dataesr/scientific-paragraphs-categorization huggingface dataset:
assignment of metagenomics data -Reads were first recruited against a database of 155 protein sequences of the four lipid desaturases (DesC3, DesC4, DesA2, DesA3) extracted from the 53 Synechococcus/Cyanobium genomes of the information system Cyanorak v2 (http://sb-roscoff. fr/Phyto/cyanorak; Pittera et al. , 2018 ; Dataset S1) using BLASTX analyses (v 2. 7. 1+; Altschul et al. , 1990)  with default parameters, limiting the results to one target sequence (--max_target_seqs 1), and considering only the results with an e-value lower than 0. 001. The recruited reads were then mapped with BLASTN (with the same options as above) against a database including the 155 above mentioned Synechococcus/Cyanobium desaturases as well as many sequences used as outgroups including 117 Prochlorococcus fatty acid desaturase sequences (Dataset S2), 185 outgroup cyanobacterial genomes from Cyanobase plus 537 genomes from proGenomes (Dataset S3), 6,092 additional non-picocyanobacterial fatty acid desaturase sequences retrieved from NCBI (Dataset S4) as well as all marine picocyanobacterial desaturases from Cyanorak.
Acknowledgements This work was funded by the  French programs EC2CO-Microbien  ( METALIC ),  ANR CINNAMON  ( ANR-17-CE2-0014-01 ) and  EMBRC France  ( INFRA-2010-2. 2. 5 ). Solène Breton and Ulysse Guyet were supported by the  Région Bretagne  and the  French Ministry of Higher Education and Research. Juliette Jouhet  and  Eric Maréchal  are supported by the  French National Research Agency  ( ANR-10-LABEX-04   GRAL Labex, Grenoble Alliance for Integrated Structural Cell Biology ;  ANR-11-BTBR-0008   Océanomics ;  ANR-15-IDEX-02 GlycoAlps Cross DisciplinaryProject ). We are grateful to the  Roscoff Culture Collection  for maintaining the Synechococcus strain used in this study. We thank the  ABIMS Platform  ( Station Biologique de Roscoff ) for their help in the metagenomics data computing.
Functional assignment of metagenomics data -Reads were first recruited against a database of 155 protein sequences of the four lipid desaturases (DesC3, DesC4, DesA2, DesA3) extracted from the 53 Synechococcus/Cyanobium genomes of the information system Cyanorak v2 (http://sb-roscoff. fr/Phyto/cyanorak; Pittera et al. , 2018 ; Dataset S1) using BLASTX analyses (v 2. 7. 1+; Altschul et al. , 1990)  with default parameters, limiting the results to one target sequence (--max_target_seqs 1), and considering only the results with an e-value lower than 0. 001. The recruited reads were then mapped with BLASTN (with the same options as above) against a database including the 155 above mentioned Synechococcus/Cyanobium desaturases as well as many sequences used as outgroups including 117 Prochlorococcus fatty acid desaturase sequences (Dataset S2), 185 outgroup cyanobacterial genomes from Cyanobase plus 537 genomes from proGenomes (Dataset S3), 6,092 additional non-picocyanobacterial fatty acid desaturase sequences retrieved from NCBI (Dataset S4) as well as all marine picocyanobacterial desaturases from Cyanorak.
All six broadsheets have in common that they are national in scope and are generally seen to report upon national public agendas. National broadsheet papers can be expected to provide the highest amount of coverage on political issues, and to have the highest agendasetting impact for policy makers and the general public  (Barkemeyer et al. , 2013). Data were collected using keyword searches of the LexisNexis repository for each of the six newspapers. All articles within the time period that contained the search terms "fracking" 1  , "shale gas", "coal seam gas" or "unconventional gas" were captured. Only the print content of the six newspapers was considered, whereas online content such as blog entries was excluded from the analysis.
Metaphor identification In a first step, web searches were performed using the search strings "fracking is like" and "shale gas is like" in order to generate an initial list of metaphors that have been used in the context of UHD. We then read and manually screened 5% of the overall sample (395 articles) to familiarise ourselves with the text corpus. In order to minimize bias, a systematic sampling procedure was applied to draw the subsample from the overall corpus, arranging all articles in chronological order and then selecting every 20 th article for manual screening. In this stage, we were explicitly open to searching for new metaphors. Based on this procedure, ten metaphors that we identified as the most potent were selected for subsequent frequency analysis (see Table  2 ). As with previous empirical studies in this field, we acknowledge that other researchers may arrive at different classifications based on the same dataset  (Nerlich and Halliday, 2007).
