dataesr/scientific-paragraphs-categorization huggingface dataset:
Chromatin immunoprecipitation Wildtype, cfp-1(tm6369), and set-2(bn129) mixed embryos were obtained by growing strains at 20°C in liquid culture using standard S-basal medium with HB101 bacteria. Strains were grown to the adult stage then bleached to obtain embryos, which were washed in M9, then frozen into "popcorn" by dripping embryo slurry into liquid nitrogen. Chromatin the alignments to BAM format. Normalized ChIP-seq coverage tracks was generated using the BEADS algorithm  (Cheung et al. , 2011; Stempor 2014). ChIP-seq peaks were called for SIN-3, HDA-1, and CFP-1::GFP in wild-type embryos using MACS2 v. 2. 1. 1  (Feng et al. 2012)  with a q-value cut-off of 0. 05 and fragment size of 150bp against summed ChIP-seq input (GEO GSE87524). Peak summits were extended 150bp upstream and downstream, creating 300bp peak regions.
Comparison of ChIP levels between wild-type and cfp-1 mutant embryos were done using SIN-3 antibody Q5986. HDA-1 ChIPs were done using Novus 38660002/Q2354 and H3K4me3 ChIPs used Abcam ab8580. The age distributions of mixed embryo collections were in the following proportions (% <300 cell / % over300cell, average of the two replicates): H3K4me3 ChIPs: WT N2, 51/49; cfp-1, 59/51, set-2, 54/46. SIN-3 and HDA-1 ChIPs: WT N2, 48/52; cfp-1, 49/51. Sequencing libraries were constructed as in (McMurchy et al. 2017). ChIP-seq libraries were sequenced using an Illumina HiSeq1500. SIN-3, HDA-1, and CFP-1::GFP ChIP-seq data processing CFP-1::GFP (GEO GSE49870), SIN-3, and HDA-1 ChIP-seq reads were aligned to the ce11 assembly of the C. elegans genome using BWA v. 0. 7. 7 (Li and Durbin 2010) with default settings (BWA-backtrack algorithm).
Acknowledgements We are grateful to  G. Benoit  for help in bioinformatics analysis,  JL. Bessereau  and  J. Govin  for scientific discussion, and  R. Margueron  for critical reading of the manuscript. We acknowledge the discovery platform and informatics group at EDyP, the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon , and the  SFR Biosciences (PLATIM and PSF). Thanks to  B. Meyer  for ASH2 antibodies and the  Caenorhabditis Genetic Center , which is supported by the  National Center for Research , for strains. Proteomic experiments were partly supported by the  Proteomics French Infrastructure  ( ANR-10-INBS-08-01  grant) and the  Labex GRAL  ( ANR-10-LABX-49-01 ). F. P. acknowledges support by the  ANR  ( N° 15-CE12-0018-01 ), the  CNRS , and the  Fondation ARC.
Determination of Field Line Opening Finally, we demonstrated by measurement of J • (E + v e × B) and by the observation of a mixture of magnetosheath and magnetosheric electrons within a flux tube that the onset of both the whistler waves and the whistler mode chorus waves on the Earthward side of the electron diffusion region is coincident with the opening of magnetic field lines via reconnection. cussions with Love Alm and Douglas Menietti are gratefully acknowledged. The entire MMS data set is available online at https://lasp. colorado. edu/ mms/sdc/public/links/. Fully calibrated data are placed online at this site within 30 days of their transmission to the MMS Science Operations Center. The data are archived in the NASA Common Data Format (CDF) and so can be plotted using a number of different data display software packages that can use CDF files.
A very comprehensive system called the Space Physics Environment Data Analysis System (SPEDAS) is available by downloading http://themis. ssl. berkeley. edu/socware/bleeding_edge/ and selecting spdsw_latest. zip. Training sessions on the use of SPEDAS are held on a regular basis at space physicsrelated scientific meetings. All of the data plots in this paper were generated with SPEDAS software applied to the publicly available MMS database, so they can readily be duplicated.
