dataesr/scientific-paragraphs-categorization huggingface dataset:
In such cases, the landmarks 58/72 (zygomatic process of the maxillae) were hard to define accurately. They were therefore considered as missing and estimated a posteriori. Thin plate spline interpolations  (Gunz et al. , 2009)  were computed to estimate missing landmarks for each group (e. g. species, cryptic lineages). This approach was implemented in the software package  'geomorph' v. 3. 5. 0 (Adams et al. , 2017)  in R (R Development Core Team, 2013). A generalized Procrustes analysis  (Rohlf & Slice, 1999)  was performed on all sets of landmarks. All specimens were scaled to centroid size, optimally translated and rotated using a least-squares criterion. The coordinates retrieved by the generalized Procrustes analysis represented the shape of the skull of each specimen. An ANOVA was performed on a subset of our data for which sex determination was available (N = 120), in order to assess sexual dimorphism in skull shape.
This analysis was performed with procD. pgls from 'geomorph' using a consensus phylogeny from  Gaubert et al. , (2018). Then, we focused on ontogenetic allometry using interspecific (N = 241) and intraspecific datasets. The cryptic lineages dataset including P. tricuspis included only Downloaded from https://academic. oup. com/zoolinnean/article-abstract/188/1/255/5618763 by biuupm Bibliotheque Universitaire UPM, frederic. delsuc@umontpellier. fr on 07 January 2020 258 S. FERREIRA-CARDOSO ET AL. 2019 The Linnean Society of London, Zoological Journal of the Linnean Society, 2020, 188, 255-275 96 specimens (Fig. 2 ; KMMA 30808 was discarded owing to imprecise information on location). Details about the analysis of the M. javanica dataset can be found in the Supporting Information (Appendix S3).
procD. lm from the package 'geomorph'. The HOS tests were complemented by graphical representations of allometric trajectories and skull shape deformations  (Adams & Nistri, 2010; Esquerré et al. , 2017). We plotted the first principal component (PC1) of the predicted values of multivariate regression of shape on log-centroid size vs. log-centroid size regressions for each species. We then assessed the significance of differences between the intercepts using a Tukey means comparison, to test for changes in shape explained by species differences in the resulting morphospace. Thin plate splines were generated using the function tps3D from the R package 'Morpho' v. 2. 5. 1  (Schlager, 2017)  in order to characterize differences in shape between the smallest and the largest specimens for each species (ontogenetic allometry).
We then visualized landmark displacement during ontogeny using the function deformGrid3d from the same package. A phenotypic trajectory analysis (PTA) was also performed as a complementary analysis to the HOS test (see Supporting Information, Appendix S3;  Adams & Collyer, 2009; Collyer & Adams, 2013). (Gaubert et al. , 2016)  to which they were attributed (right). The tree topology of the intraspecific affinities is based on  Gaubert et al. (2018). Downloaded from https://academic. oup. com/zoolinnean/article-abstract/188/1/255/5618763 by biuupm Bibliotheque Universitaire UPM, frederic. delsuc@umontpellier. fr on 07 January 2020 Second, when the HOS test revealed parallel trajectories, we performed an additional analysis (overlap test) on multivariate shape data to test for overlap in ontogenetic allometric trajectories by comparing their differences with a set of 10 000 permutations  (Piras et al.
, 2011; Esquerré et al. , 2017). Intercepts were tested at x = 5. 125, because we lacked fetuses and neonates of small size (close to x = 0), which could result in an incorrect estimate of minimum size (x = 0). The overlap test was performed only on the interspecific dataset. Finally, if slopes were overlapping between species, a third analysis was performed to identify peramorphosis/paedomorphosis. This 'heterochrony test' enables the characterization of differences in skull shape at maximum size  (Piras et al. , 2011; Esquerré et al. , 2017). Significance was assessed by comparing these differences with a set of 10 000 Downloaded from https://academic. oup. com/zoolinnean/article-abstract/188/1/255/5618763 by biuupm Bibliotheque Universitaire UPM, frederic. delsuc@umontpellier. fr on 07 January 2020 260 S.
