dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements I would like to thank  Alice Desclaux  ( IRD ) for her enthusiasm for the idea of this piece as a  MAT Photo Essay as well as Frédéric Keck (CNRS) , with whom this research began within the  LAS. Finally, I'd like  to thank  Sophie-Hélène Trigeaud (LADEC)  for her help proofreading the English version of this article.
Using a corrected version of the sister clade analysis on an updated dataset, dioecious clades have been found to have actually higher species richness than non-dioecious sister clades in angiosperms  (Kafer et al. 2014). Using a phylogenetic method to study trait evolution and species diversification on several genera with dioecious species  (Maddison et al. 2007) , dioecy was not associated with an increased extinction rate  (Sabath et al. 2016; Duan et al. 2018). Another hypothesis to explain the rareness of dioecy in angiosperms posits that reversion from dioecy to other sexual systems is frequent and that dioecy is more evolutionary labile than previously thought  (Kafer et al. 2017). This hypothesis was supported by a study of 2145 species from 40 genera in which the authors found evidence of frequent reversions from dioecy to other breeding systems  (Goldberg et al.
2012; Gayral et al. 2013; Romiguier et al. 2014). Multiple individuals were Population genomic consequences of separate sexes in plants sampled across the species' geographic ranges and sequenced by RNA-seq. A pipeline specifically designed for such data was applied to perform SNP-calling  (Tsagkogeorga et al. 2012; Gayral et al. 2013; Nabholz et al. 2014 ). The SNPs were then analyzed to estimate genetic diversity and the efficacy of selection in each species.
However, S. viscosa E exhibited a high F IT , a low heterozygosity level and low genetic diversity, which could be due to recent demographic shifts or a high selfing rate (Fig. 2 ). Proxies for species abundance were calculated based on the number of occurrences of each species retrieved from the Global Biodiversity Information Facility (GBIF) database. The geographic range of each species was extracted from to the Atlas Florae Europaeae (see Materials and methods, Fig. S6 , Fig. 3 ). Dioecious and non-dioecious species had similar geographic ranges (622. 6 versus 784 Atlas dots on average, Wilcoxon one-sided rank sum test p-value=0. 52) and GBIF species abundance (30,777 versus 23,968. 5 on average, Wilcoxon one-sided rank sum test p-value=0. 43). This is not consistent with the dead-end hypothesis which predicts smaller geographic distributions and population sizes for dioecious species.
However, the 13 species studied here have very different census population sizes (N) with some species being cosmopolitan (e. g. S. latifolia, S. vulgaris) while others are rare or even endemic (e. g. S. diclinis). Genetic diversity can be affected by N, although the relationship can be complex such that N and N e are quite different  (Kalinowski and Waples 2002). In order to make a fair comparison of dioecious and non-dioecious species, we accounted for the effect of N when looking at the effect of sexual systems on genetic diversity. Two different proxies for N were used: (i) the species abundance in the GBIF dataset, and (ii) the geographic range of each species according to the Atlas Florae Europaeae. We used a linear model to jointly assess the effects of N and sexual systems on π S. GBIF data and Atlas geographic range (estimates of N) were significantly positively associated with π S (pvalues = 0.
030 and 0. 012, t-values = 2. 529 and 3. 054, R 2 = 0. 390 and 0. 483 respectively, and see Fig. 3 ). This shows that genetic diversity is strongly influenced by N in our Silene sample set. This also suggests that, in the case of the Silene genus, GBIF abundance and Atlas geographic ranges are sensible proxies for N. After controlling for the effect of N on genetic diversity, dioecious species Population genomic consequences of separate sexes in plants had a significantly higher π S than non-dioecious species (linear model pairwise t-test p-values = 0. 142 and 0. 048, t-values = 1. 595 and 2. 247 using GBIF data and Atlas geographic range estimates respectively). Adding F IT to the linear model correlating genetic diversity and N did not significantly improve our linear model (anova p=0. 102 and p=0.