The nematode Caenorhabditis elegans has been central to the understanding of metazoan biology. However, C. elegans is but one species among millions and the significance of this important model organism will only be fully revealed if it is placed in a rich evolutionary context. Global sampling efforts have led to the discovery of over 50 putative species from the genus Caenorhabditis, many of which await formal species description. Here, we present species descriptions for ten new Caenorhabditis species. We also present draft genome sequences for nine of these new species, along with a transcriptome assembly for one. We exploit these whole-genome data to reconstruct the Caenorhabditis phylogeny and use this phylogenetic tree to dissect the evolution of morphology in the genus. We show unexpected complexity in the evolutionary history of key developmental pathway genes.
Genome sequences of nine new Caenorhabditis species We sequenced the genomes of all newly-described species to high coverage (100 -350x) using short-read Illumina technology. After identifying and removing reads from non-target organisms, we generated draft assemblies for each species, employing heterozygosityaware assembly approaches where necessary. Draft assemblies were scaffolded using assembled transcripts or long-insert (or "mate-pair") data, where available. The sequence data generated for Caenorhabditis vivipara were not of sufficient quality to generate a reference assembly and are not discussed further. All assemblies have been submitted to DDBJ/ENA/GenBank (Table  1 ). Assembly span indicated substantial variation in genome size among species (  * RNA-seq data were not generated and therefore not used to guide gene prediction.
† longinsert (or "mate-pair") data were used during assembly. BUSCO version 3. 0. 2 was used with the 'Nematoda_odb9' dataset for both genome and proteome completeness assessment. Scaffolds shorter than 500 bp were discarded prior to annotation. Wormbase version WS264 of the C. elegans genome was used  (Lee et al. 2018). ‡BUSCO does not find core genes in the complete C. elegans N2 genome. More detailed metrics are presented in Table  S3.
Given sufficient conflicting signals, concatenation approaches are known to lead to inaccurate topologies  (Kubatko & Degnan 2007). Coalescent-based summary approaches, such as ASTRAL-III, are able to accommodate conflicting signals in gene trees arising from incomplete lineage sorting  (Mirarab et al. 2014 ), but may be sensitive to gene tree estimation error  (Gatesy & Springer 2014; Roch & Warnow 2015). The different models used in BI and ML analysis (CAT-GTR and GTR, respectively) are presumably one source of conflict between these two analyses. The CAT-GTR model, unlike the GTR model, is able to accomodate heterogeneity in the amino acid substitution process at different sites in the alignment  (Lartillot & Philippe 2004). Further work is required to determine whether accounting for such heterogeneity is necessary for model adequacy for this dataset.
Future, more detailed studies of intron gain and loss in Caenorhabditis may reveal clues as to which forces govern their rates and, importantly, which mechanisms are responsible for these events. Genome sequencing projects for several other Caenorhabditis species are currently underway, and the resulting data will be vital to these efforts. The evolution of Notch/LIN-12 signalling proteins in Caenorhabditis C. elegans is a major developmental biology model, and understanding how its particular instance of nematode (and animal) development was established may reveal the logic of the evolution of development and the importance of processes such as developmental systems drift. The availability of complete genomic information for species in the genus Caenorhabditis will facilitate realisation of this research programme.
Genomics, species description and Caenorhabditis biology By describing new species of Caenorhabditis with high quality genomic data we hope to promote not only understanding of the evolution of this exciting genus, but also exploitation of these new species in deepening understanding of the pattern and process of organismal evolution. In particular, the ability to interfere with gene function through RNAi and accurately modify genomes using CRISPR-Cas editing is routine in C. elegans and becoming established in other species. Using the genomic data presented here, specific loci can be deleted or altered to test hypotheses of gene function in a rich phylogenetic context. Currently, strains corresponding to about 50 species of Caenorhabditis are available in culture. Our goal is to provide genome sequences and formal species descriptions for all these species.
The assemblies presented here are not chromosomal, unlike the genome of C. elegans, because of the inability of short-read data to bridge and resolve large repeats. Recent developments in genomic technologies make phased, chromosomal assemblies of all Caenorhabditis species' genomes an achievable goal, and we and others are using long read and other approaches to advance this goal. Approximately 20,000 new species are described each year. We also suggest that, wherever technically possible, the generation of genomic data as part of the introduction of new taxa should become standard. Complete sequencing of all taxa on earth has been proposed  (Lewin et al. 2018) , and active support should be offered to taxonomists to add their new species to this global effort.