Flexibility and power stability Flexibility is a term making reference to the possibility to operate the PEM water electrolysis plant at different power values. Again, flexibility capabilities of the entire system are mainly dictated by stack capabilities, and to a lesser extent by balance-of-plant (BoP) capabilities (at least when the BoP is adequately designed). Figure  9  shows the result of a typical flexibility test. The data were collected during operation under current-controlled (galvanostatic) conditions, a choice usually dictated by down-stream hydrogen-consumption requirements. The observed power overshoot can be corrected by using power-controlled DC power sources. Results show that during operation, the electrolysis unit can stand idle as long as necessary and then jump to either nominal or maximal current (or power) density and back within seconds.
Reactivity Reactivity is a term making reference to the time it takes to the PEM water electrolysis stack/plant to change its power set point. Again, the reactivity is mainly dictated by the water electrolysis stack, and to a lesser extent by the balance-of-plant. Figure  11  shows the situation for both primary (Figure  11-a ) and secondary (Figure  11-b ) reserves. In both cases, data were obtained during a 'hot start' experiment, i. e. when the stack temperature is kept constant at nominal operating value before turning power on/off. For the primary reserve test (Figure  11 -a), the constraint imposed during the test was to switch power density from 100 % down to 75% of nominal power density within less than 30 seconds, applying a linear power transient. For the secondary reserve test (Figure  11-b ), the constraint was to switch power density on from initial (0%) to final (100%) set points in less than 2 min and backwards (soft and emergency ramps), again using a linear power sweep.
Acknowledgement The support of the  French Research Institute on Energy Transition, Paris-Saclay Efficacité Energétique  ( PS2E ), in the frame of the  FlexiPEM  project, is acknowledged. Financial support from the  French Agence Nationale de la Recherche  ( ANR 11 EMMA 048-01 ,  ANR-RF-2015-01 ) and the  French ADEME (Ademe   1694C006 ) is also acknowledged.
Epistasis analysis An epistasis analysis was carried out to detect potential interactions between the parthenogenesis QTLs. Two analyses were performed, using either all 152 male and female progeny ('full dataset') or the subset of all the 93 male individuals. We observed significant sex-by-genotype interactions for parthenogenetic capacity. The analysis of the full dataset identified an epistatic interaction between the P2 QTL and the P1 QTL (Fig  3 ). When the same analysis was carried out with only the males, this epistatic interaction was not detected (S9 Table ). In Fig  3 , the B allele was inherited from the female parent, and the A allele from the male parent. All females were parthenogenetic and therefore their parthenogenetic phenotype was independent of the allele carried at the P2 locus.
In contrast, the phenotype of males depended on the allele carried at the P2 locus. An additional interaction was detected between the P2 QTL and the P3 QTL. In this case, the frequency of P+ individuals was higher when the maternal B allele was present at the P2 locus and the effect was strongest when the P3 locus carried the paternal A allele (Fig 3B, S10 Table ). Several additional interactions were detected between the P2 QTL and markers on several autosomes when the male-only dataset was analysed (S9 Table ).
Parthenogenetic male gametes exhibit reduced fitness in sexual crosses It is not clear why some strains of Ectocarpus exhibit male gamete parthenogenesis whilst others do not. More specifically, bearing in mind that all strains tested so far exhibit parthenogenesis of female gametes, why are male gametes not parthenogenetic in some lineages? To address this question, we investigated if there were differences in fitness between P-and P+ male gametes for parameters other than parthenogenetic growth. Specifically, we examined fertilisation success (capacity to fuse with a female gamete) and growth of the resulting diploid sporophyte. We tested twelve combinations of crosses between four P-or P+ males and five females (S14 and S15 Tables). We fitted a generalized linear mixed model with a logistic regression to the data and observed that the male P-gametes showed a higher rate of fusion with female gametes than the P+ male gametes (p = 0.
DNA extraction and library RAD sequencing A double digest RAD sequencing (ddRAD-seq) library was generated using 152 individuals from the EA1 x RB1 segregating population. Parthenogenetic individuals were selected (37 females and 36 males) as well as non-parthenogenetic males (79 individuals). DNA extraction was performed for each individual (Macherey-Nagel, NucleoSpin Plant II kit (GmbH & Co. KG, Germany) and DNA quantity was measured and standardized at 100 ng using a Pico-Green (Fischer Scientific) method for quantification. The DNA quality was checked on agarose gels. The ddRAD-seq library was constructed as in  [62]  using HhaI and SphI restriction enzymes (New England Biolabs, https://www. neb. com/). Those enzymes were selected based on an in silico digestion simulation of the Ec32 reference genome  [18]  using the R package SimRAD  [63].
