dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions This study presents an updated dataset of isotopic source signatures of CH 4 from recent atmospheric measurements, while including additional data from published literature which were not previously included. The new data are a contribution from the EMID, that results from the sampling activities performed within the MEMO 2 project. It represents a substantial contribution to the global dataset for fugitive fossil fuels and waste sources, mainly sampled in urban areas. We have highlighted two main improvements in our understanding of the CH 4 isotopic composition: (i) a more robust range of values for modern microbial sources, and a better characterisation of the δ 13 C enrichment in CH 4 from waste sources; and (ii) fossil fuel-related sources could have more depleted values than previous estimates used in global mod-Earth Syst.
Sci. Data, 14, 4365-4386, 2022  https://doi. org/10. 5194/essd-14-4365-2022 This database, mean ± sem c -44. 5 ± 0. 5/-50. 7 ± 1. 3 a -182 ± 3. 0/-210 ± 4. 5 a a for natural gas/coal; b also in  Lassey et al. (2000) ,  Houweling et al. (2000) ,  Bousquet et al. (2006 ), Gosh et al. (2015) ,  Thompson et al. (2018) ; c standard error of the mean. els. In this respect, our data confirm the analysis made by  Schwietzke et al. (2016). Finally, the new European data contain comparatively more δ 2 H measurements. In the case of fossil fuel emissions, the use of δ 2 H CH 4 is of particular interest. In general, utilising both δ 13 C and δ 2 H for CH 4 improves our ability to clearly separate fossil fuel and microbial sources, compared to δ 13 C alone. The use of δ 2 H as additional constraint could help to answer open questions regarding the CH 4 global budget.
To better understand the drivers of δ 2 H variability (except for δ 2 H of precipitation), more measurements are required, especially of pyrogenic and waste sources. The present dataset can be used for CH 4 source attribution, studies at local and regional scales, and to derive global source signatures for input to global methane cycle modelling studies. The larger dataset will also help to estimate the uncertainties to take into account when using isotopic data in top-down studies, and with prior knowledge of the specificities of the studied region, the use of isotopic data in top-down studies is a powerful tool to evaluate the bottom-up emission inventories  (Alvarez et al. , 2018; Etiope and Schwietzke, 2019; Rutherford et al. , 2021; Stavert et al. , 2021). A future improvement of this database would be to include more measurements on the African, Asian, and South American continents, where experimental studies are lacking.
Because of its https://doi. org/10. 5194/essd-14-4365-2022 Earth Syst. Sci. Data, 14, 4365-4386, 2022 potential for source characterisation, new studies should also focus on δ 2 H CH 4 measurements. The maintenance of a CH 4 stable isotope database relies on a certain transparency of different groups around the world on their work. Therefore we strongly encourage the scientific community to pursue the efforts to make scientific data open-access more systematically. Appendix A: Supplementary material   , 2022; Brownlow et al. , 2017; Klevenhusen et al. , 2009 Klevenhusen et al. , , 2010;; Levin et al. , 1993; Lu et al. , 2021; Rust, 1981; Townsend-Small et al. , 2012; Wahlen et al. , 1989)   (Beck et al. , 2012; Burke and Sackett, 1986; Day et al. , 2015; Happell et al. , 1994; Kuhlmann et al.
Acknowledgements. We thank all the staff from different organisations involved in the MEMO2, CoMet, and ROMEO projects who participated in the sample collection. We acknowledge the technical staff at  UU  and  RHUL  for the maintenance of the IRMS systems. This work was supported by the  ITN  project " Methane goes Mobile -Measurements and Modelling " (MEMO2; https://h2020-memo2. eu/, last access: 3 November 2021). Financial support. This research has been supported by the  European Union 's  Horizon 2020 Research and Innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 722479.
Acknowledgements A. P. acknowledges the financial support of  Japan Society for the Promotion of Science (JSPS)  grant number  14332  and  Science Foundation Ireland (SFI)  grant number  18/SIRG/5621. T. M. thanks support from  JST Mirai   JPMJMI19A1  and  JSPS KAKENHI JP19H00833. The authors are grateful to  Transmission Electron Microscopy Station in NIMS (Japan)  for support and discussion. Open access funding provided by  IReL.