De novo genome assembly and gene prediction Detailed methods for each species, along with all software tools used (including versioning and command line options), are available in Supplementary File 2. We performed quality control of all raw sequence data using FastQC  (Andrews & Others 2010)  and used Skewer  (Jiang et al. 2014)  and FASTQX Toolkit  (Gordon & Hannon 2010)  to remove low-quality bases and adapter sequence. Adapters were removed from long-insert data using NextClip  (Leggett et al. 2014). For each species, we identified contaminants using taxon-annotated, GC-coverage plots as implemented in blobtools (Laetsch & Blaxter 2017) (preliminary assemblies were generated using CLC Bio (CLCBio, Copenhagen, Denmark), and likely taxon origin determined using NCBI-BLAST+  (Camacho et al.
2009)  or using Kraken  (Wood & Salzberg 2014). Reads originating from contaminant genomes were discarded. We estimated the optimal k-mer length for assembly independently for each genome using KmerGenie  (Chikhi & Medvedev 2014). Preliminary assemblies were generated using several de Bruijn graph assemblers, including Velvet  (Zerbino & Birney 2008)  and Platanus  (Kajitani et al. 2014) , across several parameter values. The resulting assemblies were assessed using numerical metrics, and two biological completeness metrics, Core Eukaryotic Genes Mapping Approach (CEGMA)  (Parra et al. 2007 ) and Benchmarking Universal Single-Copy Orthologs (BUSCO)  (Simão et al. 2015 ) (using the 'Nematoda_ob9' dataset). For each species, the highest quality assembly was selected and, where possible, scaffolded with assembled transcripts using SCUBAT2 (https://github.
Phylogenetic analysis Accessions to all data used in phylogenomics analysis are available in Table  S6. For those species with available genome sequences, we identified and collected the protein sequence of the longest isoform of each protein-coding gene. For those species for which only transcriptome data was available (C. guadeloupensis and C. vivipara), open reading frames and putative protein sequences were predicted using TransDecoder  (Haas & Papanicolaou 2012). OrthoFinder (Emms & Kelly 2015) (using the default inflation value of 1. 5) was used to cluster all protein sequences into putatively orthologous groups (OGs). OGs containing loci present as single-copy in at least 27 of the 33 species (except in those species with genome assemblies known to contain regions of uncollapsed heterozygosity [i.
The protein sequences of each one-to-one OG were then aligned using FSA and gene trees estimated as previously described. ASTRAL-III  (Mirarab & Warnow 2015) , a coalescentbased method, was then used to reconstruct the species phylogeny using individual genes trees as an input. We also reconstructed the species tree using a concatenation approach. TrimAl  (Capella-Gutiérrez et al. 2009 ) was used to remove spuriously aligned regions from each alignment, which were subsequently concatenated using catfasta2phyml (available at https://github. com/nylander/catfasta2phyml). Maximum likelihood (ML) analysis was performed with RAxML (general-time reversible model (GTR)  (Tavaré 1986 ) with gammadistributed rate-variation among sites) along with 100 bootstrap replicates. Bayesian inference was carried out using the site-heterogeneous CAT-GTR substitution model  (Lartillot & Philippe 2004 ) (with gamma-distributed rate-variation among sites) implemented in PhyloBayes MPI  (Lartillot et al.
Notch-like Receptor Analysis In an existing orthology clustering set, we identified the OG that contained the C. elegans proteins LIN-12 (R107. 8) and GLP-1 (F02A9. 6) and collected the protein sequences of each member. After removing sequences that were shorter than 700 amino acids, we generated an amino acid alignment using FSA. We performed a maximum likelihood analysis using RAxML, allowing the substitution model to be automatically selected, and conducted 100 rapid bootstrap replicates. Branch lengths were extracted using a custom Python script (available at https://github. com/lstevens17/caeno-ten-descriptions), making use of the ete3 module  (Huerta-Cepas et al. 2016). We identified conserved domains in each protein sequence using InterProScan  (Jones et al. 2014). Counts of EGF-repeats were obtained from were obtained from the ProSiteProfiles database (release 2017_09)  (Sigrist et al.
