dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of random features to consider at each split and the number of bootstrapped datasets used to train each decision tree in the random forest method is approximately twothirds of the total predictors and two-thirds of the total samples, respectively  (James et al. , 2013). Appendix C: Histograms for creation of the bins In Sect. 5, different bins are created to analyze the specific contribution of clouds for both day and nighttime. Figure  C1a  shows the distribution of the ratio R CL /R CS for daytime hours, where two peaks can be easily identified: one between -0. 5 and -1 and one slightly negative with a tail in positive values. From this distribution, three different bins can be created in order to separate the effect of clouds on observed near-surface temperature variations, going from the highest cooling effect to the warming effect (rectangular semi-transparent brown boxes in Fig.
Daily rain values are obtained in the following way. For the TAH site, hourly rainfall data are available thanks to the neighboring meteorological station of Faa'a (Météo-France). On the TKH atoll, there is no automatic meteorological station. However, precipitations can be estimated from the ERA-Interim  [29, 30] , an atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts. Even if the spatial resolution of the data set is approximately 80 km, the rain data provided by ERA-Interim can be representative since the low elevation of the TKH atoll has no significant effect upon synoptic analysis. For a given water sample, chemical compositions (dew, rain, water sources) have been evaluated by classical statistical indicators (min, max, mean and standard deviation) and the volume weighted mean (VWM).
Near the ground level where dew forms, in the atmospheric boundary layer, the contribution from water vapor (about 0. 2-2% by volume) and carbon dioxide (about 0. 03% in volume) is of great importance for the radiative balance, with radiation from water vapor being by far the more important of the two. The data collected at TAH have been used to validate the model  [33] , with the meteorological parameters collected on site. Here we want to adapt and test this model from standard meteorological databases (Weather Underground, [34]) by using their historical time series. Tikehau data are not accessible in the database. For the period May-October 2005, Tahiti data have been extracted from the Faa'a airport station located at latitude 17. 77 • S, longitude 149. 31 • W, 4 m asl (distance: 2. 5 km from the measurement site).
37 × (1 + 0. 204323H -0. 0238893H 2 -(18. 0132 -1. 04963H + 0. 21891H 2 ) × 10 -3 T d ) T d +273. 15 285 4 1 -N 8. (5) The Weather Underground hourly data base  [34]  provides the quantities T a ( • C), T d ( • C), wind speed V (km•h -1 , to be transformed in m•s -1 ), but N must be determined from the sky conditions according to standard descriptions of the local meteorology, "fair", "partly cloudy", "mostly cloudy", and "cloudy". According to NOAA's national weather service glossary  [35] , the correlation sky conditions/cloud cover proposes N = 8/8 for "cloudy", 5/8 to 7/8 for "mostly cloudy", 3/8 or 4/8 for "partly cloudy" and "fair" (mainly for nocturnal conditions) corresponds to less than 3/8 opaque clouds, no precipitation, no extremes of visibility/temperature/wind. By filtering the rain or fog events and integrating the time series on a daily time-step corresponding to h > 0, calculated cumulative daily yields are obtained and compared to the corresponding measured cumulative yield.
According to the previous standard cloud coverage, we have implemented different configurations in our Matlab program, and the best correspondence is obtained for the following transformations: N = 0 for "fair", N = 4 for "partly cloudy", N = 6 for "mostly cloudy" and N = 8 for "cloudy" (Figure  2a ). A linear correlation between collected and estimated dew yields is observed during the period, with sum h (calculated)/sum h (measured) = 1. 092 ± 0. 022 (uncertainty: one standard deviation) with R 2 = 0. 984. The computed cumulative dew yield exhibits a slightly smaller value than the observed yield, i. e. , 4. 55 mm versus 4. 70 mm from the 6 months of observation, a phenomenon that was also observed when using the in-situ meteorological parameters  [33]. A stepwise increase is observed in Figure  2a.
pH Values The measurements of pH were carried out mainly at the TAH site (Table  3 ). Only a few data were measured in TKH, due to a failure of the pH-meter (dew at TAH: four samples in 2004, 31 samples in 2005; dew at TKH: no samples in 2004 and one sample in 2005; rain: no samples in 2004 and in 2005, four samples at TAH and two at TKH). At TAH, VWM dew pH TAH = 6. 05 (arithmetic mean: 6. 45 ± 0. 5) in 2004 and 5. 23 in 2005 (arithmetic mean: 5. 49 ± 0. 5) with minimum 4. 80 and maximum 7. 17. For TKH (Table  4 ), the only available measurement in 2005 corresponds to pH (dew) = 6. 720 (no data in 2004). Figure  5a  shows the pH evolution for rain and dew. As anticipated, there is no clear variation in time due to the stability of the meteorological conditions. Rain at the TAH site (Table  3 ) appears slightly more acidic than dew with VWM pH TAH,rain = 4.