Visualization of shape Variation and statistical analysis The variation of skull shapes was visualized using a PCA  (Dryden & Mardia, 1993). Data were analysed without allometric correction in order to allow for a comparison of the results with morphological discrete characters used in phylogenetic analyses. Analyses with the allometry-corrected shapes for the interspecific and P. tricuspis datasets can be found in the Supporting Information (Appendix S3). Allometrycorrected shapes were obtained as the residuals of pooled within-group regressions. This method is used to obtain a common estimate for the allometry when comparing several groups  (Sidlauskas et al. , 2011; Benítez et al. , 2013; Klingenberg, 2016). Pooled withingroup regressions were performed in MorphoJ v1. 06d  (Klingenberg, 2011).
Interspecific variation in extant pangolins A PCA was performed excluding specimens considered as juveniles (N = 173). A second PCA was performed on a dataset including juveniles and two specimens of M. culionensis (N = 243). We used a. ply surface of a micro-computed tomography-scanned P. tricuspis skull (BMNH 34. 6. 2. 92), the species most closely resembling the mean shape. The surface was then deformed to the mean skull shape of Pholidota and used to visualize the variation in skull shape along the first three principal components (PCs). Triangular mesh warping via thin plate spline was performed with the package 'Morpho'. A multivariate ANOVA was performed to assess whether skull shape differed between taxonomic groups. Pairwise comparisons between leastsquares means were performed using advanced.
procD. lm. If taxonomy had a significant effect on skull shape, a leave-one-out cross-validated linear discriminant analysis (LDA) was performed on a set of PCs explaining 90% of variance. The leave-oneout procedure allows evaluation of the accuracy with which unknown specimens can be identified (e. g. Evin et al. , 2013). Linear discriminant analyses and post hoc classification methods were performed with the 'MASS' package  (Venables & Ripley, 2002)  in R.
Intraspecific variation in P. tricuspis and M. javanica A second subset including only adult P. tricuspis specimens was analysed separately in order to describe the intraspecific variation in skull shape (N = 71). We used the cryptic genetic lineages defined by  Gaubert et al. (2016)  to test the variation in skull shape linked to geographical distribution within P. tricuspis. Specimens were sorted according to six regions (Fig. 2 ): Western Africa (WAF), Ghana (GHA), Dahomey Gap (DHG), Western Central Africa (WCA), Central Africa (CAF) and Gabon (GAB). Skull shape difference tests and cross-validated LDA described in the previous paragraph were repeated on this dataset. If a posteriori attribution errors were consistently detected between two regions, these were merged, and the protocol was repeated with the new specimen sorting in order to test for its potential in assigning specimens with unknown geographical origin.
80, P = 0. 085; F 1,106 = 2. 03, P = 0. 057) and  Downloaded from https://academic. oup. com/zoolinnean/article-abstract/188/1/255/5618763 by biuupm Bibliotheque Universitaire UPM, frederic. delsuc@umontpellier. fr on 07 January 2020 the interaction between sex and species grouping (F 6,105 = 0. 89, P = 0. 656; F 6,106 = 1. 07, P = 0. 332) did not significantly influence shape, with and without considering size as a covariate in the model, respectively (N = 120; Supporting Information, Table  S10 ). The Procrustes ANOVA performed with the adult dataset revealed a significant effect of species on skull shape (F 6,166 = 45. 02, P < 0. 001; Table  2 ). Pairwise comparisons showed that all species presented significantly different skull shapes (Supporting Information, Table  S11 ). The variation in skull shape was visualized using a PCA performed on the raw shape variables of the seven pangolin species (Fig.
delsuc@umontpellier. fr on 07 January 2020 270 S. FERREIRA-CARDOSO ET AL. 2019 The Linnean Society of London, Zoological Journal of the Linnean Society, 2020, 188, 255-275 M. javanica/M. culionensis and M. crassicaudata, with M. pentadactyla as the sister taxon  (Gaubert et al. , 2018). The M. crassicaudata-M. javanica/M. culionensis clade remains moderately supported in the Bayesian inference excluding mitogenomes (nuclear DNA only). In contrast, a moderately supported sister-group relationship between M. pentadactyla and M. crassicaudata is retrieved by morphology-based phylogenetic analyses (bootstrap value = 76;  Gaudin et al. , 2009). The list of synapomorphies from  Gaudin et al. (2009)  for the node gathering M. crassicaudata and M. pentadactyla featured only three cranial traits. Among these, only the position of the foramen ovale at the level of the anterior edge of the ectotympanic is an unambiguous synapomorphy [character 379(1)].
