dataesr/scientific-paragraphs-categorization huggingface dataset:
The operational team completes the data update procedure by preparing and releasing the pooled data and metadata sets (i. e. , files containing data for all countries combined). During this process the information collected for the countries is harmonized into categories that can be compared across data sources. Table  1  is an extract of the codebook of variables that describe the definition of a COVID-19 death for each data source in the database. The complete codebook can be downloaded at https://dc-covid. site. ined. fr/en/data/pooled-datafiles/ and in figshare  6.
Data Records "The Demography of COVID-19 Deaths" is freely available online [https://dc-covid. site. ined. fr/en/] for scientific use (see "Terms of use" page on the website: https://dc-covid. site. ined. fr/en/contact-terms-use/). A snapshot of the dataset is also available at the figshare repository  6. The database aims at addressing the needs and interests of a variety of database users, including researchers as well as non-specialists and the general public. The database consists of a collection of webpages: one for each country, one for the pooled datasets, and additional webpages including a discussion of key data issues. In the country-specific webpages [https://dc-covid. site. ined. fr/en/data/], for each country, the following files are available for download: 1) a spreadsheet file with the actual data coming from one or (if applicable) several data sources, also containing a summary of the metadata on each data source, 2) a document containing country-specific explanatory notes, and 3) all the original documentation (methodological documents and official reports) from which the data and the metadata have been extracted.
The original documentation can be used to specify data characteristics, data heterogeneity, and possible biases across different sources, for rigorous international comparisons. Each country-specific webpage also displays some summary information about coverage and the national sources of information, as well as a link to the archive containing all prior versions of the data files (Fig. 1 ). The country-specific webpage is intended for non-specialist users. It provides data in a user-friendly format along with warnings about data idiosyncrasies that may hamper country comparison. On the Pooled sets webpage [https://dc-covid. site. ined. fr/en/data/pooled-datafiles/], pooled data sets are available for expert users in. csv format. These data sets include all the data sources except for those that have been discontinued.
They can be downloaded in a zipped folder along with a file containing population estimates, the pooled metadata set, and a codebook of variables. Countries in the pooled data sets are referenced with the ISO 3166 numeric and alpha-3 codes which helps to geolocate and merge the data with indicators available in other international databases such as testing, short-term estimates of excess mortality, vaccination, and policy indicators. The following zipped folders are available:  The pooled data sets can be downloaded from the website, including previous versions of the data files. They are also available in figshare  6  updated as of mid-December 2021, along with an R code that can be used to retrieve the latest version of these datasets from the website. The pooled metadata set summaries the main characteristics of the data for proper international comparisons, such as the definition of deaths attributable to COVID-19, confirmation criteria, data coverage, co-variables, and the collection and publication patterns of each data source (Tables  1  and 2 ).
Metadata for the population estimates are also available. Through the R code available in figshare  6  , users can merge data and metadata pooled files to keep track simultaneously of changes in the data and in the definitions over time. Merged data and metadata pooled files can be used to select comparable subsets according to data characteristics. Users can explore the data availability in the database according to metadata characteristics through an online Data availability explorer [https://ineddemographiecovid19. shinyapps. io/DataViz/]. This tool shows the heterogeneity of the data across data sources and over time, as to e. g. : width of age groups, data collection systems, data coverage, identification of COVID-19 (suspected or confirmed, cause of death), reference date (e. g. , registration, publication, or occurrence date).
technical Validation Fig. 2  COVID-19 data availability by sex and age according to open age interval and the width of the age-groups by data source. This figure was created through the Data Availability Explorer here https:// ineddemographiecovid19. shinyapps. io/DataViz/. Only one date per month is shown, but the same figure for all the available dates can be visualized online. number of COVID-19 deaths having occurred during the first wave (deaths occurred up to September 15, 2020) were assessed using the age-and-sex distributions of previous all-cause mortality reported by the HMD. Further analysis considered comparisons among countries based on classic demographic indicators such as sex ratios of age-standardized and age-mortality patterns. The database has also been used by international researchers and has already led to articles published in influential journals such as The Lancet 7 , Proceedings of the National Academy of Science  8, 9  , and Demographic Research  10.
The stringency of the data collection and metadata review processes has limited the number of countries available in the database. In some cases, there may be no country specialist and/or in-country expert in the database operational team to ensure that the data available are properly interpreted. In other cases, national statistics offices in charge of disseminating COVID-19 mortality data do not provide documentation that is detailed enough to assess the quality and comparability of the available statistical information. These are important criteria for a country to be included in the database.