097 for GBIF and Atlas geographic range estimates respectively, see Materials and Methods for details). This suggests that the increase in genetic diversity observed under dioecy is not attributable to inbreeding evasion. These effects remained after correcting for phylogenetic relationships among species using generalized least square models in the R package ape  (Paradis and Schliep 2019) : N was significantly positively correlated to π S (p-values = 0. 012 and 0. 03, t-values = 3. 05 and 2. 53 for Atlas and GBIF respectively) and dioecious species had a significantly higher π S compared to nondioecious species when using Atlas geographic range estimates (p-value = 0. 048, t-value = 2. 25), but not when using GBIF estimates (p-value = 0. 14, t-value = 1. 6). In this analysis, different sets of genes were used in different species.
Because N e is known to influence the efficacy of selection, we correct for differences in N e (using π S as a proxy) between species in order to make a fair comparison of dioecious and non-dioecious species. A linear model was used to jointly assess the effects of N e (using π S as a proxy) and of the sexual system on α. π S was significantly positively correlated to α (p-value = 0. 003, t-value = 4. 07, Fig. 4 ). The same result has previously been observed in animal species  (Galtier 2016)  where the correlation between α and π S was solely due to the link between π S and ω NA. Because α = ω A / (ω A + ω NA ), when selection is more efficient (higher N e and higher π S ), the rate of deleterious substitutions ω NA is reduced, which in turn increases α. However, in the present dataset, the correlation between α and π S was both due to higher adaptive evolutionary rates and lower deleterious evolutionary rates when effective population size is high.
Indeed, π S was significantly positively linked to ω A (linear model p-value = 0. 011, t-value = 3. 19) and significantly negatively correlated to ω NA (linear model p-value = 0. 00017, t-value = -6. 15, see Fig. 4 ). These effects remained after correcting for phylogenetic relationships among species using gls models in the R package ape (Paradis and Schliep 2019): π S was significantly positively correlated to α and ω A (p-values = 0. 026 and 0. 0054, t-values = 4. 11 and 3. 64 for α and ω A respectively) and π S was negatively correlated to ω NA (p-value = 0. 0015 and t-value = -4. 49). Estimates of α, ω A and ω NA were then compared between dioecious and non-dioecious species, after controlling for the effect of N e (using π S as a proxy). Non-dioecious species had a significantly lower α and ω A and a significantly higher ω NA compared to dioecious species (linear model pairwise t-test p-values = 0.
015, 0. 00031 and 0. 019, t-values = -2. 99, -5. 65 and 2. 87 for α, ω A and ω NA respectively). These effects remained significant after correcting for phylogenetic relationships among species using gls models in the R package ape (p-values = 0. 032, 0. 0007 and 0. 058, t-values = -2. 53, -5. 03 and 2. 17 for α, ω A and ω NA respectively). Dioecious species thus experience fewer fixations of slightly deleterious mutations due to genetic drift and more adaptive evolution, which is opposite to what we expect under the dead-end hypothesis. The analyses were repeated using orthologous genes shared within the subgenus Behenantha or within the subgenus Silene as different sets of genes in different species could affect the results. The conclusions remained unchanged; differences were still in the same direction although statistical power was reduced in some cases (see Fig.
One selfing hermaphrodite subspecies (S. viscosa E) could not be included in the analysis of selection efficacy because its genetic diversity was too low. However, two hermaphrodite species that are not heavy selfers (Figure  2 ) were included in our comparison of dioecious and non-dioecious species. However, the fact that significant differences in terms of selection efficacy between dioecious and non-dioecious species remain, even without this selfing species, highlights that the advantages of dioecy that we observe do not result from comparing selfing and outcrossing species. What is more, the increased genetic diversity observed in dioecious species could not be explained by differences in inbreeding levels in our dataset. However, the main difference between dioecious species and hermaphrodites is the ability to occasionally self-fertilize, which can strongly affect the demography of a species.
