dataesr/scientific-paragraphs-categorization huggingface dataset:
Descriptive Analysis The inclusion criteria for the basic dataset were met by 13 368 cases reported from 12 countries during the study period 2009-2010 to 2016-2017 (Figure  1 ). The number of reporting countries varied each season with France and Spain reporting the majority of cases (57% and 26%, respectively). Of the 13 368 patients in ICU with laboratory-confirmed influenza virus infection, 2806 (21%) died (Table  1 ). The majority of cases were male (7604; 57%). The median age was 59 years (interquartile range, ±14 years; range, 0-104 years). Most of the cases (7566; 57%) were between 50 and 79 years of age, and 10% of the cases were 18 years old or younger. Most influenza viruses (11 809; 88%) were type A: 5223 (44%) were subtyped as A(H1N1)pdm09, 1975 (17%) were subtyped as A(H3N2), and 4611 (39%) were reported without subtype.
Underlying Conditions and Virus Type/Subtype Among 5886 cases with known influenza virus type and A subtype (A(H1N1)pdm09 and A(H3N2)) and underlying conditions (Specific dataset) (Figure  1 ), 23% percent (1349) died (Table  1 ). More cases were male (3205; 54%) than female. A total of 2552 cases (43%) were reported to have no underlying condition. One condition was reported for 2498 (42%) cases, 2 conditions were reported for 569 cases (10%), 3 conditions were reported for 198 cases (3%), and 4 to 6 conditions were reported for less than 2% of the patients. The most commonly reported underlying conditions were chronic lung disease (18%) and obesity (15%). Influenza virus A(H1N1)pdm09 was detected in the majority (3512; 60%) of the patients, followed by A(H3N2) (1286; 22%) and type B (1088; 18%).
Pregnancy Pregnancy was reported for 244 women between 17 and 59 years of age. Twenty-four (10%) of the pregnant women died. Of the 153 pregnant women with known vaccination status, 3 were vaccinated and 150 were unvaccinated. The risk of death was lower for pregnant women when restricting the analysis to the age group 17-50 years old compared with nonpregnant women of the same age group (IRR, 0. 57; 95% CI, 0. 38-0. 85) and adjusting for reporting country within the basic dataset. No difference was seen between France reporting the majority of cases and other countries as well as between pandemic and nonpandemic seasons (Supplementary Data). Within the specific dataset when restricting the analysis to women 17-50 years old and adjusting for reporting country, pregnant women were less likely to die (IRR, 0.
In addition, no information on timing of complications in relation to ICU admission was available within this dataset as well as on other complications such as secondary bacterial infections or related treatment. The present study is limited by the small number of EU countries reporting. Cases were reported from only 12 of the 31 EU/ EEA Member States, and so their cases might not be representative of all severe cases across the EU/EEA. A further limitation of this study is the differences in ICU reporting systems between Member States and the pooling of such case-based data across countries that have different underlying healthcare systems and surveillance  [26]. The imbalance between the contributions of the various countries over time might also limit the comparability of the data. Nevertheless, we assessed the variation by applying different statistical methods and identified a reasonable test to compensate for the reporting country effect according to Barros et al  [25] ; however, different underlying data collection cannot be solved by pure statistics.
However, fatality in ICU represents only a small fraction of the mortality toll of influenza, particularly in the elderly, which are less likely to be admitted to ICU and have also been shown to be less likely tested for influenza virus infection in the hospital setting  [30]. The data reported to TESSy were not complete regarding influenza virus A subtype and B lineage as well as underlying conditions. A considerable number of cases (4611; 34%) from the basic dataset had to be dropped from the specific subset analysis due to missing virus subtype information. The high proportion of unsubtyped influenza A viruses, ranging from 0. 1% during the pandemic season 2009/2010 to 60% in 2016/2017, limited determination of the dominant virus subtype for most of the seasons in the analysis. We also observed that the proportion of unsubtyped influenza A viruses was higher in A(H3N2)-predominant seasons than in A(H1N1) pdm09 seasons.
