dataesr/scientific-paragraphs-categorization huggingface dataset:
Marbled notothen ( All gDNA concentrations were quantified with a NanoDrop ND2000 spectrophotometer (Thermo scientific, Wilmington, Delaware) and/or Qubit (ThermoFisher, France) before processing gDNA for RAD libraries construction. RAD-Sequencing and demultiplexing of RAD-Seq reads RAD libraries were constructed for each species from individual fish gDNA using a single restriction enzyme, SbfI, following the standard protocol as previously described  (Amores, Catchen, Ferrara, Fontenot, & Postlethwait, 2011). Libraries were sequenced as single end 100 bp reads on one lane of Illumina HiSeq 2500. Quality control and demultiplexing of the reads was performed with the process_radtags. pl wrapper script from Stacks version 1. 44 (J. M. Catchen et al. , 2011)  using the following options: discard reads with low quality scores (-q), remove any reads with an uncalled base (-c), and rescue barcodes and RAD-seq reads (-r).
Performance measurements Runtime and peak memory usage of radsex (version 1. 0. 0) and Stacks (version 2. 2) were measured with "/usr/bin/time -v <command>" on the Genotoul computational platform (Toulouse, France) using 16 threads. For radsex , performance was measured for the process , distrib , and signif commands run sequentially with a minimum depth of 1, as this value would require the longest runtime and memory. For Stacks, performance was measured for the denovo_map. pl pipeline with the following settings: "-M 3 -n 2 -X populations:--fstats", which represent a standard usage to identify sex-specific markers. https://github. com/SexGenomicsToolkit/sgtr for sgtr. Both radsex and sgtr are also available on the conda channel Bioconda. A complete documentation for RADSex is available at https://sexgenomicstoolkit.
RADSex validation on a published dataset To assess whether RADSex can recover previous results on sex determination, we analyzed a dataset which has been used to characterize a major sex-determination locus with a STACKS-based approach  (Wilson et al. , 2014). This dataset consists of RAD-Seq reads from 31 males and 30 females from the carbio strain of Oryzias latipes , the Japanese medaka, which has a known XX/XY sex determination system with chromosome 1 as the sex chromosome  (Matsuda et al. , 2002; Indrajit Nanda et al. , 2002). We analyzed this O. latipes dataset with RADSex using a minimum depth of 10 (-d 10) to define the presence of a marker in an individual. This value of minimum depth was chosen based on the median sequencing depth in each individual computed with radsex depth : the average median sequencing depth was 34, and thus a stringent minimum depth of 10 can be used to discard markers from potential sequencing errors but still retain real markers.
(Wilson et al. , 2014)  , who found 248 RAD-seq reads associated with male phenotype mapping to a region ranging from 14. 3 to 32. 5 Mb on Ola1. However, our analysis revealed a previously unidentified Y-specific polymorphism in the sequenced population. This example highlights the effectiveness and versatility of RADSex and its visualization tools to identify, explore, and explain patterns in datasets that can be overlooked with traditional approaches. Analysis of 15 new ray-finned fish RAD-sequencing datasets RAD-Seq datasets were generated for 15 ray-finned species in which no markers for genetic sex determination were available when we initiated this work. For each species, we used RADSex to search for sex-biased markers with a minimum marker depth (-d) of 1, 2, 5, and 10. We report results for a minimum marker depth of 10 ( Table  1  ) as it is the most stringent, except in three species with the lowest sequencing depths, for which we report results for a minimum marker depth of 2 ( Supp Fig.
, 2019; Star et al. , 2016). The incomplete sex-linkage of sex-biased markers may be explained by a Y chromosome polymorphism and/or the existence of sex-reversed genetic XX females. Although Y chromosome population differences have rarely been explored, at least in fish species, the results from our re-analysis of a medaka population suggest that such polymorphism may be more frequent than expected. In addition, female-to-male sex-reversal could be a consequence of intensive rearing aquaculture conditions leading to stress induced sex-reversal  (Geffroy & Douhard, 2019)  , which has been observed in other fish species reared in laboratory or aquaculture facilities  (Pan et al. , 2019). In the nine other species ( Table  1  and Supp. Fig. 3  ), we did not find any marker significantly associated with phenotypic sex, which indicates that these species either 1) have a small undifferentiated sex locus, 2) lack a genetic sex determination mechanism, or 3) rely on a complex combination of environmental and genetic sex determination factors that prevented the detection of a clear sex-biased signal in the dataset.