Acknowledgements We dedicate this paper, and Caenorhabditis sulstoni, to  John Sulston , a mentor and colleague. We thank  Adeline Mallet  for assistance at the Ultrastructural Microscopy Platform of the  Pasteur Institute. We are very grateful to  Fabrice Besnard ,  Ludmilla Lokmane ,  Jean-Baptiste Pénigault ,  Clotilde Gimond ,  Charlie Gosse ,  Howard Baylis ,  Kevin Howan ,  Sarah Mühlberger ,  Amir Yassin  for samples. We thank  Taisei Kikuchi ,  Janna Fierst  and  Erich Schwarz  for pre-publication access to the access to genomic data. We gratefully acknowledge the  Republic of Panama  and the  Smithsonian Tropical Research Institute  for supporting collections on Barro Colorado Island. Samples were exported from Panama under Scientific Permit  SEX /A-25-12 from the  Autoridad Nacional del Ambiente.
We thank the staff of Edinburgh genomics for expert support. Edinburgh Genomics has core funding from the  UK Natural Environment Research Council  ( UKSBS PR18037 ). LS was supported by a  Baillie Gifford studentship. LF, AR and MAF were supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Ecole Normale Supérieure  and by  Agence Nationale pour la Recherche  grant  ANR-11-BSV3-013. CB and SF were supported by the  CNRS. KCK and DHAF were supported by  NSF  grant  NSF   IOS-1656736. MVR was supported by  NIH  grant  R01GM121828.
Data Accessibility All raw sequence data, reference genome assemblies and rDNA sequences have been deposited in the relevant INSDC databases (see Table  1  for accession numbers). Accessions and links to all data used in the phylogenomics analysis are available in Table  S6. Data files associated with this study have been deposited in Zenodo under the accession 10. 5281/zenodo. 1402254. All assemblies and annotations described in this paper are are available to browse, query, and download at caenorhabditis. org. Information about morphology, geographic distribution, selected gene sequence data and taxonomy of these and other Caenorhabditis species has been deposited in RhabditinaDB (rhabditina. org).
Caenorhabditis zanzibari sp. n. • Caenorhabditis panamensis sp. n. • Caenorhabditis parvicauda sp. n. • Caenorhabditis quiockensis sp. n. • Caenorhabditis sulstoni sp. n. • Caenorhabditis tribulationis sp. n. • Caenorhabditis uteleia sp. n. • Caenorhabditis vivipara sp. n. • Caenorhabditis waitukubuli sp. n. species name Temp. species number Abbrevi ation • Caenorhabditis Caenorhabditis parvicauda sp. n. C. sp. 21 Cpv Reference isolate NIC134 Sequenced strain (inbreeding rounds) NIC534 (25x) PRJEB12595 MH800325 Genome accession number rDNA accession number(s).
Genome sequencingFor C. parvicauda, C. quiockensis, C. sulstoni, C. tribulationis, C. uteleia,C. waitukubuli and  C. zanzibari, two short-insert (insert sizes of 300 bp and 600 bp, respectively) genomic libraries and a single short-insert (150 bp) RNA library were prepared using Illumina Nextera reagents and sequenced (125 bases, paired-end) on an Illumina HiSeq 2500 at Edinburgh Genomics (Edinburgh, UK). For C. becei and C. panamensis a short-insert (insert size of 600 bp) genomic library and a long-insert (insert size of 4 kb) genomic library were prepared using Illumina Nextera reagents and sequenced (100 bases, paired-end) on an Illumina HiSeq 2500 at the New York Genome Center (New York, USA). All raw data have been desposited in the relevant International Nucleotide Sequence Database Collaboration (INSDC) databases (Table.
We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model.
The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.
