dataesr/scientific-paragraphs-categorization huggingface dataset:
It cre- ates the seasonally dependent adjustment (less than 1 % in the upper stratosphere), but this correction does not add significant long-term trend. However, different optimized corrections are applied to the individual periods between instrument calibrations, which results in different amounts of increases in the retrieved ozone. To highlight changes in optimized Umkehr, the COH overpass ozone record is plotted as a reference (red line). The vertical dotted lines indicate the periods of satellite records (also see abbreviations at the top of the plot) that are combined in the COH ozone dataset. The difference between COH and operational Umkehr data is shown as a dark green line with a mean positive bias of ∼ 15 % (0 % and 5 %) in layer 8 (6 and 4, respectively) that varies seasonally and temporally.
The percent difference between optimized Umkehr and COH is shown as a light green line. The average bias is close to zero, while the seasonal changes are also reduced in layers 8 and 4. The main change in the optimized Umkehr ozone dataset is the increase/decrease in ozone amount vertically (three panels in Fig. 7 ). Also noticeable are changes in the relative shifts between calibration periods. For example, the change in the offset between COH and operational Umkehr biases (dark green line) is seen in the  2001-2006 and 2006-2011  periods. This step change offset is largely reduced in COH comparisons against the optimized Umkehr version (light green). In addition, we do not find any evidence of a step change in the optimized data in 1998 or 2004/2005 that could be related to step changes found in M2GMI  (Stauffer et al.
, 2019). Similarly, in optimized records of three other Umkehr stations (see Sect. S1), we do not find any impact from the M2GMI step changes. For example, at the MLO station, the instrumental artifacts in Dobson operations resulted in significant step change in Umkehr operational data but were completely eliminated by the optimization method (see Fig. S5a ). The importance of these changes for trend analyses is alluded to in the LOTUS 2019 report and will be further discussed in a future paper.
The optimization provides a tool for fine tuning the Umkehr retrievals, removal of the instrumental biases, and empirical evaluation of the impacts of stray light contribu- tions to the observations over different time periods. However, the optimization is not meant to reduce the bias between the reference model and Umkehr ozone profile. The models are used only as a guide to assure the continuity of optimized ozone after evaluating and removing step changes caused by Dobson instrumental artifacts, changes to the data collection protocols, and data processing. This careful approach aims at homogenizing Umkehr time series for trend analyses, reducing noise in the data, and supporting NOAA and WMO efforts in the detection of ozone recovery under the Montreal Protocol guidance. ozone datasets for analyses.
SS and KW performed the simulations and extracted model data for comparisons with ground-based stations. IP prepared the manuscript with contributions from all coauthors. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Quadrennial Ozone Symposium 2016 -Status and trends of atmospheric ozone (ACP/AMT inter-journal SI)". It is a result of the Quadrennial Ozone Symposium 2016, Edinburgh, United Kingdom, 4-9 September 2016.
Acknowledgements. Irina Petropavlovskikh  and  Krzysztof Wargan  would like to thank  Luke Oman  for developing M2GMI and for very fruitful discussions. This study was supported by  NOAA Climate Program Office's Atmospheric Chemistry ,  Carbon Cycle , and  Climate program , grant no. NA19OAR4310169 (CU)/NA19OAR4310171  (UMD). Irina Petropavlovskikh  and  Jeannette Wild  were also supported by the  NASA SAGE III/ISS  award no. NA19OAR4310169. Krzysztof Wargan  is supported by  NASA 's Global Modeling and Assimilation Office core funding. Financial support. This research has been supported by the  Climate Program Office  (grant no. NA19OAR4310169 ). Review statement. This paper was edited by  Mark Weber  and reviewed by two anonymous referees.
Data availability. The datasets used in this study can be downloaded from https://gml. noaa. gov/aftp/data/ozwv/Dobson/AC4/ Umkehr/Monthly/ (last access: 21 May 2021; Petropavlovskikh and Miyagawa, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-15-1849-2022-supplement. Author contributions. IP and KM analyzed the data and wrote the manuscript. KM, AMB, BJ, SS, KW, RQ, GA, and SGB prepared the ground-based ozone data; JW, LF, and EB prepared satellite.
