dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the small size of the available annotated dataset, to address this task we implemented a simple string matching algorithm that relies on a gazetteer containing a set of Twitter usernames and hashtags extracted from the training data, and a list of very common news agencies (e. g. BBC, CNN, CNBC). If no matches are found, the algorithm extracts the NEs from the tweets through  (Nooralahzadeh et al. , 2016) 's system, and applies the following two heuristics: i) if a NE is of type dbo:Organisation or dbo:Person, it considers such NE as the source; ii) it searches in the abstract of the DBpedia 7  page linked to that NE if the words "news", "newspaper" or "magazine" appear (if found, such entity is considered as the source). In the example above, the following NEs have been detected in the tweet: "The Guardian" (linked to the DBpedia resource http://dbpedia.
org/page/ The_Guardian) and "Greek crisis" (linked to http://dbpedia. org/page/Greek_ government-debt_crisis). Applying the mentioned heuristics, the first NE is considered as the source. Most of the errors of the algorithm are due to information sources not recognized as NEs (in particular, when the source is a Twitter user), or NEs that are linked to the wrong DBpedia page. However, in order to draw more interesting conclusions on the most suitable methods to address this task, we would need the increase the size of the dataset.
Discussion and Future work This paper investigated argument mining tasks on Twitter data. The main contribution is twofold: first, we propose one of the very few approaches of argument mining on Twitter, and second, we propose and evaluate two new tasks for argument mining, i. e. , facts recognition and source identification. These tasks are particularly relevant when applied to social media data, in line with the open popular challenges of fact-checking and source verification to which these results contribute. The issue of argument detection on Twitter has already been addressed in the literature. Bosc et al. (2016a,b)  address a binary classification task (argument-tweet vs. non argument), as first step of their pipeline. Goudas et al. (2015)  experiments machine learning techniques over a dataset in Greek extracted from social media.
They first detect argumentative sentences, and second identify premises and claims. However, none of them is neither interested in distinguishing facts from opinions nor to identify the arguments' sources. An argumentation-based approach is applied to Twitter data to extract opinions in  (Grosse et al. , 2015) , with the aim of detecting conflicting elements in an opinion tree to avoid potentially inconsistent information. Both the goal and the adopted methodology are different from ours. Being it a work in progress, several open issues have to be considered as future research. Among them, we are currently extending the dataset of annotated tweets both in terms of annotated tweets per topic, and in terms of addressed topics (e. g. Brexit after the referendum, Trump), in order to have more instances of facts and sources.
, 2015) ), and 75% of the assembly length is covered by 86 scaffolds (L75 statistic). Chromosphaera has 12,463 annotated genes comprising 85. 5% of the BUSCO set, and its L75 statistic is 187 scaffolds. In contrast, Abeoforma and Pirum have larger genome assemblies (101. 9 and 84. 4 Mb), but these are fragmented (L75 = 25,133 and 25,440 scaffolds) and incomplete (11. 9% and 17. 0% of BUSCO). These lower contiguities are reflected in their partial gene predictions (Figure  1A , Figure  1 -figure supplement 1), which consequently hindered the detection of BUSCO orthologs. Overall, together with Capsaspora, the two choanoflagellates and three already available ichthyosporeans, our expanded dataset now comprises 10 genomes from all unicellular Holozoa lineageseight more than in previous genome analyses  (Fairclough et al.
The new Chromosphaera (gen. nov. ) helps resolve the phylogeny of Holozoa To have a robust phylogenetic framework for our comparative analyses, we investigated the phylogenetic relationships between holozoans with a phylogenomic analysis based on the dataset developed in  Torruella et al. (2015). We classified the newly identified Chromosphaera perkinsii (gen. nov. , sp. nov. ) as a member of Ichthyosporea, in the order Dermocystida, as it clusters with Sphaerothecum destruens in our phylogenomic analysis (Figure  2 ; BS = 100%, BPP = 1). Therefore, Chromosphaera, isolated from shallow marine sediments in Hawai'i, is the first described putatively freeliving dermocystid Ichthyosporea. Indeed, all described dermocystids are strict vertebrate parasites, whereas ichthyophonids are typical animal commensals or parasites (although free-living species have been described and some have been identified in environmental surveys of marine microbial eukaryotic diversity)  (del Campo and Ruiz-Trillo, 2013; Glockling et al.