In May 2020, in response to the COVID-19 pandemic, the Human Mortality Database (HMD)  6  team established a new open data resource on weekly mortality: the Short-term Mortality Fluctuations (STMF) data series. The present "Data descriptor" describes the STMF. STMF is the first international scientific database providing harmonized, uniform, and fully documented data on weekly deaths and all-cause mortality. Transparent methodology, rigorous data quality checking procedures, and careful documenting of the original data sources implemented in the STMF strengthen the scientific evidence on the scale of the pandemic and facilitate research on the effectiveness of varying policy responses.
Methods The raw data provided by our various sources are checked and processed prior to inclusion in the STMF database. We distribute deaths at an unknown age and split data by age and sex. Nevertheless, the data is not adjusted for death undercount nor smoothed. All known country-specific quality issues are documented in the country-specific metadata file. STMF covers a subset (38 out of the 48) of countries or areas included in the HMD. Consequently, it is limited by design to populations where death registration and census data are virtually complete, one of the main criteria for inclusion in the HMD. Since a well-functioning vital registration system requires substantial resources, all the STMF countries are developed and relatively wealthy nations. The length of the country-specific data series depends on the availability of weekly mortality data collected by the National Statistical Offices.
Because of the lack of historical series of such data, the STMF covers a relatively short period. The longest data series is available for Finland and it covers the period from 1990 to 2020. Most of the country series (23 out of the 38) begin in 2000. Chile, Greece, and Germany have the shortest data series, beginning in 2016/17 (see details in the "Usage Note" section). The Russian data series does not include data for 2020. The Russian Federal State Statistical Service publishes weekly death counts only as part of the standard yearly statistical report, and therefore the counts arrive after a significant delay: data for 2020 is likely to be published in September 2021. Data processing starts with the collection of annual population exposures and weekly death counts. We use population exposures from the HMD.
For the most recent year(s), when the HMD estimates are not available, they are complemented by the forecasted projections. The Lee-Carter model 7 is used to extrapolate annual deaths that consequently serve as input to estimate age-specific population exposures and death counts under the assumption of zero migration. The Lee-Carter model is fitted using the available HMD data series starting from 2005. The choice of the reference period in the Lee-Carter model may significantly affect the long-term forecast  7, 8  but has little effect on the STMF estimates such as death rates and ungrouped death counts. We used relatively short reference period in order to 1) be consistent across countries and 2) focus only on the most recent changes in mortality. These two conditions are limited us to period starting from the early 2000s.
The sensitivity analyses (not presented here) have shown that choosing any population exposure forecasting reference period between 2000 and 2010 produces less than 0. 5% change in death rates. Forecasting was implemented using the R package "demography"  9. In general, the forecasted data were used for the last 3-4 years, i. e. 2018-2021. In the next step, the original data are split into standard age groups. This procedure is applied for each year independently using proportions from annual age-specific death counts (observed or forecasted): A similar procedure is applied when sex-specific data are not available. Sex-specific weekly death counts in each age group are estimated using the observed sex-ratio in the annual total death counts in the same age group: + = + ⋅ + + D x x a D x x b D x x a D x x b ( , ) ( , ) ( , ) ( , ) + = + ⋅ + +.
D x x a D x x a D x x a D x x a ( , ) (, ) ( , ) ( , ) y w males y w total y males y total , , Finally, the age-specific death rates are calculated as follows: denotes annual population exposures in age interval in year y. Thus, these rates are directly comparable with annual death rates. + = + + m x x a D x x a E x x a ( , ) ( , ) ( , The STMF does not perform any adjustment for data incompleteness. It is inevitable that information about deaths appears in statistics with a certain delay. Post-mortems (when required), medical certification, civil registration, data processing, tabulation, and checking takes time. For this reason, the last available weeks in 2020 for data series by date of occurrence are incomplete. Besides, all data for the current year for all countries, and (in some cases) for the previous year, should be considered as preliminary.
For the STMF, we collect deaths by date of occurrence. This type of data is preferred for analysis since artificial fluctuations typically affect death counts by date of registration. However, depending on the country, weekly death counts are sometimes provided by date of registration only. There is no way to convert the date of registration into the date of occurrence. All countries included in the STMF except Northern Ireland, Scotland, and England and Wales provide deaths by date of occurrence. These exceptions provide data according to the week of registration. For each country, the exact type of input data is indicated in the metadata file as well as in the input file providing the original raw death counts.
