dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of data and materials Coding sequence alignments and gene-specific phylogenetic trees of 48 avian species from Jarvis et al. [57]  were retrieved at GigaDB  [74] , https:// doi. org/10. 5524/101041. Two additional sets of multiple sequence alignments from Botero-Castro et al. [32]  were retrieved at https://doi. org/10. 6084/ m9. figshare. 5202853. The Bio++ source code and custom scripts used in this study have been deposited at the Zenodo research data repository and are available at https://doi. org/10. 5281/zenodo. 2149686. The Bio++ source code and a tutorial on the computation of d N and d S based on stochastic mapping, taking into account gBGC, have also been deposited in a public repository under the link: https://github. com/BioPP/supp-mat/ tree/master/mapdNdS.
[32] 1077 Dataset from Figuet et al. 2016 + alignment filtering based 0. 17 2. 80 × 10 -1 0. 42 4. 00 × 10 -3 (Figuet+HMMclean) on HMMclean Botero-Castro et al. [32] 1077 + 1245 Figuet+HMMclean dataset + 1245 previously non-annotated 0. 23 1. 30 × 10 -1 0. 59 7. 34 × 10 -5 (Botero-Castro) GC-rich genes present in at least 10 species This study 7986 Alignments from Jarvis et al. [57] 0. 08 6. 09 × 10 -1 0. 19 2. 19 × 10 -1 Max missing data of 6 species.
The genetic analyzer ABI PRISM 3130xl (Applied Biosystems, Foster City, California, USA) and the software SeqScape (Applied Biosystems, Foster City, California, USA) were used for electropherograms development and analysis. To rule out mutations in other HDL-related genes, APOA1, LPL and LCAT genes were also sequenced in the proband. The first 2 genes were analyzed by an already published approach  [16] , the third gene by a Sanger sequencing approach using a specific set of primers (unpublished). The gene variants were labeled according to the Human Genome Variation Society nomenclature (version 15. 11) (http:// varnomen. hgvs. org/. The GenBank NM_005502. 3 and NP_005493. 2 were used for the analysis of, respectively, human ABCA1 cDNA and protein. The in silico analysis was performed using Polyphen (http://genetics.
Shell and aperture length and width (shell morphology) were measured on these photographs with the software ImageJ  (Schneider et al. 2012). Shell thickness was measured with an electronic caliper at the nearest 0. 01mm at the edge of the aperture. Shorter and narrower shell and aperture dimensions (after adjusting for mass) and thicker shell are adaptive anti-predator responses  (Auld and Relyea 2011 ).
Statistical analysis The multigenerational effect of predator cues on anti-predator behavior (i. e. snail position above/on or below the water surface) was analyzed using generalized linear mixed models (GLMM) assuming a binomial distribution (logit link function). Shell and aperture length and width were analyzed with a principal component analysis (PCA) with the package FactoMineR  (Lê et al. 2008)  to assess shell morphology. They were highly correlated and the first component (PC1) explained most of variance in shell morphology (95. 8%; Appendix 2): a high value of PC1 was associated with a longer and wider shell and aperture, whereas a low value was associated with a smaller and narrower shell and aperture. PC1 was then used as a proxy for shell morphology and response variable in linear mixed model.
We used type 2 F-tests for significance of fixed effects in the LMM and likelihood ratio tests in the GLMM (lmerTest package;  Kuznetsova et al. 2017). We tested significance of random effects with likelihood ratio tests comparing models with or without the tested random effect in the full fixed effect structure. All statistical analyses were performed with R 3. 4. 1 (R Core Team 2017) and the glmer() and lmer() functions from the lme4 package  (Bates et al. 2015 p. 4).
Acknowledgements Thanks to  P. Gibert  for advice on the manuscript. We thank  L. Bensousoussan  for help on data analysis. We are grateful to the  PCI  recommender  Troy Day  as well as two reviewers ( Stewart Plaistow  and an anonymous reviewer) for their useful and highquality reviews based on a previous version of the manuscript. This preprint has been reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi. org/10. 24072/pci. evolbiol. 100076).
1 The BTs were calculated including selected meteorological parameters (i. e. , air pressure, relative humidity, sun flux, and potential temperature, as well as rainfall and mixing depth) by using the HYSPLIT download package  (version 4, Revision 664, October 2014). This resulted in an ensemble of 74 496 individual BTs, which were archived as ASCII files. The further processing of these "raw" BT files was conducted in IGOR Pro  (version 6. 3. 7, Wavemetrics, Inc. , Portland, OR, USA). Figure  1  shows two exemplary 14 d BT ensembles for selected wet and dry season months. In the context of the 1 This time period from January 2008 to June 2016 was chosen for two main reasons: (i) it includes 8. 5 years of BT data, which is a comparatively broad statistical basis to retrieve characteristic BT clusters for the ATTO region.