e. , three intricate disulfide bridges that confer very high stability to proteins; Fig. 4 )  35. Hence, it is likely that rigidity originates from the structure formed by these cysteines that are highly conserved in the protein family, as indicated by the Consurf analysis (Figs. S1  and S4 ). Thus, we sought to determine whether these disulfides are formed and whether they influence the stability and/or the oligomerization state of the protein by covalent bonds. A single peak corresponding to the theoretical mass of MLP124266 monomer was obtained by mass spectrometry (data not shown). The titration of free thiol groups in an untreated recombinant MLP124266 gave an averaged value of 1 mole SH per mole of protein. Considering the presence of 8 cysteines in the protein, these results are consistent with the existence of three intramolecular disulfide bridges (Fig.
5A ). Residues 1-14 and 150-151 having missing assignments are not defined in the final models. This arrangement of secondary structure produces a cone-shaped fold for the protein, which generates a distinctive hydrophobic cavity (Fig. 5B , Fig. 5C ). To identify potential structural homologs of MLP124017, we performed structural similarity searches using the Dali server  36. Queries identified SBAL_0622 (PDB code 3BLZ) and SPO1084 (PDB code 3FKA) as the closest structural homologs with the highest Z-score of 6. 0 and 6. 3, respectively, and a RMSD of 4. 1 and 3. 5 Å, respectively (Fig. S6 ). These two proteins, which are from the bacteria Shewanella baltica and Ruegeria pomeroyi, have no known function, but share a common Nuclear Transport Factor 2-like (NTF2) fold. The NTF2 superfamily comprises a large group of proteins that share a common fold and that are widespread in both prokaryotes and eukaryotes  37.
0 software (Bruker) and analysed with NmrViewJ 64 , CcpNmr 65 and ARIA2  66. Structure calculation. For MLP124017 structure calculation, NOE peaks identified in 3D  15  N-NOESY-HSQC and 2D NOESY experiments were automatically assigned during structure calculations performed by the program CYANA 2. 1  67. The 15 N, H N ,  13  C' ,  13  Cα, Hα, and  13  Cβ chemical shifts were converted into ϕ/Ψ dihedral angle constraints using TALOS + (v. 1. 2)  68. Hydrogen bond constraints were determined according to 1 H/ 2 H exchange experiments of backbone amide protons (H N ). Each hydrogen bond was forced using following constraints: 1. 8-2. 0 Å for H N ,O distance and 2. 7-3. 0 Å for N H ,O distance. Final structure calculations were performed with CYANA (v. 2. 1) using all distance and angle restraints (Table  S3 ).
600 structures were calculated with CYANA 2. 1, of which the 20 conformers with the lowest target function were refined by CNS (v. 1. 2) using the refinement in water of RECOORD  69  and validated using PROCHECK  70. For MLP124266 structure calculation, 783 NOE peaks identified in 3D  15  N-NOESY-HSQC or 2D NOESY spectra, 75 φ/Ψ dihedral angles generated by DANGLE  71  and 10 hydrogen bonds were input as unambiguous restraints in ARIA2. Covalent disulfide bonds between Cys 39 -Cys 55 , Cys 44 -Cys 58 and Cys 50 -Cys 64 were also introduced. Among the 400 structures generated by ARIA2, 20 models of lowest energy were refined in water (Table  S2 ). NMR assignment and structure coordinates have been deposited in the Biological Magnetic Resonance Data Bank (BMRB code 34423 and 34298) and in the RCSB Protein Data Bank (PDB code 6SGO and 6H0I), respectively.
Acknowledgements The authors would like to thank  Prof. David L. Joly  ( Université de Moncton, Nouveau Brunswick, Canada ) and  Prof. Hugo Germain  ( Université du Québec à Trois-Rivières, Québec, Canada ) for early discussions about candidate effector selection and continuous collaborations and discussions on the topic through the years. The authors would also like to acknowledge the  French EFFECTOME network  financed by  INRA Plant Health and Environment and Ecology of Forest, Grasslands  and  Freshwater Divisions , as a fruitful platform for sharing ideas and creating collaborations. Access to the Bruker DRX 600 of the  UMS2008 IBSLor Biophysics and Structural Biology Core Facility  ( Université de Lorraine-CNRS-INSERM ) was appreciated. The  UMR1136  is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d' Avenir" program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE).