Main roads are marked by the black dashed lines. Ergo, the central point of this study is to investigate how the host rocks of different mineral deposits at the TMP evolved, examine their general geochemistry characteristics, pinpoint the main differences and similarities and elucidate how and why these magmas became fertile. The data approach given by  Loucks (2014) , for example, where the author constrains the fertility of plutons by looking at their geochemical signature (i. e. copper-producing plutons show higher whole-rock Sr/Y and V/Sc ratios, used as proxies to the hydrous condition of the magmas) is rather pertinent for our purpose. In this regard, we gathered information and organized a geochemistry database for selected Au -Cu -Mo mineral deposits and occurrences identified in the TMP.
Methods The results presented in this contribution refer to whole-rock and zircon geochemistry/isotopic analyses. The whole-rock geochemistry database counts with published and unpublished results from selected deposits in order to cover most part of the TMP, and involves results from  Biondi et al. , 2018 , Cassini, 2016 , Echeverri Misas, 2015 , Gutiérrez, 2018 , Juliani et al. , 2002 , Lamarão et al. , 2002 , Lopes and Moura, 2019 , Tokashiki, 2015  (geochemistry database is available as supplementary material). In-situ measurements of trace-elements in zircon grains were performed in OMS samples collected in central TMP. Three new U-Pb dating results are presented coupled with two reexamined analyses published by  Cassini et al (2020). Whole-rock geochemistry data was treated with  GCDKit (Janoušek et al.
, 2006)  and modelling routines of fractional crystallization were performed using further R programming  (Janoušek et al. , 2016). Major elements were modeled through simple mass-balance and trace-elements through Rayleigh crystallization using partition coefficients from  Rollinson (1993)  and GERM database (available at http://earthref. org). Trace-elements and U-Pb geochronology analyses in zircon grains were performed at the Department of Earth Sciences (ETH -Zürich) using a Thermo Element XR, sector field, single collector ICP-MS coupled to a 193-nm Resolution S155 (ASI) laser ablation system. The later counts with a two-volume Laurin Technic ablation cell fluxed by high-purity He at 0. 5 l min-1 rate. The 20 μm spots were ablated for 30 s with a repetition rate of 3 Hz and a laser energy of 2 J cm-2.
Ar was admixed to the aerossol within the ablation funnel (0. 96 l min-1) to carry the ablated material for ionization. The integration time per point was 0. 662 s, and elemental concentration was calculated using the software Iolite  (Paton et al. , 2011). The stoichiometric Si content of zircon (15. 2 wt%) was used as internal standard for trace element quantification and U-Pb data reproducibility was assured by analyses of reference zircon grains (AUSZ7-1, OG-1, Plešovice and 91500). U-Pb and trace element measurements were acquired at the same spot, and fractured or metamict domains were avoided. Zircon trace element results were treated with GCDKit and U-Pb data with IsoplotR  (Vermeesch, 2018) , results are also available as supplementary material. We use the XmeltCe4+/XmeltCe3+ index  (Smythe and Brenan, 2016) , to constrain the fO2 of the magmas from central TMP (equations 1 and 2).
Whole-Rock geochemistry In this section we directly compare whole-rock geochemistry analyses from the different TMP deposits. As a first selection criteria and in view of the thick weathering profile at the Amazonian region, the whole-rock geochemical data were plotted on the MFW diagram after Ohta and Arai (2007) (Fig. 3 ). The diagram was idealized with basis on a dataset of igneous rocks and their respective weathered products, where principal component analysis allows the M and F vertices to capture mafic to felsic differentiation sequence, whereas the W apex relates with the degree of weathering. Accordingly, the samples that plot on the center of the diagram and scatter towards the W vertice likely present some degree of weathering that might disturb their original geochemical signature and, consequently, these samples were excluded from the database.
Ti-in-zircon thermometry The Ti-in-zircon (TZirc. Ti) thermometer after  Ferry and Watson (2007)  was applied to constrain magmatic temperatures and the results are summarized on Table  3  (values below 620 °C and above 970 °C were discarded and not considered on our database). The plot from Fig. 12a  shows how TZirc. Ti and zircon saturation temperature (TZirc. Sat)  (Watson and Harrison, 1983 ) relate for each sample/grain analyzed and, accordingly, results distribute in three fields: i) upper part of the plot where TZirc. Ti > TZirc. Sat + 100, hence zircon-undersaturated field; ii) shaded area between 1:1 and 1:100 ; and iii) bottom part of the plot with TZirc. Ti < TZirc. Sat, zircon-oversaturated field. Whereas autocrystic zircons will plot on the bottom and eventually on field ii, inherited grains (ante-and xenocrysts) spread through all fields.
