dataesr/scientific-paragraphs-categorization huggingface dataset:
Oligodon sublineatus Duméril, Bibron & Duméril, 1854 Figs 1-3; Tables  1, 2  Remarks. Standard morphometric and meristic data of the two syntypes are presented in Table  1. We hereby recognise two syntypes: the larger specimen (MNHN 3238) and the smaller specimen (MNHN 3239). Uncertainties still exist in Oligodon taxonomy and O. sublineatus may represent a cryptic species complex in Sri Lanka (see table 2 showing the wide range of subcaudal and ventral counts within O. sublineatus), therefore it is necessary to stabilize the name with a recognised lectotype. There are two main reasons for selecting MNHN 3238 as the lectotype: (1) it was used in the original description and its morphometric data has been provided and (2) it is a fully grown, well-developed and well preserved adult specimen in good shape.
Acknowledgments We are very grateful to  Gernot Vogel ,  A. de Silva ,  P. Uetz ,  W. M. S. Botejue ,  D. E. Gabadage ,  N. Gamage ,  M. M. Bahir ,  M. C. De Silva ,  H. Jayasinghe ,  V. Weeratunga ,  M. Madawala ,  S. Fernando ,  S. Wellapulliarachchi ,  P. Samarawickrama ,  M. Wickramasighe ,  D. Kandambi ,  P. Pieris , and  G. N. Karunarathna  who provided valuable support which enabled us to complete this study. We also thank  N. Wickramasinghe ,  S. Kasthuriarachchi ,  M. Nandasena ,  C. Munasinghe ,  L. Somaratne ,  R. Dasanayake ,  M. B. Vaidyasekara ,  C. Kotalawala ,  A. Samanthika ,  P. Gunasiri  and  R. Wickramanayake  at  National Museum of Sri Lanka (NMSL ) for assisting us while examining collections under their care. Special thanks to  D. Vallan  for providing information linked to the specimens at NMB.
Such networks rely on methods from graph theory that have recently been adapted to address an increasing number of biological questions using large molecular datasets  [30, 31, [38] [39] [40] [41] [42] [43]. With such analyses, combining massive sequence data produced from numerous studies becomes feasible, and diversity patterns can be inferred that otherwise would not be apparent from individual studies alone. For example, a count of phylotypes in two separate datasets provides no information about which of these phylotypes are the same, while sequences from different studies can be directly compared in a single network analysis. Phylogenetic analysis of short read data has made great strides in recent years, following the development of phylogenetic placement algorithms for incorporating short read data into a reference phylogeny (for example,  [44, 45] ).
Results and discussion The large scope of network analyses Sequence similarity networks are inclusive graphs, easily accommodating substantial molecular datasets (typically millions of sequences, but billions are possible). They provide a unified comparative framework for these sequences, which can be analyzed with the methods and tools of graph theory  [54]. In such graphs, the nodes represent the objects to be compared (here ciliate V4 sequences), each pair being linked by an edge if there is significant similarity between the two corresponding nodes (here a minimum % identity (%ID), E-value, length, and alignment cover spanning over the two sequences). The resulting networks provide multiple lines of evidence to analyze genetic diversity in large molecular datasets at various sequence similarity levels, hence at various taxonomical levels, and consequently to test ecological theories (Table  1 ).
Shortest path Shortest distance between a pair of nodes Measure to quantify the divergence between a pair of sequences (for example, a long shortest path between an environmental sequence and a sequence from a cultured ciliate indicates a high divergence between these sequences, since these sequences are not direct neighbors in the graph). See Figure  2D. Listed are the most important network terms introduced into microbial ecology in the framework of this study. The table indicates how these methods can be applied to HTS data to address fundamental questions on the diversity and distribution of microbial organisms. HTS, high-throughput sequencing. Weighted sequence similarity networks displaying sequences as nodes (black nodes represent environmental sequences, red nodes represent sequences of cultured ciliates), connected by edges reflecting their %ID obtained from a BLAST analysis (see list of weighted edges in which the color code reflects the %ID; red for 99%, orange for 97%, pink for 96%).
