dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We acknowledge the support of the  Belgian Antarctic program  lead by the  Belgian Federal Science Policy (BELSPO)  allowing the installation and maintenance of the seismic station (project  GIANT-LISSA , grants  EA/33/2A  and  EA/33/2B ). We are thankful to the  BELSPO  project  HYDRANT  (grants  EA/01/04A  and  EA/01/04B ,  PI Nicole van Lipzig ) that supported installation and maintenance of the automatic weather station at PEA together with technical support by the  Institute for Marine and Atmospheric Research (IMAU) ,  Utrecht University. We thank  Carleen Reijmer  ( IMAU ) for providing AWS6 and AWS7 data at Svea base. Niels Souverijns  is acknowledged for providing access to the webcam data now supported under  BELSPO  project  AEROCLOUD. We are grateful to the  International Polar Foundation  for the logistic and field support in such a difficult environment.
We are also grateful for constant development of the following open-source software packages used in this study: Python (python. org),Matplotlib (Hunter, 2007), Obspy (Beyreuther and others, 2010) and QGIS (qgis. org). We also benefited from the Quantarctica database distributed by the  Norwegian Polar Institute  (quantarctica. org). IG thanks  FCT/MCTES  for the financial support to CESAM ( UID/AMB/50017/2019 ), through national funds. We thank  Trevor Williams  for proofreading the manuscript while at sea. The manuscript profited from constructive reviews from  Evgeny Podolskiy  and an anonymous reviewer. This is IPGP contribution 4044.
Towards identification of varying maternal factors in cavefish To obtain an exhaustive molecular view of maternal transcriptomic differences between surface and cavefish, RNA-sequencing was performed on Astyanax embryos at 2-cell stage (surface fish, n=2 samples; cavefish, n=3 samples; and reciprocal F1 hybrids, n=3 samples each). The dataset (between 75 and 100 million paired reads per sample) was analyzed through the European Galaxy Server and reads were aligned to the Surface Fish Astyanax genome (NCBI, GCA_000372685. 2 Astyanax_mexicanus-2. 0). The sample-tosample distance analysis grouped the four types of samples in two clear categories, strictly depending on their maternal contribution (Figure  8A ). Similarly, Principal Component Analyses (PCA) analyses clustered the samples from hybrid embryos together with those coming from their maternal morphotype (Figure  8  -figure  supplement A ).
Hence the most strongly dysregulated genes are not the ones involved in metabolism but those involved in signaling and cell interactions. Together, these results show that the RNA composition of the cavefish and surface fish eggs shows a strong maternal signature, and thus oocyte content could contribute to the developmental evolution of cavefish phenotype. Finally, we picked two candidate genes from the transcriptomics dataset, that were directly relevant to our findings in the previous section: Oep (one-eyed pinhead, also named tdgf1), a Nodal co-receptor necessary for dkk1b induction and shield formation and whose maternal and zygotic mutant  (MZoep)  shows defects in margin internalization and fate specification in zebrafish (Carmany-Rampey  & Schier, 2001; Zhang et al. , 1998) ; as well as the maternal ventralizing transcription factor Vsx1 (Visual System homeobox 1) which regulates flh and ntl expression and is involved in axial versus paraxial mesoderm specification and migration  (He et al.
Ma et al. (2018)  have also recently described increased pou2f1b, runx2b, and axin1 mRNA levels in unfertilized cavefish eggs as compared to surface fish eggs. These genes also show differential expression in our transcriptomic dataset. Classification of DEGs based on their biological role showed an enrichment on certain biological processes that may have been key for cavefish evolution. Relevant to this work we found that 6. 5% of the "top DEGs with fold-change>5" are involved in signaling (Figure  8C ). Some of these genes are regulators of the Wnt pathway (i. e. sFRP2, dkk2 and wnt11) that is important for the establishment of the embryonic organizer. Members of other signaling pathways are also greatly modified (i. e. FGF, BMP, Nodal). Our interpretation is that the origin of the induction of organizers with different properties in the two morphs might stem in an upstream maternally-regulated event, with a domino effect leading to morphological and functionally diverse brains.
