dataesr/scientific-paragraphs-categorization huggingface dataset:
On the one hand, MCoEM only makes use of 13 from the 15 available classes. The two remaining classes corresponds to the 12th and 15th templates in the middle column of Figure  10. Figure  11  plots the weight evolution for each class as MCoEM moves forward and shows that those two classes have quickly become unused by the algorithm. The first ten classes weight is slightly lower than 1/10 which is in line with the dataset. On the other hand, SAEM maintains the 15 classes alive all throughout the algorithm. In this example, SAEM appears more robust than MCoEM for inferring a mixture model. However, we believe that the stability of MCoEM can be improved by increasing the number of iterations before the first parameter update (only 50 in our simulation), hence avoiding this degeneracy problem.
Classification When considering real-time classification applications, the MCoEM methodology may prove more adequate than SAEM: indeed as soon as the first estimate of θ is available, a classifier can be implemented. Of course, the rate of correct classification is expected to improve upon random guessing as soon as the templates take shape. Both algorithms produce a sequence of parameter estimates. However, since iterations of MCoEM and SAEM have different complexity, we consider θt , the parameter estimate after a runtime of t time units, as a fair way to compare both methods. Learning parameters of the mixture of deformable models (5) allows to classify labeled observations {( Ỹ1 , V 1 ),. , ( ỸN , V N )} gathered in a testing dataset. There is no overlap    between those testing observations and the data {Y 1 ,.
Background: Nicotiana attenuata (coyote tobacco) is an ecological model for studying plant-environment interactions and plant gene function under real-world conditions. During the last decade, large amounts of genomic, transcriptomic and metabolomic data have been generated with this plant which has provided new insights into how native plants interact with herbivores, pollinators and microbes. However, an integrative and open access platform that allows for the efficient mining of these -omics data remained unavailable until now. Description: We present the Nicotiana attenuata Data Hub (NaDH) as a centralized platform for integrating and visualizing genomic, phylogenomic, transcriptomic and metabolomic data in N. attenuata. The NaDH currently hosts collections of predicted protein coding sequences of 11 plant species, including two recently sequenced Nicotiana species, and their functional annotations, 222 microarray datasets from 10 different experiments, a transcriptomic atlas based on 20 RNA-seq expression profiles and a metabolomic atlas based on 895 metabolite spectra analyzed by mass spectrometry.
Background Nicotiana attenuata, is a diploid wild tobacco native to the Great Basin desert of the United States with populations across Utah, Nevada, Arizona, Oregon, and California. This plant has adapted to an ecological niche defined by the post-fire environment, where soils tend to be nitrogenrich and biotic stresses are highly dynamic  [1]. During the last decade, N. attenuata has been developed as a model organism to study plant-environment interactions in its native environment  [2] [3] [4] [5] [6] , and a large number of transcriptomic and metabolomic datasets have been generated with this plant. For example, more than 230 transcriptomic data from N. attenuata have been submitted to the NCBI GEO database. However, to efficiently analyze, explore and visualize such genome-wide metabolomic and transcriptomic data remain challenging for individual researchers.
Genomic data The NaDH includes 33,449 and 27,911 predicted gene models from N. attenuata (release r2. 0) and N. obtusifolia genomes (release r1. 0), respectively. For comparative genomic analysis, additional gene sequences and structures from nine dicot plant genomes are also included in the database (Table  1 ). To provide functional annotations, the predicted enzyme commission (EC) identities, gene ontology (GO) terms, and protein domains are included. Fig. 1  Overview of data structure and utilities in the NaDH. The NaDH consists of 10 major utilities, which can be accessed by either gene ID or metabolite spectrum ID.
Gene families and phylogeny The NaDH includes 23,340 homologous groups constructed based on protein coding sequences from 11 eudicot species (Table  1 ). PhyML was used to construct phylogenetic trees with high confidence from these homologous groups that contain more than two genes. In total, 16,305 trees containing 255,404 genes (of which 28,610 are from N. attenuata) are included in the NaDH. In addition, 81,859 gene duplication events detected from high confidence phylogenetic trees (approximate Bayes branch supports of greater than 0. 9 for the target node and its two child nodes) using the species-overlapping algorithm implemented in Notung-2. 6 [19, 20]  are also included in the database. The majority of gene duplication events in N. attenuata are found at the Solanaceae branch (Table  2 ), consistent with the observation that species of Solanaceae share a wholegenome triplication event.