1b  and Supplementary Fig. 4 ). As expected under balancing selection, the het genes show intermediate allele frequencies over the sampling time period. This pattern includes the het-c gene (Fig. 1b ), which is the interacting partner of het-d and het-e  38  , providing further support for the hypothesis that HNWD genes are also under balancing selection  31. Two reproductively isolated groups exist within P. anserina. Previously, van der Gaag 39 performed pairwise crossings of around  half the Wageningen collection (plus the French reference strain S), recording various degrees of sexual and vegetative incompatibility. To gain insight into the population structure, we re-coded these observations into a distance matrix of mating success for 45 strains, for which we also have Illumina data (Supplementary Table  3 ).
The het-r and het-v loci induce reproductive isolation in the wild. Once the molecular basis of vegetative and sexual incompatibility was identified, we set out to further characterize the Wageningen population with regard to the two RI groups. Although the effects of the het-r/v interaction on sexual incompatibility are significant, they are not absolute. Hybrids can be produced in the laboratory (Fig. 3e , left) and the RV reaction is thermosensitive, losing effect at 32 °C (ref. 58 ). To evaluate if the RI group effects we observed in the laboratory crosses hold in wild populations, we analysed the population genomic dataset to determine their het-r/v alleles. We reason that if, despite sexual incompatibilities, the two RI groups are mixing in nature, we should encounter the three possible viable genotypes between het-v and het-r (that is, rV, RV1 and the recombinant rV1; Fig.
In the case of het-v, we found that het-Va and het-Vb are present in at least some species but there is also evidence of introgression of het-v between P. anserina and P. pauciseta (Supplementary Fig. 19 ). These observations suggest a dynamic evolution of the het-v locus throughout the divergence of this species complex but without population sampling from the other Podospora species we cannot draw concrete conclusions. Looking at other genera of the order Sordariales 66 , we also found that het-Va and het-Vb show conserved synteny in Sordaria macrospora and a number of Neurospora species (Supplementary Table  6 ). As large genomic datasets of the Neurospora species are available, we investigated the distribution of the het-v alleles across the genus. We found that only Neurospora perkinsii, N.
Methods We used the packages ggplot2 v. 3. 0. 0 (ref. 71 ), cowplot v. 1. 0. 0 (ref. 72 ), gridExtra v. 2. 3 (ref. 73 ), hexbin v. 1. 27. 3 (ref. 74 ), rstatix v. 0. 7. 0 (ref. 75 ) and ggpubr v. 0. 4. 0 (ref. 76 ) for data visualization and analysis. Animal silhouette figures were taken from http://www. printableparadise. com/printable-animal-silhouettes. html. Most bioinformatics analyses were performed using the workflow manager Snakemake v. 5. 4. 4 (ref. 77 ).
Fungal material. Including eight previously sequenced strains  29  , our genomic dataset consisted of 106 P. anserina strains sampled in Wageningen, the Netherlands, and kept at the Laboratory of Genetics of the Wageningen University and Research  32, [78] [79] [80]. These strains are identified with 'Wa' followed by a unique strain ID number (Supplementary Table  1 ). Most of the strains were obtained between 1991 and 2010 by isolating a single spore from a fruiting body (perithecium) in herbivore dung. Each spore was grown and selfed one or two times to obtain sexual spores that were stored at -80 °C. We further expanded the collection by sampling during 2016 and 2017 (see below) but the strains from 2017 were not sequenced. In addition, we included whole genome sequences of the P. anserina French strains S, Y and Z sampled in 1937  81, 82  , the strains CBS433.
2. 19. 0 (https://broadinstitute. github. io/picard/). We used HaplotypeCaller of the Genome Analysis Toolkit (GATK) v. 4. 1. 1 (ref. 88 ) for indel realignment and variant discovery across all (haploid) samples simultaneously, following the GATK Best Practices recommendations  89. For subsequent analyses we used only the SNP data (that is, the indels were excluded). Data associated with the mitochondrial scaffold were discarded. VCF file manipulation was done with VCFtools v. 0. 1. 16 (ref. 90 ) and BCFtools v. 1. 9 (ref. 91 ). Visual inspection of the mapped reads revealed low levels (<10% of the total depth of coverage, typically 1-3%) of index-hopping in some samples  92. Because the sequenced strains are haploid and have a high read coverage (>80×), the low level of contaminating reads did not affect base calling.
org/10. 5281/zenodo. 6323682. This analysis included all samples sequenced, as at least the French strain S also has mating data available to compare it with. However, using only the Wageningen samples leads to similar results. The two samples with intermediate values of PC1 (next to the line at PC1 = 0) in Fig. 2a  (Wa60+ and Wa61-), have a V1 allele (that is, they belong to RI group RV1) but are recombinant with the RI group rV for the linked sites surrounding the het-v locus. Removing these two samples had no effect on downstream analyses.
