dataesr/scientific-paragraphs-categorization huggingface dataset:
Each sample was sequenced for 48 h on one R9. 4 flow cell. We obtained more than 9 billion Illumina (1. 4 Tb) and 7 million Nanopore (21. 5 Gb) reads from the 69 soil metagenomes (mean: 19. 9 Gb, minimum: 0. 7 Gb, maximum: 82. 9 Gb) (Additional file 1: Table  S1 ). The quality of the raw Illumina data was verified with fastQC v0. 11. 9  [43]  and multiQC v1. 8  [44]. Cutadapt v1. 16  [45]  was then used to trim sequencing adapters and low-quality base calls (q < 20) and to filter out short reads (< 50 bp). Nanopore data were basecalled with GPU guppy v4. 0. 11 using the high-accuracy model and applying a minimum quality score of 7. The quality of the basecalled Nanopore data was assessed with pycoQC v2. 5. 0. 21  [46]  and adapters were trimmed with Porechop v0. 2. 4  [47]. S1. e In situ ecosystem-level nitrous oxide (N 2 O) and methane (CH 4 ) fluxes measured from the 43 sites using a static, non-steady state, non-flow-through system.
Taxonomic profiling Taxonomic profiles of the microbial communities were obtained using a read-based approach, i. e. , based on unassembled Illumina data. Due to differences in sequencing depth across the samples, the dataset was resampled to 2,000,000 reads per sample with seqtk v1. 3  [48]. Reads matching the SSU rRNA gene were identified with METAXA v2. 2  [49]  and classified against the SILVA database release 138. 1  [50]  in mothur v1. 44. 3  [51]  using the Wang's Naïve Bayesian Classifier  [52]  and a 80% confidence cut-off. Differences in community structure across soil ecosystems and depths were assessed using non-metric multidimensional scaling (NMDS) and PERMANOVA with the package vegan v2. 5-6 in R v3. 6. 3 (functions metaMDS and adonis, respectively)  [40, 41]. The relationship between community structure, soil physicochemical properties, and elevation was also assessed using PERMANOVA and distance-based redundancy analysis (db-RDA) with forward selection with the package vegan v2.
5-6 in R v3. 6. 3 (functions adonis and capscale/ordistep, respectively)  [40, 41]. The physicochemical dataset included only pH, moisture, and SOM due to a high amount of missing data for the other variables, and barren sites were not included in the PERMANOVA procedure due to the limited number of samples. Moisture and SOM were log-transformed prior to analysis. Relationships between the abundance of individual genera and N 2 O flux rates were assessed using linear regression in R v3. 6. 3  [40].
2  [59]  after discarding contigs shorter than 2500 bp. The two Illumina co-assemblies and the two individual Nanopore assemblies yielded more than 4 million contigs longer than 2500 bp, with a total assembly size of 21. 1 Gb. Gene calls were predicted with prodigal v2. 6. 3  [60]. Single-copy genes were identified with HMMER v. 3. 2. 1  [61]  and classified with DIAMOND v0. 9. 14  [62]  against the Genome Taxonomy Database (GTDB) release 04-RS89  [63, 64]. Illumina reads were mapped to the contigs with bowtie v2. 3. 5  [55]  and SAM files were sorted and indexed using SAMtools v1. 9  [56]. The co-assemblies covered a significant fraction of the original metagenomic data, with an average read recruitment rate of 54. 6% across samples (minimum: 22. 9%, maximum: 75. 8%). Due to their large sizes, Illumina co-assemblies were split into 100 smaller clusters based on differential coverage and tetranucleotide frequency with CONCOCT v1.
Gene-centric analyses Functional profiles of the microbial communities were obtained using a gene-centric approach based on assembled data. For each (co-)assembly, gene calls were translated to amino acid sequences and searched against the KOfam hidden Markov model (HMM) database with KofamScan v1. 3. 0  [67]. Only matches with scores above the pre-computed family-specific thresholds were kept. Genes putatively identified as denitrification genes (nirK, nirS, norB, and nosZ) were submitted to further analyses to identify false positives consisting of distant homologues that are not involved in denitrification. Amino acid sequences were aligned with MAFFT v7. 429  [68]  and alignments were visualized with Unipro UGENE v38. 1  [69]. Sequences were then inspected for the presence of conserved residues at positions associated with the binding of co-factors and active sites: nirK, Cu-binding and active sites  [70] ; nirS, c-heme and d 1 -heme binding sites  [71] ; norB, binding of the catalytic centres cyt b, b 3 , and Fe b  [72] ; nosZ: binding of the Cu Z and Cu A centres  [72].
