dataesr/scientific-paragraphs-categorization huggingface dataset:
These include the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) from 2004 to the present  (Bernath 2017 ) and the Global Ozone Monitoring by Occultation of Stars (GOMOS) for 2002-12. For GOMOS, two temperature datasets have been released recently: one for the stratosphere  (Sofieva et al. 2019) , and another dataset for the upper stratosphere and the mesosphere  (Hauchecorne et al. 2019 ). Exploration of these data for potential use in trend analyses is the subject of future research.
7 K near the tropopause, gradually increasing into the stratosphere  (Scherllin-Pirscher et al. 2011a ,b, 2017). For monthly zonal means, the total uncertainty estimate is smaller than 0. 15 K in the upper troposphere and lower stratosphere, and up to 0. 6 K at higher latitudes in wintertime  (Scherllin-Pirscher et al. 2011a). As the time delay measurement of the refracted signals is based on precise atomic clocks, this enables longterm stability and traceability to the Système International (SI) unit of the second  (Leroy et al. 2006). Therefore, data from different RO missions can be merged to a seamless record without intercalibration or requiring substantial temporal overlap  (Foelsche et al. 2011; Angerer et al. 2017). Continuous observations are available from several RO satellite missions.
1) RADIOSONDE OBSERVATIONS Reliable radiosonde temperature records commenced in 1958. Observations are made once or twice per day at stations that are mainly located on Northern Hemisphere continents. The weather balloons reach up to about 25 km until they burst. Depending on wind conditions, typical drift distances are a few kilometers in the lower troposphere to about 50 km in the lower stratosphere  (Seidel et al. 2011). All radiosonde datasets have limited coverage in the tropics. Different countries use different instrument types, and instrumentation has changed over time (e. g. ,  Thorne et al. 2011). A further problem is that radiosondes are affected by radiation biases during daytime measurements  (Sherwood et al. 2005; Ladstädter et al. 2015). To reduce data discontinuities and residual cooling biases in radiosonde-derived CDRs, a number of different adjustment techniques have been developed.
Several centers have produced homogenized radiosonde products using different methods. NOAA's Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC)  (Free et al. 2004 ) is based on spatial averages of adjusted temperature data  (Lanzante et al. 2003 ) from 1958 to 1995. Since 1996, it is based on the Integrated Global Radiosonde Archive (IGRA) station data using a first difference method  (Free et al. 2004 ) and the record is not fully homogenized. RATPAC-A, version 2, data (NOAA NCEI 2019) are provided as zonal yearly anomalies. The Hadley Centre Atmospheric Temperature dataset (HadAT) from the Met Office  (Thorne 2005; McCarthy et al. 2008 ) uses a larger number of stations; however, it is only available for 1958-2012 and not updated to the present (www. metoffice. gov.
uk/hadobs/hadat/). Sherwood et al. (2008)  constructed a radiosonde record based on an iterative universal kriging (IUK) method. The record is available only until 2015 and not updated to present  (Sherwood and Nishant 2015). Haimberger et al. (2008)  introduced the homogenized Radiosonde Observation using Reanalysis (RAOBCORE) and Radiosonde Innovation Composite Homogenization (RICH) datasets. Break points are determined either by using composites of neighboring observations as reference (RICH) or by comparing to departures from a reanalysis background (RAOBCORE). RICH is independent of the background, but interpolation errors may be large where sampling is sparse such as in the tropics and the Southern Hemisphere. RAOBCORE reduces interpolation errors at the cost of slight background dependence on ERA-Interim  (Haimberger et al.
2012). Gridded RAOBCORE and RICH data have been updated to the end of 2018  (Haimberger 2019). The data are provided as monthly means at 108 3 108 resolution. Radiosonde temperature data are also available from GRUAN radiosonde stations (https://www. gruan. org/; Bodeker et al. 2016). GRUAN is a reference observing network of quality measurements of ECVs to reduce uncertainty in climate monitoring  (Seidel et al. 2009; Thorne et al. 2013 ). As of 2019, GRUAN comprises of 26 sites, 12 of which have been certified. We do not use GRUAN data because records start in 2009 and are too short for reliable trend estimation. In this study, we use the RICH and RAOBCORE radiosonde records of the University of Vienna. In addition, we also use radiosonde data from the ERA-Interim archive (denoted RS-VAIS), restricting our attention to data from the Vaisala RS80, RS90, RS92, and RS41 radiosondes from 1995 onward.