Population genetic analyses. To investigate the genetic diversity of P. anserina, we used the R package PopGenome v. 2. 6. 1 (ref. 94 ) to estimate the following population genetic parameters: the average pairwise nucleotide diversity within a population (π) and between two populations (D xy ) (ref. 95 ), Watterson's theta (θ W ), the Tajima's D statistic  96  and the fixation index F st (refs. 97, 98 ). All analyses were done on windows of 10 kb with steps of 1 kb. Of note, standard VCF files only contain sites that are variable but do not specify sites with missing data that NAtuRE ECoLoGy & EVoLutIoN | www. nature. com/natecolevol.
5281/ zenodo. 6323682. We found that the overall diversity in all sampled strains (Supplementary Table  1 ) is very low (π = 0. 000497). Still, we restricted the analyses to only the Wageningen strains (π = 0. 000492) to ensure the patterns correspond to a single population. LD was calculated using the squared allele frequency correlations (r 2 ) (refs. 101, 102 ). We calculated chromosomal LD decay by combining the methodology of ref. 103  and ref. 104. Specifically, for each chromosome we randomly sampled 30 × 50 kb long windows and extracted all SNPs present in the high-quality SNP with singletons removed. We used VCFtools to calculate r 2 for pairs of loci within each window (-hap-r2). We evaluated the decay of LD with distance using a nonlinear regression  105  following equation 1 of ref.
02 (that is, the variant is present in at least two individuals). The SNPs were processed using the v. 1. 10. 0 of the vcfR package  99. As expected given random chromosomal segregation, het-v in chromosome 5 has very low linkage levels (r 2 < 0. 01) with genotyped het genes in different chromosomes (het-z and het-q). Still, we found a very strong association with het-r in chromosome 2 (r 2 = 0. 926) as hypothesized due to their interaction but also a moderate association with het-s in chromosome 3 (r 2 = 0. 246). The latter value, however, falls within the distribution of r 2 estimates between het-v and SNPs along the genome in general (Supplementary Fig. 14b ). Cloning and characterization of het-v. Deletion strains were obtained by constructing deletion cassettes in which the Escherichia coli hygromycin B phosphotransferase (hph) or the Streptomyces noursei nourseothricin resistance (nat1) genes were inserted between ~500-700 bp PCR fragments corresponding to the flanking sequences of the region of interest (position of the flanking regions is given Supplementary Fig.
Hence, we generated two point mutants, Y233A and Y233F, which confirmed that the methyltransferase activity of this gene is required for V1/V incompatibility but dispensable for R/V incompatibility (main text). In addition, the two point mutants display a slightly different phenotype: Y233F leads to loss of the barrage reaction to the V tester (and in that sense resembles the wild-type allele), whereas Y233A does not, consistent with the fact that the Y to F substitution is more conservative than Y to A. Homologues of het-Va (displaying the same domain architecture with a SET/Rubisco/MYND domains) were found only in filamentous fungi in a limited number of species (125 blast hits with e-value <10 -50 in the JGI fungal database with 2027 genome entries on 5 May 2021). Gene het-Vb encodes a 771 amino acid long protein with a N-terminal domain (residue 1 to ~140) showing remote similarity with the pore-forming domain of the mammalian MLKL pseudo-kinase involved in execution of necroptotic cell death  115.
The fungal HeLo and HeLo-like domains involved in the induction of cell death by membrane permeation and in [Het-s]-mediated incompatibility also show homology to MLKL  50. The N-terminal region (~535-675) of the het-Vb encoded protein shows homology to TUDOR domains. TUDOR domains are beta-sheet rich globular domains often repeated in tandem and involved in methyl binding  49. These low-level homologies were detected using the HHpred server. Gene het-Vb homologues were found only in filamentous fungi in a limited number of species (63 blast hits with e-value <10 -50 in the JGI fungal database with 2027 genome entries on 5 May 2021). In a number of species, homologues of both het-Va and het-Vb were found. Genotyping of the het genes. To genotype their allorecognition genes, all samples with Illumina data were assembled de novo using SPAdes v.