Sequences which did not contain the correct amino acid at these positions were removed. Finally, resulting amino acid sequences were aligned with MAFFT v7. 429  [68]  along with reference sequences from the genome of cultured denitrifiers  [14]  and a maximum-likelihood tree was computed with FastTree v2. 1. 11 [73]  using the LG + GAMMA model. Annotation of denitrification genes was also performed for previously published genomes retrieved from GenBank. These included a set of 1529 MAGs obtained from soils in Stordalen Mire, northern Sweden  [37] , and all (n = 69) genomes of Acidobacteriota strains and candidate taxa (accessed on 9 October 2020). The abundance of functional genes was computed based on read coverage with CoverM v0. 6. 1  [74]. For this, Illumina reads were mapped to the contigs with minimap v2.
Phylogenomic analyses of MAGs and metabolic reconstruction Phylogenetic placement of MAGs was done based on 122 archaeal and 120 bacterial single-copy genes with GTDB-Tk v1. 3. 0  [76]  and the GTDB release 05-RS95  [63, 64]. Acidobacteriota MAGs containing denitrification genes were submitted to further phylogenomic analyses alongside all genomes of Acidobacteriota strains and candidate taxa available on GenBank (n = 69; accessed on 9 October 2020). For this, the amino acid sequence of 23 ribosomal proteins was retrieved for each genome with anvi'o v6. 2  [59]  and aligned with MUSCLE v3. 8. 1551  [77]. A maximum likelihood tree was then computed based on the concatenated alignments with FastTree v2. 1. 11 using the LG + GAMMA model  [73]. Escherichia coli ATCC 11,775 was used to root the tree.
For metabolic reconstruction, MAGs were annotated against the KOfam HMM database  [67]  with HMMER v. 3. 2. 1  [61]  using the pre-computed score thresholds of each HMM profile. The anvi-estimate-metabolism program in anvi'o v6. 2  [59]  was then used to predict the metabolic capabilities of the MAGs. A metabolic pathway was considered present in MAGs containing at least 75% of the genes involved in the pathway. Carbohydrateactive enzymes (CAZymes) were annotated with dbCAN v. 2. 0 based on the dbCAN v7 HMM database  [78]. Only hits with an e-value < 1 × 10 -14 and coverage > 0. 35 were considered.
A manually curated genomic database from tundra soil metagenomes Using anvi'o  [59] , we obtained 8,043 genomic bins and manually curated these to a set of 796 medium-and high-quality MAGs (≥ 50% complete and ≤ 10% redundant according to the MIMAG standard  [66] ) (Additional file 1: Table  S2 , Additional file 2: Fig. S3 ). According to estimates based on domain-specific single-copy genes, the obtained MAGs were on average 65. 4% complete (minimum: 50. 0%, maximum: 100. 0%) and 2. 7% redundant (minimum: 0. 0%, maximum: 9. 9%) (Additional file 1: Table  S2 ). Phylogenomic analyses based on 122 archaeal and 120 bacterial single-copy genes placed the MAGs across 35 bacterial and archaeal phyla according to the GTDB classification  [63, 64]  (Additional file 2: Fig. S3 ). The most represented phyla were Acidobacteriota (n = 172), Actinobacteriota (n = 163), Proteobacteria (Alphaproteobacteria, n = 54; Gammaproteobacteria, n = 39), Chloroflexota (n = 84), and Verrucomicrobiota (n = 43).
Unsurprisingly, co-occurrence of genes encoding the three enzymes was also not observed in any of the other genomic bins of lower quality that were discarded from the final MAG dataset (i. e. , bins that were < 50% complete and/or > 10% redundant). To verify if microorganisms with truncated denitrification pathways are common in other tundra systems, we expanded our analysis to 1529 MAGs recovered from permafrost peatland, bog, and fen soils in Stordalen Mire, northern Sweden  [37]. Among these, 225 MAGs (14. 7%) contained denitrification genes (Additional file 2: Fig. S5 ). MAGs encompassed a similar taxonomic profile as observed in the Kilpisjärvi dataset, and MAGs with truncated denitrification pathways were also the norm in Stordalen Mire soils. Only one MAG, assigned to the Gammaproteobacteria genus Janthinobacterium, encoded all the Nir, Nor, and Nos enzymes required for the reduction of NO 2 -to N 2.
