dataesr/scientific-paragraphs-categorization huggingface dataset:
1992). We used data only from Grand Terre East, Grand Terre West, and Malabar, because no individuals were marked on Picard. In total, we used data from 166 females, 98 males, and 63 individuals of unidentified sex. Using the length of the third scute as a time-varying individual covariate, we tested for the effect of body size on survival while taking into account variation among islands and sexes. We applied a goodness-of-fit test using RELEASE TEST 2 + 3 to test whether our global model fits the data. Because this dataset is considerably smaller than the count dataset, we used Akaike's information criterion corrected for small sample size (AIC c ) for the identification of the most parsimonious  (Burnham and Anderson 2002). Based on the estimated annual survival rates and their standard deviation, we bootstrapped survival rates for 40 consecutive years, the cumulative product of which gave the resulting fraction of initially marked adults alive after 40 years for each sex and island.
Acknowledgments We are very grateful to all the Aldabra staff and volunteers who collected the tortoise data over the last 15 years, without which this analysis would not have been possible. We thank  Gabriela Schaepman-Strub  and  Erik Postma  for useful comments on an earlier version. Further data collection and analysis was supported by an award from the  Forschungskredit of the University of Zurich  ( K-74324-02-01 ) to establish a  Zurich-Aldabra Research Platform (ZARP). The Queen's College, Oxford , also provided financial support to LAT.
Of these, 17 characters are binary, whereas 17 are multistate. All the latter characters have two or three derived states. Owing to the lack of a priori information, all characters were unordered and equally weighted (Fitch optimality criterion). The data matrix (Supplementary Text S3) was built under Mesquite 2. 6  22  and processed with TNT  23  with the 'traditional search' option (using TBR). Nomenclatural acts. This published work and the nomenclatural acts it contains have been registered in ZooBank, the proposed online registration system for the International Code of Zoological Nomenclature (ICZN). The ZooBank LSIDs (Life Science Identifiers) can be resolved and the associated information viewed through any standard web browser by appending the LSID to the prefix 'http:// zoobank. org/'.
Acknowledgments We acknowledge very much the students and staff of the  Lebanese University  for their helping hand both on campus and in the field in the Bekaa and, especially,  G. Kachacha  for his commitment. We are indebted to  I. Antoñanzas-Asso  for sorting the sediment. We sincerely thank  C. Denys  and  C. Argot  ( Muséum national d'Histoire naturelle, Paris ) and  F. Mayer  and  S. Jancke  ( Museum für Naturkunde, Berlin ) for making available to RLA the ctenodactylid material under their care. L. Marivaux  and  B. Marandat  ( Université Montpellier 2, Montpellier ) kindly answered our enquiries. We also express our gratitude to  A. Winkler  ( Southern Methodist University, Dallas ) and  P. Mein  ( Université Claude Bernard Lyon I, Villeurbanne ) for having improved the manuscript.
NGS data analysis Raw sequences generated by the NGS experiments were processed using USEARCH v8. 0. 1623 (Tiburon, USA) for linux in order to perform a de-novo OTU clustering analysis  [71, 72]. A quality filtering step was applied in order to keep sequences with an expected number of errors <1. 5 and a length >150 nt. After the dereplication and the sorting of the remaining sequences, the latter were clustered into representative OTUs using a radius percentage of 3. 0. The resulting OTUs were then scanned to detect and to remove the chimera. These steps were performed on a pool of sequences generated by all samples from the 3 experiments (0, 4, and 8 months), as recommended in the USEARCH manual in order to make the results of each experiment comparable. The quality-filtered sequences from all samples of each experiment were mapped against the non-chimeric OTUs (n = 343) and one OTU table was therefore generated for each experiment.
The taxonomic assignment of each OTU was carried out using a local BLAST against the Greengenes database version 13. 5  [73]. The Greengenes database was divided into 4 sub-databases according to the level of taxonomic definition (genus, family, order, phylum). The taxonomic assignment of each OTU was based on a blast hit at the species, genus, family or order level if a > 97 %, > 95 %, > 90 % or > 85 % identity was found in the corresponding databases, respectively. The OTU tables and the OTU taxonomic assignments were the starting point of the downstream analyses which were performed using the R software (version 3. 1. 2. , Vienna, Austria). A second level of quality-filtering based on OTU abundance was carried out to discard those OTUs with a number of sequences <0. 005 % of the total number of sequences, as recommended before  [74].