DISCUSSION We demonstrate that RADSex is an efficient and, thanks to its simple usage and visualization options, user-friendly tool to identify sex-determination systems and sex-biased markers from RAD-Seq data. Unlike other popular RAD-Seq analysis software, e. g. STACKS (J. M. Catchen et al. , 2011)  and PyRad  (Eaton, 2014)  , which catalog polymorphic loci, RADSex creates monomorphic markers. RADSex therefore counts each allele at a locus defined as polymorphic by other software as a separate marker. Working with monomorphic markers allows statistical comparison of populations using straightforward presence/absence tests, yielding results that are comparable between datasets. In contrast, the outcome of grouping sequences into polymorphic markers can be sensitive to parameter values  (Paris et al.
altivelis ) and the tench ( T. tinca ) datasets: in both species, the tile plot revealed a complete sex-linkage of several markers significantly associated with male phenotype, indicating a male heterogametic sex determination system. Besides these straightforward cases, the tile plot visualization is most useful to identify the sex-determination system in more complex datasets, for instance in the presence of outliers and sequencing biases. These complex datasets can then be further explored with the marker depths heatmap visualization, for instance to confirm whether sex-biased markers are absent or present in specific individuals. Using this heatmap, we were able to quickly identify sex-reversed individuals responsible for the non-complete sex-linkage of sex-biased markers in a publicly available medaka dataset  (Wilson et al.
, 2014)  , as well as outliers in our own common carp ( C. carpio ), Atlantic cod ( G. morhua ), banded knifefish ( G. carapo ), and common molly ( P. sphenops ) datasets. These outliers could arise from human errors in assessing phenotypic sex; in particular, in the common molly, males are externally indistinguishable from females until puberty, which occurs late in some males, and immature testes resemble non-reproductively active ovaries. Because in this species sex was determined from secondary sex characters and macroscopy of the gonads, but not histology, it is possible that the relatively high number of females in which we found male-biased markers were immature males that were mis-sexed as females. However, the presence of outliers could also hint at a more complex sex determination system, for instance polygenic sex-determination  (Moore & Roberts, 2013)  , autosomal modifiers that are polymorphic in the population  (Wu, 1983)  , or simple monofactorial genetic sex determination systems with some sex-reversed individuals triggered by environmental factors  (Baroiller & D'Cotta, 2016; Dupoué et al.
, 2019; Wessels et al. , 2017). With a high occurrence of outliers, sex-biased markers may no longer be significantly associated with phenotypic sex, yet these markers can still be biologically relevant. Because RADSex creates non-polymorphic markers, the number and distribution of markers between individuals is only affected by a single parameter controlling the minimum depth to consider a marker present in an individual. An overly high minimum depth value can lead to false negatives, i. e. markers considered absent from an individual because of insufficient sequencing depth, and a very low value can create false positives, i. e. markers considered present in an individual because of sequencing errors. Moreover, because the probability of association with phenotypic sex for a marker is adjusted using Bonferroni correction, the stringency of the test for significance increases with the total number of markers identified in the dataset, which is affected by the minimum depth parameter.
In the 15 datasets that we generated, median sequencing coverage in an individual was between 30x and 100x, except for three species for which it was lower than 30x ( Supp. Fig. 2  ). For all species except the three species with a low sequencing coverage, we found that minimum depth values between 5 and 10 provided a good balance between stringency and minimizing false negatives. However, some datasets may require using low minimum depth values, for instance the three datasets for which median individual sequencing depth was low, or species with large genome sizes like some sharks  (Hara et al. , 2018)  or some amphibians  (Sclavi & Herrick, 2019)  for which sequencing all individuals of a population with sufficient depth can be costly. In such cases, the memory efficiency of RADSex enables the analysis of large datasets with minimum depth as low as one, which can require a lot of memory with other software.