Cluster analysis of backward trajectories Based on the entire set of 74 496 3 d BTs (1 January 2008-30 June 2016) we conducted a k means cluster analysis (CA, with Euclidian distances) -the most commonly used partitioning clustering approach -which provides a (nonsupervised) classification of the trajectories' spatiotemporal variability  (MacQueen, 1967; Kassambara, 2017). The 3 d BTs were selected as input data for the clustering to retrieve the BT's spatiotemporal variability over the South American continent and, particularly, over the NE Amazon Basin. The clustering was performed with the statistical software package PASW Statistics (version 18. 0. 0, IBM, Armonk, NY, USA). The 74 496 BTs, each with 72 pairs of latitude and longitude coordinates, represented the input data for the clustering.
Geographic analysis by means of selected GIS data sets We conducted a detailed analysis of the land cover type and/or change in the ATTO BT footprint region using a number of different GIS data sets processed using the QGIS software package ("Las Palmas" version 2. 18. 2, QGIS development team). The QGIS software (formally known as Quantum GIS) is freely available under http://www. qgis. org (last access: 25 February 2018). All GIS data sets were handled using the coordinate reference system WGS84 (world geodetic system from 1984). A detailed list of all GIS data sets used in this study, together with a short summary of relevant information, web links, and references, can be found in the Supplement Sect. S1. 1. The classification of land cover types as well as the quantification of land cover changes and dynamics is a complex task for various reasons: GIS data sets are often based on different spacecraft instruments and data acquisition methods (e.
Analysis of backward trajectory footprint region in relation to geographic data Using scripts written in Python 2. 7, an analysis weighted by air mass residence time of several GIS data sets was conducted to assess the relative significance of land cover categories, forest cover, and loss as well as fire events for the ATTO observations. Each data set was weighted using rasterized BT density maps. The rasterized BT density maps were calculated with cell sizes of 0. 09 • by 0. 09 • resolution. For each raster cell, the total length of intersecting trajectories within the cell was calculated. The sum of path lengths was then set in relation to the highest sum found at the ATTO site, where all trajectories crossed the same cell. To calculate values for the cluster footprints, the GIS raster maps for land cover, forest cover, and forest loss were combined with the air mass residence time map.
Protected areas The conservation efforts to protect the Amazonian forests are manifold. Often, protection is initiated as a response to deforestation frontier expansion  (Nepstad et al. , 2006). Worldwide, a large variety of different types of conserved areas (e. g. , in terms of their legal, control, and habitation status) exists. According to the world database on protected areas (WDPA) classification, about 14 types are relevant in the ROI foot (Fig. 21a ). About 41 % of the actual footprint region is protected. Basin-wide, about half of the forest area has some protection status, whereas only 10 %-20 % is considered strictly protected  (Barber et al. , 2014; ter Steege et al. , 2015). The establishment of protected and controlled areas, such as parks, reserves, and indigenous lands, can be an effective tool to reduce or even inhibit deforestation and fire occurrence (e.
Summary and conclusions This study presents a backward trajectory (BT) and geographic data analysis for the ATTO site in the central Amazon Basin. It extracts spatiotemporal BT patterns in relation to the current state and anticipated future change of the land cover within the ATTO footprint. The overall aim of this work is to provide a robust characterization of the effectively probed land cover mosaic in the footprint region to embed atmospheric observations at ATTO into a broader Amazonian context. Since the footprint regions of the ATTO site and the nearby ZF2 site are similar, the present work may further act as a resource for the interpretation of ZF2 results. We envision that this work may serve as a resource and look-up reference for the interpretation of current and future observations in the ATTO-relevant and vulnerable eastern basin, in which the progressing climate and land use changes are influences of crucial importance.
Data availability. The following data from this study have been deposited under https://doi. org/10. 17617/3. 2g (Pöhlker et al. , 2019)  for use in follow-up studies: (i) a time series, with the cluster membership numbers resulting from the k-means cluster analysis of the HYSPLIT backward trajectories (see Sect. 2. 2 and 2. 4) in NASA Ames format (named "ClustMemb. dat"); (ii) the center lines of the 15 k-means backward trajectory clusters (as plotted in Figs. 4, 7, and several further maps), provided as ASCII and shapefile formats (named "ClustCent. dat" and "ClustCent_Shape. zip"); (iii) high-resolution versions of all figures, as well as selected supplement figures; (iv) the air mass residence time maps and contour lines of the BT ensembles in Figs. 7  and 8 , provided as shapefile format (named "BTContour_Shape.