Data records The STMF provides open and easy access to death counts and rates for all-cause mortality cross-classified by week, year, sex, age, and country in a standard format along with a complete description of data formats, data processing, and methods. The database contains also the original input data used to produce these output estimates. In addition, detailed country-specific documentation is provided along with precise references to the source and provenance of the original data. Figure  2  provides information about data availability across all STMF countries. The data, metadata, and detailed documentation are freely available for all users at the HMD website (www. mortality. org). We use CC-BY 4. 0 license 10 for the STMF data. The snapshot of the dataset described here is available at the figshare repository  11.
A detailed description of data formats and methods is given in the STMF Note  12. The country-specific metadata including information about data sources and shape of the original data is provided as a separate PDF file. We also provide original raw data used for calculation in a standardized format as a set of comma-separated (CSV) files. STMF data series are presented in tabular form. Data is available in two formats: as an Excel file and as a comma-separated (CSV) file. Both files contain all available data series and provide the same data in two different formats. Each line (record) in the Excel and CSV files has an identical structure. The first four columns of the data matrix define the country (ISO-3 country codes), year, week of occurrence or registration, and sex (males, females, and both sexes combined) for the death counts and death rates that are showed in the next columns.
The rates and counts are given by age groups (0-14, 15-64, 65-74, 75-84, and 85+) and for all ages combined. STMF death rates are defined as weekly rates, i. e. number of deaths per person-week (see Methods section). The last three columns include explanatory indicators: "Split" indicates whether the data were split from aggregated age groups; "SplitSex" indicates if the original data are available by sex (0) or data was split by sex (1); "Forecast" is equal to 1 for all years where forecasted population exposures were used to calculate the weekly death rates. The Excel file also provides a summary information relative to the whole dataset on the first worksheet. The following worksheets present country-specific data. Each worksheet contains all of the series available for the corresponding country.
Thus, the standardized output file includes five age groups: child mortality (0-14), adult mortality (15-64), and three age groups for old-age mortality (65-74, 75-84, 85+). Meanwhile, the STMF also provides data in their original classification to allow users to reclassify data using a different age grouping depending on their specific needs. The raw data provided by our various sources are checked and processed prior to inclusion in the STMF database. We distribute deaths at an unknown age and split data by age and sex. Nevertheless, the data is not adjusted for death undercount and is not smoothed. Raw data is provided as a set of country-specific files. Each record in the data file refers to the death count for one particular week. Each death count (field "Deaths") corresponds to a particular population/country name (field "PopName"), geographical area (field "Area"), calendar year (field "Year"), calendar week ("Week"), sex (field "Sex"), age/lower limit of age interval (field "Age"), the length of the age interval (field "AgeInterval"), the type of data (field "Type"), and the type of data access (field "Access").
All known country-specific quality issues are documented in the country-specific metadata dossier with a standardized structure. The metadata systematically includes the following sections: (statistical) coverage, time coverage, (format and shape of the) original data on death, manipulation applied to the original data to obtain the output data, information on the data source and data provider, additional notes, and references (to the data sources). The metadata is presented as a single PDF file. To facilitate research on mortality outbreaks and seasonal variations by professional users and to enhance basic understanding of the data by non-professional users, we added a visualization layer to the database  3. The STMF visualization tool is an open-sourced, web-based shiny  20  application focusing on displaying excess mortality in weekly death counts or death rates.
Technical Validation The STMF is limited to populations included in the HMD. The selection criteria to include countries in the HMD rely on completeness of coverage and general quality of statistics on deaths, births, and population exposures. Before deciding to include data for a new country, extensive checks are performed to assess the reliability of the input data and the coverage and quality of population statistics of the country  21. Thus, the STMF includes only countries with virtually or fully complete coverage of vital statistics and meeting strict formal criterions regarding the data quality. In addition to country-specific knowledge based on the HMD experience, a set of additional data quality indicators is computed to check consistency of the STMF data series during each update.
These indicators include both internal (sums by age and sex match respective totals; processed death counts against the raw data; sex ratios in death rates) and external (sum of weekly deaths counts over the year is close to yearly official figures) consistency checks. A series of diagnostic charts are produced to check the plausibility of seasonal trends, consistency of mortality trends over time, and to detect possible outliers. All suspicious cases are carefully investigated by internal experts. If needed, we contact the official data provider to clarify the issue. All unresolved data issues are carefully documented in the metadata. Nevertheless, for the first year in the data series, a few first weeks might be missed while, for the last year, data collection might still be in progress and the last weeks might be missed.
A blue color indicates that the original data are provided by sex and for age groups detailed enough to create STMF age groups without additional adjustments. A yellow color indicates that at least some of the original data for that year are provided by broad age group and sex. The red squares point to original data available only by broad age groups, i. e. STMF data were split by age and sex. A star (*) indicates that data is available only by date of registration instead of date of occurrence. Sensitivity to methods. The main goal of the STMF is to provide data for research purposes. The specific aim of the STMF is to provide high-quality consistent data free of any bias caused by methods or models used to adjust the original statistical information. Following this principle, the SMTF puts maximum efforts to collect the most-detailed original data.