Magmatic fO2 estimates In order to make the data easier to read and comparable with other paper's datasets, we treat magmatic fO2 in log units above or below the fayalite-magnetite-quartz (FMQ) oxygen buffer (results are summarized on Table  3 ). The graph from Fig. 12b  shows magmatic temperature (TZirc. Ti) plotted against ΔFMQ (xenocrysts, high La and low LREE-i zircon grains were ruled out). Sample USP14-83 shows the highest fO2 values and zircon grains plot on the top part of the graph (values higher than ΔFMQ + 4). However, these results should be treated cautiously, as the sample shows predominantly low LREE-i zircon grains. The granodioritic porphyry (sample USP14-144) shows the second highest fO2 values, with ΔFMQ from 0 to + 4. Samples USP14-91 and 136 cover a similar and narrower fO2 range, with ΔFMQ between -1 and +1.
Acknowledgements The results presented in this research were funded by the  National Council for Scientific and Technological Development (CNPq)  through the processes  550342/2011-7  and  573733/2008-2 , and by the  Society of Economic Geologists (SEG)  through its McKinstry fund. The authors are also thankful for the  ETH's Department of Earth Sciences  for the in-situ zircon analyses. Scholarships for Lucas V. Cassini were provided by the  Coordination for the Improvement of Higher Education Personnel (CAPES)  and  CNPq from Brazil , and by the  French agency Campus France  through its  Eiffel Bourse  d'excellence (# P714383D ).
Whole-genome sequencing and genotype calling The reference rice genome (Nipponbare/japonica) was downloaded from the Phytozome database  53, 54. The paired-end raw reads were first quality-evaluated using FastQC v0. 11. 9 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/)  55  and then mapped to the reference genome using BWA v0. 7. 15-r1140 with default parameters. Tandem Repeats Finder v4. 09  56  was used with default parameters to scan the genome tandem repetition of DNA sequences. To obtain high-confidence SNP markers that can differentiate the two parental alleles, we used the analysis strategy described below. First, for parental and F1 samples (with a mean depth of 14. 3×), SNPs and SVs were predicted from whole-genome resequencing datasets using inGAP-family  57. Then, potential false-positive SNPs that come from sequencing errors, small indels, tandem repeats and SVs were identified and filtered by inGAP-family  57, 58.
Furthermore, those SNPs only present in one of the parents (each with two replicates, homozygous genotype), and with a heterozygous genotype in F1 plants (two replicates, reference allelic ratio larger than 0. 3 and less than 0. 7) and sequencing depths larger than three, were kept as high-confidence markers. In the end, we obtained a set of 267,753 high-confidence SNP markers for subsequent analysis. For F2, T0, T1, and T2 samples (with a mean depth of 1. 9×), the read count and genotype profile for SNPs were generated by inGAP-family, and then a sliding window-based method was used to construct the genome-wide genotype landscape across samples, with a window size of 200 kb and a sliding size of 100 kb. The genotype profile was visualized by using ggplot2 v3 3. 5 package in R environment.
Microscopy analysis of meiotic products by clearing and callose detection by aniline blue staining Ovaries and anthers collected at pre-anthesis stages (white or paleyellow anthers) from independent T2 plants of apomictic events T314 15. 1 and T314 37. 7 (and of replicated samples collected on T3 plants of event T314 15. 1) were fixed in Carnoy's or FAA fixatives and rinsed in 70% ethanol. Dissected ovaries were mounted in Hoyer's clearing medium and gently squashed by pressure on the coverslip to expose ovules. Callose detection using aniline blue staining was performed as described  60. Observation and imaging were done on a Zeiss Axioimager Z2 microscope equipped with DIC, CFP emission filter; ×40 or ×63 (NA1,4) oil immersion lenses, and a sCMOS camera (Hamamatsu ORCA Flash V2). Images were processed using ImageJ software.
Statistical analyses As most traits did not follow a normal distribution, the statistical significance of differences between transgenic lines and controls was determined using non-parametric Kruskal-Wallis tests and an α-risk of 0. 05. Multiple pairwise comparison was performed according to Dunn's procedure with a Bonferroni-corrected significance level of the p-value. For panicle fertility, we used Duncan's test with a significance of 0. 05%. The XLSTAT software (Addinsoft, Paris, France) was used for the statistical analyses.