Libraries were generated from purified total RNA using polyA selection (llumina TruSeq Stranded Protocol). Samples were sequenced for between 75 and 100 million reads (paired-end, 51-35bp) using the NextSeq 500/550 High Output Kit v2 (75 cycles). Following sequencing, raw data were retrieved (fastq-formatted files) and used for subsequent sequence alignment and expression analyses. Raw sequencing data are available through the NCBI Sequence Reads Archive (SRA) under BioProject accession PRJNA545230. RNA-sequencing reads from each of the four conditions (surface fish, cavefish and reciprocal F1 hybrids) were trimmed using Cutadapt 1. 15 and quality control was assessed using FastQC (v0. 11. 5). All downstream analyses were done using the European Galaxy Server (https://usegalaxy. eu, (Afgan et al.
6,  (Love et al. , 2014) ). Based on the PCA and sample to sample distance analyses, data from F1 hybrid were combined with their respective mother morphotype for the DEG analysis. Only genes with a FDR<0. 01 (false discovery rate, p value adjusted for multiple testing with the Benjamini-Hochberg procedure) and absolute fold change higher than 1. 5 (log2(FC)>0. 58) were kept as significantly over-or under-expressed in cavefish compared to surface fish. Mapped reads were visualized using the genome browser IGV (http://www. broadinstitute. org/igv/) (Robinson et al. , 2011). A Gene Ontology Annotation file for the Astyanax transcriptome (from NCBI database) was generated using OmicsBox (formerly Blast2GO, https://www. biobam. com/) following the general workflow presented by the software: BLAST with CloudBlast (restricted to the teleosteii database, keeping the top 20 results with an e-value of 10^(-5)), followed by mapping (GO version April 2019), annotation and InterProScan analysis in parallel.
Acknowledgements We thank  Stéphane Père ,  Victor Simon  and  Krystel Saroul  for care of our Astyanax colony, and  François Agnès  and all other members of the DECA team for fruitful discussions and important suggestions to our study. This work has benefited from the facilities and expertise of the QPCR and sequencing platforms of I2BC, Gif sur Yvette. We thank  Yan Jaszczyszyn  from the I2BC sequencing platform for fruitful interactions. Grant support: FRM (Equipe FRM DEQ20150331745 RETAUX),  CNRS  and  Becas Chile.
Acknowledgements. This work is a contribution to the  MIS-TRALS/HyMeX  programme through the  ASICS-MED  ( ANR-12-BS06-0003 ) project funded by the  French National Agency for Research (ANR). Data were obtained from the  HyMeX  programme, sponsored by grants from  MISTRALS/HyMeX and Météo-France. The authors acknowledge the international Argo programme, the  LEFE/GMMC  programme and the  French NAOS  project for supporting the deployment of profilers. Argo and CTD data were collected and made freely available by the  CORIOLIS  project (http://www. coriolis. eu. org, last access: 8 February 2019) and programmes that contribute to it. We acknowledge the crews of  R/V Suroit  and  Tethys II  and the scientists involved in the different cruises mentioned in this paper. Numerical simulations were performed using HPC resources of CALMIP ( CALcul en MIdi-Pyrénées , projects  1247 ,  09115  and  1325 ) and  GENCI  ( Grand Equipement National de Calcul Intensif , project  010569 ).
Acknowledgements. This study was funded by the  Agence Française pour la Biodiversité  (research program supervised by J. M. P. ) and the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche  (Ph. D. grant to N. B. ). This work was carried out under the licenses  08/2015  and  07/2016  delivered by the  Préfecture de la Loire-Atlantique. We thank the  Parc Natural Régional de Brière , notably  J. P. Damien , for supporting our research activities on amphibian ecology. The authors declare that they have no conflict of interest. We are also grateful to two anonymous reviewers for their constructive comments on an earlier version of the manuscript. A. Buchwalter  edited the final version of the article.
The analysis revealed the existence of two genetic groups exclusively located in France in our sample (French 1 and French 2 hereafter) of 76 and 49 genotypes, respectively. Among the 20 genotypes of the third group, seven were defined as North Swedish in the 1001 genomes dataset  45. Consistently, the 20 "Swedish" genotypes, although not all in Sweden (Fig. 1a ), were mainly located in cold environments and woodland-shrubland in Whittaker's classification (Fig. 1b ). We considered the 76 genotypes from group 4 as "Central European" (Fig. 1a ), typically living at intermediate temperatures and rainfall (Fig. 1b ). 83 genotypes composed the group 5, all located in Western Europe (Fig. 1a ), in a range of relatively warm environments with intermediate rainfall. Leaf economics of A. thaliana.