Quality filtering and reference mapping The ddRAD-seq sequencing data was analysed with the Stacks pipeline (version 1. 44,  [31] ). The raw sequence reads were filtered by removing reads lacking barcodes and restriction enzyme sites. Sequence quality was checked using a sliding window of 25% of the length of a read and reads with <90% base call accuracy were discarded. Using the program PEAR (version 0. 9. 10,  [64] ) paired-end sequencing of short fragments generating overlapping reads were identified and treated to build single consensus sequences. These single consensus sequences were added to the singleton rem1 and rem2 sequences produced by Stacks forming a unique group of singleton sequences. For this study, paired-end reads and singleton sequences were then trimmed to 100 bp with the program TRIMMOMATIC  [65].
The genome of the male parent of the population (strain RB1) was recently sequenced to generate an assembly  [66]  guided by the Ectocarpus species 7 reference genome published in 2010  [67]. We performed a de novo analysis running the denovo_map. pl program of Stacks. Firstly, this program assembles loci in each individual de novo and calls SNPs in each assembled locus. In a second step, the program builds a catalog with the parental loci and in a third step, loci from each individual are matched against the catalogue to determine the allelic state at each locus in each individual. We then used BWA (Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303. 3997) to align the consensus sequence of the catalog loci to the reference genome and used the Python script "integrate_alignments.
Genetic map construction and QTL mapping The vcf file obtained with the Stacks pipeline was first filtered to keep only loci with maximum of 10% of missing samples and samples with a maximum of 30% of missing data. The program Lep-MAP3 (LP3)  [69]  was used to construct the genetic map (S18 Table ). LP3 is suitable to analyse low-coverage datasets and its algorithm reduces data filtering and curation on the data, yielding more markers in the final maps with less manual work. In order to obtain the expected AxB segregation type for this haploid population, the pedigree file was constructed by setting the parents as haploid grand-parents and two dummy individuals were introduced for parents. The module ParentCall2 of LP3 took as input the pedigree and the vcf files to call parental genotypes.
The module SeparateChromosomes2 used the genotype call file to assign markers into linkage groups (LGs). Several LOD score limits were tested to obtain an optimal LOD score of 8 giving a stable number of LGs. The module JoinSingles2All was then run to assign singular markers to existing LGs by computing LOD scores between each single marker and markers from the existing LGs. The module Order-Markers2 then ordered the markers within each LG by maximizing the likelihood of the data given the order. Sex averaged map distances were computed and 10 runs were performed to select the best order for each LG, based on the best likelihood. This module was run with the parameters grandparentPhase = 1 and outputPhasedData = 1 in order to obtain phased data for QTL mapping. This phased data was converted to fully informative genotypic data using the script map2gentypes.
Expression values were represented as TPM and TPM<1 was applied as a filter to remove noise if both replicates of both samples exhibit it. Differential expression was analysed using the DESeq2 package (Bioconductor;  [73] ) using an adjusted p-value cut-off of 0. 05 and a minimal fold-change of two. The reference transcripts were blasted to the reference genome Ec32 predicted proteins (http://bioinformatics. psb. ugent. be/orcae/overview/EctsiV2) (e-value cut-off = 10e -5 ) and the orthology relationship between Ectocarpus species 1 and Ec32 (Ectocarpus species 7) was established based on the best reciprocal blast hits.
SNP and indel detection method Draft genomes sequences are available for the parent strains RB1 and EA1  [66]. Using Bowtie2, we aligned the EA1 genome against the RB1 genome and generated an index with sorted positions. The program samtools mpileup  [74]  was used to extract the QTL intervals and call variants between the two genomes. The positions of variants between the two genomes were identified and filtered based on mapping and sequence quality using bcftools  [75]. The annotation file generated for the RB1 genome was then used to select SNPs and indels located in exons of protein-coding genes for further study (bcftool closest command). The effect of polymorphism on modification of protein products was assessed manually using GenomeView  [76] , the RB1 genome annotation file (gff3) and the vcf file for each QTL region.