Acknowledgements We wish to thank  Murielle Portefaix  ( INRAE ) for assistance with tissue culture,  Eve Lorenzini ,  Rémy Michel ,  Sémi Melliti , and  Christian Chaine  for greenhouse support,  Dr. Frédéric Bakry  and  Dominique Dambier  for assistance and advice in flow cytometry,  Armelle Soutiras  and  Mathilde Singer  for assistance in grain quality analyses,  Matthieu Dejean  for the grain morphological script using the open source  ImageJ program ,  Pierre Mournet , for advice on the  Great Regional Technical Platform (GPTR) of genotyping at Cirad. This research has been conducted on the functional analysis (AFEG), cell imaging (PHIV), and biochemical phenotyping (PPB) platforms of  CIRAD. The support of  Dr. Christophe Périn  at the initiation of the project is acknowledged. M. B.
This work particularly benefits from ground-based and in situ observations, rarely available in tropical regions, and not used for comparisons with model outputs so far. This provides a model assessment at a more local scale. The model initialisation is driven from information about injection parameters available in the literature. We use updated and improved satellite SO2 datasets in comparison with former studies to examine the modelled evolution of SO2. We discuss explanations for the model-measurement discrepancies at the large and local scale, such as the pertinence of the injection parameters and the importance of ash co-injection as reported in recent studies.
Space-Borne and In Situ Observations IASI is a nadir-looking remote sounder on board the Meteorological Operational satellite (MetOp-A) launched in October 2006 into a Sun-synchronous polar orbit. IASI provides global coverage of the thermal outgoing radiation of the Earth in the 645-2760 cm -1 (3. 62 to 15. 5 μm) spectral range twice a day. The footprint is 12 km in diameter at nadir and the swath width is around 2200 km. Its spatial coverage makes the instrument suitable for monitoring a range of atmospheric species  [33] , in particular for detecting and tracking volcanic SO2 clouds  [34, 35]. IASI data have been interpolated on a 0. 25 × 0. 25° lat-long grid twice a day, corresponding to 09:30 and 21:30 LST. In this study we have used the latest available version of the IASI SO2 product which is an update of the dataset used in  Clarisse et al.
In this work we use extinction at 675 nm, center slit only, as in previous reported studies  [31, 40, 41]. The 675 nm relative accuracy and precision derived from comparisons with other satellite datasets are on the order of 20%  [39]. Data are provided from 10 to 40 km in altitude on a 1 km vertical grid. In our study, we use cloud filtered data as explained in  Taha et al. (2021)   [39]. The OMPS-LP algorithm identifies cloud-top height using the cloud detection method described in Chen et al. (2016)  [42]. Cloud type classifies the identified cloud as cloud, enhanced aerosol or Polar Stratospheric Cloud (PSC). The enhanced aerosol definition requires the cloud altitude to be at least 1. 5 km above the tropopause. To avoid removing aerosols from fresh volcanic or fire plumes, data have been filtered by removing the extinction coefficient at and below cloud-top height only if the reported cloud-top height is in the troposphere.
The Differential Absorption Lidar (DIAL) system designed for stratospheric ozone monitoring  [45]  has been used to retrieve aerosols profiles in the 15-38 km altitude range. This instrument has been implemented in Saint-Denis de La Réunion since 2000 and moved to the Maïdo observatory in early 2013. The technical details and evaluation of its performance are given by  Baray et al. (2013)   [45]. The current configuration of the DIAL system mainly detects signals in the UV regions of the spectrum (308, 332, 355, and 387 nm). More details can be found in  Bègue et al. , 2017 [24]. For this study, we use the same dataset as Bègue et al. , 2017  [24] , i. e. , daily records of backscattering signals obtained from the Maïdo facility between 1 November 2014 and 30 November 2016 (106 profiles). A lidar radio value of 60 sr is used which is quite common for volcanically quiescent conditions and periods of moderate eruptions  [46].
WACCM transports water vapour, chemical species, and temperature consistently via a flux-form semi-Lagrangian transport scheme  [59, 60]. The chemical module is based upon the three-dimensional chemical transport Model for Ozone and Related Chemical Tracers, Version 4  [61]. The WACCM atmospheric chemistry scheme includes a detailed sulphur cycle and key stratospheric nitrogen (NOy), and halogenated (i. e. , chlorine and bromine) and hydrogenated (in particular HOx radicals) compounds. The model includes emissions of Car-riation du contenu des aérosols aux latitudes t bonyl Sulfide (OCS) and sulphur dioxide (SO2), two primary sulphur emissions of importance for the UTLS region. OCS was prescribed using data from  Kettle et al. (2002)   [62]. For SO2, NH3, black carbon, organic carbon, NOx, CH4, and CO, the MACCity data set including both anthropogenic and biomass burning emissions was used  [63, 64].