2009). Deviations from the zonal mean are seen as eddy signals in the subtropics  (Scherllin-Pirscher et al. 2012). We use the Niño-3. 4 SST index as an ENSO proxy. This is the spatially averaged SST in the Niño-3. 4 region (58S-58N and 1708-1208W). By definition, El Niño or La Niña periods occur if 5-month running means of SST anomalies in this region exceed 10. 4 K or 20. 4 K, respectively, for at least six months  (Trenberth 1997). Our multiple regression relied on version 5 of the Extended Reconstructed Sea Surface Temperature dataset (ERSSTv5;  Huang et al. 2017). To account for lags between this measure of ENSO variability and the response of tropospheric temperature, we used a lag of 3 months for the monthly ERSSTv5 (1981-2010 base period) Niño-3. 4 index  (CPC 2019). Tropical stratospheric variability is dominated by the QBO, which has a period of about 28 months.
1993). Explosive volcanic eruptions such as El Chichón in 1982, Mount Pinatubo in 1991  (Robock 2000)  and also minor volcanic eruptions after 2000 affect short-term temperature trends in the troposphere and stratosphere  (Solomon et al. 2011; Stocker et al. 2019). As a proxy for the effects of volcanic eruptions we compute the stratospheric aerosol optical depth over 15-25 km from the monthly mean Global Space-Based Stratospheric Aerosol Climatology (GloSSAC), version 1. 0, averaging over the tropics and subtropics  (Thomason 2017; Thomason et al. 2018). In addition, we used observed surface temperature trends to compare with trends in the free atmosphere. We employed the HadCRUT4 dataset for this purpose (Met Office and the Climatic Research Unit, University of East Anglia, United Kingdom; HadCRUT4 2020;  Morice et al.
Monthly mean temperature time series from the four selected lidar stations are presented. Equivalent temperatures have been computed from lidar profiles using the SSU3 weighting function from STAR, by sampling and weighting the lidar temperature at the respective height levels (ftp://ftp. star. nesdis. noaa. gov/ pub/smcd/emb/mscat/data/SSU/SSU_v2. 0/Weighting_ Function/). The vertically weighted lidar results are compared to collocated SSU3 data at grid points corresponding to the location of the respective lidar stations. A constraint in this comparison is the limited temporal sampling of the lidars at individual stations (measurements are limited to clear nights) in contrast to the full monthly sampling of SSU. This may partly explain the larger variability of the lidar temperature series.
In general, the variability in lidar temperatures is higher at the more northerly HOH and OHP stations (47. 88 and 448N, respectively) and smaller for the tropical MLO station (19. 58N). Lidar temperature anomalies are well correlated with the SSU3 time series: correlations are 0. 76, 0. 70, 0. 73, and 0. 72 for SSU-AMSU versus HOH, OHP, TMF, and MLO (respectively). All linear trends in Fig. 4 , both for the lidar data and SSU, were computed for the time periods dictated by the length of individual lidar records. At HOH, the linear trend of 20. 39 6 0. 59 K decade 21 for the lidar is smaller than the trend of 20. 59 6 0. 38 K decade 21 for SSU-AMSU (Fig. 4a ). Although the latter is statistically significant, the trend in HOH lidar data is not significant because of the larger variability of lidar data.
At TMF, the lidar trends are larger than SSU trends, specifically due to the differences in the first half of the time series. These differences are likely associated with a warm temperature bias in the first few years of the lidar record. The warm bias was caused by the presence of signal-induced noise in the raw lidar data complicating the extraction of background noise. The pre-1996 temperature data at TMF should therefore be considered with caution. A full reanalysis of these data is currently being undertaken, with the expectation of a more accurate TMF record during these early years. At OHP, lidar trends are of a similar magnitude as SSU-AMSU trends over the time period considered, but these are not statistically significant due to the large variability. Note that data at the end of the OHP time series are not included in this work as they are currently being investigated for biases.