Cross-sectional data allow comparing the productivity of individuals with different measurable characteristics. Differently from cross-sectional studies, longitudinal studies allow to observe the same individual over time and to control for measurable and unmeasurable time-invariant characteristics  (Wooldridge 2006). New in our work is the use of fine-grained information on the scientists' family status to exploit the complexity of childbearing responsibilities. Additionally, we use a longitudinal dataset to overcome properly the issue of controlling for unmeasurable time-invariant characteristics. We survey the entire population of French physicists active at the Institute of Physics (INP) in June 2017. Our survey has been addressed to 1,085 individuals and obtained positive replies from the 621 (57.
2 ' It is with the objective of promoting gender equality that CNRS launched the Mission for Women's integration (2014). In recent years, progress towards gender equality has been made: in 2017, 43% of the CNRS employees were women. 3 However, women's presence has not increased equaly across disciplines. Looking at the Ph. D. students' composition, the female share in hard science was only 30% in 2017. 4 In the same year, at higher career levels, institutes like INP count only 19% of female physicists. This lower percentage of female INP researchers reflects a significant gender bias in disciplines such as physics, mathematics, and chemistry. INP aggregates scientists with high academic profile and productivity. Publication data retrieved from the Web of Science bibliometric dataset (Clarivate Analytics) shows that during the period 2001-2016, INP produced approximately 52,000 publications in Physics.
Data collection We surveyed all French physicists affiliated to INP and active in June 2017. We contacted the 1085 scientists working in the five main study areas in which the institute specializes: theoretical physics, condensed matter and optics, atoms, molecules, and quantum physics. The survey was developed and conducted in French avoiding challenges of translation. We surveyed scientists on their family characteristics. Specifically, we inquired if a scientist had children, andif this was the casewe asked to detail the birth year of each child. We reconstructed from their Curricula Vitae their career advancements, identifying the year of promotion (if any) from Junior Researcher Second Level to Senior Research Exceptional Level. To complete the tracking of their career advancements in the survey, we asked to specify the beginning and end year of promotion as head of a research unit, if any occurred.
The survey was launched online, with INP administration providing us with the complete list of their scientists with their email contacts. Each scientist on the list received a link to access and fill in a form with our set of questions. 7 In total, we received 621 usable answers. The overall response rate is 57. 24%. We obtained 364 responses after the first round (June 2017), and 257 additional responses with the first and second reminders (177 and 80 additional responses, respectively in July 2017 and January 2018). We performed a series of statistical tests showing that respondents are similar to non-respondents (see Appendix A). Female responded in the 68. 27% of the cases, while males responded in 54. 62% of the cases. We complemented the survey data with demographic data obtained from the French Ministry of Education, and we reconstructed the full scientists' publication record by collecting their publications on the Web of Science dataset (Clarivate Analytics).
Data description The way in which we framed the questions allowed us to build a longitudinal dataset having important advantages compared to similar studies. While extant studies used surveys to capture a snapshot of the situation in a given period, having asked the exact birth year of each child and the exact period when they had been the head of a research unit, we reconstructed the yearly scientist' family and academic rank. Observing each of our 621 scientists along the observation period 2001-2016, we end up with 9,021 observations. Not all our scientists are present in the observation period 2001-2016, due to their different entry and exit year at INP. In our sample, 67. 3% of scientists are observed for the full period, whereas the minimum number of observations per scientist is five years (0.
Variable construction Based on the survey data, the data released by the French Ministry of Education, and the bibliometric data collected on the Web of Science database, we calculate our variables of interest and controls. We measure scientist's publication productivity as the Number of publications authored by the scientist in a given year t. Among the variables related to the scientist's demographic characteristics, we consider the dummy variable Female that equals one if the scientist is a woman, zero otherwise, and Age that corresponds to the age of the scientist in year t. 8 To measure family characteristics, we look at the Total number of children that equals to the number of children born until year t. Considering that children have different needs at the various stages of their life, we distinguish the cases where in the family there is a child in their first years of life.