The numbers of markers significantly associated with phenotypic sex can provide an estimate of the size of the sex determining region, with a low or high number of sex-specific markers reflecting a small or a large non-recombining region, respectively. It is worth noting, however, that other factors can affect this number, for instance the level of differentiation between males and females within the non-recombining region, or bias in GC content within this region  (Sigeman et al. , 2018; Smeds et al. , 2015)  potentially leading to an over-or under-representation of RAD-Seq markers depending on the restriction enzyme used for library construction. An extreme case is a complete absence of markers associated with phenotypic sex for any value of minimum depth, which we observed in nine of our datasets.
However, when a genome is available, locating sex-biased markers provides information on the size and level of differentiation of the sex-determining region and can help to identify candidate genes involved in sex determination. The manhattan plot, circos plot, and contig plot visualizations included in RADSex quickly display the location of the sex-biased markers over the whole genome or over selected regions to identify the sex-determining region. Furthermore, the sex-bias metric and probability of association with sex computed with RADSex can be more effective than F ST computed on polymorphic loci to detect variability in the sex-determining region. This was illustrated by our reanalysis of a publicly available Japanese medaka dataset  (Wilson et al. , 2014)  , in which we detected multiple Y chromosome haplotypes that were not found in the original study.
As part of this study, we used RADSex to investigate the sex determination systems of 15 species sampled broadly across the ray-finned fish phylogeny (Sup. figure X). We identified XX/XY SD systems in six of these 15 species, and we did not find evidence of a ZZ/ZW SD system in any of the other nine species. Although our sampling is too limited to make general inferences about teleost sex determination, the predominance of XX/XY over ZZ/ZW SD systems in our datasets is in agreement with previous findings that in teleosts, transitions from ZZ/ZW to XX/XY SD systems are more frequent than the reverse  (Pennell, Mank, & Peichel, 2018)  , which would result in XY SD systems being prevalent in this clade. All our datasets were generated using single-digest RAD-Seq, but the workflow accepts double-digest RAD-Seq data as well.
Furthermore, although all species included in this study are fish, we did not use any fish-specific assumptions in RADSex's implementation, and therefore the computational workflow can be applied to RAD-Seq data from any species. Finally, we specifically developed RADSex to study sex determination, and this design decision is reflected in the wording of this manuscript. However, the computational workflow was designed to be generic, and therefore both the radsex software and the sgtr R package could be used for other large and major quantitative trait loci with contrasting binary phenotypes. A table containing the sequencing depth of each marker in each individual is computed using the process command from radsex. This table is then used as input for the distrib command to compute the distribution of markers between males and females, and this distribution is visualized with the radsex_distrib() function of sgtr.
RADSex's runtime was linearly and positively correlated to the number of RAD-seq reads, ranging from 139 seconds for Tinca tinca , whose dataset contained ~130 M. RAD-seq reads , to 1106 seconds (18 min and 26 s) for Sander vitreus , whose dataset contained ~262 M. RAD-seq reads ( Fig. 3. A ). RADSex was on average 42 times faster than STACKS on the same dataset, ranging from 26 times faster for Danio choprae to 88 times faster for Danio albolineatus ( Fig 3. B ). RADSex's peak memory usage was also linearly and positively correlated to the number of RAD-seq reads, ranging from 2. 4 Gb for Poecilia sphenops to 17 Gb for S. vitreus ( Fig 3. C ). RADSex used on average 1. 9 times less memory than STACKS to analyze the same dataset, ranging from 1. 2 times less memory for D. choprae to 3. 4 times less memory for D.