ANOVA of shape ~ taxa/geographical groups of adult specimens of the interspecific and Phataginus tricuspis datasets Downloaded from https://academic. oup. com/zoolinnean/article-abstract/188/1/255/5618763 by biuupm Bibliotheque Universitaire UPM, frederic. delsuc@umontpellier. fr on 07 January 2020 264 S. FERREIRA-CARDOSO ET AL.
ACKNOWLEDGEMENTS We thank  Steffen Bock ,  Frieder Mayer ,  Detlef Willborn  (MfN),  Roberto Portela Miguez ,  Louise Tomsett  (BMNH),  Géraldine Veron  (MNHN),  Chris Conroy  (MVZ),  Darrin Lunde ,  Paula Bohaska ,  John Ososky  (USNM) and  Eleanor Hoeger  ( AMNH ) for access to collections. We acknowledge  Farah Ahmed ,  Amin Garbout  (BMNH) and  Renaud Lebrun  ( ISEM ) for assistance with micro-computed tomography scanning. We also thank  Pierre-Henri Fabre ,  Quentin Martinez , and  Julien Claude  ( ISEM ) for fruitful discussions. Finally, we thank two anonymous reviewers for helpful comments on the manuscript. S. F. -C. , L. H. and F. D. were supported by a  European Research Council (ERC)  consolidator grant (ConvergeAnt # 683257 ). L. H. and F. D. were supported by  Centre National de la Recherche Scientifique (CNRS).
This research received support from the  Synthesys  Project, https:// www. synthesys. info/,financed by the  European Community Research Infrastructure Action  under the  FP7  ( GB-TAF-5606  and  BE-TAF-5661 ). This work was supported by  PANGO-GO  ( ANR-17-CE02-0001 ) and by  'Investissements d'Avenir ' grants managed by  Agence Nationale de la Recherche Labex CEMEB  ( ANR-10-LABX-0004 ),  Labex NUMEV  ( ANR-10-LABX-0020 ). This is contribution ISEM 2019-150 of the  Institut des Sciences de l'Évolution de Montpellier.
Acknowledgments: The authors would like to thank the  KARST observatory network (SNO KARST) initiative  at the  INSU/CNRS , which aims to strengthen knowledge sharing and promote cross-disciplinary research on karst systems at the national scale. We also thank  Météo-France  for providing rainfall data and  BRGM (French geological survey)  for providing recent discharge data. We also would like to dedicate this contribution to the memory of  Alain Mangin  and to his long-term invaluable care of collecting data on these two Pyreneans watersheds.
, 2015; McNicholas and Mass, 2018; Price et al. , 2018; Hintz et al. , 2019) , vehicles  (Anderson et al. , 2012; Mahoney and O'Sullivan, 2013)  and communication networks  (Zinevich et al. , 2009). Muller et al. (2015)  provided a comprehensive review of "crowdsourcing" efforts in the atmospheric sciences. Since this review was published, new advancements have been made with crowdsourced datasets. Some of the most recent advancements include the collection and quality-control of atmospheric pressure observations from smartphones  (Kim et al. , 2015 (Kim et al. , , 2016;; Madaus and Mass, 2017; McNicholas and Mass, 2018; Price et al. , 2018; Hintz et al. , 2019). Kim et al. (2016)  was the first to apply machine-learning methods to bias correct smartphone pressure observations (SPOs). McNicholas and Mass (2018)  demonstrated an efficient machine-learning approach to SPO bias correction that benefited from non-meteorological smartphone sensor data.
Clark et al. (2018)  examined the use of PWSs and made considerable progress in the quality control and use of such data. Examples of successful assimilation of such observations into operational Numerical Weather Prediction (NWP) models are currently few and far between. The integration of observations from PWSs into the NOAA Meteorological Assimilation Data Ingest System (NCEP, 2019) dataset is an early example. Also, in the U. S. , the utility of PWSs has been of increasing interest for forecasts of severe convection  (Madaus et al. , 2014; Carlaw et al. , 2015; Sobash and Stensrud, 2015; Gasperoni et al. , 2018). A meeting on the use of crowdsourced data in NWP and weather forecasting was held in Copenhagen 4-5th December 2018 at the Danish Meteorological Institute (DMI), with two main purposes.