Method and results This study aims to investigate the impact of the family characteristics on the gender publication gap. To this purpose, we proceed in two steps. First, we conduct an exploratory analysis based on the descriptive statistics of our data and, second, we estimate the parameters (b) of Equation (  1 ) with a formal multiple regression model applying Ordinary Least Squares (OLS). Publication productivity it = b 0 + b 1 demographic characteristics it + b 2 family characteristics it + b 3 Career advancement it + 1 it (1) Where i and t refer to the scientist i observed at time t. Our 621 individuals are observed over the period 2001-2016, for a total of 9021 observations. Individuals are observed on average for 14. 52 years. Figures  2  and 3  show how publication productivity varies by family size (for the total sample) and by children age (for the 248 physicists sub-sample).
99 papers per year). Figure  4  provides a graphical representation of the regression results reported in Table  4 , Column 4. 10 Specifically, it shows the predicted scientist's publication productivity for a representative scientist when family characteristics vary. We assume that the representative scientist is a junior researcher (first class), not the head of a lab, and 40-year-old. This scientist's profile is the most frequent in our dataset (see the average characteristics of the scientists in our sample reported in Table  2 ). For family characteristics, in Figure  4 , we 9 There is a positive correlation between the number of papers published by a scientist when she enters the observation period and the later yearly productivity. One paper more in the initial stock guarantee 0. 72 paper more by year.
Discussion and conclusions This paper contributes to the gender literature by analysing the role played by family characteristics on male and female scientists' publication productivity. Unique to our study is the use of longitudinal data. We surveyed all the physicists affiliated to INP in June 2017 and reconstructed their full data record, including family status, academic rank, and publications over the period 2001-2016. We find that family characteristics play a different role for men and women. The paper addresses a number of econometric challenges faced in the attempt to accurately quantify the relationship between family-related explanatory factors and publication productivity. Family characteristics are the observable characteristics most commonly suggested as an explanatory factor of the productivity gender gap  (Ceci and Williams 2010, 2011).
Although quantitative isotope data from speleothems has been used to evaluate isotope-enabled model simulations, currently no consensus exists regarding the most appropriate methodology through which to achieve this. A number of modelling groups will be running isotope-enabled palaeoclimate simulations in the framework of the Coupled Model Intercomparison Project Phase 6, so it is timely to evaluate different approaches to using the speleothem data for data-model comparisons. Here, we illustrate this using 456 globally distributed speleothem δ 18 O records from an updated version of the Speleothem Isotopes Synthesis and Analysis (SISAL) database and palaeoclimate simulations generated using the ECHAM5wiso isotope-enabled atmospheric circulation model. We show that the SISAL records reproduce the first-order spatial patterns of isotopic variability in the modern day, strongly supporting the application of this dataset for evaluating model-derived isotope variability into the past.
This fractionation is generally poorly constrained  (McDermott, 2004; Fairchild and Baker, 2012) , does not account for any kinetic fractionation at the time of deposition and is not suitable for aragonite samples. Thus, using a single standard correction and not screening records for mineralogy introduces uncertainty into the data-model comparisons. SISAL (Speleothem Isotopes Synthesis and Analysis), an international working group under the auspices of the Past Global Changes (PAGES) project (http://pastglobalchanges. org/sisal,last access: 2 August 2019), is an initiative to provide a reliable, well-documented and comprehensive synthesis of isotope records from speleothems worldwide  (Comas-Bru and Harrison, 2019). The first version of the SISAL database (SISALv1:  Atsawawaranunt et al. , 2018a, b)  included 381 speleothem-based isotope records and metadata to facilitate quality control and record selection.
A major motivation for the SISAL database was to provide a tool for the benchmarking of palaeoclimate simulations using isotopeenabled models. In this paper, we examine a number of issues that need to be addressed in order to use speleothem data, specifically data from the SISAL database, for model evaluation in the palaeoclimate context and make recommendations about robust approaches that should be used for model evaluation in CMIP6-PMIP4. We focus particularly on interpretation issues that could be overlooked in using speleothem records and we show the strengths and limitations of different comparison techniques. We use the MH and LGM time periods, partly because the midHolocene and lgm experiments are the "entry cards" for the CMIP6-PMIP4 simulations and partly because these are the PMIP time periods with the best coverage of speleothem records.