Transcriptomic data The NaDH contains expression profiles from both RNAseq and microarray datasets  [21] [22] [23]. For the RNA-seq datasets (Illumina HiSeq 2000, pair-end sequencing, NCBI accession number: PRJNA317743), the expression level (transcript per million, TPM)  [24]  of each gene from different tissues sampled from leaves, seeds, roots, stems and flowers are included (Table  3 ). In total, 21,970 genes were expressed in at least one tissue (TPM greater than 5). Roots contain the largest number of expressed genes (Table  3 ). For the microarray dataset, 222 microarrays (based on Agilent platform: GPL13527) from N. attenuata leaves, roots and flowers are included. The probes of this microarray platform were mapped to the N. attenuata genome and the uniquely mapped probes were annotated according to gene predictions.
Metabolomic data Metabolomic data from 14 isolated tissues of N. attenuata growing under controlled conditions in glasshouse were curated. This includes a pool of all non-senescing rosette leaves, combined lower, middle and higher segments of the stem, the complete root system, dried seeds, complete floral buds of 8 mm length, complete sepal ring, the nectary, the ovary (not including the nectary), the style, anthers, filaments (not including anthers), and the corolla tube and limb, collected at anthesis. Pools of 100 mg tissues were extracted using 80% methanol. Independent extractions were also conducted with 20% methanol. Samples were analyzed using UHPLC-ESI/qTOF-MS in positive ion mode. MS/MS data collection was achieved via a previously-described pipeline  [25]  and 895 reconstructed MS/MS spectra were obtained  [26].
Gene-to-gene co-expression To facilitate the identification of co-regulated genes in N. attenuata, we calculated the pairwise expression correlation co-efficiency based on RNA-seq data from 20 different tissues using three different methods: Gini, Spearman and Pearson  [27]. In total, 15,216 informative genes (with TPM greater than 5 in at least one tissue and a variance greater than 1) were used and gene pairs with absolute expression similarity greater than 0. 65 were considered for the final dataset. The raw reads information and methods used for generating these data are available under NCBI accession number PRJNA317743 All data are stored in NaDH and can be visualized in a network graph.
Metabolite-to-metabolite co-expression Metabolite-to-metabolite tissue associations were calculated using Ochiai similarity based on binary metabolite dataset (containing 14 tissue vectors), with a cutoff of 2 on the ZMAD transformed values  [28]. The metabolite-tometabolite pairwise associations were calculated across the dataset by comparing each metabolite spectrum with the other metabolite spectra  [26]. All data are stored in the underlying database for fast accessing under the utilities of NaDH.
Gene-to-metabolite co-expression Gene-to-metabolite tissue associations were calculated using Ochiai similarity with binary gene and metabolite data (presence and absence) generated from 12 shared tissues between the transcriptome dataset and the metabolome dataset (a cutoff of transcriptome dataset is ZMAD transformed TPM greater than 3 and a cutoff of metabolome dataset is ZMAD transformed value greater than 2). Gene expression was centered by median and medianabsolute-deviation (MAD) to obtain a relative expression level  [29]. In total, 23,075 genes and 895 metabolite spectra with expression levels above the threshold were used for the network constructions  [26]. The pairwise correlations were calculated using Ochiai correlations based on the transformed binary values  [28]  and only Ochiai correlation coefficient (occ) greater than 0.
We then test the assumption that tooth crowding is less frequent in ancient and modern wild wolf populations by examining 750 modern dogs and 205 modern wolves from across the modern geographic range of Canis lupus as well as 66 Late Pleistocene wolves from Alaska. Our results demonstrate that landmark-based metrics provide a reliable approach for recording and analysing tooth crowding. Although it is likely that the relatively low frequency of tooth crowding found in our modern dog dataset (~6%) in part reflects the 'modern' morphology of domestic breeds, the higher frequency of crowding in both modern (~18%) and ancient (~36%) wolves strongly suggests that current assumptions linking tooth crowding with the process of early domestication (at least in dogs) should be critically re-evaluated, and that further investigations into the drivers behind these developmental patterns should be pursued.