Our results suggest that established dioecious Silene species are genetically healthy. Our work revisits the fascinating question of why dioecy is rare in angiosperms, since the dead-end hypothesis does not seem to apply, at least to our sample of species. However, our dataset might suffer from a "survivor bias" and we cannot exclude that potential handicaps of dioecy could lead to very early extinction during the emergence of new dioecious populations from hermaphrodite lineages. At a macro-evolutionary scale, this rapid dead-end would lead to an observed low rate of transition towards dioecy. However, current estimates do not show low transition rates to dioecy in angiosperms  (Goldberg et al. 2017) , which goes against the possibility of an early dead-end of dioecy. An alternative hypothesis to explain the rarity of dioecy is that rates of transition to dioecy could be low in angiosperms and/or dioecy could be more evolutionary labile than previously thought and easily lost and reversed to non-dioecious states depending on environmental conditions  (Goldberg et al.
Phylogenetic reconstruction and phylogenetic signal Orthologous ORFs among species were obtained using best reciprocal blast hits among the 14 species (all species from the Silene genus and Dianthus chinensis). For each orthologous ORF, the nine species were added one by one into the alignment using MACSE  (Ranwez et al. 2011 ). The data were filtered so that a maximum of 20% missing data per site and per species was allowed, for a total of 55,337 variable nucleotides across 1,294 ORFs. We used the maximum number of ORFs available and did not restrict our analysis to autosomal ORFs identified in dioecious species. The phylogenetic tree was reconstructed using the GTRGAMMA model in RaxML  (Stamatakis 2006). Bootstrap support values were generated using 100 replications. Phylogenetic signal and its significance were computed using the R function phylosig in the phytools R package  (Revell 2012).
Acknowledgements We would like to thank  Jos Kafer  for his help with fieldwork and a number of colleagues for sending us seeds:  J. J. Wieringa ,  Deborah Charlesworth ,  Bohuslav Janousek ,  Tatiana Giraud ,  Honor C. Prentice ,  Salvatore Cozzolino ,  Delphine Griveau ,  Henk Schat ,  Cristina Gonnelli ,  Alessia Guggisberger ,  Sophie Karrenberg ,  Adrien Favre ,  Maria Domenica Moccia ,  Aria Minder ,  Carmen Rothenbuehler ,  Massimiliano Gnesotto  and  Kerstin Kustas. AM, RT and GABM thank  Elise Lacroix  technician of the  Univ. Lyon 1 greenhouse and HM, SGa and PT thank  Eric Schmitt  for taking care of the Silene plants. We are grateful to  Sylvain Santoni  and the AGAP platform staff for the RNA extraction and RNA-seq. The numerical results presented in this paper were carried out using the computing cluster of  Laboratoire Biométrie et Biologie-Pôle Rhone-Alpin de Bioinformatique (LBBE-PRABI ) at  Univ.
Lyon 1 and the  HPC service of the Centre de Ressources Informatiques (CRI)  of  Univ. Lille 1, for which we thank  Stéphane Delmotte  and  Bruno Spataro. We thank  Nicolas Galtier  for help with his program sfs. HM, SGa and PT are grateful to  Jonathan Aceituno  for technical support in R and to  Céline Poux  for discussion on phylogenetic inference. We are thankful to  Brandon Gaut  for commenting on the manuscript. This work was supported by the  Agence Nationale de la Recherche  ( ANR-11-BSV7-013-03 , TRANS) to SGl, GM, and PT, the  Swiss National Science Foundation  ( 31003A-182675 ), and a PhD fellowship from the  French Research Ministry  to HM.
In contrast to other biological disciplines in which new (mostly more precise) data types often completely replace old ones, data used in taxonomy may retain their significance over centuries. Comparisons of geographical occurrence records or morphological traits, such as hair, scale, or stomata density as well as co-occurrence, like host-parasiterelationships, generate additional value for our understanding of biological, evolutionary, and ecological processes. Since the Renaissance, the sustainable maintenance of such data has been ensured by books and other print media and, of course, by physical specimens stored in natural history collections. Computerization of science and society over the last 40 years has led to profound transformations, increasing the speed of data traffic and opening unprecedented opportunities to quickly generate and transmit visual and acoustic information, the latter highly important for the study of birds and amphibians.