The corresponding color code is used on the networks (right panel) to explore and structure the data. The mock dataset results in: A) two connected components when the minimum %ID required to connect two nodes is ≥97%; B) a single giant component when the minimum %ID required to connect two nodes is ≥96%; C) finer-level structure within components can be further detected using methods of community clustering, such as the Louvain Communities (see Methods). %ID, % identity. Components  [55]  (GCC, Figure  1B ). Second, connected components can be further partitioned to identify densely connected regions within them, known as communities in graph theory. In each community, nodes are more connected to other nodes within the given community than to external nodes (Figure  1C ). We used Louvain communities  [46]  (LCs) of level 1 as the finer level of sequence similarity, that is, as the tightest clusters of similar sequences.
In other words, Figure  3  reveals the similarity relationships between sequences from cultured organisms and from an increasing number of environmental projects. Ciliate sequences obtained by BioMarKs (represented by all non-pink LCs in the right graph, Figure  3  (t)) substantially increase the diversity that was known from sequences of described organisms (represented by all non-green LCs in the left graph, Figure  3 (t-2 )). For cDNA sequences, the number of LCs increases 1. 4 fold and for DNA 2. 3 fold. When all ciliate sequences from the environmental reference database are also considered in the GCC network analyses, we find that a large proportion of BioMarKs diversity was, in fact, discovered previously in environmental molecular diversity surveys (non-pink nodes in Figure  3  (t) and non-green nodes in Figure  3 (t-1 ), respectively).
Sequences of ciliates with restricted geographical or habitat distribution will tend to connect with each other in our networks, and the assortativity of their respective labels will be high (Figure  2A ). Hence, the proportion of connected components with significantly higher assortativity values than expected by chance for any location or depth in our graphs indicates to which extent we observed some geographical or habitat structure in the distribution of ciliates in our data set. The majority of the three tested labels for habitats, and of the eight tested labels for locations, were significantly more assortative in the analyzed networks, indicating a notable geographical and habitat structure of BioMarKs ciliate sequences (Additional file 7: Table  S4 ). The structuring effect is especially emphasized in the cDNA dataset for networks generated at thresholds ≥95% sequence similarity (Additional file 7: Table  S4 ).
Network analysis in microbial biogeography We present network analyses as highly efficient tools to exploit massive HTS datasets and identify novel diversity. Further, network analyses appear as a useful means to address fundamental subjects in ecology using environmental diversity inventories. Such subjects include the analyses of microbial distribution patterns that allow us to draw conclusions about the underlying biological causes. For instance, while closely related ciliate sequences grouped together in our networks, the in-depth analyses of these groups showed that they are structured with regard to habitat or geographic location. With a broad ecological perspective, this can be interpreted as taxonomically closely related groups (depending on the resolution of sequence similarity we choose) which form distinct subcommunities in different habitats or at different geographic locations in European coastal waters.
Constitution of the dataset A first reference database of ciliate sequences was built including 308 V4 SSU-rDNA sequences from GenBank. Only sequences from cultured and morphologically identified isolates were selected. Additionally a second reference database of 82,560 environmental ciliate SSU-rDNA sequences from earlier diversity studies was generated from GenBank. Moreover, environmental samples were collected at eight European marine coastal sites part of the biodiversity of marine eukaryotes project (BioMarKs  [48] ). Information about sampling procedure, filter preparation, extraction of nucleic acids, sequencing strategy, sequence quality check and taxonomic assignment (on the phylum level) of the samples can be found in Logares et al. (2014)  [18]. Stringent cleaning options were used (exact primer matching, removing low quality score sequences using sliding windows of 50 bp, and chimera checking) to guarantee the quality and reliability of the final dataset.
Each sequence was labelled based on its origin (cultured organism, earlier environmental project, Bio-MarKs environmental sample), depth in the water column (subsurface, DCM, sediment), and sampling site of the BioMarKs project  [48]. In all environmental datasets, only sequences which could be assigned to a culture sequence belonging to the phylum Ciliophora and had at least 300 bp in length were considered, amounting to 85,482 BioMarKs ciliate V4 SSU-rDNA sequences. Furthermore, datasets were dereplicated (when identical, only unique sequences were kept for each sampling site in the BioMarKs database), which resulted in a final dataset of 25,842 unique BioMarKs sequences (8,931 DNA and 16,911 cDNA sequences, respectively), 308 ciliate culture sequences and 928 ciliate sequences of earlier environmental studies (listed in Additional file 1: Table  S1 ).