Fig. 3  Metabolic potential for denitrification in tundra soils. a Distribution of denitrification genes across 110 metagenome-assembled genomes (MAGs) recovered from tundra soils in Kilpisjärvi, northern Finland. Genes encoding the nitrite (nirK/nirS), nitric oxide (norB), and nitrous oxide (nosZ) reductases were annotated using a three-step approach including (1) identification using hidden Markov models from the KOfam database, (2) manual inspection for the presence of conserved residues at positions associated with the binding of co-factors and active sites, and (3) phylogenetic analyses along with sequences from archaeal and bacterial genomes (Additional file 2: Fig. S6 ). More information about the MAGs can be found in Additional file 1: Table  S2. b Phylum-level relative abundance of microorganisms harbouring denitrification genes across the different soil ecosystems, computed as a proportion of reads mapping to each MAG.
Relative abundances were computed as the proportion of reads mapping to each MAG. MAG taxonomy is based on the Genome Taxonomy Database (GTDB) release 05-RS95 Ellin6529 MAGs that were dominant in the fen sites, namely coxABC and cydAB (Additional file 1: Table  S3 ). No genes involved in carbon fixation, dissimilatory sulfate reduction, dissimilatory nitrate reduction to ammonia, and nitrogen fixation were found in any of the dominant norB-and nosZ-containing MAGs. To elucidate the phylogenetic placement of the Acidobacteriota MAGs and to verify if the potential for NO and N 2 O reduction is present in other members of this phylum, we analysed all available genomes of Acidobacteriota strains and candidate taxa available on Gen-Bank. This revealed that genes encoding the Nir and Nos enzymes are widespread across the phylum Acidobacteriota (Fig.
Our genome-resolved survey revealed that denitrification across different tundra soil ecosystems is dominated by microorganisms with truncated denitrification pathways (i. e. , harbouring only a subset of the genes required for complete denitrification), most of which represent poorly characterized taxa without cultured representatives. The congruence of these findings in both our original dataset of northern Finland soils and a re-analysis of a comprehensive metagenomic dataset from soils in northern Sweden  [37]  suggests that truncated denitrification pathways are not a methodological artifact arising from the metabolic reconstruction of fragmented genomes. Indeed, recent genome-resolved investigations have shown that cross-feeding between microorganisms with truncated metabolic pathways, also known as metabolic handoffs, are the norm across a wide range of ecosystems such as grassland soil, aquifer sediment, groundwater, and the ocean, and not only in relation to denitrification but other redox transformations as well  [30, 89, 90].
Although it has been established that denitrification is a community effort performed by different microbial populations  [12] [13] [14] [15] , these genome-resolved metagenomic studies are beginning to reveal a more indepth, ecosystem-centric representation of the denitrification pathway. In addition to their predominance in genomic databases  [14] , it appears that truncated denitrifiers are also dominant within defined ecosystems across various terrestrial and aquatic biomes, including the tundra. It has been suggested that the partitioning of metabolic pathways across different populations via metabolic handoffs is advantageous as it eliminates competition between enzymes accelerating substrate consumption  [15, 31]  and provides flexibility and resilience to the communities in face of environmental disturbances  [30].
Acknowledgements We would like to acknowledge  CSC-IT Centre for Science  for providing the necessary computing resources and  Kimmo Mattila  for IT support; the staff from the  Kilpisjärvi Biological Station ,  Tanja Orpana ,  Aino Rutanen ,  Anniina Sarekoski ,  Johanna Kerttula , and the members of the  BioGeoClimate Modelling Lab  for assistance with fieldwork and soil characterization;  Jillian Banfield  and  Christina Biasi  for helpful discussion;  Laura Cappelatti  for proof-reading the manuscript;  Murat Eren ,  Sebastian Lücker ,  Donovan Parks , and  Antonios Kioukis  for tips, recommendations, and troubleshooting; and all anonymous reviewers who provided important insights to the original manuscript.
Funding This work was funded by the  Academy of Finland  (Grants  314114  and  335354 ) and the  University of Helsinki. SV was funded by the  Microbiology and Biotechnology Doctoral Programme (MBDP). AMV was funded by the  Academy of Finland  (Grant  286950 ), the  Otto Malm Foundation , and the  Gordon and Betty Moore Foundation  (Grant  8414 ). MEM was supported by the  Academy of Finland  (Grants  314630  and  317054 ). Open access funded by  Helsinki University Library.
Availability of data and materials Raw metagenomic data and assembled MAGs have been submitted to the European Nucleotide Archive (ENA) under the project PRJEB41762. MAGs can also be downloaded from https:// doi. org/ 10. 6084/ m9. figsh are. 19722 505. All the code used can be found in https:// github. com/ Arcti cMicr obial Ecolo gy/ Kilpi sjarvi-MAGs.