Acknowledgements. This work has been supported by the  Max Planck Society (MPG). For the operation of the ATTO site, we acknowledge the support by the  German Federal Ministry of Education and Research (BMBF  contract nos. 01LB1001A  and  01LK1602B ) and the  Brazilian Ministério da Ciência, Tecnologia e Inovação (MCTI/FINEP  contract  01. 11. 01248. 00 )as well as the  Amazon State University (UEA) ,  FAPEAM, LBA/INPA , and  SDS/CEUC/RDS-Uatumã. This paper contains results of research conducted under the Technical/Scientific Cooperation Agreement between the  National Institute for Amazonian Research , the  State University of Amazonas , and the  Max-Planck-Gesellschaft  e. V. ; the opinions expressed are the entire responsibility of the authors and not of the participating institutions. Céline Degrendele  was funded by the core facilities of the  RECETOX Research Infrastructure , project  LM2015051 , and by  Actris-CZ   RI  project  CZ.
and its transformation in the ATTO footprint Appendix A: List of acronyms, abbreviations, and symbols Acronym Description PERSIANN-CDR Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks for Climate Data Record Acronym RENCA Description national reserve of copper and associates a. g. l. ROI above ground level region of interest AMAZE-08 Amazonian Aerosol Characterization Experiment 2008 ROI foot region of interest covering the continental part of the ATTO BT AMO Atlantic multidecadal oscillation footprint (62, 40 • W, 8 • S, 6 • N) ATTO SEDAC Amazon Tall Tower Observatory Socioeconomic Data and Applications Center BL SPOT boundary layer Satellite Pour l'Observation de la Terre BT SRTM back trajectory Shuttle Radar Topography Mission CA SST cluster analysis sea surface temperature CIAT SUR International Center for Tropical Agriculture Suriname E SW BT clusters towards east BT cluster towards southwest ENE TNA BT clusters towards east-northeast tropical northern Atlantic ENSO TSA El Niño-Southern Oscillation tropical southern Atlantic ESA UNLC European Space Agency UN land cover classification ESE UNEP BT clusters towards east-southeast United Nations Environment Program f UTC frequency of occurrence of BTs coordinated universal time FAO WDPA Food and Agriculture Organization World Database on Protected Areas FFDAS WGS 84 Fossil Fuel Data Assimilation System World Geodetic System from 1984 FLEXPART FLEXible PARTicle dispersion model WWF World Wildlife Fund FoO WMO frequency of occurrence World Meteorological Organization GFAS Global Fire Assimilation System GHCN Global Historical Climatology Network GIS geographic information system GUF French Guiana GUY Guyana HYSPLIT Hybrid Single-Particle Lagrangian Trajectory Model INPE Instituto Nacional de Pesquisas Espaciais IPCC Intergovernmental Panel on Climate Change ISLSCP II International Satellite Land-Surface Climatology Project, Initiative II ITCZ intertropical convergence zone IUCN International Union for Conservation of Nature LBA Large-Scale Biosphere-Atmosphere Experiment in Amazonia LRT long-range transport MODIS Moderate Resolution Imaging Spectroradiometer NASA National Aeronautics and Space Administration NCAR National Center for Atmospheric Research NDVI normalized difference vegetation index NE BT clusters towards northeast NOAA National Oceanic and Atmospheric Administration ONI Oceanic Niño Index OSM OpenStreetMap P precipitation rate P BT cumulative precipitation along 3 d BTs.
Methods: Three partners, that performed virus sequencing using different commercial library preparation kits and NGS platforms, gathered together and shared with each other 75 raw datasets which were analyzed separately by the participants to produce a consensus sequence according to their own bioinformatics pipeline. Results were then compared to highlight discrepancies, and a subset of inconsistencies were investigated more in detail. Results: In total, we observed 526 discrepancies, of which 39. 5% were located at genome termini, 14. 1% at intergenic regions and 46. 4% at coding regions. Among these, 10 SNPs and 99 indels caused changes in the protein products. Overall reproducibility was 99. 94%. Based on the analysis of a subset of inconsistencies investigated more in-depth, manual curation appeared the most critical step affecting sequence comparability, suggesting that the harmonization of this phase is crucial to obtain comparable results.