This second level of quality-filtering resulted into an OTU table including 285 OTUs. After rarefaction, the αdiversity indexes (Richness, Chao1, Simpson and Shannon) were computed for each sample with the phyloseq R package  [75]  while the β-diversity, which involves the assessment of dissimilarity between the composition of communities by taking into account the evolutionary relationship between sequences, analyses were carried out by computing weighted UniFrac distances with the GUnifrac R package  [76]. The UniFrac analysis was based on a phylogenetic tree of the OTUs which was obtained with the UPGMA algorithm after OTU sequence alignment with MUSCLE function  [77]  from the muscle R package. The α parameter of the weighted UniFrac distance analysis was set to 0. 5 in order to avoid that distances be dominated by highly abundant lineages.
The weighted UniFrac distance matrix was visualized using a principal coordinates analysis (PCoA) with the cmdscale function from the GUnifrac R package. In addition, the ADONIS function of the same package was used to perform a permutational multivariate analysis of variance of the weighted UniFrac distance matrix in order to detect the association between the Ag NP dose and the β-diversity. The association between Ag NP dose and each outcome (including αand β-diversities as well as phyla, family and genera relative abundance) were assessed via linear trend effects which were computed using linear regression model by assuming equally spaced groups (0, 46, 460, and 4600 ppb). When the effect of the Ag NP dose was assessed on multiple outcomes (e. g. 11 family relative abundances or 4 α-diversity measures), p-values were corrected with the Benjamini-Hochberg method in order to control the False Discovery Rate (FDR)  [78] , and the significance threshold for the adjusted p-values was set at 0.
Acknowledgements We warmly thank  Prof. Eric Gaigneaux  ( Faculty of Bioscience Engineering & Institute of Condensed Matter and Nanosciences, Université catholique de Louvain, Louvain-la-Neuve, Belgium ) for N 2 physisorption analyses of  Ag NP ,  Dr Caroline Bouzin  and  Marc de Bournonville  ( Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Louvain-la-Neuve, Belgium ) for their help with histological analyses,  Dr Jan Mast  ( Coda-Cerva, Brussels, Belgium ) for Ag NP characterization. This work is in part a contribution to the  Labex Serenade  (no  ANR-11-LABX-0064 ) funded by the " Investissements d'Avenir" French Government program  of the  French National Research Agency (ANR)  through the  A*MIDEX project  (no  ANR-11-IDEX-0001-02 ) and the French X-ray CT platform called Nano-ID was funded by the  EQUIPEX  project  ANR-10-EQPX-39-01.
de 8 Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Straße 17, 10315 Berlin, Germany Full list of author information is available at the end of the article MLV diversity  [2, 3] , these datasets are often exceptionally complex, consisting of tens of thousands to many millions of sequence reads. These high-throughput data sets are not amenable to standard phylogenetic analysis, as there are substantial challenges for computing, evaluating, and visualizing alignments and phylogenies for such large data sets. In our analysis of NGS-generated data, we overcome these challenges by using a clustering approach to determine the distribution of MLVs in two wild-caught and three inbred laboratory strains of M. musculus. In addition, we also performed detailed sequence comparisons to determine the presence of specific viral reference sequences in these mice.
Cluster analysis of MLV diversity To determine the diversity of MLVs and their distribution in the different mice, we used the Markov Cluster Algorithm as implemented in the TRIBE-MCL software  [17]. In this approach, sequences are grouped ("clustered") based on pairwise similarity measures such as BLAST E-values  [18]. Filtered NGS reads and selected reference sequences from the C57BL6/J genome of Xmv, Pmv and Mpmv (  [4]  were grouped into 7,041 sequence clusters, 5,815 of which were singletons. We further analyzed all clusters that contained at least 50 reads; smaller clusters were only considered if the clustering process assigned at least one of the reference sequences to it. For each of these clusters, we determined which of the MLV target region it corresponded to. We also determined which mouse samples were represented in each of these groups.