These studies have produced tooth-crowding indices of continuous data that are then directly compared with other datasets, where indices of tooth crowding in wild and domestic specimens have previously been calculated (e. g. Clark, 1996; van Wijngaarden-Bakker, 1974; Walker and Frison, 1982; Ovodov et al. , 2011). For instance,  Ovodov et al. (2011)  measured the tooth-crowding index of a 33,000-year-old canid from Razboinichya Cave (Russia) and compared it to crowding indices reported from  Clark (1996)  Neolithic dog samples, as well as  Benecke (1994)  P redmostí (Czech Republic) canid mandibles, but not with other contemporaneous specimens. The authors do, however, urge caution when using only tooth crowding as evidence for the possible presence of early domesticate dogs  (Ovodov et al.
, 2011). The reference datasets used as a baseline for tooth crowding in wild wolf populations are often limited both in number of specimens and geographic coverage (e. g. only modern European wolves are used in  Clark, 1996). Inconsistency of the methods and metrics, as well as the development of study specific measurements (i. e. Dimitrijevi c and Vukovi c, 2012), also hinders comparisons with previously measured archaeological and wild canid specimens. Statistical analyses are additionally lacking in previous studies of tooth crowding. Although  Benecke (1987)  and Dimitrijevi c and Vukovi c (2012) use discriminate analysis on mandibular and maxilla measurements to study Upper Palaeolithic canids from Northern Europe and Mesolithic/Early Neolithic dogs from the Danube Gorge respectively, neither attempted to discriminate levels of tooth crowding between wild and domestic animals, even though both studies continue to note the importance of tooth crowding as an indicator of domestication  (Benecke, 1987:33; Dimitrijevi c and Vukovi c, 2012).
, 2013). The leave-one-out cross validation method iteratively treats each specimen as an unknown and attempts to identify it. Thus, the percentage of correctly reidentified specimens provides an approximation for the ability to identify an unknown specimen to a given group, and is reported as a percentage of specimens correctly classified (cross-validation percentage; CVP). These analyses were applied to the complete dataset, as well as the subsets of the wolf-like breeds and Pariah dogs. CVA was conducted using both the Spacing Ratio and the Rotation Ratio measurements, to examine which (if any) method provides better discrimination between groups. The variation within SR crowding scores due to differences in size was examined with a regression between the SR score and the toothrow centroid size.
This was carried out on the global dataset as well as for each continent separately (North America and Eurasia), and p values were adjusted for multiple comparisons using the Bonferroni method. All calculations of the previous measurements and analyses were executed in R (using the interface RStudio v0. 99. 467) (R Core  Team, 2015). The author written R code for calculating the crowding metrics is available in the Supplementary information (SI 1).
Geographic variation in modern wolves Tooth crowding was found to have a significant (p < 0. 001), but weak positive correlation (adjusted R 2 ¼ 0. 054) with latitudinal change. The results of the global Procrustes superimposition were non-significant over the whole geographic range of the dataset (p ¼ 0. 81) and within each continent (Eurasia p ¼ 0. 67; North America p ¼ 0. 32). This suggests that the influence of geography on the variation of tooth crowing does not need to be considered.
While we cannot exclude the possibility of wolf-dog hybridisation as an explanation for crowding (hybridisation being well documented, see Vil a and  Wayne, 1999; Moura et al. , 2014; Randi, 2011; Verlag and Service, 2002; Crockford, 2000) , the large size of our dataset, along with the inclusion of Pleistocene wolves, minimises any likely influences. Interestingly, the highest proportion of crowded specimens was observed in the Late Pleistocene wolves from Alaska. These specimens mostly pre-date the arrival of humans in North America  (Goebel et al. , 2008 ; although see  Bourgeon et al. , 2017)  adding support to our observation that a degree of mandibular tooth crowding should be expected in the range of natural ecomorphs represented within Canis lupus. While no crowding index value has been (nor should be) definitively shown to determine wild/domestic status, and previous studies have similarly recorded this large overlap in crowding scores between wild and domestic specimen (e.