This transformation has also affected taxonomy, which has undergone a profound transformation, triggered mostly by molecular-genomic information that is fundamentally altering understanding of evolution and speciation. Moreover, the growing demand for taxonomic information in other branches of science has raised the need for speedy species identification (defined as the decision that an organism either belongs to an already named species or requires description and naming), which demands the combination and crosslinking of datasets or different layers of data. Automation of at least part of the taxonomic workflow, while maintaining comparability with historical data and knowledge, is needed if we are to speed up species naming and identification (of already named species). However, automated species identification by machine learning tools or DNA metabarcoding techniques can only work with comprehensive character comparison and integration with previous information.
Recognition of regularities and irregularities in sets of characters provides density estimations to discover data clusters, but needs expert verification and control. There are currently two million species names and hundreds of millions of specimens in natural history collections  (Short et al. 2018 ), with about 20,000 new species being named each year (Zhang 2011; but see next paragraph for a finer-scale analysis). Natural history collections and libraries have started digitizing specimen catalogs and historical literature. Many name-bearing-type specimens are represented by high-resolution images on the web. Over one billion species occurrence records are available through the Global Biological Information Facility (gbif. org, accessed May 2019), and numerous curated species databases have been compiled for the "Catalogue of Life" (catalogueoflife.
org; accessed May 2019). Although most genetic data are not generated by taxonomists, sequence information in the INSDC databases (www. insdc. org: GenBank, https://www. ncbi. nlm. nih. gov/genbank/), European Nucleotide Archive (ENA, https://www. ebi. ac. uk/ena), DNA Data Bank of Japan (DDBJ, http://www. ddbj. nig. ac. jp, all accessed May 2019) is growing fast and represents a valuable resource for taxonomists. In fact, sharing of molecular data, as imposed by many journals, is an excellent example of how scientific research benefits from data exchange at a global scale.
Importance of digital availability of primary data The management (storage and retrievability) of data packages (Fig. 1 ) produced in alpha-taxonomic studies-dealing with taxonomic revision, the "sinking" of synonyms, and description of new species-is, however, not keeping pace with these modern developments. The number of species descriptions per year has increased for fungi and vertebrates, but has been decreasing for plants and insects for some time (Fig. 2A ). Authors in the journal Zootaxa, here used as a benchmark, have published 13% of all animal species descriptions from 2004 onwards (Index of Organism Names, http://www. organismnames. com, accessed June 2019). Of these descriptions, many do not make reference to molecular data (Fig. 2B ), and in the year 2018, only about 14,000 DNA sequences deposited in GenBank were linked to Zootaxa papers.
With 2321 papers published in Zootaxa in 2018, this corresponds to an average of six DNA sequences per taxonomic study. The use of DNA information in alpha-taxonomic work differs, however, among groups of organisms, as illustrated by a survey we undertook of 2208 papers published in 2018 (Fig. 2B ): While taxonomists working on fungi heavily relied on molecular data, this was only true for a small proportion of taxonomic studies on plants and, especially, insects (Fig. 2B ). Information in DNA sequences is ideally suited to be used in crisp diagnoses of type material, and this is increasingly being recognized and should be promoted in all Codes of Nomenclature, not just those for fungi and prokaryotes  (Renner 2016 ). Among the 2208 surveyed papers, genomescale data sets (such as RADseq, Sequence capture, RNAseq, full genomes) were only used in one paper in mycology (a draft genome), and not at all in zoology or in botany.
Also, at the moment, all Codes of Nomenclature require text, in addition to a specimen, for a new species to be named; the image of a DNA sequence or a link to DNA sequences in a data bank, linked to the type specimen, is insufficient for naming a new species. The storage of non-molecular raw data associated with species descriptions or taxonomic revisions also has remained problematic, and such data are therefore often lost for future re-and meta-analyses  (Schmidt et al. 2016). For instance, the journals Phytotaxa and Zootaxa do not provide a default option for the publication of supplementary primary data, which could be tables, spreadsheets, images, audio files, or other kinds of data. Data packages related  Morphometry" to all sets of measurements realized and reported with a comparative perspective on a set of several specimens, and "3D imagery" to every studies having generated data using tomographic methods (mostly 3D X-ray μCT-scan, plus one paper using synchrotron radiation μCT-scan) to a taxonomic study can be diverse (Fig.