, 2013). Our analysis confirms our previous results with regards to the phylogenetic relationships within Holozoa: the Teretosporea, comprising Ichthyosporea and the small free-living osmotroph Corallochytrium  (Raghukumar, 1987) , are a sister-group to all the other holozoans (filastereans, choanoflagellates and animals) with improved statistical support (Figure  2 ; BS = 93%, BPP = 0. 85). The monophyly of Teretosporea rejects alternative scenarios such as the 'Filasporea' hypothesis (a grouping of Filasterea + Ichthyosporea)  (Ruiz-Trillo et al. , 2008; Liu et al. , 2009)  or the status of Corallochytrium as an independent opisthokont lineage. Source data 1. BVD57 phylogenomic dataset  (Torruella et al. , 2015)   Trends in the evolution of genome size, synteny and gene conservation across Holozoa Independent increases in genome size in Metazoa and unicellular holozoans We found that Metazoa typically have larger genomes than their unicellular relatives: early-branching animals are within the 300-500 Mb range  (Elliott and Gregory, 2015a; Simakov and Kawashima, 2017)  and most unicellular holozoans have relatively compact genomes, like Corallochytrium, Capsaspora or Chromosphaera (24.
, 2012; Simakov and Kawashima, 2017; Sebe-Pedros et al. , 2010; Tordai et al. , 2005; Ekman et al. , 2007; Hynes, 2012; Deshmukh et al. , 2010; Grau-Boveet al. , 2015). Here, we present a comprehensive study of gene diversification in Holozoa, using our taxon-rich genomic dataset to reconstruct its effect in the animal ancestry. We thus performed a comparative analysis of protein domain architectures across eukaryotes, using the rates of domain rearrangement (or shuffling) as a proxy for gene family diversification. We compared the phylogenetic distribution of protein domain co-occurrences across species and gene families (using a dataset comprising 26,377 gene families or clusters of orthologs derived from 40 eukaryotic species (see Methods). We inferred rates of domain rearrangement at ancestral nodes of the eukaryotic tree using a probabilistic birth-and-death model (Csu ˝ro ¨s and Miklos, 2006) to reconstruct the content of specific protein domain architectures in ancestral genomes (available as Figure  7 ).
All PE and MP libraries were prepared and sequenced at the CRG Genomics Unit (Barcelona), using Illumina HiSeq 2000 and the Trueseq Sequencing Kit v3 (Abeoforma and Corallochytrium) or v4 (Pirum and Chromosphaera). The only exception was Corallochytrium PE libraries, which were sequenced at the Earlham Institute Genomics Unit (Norwich, UK) using Illumina MiSeq and the Trueseq protocol v2. Genome sequencing data has been deposited in NCBI SRA under the BioProject accession PRJNA360047. RNA-seq data was produced for Chromosphaera and Abeoforma. RNA extractions were performed from confluent axenic cultures grown in three 25 ml flasks for 5 days. RNA was extracted using Trizol reagent (Life Technologies, Carlsbad, CA, USA) with a further step of Dnase I (Roche) to avoid contamination by genomic DNA, then purified using RNeasy columns (Qiagen).
We sequenced PE libraries of 125 bp with an insert size of 250 bp, yielding 168Á10 6 reads for Chromosphaera and 178Á10 6 for Abeooforma; which were constructed using the Trueseq Sequencing Kit v4 (Illumina, San Diego, CA). The libraries were sequenced in one lane of an Illumina HiSeq 2000 at the CRG genomics unit (Barcelona). All transcriptome sequencing data has been deposited in NCBI SRA using the BioProject accession PRJNA360056.
2. 1 scaffolding module  (Kajitani et al. , 2014). Each assembly was later processed using the GapCloser module from SOAPdenovo assembler with PE data, in order to extend the scaffolded contigs by shortening N stretches  (Luo et al. , 2012). Genome assembly statistics (genome size, N50, L75) were calculated using Quast v2. 3  (Gurevich et al. , 2013) , and completeness was assessed using the BUSCO v1. 1  (Sima ˜o et al. , 2015)  database of universal eukaryotic genes, based on the predicted transcripts.