Assimilation rate (A mass ) was the most variable trait among the leaf economics traits in our dataset (15-fold; from 40. 9 to 608. 9 µmol g -1 s -1 ) while leaf mass per area (LMA) and leaf lifespan (LLS) varied 5 and 3. 5 fold (from 18. 7 to 101 g m -2 , and from 15 to 53. 5 days), respectively. In regard to interspecific meta-analyses, variation in A mass was wide (from 5 to 660 µmol g -1 s -1 3 ) and was relatively weak for LMA (from 14 to 1,500 g m -2 3 ) and for LLS (from 0. 9 to 288 months 3 ). Pairwise regression revealed strong correlations between traits, independently of the genetic structure of the sample (r² Amass-LLS = 0. 32, r² Amass-LMA = 0. 73, r² LMA-LLS = 0. 38, all p-value < 0. 01). The 3-dimension space shaped by traits covariations in A. thaliana was comparable to the interspecific spectrum previously observed 3 : genotypes are ranked from low A mass and high LMA and LLS, toward high A mass and low LMA and LLS (Fig.
87, P < 0. 001; Fig. S1h ). Furthermore, life history and performance at the plant level co-varied in parallel with this leaf level phenotypic variation. Relative growth rate (RGR) and age at maturity (AM) were negatively correlated (Fig. 3 ) in our dataset, consistently with the slow-fast continuum pattern: fast growing genotypes reproduced earlier than slow growing ones. As for the LES, we performed a PCA with slow-fast traits and we assigned a position along the continuum for each genotype according to its score on PC1. The positions of the genotypes along the leaf economics spectrum and the slow-fast continuum were tightly correlated (Fig. 4a ) and the slope did not differ significantly when taking the kinship matrix of the genotypes as a covariate. In other words, slow growing and late reproducing genotypes have low leaf carbon assimilation rate and long leaf lifespan, whereas fast growing and early reproducing genotypes have high leaf assimilation rate and short leaf lifespan.
This suggests that slow-fast traits behaved like outlier variants that diverged among lineages due to the effect of diversifying selection. Our data do not support the same hypothesis for LES traits, despite a tight coordination with slow-fast traits. Climatic drivers of A. thaliana phenotypes. We investigated whether annual rainfall and temperature, two key climatic variables in plant functional biogeography, explain the position of the genotypes along the slow-fast continuum and leaf economics spectrum axes. We extracted 19 climatic variables at the collecting sites of the genotypes on CHELSA website (www. chelsa-climate. org/) related to temperature and precipitation, their temporal variability and extremes. Consistently with the population structure analysis, only the slow-fast continuum axis was significantly correlated with MAR (r = -0.
Relative growth rate (RGR, mm² mm -2 d -1 ) and leaf lifespan (LLS, d) were estimated from automated daily pictures of the rosettes. More precisely, a sigmoid curve was fitted to rosette area as a function of time in order to extract growth parameters, where RGR was calculated as the slope at the inflection point  [80] [81] [82]. Using daily pictures, we tracked three consecutive leaves from birth (emergence) to death (full senescence). For each plant, leaf duration was calculated as the average number of days from leaf emergence to senescence. F st and p st estimates. In order to perform population genetic analyses, genetic groups were identified by genetic clustering of 378 genotypes, using the 250 K SNPs data available from Horton et al. 44. Clustering was performed with ADMIXTURE  83  after linkage disequilibrium pruning (r 2 < 0.
To determine the neutral F ST value, we calculated the median of the significant F ST values of the intergenic SNPs. More specifically, we filtered the SNPs dataset keeping the intergenic SNPs only, assuming that they are rather prone to endure neutral differentiation processes. We then calculated a F ST value for each intergenic SNP with the attribution of population described above. To test for the significance of the F ST values, we randomized 1000 times the population attribution to the genotypes in order to provide a null distribution of F ST. F ST values higher than the 95 th quantile of their null distribution were stated as significant. The phenotypic population structure is ideally evaluated using Q ST , an analogue of F ST measure  85, 86. We estimated Q ST using a phenotype-based surrogate (P ST )  86  which depends on among-population and overall heritability of the trait (c and h², respectively) computed as follow: σ σ σ = + P c c h ST B B W 2 2 2 2 where B and W are the between-and within-population part of variance, respectively.