Conclusions This study represents the first comprehensive examination of mandibular tooth crowding in a large geographical and temporal dataset of dogs and wolves. Our results show firstly that systematic quantification of tooth position in the canid mandible is achievable using landmark-based metric analysis, and that these metrics can be used to quantitatively explore instances of tooth crowding in canids. In addition, the Spacing Ratio measurement provides a robust quantification of mandibular tooth crowding, and measuring the toothrow by accounting for curvature of the hemimandible is crucial to avoid overestimating crowding. While there were significant differences in crowding between wolves and domestic dogs, the results of discriminant analyses provided low cross-validation values, demonstrating that wolves and domestic dogs cannot be confidently separated based on tooth crowding alone, contrary to the geometric morphometric approach which incorporates more of the morphology of the toothrow, and appeared a promising tool for discriminating between groups.
Acknowledgements We are grateful to the curators and staff of many museums and institutions for kindly providing access to collections under their care, especially  Marc Nussbaumer & Andre Rehazek  of  Naturhistorisches Museum Bern ,  Jack Tseng  of the  American Museum of Natural History , and  Richard Meadow of Peabody Museum, Harvard University. Additionally, we are thankful to  Richard Allen ,  Anna Linderholm  and  Angela Perri  for their help with sourcing and collecting data for this study. This work was supported by  NERC  ( NE/ K003259/1 ), and the  European Research Council  ( ERC-2013-StG 337574-UNDEAD ).
1. There is an urgent need for large-scale botanical data to improve our understanding of community assembly, coexistence, biogeography, evolution, and many other fundamental biological processes. Understanding these processes is critical for predicting and handling human-biodiversity interactions and global change dynamics such as food and energy security, ecosystem services, climate change, and species invasions. 2. The Botanical Information and Ecology Network (BIEN) database comprises an unprecedented wealth of cleaned and standardised botanical data, containing roughly 81 million occurrence records from c. 375,000 species, c. 915,000 trait observations across 28 traits from c. 93,000 species, and co-occurrence records from 110,000 ecological plots globally, as well as 100,000 range maps and 100 replicated phylogenies (each containing 81,274 species) for New World species.
Here, we describe an r package that provides easy access to these data. 3. The bien r package allows users to access the multiple types of data in the BIEN database. Functions in this package query the BIEN database by turning user inputs into optimised PostgreSQL functions. Function names follow a convention designed to make it easy to understand what each function does. We have also developed a protocol for providing customised citations and herbarium acknowledgements for data downloaded through the bien r package. 4. The development of the BIEN database represents a significant achievement in biological data integration, cleaning and standardization. Likewise, the bien r package represents an important tool for open science that makes the BIEN database freely and easily accessible to everyone.
| INTRODUCTION Understanding the distribution and diversity of organisms relies on the availability of large quantities of standardised, robust, and comprehensive biological data. Furthermore, developing ecological theories that are both general and predictive also necessitates highquality data. There is an especially urgent need for large quantities of botanical data across differing spatial scales. Despite the crucial importance of plants for ecosystem functioning and services, plants have lacked comprehensive datasets similar to those of less-speciose clades such as birds and mammals, which have trait  (Myhrvold et al. , 2015) , phylogenetic  (Bininda-Emonds et al. , 2007; Faurby & Svenning, 2015; Jetz, Thomas, Joy, Hartmann, & Mooers, 2012)  and range information  (BirdLife International, 2013; IUCN, 2013)  available for most species.
nceas. ucsb. edu/bien/) database is an unprecedented collection of integrated and standardised New World botanical data  (Enquist, Condit, Peet, Schildhauer, & Thiers, 2016). This has resulted from the efforts of more than 50 scientists from 20 institutions across the globe. The BIEN database is constructed from a workflow that performs three essential tasks. First, it integrates multiple data types (e. g. trait data, occurrence records, ecological plot data, Table  1 ) from numerous sources (Tables  S1-S3 ). Second, utilizing open source tools, it assesses and corrects taxonomic errors, standardizes taxonomy, validates geographic information, and standardizes data formats. Third, it generates species-level distribution models and phylogenies. The current version of the BIEN database (bien 3.