GO term enrichment analysis A Gene Ontology enrichment analysis was performed using two lists of genes: a predefined list that corresponded to genes from all three QTL intervals and a reference list including all putative genes in the mapped scaffolds based on the Ec32 reference genome and that had a GO term annotation. The analysis was carried out with the package TopGO for R software (Adrian Alexa, Jo ¨rg Rahnenfu ¨hrer, 2016, version 2. 24. 0) by comparing the two lists using a Fisher's exact test based on gene counts.
Fitness measurements Reproductive success was assessed in the segregating population by measuring the capacity of male P+ and P-gametes to fuse with female gametes and by measuring the length of the germinating sporophytes derived from these crosses. For this, we crossed males and females as described in  [60]. Briefly, we mixed the same amount of male and female gametes (app. 1x10 3 gametes) in a suspending drop, and the proportion of gametes that succeeded in fusing was scored. Two different P+ males (Ec236-34 and Ec236-245) and two different P-males (Ec236-10 and Ec236-298) were crossed with five different females (Ec236-39; -203; -65; -284 and Ec560) (S14 Table ). Between 18 and 254 germlings were counted for each cross. To determine whether there was a difference between the capacity of P+ and P-male gametes to fuse with female gametes, we applied a generalized linear mixed model with a logistic regression (family = binomial) using the glmer function from the R package lme4 (details in S14 Table ).
The model was run with and without observation-level random effects to account for overdispersion  [77]. The length of zygotes derived from three different crosses between female strains and either male P-or male P+ strains were measured after between 3h to 4 days of development using Image J 1. 46r  [78]. For all datasets, the assumption of normality (Shapiro test) and the homoscedasticity (Bartlett's test) were checked. The latter's assumptions were not met for zygote length, and consequently statistical significance differences at each time of development was tested with a non-parametrical test (Mann Whitney U-test, α = 5%).
5A ). Transcriptomic data from the dark incubation experiments of N. oceanica and N. gaditana indicate that the genes coding for PKS-Clade I and PKS-Clade III enzymes were downregulated or did not exhibit significant changes under dark conditions, while those coding for PKS-Clade II were upregulated (Fig. 3 ), and we did not detect genes coding for PKS-Clade IV in our transcriptomes. PKS-Clade II enzymes CCMP1779j6720 and Naga_100086g4 increased their expression in the dark treatment by 3. 2-and 2. 5-fold, respectively (Fig. 3 ) suggesting they can be potentially involved in the hydroxylation of fatty acids. Interestingly PKS-Clade II enzymes possess domains for PKS-KAS (IPR020841), AT (IPR020801) as well as an adenosine monophosphate (AMP) binding domain (IPR000873), a phosphopantetheine-binding ACP domain (PPT, IPR009081), and a ketoacyl-ACP-reductase (KAR, IPR013968) domain (Fig.
thaliana  (Li-Beisson et al. 2010) , Apis mellifera  (Teerawanichpan et al. 2010) , Calanus finmarchicus (Teerawanichpan and Qiu 2012), and Euglena gracilis  (Teerawanichpan and Qiu 2010). However, we could not find any protein sequence containing the conserved motif [IVF]X[ILV]TGXTGF  [MLV] [GA] which corresponds to the FAR catalytic site  (Hofvander et al. 2011, Teerawanichpan and Qiu 2012) , and none of the Nannochloropsis proteins belong to any FAR family (IPR026055, IPR008670, IPR016836 and IPR003157). Furthermore, similarity searches (blastp) of known FARs against the deduced amino acid sequences of Nannochloropsis genomes produced hits with low (bit score <50) similarity (data not shown). Indeed, a recent study also failed to detect genes coding for FARs in Nannochloropsis genomes  (Sorigue et al.
32 mm, coated with CP Sil-5 (thickness 0. 12 mm). Identification of the lipids was achieved using GCmass spectrometry (GC-MS) with an Agilent 7890B GC coupled to an Agilent 5977 mass spectrometer (Agilent). Identification of the LCDs, LCAs, and LCHFAs was based on the fragmentation mass spectra obtained in full scan (m/z 50-800) as described by  Versteegh et al. (1997). To discriminate between C 30:0 diols and the C 32:2 alkenols, which co-elute on the GC, we determined the relative contributions of each compound to the total peak area in the GC-MS chromatogram (MassHunter software, Agilent) in which the two compounds are partially separated. For the stable isotope experiment, we analyzed two filters corresponding to the two treatments of N. oceanica. The extraction set-up consisted in saponification followed by acid hydrolysis as described above.