Usage Notes "The Demography of COVID-19 Deaths" database is an open access database [https://dc-covid. site. ined. fr/en/]. No registration is required. Anyone can browse the database content and download the data, which are available both as spreadsheets and. csv files. Specific citation guidelines are indicated in a "Terms of use" page on the website. Users are invited to contact the database team at eo-dc-covid@listes. ined. fr with their questions or suggestions, or to volunteer their help with accessing data from additional countries. In addition to providing data to users, the "The Demography of COVID-19 Deaths" database highlights the potential uses that may be made of the data, while pointing out the specificities, differences and shortcomings of the data sets [https://dc-covid. site.
ined. fr/en/presentation]. In this sense, a core feature of the database is to illustrate critical data issues to be considered when conducting comparisons over time and/or across countries. Simple cross-country examples are given to illustrate these issues and demonstrate the need to take them into consideration when analyzing pandemic mortality data. Users are also invited to check the Data availability explorer before engaging in international comparison. The database team's first analysis pointed out the need to take data heterogeneity into account when identifying the national and transnational characteristics of COVID-19 mortality rates and trends  5. The availability and degree of detail of the COVID-19 death counts by age and sex varies between data sources. Variations range from differences in the open-ended age interval (the maximum age to which deaths are reported) and the Fig.
These differences in data collection system types and data release procedures may affect the degree of completeness of the data: the faster the data is released, the less accurate they are likely to be. The "Demography of COVID-19 Deaths" database is the only database so far to collect, update, and systematize the metadata associated with the mortality attributed to COVID-19. Detailed metadata enable users to make informed decisions regarding the most suitable data for their comparative analyses and to understand the main limitations of the results. This is a major strength of the database because most reported COVID-19 statistics come from ad hoc systems. Its user-friendly structure helps users with varying levels of expertise to find the information collected at any time, as it provides continuous access to previously released files and official documentation for each country.
Code availability The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses are available on GitHub (https://github. com/covid19-hg/), and the code for the Mendelian randomization and genetic correlation pipeline is available at GitHub (https://github. com/marcoralab/MRcovid). Codes for implementing the multivariable Mendelian randomization analysis and subtype analyses are available at GitHub (https://github. com/marcoralab/multivariate_MR and https://github. com/mjpirinen/covid19-hgi_subtypes).
available online (https://www. covid19hg. org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table1. We used publicly available data from GTEx (https://gtexportal. org/ home/), the Neale laboratory (http://www. nealelab. is/uk-biobank/), the Finucane laboratory (https://www. finucanelab. org), the FinnGen Freeze 4 cohort (https://www.
The spatial resolution of each camera varies with the position of the bubble, but the typical value of the two cameras are 28. 9 and 57. 1 μm pixel -1. An approximate lower bound for the size of the smallest resolved bubble is then d min ≈ 200 μm. The trajectories taken by the bubbles are then determined using the Python package Trackpy  (Allen et al. 2021) , which implements the algorithm from  Crocker & Grier (1996). Such trajectories are shown in figure  3(b ). Using the three-dimensional map of the turbulence statistics obtained from PIV, we compute the bubble's size relative to the local Hinze scale d/d H (computed with the local value of ) at each bubble location, which is encoded in the colour in the figure. The mean dissipation rate at the break-up locations is = 0. 52 m 2 s -3 , with a standard deviation of 0.
After doing so, we store the sizes of the parent bubble and child bubbles, as well as the turbulence characteristics spatially interpolated at the initial break-up location. To remove spurious break-ups, we discard those for which the sum of the calculated volumes of the m child bubbles is less than 50 % of, or more than 200 % of, the calculated volume of the parent bubble. In total, we captured 162 bubble break-ups with this dynamical tracking approach that fit the volume conservation criteria. Figure  13(a)  shows the distributions of the break-up conditions (the Hinze scale at the break-up location and the parent bubble size) for the aggregated dataset, which we later break down by the parent bubble's size relative to the Hinze scale. The parent bubble diameter d 0 is typically slightly larger than the Hinze scale, as the distribution of d 0 (the green line) is located just to the right of that of d H (the dashed red line).
With the assumption that the initial splitting event happens nearly instantly after the bubble is released into the turbulence, to apply the same definition of the duration of the break-up, we define m for this dataset as the number of resolved bubbles present after one eddy turnover time T turb (d 0 ) = -1/3 d 2/3 0 has elapsed after the cavity release, which are denoted by the open circles in figures 8(a) and 17(a). We invoke (C1) to apply a slight adjustment to these numbers in order to extrapolate results to the finer spatial resolution of the tracked break-up experiment, as discussed in Appendix C. The number of bubbles in the extrapolated range constitutes about 30 % of those in the observable range. These adjusted values are shown as the filled-in light blue circles in figure  17(a).