8a ). The grey shading represents the 25th to 75th quartile and the grey bars the 95% confidence interval of bootstrapped observational values. For plotting purposes, a shows the mean M -30 for IPSL-CM6A and c shows the ratio a a / +3 EUREC 4 A data MRI-ESM2 BCC-CSM2 IPSL-CM6A CanAM4 HadGEM2-A CNRM-CM6 HadGEM3-GC31 MIROC6 MPI-ESM IPSL-CM5A M (mm s - M R for BCC-CSM2. In c, the upper end of the observational 95% confidence interval (at 6. 75) is cropped.
Observations from the Barbados Cloud Observatory (BCO)  15  and the RV Meteor  44  provide further context at the western and eastern boundaries of the EUREC  4  A circle. A typical flight day of HALO consisted of two sets of three consecutive circles lasting about 3 h and comprising 30-36 sondes (sometimes defined as circling  9, 22, 29  ). The 3-h circle sets are separated by a 1. 5-h break to refuel the ATR. The circle patterns were flown from 22 January to 15 February with different starting times between 04:30 and 19:30 local time (LT) to sample the diurnal cycle. Four more single-dropsonde circles are also used, three of which were flown by the P3 aircraft  45  during nighttime (starting at 00:15 LT on 9 and 10 February, and at 01:30 LT on 11 February). In total, the dataset comprises 73 circles (1-h scale) and 24 sets of three consecutive circles (3-h scale), for which 16 have coincident ATR data.
Observations For estimating the cloud-base mass flux M, R and many other variables, we use dropsonde data from the JOANNE dataset  28  , namely Level 3 (gridded quality-checked sondes) and Level 4 (circle products) vertical profiles of thermodynamic quantities, wind and mesoscale vertical velocity, W. The HALO dropsondes are corrected for a dry bias by multiplying the relative humidity by 1. 06 (ref. 28 ). For the cloud-base cloud fraction C, we use the BASTALIAS lidar-radar synergy product  31  , which includes both cloud and drizzle (but not rain) and constitutes an upper bound on C. We also test the relationships for three further estimates of C: • The non-drizzling cloud product from the radar-lidar synergy (C only ), which excludes drizzle and constitutes a lower bound on C. • In situ estimates from a microphysical probe defined on the basis of thresholds of liquid water content plus particle size (C pma ).
Acknowledgements We thank all the scientists, engineers, technicians, pilots and people from Barbados who made the EUREC 4 A data collection possible (see ref. 9 for the complete list). We acknowledge  J. Delanoë  and  P. Chazette  for making the horizontal radar-lidar cloud measurements on board the ATR possible,  M. Lothon ,  P. -E. Brilouet  and the  SAFIRE team  for making the turbulence measurements and ATR flights possible,  M. Wirth  and  S. Gross  for producing sub-cloud-layer height retrievals from the  WALES lidar onboard HALO ,  C. Fairall  and  E. Thompson  for insights about SSTs and surface fluxes,  E. Siddle  for sharing the AutoNaut Caravela data,  I. Schirmacher  for comparison with her RV Meteor surface flux estimates,  J. Röttenbacher  for making RV Meteor data available,  I.
Data availability All data used in this study are published in the EUREC  4  A database of AERIS (https://eurec4a. aeris-data. fr/, last accessed: 28 July 2022). We use v2. 0. 0 of the JOANNE dropsonde data  28  (https://doi. org/10. 25326/246). The specific ATR datasets 31 used are the BASTALIAS product (https:// doi. org/10. 25326/316), the turbulence measurements 49 (https://doi. org/10. 25326/128) and the PMA/Cloud composite dataset (https:// doi. org/10. 25326/237). The specific HALO datasets 29 used are cloud masks derived from WALES cloud-top height estimates (https://doi. org/10. 25326/216), HAMP Cloud Radar (https://doi. org/10. 25326/222) and specMACS (https://doi. org/10. 25326/166), and the flight segmentation product (https://doi. org/10. 5281/zenodo. 4900003). From the BCO 15 , we used ceilometer (https://doi.
org/10. 25326/367) and cloud radar data (https://doi. org/10. 25326/55). From the RV Meteor 44 , we used standard dship meteorological data for the EUREC 4 A Meteor cruise M161 (retrieved from http://dship. bsh. de/, last accessed: 28 June 2022), surface heat fluxes (https://doi. org/10. 25326/312), ceilometer measurements (https://doi. org/10. 25326/53) and cloud radar data (v1. 1, https://doi. org/10. 25326/164). We further used data from AutoNaut Caravela 52 (https://doi. org/10. 25326/366) and 10-min air-sea flux data (v1. 3, https://doi. org/10. 25921/etxb-ht19) from the RV Ronald Brown  54. Also, we used CLS Daily High Resolution Sea Surface Temperature maps (retrievable through the AERIS operational centre https://observations. ipsl. fr/aeris/eurec4a-data/SATELLITES/CLS/SST/, last accessed: 28 June 2022, or directly from https://datastore.