To reach this goal, sequenced reads generated by three laboratories were gathered together to constitute a unique set of 75 raw data, which was then shared among the participants for BI analysis. The dataset encompassed either VHSV or IHNV field strains as well as one recombinant VHSV. Both viral species have the same genome architecture, constituted by a negative-sense, single-stranded RNA molecule consisting of six genes in the order 3′-N-P-M-G-NV-L-5′. They encode six structural and non-structural proteins, namely the nucleocapsid protein (N), the phosphoprotein (P), the matrix protein (M), the glycoprotein (G), the non-virion protein (NV) and the polymerase protein (L)  [11]. The participants were asked to produce a consensus sequence for each strain according to their own BI methodology, and complete genomes were then compared to highlight any discrepancy in terms of type (Single Nucleotide Polymorphisms -SNPs and deletions/insertions -indels) and localization (coding regions, intergenic regions, genome termini).
Study design Seventy-three VHSV (n = 55) and IHNV (n = 18) viral strains collected by the Novimark consortium were used for the exercise [Additional file 1] and processed by three participant laboratories, later referred as Lab 1, Lab 2 and Lab 3, according to the protocols and sequencer machines available at their facilities. In detail, 36 VHSV and 13 IHNV were sequenced with Illumina MiSeq by Lab 2 and Lab 3, and 19 VHSV and 5 IHNV were sequenced with Ion Proton™ Sequencer by Lab 1. Additionally, a calibrator specimen constituted by a recombinant VHSV strain (see below) was included in the exercise and analyzed with both sequencing technologies (Fig. 1 ). All the viruses were subject to library preparation and whole genome sequencing (WGS) according to the protocols reported below. In total, 75 unique raw datasets, later referred as "raw data" or "sample(s)", were generated by Lab 1, Lab 2 and Lab 3 according to the methodologies implemented at their facilities, and shared in FASTQ format, either as a single file or as two paired files, via a secure File Transfer Protocol (FTP) site.
Double-stranded cDNA was synthesized using NEBNext mRNA Second Strand Synthesis Module (Euroclone), purified with MagSI-NGSPREP PLUS beads (MagnaMedics) and quantified with Qubit dsDNA HS assay kit (ThermoFisher Scientific). The cDNA library was prepared using Illumina Nextera XT DNA Sample Preparation kit (Illumina), and fragments were selected with MagSI-NGSPREP PLUS beads (MagnaMedics). Library was checked for quality and size with Agilent 2100 Bioanalyzer (Agilent High Sensitivity DNA kit, Agilent Technologies) and sequenced with MiSeq v3 Reagent Kit (300PE) using Illumina MiSeq platform.
The purified vector was then subject to sequencing using primers listed in Additional file 2 and the BrilliantDye™ Terminator Cycle Sequencing Kit (Nimagen) according to the manufacturer's instructions. Sequencing reactions were then purified with CENTRI-SEP 96 Well Plates (Princeton Separations) and sequenced in a 16-capillary ABI PRISM 3130xl Genetic Analyzer (Applied Biosystems). Sequence data were assembled and edited using the SeqScape software v2. 5 (Applied Biosystems) and the final consensus was compared with the genome sequences of r23/75 obtained with NGS. The VHSV complete genome of p23/ 75 is publicly available under the GenBank acc. no. MK792283.
Bioinformatics analysis Lab 1 Reads were cleaned with Trimmomatic v0. 36  [15]  (ILLUMINACLIP: oligos. fasta: 2:30:5:1: true; LEADING: 22; TRAILING: 22 for Ion Proton™ sequencing and 28 for Illumina MiSeq sequencing; MAXINFO: 40:0. 2; MINLEN: 36). Bowtie2 v2. 2. 5  [16]  was adopted to perform a rapid alignment with down-sampled reads on the local NT database. VHSV or IHNV complete genomes with the highest number of matching reads were used as a primary reference to align all cleaned reads with BWA v0. 7. 15  [17]. Based on the alignment, the coverage was estimated using an in-house perl script, bam stat v1. 0. 13 (http://bamstats. sourceforge. net/) and bam2fastx v1. 0 (https://github. com/PacificBiosciences/bam2fastx). Raw reads were down-sampled to fit an estimated global coverage of 80X fold and were de novo assembled with Mira v4.