PCR was performed using Invitrogen Taq Polymerase using standard supplied buffers. Cycling conditions were 3 minutes 94 °C followed by 40 cycles of 30 sec 94 °C, 45 sec 54 °C and 45 sec 72 °C with a final 10 minute 72 °C extension. Water controls were always run as negative controls for PCR (data not shown). Contamination, especially from PCR reagents, was not detected at any point. Triplicate PCR products were pooled and purified using the QIAquick PCR Purification Kit (Qiagen).
Sequencing PCRs were performed in triplicate to minimize the inherent amplification bias of any given PCR of multicopy loci. The PCR products were verified by gel electrophoresis and, based on the intensity of the products, pooled in equal amounts for each of the three reactions. Each pool had a unique multiplex identifier (MID) (Roche Life Sciences) ligated to the products, which allowed for computational sorting of reads by animal post-sequencing. A 1/8 plate 454 FLX Titanium run was used to generate sequence data. The 454 sequence reads generated in this study were separated by MID using sfftools (Roche Life Sciences) for standard MIDs. Low quality reads were excluded from the analysis, resulting in a data set of approximately 103,761 reads.
Clustering analysis For each mouse, cd-hit-est  [25]  was used to remove redundant reads at 100 % sequence identity, resulting in a reduction from 103,761 to 69,201 sequence reads. In addition, sequences shorter than 250 bp were removed, resulting in a final data set of 55,979 sequence reads. This data was combined with a set of 204 unique reference sequences from representative Xmv, Pmv and Mpmv MLVs (target region 1: 47 reference sequences, targets 2-3: 46, target 4: 37, target 5: 30, target 6: 44) into a single file and used to generate a matrix of pairwise BLASTN E-values  [18]. The software Tribe-MCL  [17]  was then used to cluster sequences into families with an inflation value of 9. Tribe-MCL uses a Markov cluster (MCL) algorithm. In this approach, pairwise sequence similarity information for a set of sequences is used to construct a weighted graph, which is then converted into a Markov matrix.
Next, simulation of stochastic flow in graphs is used to iteratively expand and inflate this matrix, with the goal of adjusting the edges until discrete and fully connected clusters are evident. Sequence clusters that contained reference sequence matches for target regions 1, 2, 3, 4, 5 and 6 were directly used for further analysis. BLAST was used to assign reference sequences to all families with at least 50 sequences to which no reference was assigned during the clustering step. Specifically, each sequence in these families was compared to a database of the reference sequences, and the single best match with an E-value of at least 1-20 was recorded. This information was combined for each family, resulting in an assignment of reference sequences to families to which no reference was assigned during clustering.
Assignment of sample sequences to specific reference sequences For two separate sets of reference sequences (1. XMRV consensus, PreXMRV-1, PreXMR-2  [7] ; 2. Xmv, Pmv and Mpmv sequences  [4] ), we computed the pairwise sequence identity among the reference sequences as well as between the reference sequences and the most similar sample sequence from each mouse. For the latter values, the single most similar sample sequence to each reference from each mouse for each MLV target region was first identified using BLASTN. Subsequently, pairwise identities were computed from pairwise optimal alignments using the water program of the EMBOSS package  [26]. Computational analyses were implemented using custom Perl scripts that made use of BioPerl  [27].
Acknowledgements The authors thank  Diethard Tautz ,  Pin Cui ,  Kyriakos Tsangaras ,  Karin Hönig ,  Susan Mbedi , and  Esther Maldener  for excellent advice and technical support, and  John L. Goodier  ( McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore MD, USA ) for providing DNA from mouse inbred strains. The high-throughput sequencing was performed at the  Berlin Center for Genomics in Biodiversity Research (BeGenDiv). Funding for the project came from a seed funding program from  University of Saarland (JM). JM is furthermore supported by grants from  DFG. ADG and ALR were supported by Grant Number  R01GM092706  from the  National Institute of General Medical Sciences (NIGMS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIGMS or the National Institutes of Health.