Transcriptome assemblies were produced to support PASA and Augustus gene predictions. RNAseq PE libraries were assembled using genome-guided Trinity v2. 0. 6 and STAR v2. 5 (for Corallochytrium, Chromosphaera and Ichthyophonus) or de novo Trinity (Pirum and Abeoforma, assemblies from  (Torruella et al. , 2015; Grabherr et al. , 2011; Dobin and Gingeras, 2015). In the case of the Corallochytrium, Chromosphaera and Ichthyophonus genome-guided assemblies, quality control was performed as indicated above for the genomic libraries, using the RNA-seq data generated for this study (Chromosphaera) or in  (Torruella et al. , 2015)  (Ichthyophonus accession: PRJNA264423; Corallochytrium accession: PRJNA262632). A minimum k-mer coverage = 2 was used in all Trinity assemblies. Transcriptome assemblies were annotated with Transdecoder using Pfam release 29 protein domain database, in order to obtain mRNA and translated peptides.
Next, PASA annotations were obtained from assembled transcripts, mapped to the genome using GMAP and BLAT v35  (Kent, 2002; Wu et al. , 2016). Only high quality mapping was accepted, with a minimum of 95% identity and 75% transcript coverage. We then trained Augustus independently, using protein and mRNA predictions (mapped to the genome with Scipio 1. 4  (Keller et al. , 2008) , BLAT and GMAP), followed by an optimization round of the species-specific parameters. After the training, an Augustus prediction was performed using the optimized parameters. Finally, all annotations were consolidated using Evidence Modeler. In this step, gene models from PASA and Augustus were given higher relative weights than ab initio-predicted models (10 and 5 times more reliability, respectively).
Phylogenomic analysis We used an improved version of the dataset published by Torruella et al. (Torruella et al. , 2015) , adding nine single-copy protein domains to the previous version (which included 78 alignments) according to the methodology developed in  (Torruella et al. , 2012). Since Abeoforma and Pirum genome assemblies were fragmented and contained partial gene models, we used transcriptome assemblies from  (Torruella et al. , 2015)  instead. We compiled a 57-taxa dataset of Unikonta/Amorphea species (hereby termed BVD57 taxa matrix; including Holozoa, Holomycota, Breviatea, Apusomonadida and Amoebozoa; 24,021 amino acid positions). This dataset represents a ~10% increase in the number of aligned positions, compared to the original S70 dataset from  (Torruella et al. , 2015). We used the BVD57 dataset to build ML phylogenetic trees using IQ-TREE v1.
Generation of a species tree and ortholog datasets for comparative analyses Our comparative genomics analyses are based on a dataset of 42 complete eukaryotic genomes, with a focus on unicellular and multicellular Holozoa, and using relevant outgroups from the Holomycota, Apusomonadida, Amoebozoa, Viridiplantae, Stramenopila, Alveolata, Rhizaria and Excavata groups. The complete list of species, abbreviations and data sources is available as Figure  2 source data 1. Since ancestral state reconstruction requires the assumption of an explicit species tree, we classified the 42 genomes in our dataset according to a consensus of phylogenomic studies  (Torruella et al. , 2015; Derelle et al. , 2015; He et al. , 2014)  and our own results. We remained agnostic about the internal topology of SAR  (Burki et al.
, 2016) , Fungi  (Torruella et al. , 2015) , the contentious hypotheses for the root of eukaryotes (namely, 'Opimoda-Diphoda' or 'Excavata-first')  (Derelle et al. , 2015; He et al. , 2014)  and the earliest-branching animal group (Porifera or Ctenophora)  (Whelan et al. , 2015; Simion et al. , 2017). All these cases were recorded in our species tree as polytomic branchings. We inferred two different ortholog datasets using the predicted proteins from the afore-mentioned genomes, using Orthofinder v0. 4. 0 with a MCL inflation = 2. 1  (Emms and Kelly, 2015). The first database included 40 eukaryotic species (excluding the low-quality gene models of Pirum and Abeoforma), whose genes were classified in 162,559 clusters of orthologs, 26,377 of which contained >1 gene (henceforth, 'orthocluster').
Gene family evolution analyses Retrieval of homologous sequences Retrieval of homologous protein sequences was performed by querying orthologs or protein domain HMM profiles (depending on the gene family; see below) against a database of protein sequences from 69 selected eukaryotic genomes and transcriptomes (Figure  2 -source data 1). Since Abeoforma and Pirum genome assemblies were fragmented and contained broken gene models, we used transcriptome assemblies from  (Torruella et al. , 2015)  instead. The following gene families were defined by its catalytic/representative protein domain: type IV collagen (PF01413), TAZ zinc finger TFs with HAT/KAT11 domains (PF08214), Upf1 (PF09416), Upf2 (PF04050), Upf3 (PF03467), Smg1 (PF15785), Smg8/9 (PF10220), eRF1 (combination of PF03463 +PF03464+PF03465), Y14 (PF09282), Magoh (PF02792) and MLN51/CASC3 (PF09405).