0. 2  [18]  and, after being cleaned with Trimmomatic, also with SPAdes v3. 10. 0  [19]. Subsequently, assembled contigs were aligned against the integrated NT database using BLAST  [20]. The best match (i. e. the longest sequence corresponding to a complete genome of VHSV or IHNV) was selected as reference for a BWA alignment. The generated SAM file was then used to compute the mpileup using samtools v1. 9  [21] , and variant calling was performed with bcftools v1. 9  [21]. Called variants were then used to produce a consensus sequence with vcfutils. pl v1. 9  [21]  and seqtk v1. 2 (https:// github. com/lh3/seqtk). Finally, assembled contigs and BWA alignments were visually inspected for ambiguous bases, indels, ORF integrity, genome integrity and, if required, were manually adjusted.
Lab 2 Unless otherwise specified, pre-processing of raw data, reads alignment, variant calling and alignment of the consensus sequences against the reference genomes were done using CLC Genomics Workbench v4. 9 (https://www. qiagenbioinformatics. com/). Sequences were trimmed based on quality scores with a cumulative error probability of 0. 03, and including a maximum of 3 ambiguous bases. Sequences shorter than 40 bp were discarded. Sequencing adaptors were trimmed with mismatch and insertion costs of 2 and 3, respectively, and with a minimum internal and end scores of 10 and 3, respectively. Reads were aligned against the following reference sequences: VHSV 23-75 (GenBank acc. no. FN665788), VHSV MI03GL (GenBank acc. no. GQ385941), VHSV SE-SVA-14-3D (GenBank acc. no. AB839745) and IHNV (GenBank acc.
Lab 3 Reads quality was assessed using FastQC v0. 11. 2 (https:// www. bioinformatics. babraham. ac. uk/projects/fastqc/). Illumina MiSeq raw data were cleaned by removing: a) reads with more than 10% of undetermined ("N") bases; b) reads with more than 100 bases with Q score below 10; c) duplicated paired-end reads. Ion Proton™ raw data were cleaned by removing duplicated reads. Filtered reads were clipped from Illumina adaptors Nextera XT or Ion Pro-ton™ adaptors with scythe v0. 991 (https://github. com/ vsbuffalo/scythe). Low quality ends were trimmed with sickle v1. 33 (https://github. com/najoshi/sickle) over a range with minimum average quality of 25 for Illumina MiSeq reads, and 15 for Ion Proton™ reads. Reads shorter than 80 bases or unpaired after previous filters were discarded. Filtered reads were aligned against the integrated NT database (version 8 February 2017) with BLAST v2.
6. 0+ adopting default parameters and e-value <10e-50. Alignment hits matching against VHSV and IHNV complete genomes, with sequence similarity ≥95% and match length of the query ≥99%, were selected. For each sample, sequences with the highest number of matching reads were chosen as proper reference genomes. Reads were then re-aligned against their respective reference genome selected as above using BWA v0. 7. 12 and standard parameters. Before SNPs calling, alignments were processed with SAMtools v0. 1. 19 for conversion into BAM format and sorted by position. Subsequently, potential errors of the alignment were corrected with Picardtools v2. 1. 0 (http://broadinstitute. github. io/picard/) and GATK v3. 5  [24] [25] [26] , reads were re-aligned in the proximity of indels and base quality was recalibrated.
Consensus sequences comparison The consensus sequences produced by Lab 1, Lab 2 and Lab 3 were saved in fasta format and collected for subsequent analysis via a secure FTP site. Complete genomes of VHSV and IHNV were aligned separately with MAFFT v7. 294b  [29]  using standard parameters. Differences between the consensus sequences produced by the participants related to the same viral strain were identified with a perl script developed in-house and available upon request. For the recombinant calibrator, consensus sequences obtained with NGS were compared also with the Sanger sequence (Genbank acc. no. MK792283). Discrepancies were evaluated by taking into account their genome localization, namely a) coding regions (CDS), consisting of viral genes translated into viral proteins; b) non-coding intergenic regions, consisting of untranslated sequences that intersperse viral coding regions; c) genome termini, consisting of untranslated regions at the 3′ and 5′ ends of the viral genome.
Discrepancies analysis Consensus sequences obtained from the analysis of 75 raw data were collected, and genomes referring to the same viral strain were compared to highlight any inconsistency. In total, our analysis revealed 526 discrepancies, with an average of 7. 0 differences per sample (d/s). A distribution of all discrepancy types occurred, also in respect to sequencing technology, is shown in Fig. 2. A more detailed breakdown of the discrepancies observed in each sample is provided in Additional file 4, where the number of inconsistencies is shown as regards type, genome location, viral species and sequencing technology. The ratio between the total number of discrepant sites and the cumulative genome size for all the raw data was 0. 06%. This value was used as a means to evaluate PT reproducibility, corresponding to 99.