As shown in their paper, once collapsed to d 0 /d H , the dependence on the dimensional bubble size nearly disappears. The four datasets (our two experiments, those from  Vejražka et al. (2018) , and DNSs from  Rivière et al. 2021 ) produce a coherent picture regarding the number of bubbles formed. When d 0 /d H is small, break-ups tend to be binary, producing on average two child bubbles after T turb (d 0 ). As d 0 /d H increases, the number of child bubbles increases. Surface tension is less effective at preventing the severe deformation of larger bubbles, leading to more complex deformed bubble shapes that yield a greater number of child bubbles. The orange curve in figure  17.
A stochastic model for each break-up The Monte Carlo approach involves running many iterations of a stochastic model and developing a statistical representation of the aggregated results. Each discrete simulation of a break-up mirrors the physical processes involved: the bubble, sketched in figure  18 (a), is first deformed into two lobes, shown in (b), and then some number of capillary bubbles are created as the neck separating the lobes collapses to create the two inertial child bubbles. For each iteration (i. e. one simulated breakup) at a given value of Δ, we first define the number of bubbles m that will be produced by picking a value of m from the distribution r(m ; d 0 /d H ) given by (5. 3), adding m min = 2, and rounding to the nearest integer. We pick δmin = 0. 07 3 in order to match the experimental dataset on which the parameterisation of m is based.
Yet, it remains to be seen whether preconditioning due to ice-phase processes occurs often and widely enough to impact statistics of cloud morphology and cloud albedo. In this study, we investigate the connections between cloud phase, cloud organization, and cloud albedo in the SO. In order to investigate these connections for a wide range of cases, we use active satellite data of the Afternoon Constellation (A-Train) from 2007 to 2010. The cloud phase is analyzed by using a vertically integrated cloud-phase classification which we describe in Sect. 2. 2. Moreover, we focus on the seasonal changes during austral winter (June-August, JJA) and summer (December-February, DJF) because these seasons have the highest frequency of occurrence of open and closed MCC clouds, respectively. In Sect. 3.
This is also the oversampled vertical resolution of CloudSat (effective vertical resolution is 480 m). Thus, this distance ensures that multi-layer clouds are two separated clouds with a sufficiently large separation. In order to assign one cloud phase to a certain data point in DARDAR, we need to reduce the DARDAR cloud classification in the vertical dimension. Therefore, all data points are classified into MPCs, liquid clouds, or clear, depending on their vertical phase distribution (Fig. 1b ). Here, we only analyze pixels. Liquid clouds are considered to be clouds that only consist of Liq, Sup, or Sup above Liq (Sup → Liq). For MPCs, we consider five different types: only Mix, Mix above Ice (Mix → Ice), Sup above Ice (Sup → Ice), any combination of Sup and Mix (Sup ↔ Mix), and any combination of Sup and Mix above Ice (Sup ↔ Mix → Ice).
MCC classification The MCC regime identifications are developed by applying the supervised neural network algorithm designed in  Wood and Hartmann (2006)  to C6. 1 of the MODIS Aqua LWP swath data. The algorithm uses the power density function and power spectrum of LWP to determine whether swath sub-scenes (256 km × 256 km areas) fall into one of three categories: open MCC, closed MCC, or cellular but disorganized. See  Eastman et al. (2021)  for more information on the C6. 1 MCC identifications. To collocate the MCC data set with the CloudSat track, the haversine distance for all DAR-DAR data points to the middle of each MCC scene is calculated. The MCC regime of the nearest MCC scene within a radius of 128 km is set for each DARDAR data point.
Discussion and conclusions To date, only a few studies have investigated the potential link between cloud organization and cloud phase in stratocumuli  (Abel et al. , 2017; Eirund et al. , 2019a; Tornow et al. , 2021). All of them are based on field campaigns in the Northern Hemisphere which observe particular cases and extensively analyze their processes with numerical models. Thus, in this study, we explore whether this link between cloud phase and morphology can also be found in SO cloud statistics obtained from DARDAR. An advantage of using remote-sensing data is that they cover a broad variety of cases and have almost global coverage. The spatial coverage of passive satellites would be even greater than that of active satellites. However, the cloud phase in passive instruments can only be evaluated at cloud top and often show a supercooled layer there (e.