All GC-MS parameters were also identical, but the total run time was 60 min. The isotopic composition of the different compounds was analyzed using GC-isotope ratio mass spectrometry, using an Agilent 6890 GC coupled to a Thermo Delta Plus isotope ratio mass spectrometer (IR-MS, ThermoFisher, Landsmeer, Netherlands). Separation was achieved on a ZB-5MS column with a length of 60 m, a column diameter of 0. 32 mm and a film thickness of 0. 25 mm. Oven program was identical as that described for the GC-FID and GC-MS, but the end temperature of 320 C was held for 30 min (total run time of 80 min). The injection volume was 1 mL, the four fractions were all analyzed in triplicate, and the reported data represent averaged values. The isotopic compositions are reported in units of atom percent (At%).
Genomic analyses To identify genes potentially involved in the biosynthesis of LCAs, LCDs, and LCHFAs in Eustigmatophyceae, we constructed a local blast database  (Altschul et al. 1990 , Camacho et al. 2009 ) using the predicted proteins from the genomes of eight Nannochloropsis strains (Nannochloropsis sp. CCMP531, N. gaditana B-31, N. gaditana CCMP526, Nannochloropsis granulata CCMP529, N. oceanica CCMP1779, N. oceanica IMET1, Nannochloropsis oculata CCMP525, and N. salina CCMP537, Supplementary Data). Conserved protein domains were searched using Interproscan  (Jones et al. 2014)  or by manually enquiring for specific AA motifs. Specifically, we searched for proteins containing the motifs HWYHH, GMGCSAG and [D/E]TACSSS or H[G/A]TGT, which correspond to highly conserved regions of Á0-ELOs  (Hashimoto et al.
2008) , FAEs  (Millar et al. 1999)  and PKSs  (Shelest et al. 2015)  enzymes, respectively. Moreover we searched for genes potentially coding for the conserved motif [I/V/F]X[I/L/ V]TGXTGF[M/L/V][G/A] which corresponds to the catalytic site of FARs  (Hofvander et al. 2011, Teerawanichpan and Qiu 2012). The presence and position of TMHs in Á0-ELOs, FAEs, and PKSs proteins was assessed using TMHMM  (Krogh et al. 2001). We carried out similarity searches of known proteins from other species against the locally built Nannochloropsis database as well as similarity searches of Nannochloropsis proteins potentially involved in the biosynthetic processes against the nonredundant (NR)  (Pruitt et al. 2005 ) and the Swissprot  (Bateman et al. 2017)  databases, using blastp  (Camacho et al. 2009). To compare putative Nannochloropsis enzymes involved in the biosynthetic processes with known and unknown proteins from other species, we performed phylogenetic analyses on four protein families: FAEs, PKSs, the HADs and the WS-DGATs.
We aligned putative Nannochloropsis FAEs with known FAE proteins from A. thaliana, Brassica napus  (Joubes et al. 2008)  as well as a FAE known to elongate hydroxy fatty acids from P. fendleri  (Moon et al. 2001). Sequences were aligned using MAFFT-linsy  (Katoh and Standley 2013)  and poorly aligned regions (regions containing >50% gaps) were trimmed from the alignment which finally consisted of 50 sequences and 195 unambiguously aligned positions. We analyzed the KAS domain of PKSs (KAS-PKS) using Nannochloropsis proteins previously identified as PKSs  (Shelest et al. 2015 , Alboresi et al. 2016)  as well as other proteins containing the KAS-PKS domain (IPR020841) and/or containing the conserved motifs [D/ E]TACSSS and H[G/A]TGT. Sequences were then aligned to a pre-existing alignment of 92 KAS-PKS sequences  (Kohli et al.
2016 ) and trimmed as described above. The final alignment consisted of 138 sequences and 173 AA positions. For HADs, we extracted two AA sequences containing a domain for HAD (IPR010084) from N. oceanica and N. gaditana, respectively. We then searched for homologs in the Nannochloropsis and Swissprot databases, aligned and trimmed the sequences as described above and the final alignment included 50 AA sequences and 147 positions. For the WS-DGAT phylogeny, we downloaded known AA sequences from the Swissprot database, searched for homologs in Nannochloropsis, aligned the sequences and trimmed the alignment as described above. The alignment included 43 AA sequences and 245 positions. Phylogenetic trees were constructed using both maximum likelihood (ML) and neighbor joining (NJ) algorithms based on 1,000 bootstraps.