We used a different approach, in an effort to make our database more computationally practicable and to avoid the uncertainties of the statistical interpolations of climatological variables over complex areas. More specifically, we projected WRF outputs to different elevation bands to generate simulations for multiple elevations. This procedure allows one to study the elevation-dependent characteristics of the snowpack over different mountain ranges, preserving the WRF output resolution. Our procedure uses the physically based Factorial Snow Model (FSM;  Essery, 2015) , which is fed by ERA-Interim reanalysis  (Berrisford et al. , 2011) , and downscaled by the WRF model. The final products of our analysis are simulated daily time series of SD and SWE at different elevations from 1980 to 2014.
Data and methods We used an existing WRF simulation (cell size: 10 km) for the whole Iberian Peninsula (Fig. 1 ), with a 3 h time step from January 1979 to November 2014, as input data for the FSM. Most inputs of the FSM were extracted directly from the WRF simulation, but some were calculated using other submodels. We projected the WRF outputs and derived variables to different elevation bands, from 500 to 2900 m a. s. l. at steps of 100 m, from the WRF pixel elevation using several hygrometric and psychrometric formulas and elevation lapse rates. FSM outputs were aggregated at a daily time step in order to increase the manageability of the data. Validation was performed at different steps of the workflow using different observational data sources. Figure  2  shows the workflow completely, which is described in more detail below.
Meteorological driving data The meteorological variables were calculated using the WRF model, a mesoscale climate model. Previous researchers used this model to simulate climate at regional scales for analysis of past, present, and future conditions  (Chen et al. , 2011; Heikkilä et al. , 2011). The spatial resolution of our simulation is 0. 088 • (∼ 10 km) and the time step is 3 h. ERA-Interim reanalysis  (Berrisford et al. , 2011)  was used as driving data for the WRF model. With this procedure all meteorological variables for running snowpack models were generated for the whole Iberian Peninsula. The WRF configuration was described in detail by  García-Valdecasas Ojeda et al. (2017). This simulation provided the following variables: wind speed (Ua), surface temperature (T ), precipitation (Pr), relative humidity (RH), shortwave incoming radiation (SW), and atmospheric pressure (Ps).
Validation procedure Validation was performed at different resolutions and at different steps of the workflow, using all available observational data (Fig. 2 ). Previous studies  (Argüeso et al. , 2012; García-Valdecasas-Ojeda et al. , 2016 ) simulated temperature and precipitation using WRF at different timescales compared with the grids, based on observations from Spain02 (Herrera et al. , 2012) and PT02  (Belo-Pereira et al. , 2011) , highresolution precipitation and temperature gridded datasets for Spain and Portugal, respectively. The results indicated proper simulation of the major patterns of precipitation and temperature, even for extreme events. Subsequent research showed that the downscaling made by WRF provided improved ac-curacy compared to ERA-Interim data, due to the higher resolution (García-Valdecasas  Ojeda et al.
, 2017). In this work, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite sensor to validate our snow cover product for the period September 2000 to November 2014. Similarly, we used data from telenivometers, which were available in the Pyrenees from October 2009 to June 2014. First, we compared MODIS data with the SD and SWE time series (10 km resolution). MODIS snow maps were generated using the same workflow for each mountain range in the study area (Pyrenees, Cantabrian Mountains, Central System, Iberian Range, and Sierra Nevada). We downloaded all the available MOD10A1 and MYD10A1 products (version 5) from the National Snow and Ice Data Center  (Hall et al. , 2006). The original granules were mosaicked and reprojected from the sinusoidal system to the Universal Transverse Mercator (UTM) reference system.
Then, we ran a gapfilling algorithm, using the binary snow product to avoid data losses due to cloud cover  (Gascoin et al. , 2015). This provided gap-free daily maps showing the presence and absence of snow in each mountain range from 2000 to 2014. From these maps, the probability of snow was calculated as follows: P (Snow) = N s N × 100, (7) where P (Snow) is the probability of snow (%), N s is the number of days with snow, and N is the total number of days of the period. Snow probability maps were also calculated from the FSM snow cover maps. In this work, we chose a threshold of 0. 11 m for SD and a threshold of 40 mm for SWE  (Gascoin et al. , 2015)  in the FSM time series. This allowed us to generate snow cover maps from FSM outputs. Then, we aggregated the MODIS pixels (500 m) to the simulation grid (∼ 10 km), with averaging of the values of MODIS pixels to make them comparable.