1 ) and require being stored in different distributed repositories. Other journals, such as PhytoKeys (http://www. phytokeys. com) and ZooKeys (http://www. zookeys. com), do offer this possibility and have set new standards in taxonomic publishing and especially dissemination, in full compliance with the current Codes of Nomenclature. These journals use semantic mark up, or tagging, a method that assigns markers, or tags, to text strings such as taxonomic names, gene sequences, localities, and designations of nomenclatural novelties  (Penev et al. 2010 (Penev et al. , 2018)). Tagged information can then be saved in machine-readable languages like XML (eXtensible Markup Language). Semantic tagging allows not only computerized methods of archiving and data mining from articles but also provides the basis for so-called semantic enhancements, which permit the linking to related articles or data.
The definition of "specimen" varies among groups of organisms; in zoology, it typically is an individual voucher specimen stored in a collection. In botany, it is a complete or partial plant stored in an herbarium. In paleontology, it may be a stone including one or more fossils Fig. 2  Scheme of a data package in taxonomy centered around a specimen. This example is from zoology, and data types will obviously differ among taxa stored in a collection. For prokaryotes, protists and fungi, the preserved entities usually are either dried herbarium specimen or strains cultivated in a laboratory and preserved in culture collections; in other cases, microscopy slides or samples of multiple individuals might be treated as a specimen. Despite heterogeneous species concepts and problems in applying these across the Tree of Life, the availability of as many data per specimen as possible will benefit future taxonomic work.
Benefits of data sharing in taxonomy Taxonomists rarely work in large teams; for instance, half of the 2311 papers published in Zootaxa in 2018 were authored by only one or two researchers. Such small teams may lack institutional support for performing elaborate bioinformatic tasks, and submitting data to repositories may become an additional time-consuming burden. However, the benefits of data availability and the possibility of re-use by other researchers are large, both for the progress of the discipline as a whole, and for the individual researcher who aims at recognition and visibility for his or her work. For the past eight years, the National Science Foundation, USA (NSF), has sponsored the 10-year 100-million USD program "Advanced Digitization of Biodiversity Collections," which has paid for nearly 62 million specimens to be digitally photographed from multiple angles for specific research studies.
It is now expanding efforts to develop a standardized, upgradable system for linking disparate databases to create "extended specimens. " Users will see not only a specimen but also data, such as DNA sequences and environmental information from its collection site. Such information can help assess shifts in faunas and floras that occurred with changes in climate or land use. Unfortunately, despite at least a decade of calls for image sharing (e. g. Pyle et al. 2008; Seltmann 2008; Winterton 2009) , the datasets underlying classical drawings or image documentation of categorical character states are rarely published. This requires the next taxonomist working on the same group of organisms to re-score the very same specimens for the very same characters, forgoing the opportunity to continuously optimize taxonomic treatments  (Favret 2014).
The NSF has required such plans since January 2011, and all such plans are reviewed as an integral part of the proposal. For a researcher to receive continued NSF funding, data availability from previous projects is checked by the reviewers. An imitation of the outstanding success of data publication by the mutualism of journals and INSDC is currently unfortunately not feasible for other data. Nevertheless, an additional incentive for digital data storage is the option of publishing data sets and their description in vetted repositories (e. g. www. pangaea. de) and in new data journals, such as Biodiversity Data Journal (https://bdj. pensoft. net/), Scientific Data (www. nature. com/sdata/), or GigaScience (https://academic. oup. com/gigascience), which increases the availability, visibility, and reusability of the data.
Sustainable data repositories for long-term data storage and re-use Sharing and re-using data requires companies or institutions that maintain sustainable long-term data storage and ensure the technical implementation of data storage capacity  (Bach et al. 2012; Allison et al. 2015) , a task complicated by the lack of long-term funding for the relevant databases. For example, extensive efforts are required to fulfill standards in this field, such as mirroring data, as successfully implemented by the INSDC databases that store molecular data. Moreover, longterm data storage is only reasonable if the re-use of the stored data is adequately facilitated, which for alpha-taxonomy is determined by the ease of submitting data, linking data, and finding data. These factors, in turn, depend on the curation level of the archived data.