Aiming for fast weather forecasting, a data assimilation cycle with a 5-minute update rate is necessary; therefore, an ultra-rapid data assimilation method is proposed. A real-world application employs the high resolution project observations in a 5-minute assimilation cycle for the regional operational weather model COSMO-D2, focusing on the model performance optimisation near the surface and its predictions along road sections in Germany, where the current observation network is not dense enough. First results, comparing car measurements, nearby weather stations and model analysis and forecasts were presented. E. Mallet and S. Al Ali (Météo France) first gave a brief overlook of crowdsourcing activities at Météo-France. Those activities focus on the use of human observations from "expert" non-professional observers and "citizen" observers, and automated observations collected from PWSs, agricultural networks and connected vehicles.
Then, the presentation focused on two ongoing projects: (1) The first action concerns the crowdsourcing module in Météo-France's mobile application that allows mobile users to report the observed weather and to post pictures of the sky. The module provides, without access restriction, a simple entry of almost twenty phenomena to the users, who in their turn will select the observed phenomena and report the observed weather condition on a regular basis. Based on this module, more than 10,000 observations are collected daily, and more than 40,000 in high-risk situations. Visualisation of crowdsourced data is already available to forecasters, and the next step is to feed it to operational databases in order to expand its possible uses. (2) The second action concerns the potential use of vehicle observations for meteorological applications which is the subject of a partnership between Météo-France and Continental.
The aim is to infer weather (precipitation and low visibility) and road conditions (dry, wet, slick) at a particular location in time, through the analysis of vehicle data elements (temperature, wiper and headlight statuses, velocity, and the activation of ABS and ESP systems). The experimental campaign started in November 2016 and is still ongoing. The fleet consists of hundreds of vehicles, transmitting data through a connected dongle. Data filtering and quality checking routines were developed, and vehicle observations were evaluated against meteorological data. Machine learning classification algorithms were developed, using data from meteorological observation merging products as references for hydro-meteor discrimination and visibility. The preliminary results were promising and also showed the need to combine multiple parameters in order to successfully derive weather observations.
K. O'Boyle (Met Office) presented how The Met Office view crowdsourcing as distinct from citizen science (see section 4). There is a long history of citizen science at the Met Office. The Weather Observations Website (WOW) (wow. metoffice. gov. uk) is the Met Office citizen science portal. WOW has global reach, and is a platform for anybody to submit, share and display their weather observations, either manually or by connecting a PWS using APIs. WOW data is being trialled in nowcasting applications, but is not yet assimilated into NWP. Investigations into other opportunistic observations are ongoing at the Met Office, including collecting data from vehicles. M. Clark (Met Office) presented on an automated quality control and gridding process for citizen science data. There has been a focus on Met Office WOW data from PWSs to create high resolution surface analyses.
Parameter values from each WOW site are constrained to have the same longterm mean as neighbouring official sites, but are otherwise allowed to vary freely, as it is assumed that shorter-term, temporary deviations are the signature of genuine small scale features which are worth retaining in the analysis. A series of case studies have shown that there is value in this approach. S. L. Dance (University of Reading) gave an overview of the DARE: Data Assimilation for the REsilient City project. This is a UK Engineering and Physical Sciences Research Council (EPSRC) Senior Fellowship in Digital Technology for Living with Environmental Change. The vision for the project is to use "datasets of opportunity", such as CCTV and vehicle observations, alongside scientific observing networks, such as satellite data  (Mason et al.
, 2018)  to improve predictions of urban natural hazards such as flooding and high impact weather. There are many potential benefits of such data, including the availability of large numbers of inexpensive observations, in areas where there are people but there may be few sources of scientific observation data. For example, (1) air traffic management reports have potential to provide observations of temperature inversions in the boundary layer  (Mirza et al. , 2016 (Mirza et al. , , 2019)). (  2 ) In many locations around the world, the population has access to smartphones, but ground-based scientific observations are sparse. Furthermore, there are a number of issues in collecting 'datasets of opportunity' for use in assimilation. These include the need for metadata such as time and location in order to carry out the assimilation, versus data protection for the data provider, who may be a private individual.