Graph construction Two types of sequence similarity networks (Figure  1 ) were constructed in this study to compare the consilience between the information within BioMarKs DNA sequences on the one hand and RNA sequences on the other hand. We used (1) a network with BioMarKs DNA plus reference sequences (10,167 sequences) and (2) a network with BioMarKs cDNA plus reference sequences (18,147 sequences). In both cases, sequences were used as an input file for EGN  [80]  choosing the following options: BLASTn E-value <1e-5; minimum hit identity threshold 60%; minimum hit length 40% of the smallest homolog. This protocol excluded singletons and resulted in disconnected networks at various sequence similarity thresholds (85%, 90%, 95%, 96%, 97%, 98%, 99%). Meaning that in a network with a sequence similarity threshold of 97%, two nodes (where each node represents one sequence from the dereplicated DNA or cDNA dataset, respectively) are connected by an edge only if they share a sequence similarity of at least 97% (Figure  1A ).
Graph analyses Clusters exclusively comprising sequences with the same label (for example, BioMarKs sequences or cultured ciliate sequences) were quantified. Using Igraph library scripts  [83]  in the R statistical computing environment  [84] , we applied two measures of dissimilarity between BioMarKs sequences, environmental sequences obtained in previous projects, and sequences from cultured ciliates. First, we used the minimal shortest path between all pairs of nodes of interest, expressed in number of edges (Table  1 , Figure  2D ); in other words, the minimal number of edges that must be crossed to connect any environmental node (that is, a BioMarKs sequence or a sequence from a previous environmental project) and its closest node of a cultured ciliate sequence (infinite when no such path existed).
Second, we used the closeness of environmental nodes and nodes from cultured ciliates in our graphs (Table  1 , Figure  2D ). This closeness quantifies the centrality of sequences in the graph: more peripheral sequences are more different from most other of the dataset. The distributions of closeness values for these groups of nodes were compared using the Kolmogorov-Smirnov test (P <0. 05 and P <0. 01) with the unilateral option (test 1: cultured sequences versus sequences from former environmental studies; test 2: cultured sequences versus all environmental sequences (former environmental studies plus BioMarKs)).
Figure1Mock sequence similarity networks. Weighted sequence similarity networks displaying sequences as nodes (black nodes represent environmental sequences, red nodes represent sequences of cultured ciliates), connected by edges reflecting their %ID obtained from a BLAST analysis (see list of weighted edges in which the color code reflects the %ID; red for 99%, orange for 97%, pink for 96%). The corresponding color code is used on the networks (right panel) to explore and structure the data. The mock dataset results in: A) two connected components when the minimum %ID required to connect two nodes is ≥97%; B) a single giant component when the minimum %ID required to connect two nodes is ≥96%; C) finer-level structure within components can be further detected using methods of community clustering, such as the Louvain Communities (see Methods).
Figure3Sequence similarity network showing the giant connected component (GCC) of each dataset. GCC were constructed at the most inclusive sequence similarity threshold (≥85%) for DNA (above) and cDNA (below) networks. Supernodes of the GCC represent Louvain communities (LCs); the size of the supernode indicates the number of sequences in the respective LC, the color indicates the proportion of targeted sequences in the respective LC (for example, sequences from cultured ciliates at t-2, sequences from cultured ciliates and previous environmental samplings at t-1, sequences from BioMarKs at t). The figure should be read along the time of data generation axis from left to right to follow the gradual discovery of ciliates by Sanger sequencing (t-2) compared to previous environmental studies (t-1) and finally to the current 454 pyrosequencing study (t).
Publicly available SSU-rDNA sequence data included into the environmental reference database. The table shows which studies had been included into the environmental reference database of our sequence similarity network approach. The first column gives the number of V4-SSU-rDNA sequences of each study which fulfilled our requirements and were thus incorporated into the environmental reference database. Additional file 2: FigureS1. Proportion of CCs (above) and LCs (below) in DNA and cDNA networks which exclusively contained BioMarKs sequences. DNA data are shown in red, cDNA data in blue. Composition of each CC and LC was analyzed at all sequence similarity thresholds to detect references from cultured ciliate sequences and sequences of former environmental studies. Proportion of CCs and LCs displayed here only contain sequences of BioMarKs project.