Homologs were thus retrieved by querying Pfam protein domains (29th Pfam release  (Punta et al. , 2012) ), using HMMER v3. 1b2 (HMMER, 2015) searches with hmmersearch, using the profile-specific gathering threshold cut-off. In the case of LIM homeodomain genes, we queried the genomes/transcriptomes of all available unicellular holozoans (see taxon sampling above) using the homeobox HMM (PF00046), and restricted the subsequent phylogenetic analysis (see below) to sequences that clustered with known LIM-HD genes from the HomeoDB database in blastp searches  (Zhong and Holland, 2011). In the case of the eRF3, eIF4A3, Smg5/6/7 and SRSF1-9 gene families, we queried the genomes/ transcriptomes mentioned above using blastp searches of the human orthologs of these gene families (Uniprot accession numbers: eRF3 is P15170; eIF4A3 is P38919; Smg5/6/7 are Q9UPR3/ Q86US8/Q92540; SRSF1-9/TRA2  (Plass et al.
Analysis of repetitive elements Repetitive regions were annotated in Holozoa genomes using RepeatMasker open-4. 0. 5  (Smit et al. , 2015)  and annotations from the 20150807 release of GIRI RepBase database  (Bao et al. , 2015). We used the Eukaryota-specific database, with either the slow high-sensitivity search mode (unicellular holozoans) or the default search mode (metazoans); and stored the genome coordinates of TEs, low complexity repeats, tRNA genes, simple repeats and satellite regions. Internal similarity of each genome's TE complements was analyzed with blastn self-alignments of all TEs (considering a minimum 70% identity and 80 bp alignment length), and the distribution of percentage identity values was plotted using R.
Then, we calculated the gene synteny ratio r of each species pair i-j as follows: r ij ¼ c ij À s ij N ij c max À s max N max where c denotes the number of syntenic orthologs between i and j; s is the number of spurious syntenic orthologs averaged over 100 random replicates; and N is the number of comparable ortholog pairs between i and j. Values are normalized to the 0-1 interval using the maximum values of the dataset as a reference, i. e. Sphaeroforma and Creolimax. A heatmap representing the degree of similarity in pairwise species comparisons was produced using the synteny ratio (R gplots library  (Warnes et al. , 2016) ). Species were clustered according to their mean synteny. The same analysis was performed using the database of 40 eukaryotic genomes, which excluded Abeoforma and Pirum.
Analysis of coding sequence conservation From our ortholog database using 40 eukaryotic genomes (excluding Pirum and Abeoforma, which had lower-quality gene annotations due to their fragmented assemblies), we selected 143 orthoclusters present in all unicellular holozoans, plus Amphimedon queenslandica, Trichoplax adhaerens, Homo sapiens and Nematostella vectensis (as representative animal genomes). We aligned each group of orthologs using MAFFT G-INS-i  (Katoh and Standley, 2013) , trimmed the alignments using trimAL automated algorithm  (Capella-Gutierrez et al. , 2009) , and inferred maximum likelihood trees for each ortholog group using RAxML v8. 2. 0  (Stamatakis, 2014)  and the LG amino acid substitution model. Then, for each tree, we recorded all pairwise phylogenetic distances between species as measured by substitutions per alignment position using the cophenetic module of the ape v3.
Comparative analysis of intron content Intron content of a subset of 40 eukaryotic genomes (excluding Abeoforma and Pirum, which had lower-quality gene annotations due to their fragmented assemblies) was analyzed using a set of single-copy orthologous genes, and used to reconstruct ancestral states as described by Csu ˝ro ¨s et al. (Csuros et al. , 2011; Csuro ¨s et al. , 2007 , Csuro ¨s et al. , 2008). We then selected orthocluster present as single-copies in 80% of our species dataset, allowing for paralog genes to occur in just one species per group (if that was the case, the best-scoring copy in BLAST alignments was kept). This yielded a group of 342 nearly paneukaryotic genes, whose protein translations were then aligned using MAFFT v7. 245 G-INS-i algorithm  (Katoh and Standley, 2013)  and annotated with their intron coordinates (retrieved from their respective genome annotations).