Collaborative work with the UK Met Office, is assessing the potential of temperature observations recorded by vehicles. The preliminary findings showed that the data collection method was not reliable for collecting large temperature data sets. Furthermore, for the initial data sets collected, it was shown that temperature measurements had a negative correlation with the speed of the vehicle. It was concluded that a new data collection technique was required, and a more detailed study was vital before the benefits of assimilating vehicle temperatures could be assessed. D. Blaauboer (KNMI and EUMETNET) presented shortly the KNMI-activities in the domain of crowdsourcing. These include participation in the WOW project of UK Met Office, application of car data (temperature sensor, wiper data), smartphone data, damage reporting app (to report weather impacts by the public), wind data from hot air balloons.
EUMETNET, the grouping of 31 European National MetServices, recognised the emerging availability and application opportunities of crowdsourced data and the Internet of Things among many of its members. Therefore EUMETNET has organised a few dedicated workshops on this subject with the aim to bring experts in this field together, to foster networking and possibly create a platform or programme in near future to develop common applications to the benefit of all. M. Dahoui (ECMWF) presented an overview of the importance on in-situ data in global NWP. It was shown that there are data gaps in the surface observations received at ECMWF and the potential and challenges for using crowdsourced data to fill these gaps were described. Also, it was stressed that crowdsourced data can be important for verification purposes.
A denser network is useful to detect small scale features and rapid changes of the atmosphere, so observations have also the potential to improve the forecast verification aspects leading to a better understanding of model performance. The usage of crowdsourced observations is however very challenging. It was suggested that data collection and pre-processing needs a collaborative effort between NWP centres through coordination of the WMO, the industry and the private sector to improve and unify standards and to agree on best practices. A common and shared use of operationally managed data hubs (such as the MetOffice Weather Observation Website) is a cost-effective solution to manage the diversity of data sources and formats. A good understanding of the error characteristics of the observations is necessary to allow proper data selection and error specification.
In the terminology proposed by the Met Office (i), there is a clear separation between citizen-science data and 'crowdsourced' data, wherein the ECMWF proposal (ii) the term 'crowdsourced' data is a collective term. It is recommended that authors define their usage of these terms. II. Obtaining useful crowdsourced data may involve collaboration with commercial entities, such as manufacturers of PWSs or vehicles. In some cases, collaborations of this kind mark a step change in the way universities and meteorological institutes have previously operated. For professional use, crowdsourced data needs to be as unprocessed as possible when received. Working in collaboration with manufacturers may enable this. Some of the workshop participants have built successful collaborations with commercial entities, taking a "virtuous circle" approach, whereby data is provided by a manufacturer, and in return the meteorological institution provides forecast data or quality controlled observational data.
To make them comparable, methods to deal with multiscale comparisons are required for example, filtering or superobbing. Further, other themes seemed to be well established. There was a general agreement that crowdsourced data can provide useful observations in areas otherwise devoid of observations. It was discussed whether stationary platforms (e. g. , PWS) are easier to implement in existing systems than moving platforms (e. g. , vehicles, SPOs). In general, stationary platforms are believed to be easier to bias-correct than moving platforms. Also, new data sources should be seen to supplement conventional observation networks rather than a replacement, as trusted observations are required as a reference for new data sources. A nested platform of reference may be a good way of organising networks in the future, for example, SYNOPs used as a reference for the quality control of PWS data, and PWS then used as a more dense reference dataset for observations from mobile platforms.
It is generally thought that the lesser degree of interaction required by the participant the higher the volume of data that can be collected. It is not clear if either of the two are of superior quality. Further, the following recommendations are made. An organised community of those involved in crowdsourcing activities would be beneficial. EUMETNET would provide a good forum for this, however, such a forum should not be restricted to European countries. This forum could be a simple, independent, platform accessible via a website. Regarding vocabulary, it would be beneficial for the community to agree on common terminology related to crowdsourcing. To realise the full potential of crowdsourced data for NWP, issues of data quality, privacy, and availability will need to be addressed. Data quality could be enhanced by prioritising the collection of accurate metadata.