4 as of 15 January 2017) contains 81,108,996 occurrence records that stem from 378,502 species from across the globe. While the data are global in extent (Figures  1  and 2 ), data integration efforts have focused on the New World (North and South America, Table  2 ). The BIEN database differs from other biodiversity databases in the breadth of data types it contains (Table  1 ), the integration of these data, the degree of validation and standardization the data goes through, and the amount of additional metadata added. Most other databases focus on a single data type, such as TRY  (Kattge et al. , 2011)  with trait data and GBIF (Global Biodiversity Information Facility, 2012) with occurrence records, where BIEN integrates multiple data types, including occurrence, plot, trait, and phylogenetic data.
Data within the BIEN database are also put through standardization and validation procedures, thereby increasing data quality and ease of use. For example, BIEN occurrence data undergo additional taxonomic and spatial validations that aren't used by GBIF (Global Biodiversity Information Facility, 2012), and  T A B L E 2 Summary of the number of occurrences within bien 3. 4 F I G U R E unlike Traitbank  (Schulz, 2016) , BIEN trait units and names have been standardised. The wealth of information contained in the BIEN database is housed at the National Center for Ecological Analysis and Synthesis (NCEAS) in Santa Barbara, CA. The database is open-access and can either be accessed by a graphical user interface (GUI) at BIENdata. org or through the newly developed bien package for r that we introduce here (R Core Development Team, 2015).
| DATA CLEANING AND INTEGRATION WORKFLOW As described by  Enquist et al. (2016, in prep. ) Range maps were built for each species using a method determined by the number of observations of that species. A species with a single record was assigned a range that included only the 100 km 2 cell where it was found. Ranges for species with 2-3 records were rectangular bounding boxes with the limits set by the minimum and maximum latitude and longitude of all occurrences. Ranges for species with 4-9 records were built with convex hulls (the minimum-fitting polygon that encompasses all occurrences of that species). For species with >9 records, we built species distribution models using the Maxent algorithm  (Phillips, Anderson, & Schapire, 2006). Only one occurrence record per cell (in cases of multiple records) was used for Maxent model building.
Maxent model building generally followed the recommendations outlined in  (Merow, Cory, & Silander, 2014; Merow, Cory, Smith, & Silander, 2013). Model settings were chosen to balance overfitting, which underestimates range sizes, with underfitting, which results in excessively smooth models that over predict range size. Only linear, quadratic, and product features were used and regularization was set at the default value. Environmental predictors for SDMs were obtained from the WorldClim current   Maxent's continuous predictions were converted to binary presence/absence predictions by choosing a threshold based on the 75th percentile of the cumulative output. We constructed a phylogeny of 18,641 species based on a standardised list of New World species and the gene regions atpB-rbcL, ndhF, psbA, psbA-psbH, rbcL and trnT-trnL-trnF marker regions, using the software PHLAWD  (Smith, Beaulieu, & Donoghue, 2009).
The phylogeny was constructed with RAxML (7. 3. 0; Stamatakis, 2006)  with an unconstrained ML search and divergence times were estimated using penalised likelihood and the treePL software package  (Smith & O'Meara, 2012). Additional details on the methodology used to extract these data from GenBank and align them are presented in  Hinchliff and Smith (2014). Onto this phylogeny, we grafted the additional taxa from the BIEN dataset using taxonomy (genus membership) as a guide for the remaining c. 72,000 species. This grafting was repeated to create a set of 100 phylogenies to account for uncertainty in placement of species without genetic information. Additional information on the BIEN phylogenies is available online (http://bien. nceas. ucsb. edu/bien/biendata/bien-2/phylogeny/).
| DATA LICENSE The data and derived products are distributed via CC-BY-NC-ND Creative Commons license (https://creativecommons. org/licenses/by-nc-nd/4. 0/). This allows redistribution and re-use of a licensed work on the conditions that the creator is appropriately credited, that use is noncommercial, and that users do not distribute derivative works. Users interested in uses of these data and products outside the scope of this license are encouraged to contact the BIEN working group (bien. working. group@gmail. com) to discuss alternative licensing options.