Observed trends in the tropical stratosphere point to a slight cooling from about 20. 1 to 20. 22 K decade 21 in the lower stratosphere observed in RO and radiosondes, which increases to 20. 45 K decade 21 in the upper stratosphere in the SSU observations. The largest trend differences among the different datasets are evident in the tropopause region. In this region we are more confident in trends from RO observations than in those from radiosonde observations. Inai et al. (2015)  reported a pressure bias in Vaisala RS80 sensors that could lead to an artificial trend in temperature. Transitioning from the RS80-dominated time period before 2007 to the RS92-dominated period thereafter may have contributed to weaker TTS trends in the radiosonde record. In addition, radiosonde information is sparser in the tropics and Southern Hemisphere and clustered over Northern Hemisphere continents.
4 at slightly lower altitudes. Secondary effects such as phase changes have only a small effect on the amplification (less than 0. 1). Comparison of the standard deviation ratios for the period 1979-2018 (Fig. 13a ) shows that for short-term variability, the RICH and RAOBCORE radiosonde records follow the expected moist adiabatic lapse rate up to 8-10 km. Above 10 km, these radiosonde datasets show no further amplification. There is also close agreement between the theoretical expectation of the standard deviation ratios and the MSU TLT and TMTcorr results (with weighting function peaks near 2 and 5 km, respectively, which represent thick vertical layers but are indicated as points). MSU TTScorr shows no further amplification relative to TLT and TMTcorr. Now consider scaling ratios for multidecadal tropospheric and surface trends over the period 1979-2018 (Fig.
13d ), each of the datasets show amplification with altitude, but with considerable differences in detail. The RS-VAIS radiosonde and RO trends show strongest amplification (up to a factor of 3. 0) in the upper troposphere above 8 km, while the RAOBCORE and RICH data have nearly constant amplification (;2. 0) with height above ;3 km. MSU RSS and STAR data show small amplification with STAR TMTcorr being closest to the theoretical lapse rate. TTScorr does not show any amplification, with only slight amplification from UAH TTScorr. This is most likely because the TTScorr weighting function still contains nonnegligible cooling contribution from the lower stratosphere that causes the TTScorr to underestimate the warming trends in the upper troposphere. When interpreting Fig. 13 , one should note that only the best estimate trend curves are shown for clarity.
From Fig. 10 , however, one can see that there is substantial uncertainty in trends derived from all datasets. In addition, the scaling ratios depend sensitively on the HadCRUT4 surface trends, and a reduction of the best estimate from 0. 14 to 0. 11 K decade 21 , which is well within the observational uncertainty range in the tropics  (Morice et al. 2012) , would enhance the scaling factors of the upper-air datasets by 20%. Despite this caveat, the datasets in Fig. 13d  provide evidence for amplified warming in the tropical upper troposphere over the  RO period (2002-18). All observations except MSU TTScorr are able to represent the amplification of month-to-month variability, with RO and RS-VAIS showing larger upper-troposphere variability. For the multidecadal trends (Figs. 13b, d ), the STAR MSU TMTcorr shows an amplification while TTScorr does not.
Summary and conclusions This study provides an overview of updated atmospheric temperature trends estimated from observations and assesses the uncertainties and limitations of currently available data records. Atmospheric observations over the satellite era  have been investigated from satellite-based sensors as well as from groundbased instruments. We constrained our analysis to data records that have been updated until the end of 2018. We analyzed layer-average temperature from new merged data records of microwave sounders (MSU-AMSU) provided by three data centers and from two merged records of stratospheric sounders (SSU-AMSU and SSU-MLS). We compared these datasets to lidar temperatures from four long-term stations and to two gridded homogenized radiosonde records. We also relied on novel GNSS RO from three different centers.
Acknowledgments. The authors express their gratitude to SPARC for supporting the ATC activity in their work described in this article. These activities have been undertaken under the guidance and sponsorship of the World Climate Research Programme. We acknowledge the groups at NOAA/STAR, RSS, and UAH who have produced and made available the satellite temperature data sets used in this study. We acknowledge UCAR/CDAAC (Boulder, CO, United States) for providing access to RO phase, orbit data, and temperature data, as well as to ECMWF (Reading, United Kingdom) for giving access to analysis and forecast data. We also thank the WEGC EOPAC team for providing the OPSv5. 6 RO data, and the ROM SAF team for providing the ROM SAF CDR v1. The lidar data used in this publication were obtained from the Norwegian Institute for Air Research (NILU) as part of the Network for the Detection of Atmospheric Composition Change (NDACC) and are publicly available (see http://www.
ndacc. org). The GloSSAC data set was obtained from the NASA Langley Research Center Atmospheric Sciences Data Center. We acknowledge FU Berlin (Berlin, Germany) for providing Singapore wind data. For C. -Z. Zou, the views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or U. S. Government position, policy, or decision. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Q. Fu was supported by the NASA grant 80NSSC18K1031. L. Haimberger was supported by FWF grant P28818-N29 and EU 7FP grant 607029 (ERA-CLIM2). A. K. Steiner and F. Ladstädter were funded by the Austrian Science Fund (FWF) under research grant P27724-NBL (VERTICLIM), and by the FFG-ALR project ATROMSAF1(ASAP-13 859771).