Samoylov Island site information Point-scale simulations were carried out using data from the Samoylov Island field site in the Lena River delta, Siberia. Figure  2  shows the location of Samoylov Island in the context of the whole Arctic permafrost region. There is a large quantity of data available from this site, making it a good site for detailed process evaluation (e. g. Yi et al. , 2014). The landscape is formed of ice-wedge polygonal tundra with ponds and thermokarst lakes. There is an abundance of mosses and organic soil, so including the model developments described above has a notable impact on the JULES simulations. A typical soil profile is shown in Fig. 3b , highlighting the moss and organic layers. Figure  3a  shows an aerial view of the monitoring site, including the meteorological station, soil temperature monitoring and active layer monitoring grid (only polygon centre points are highlighted as these data are used for evaluation).
Forcing data The meteorological driving data were prepared using observations from the site combined with reanalysis data for the    (Weedon, 2013). This is produced using the same techniques as the WFD but is instead based on the ERA-Interim reanalysis data (ECMWF, 2009) and covers the period 1979-2012. For the time periods where observed data were available from Samoylov, correction factors were generated by calculating monthly biases relative to the WFDEI data. These corrections were then applied to the time series from 1979 to 2010 of the WFDEI data. The WFD before 1979 was then corrected to match this data and the two data sets were joined at 1979 to provide gap-free 3-hourly forcing from 1901 to 2010. Meteorological station observations were used for all variables except snowfall, which was estimated from the observed snow depth by treating increases in snow depth as snowfall events with an assumed snow density of 180 kg m -3.
Acknowledgements. The authors acknowledge financial support by the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) project  PAGE21 , under  GA282700. E. J. Burke  was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme   GA01101. M. Langer  is also supported by a post-doctoral research fellowship (A. v. Humboldt Foundation). Work was facilitated by a PAGE21 young researcher exchange grant. Thanks go to the  Russian-German expedition team  for logistical support during the Samoylov field work. Thanks to  Niko Bornemann ,  Steffen Frey  and  Günter Stoof  for data collection and compilation. Edited by:  D. Lawrence.
© 2015 Melodelima et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Acknowledgments The optical photographs were taken by  C. Lemzaouda  and  P. Loubry (CNRS). We thank  C. Argot ,  P. Tassy ,  G. Billet , and  C. Sagne  ( MNHN ) for granting access to the material of Hyaenodonta under their care. We are also indebted to  C. Bouillet  ( MNHN ) for helping preparing the skull of the holotype of K. langebadreae. We thank  R. Tabuce  ( CNRS-Université Montpellier 2 ) for having permitted the utilization of the picture of Furodon crocheti. We also thank  Shawn P. Zack ,  Lars Werdelin ,  Gregg Gunnell ,  Margaret E. Lewis  and one anonymous referee and the  Academic Editors-Nathan Jeffery and Matt Friedman -for their very constructive comments that improved the manuscript.
A database providing information on mosquito specimens (Arthropoda: Diptera: Culicidae) collected in French Guiana is presented. Field collections were initiated in 2013 under the auspices of the CEnter for the study of Biodiversity in Amazonia (CEBA: http://www. labexceba. fr/en/). This study is part of an ongoing process aiming to understand the distribution of mosquitoes, including vector species, across French Guiana. Occurrences are recorded after each collecting trip in a database managed by the laboratory Evolution et Diversité Biologique (EDB), Toulouse, France. The dataset is updated monthly and is available online. Voucher specimens and their associated DNA are stored at the laboratory Ecologie des Forêts de Guyane (Ecofog), Kourou, French Guiana. The latest version of the dataset is accessible through EDB's Integrated Publication Toolkit at http://130.
French Guiana is mainly covered by primary rainforest and its inhabitants (ca. 250,000) are mostly distributed along the coast  (Gond et al. 2011). While some evidence suggests that the Guiana Shield could be an early center of speciation for mosquitoes in the Neotropics  (Navarro et al. 2007) , the mechanisms explaining the high mosquito diversity in the region remain poorly understood. This work is an ongoing process and should help to understand mosquito distribution across French Guiana. This database will also be used to disseminate biodiversity information related to future studies on mosquito distribution in French Guiana in general and in medical entomology and ecology in particular. We aim to promote the best practices for recording and sharing biodiversity data within our research community, and highly encourage foreign institutions to do the same.