Data availability. The DARDAR-MASK v2. 23 products are available on the AERIS/ICARE Data Center (https://www. icare. univ-lille. fr/, last access: 2 December 2020,  Ceccaldi et al. , 2013). MODIS cloud retrievals (MYD06_L2. 6) are also obtained from AERIS/ICARE Data Center  (Platnick et al. , 2015). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-10247-2022-supplement. Author contributions. JD performed the data analysis and wrote the article with the support of AP. AP established the initial ideas of this study and contributed to its design. OS provided and collocated the remote-sensing retrievals of MODIS on the CALIPSO track. ILM and RW provided the mesoscale-cellular convective (MCC) classification. All authors contributed to the discussion of the results and editing of the manuscript.
Financial support. The research from  Jessica Danker  and  Anna Possner  has been supported by the  Federal Ministry of Education and Research (BMBF)  " Make our Planet Great Again -German Research Initiative " (grant no. 57429624 ) implemented by the  German Academic Exchange Service (DAAD). Research by  Isabel L. McCoy  is supported by the  NOAA Climate and Global Change Postdoctoral Fellowship Program , administered by  UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS)  under award no. NA18NWS4620043B. This open-access publication was funded by the  Goethe University Frankfurt.
The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data.
At the beginning of the COVID-19 outbreak, ISARIC adapted the ISARIC-WHO Clinical Characterization Protocol and data tools  7  to facilitate global research collaboration and accelerate the understanding of COVID-19 as part of the public health response to the pandemic  1, 8, 9. Between January 2020 and September 2021, information about the clinical presentation, treatment, and outcomes of more than 705,000 patients with COVID-19, hospitalized across 62 countries, was aggregated to form the ISARIC-COVID-19 dataset. Clinical teams in 1,559 participating institutions collected the data. Figure  1  shows the number of patients per country included in the database as of September 2021  1, 4, 10. The number of patients included in the dataset continues to grow as data collection continues across the globe.
The objective of the dataset is to accelerate understanding of COVID-19 through access to detailed clinical information on infected patients from a range of settings. Access to data facilitates science, improves scientific transparency and integrity, and has played a substantial role in the generation of knowledge that has led to better patient management and vaccine production for COVID-19  11. The diversity of populations, regions, and resource levels from which the data originate increases the generalizability of the evidence generated and supports comparisons across them. By collating, standardizing, and sharing large volumes of disparate data, curation and governance efforts are invested centrally by a specialised team, enabling efficient data access, and analysis by many researchers focused on the questions most relevant to the patients in their settings.
Methods Data collection. Standardized clinical data of patients with suspected or confirmed COVID-19 are collected on the ISARIC-WHO case report forms (CRFs) (https://isaric. org/research/covid-19-clinical-research-resources/ covid-19-crf/) or site-specific iterations of these forms. These forms are available in multiple languages to support accessibility for a global response. Sites implement data collection contemporaneously to clinical care. Data are collected through direct observation and/or reviewing and extracting electronic health records or patient registries. Data can be submitted to ISARIC by completing the CRF on the Research Electronic Data Capture platform (REDCap version 10. 6 Vanderbilt University  14  ) hosted by the University of Oxford. Alternatively, institutions using other data collection forms and/or a different data management system can share patient data in any format to the ISARIC COVID-19 data platform, hosted by the Infectious Diseases Data Observatory (IDDO, www.
iddo. org). Data were prospectively collected on patients with clinical suspicion or laboratory confirmation of SARS-CoV-2 infection and admitted to a participating hospital or ward. Recruitment aimed to include all identified patients; however, resource constraints limited enrolment when patient numbers surged and health systems became overwhelmed. In such cases, or in sites where prospective data collection was impossible, data were extracted from electronic health records. Ethics approval and informed consent were obtained according to local regulations, which included a waiver of consent to collect de-identified data at several sites due to the burden on front-line Fig. 1  The number of patients per country is included in the ISARIC COVID-19 database. Data standardization -review and edit checks.
Data is run through Pinnacle 21 ®  (community version)  software, a CDISC standards compliance-verification tool that checks the standard SDTM implementation guide rules and requirements for regulatory submission. The resulting checks and warnings are assessed for applicability to the individual dataset. The data are also run through standard edit checks to identify possible mapping errors separate from SDTM conformance. The curator adjusts the mapping as needed to make corrections. Figure  2  describes the workflow from data acquisition to the final, pooled dataset that researchers can access to conduct their research.