59. We used the Geneious 10. 1. 3 software (https://www. geneious. com) to align the resulting sequences to the long-read data references to confirm allelic identity. The WD40 repeat structure of the strain Wa63 is considered to be representative of het-R; all other allelic variants are considered to be het-r  36. The Sanger sequencing results were consistent with the PCR method. We reasoned that if the RI groups do not mix in nature, we would encounter a strong deficit of recombinant individuals (rV1). This is a conservative approach to test for reproductive isolation because: (1) errors in the genotyping method above are more likely to favour an r allele since there are many more ways to produce the mutant version (r) rather than finding exactly 11 WD40 repeats in a specific order (R); and (2) spontaneous vegetative mutations are known to occur, probably breaking R into an r allele  36, 120.
5] [126]. The custom script query2haplotype v. 1 (available at https://github. com/SLAment/Genomics/blob/master/BLAST/query2haplotype. py) was used with parameters -s 100 -e 1 and -f 100. An absence of hits was taken as evidence that that gene is not present in that genome and those with partial hits were considered fragments. The annotation for the gene models of N. crassa and N. tetrasperma (FungiDB, https://fungidb. org/fungidb/) place unsupported introns into the sequences to avoid premature stop codons. We assume that the intron structure matches that of P. anserina as this holds true for the supported introns and for the distantly related sequences from Magnaporthe oryzae. Given this rational, sequences with premature stop codons were assumed to represent putative pseudogenes. Genotyping of the Spok genes.
Acknowledgements We would like to thank  M. Grudzinska-Sterno  for valuable assistance with laboratory work, as well as  R. Debuchy  and  M. Sicault-Sabourin  for assistance in the positional cloning. Next Generation sequencing was possible thanks to the support of the  National Genomics Infrastructure/Uppsala Genome Center  on massive parallel DNA sequencing. The computations were performed on resources provided by  Swedish National Infrastructure for Computing (SNIC)  through  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  under the projects  SNIC 2017/1-567  and  SNIC 2019/8-371. We would also like to thank  S. Tusso  for useful advice and code peer review. Funding was provided by the  European Research Council (ERC)  with the grant no. ERC-2014-CoG , project  648143  SpoKiGen (H.
Introduction The oceans mitigate climate change by absorbing both atmospheric CO 2 corresponding to a significant fraction of anthropogenic CO 2 emissions  (Friedlingstein et al. , 2019; Gruber et al. , 2019)  and most of the excess heat in the Earth system caused by the enhanced greenhouse effect  (Cheng et al. , 2017 (Cheng et al. , , 2020)). The objective of GLODAP (Global Ocean Data Analysis Project; http://www. glodap. info, last access: 27 June 2022) is to provide high-quality and bias-corrected water column bottle data from the ocean surface to the sea floor. These data should be used to document the state and the evolving changes in physical and chemical ocean properties, e. g. , the inventory of anthropogenic CO 2 in the ocean, natural oceanic carbon, ocean acidification, ventilation rates, oxygen levels, and vertical nutrient transports  (Tanhua et al.
, 2021). The core quality-controlled and bias-adjusted variables of GLODAP are salinity, dissolved oxygen, inorganic macronutrients (nitrate, silicate, and phosphate), seawater CO 2 chemistry variables (dissolved inorganic carbon -TCO 2 , total alkalinity -TAlk, and pH on the total hydrogen ion, or H + , scale), the halogenated transient tracers chlorofluorocarbon-11 (CFC-11), CFC-12, CFC-113, carbon tetrachloride (CCl 4 ), and sulfur hexafluoride (SF 6 ). Other chemical tracers are measured on many cruises included in GLODAP, such as dissolved organic carbon and nitrogen, as well as stable and radioactive isotope ratios. In many cases, a subset of these data is distributed as part of the GLODAP data product; however, such data have not been extensively quality controlled or checked for measurement biases in this effort.