Ho and X. Zhou 2020, unpublished manuscript). An overview on RO data processing and a description of retrieval steps for each specific dataset is given inSteiner et al. (2020, their Table , the ROM SAF CDR v1. 0 version of ROM SAF [Danish Meteorological Institute (DMI) Copenhagen, Denmark] (ROM SAF 2019; Gleisner et al. 2020), and UCAR/ NOAA data [UCAR COSMIC Data Analysis and Archive Center (CDAAC); Boulder, Colorado, and NOAA] (UCAR CDAAC 2019; S. -P.
Data and Data sources Data on bilateral trade flows were obtained from the United Nations Comtrade database. Information on population, and gross domestic product were compiled from the World Development Database (WDI). Data on common border, bilateral distance, common language, and colonial ties were obtained from the Centre d'Etudes Prospective et d'Informations Internationals (CEPII) database. The economic policy uncertainty, our variable of interest, was obtained from the uncertainty index developed by  Ahir et al. (2018). The overall sample used in our study consist of 126 countries and span 1996-2014. The uncertainty index was constructed from the Economics Intelligence Unit (EIU) reports from 1996-2018, and comprises 143 countries. The index capture the average frequency of uncertainty related to key economic and political developments as reported in the EIU report.
Availability of data and material The data that support the findings of this study are available from the Centre d'Etudes Prospective et d'Informations Internationals (CEPII) database and from the Economic Policy Uncertainty website. Restrictions may apply to the availability of these data. Data are available at: http://www. cepii. fr/CEPII/fr/bdd_modele/presentation. asp?id=32, and https://www. policyuncertainty. com/wui_quarterly. html.
16S rRNA gene amplicon sequencing DNA was extracted as described above and sent for Illumina amplicon sequencing at the Genome Quebec Innovation Centre (McGill University). DNA was sequenced using the Illumina MiSeq PE300 platform with modified versions of primers 926f and 1392r (926f-modified: AAACTYAAAKGAATWGRCGG; 1392r-modified: ACGGGCGGTGWGTRC) which target the V6-V8 variable region of the 16S rRNA gene from bacteria and archaea, as well as the 18S rRNA gene in eukarya. After sequencing, raw data was processed using the QIIME 2  31  Core 2020. 2 distribution. Primers were removed using the cutadapt 32 plugin. Sequences were denoised and merged into amplicon sequence variants (ASVs) using DADA2  33  (forward sequence truncated at 260 bp, reverse sequence truncated at 240 bp). Taxonomy was assigned to ASVs using the feature-classifier plugin  34  with the classify-sklearn method using a pre-trained Naïve Bayes Classifier with the SILVA 132 99% OTUs full-length sequences database (available at https://docs.
Acknowledgements The authors would like to thank:  Shen Guo  for conducting GC sampling and DNA extraction during phase III of the biotransformation experiment;  Dr. Courtney Toth  for performing the preliminary steps in the QIIME 2 pipeline for the analysis of the 16S rRNA amplicon sequencing data; and  Suly Rambinaising  for performing the qPCR runs with the Desulfuromonadales/Geobacter primer. L. P. J. was supported by the  NSERC   CREATE RENEW program  [180804567], the  Government of Ontario  through the  Ontario Graduate.
In this paper, the applicability and usefulness of an electron-impact broadening dataset for Co II lines for investigations of white dwarf and A-type star atmospheres is analyzed. Stark broadening of the lines in the spectra of hot and dense celestial objects such as white dwarfs (WD), because of specific conditions of high electron density and high temperature in their atmospheres, usually dominates on Doppler broadening. Consequently, particular attention has been payed to hydrogen-rich (DA) and helium-rich (DB) types of WD, trying to figure out if a change in the physical conditions in their atmospheres, such as effective temperature or surface gravity, affects the relationship between thermal Doppler and electron-impact broadening for particular spectral lines. Data 2020, 5, 74 2 of 9.