Taxonomic coverage Description: This database concerns all mosquito (Diptera: Culicidae) species inhabiting French Guiana. Most specimens have been identified to species level or at least to genus level. The identifications were made by the first author based most of the time on the examination of immature and adult specimens, and by using the latest taxonomic publications on the genus or on the subgenus concerned (e. g. Harbach and Peyton 2000 , Motta and Lourenço-de-Oliveira 2000 , Zavortink 1979 ). The validation of species and subspecies is based on "A Catalog of the Mosquitoes of the World (Diptera: Culicidae)"  (Knight and Stone 1977)  and its supplements  (Knight 1978; Ward 1984 Ward , 1992;; Gaffigan and Ward 1985) , and the "Systematic Catalog of Culicidae"  (WRBU 2015). The internal classification of the tribe Aedini is based on  Wilkerson et al.
(2015). Until now, the database was mostly filled with data from studies conducted on mosquitoes breeding in phytotelmata, which explains why the Sabethini are particu-larly well represented in the current dataset (Fig. 1 ). Consequently, clades like the Anophelinae, Culicini and Mansoniini are highly underrepresented and the tribes Aedeomyiini and Uranotaeniini are not at all represented (Fig. 1 ). The dataset presently contains 19 genera and 81 species, including occurrences of twelve species recently recorded in French Guiana  (Talaga et al. 2015) , namely: Onirion sp. cf. Harbach & Peyton (2000) , Sabethes (Peytonulus) hadrognathus  Harbach, 1995, Sa.
Title: Mosquitoes of French Guiana Personnel: Stanislas Talaga Study area descriptions: Collecting trips were conducted in various locations throughout French Guiana ranging from urban to pristine environments. Design description: This database was originally built from studies on mosquitophytotelm associations at the scale of French Guiana. Immature mosquitoes were collected from at least 30 water-holding structures per phytotelm species, per locality. However, the extent of the sampling area was not standardized between the different localities. The database also contains some records of opportunistically sampled immature and adult mosquitoes conducted by the first author. Funding: Data for this resource have been obtained within the framework of the projects BIOHOPSYS and DIADEMA from the CEBA (CEnter for the study of Biodiversity in Amazonia) and thanks to a PhD fellowship from the Université Antilles-Guyane awarded to Stanislas Talaga.
Specimen codes are based on the name of the collection followed by a unique serial number as proposed by  Gaffigan and Pecor (1997). The same code was used for all of the biological material issued from the same specimen. When it was impossible to bring live samples back to the laboratory and rearing was not possible either, specimens were stored directly in 95% ethanol in the field. Selected specimens were photographed using a Leica DFC450 camera mounted on a Leica MZ16 macroscope under a light dome simulating natural light. Images were Z-stacked using the Leica LAS Z-stacking module. Montage pictures and collecting information for each specimen are stored in an online Voseq database  (Peña and Malm 2012)  managed by the EDB laboratory (Fig. 3 ) and viewable through the Guyanensis GIS web platform at http://guyanensis.
ups-tlse. fr, through the Global Biodiversity Information Facility (GBIF) at http://www. gbif. org/dataset/5a8aa2ad-261c-4f61-a98e-26dd752fe1c5 or alternatively through the local Integrated Publishing Toolkit (IPT) at http://130. 120. 204. 55:8080/ipt/resource. do?r=mosquitoes_of_french_guiana Specimens are initially curated at the Ecofog laboratory by Stanislas Talaga and can be deposited in museums for further taxonomic study. Quality control description: Considering different sources of GPS errors (such as ionosphere delay and signal multipath), we estimate the accuracy of the coordinates to be around 30 meters at a 95% confidence level.
Acknowledgments We would like to thank  Pierre Solbes  and  Sébastien Cally  from the  EDB laboratory  for technical support,  Marceau Minot ,  Frédéric Petitclerc  and the team of the  Unité d'Entomologie Médicale (UEM) of the Pasteur Institute of Cayenne  for field help,  Elise Bayle  for the creation of the map,  Clémence Mouza  for the pictures of voucher specimens and  Andrea Yockey-Dejean  for proofreading the manuscript. We acknowledge  Paul Beuk  and  Fransisco Collantes  for their suggestions and comments on the early version of the manuscript. This study was funded by  Investissement d'Avenir  grants managed by the  French Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ;  TULIP :  ANR-10-LABX-41 ,  ANR-11-IDEX-0002-02 ).