Acknowledgements We thank  PA Jachiet  for technical help. We are grateful to the  CNRS-UPMC ABiMS bioinformatics platform  (http://abims. sb-roscoff. fr)for providing computational resources and support. The BioMarKs 454 sequence data were generated as part of a study supported by the  EU - FP7 ERA-net program BiodivERsA , under the project  BioMarKs  ( 2008-6530 ). We thank all members of the  BioMarKs consortium  who participated in field sampling and data generation. We also appreciate the support of the  Research Unit BioComp at the University of Kaiserslautern , funded by the  Research Initiative of Rhineland-Palatinate. This work was supported by a grant from  Stipendienstiftung Rheinland-Pfalz  to D. F. ; a grant from  University of Kaiserslautern  to L. B. ;  Deutsche Forschungsgemeinschaft (DFG , grant #  DU1319/1-1 ) to M.
D. ; DFG (grants #  STO414/3-1 ,  STO414/3-2  and  STO414/11-1 ) to T. S. , and by a grant from  Region Ile-de-France (DIM Malinf)  to S. K. E. B. is funded by the  European Research Council  under the European Community's  Seventh Framework Programme FP7  ( 2007-2013 Grant Agreement n°615274 ). We greatly appreciate the comments of three anonymous reviewers for their constructive comments on our manuscript.
2 ). Therefore, novel approaches integrating multisensors acquisitions can help to improve understanding of the various environmental, physical, climatic, and human factors influencing biodiversity, by monitoring spatial and temporal variations in species composition. Adding a further confounding factor, the relationship of beta-diversity with environmental heterogeneity is also scale dependent, perhaps even more than alpha-diversity. Areas of ecological transition, where the factors influencing patterns of biodiversity distribution change at different spatial scales, represent therefore a particular challenge for field monitoring. Yet, it is in precisely such areas where remote sensing may be especially helpful, enabling swift and easy computation of proxies of vegetation heterogeneity at different spatial scales, to generate hypotheses about scales at which such ecological transitions may be taking place: this can then be tested using appropriately designed field datasets.
Such information is crucial for a continuous global biodiversity analysis and an improved understanding of our current global challenges. Quantifying and monitoring global biodiversity using remote sensing-based techniques will require increasingly complex data analyses. These can only be implemented in the future with a completely new orientation of Big Data analysis using Linked Open Data (LOD) approaches as well as the rapidly growing Open Database Initiative -Freebase  (Lausch et al. 2015). LOD availability is constantly evolving, also under the umbrella of Volunteering Geographic Information  (Fonte et al. 2015) , whereby all large datasets that are freely available on the Internet can be related through semantic networks. The basic advantage of LOD models compared to traditional data-mining models is that, due to already existing semantic links, it is not up to the analyst to decide the criteria to link data in the model  (Bizer et al.
Acknowledgments DR was partially funded by: (i) the  EU BON (Building the European Biodiversity Observation Network)  project, funded by the  European Union  under the 7th Framework programme, Contract No. 308454  and (ii) by the  ERA-Net BiodivERsA , with the national funders  ANR ,  BelSPO  and  DFG , part of the 2012-2013 BiodivERsA call for research proposals. This work benefited from support from  EU COST Action   TD1202  'Mapping and the  Citizen Sensor '.
