinces and states (of the total of 132 countries in the Google dataset, these characteristics were present in only 39% of them).
To overcome with these missing data, we opted for a single imputation method by adding the average of the values by country and by category. This choice is justified by its simplicity to generate the complete dataset  [54]. Each variation in mobility within the dataset is associated with spatial sub-spaces in two different granularities: national and its political-administrative subdivisions (provinces or states). Thus, the analysis of spatial auto-correlation was used to reveal whether there is any association between these variations of nearby mobility in spaces, whether they form clusters or if they occur at random. We considered two periods that differ from each other by the beginning of the gradual reopening of the economy and the social activities: 15 February to 31 May 2020, and 1 June to 27 October 2020.
Discussion In a context in which pharmaceutical solutions are not available to retain the advancement of the novel coronavirus worldwide, social distancing occupies a central position as mitigation actions against the Covid-19. Using geolocation data from cell phones from Google's Community Mobility Reports, it was possible to identify variations in the circulation of people in South America's selected countries, and, using the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT) to measure the level of rigidity of measures of social distancing, it is possible to identify also important differences between the countries analysed. In this work, we considered eight months (from 15 February to 27 October 2020) to analyse the trend of population mobility in South America, which, shortly after the World Health Organisation declared the Covid-19 pandemic, already had a considerable number of cases and deaths from the new coronavirus worldwide.