Note that within-population variance is not multiplied by 2 since A. thaliana is mainly autogamous and homozygote at all loci. Under the null assumption (H0: c = h² = 1), P ST and Q ST are analogous. Sensitivity analysis consists in calculating the P ST value and its 95% confidence interval for a gradient of hypothetical c and a given h². P ST is a robust estimator of Q ST when the critical value of c/h² is low, i. e. the minimum value of c/h² for which the lower limit of P ST 95% CI is higher than the neutral F ST. Then, a value of Q ST higher than neutral F ST means that the phenotypic differentiation between populations is larger than expected by demographic events alone, in particular genetic drift, and is thus indicative of diversifying selection on traits  41, 87. We used parametric bootstrap method to generate 95% CI around P ST values with the package MCMCglmm in R (10,000 iterations).
statistical analysis. Climate variables at the sampling sites of each genotype were extracted from the CHELSA database (http://www. chelsa-climate. org/), with a 2. 5 arc-minutes resolution. The effect of climatic variables on traits was tested using linear model regressions. All analyses were performed in R 3. 4. 1 (R Core Team, 2017). Whittaker's biomes were plotted using the BIOMEplot function provided by G. Kunstler (https:// rdrr. io/github/kunstler/BIOMEplot/src/R/biomes-plot. R). All leaf and plant traits, but RGR, were log 10 transformed when Gaussian distribution is required for statistical analyses. Principal component analysis (PCA) was performed using the package FactoMineR. The package nlme was used to perform linear models and phylogenetic generalized least squares regressions.
We performed phylogenetic regressions including a relatedness matrix as covariance matrix, obtained after running the PLINK-make-rel command across the 250 K SNPs from the RegMap data. SMA regressions between leaf and plant traits were performed using the package SMATR  88  , and phylogenetic SMA regressions using the Phyl. RMA function of the Phytools package. The phylogenetic tree required for SMA regression has been produced with Tassel using the RegMap SNPs data  44.
Acknowledgements We thank  Myriam Dauzat ,  Alexis Bédiée  and  Gaëlle Rolland  for their technical assistance during trait measurements and for the environmental control in PHENOPSIS. We thank  Fabrice Roux  and his group ( Laboratory of Plant-Microbe Interactions, Toulouse, France ) for the seeds of the natural accessions used in our study. This work was supported by  INRA   Dpt EA grant , the  European Research Council (ERC)  (' CONSTRAINTS ': grant  ERC-StG-2014-639706-CONSTRAINTS ), and the  French Agency for Research (ANR  grant  ANR-17-CE02-0018-01 , '  AraBreed '). This publication has been written with the support of the  Agreenskills fellowship program  which has received funding from the  EU 's  Seventh Framework Program  under the agreement  N° FP7-609398  (Agreenskills contract  3215  '  AraBreed ').
F. Li et al. :  Historical global multi-model estimates of fire emissions Abstract. Fire emissions are a critical component of carbon and nutrient cycles and strongly affect climate and air quality. Dynamic global vegetation models (DGVMs) with interactive fire modeling provide important estimates for longterm and large-scale changes in fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-theart global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most upto-date fire emission factor table based on field and laboratory studies in various land cover types.
, 2011; Kaiser et al. , 2012; Krol et al. , 2013; Konovalov et al. , 2014; Ichoku and Ellison, 2014; Darmenov and da Silva, 2015; van der Werf et al. , 2017; Heymann et al. , 2017). Satellite-based fire emission estimates are available globally but cover only the present-day period, i. e. , since 1997 for the Global Fire Emissions Dataset (GFED) and shorter periods for others. Historical change in fire emissions has been inferred from a variety of proxies, such as ice-core records of CH 4 (isotope δ 13 CH 4 from a pyrogenic or biomass burning source), black carbon, levoglucosan, vanillic acid, ammonium, and CO  (Ferretti et al. , 2005; McCornnell et al. , 2007; Conedera et al. , 2009; Wang et al. , 2012; Zennaro et al. , 2014) , site-level sedimentary charcoal records  (Marlon et al. , 2008 , 2016 ), visibility records (van Marle et al.