Anthropogenic CH4 emissions were added from the EDGAR v4. 2 databases (available at https://edgar. jrc. ec. europa. eu; accessed on 1 April 2020); biogenic CO emissions were added from the Model of Emissions of Gases and Aerosols from Nature-Monitoring Atmospheric Composition and Climate (MEGAN-MACC) database  [65]. CH2O was prescribed according to the IPCC RCP8. 5 scenarios  [66] , and for H2 the ECCAD-GFED3 database was used  [67]. For CO2, N2O, CCl4, CF2ClBr, CF3Br, CH3Br, CH3CCl3, CH3Cl, CFC11, CFC113, CFC12, and HCFC22 emissions, lower boundary conditions were prescribed following CCMI/RCP8. 5 data. WACCM is associated with the Community Aerosol and Radiation Model for Atmospheres (CARMA) microphysical module which contains a sectional aerosol scheme  [68]. The module contains microphysical treatments of sulphuric acid aerosols which are relevant to address volcanic eruptions  [22]  and three types of Polar Stratospheric Clouds (PCS): Supercooled Ternary Solutions (STS), Nitric Acid Trihydrate (NAT) and ice.
Computed volcanic injections of SO2 in terms of altitude and sulphur burden (Table  1 ) are based on updates of the database provided by  Mills et al. (2016)   [83]  using results available in the literature which are largely based on satellite and in situ observations. Depending on the volcanic event, more or less information on the injection characteristics is already available. For example, for the Kelud eruption, the total amount of injected SO2 has been estimated to be 0. 1-0. 2 Tg  [84]  and the injection altitude is based on the space-borne and in situ observations by  Vernier et al. (2016)   [85]. Note that this SO2 burden differs from the one by  Mills et al. (2016)   [83]  who assumed an injection of 0. 3 Tg of SO2. Our injection characteristics differ from the modelling work of  Zhu et al.
(2020)   [86]  who used a nonuniform vertical distribution of SO2 and a more geographically spread injection using an adapted version of WACCM-CARMA. Furthermore,  Zhu et al. (2020)   [86]  have made several more complex simulation cases regarding the eruption properties (i. e. , ash and water injection, microphysical interactions involving ash, gaseous sulphuric acid and pure sulphate, SO2 uptake on ash) and subsequently impacting the SO2 lifetime and the aerosol production. The Calbuco injection parameters are based on the work of Bègue et al. (2017)  [24]  who have combined space-borne observations by IASI and by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. For the eruptions of the Ambae, Raikoke and Ulawun volcanoes, our database is completed following Kloss et al.
The Kelud and Ambae Tropical Eruptions Extinction data from satellite observations have been used for model and observational assessment of stratospheric aerosol impacts from moderate-magnitude volcanic eruptions in a number of studies  [87, 91] , including the WACCM-CARMA model  [22, 41]. Figure  2  presents a time series of daily and zonal means of extinction coefficient at 675 nm in the tropics (20° S to 20° N) from 2013 to 2019 based on OMPS measurements and WACCM-CARMA simulations. OMPS extinction anomalies are calculated with respect to the 2013 volcanically quiescent year whereas WACCM-CARMA anomalies are derived from the volcano-on minus the volcano-off simulation. Note that WACCM-CARMA anomalies result in very similar values when calculated with respect to the year 2013 as done for OMPS.
Note that such transport features are more apparent if the OMPS SAODs are derived for maximum altitudes closer to the tropopause (not shown). We note that the signal of the summer 2017 Canadian wildfires is visible in OMPS data in the northern hemisphere (Figure  3 ). Kloss et al. (2019)   [40]  have reported that a small part of the fire plume was transported towards tropical latitudes via the Asian monsoon anticyclone circulation but its signature is not visible in the zonally averaged extinctions presented in Figure  2. No smoke aerosols from the Canadian fires have been computed in our WACCM-CARMA simulation conversely to the work of Yu et al. (2019)  [93]. Integrating the SAOD data on latitudinal bands provides another insight into the differences between the observations and the simulations (Figure  4a ).
(2018)   [22]  for the midlatitude Sarychev eruption in 2009 and have been partly attributed to a bias in the sampling of the OSIRIS version 5. 07 datasets. Since then, sampling issues have been addressed with the release of OSIRIS version 7  [95]. As discussed by  Kloss et al. (2021)   [41] , differences between model and OMPS observations may be partly attributed to sampling issues since the model outputs are provided globally twice a day whereas OMPS reaches a global coverage every ~3 days.