ACKNOWLEDGEMENTS We thank all colleagues who helped us improve the beta version of RADSex by testing it and providing feedback. This project was supported by funds from the  "Agence Nationale de la Recherche'', the "Deutsche Forschungsgemeinschaft'' (ANR/DFG ,  PhyloSex  project,  2014-2016 ,  SCHA 408/10-1 , MS ), the  National Institutes of Health (USA)  grant  R01GM085318 ,  JHP ), and the  National Science Foundation (USA) Office of Polar Programs  (grants  PLR-1247510  and  PLR-1444167  to HWD and grant  OPP-1543383  to JHP, TD, and HWD). The MGX core sequencing facility was supported by  France Genomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). RF was partially supported by  Swiss National Science Foundation  grant  PP00P3_170664  to RMW.
Software used in the analyses All analyses were performed using version 1. 0. 0 of radsex and figures were generated using version 1. 0. 1 of the sgtr R package in R version 3. 5. 2. Both radsex and sgtr are released under GPLv3 license; releases and source code are available at https://github. com/SexGenomicsToolkit/radsex for radsex and DATA ACCESSIBILITY All RAD-Sequencing experiments have been submitted to Genbank under the bioproject PRJNA548074. A computational workflow implementing all the analyses performed in this study, including generating figures, is available at https://github. com/RomainFeron/paper-sexdetermination-radsex. RADSex is released under GPLv3 license; source code, installation instructions, and documentation are available at https://github. com/SexGenomicsToolkit/radsexex.
see Fig. 4. B for an example from a real dataset). Markers obtained with the signif command can also be used as input for the radsex_markers_depth() function from sgtr if they were exported as a table of marker depths. Tiles for which association with sex is significant are highlighted with a red border. Simplified examples of tile plots for several sex-determining systems are presented in Fig. 2. Next, RADSex can filter and cluster markers based on depth across individuals to identify consistent absence or presence of markers in some individuals. Markers can be filtered based on presence in males and females using the radsex subset command and exported in the same format as the table of marker depths ( Supp. Fig. 1 ). After extracting a subset of markers, a heatmap showing the depth of each marker in each individual can be generated using the radsex_markers_depth() function from sgtr.
INTRODUCTION 1. 1 Problem statement The joint use of surface reflectances from different sensors can be challenging because of differences in sensor characteristics, ground segment algorithms and their parameters, and exogenous data used for corrections. All those factors can affect the coherency between surface reflectances and therefore create unwanted artefacts in operations leveraging direct comparison or statistical learning. To tackle this issue, the standard physicsbased method includes careful modelling of effects induced by sensor differences as well as the use of common algorithms and parameters for both sensors, as demonstrated in  (Claverie et al. , 2018)  for the constitution of an harmonised Sentinel-2 and Landsat 8 dataset. When it comes to jointly using Pleiades and Sentinel-2, differences in sensors characteristics include (i) a factor of 5 in spatial resolution, (ii) different viewing angles for each acquisition compared to fixed nadir view, which causes BRDF variations, and (iii) differences in spectral sensitivities.
Additional differences exist in ground segment algorithms, especially when using L2A Sentinel-2 products, for which endogenous atmospheric correction parameters are estimated from dedicated spectral bands, unavailable on sensors like Pleiades. This results in potentially non-linear reflectance discrepancies, as shown in figure  1. One could of course benefit from the parameters estimated during Sentinel-2 atmospheric corrections, but many of those parameters vary rapidly in time, while Pleiades and Sentinel-2 pairs can be several days apart in time. Nonetheless, some Pleiades data providers deliver images that have already been processed for atmospheric corrections, with undisclosed algorithms and parameters. In our case, level 2A Sentinel-2 time series contain fairly accurate estimations of surface reflectances with cloud and snow screening  (Lonjou et al.
Radiometric Top of Canopy mapping For the Aude1 dataset, surface reflectances are already very consistent between Pleiades and Sentinel-2, as one can see in the performances chart of all methods in figure  3 : visible bands have a RMSE of 0. 01, whereas NIR band has a RMSE of 0. 07. Nevertheless, we can see on scatter plots (figure  4 ) that reflectances are over-estimated in Pleiades image for the NIR band, and that for the green band a slight under-estimation of low reflectances and over-estimation of high reflectances exist. This is expected since the spectral bandwidths of Pleiades bands are larger than those of Sentinel-2. Even in this case, where level of radiometric calibration is equivalent and differences are small, the estimation of more consistent values between sensors is important.