(2009) (Generalized Additive Models, GAM) Thematic Mapper Plus (ETM+) data including remote sensing variables Multivariate regression models including Walnut fruit forests, Kyrgyzstan/Central Feilhauer and Schmidtlein (2009) remote sensing data as covariates Asia, ASTER data Regression models testing images with Tropical dry forests, India, Landsat Enhanced Nagendra et al. (2010) different spatial and spectral resolution Thematic Mapper Plus (ETM+)/IKONOS data Neural networks for predicting species Bornean tropical rainforests, Malaysia, Asia, Foody and Cutler (2003) richness and abundance Landsat Thematic Mapper (TM) data Beta-diversity Mantel-based correlation between species Worldwide, WWF Ecoregion database, He and Zhang (2009) assessment compositional turnover and spectral MODIS data distance Multivariate approaches (based on Walnut fruit forests, Kyrgyzstan, Central Feilhauer and Schmidtlein (2009) detrended correspondence analysis, DCA) Asia, ASTER data to find beta-diversity gradients Quantile regression applied to species beta- Tropical dry forests, India, Landsat ETM+ Rocchini and Cade (2008) diversity estimate (spectral distance decay) data Beta-diversity mapping based on explicit Tropical forests, Amazon, Peru, data from F eret and Asner (2014a) distance maps the Carnegie Airborne Observatory (CAO) airborne taxonomic mapping system (AToMS) Rows are ordered based on a complexity criterion, that is, following an increase in the complexity of the approaches being used.
Materials and Methods To assess the biological affinities of these early Vanuatu settlers, we have generated two distinct comparison datasets (SI Materials and Methods). The first is a composite sample of 12 archaeological adult specimens that were chronologically selected to help in evaluating Lapita-associated population origins. It comprises Late Pleistocene and Holocene individuals from the Sahul region, pre-Neolithic individuals from Island Southeast Asia (ISEA), and immediately post-Lapita and more recent prehistoric individuals from Remote Oceania (Table  S2 ). The second dataset consists of nine samples selected from the database of Howells  (33) , representing five geographically distinct human populations from East Asia (China), Western Micronesia (Guam), Island Melanesia (mainly Tolai from New Britain), Australia, and Eastern Polynesia (Table  S3 ).
This dataset originally included a total of 707 adult individuals out of which subsamples of 54 specimens (27 male and 27 female) were sampled in each of the five geographical groups (n = 270). Craniofacial shape affinities were assessed with series of multivariate analyses, including principal component analyses (PCA) on size-corrected measurements of the neurocranium and upper face and linear discriminant analyses (LDA), which have been used to predict the classification of the archaeological specimens, including the five Teouma individuals (SI Materials and Methods and Table  S4 ).
2) OBSERVATIONS Observations were used to provide a comparison with the different UM simulations. Rainfall was taken from the satellite-derived Tropical Rainfall Measuring Mission product TRMM-3B42 at 0. 258 3 0. 258 resolution  (Huffman et al. 2007) , which combines measurements from multiple satellites, calibrated with the use of surface rain gauges. Shortwave radiative fluxes were derived from the Land Surface Analysis Satellite Application Facility at ;3-km resolution  (Geiger et al. 2008). Land surface temperature data from the ECMWF operational analysis were also used (50-km resolution), which combine model simulations with all the surface and radiosonde observations available. As part of the special observation period of the African Monsoon Multidisciplinary Analyses campaign the observational network was greatly enhanced during the 2006 monsoon season  (Parker et al.
A run driven by observed weather is used to describe the crop-weather relationships with realistic weather inputs, and is used to validate the processes in the runs forced by model data. Crop yield observations are also used for comparison with the crop-model output, from a combination of data from the FAO CountrySTAT database (FAOstat 2012; http://faostat3. fao. org/home/E), the Ghana Meteorological Agency, and the Thematic Data Base Management System (TDBase 2001; ''La banque des donnees tabulaires du Systeme Integre pour l'Alerte Precoce''). Data span the period 1983-2009, although data availability varied by region (Fig. 2a  shows the combined average for all datasets). Five different runs were performed with different weather inputs and resolutions. Four runs were driven by the UM simulations described in section 2a(1).
3b, c ). Parameterizations of convection, on the other hand, fail to form the organized thunderstorms that are observed, and produce frequent, light rainfall spread over large areas. In both runs the vast majority of rainfall events are light to moderate, with few instances with no (15%-20%) or heavy (,10%) rainfall (Figs. 3d, e ). The differences in resolution between the datasets will affect the profiles shown in Fig. 3 , as averaging the data over larger grid boxes will tend to reduce both the highest rainfall intensities and the number of grid points with no precipitation. This alone, however, cannot explain the differences. The satellite observations are the coarsest dataset and have a tendency to underpredict heavy rainfall events compared to rain gauges  (Jobard et al. 2011 ), but despite this they show many more instances of heavy and no rainfall compared to the models with parameterized convection.