Comparative analysis of protein domain architecture evolution Protein domain architectures of the 40 eukaryotic species subset (excluding Abeoforma and Pirum, which had lower-quality gene annotations due to their fragmented assemblies) were computed using Pfamscan and the 29th release of the Pfam database  (Punta et al. , 2012). For each protein, the domain architecture was decomposed into all possible directed binary domain pairs (ignoring repeated consecutive domains; i. e. from protein A-B-B-C, the pairs A-B, A-C and B-C were built), and linked to its presence in its corresponding orthocluster (see Generation of a species tree and ortholog datasets for comparative analyses section). The final output was a numerical profile of species distribution for each combination of domain pairs in orthoclusters (considering that a cluster can contain more than one pair, and a pair can be present in more than one cluster, and thence the number of occurrences is recorded).
Comparative analysis of individual protein domain evolution We mapped the presence of individual protein domains across our dataset of 40 eukaryotic species (excluding Abeoforma and Pirum), as predicted by Pfamscan and the 29th release of the Pfam database  (Punta et al. , 2012). Using this numerical profile of domain presence in extant genomes, we computed the gains and losses at ancestral nodes using the Dollo parsimony algorithm as implemented in Count  (Csuro ¨s, 2010).
including 87 unaligned protein domains (with PFAM accession number) per species. DOI: 10. 7554/eLife. 26036. 008 Figure supplement 1. Phylogenomic analysis of the BVD57 matrix using (A) IQ-TREE maximum likelihood and the LG + R7+C60 model (supports are SH-like approximate likelihood ratio test/UFBS, respectively); (B) IQ-TREE maximum likelihood and the LG + R7+PMSF model (fast CAT approximation; non-parametric bootstrap supports); and (C) Phylobayes Bayesian inference under the LG+G7 + CAT model (BPP supports). DOI: 10. 7554/eLife. 26036. 009.
Acknowledgements This work was supported by an  ERC   Consolidator Grant  ( ERC-2012-Co-616960 ), support from the  Secretary's Office for Universities and Research of the Generalitat de Catalunya  (project  2014 SGR 619 ) and two grants from the  Spanish Ministry for Economy and Competitiveness (MINECO ;  BFU2011-23434  and  BFU2014-57779-P , the latter with  European Regional Development Fund  support), all to IRT. XGB was supported by a pre-doctoral  FPI grant  from  MINECO  (except for the January-March 2015 period). GT was funded by a  European Marie Skłodowska-Curie Action  (704566 AlgDates). HS was supported by  JSPS   KAKENHI 16K07468  and research grants from the  NOVARTIS foundation for the Promotion of Science, ITOH Science Foundation , and JUTEN grant from the  Prefectural University of Hiroshima.
But, in ancient censuses, there is usually no family level information, only household and individual levels. Therefore, family heads that are not household heads are not captured and classifications of households from historic data: nominal rolls and later population censuses 3 do not enable us to know the types of families (for instance, couples, lone males or females with children) living in complex households. Several scholars have used ancient population files to describe complex households more precisely. Some focussed specifically on the main families in households 4. Others mostly worked on life course changes in complex households based on individual events: births, marriage, deaths and migration 5. But, no methodology has been developed to capture lone females and mothers who are family heads and not household heads in household datasets.
About half of widows and divorced females are not household heads Comparing the numbers of divorced females who are household heads and all divorced females reveals immediately the extent of knowledge gaps resulting of household data alone. The Fiji 2003 HIES recorded 1,049 divorced female household heads and 2,403 divorced females altogether. Thus, an analysis of socioeconomic characteristics of divorced females based on household heads would miss more than half of the population. The proportions are not much different for widows and are always higher for females than for males (table  1 ). Headship rates by sex, age and marital status provide more information on levels of headship for females -and also young married couples. It appears again that only a fraction (45%) of the 25-34 years old urban divorced Fijian females are household heads (table 2 and figure  2 ).
Data The main information presented in this paper is based on the variables: sex, age, marital status and relation to household head. These variables are available in censuses, including some 19 th century censuses. If not directly available, the marital status of household heads can be processed from most IPUMs data files. The only variable specific to surveys used in this paper is income -that is however available in most US censuses -. Other information, like household equipments can be used instead of -or together with -income. The variable 'relation to household head' is used to show the various living arrangements of widowed, divorced or separated females who are not household heads and can be linked with their individual socio-economic characteristics. Additional variables can be constructed: numbers of children and elderly -defined by age (see below) -, and numbers of active, or preferably employed, people.