In addition, the manual data retrieval is time consuming and prone to data handling errors  (Tanhua et al. , 2021). For biogeochemical data there is the added complexity of different levels of standardization and calibration and even different units and scales used for the same variable such that the comparability between datasets is often poor. Standard operating procedures have been developed for some variables  (Dickson et al. , 2007; Hood et al. , 2010; Becker et al. , 2020) , and certified reference materials (CRMs) exist for seawater TCO 2 and TAlk measurements  (Dickson et al. , 2003)  and reference materials for nutrients in seawater (RMNS, certified based on International Organization for Standardization Guide 34;  Aoyama et al. , 2012; Ota et al. , 2010). Despite all this, biases in data still exist.
ncei. noaa. gov/access/ocean-carbon-acidification-data-system/oceans/ GLODAPv2_2022/cruise_table_v2022. html, last access: 15 August 2022). This most recent GLODAPv2. 2022 data product builds on earlier synthesis efforts for biogeochemical data obtained from research cruises, namely, GLODAPv1. 1  (Key et al. , 2004; Sabine et al. , 2005) , Carbon dioxide in the Atlantic Ocean (CARINA)  (Key et al. , 2010) , Pacific Ocean Interior Carbon (PACIFICA)  (Suzuki et al. , 2013) , and notably GLO-DAPv2  (Olsen et al. , 2016). GLODAPv1. 1 combined data from 115 cruises with biogeochemical measurements from the global ocean. The vast majority of these were the sections covered during the World Ocean Circulation Experiment and the Joint Global Ocean Flux Study (WOCE/JGOFS) in the 1990s, but data from important "historical" cruises were also included, such as from the Geochemical Ocean Sections Study (GEOSECS), Transient Traces in the Ocean (TTO), and South Atlantic Ventilation Experiment (SAVE).
GLO-Table  1. Variables in the GLODAPv2. 2022 comma separated (csv) product files, their units, short and flag names, and corresponding names in the individual cruise exchange files. In the MATLAB product files that are also supplied a "G2" has been added to every variable name (e. g. , G2cruise  DAPv2, which forms the basis for the update presented here, was released in 2016 with data from 724 scientific cruises, including those from GLODAPv1. 1, CARINA, and PACI-FICA, as well as data from 168 additional cruises. GLO-DAPv2 not only combined all previous efforts, but it also created ocean-wide consistency across all cruise data through an inversion analysis. A particularly important source of additional data was the cruises executed within the framework of the "repeat hydrography" program  (Talley et al.
Full updates involve a reanalysis, notably crossover and inversion, of the entire dataset (both historical and new cruises) in which all data points are subject to potential adjustment. This was carried out for the creation of GLO-DAPv2. For intermediate updates, recently available data are added following quality control procedures to ensure their consistency with the cruises included in the latest GLODAP release. Except for obvious outliers and similar types of errors (Sect. 3. 3. 1), the data from previous releases are not changed or adjusted during intermediate updates. Note that the GLODAP mapped climatologies  (Lauvset et al. , 2016)  are not updated for these intermediate products. A naming convention has been introduced to distinguish intermediate from full product updates. For the latter the version number will change, while for the former the year of release is appended.
The exact version number and release year (if appended) of the product used should always be reported in studies rather than making a generic reference to GLODAP. Creating and interpreting inversions, as well as other checks of the entire dataset needed for full updates, are too demanding in terms of time and resources to be performed every year or every 2 years. The aim is to conduct a full analysis (i. e. , including an inversion) again after the third GO-SHIP survey has been completed. This completion is currently scheduled for 2024, and we anticipate that GLO-DAPv3 will become available a few years thereafter (pending funding). In the interim, the fourth intermediate update is presented here, which adds data from 96 cruises to the last update, GLODAPv2. 2021 (Lauvset et al. , 2021).
Key features of the update GLODAPv2. 2022 contains data from 1085 cruises covering the global ocean from 1972 to 2021, compared to 989 for the period 1972-2020 for the previous GLODAPv2. 2021 (Lauvset et al. , 2021). Information about the 96 cruises added to this version is provided in Table  A1  in the Appendix. Cruise sampling locations are shown alongside those of GLODAPv2. 2021 in Fig. 1 , while the coverage in time is shown in Fig. 2. Not all cruises have data for all the abovementioned 13 core variables. For example, cruises with only seawater CO 2 chemistry or transient tracer data are still included even without accompanying nutrient data due to their value towards the computation of carbon inventories. In a few cases, cruises without any of these properties are included because they do contain data for other carbon-related tracers such as carbon isotopes, with the intention of ensuring their wider availability.