Dataset and Methods of Research Stark broadening theory has its application both in laboratory research as well as in astrophysical plasma  [4, [7] [8] [9] [10] [11] [12]. For example, from the astrophysical point of view, Stark broadening data are always of interest when the Stark broadening contribution to the considered line profile is not negligible, such as in the cases of the interpretation, synthesis and analysis of stellar spectral lines, the determination of chemical abundances of elements from equivalent widths of absorption lines, the calculation of radiative transfer through stellar atmospheres and subphotospheric layers, opacity calculations, radiative acceleration considerations, nucleosynthesis research and other astrophysical topics. In the investigation of laboratory plasma, Stark broadening theory can help, for example, in plasma diagnostics, for the determination of the density and temperature of the plasma.
There are no other experimental or theoretical data for Stark broadening of Co II spectral lines analyzed here. As follows from our work, measurements of Stark broadening of Co II spectral lines will be of interest not only for comparison with the results obtained here but also for analysis and synthesis of stellar Co II spectral lines. This set of data, previously published as a hard copy in Ref. [3] , is available online here in computer readable form. It will be implemented later and in the STARK-B database  [35] [36] [37] [38] , which is also a part of the Virtual Atomic and Molecular Data Center (VAMDC)  [39, 40]  and may be accessed through its portal  [41].
The Institut Pasteur in New Caledonia thus received serum samples collected in WF in 2019 and in NC in 2017-2020. Viral RNA was extracted from these samples using the QIAamp Viral RNA extraction kit (Qiagen, Hilden, Germany). Samples identified as DENV-2+ by serotyping PCR  [16]  were further processed for sequencing. Ethic approval for human samples analysis was granted by a Persons Protection Committee (CPP Sud-Est II n • Eudract 2019-A03114-53) and by the Consultative Ethics Committee of New Caledonia.
Sequencing of DENV Strains The E genes from DENV-2+ samples were amplified and sequenced as described previously  [16]. Overlapping fragments were assembled with Staden Package (Medical Research Council, Laboratory of Molecular Biology, Cambridge, England)  [17]. Sequences have been deposited into GenBank under accession numbers MN548842-MN548855, MN566109-MN566112, and MT799878-MT799882.
Maximum Likelihood Phylogenetic Analysis Nucleotide sequences were aligned with Clustal W integrated in Bioedit version 7. 2. 5 software  [18]. A maximum likelihood phylogenetic analysis was carried out with MEGA version 10. 0 software  [19] , using 89 E gene nucleotide sequences, among which 66 were retrieved from GenBank and 23 were generated in the current study. Sequences retrieved from GenBank encompass all 5 DENV-2 genotypes. Sequences from the last DENV-2 outbreak in the South Pacific were retrieved (1997-1999), along with recent (2010-2019) sequences from the Pacific and South East Asia. The evolutionary distances were computed using the Tamura-Nei 93-parameter method defined as the best evolutionary model with MEGA for our set of data. A discrete gamma distribution was used to model evolutionary rate differences among sites (3 categories).
Maximum Clade Credibility Phylogenetic Analysis A maximum clade credibility (MCC) tree was built for 48 complete E gene nucleotide sequences (1448 nt) of isolates collected in the Pacific and South East Asia between 2010 and 2020, among which 28 were retrieved from GenBank and 20 were generated in the current study. A codon-partitioned model was applied using the Tamura-Nei 93 substitution model with a gamma distribution. Different model combinations were tested, using either a strict clock, an uncorrelated relaxed clock with a lognormal relaxed distribution, or a relaxed clock with an exponential relaxed distribution. A constant, or a Bayesian Skygrid estimate of the population size, was applied. Path sampling (PS) and stepping stone sampling (SS) marginal likelihood estimates (MLE) were computed for these models (Table  S1 ).
The lowest MLE were obtained for a constant population size and a relaxed clock with a lognormal relaxed distribution. This model was further used to generate the MCC tree. Both mixing of individual chains and sufficient effective sample size were ensured by running each data for 20M generations, sampling every 10k generations using BEAST v1. 10. 4  [20]. After discarding 10% burn-in, a consensus file was produced using TreeAnnotator (BEAST package) and was visualized using FigTree 1. 4. 4.