Usage Notes Currently, the STMF dataset includes weekly death counts and rates for 38 countries/areas (see section Data Records for details). We plan to include more data series shortly and provide periodic updates based on the most recent data available from each country. In the short term, the project team aims at ensuring timely access to the most recent weekly mortality data for researchers, the media, and policymakers during the COVID-19 pandemic. In the longer-term, the STMF is seen as a continuously updated dataset that will be useful for addressing mortality fluctuations, not only from epidemic diseases but also from other acute episodes with impacts on national mortality rates. Thus, users are encouraged to visit the HMD website (www. mortality. org) to get the most recent version of the dataset.
The STMF collection is a unique data resource aimed to provide reliable and uniform mortality data for scientific investigation. The main advantages of the STMF are that: • The database follows four main guiding principles first formulated for the HMD  6, 21  : ° comparability of the series across time and space; ° use of transparent and standardized methods for processing the data; ° uniform presentation of the data in XLSX and CSV formats facilitating their use by various groups of potential users; free and easy access to the data, metadata, and documentation; ° reproducibility via extensive documentation and provision of original input data. • In addition to weekly death counts, the STMF provides age-specific death rates. • The data series are regularly updated (currently weekly). In the medium-long term, we expect to update the data series quarterly.
However, whenever critical circumstances call for it, as in the current COVID-19 pandemic, the series will be updated according to the required frequency. • Users are encouraged to contact the team with any concerns or questions regarding the country series in general or specific data points in particular. • An online visualization toolkit providing easy access and graphical representation of excess mortality estimates is available on the website. The STMF has several limitations: • It provides series at the national level though short-term mortality fluctuations may vary within countries. • It doesn't provide data by cause of death. In theory, such statistics might be added to the available data series but would not include the most recent years, because tabulations by causes of death need much more time for processing and therefore, they may be published with very long delays.
• Data quality requirements impose a restriction on the number of populations that can be potentially included in the STMF. The STMF provides data of high quality in a standardized format. There are, however, a few points that a user needs to be aware of. 1) The data on deaths are classified by date of registration or date of occurrence, depending on the country. There is no way to convert the date of registration into the date of occurrence. The exact type of data and further details on the data collection system relevant for the STMF are indicated in the metadata file. If the data are provided by date of registration (as it is in England and Wales, Northern Ireland, and Scotland), there could be artificial fluctuations in weekly death figures related to various circumstances (e. g. end of statistical periods, weekends, public holidays) as well as moderate shifts in trends and their turning points  22.
2) Data for the most recent periods are provisional for all countries. Furthermore, data for the last available weeks are incomplete due to the registration lag (time between deaths and time of their registration). The degree of incompleteness varies across countries, time, and age groups. Thus, statistical offices revise these data in the course of future updates. Usually, the weekly data is subject to revision until the yearly final tabulations are officially published (usually in the next calendar year). Each STMF country update takes these revised data into account in addition to any new data point. Each update of the annual data series in the HMD implies the recalculation of the STMF series in order to take into account the most recent HMD data, including population exposures. 3) Deaths and death rates are provided by calendar week starting from Monday and following the general ISO-8601 guidelines.
Therefore, all weeks contain 7 days, including the first and the last weeks of the year, which is essential for comparability. There are four exceptions regarding the week beginning day: for the USA, weeks begin on Sunday; for England and Wales and Northern Ireland, on Saturday; and for Australia begins on January 1 st. Nevertheless, as the also include 7-day weeks, data are comparable. Details are provided in the metadata file. 4) Each year in the STMF refers to 52 weeks. In some cases, the first week of a year may include several days from the previous year and the last week of a year may include days (and their corresponding deaths) from the next year. In particular, it means that a statistical year in the STMF is not exactly equal to the statistical year in the annual country-specific statistics.
Acknowledgements This project would not have been possible without the enthusiasm and assistance of a large number of individuals at various institutions (see full list in the STMF Note 12 ). We are grateful for all their contributions. The work on the STMF was partly supported by the  Volkswagen Foundation  (project "Strengthening a reliable evidence base for monitoring the  COVID-19 and other disasters "). DAJ, VMS, and DAL's contributions were partly undertaken within the framework of the  HSE University Basic Research Program. MB was funded by grants from the  National Institutes of Health  ( R03-AG058110  and  P30-AG012839 ), the  Society of Actuaries  and the  UK Institute of Actuaries. The funding bodies had no role in the design of the study, data collection, analysis, interpretation of data, or in writing the manuscript.