, 2010; Lamarque et al. , 2010; Marlon et al. , 2016; van Marle et al. , 2017b; and references therein). This limits their utility for quantifying the uncertainties in global and regional reconstructions of fire emissions and the corresponding impacts on estimated historical changes in carbon cycle, climate, and air pollution. A small number of studies also investigated the drivers of fire carbon emission trends  (Kloster et al. , 2010; Yang et al. , 2014; Li et al. , 2018; Ward et al. , 2018). However, these studies could not identify the uncertainty source in recent model-based reconstructions or help understand the inter-model discrepancy in projections of future fire emissions because only a single DGVM was used in each. This study provides a new dataset of global gridded fire emissions, including carbon and 33 species of trace gases and aerosols, over the 1700-2012 time period, based on nine DGVMs with different state-of-the-art global fire models that participated in FireMIP.
The dataset provides a basis for developing multi-source (e. g. , satellite-based products, model simulations, and/or fire proxy records) merged fire emission reconstructions and methods. It also, for the first time, allows end users to select all or a subset of model-based reconstructions that best suit their regional or global research needs. Importantly, it enables the quantification of the uncertainty range of past fire emissions and their impacts. In addition, the model-based estimates of fire emissions are comprehensively evaluated through comparison with satellite-based products, including amounts, spatial distribution, seasonality, and interannual variability, thus providing information on the limitations of recent model-based reconstructions. We also analyze the simulated long-term changes and the drivers for each DGVM and inter-model differences.
Models in FireMIP Nine DGVMs with different fire modules participated in FireMIP: CLM4. 5 with the CLM5 fire module, CTEM, JSBACH-SPITFIRE, JULES-INFERNO, LPJ-GUESS-GlobFIRM, LPJ-GUESS-SIMFIRE-BLAZE, LPJ-GUESS-SPITFIRE, MC2, and ORCHIDEE-SPITFIRE (Table  1 ; see  Rabin et al. , 2017 , for a detailed description of each model). JSBACH, ORCHIDEE, and LPJ-GUESS used the variants of SPITFIRE  (Thonicke et al. , 2010)  with updated representation of human ignition and suppression, fuel moisture, combustion completeness, and the relationship between spread rate and wind speed for JSBACH  (Lasslop et al. , 2014) , combustion completeness for ORCHIDEE  (Yue et al. , 2014 (Yue et al. , , 2015)) , and human ignition, post-fire mortality factors, and modifications for matching tree age/size structure for LPJ-GUESS  (Lehsten et al.
, 2009; Rabin et al. , 2017). The global fire models in the nine DGVMs have diverse levels of complexity  (Rabin et al. , 2017). SIMFIRE is a statistical model based on present-day satellite-based fire products  (Knorr et al. , 2016). In CLM4. 5, crop, peat, and tropical deforestation fires are empirically/statistically modeled  (Li et al. , 2013). The scheme for fires outside the tropical closed forests and croplands in CLM4. 5  (Li et al. , 2012; Li and Lawrence, 2017) , fire modules in CTEM  (Arora and Boer, 2005; Melton and Arora, 2016) , GlobFIRM  (Thonicke, 2001) , and INFERNO  (Mangeon et al. , 2016)  are processbased and of intermediate complexity. That is, area burned is determined by two processes: fire occurrence and fire spread, but with simple empirical/statistical equations for each process.
Fire modules in MC2  (Bachelet et al. , 2015; Sheehan et al. , 2015)  and SPITFIRE variants are more complex, which use the Rothermel equations  (Rothermel, 1972)  to model fire spread and consider the impact of fuel composition on fire behavior. How humans affect fires differs among these global fire models (Table  2 ), which influences their estimates of fire emissions. GlobFIRM does not consider any direct human effect on fires and the MC2 fire model only considers human suppression on fire. CLM4. 5 models fires in croplands, human deforestation and degradation fires in tropical closed forests, and human ignition and suppression for both occurrence and spread of fires outside of tropical closed forests and croplands. Burned area in SIMFIRE and human influence on fire occurrence in other models are a nonlinear function of population density.
e. , burning of soil organic matter is not included (Table  2 ). In the FireMIP models, fire carbon emissions are calculated as the product of burned area, fuel load, and combustion completeness. Combustion completeness is the fraction of live plant tissues and ground litter burned (0 %-100 %). It depends on PFT and plant tissue type in GlobFIRM and in the fire modules of CLM4. 5 and CTEM, and is also a function of soil moisture in INFERNO. Combustion completeness depends on plant tissue type and surface fire intensity in SIMFIRE, fuel type and wetness in the SPITFIRE family models, and fuel type, load, and moisture in the MC2 fire module.