We evaluated those methods on four different datasets over two different sites in France, learning one model for each dataset, since we do no seek for generalisation. We saw that using a MLP based architecture does not bring much improvement with respect to linear regression if Pleiades products are already calibrated at Top of Canopy level. Both can yield significant improvements especially in the NIR band, where the RMSE error is more than twice smaller. However, when using Top of Atmosphere Pleiades products as input, using a MLP based architecture brings 10% to 25% of improvement to all bands with respect to linear regression. We can assume that the correction to learn is more non-linear in that case, since it contains the removal of the contribution of the atmosphere. At first sight, using a convolutional architecture seemed to yield even better results but this is mostly due to the learning of spatial discrepancies such as mis-registration or incomplete MTF knowledge, and such a model will alter the high spatial frequency of the Pleiades image when applied at full resolution.
This study proposes a geopolitical analysis of opinion dynamics based on a statistical exploration of a press dataset covering 2014-2019. This exploration questions three case studies of geopolitical and international interest: international migration, political borders, and pandemics. Through the framework of geopolitical agenda, the aim of this study is to question the "crisis" status of changes in the media coverage of the three topics in a crossanalysis and multilingual analysis of 20 western European newspapers. It concludes that there is a prevalence of national agendas.
Indeed, analyzing the public sphere using big data requires transferring the concept into methods of analysis. However, the crisis, as a rupture, lends itself well to statistical modeling. In this study, we undertake a statistical analysis to contextualize and explore geopolitical crises in the daily press prior to in-depth thematic studies. Based on the exploration of a massive corpus of short texts (titles and first sentences of an article) could lead to more qualitative analysis of the full text of the articles or use of methods with more substantial and costly computational procedures, such as precision grammar. This study makes a novel contribution to the literature by undertaking a multilingual and comparative analysis of the agendas of three geopolitical crises over a relatively long period  (2014) (2015) (2016) (2017) (2018) (2019)  and a large sample of media outlets located in five countries (France, Germany, Spain, Italy, and the United Kingdom).
For this purpose, we use an interactive dashboard (TELEMAC) that has been developed within a European research in computational social sciences applied to opinion dynamics (Acknowledgments) to visualize and compare the spatial and temporal distribution of crises from a multidimensional perspective. The rest of the paper is organized in four sections as follows. 1) Conceptual framework. First, we present the concept of geopolitical agenda, which could be a framework for investigating the notion of "crisis" as a way to question the changing perception of topics in the daily press. We present this conceptual framework and the related methods as a list of dimensions that should be combined in the phase of data collection and exploration. We also present three case studies of crisis (borders conflicts, international migration, and pandemics).
2) Data collection and exploration. Then, we present the corpus of news that has been collected, the original data source (an RSS feeds dataset from media outlet), and how these data have been enriched (e. g. , addition of information related to detection of topics of geopolitical interest, and to the detection of foreign countries mentioned in the news). We also briefly present the TELEMAC interface and methodology used for the statistical analysis presented in the results. 3) Results. In this section, we present a selection of results obtained through the application of statistical models to each of the three topics for the entire period of observation. 4) Discussion. The general results of the analysis are that the salience of each topic is comparable in each country of observation, and the geopolitical agenda is mainly structured at the national level.
Data Collection Our analysis is based on a corpus of broadsheet mainstream daily newspapers. Obviously, this selection does not reflect the diversity of the media sphere in each of the five countries of observation. We stand on three criteria to build the corpus. • First, following  Segev (2016) , we select daily national newspapers, because they are media with a specific focus on international news as sensors of the media sphere. • Second, we select media outlets fulfilling data quality criteria (see the Supplementary Material). • Third, we analyze four broadsheet newspapers by country (available in the database of Media Cloud) to ensure a minimal diversity of editorial lines (e. g. , mixing left-with right-oriented newspapers. However, the sample is further reduced to 18, because some newspapers do not fulfill the expected conditions of the minimum number of news articles.