We then analysed each sampling time separately, to determine whether the effects of the experimental treatments on the enzymatic activity of β-glucosidase and acid phosphatase varied over time, using the same models as for the means. In the models for April, May and July, we further tested whether the response of enzymatic activities to the experimental treatments and plant community characteristics depended on the N:P ratio in the soil, by adding N:P ratio as a covariate to the models (we only had N:P ratio data for these sampling times). In addition, we tested whether the averaged N:P ratio across the year, the C:N and the C:P ratio affected the enzymatic activities by adding each ratio to our models for the mean enzyme activities (C was measured only once per year). All continuous variables were standardised to a mean of zero and a standard deviation of 1, so we could compare their effects.
Hydrological Models We consider three hydrological models of varying complexities; GR4H, SMASH, and MARINE. The models are shown in Figure  1 , and their description is given in Table  1. Here, we present their general formulations, but the details of the modeling operators are given in Appendix A. Note from Table  1  that the MARINE model is used with a finer spatio-temporal resolution compared to SMASH. This has a limited impact on the results since all models are forced with the same rainfall data (spatially averaged to the catchment scale in the case of the lumped GR4H model). We considered a 2D-spatial domain Ω (catchment) covered by a regular rectangular grid of resolution ∆x (in the case of the distributed models). The unique constraint applied to this lattice is that a unique point has the highest drainage area, that is the catchment outlet, given the flow directions.
Data This section describes the various data used in the study. For a fair assessment of the models, the same input of rainfall and, for the specific case of the continuous models (SMASH and GR4H), potential evapotranspiration (PET), were used: 1. Discharge: Observed discharges at gauged outlets of Vogue (Ardeche) and Anduze (Gardon) were used for model calibration and validation. Discharge series were extracted from the national banque hydro (http://www. hydro. eaufrance. fr/, last access: 10 March 2021).
2. Rainfall: We used rainfall data from the radar observation reanalysis ANTILOPE J+1, which merges radar and in situ gauge observations. These data are provided by Météo-France. Rainfall averages were used as the input for the 3 models depending on the grid resolution, in this case, a grid of 1 km 2 for the distributed models (SMASH and MARINE) and a spatial average at the scale of the catchment size in the case of the lumped model (GR4H).
3. Potential evapotranspiration (PET): The interannual temperature data were provided by the SAFRAN reanalysis and then used to calculate the potential evapotranspiration using the Oudin formula  [50]. PET is at the same resolution as the rainfall data. These data are specific to the continuous models (GR4H and SMASH). 4. Physiographic data: The soil thickness and texture maps were derived from the surveys provided by the INRA and BRGM. Soil classes and, consequently, the suction, porosity, and saturated conductivity were derived from the soil texture using the Rawls and Brakensiek relations  [48]. The vegetation and land use from the 2000 Corine Land Cover provided by the Service de l'Observation et des Statistiques (SOeS) of the French Ministry of Environment (www. ifen. fr) were used to derive the surface friction.
Calibration and Validation We calibrated each of the three hydrological models, GR4H, SMASH, and MARINE, with their dedicated methods presented in Section 2. 1. The methods enabled adequate calibrations for each model, as will be presented later in Section 3. 2. In order to perform fair comparisons, considering a comparable amount of hydrological information learned by the models in the calibration phase, we performed the calibration and validation using the split-sample test procedure  [55] , which involved dividing the data into two subperiods. We considered a time series of 13 years at an hourly time step, and we divided it into two subperiods of 7 years each for the calibration and validation. Period 1 is defined from 1 August 2006 to 1 August 2013, while Period 2 is defined from 1 August 2012 to 1 August 2019.
A lumped model might not perform as efficiently as a distributed model in the case of spatialized rainfall flood events (e. g. ,  [31] ), but is generally easier to calibrate compared to distributed models, requiring more constraints regarding spatial overparameterization. Users who wish to apply the studied models, which is feasible from worldwide databases with little preprocessing, are advised to consider the longest rainfall flood time series available in order to enhance parameters' representativity. Looking at the process representation, SMASH is the least complex. Recall that while GR4H has a non-conservative water exchange operator revealed as sensitive, MARINE has a subsurface component to account for lateral transfer. The poor performance of SMASH in terms of simulated moisture enhances this aspect.