We use an updated version of the SISAL database (SISALv1b:  Atsawawaranunt et al. , 2019)  and simulations made with the ECHAM5-wiso isotope-enabled atmospheric circulation model  (Werner et al. , 2011)  to explore the various issues in making data-model comparisons. Our goal is not to evaluate the ECHAM5-wiso simulations but rather to use them to illustrate generic issues in data-model comparison with speleothem isotope data. Section 2 introduces the data and the methods used in this study. Section 2. 1 introduces the isotope-enabled model simulations for the modern , the midHolocene and the lgm experiments, explains the methods used to calculate weighted simulated δ 18 O values, and provides information about the construction of time slices. Section 2. 2 presents the modern observed δ 18 O in precipitation (δ 18 O p ) used.
Section 2. 3 introduces the speleothem isotope data from the SISAL database and explains the rationale for screening records. Section 3 describes the results of the analyses, specifically the spatio-temporal coverage of the SISAL records (Sect. 3. 1), the representation of modern conditions (Sect. 3. 2), anomaly-mode time-slice comparisons (Sect. 3. ) and the comparison of δ 18 O gradients in absolute values along spatial transects to test whether the model accurately records latitudinal variations in δ 18 O across time periods  (Sect. 3. 4). Section 4 provides a protocol for using speleothem isotope records for data-model comparisons and Sect. 5 summarizes our main conclusions.
, 2012). For the lgm experiment  (Werner et al. , 2018) , orbital parameters, GHG concentrations, land-sea distribution, and ice sheet height and extent followed the PMIP3 guidelines. Climatological monthly sea ice coverage and SST changes were prescribed from the GLAMAP dataset  (Paul and Schäfer-Neth, 2003). A uniform glacial enrichment of sea surface water and sea ice of +1 ‰ (δ 18 O) and +8 ‰ (δD) on top of the present-day isotopic composition of surface seawater was applied. For the ocean surface state of the corresponding control simulation, monthly climatological SST and sea ice cover for the period 1979-1999 were prescribed. All the ECHAM5-wiso simulations were run at T106 horizontal grid resolution (approx. 1. 1 • × 1. 1 • ) with 31 vertical levels. The midHolocene and lgm experiments were run for 12 and 22 years, respectively, and the last 10 (midHolocene) and 20 (lgm) years were used to construct the anomalies.
Modern observations We use two sources of modern isotope data for assessment purposes: (i) δ 18 O p measurements from the Global Network of Isotopes in Precipitation (GNIP) database (IAEA/WMO, 2018) and (ii) a gridded dataset of global water isotopes from the Online Isotopes in Precipitation Calculator (OIPC:  Bowen and Revenaugh, 2003; Bowen, 2018). The GNIP database (IAEA/WMO, 2018) provides raw monthly δ 18 O p values for some part of the interval March 1960 to August 2017 for 977 stations. Individual stations have data for different periods of time and there are gaps in most individual records; only two stations have continuous data for over 50 years and both are in Europe (Valentia Observatory, Ireland, and Hohe-Warte, Vienna, Austria). Most GNIP stations are more than 0. 5 • away from the SISAL cave sites, precluding a direct global comparison between GNIP and SISAL records.
However, the GNIP data can be used to examine simulated interannual variability. Annual wδ 18 O averages were calculated from GNIP stations with enough months of data to account for more than 80 % of the annual precipitation and 5 or more years of data. Annual wδ 18 O p data were extracted from the ECHAM5-wiso simulations at the location of the GNIP stations for the years for which GNIP data are available at each station. We exclude GNIP stations from coastal locations that are not land in the ECHAM5-wiso simulation. This dual screening results in only 450 of the 977 GNIP stations being used for comparisons. Boxplots are calculated with the standard deviation of annual wδ 18 O p data. The OIPC dataset provides a gridded long-term     2017). The OIPC data can be used to evaluate modern spatial patterns in both the SISAL records and the simulations.