dataesr/scientific-paragraphs-categorization huggingface dataset:
For all datasets, we computed the spectral density profile and clustered them using hierarchical and k-medoid clustering. Second, we used a tanager phylogeny (350 spp. ) (Thraupidae) with 27 phylogenetically corrected principal component traits (pPC traits) spanning traits related to song, plumage, and resource-use taken from  Drury et al. (2018). Ideally, we would have used non-phylogenetically corrected PCs but these were not available. We computed the spectral density profiles for the pPC traits and clustered them using hierarchical and k-medoid clustering and computed their spectral density profile summary statistics. Finally, we used a geometric morphometrics dataset consisting of 399 three-dimensional Procrustes superimposed landmark coordinates describing the external brain shape of 48 species of New World monkeys (Platyrrhini )  (Aristide et al.
5D ). Splitter values obtained from the randomized datasets are similar to those obtained from the original datasets, suggesting that phylogenetic signal has little effect on splitter values (Supplemental Fig. 1 ). Importantly, the fragmenter and tracer values are sensitive to the introduction of erroneous data, although not dramatically (Supplemental Fig. 2 ). When a considerable amount of sampling variance (equal to three times the standard error) is introduced on 10% of tips, fragmenter and tracer values decrease only slightly. The impact of erroneous data only becomes appreciable when it is introduced on a large proportion of tips (≥ 30%). However, it is only when 100% of tips are affected by erroneous data that the inference of fragmenter and tracer values begins to approach that of AC models (β = 1.
Applications Traditionally, likelihood-based models are fit to phylogenetic trait data and the model showing the best support is inferred as the generative one. Oftentimes the difference in support between models is small and therefore finding traits with similar evolutionary histories or comparing those evolutionary histories can be difficult. The ability of our approach to directly compare the spectral density profiles of the nMGLs of different traits on the same tree allows us to find clusters of traits with similar evolutionary histories and then compare those histories in a multidimensional space defined by interpretable parameters without needing to qualify differences based on estimated likelihoods. By simulating datasets under different trait models on the Cetacean phylogeny, we are able to visualize how distinguishable these models are from one another under different parameter values (Fig.
Because the spectral density profile of the nMGL is computed directly from the phylogeny and trait data, it provides a comprehensive rendering of the structure of trait evolution across a phylogenetic clade. Consequently, the spectral density profiles of different traits on a phylogenetic tree, unlike likelihood values or summaries of phylogenetic signal, can be clustered absent any a priori model specification. We show that this is successful in distinguishing between phylogenetic trait data generated under different macroevolutionary processes and sensitive to the parameter values under which those processes are generated. Hence, in the same way that spectral density profiles have been used for identifying principal patterns of diversification in vertebrates  (Lewitus and Morlon 2016b) , they can be used for identifying principal patterns of phenotypic evolution across multiple traits within clades, as we have illustrated here with two empirical datasets.
Spectral density profiles can also be used to quickly evaluate how distinguishable different trait evolutionary processes are, as we have illustrated here on the Cetacean phylogeny. This can be very useful when developing new models, to make sure they will be distinguishable before putting all the effort into develop likelihood-based inferences for these models. Similarly, although it is impossible to separate the relative contribution of phylogenetic and trait distances on the SDP, it is possible to compare SDPs for the same trait data across multiple versions of a phylogeny (e. g. , a posterior distribution of trees generated by Bayesian inference) and thus estimate the effect of tree construction on inferences of trait evolution. We can also anticipate that spectral density profiles will be useful to compute the distance between simulated and real data in Approximate Bayesian Computation approaches  (Beaumont 2010)  for fitting models of phenotypic evolution that are not amenable to likelihood computation (e.
The relationship between the tracer and fragmenter gives some indication as to whether convergence has likely occurred: the ratio of tracer to fragmenter will be higher if the discretization of trait values in phenotypic space shows a strong phylogenetic signal (i. e. , in the absence of convergence). We show, for example, that a two-peak macroevolutionary landscape results in high fragmenter values, but relatively lower tracer values than occur under a DC model, indicative of the high level of phenotypic convergence in the macroevolutionary landscape model and low level of phenotypic convergence in the DC model. Of course, we cannot assign a threshold value for convergence, above which the tracer to fragmenter ratio conclusively evinces phenotypic convergence. However, for a given analysis of different trait data on a tree, we recommend comparing tracer to fragmenter ratios between analyses, in order to deduce the comparative levels of convergence between datasets.
Finally, the splitter of the nMGL is diagnostic of the bipartiteness of the graph and therefore, in terms of phylogenetic trait data, higher splitter values indicate a bimodal distribution of trait values with high phylogenetic signal. We analyse a previously published dataset on pPCs for tanagers  (Drury et al. 2018)  to show the usefulness of clustering phylogenetic trait data to identify and characterize traits with similar evolutionary histories among a set. Our result, that the evolution of song-related traits is distinct from that of plumage-and resource-use-related traits, is consistent with those found in  Drury et al. (2018)  for species that are year-round territorial and/or found in dense habitats. The high tracer and fragmenter values in plumage and resource-use traits suggests the discretized trait space of these traits possesses a high phylogenetic signal, while the low tracer and fragmenter values in song traits suggests low phylogenetic signal and non-discretized trait space.
We analyse a dataset of 399 landmarks on the endocrania of 48 species of New World monkeys. We show that these landmarks cluster into three groups. Landmarks within each cluster delineate meaningful regions of the external brain morphology, which suggests that each of these regions evolved differently. Cluster 1, which mostly represents the anterior frontal, parietal, and cerebellar regions, shows these regions have evolved into a discretized trait space with high phylogenetic signal, whereas cluster 2, which defines the temporal, occipital, and stem regions, shows these regions have evolved in a more uniform space with low phylogenetic signal. Cluster 3, which defines the posterior and ventral areas of the frontal region and part of the temporal region, also shows evidence of these regions evolving into a discretized trait space, but with higher levels of convergence than the regions of cluster 1.
Despite the differences in approach, these results align well with a previous analysis of the same dataset conducted using PCA  (Aristide et al. 2016 ), which suggests that, during the adaptive radiation of New World monkeys, brain shape evolved first into discrete regions of morphospace, with subsequent bursts of evolution generating convergence among clades. Moreover, according to  Aristide et al. (2016) , the different stages of this diversification can be associated to the evolution of particular regions of the brain. For example, coincident with our results for cluster 1, the anterior frontal region would have diversified early into discrete trait optima, while convergent changes would be mostly associated with other areas of the frontal region, in agreement with our cluster 3. Overall, our results support the idea that there has been differential selection on different brain regions in New World monkeys, due both to an early adaptive radiation and convergence on ecologically relevant traits  (Rosenberger 1992; Gavrilets and Losos 2009; Aristide et al.
Acknowledgments We would like to thank  Alex Bjarnason ,  Julien Clavel ,  Jonathan Drury ,  Carmelo Fruciano ,  Odile Maliet ,  Marc Manceau ,  Olivier Missa , and  Guilhem Sommeria Klein  for helpful comments on the manuscript. EL would like to thank  Evan Charles  for helpful discussion. Funding was provided by the  CNRS  and a grant (PANDA) from the  European Research Council (ERC)  attributed to HM. The views expressed are those of the authors and should not be construed to represent the positions of the  U. S. Army , the  Department of Defense , or the  Department of Health and Human Services.
To test the hypothesis that the endocranial cavities of extinct bird-line archosaurs conservatively documented the evolutionary pathway of the brain through time, we compiled a representative database of endocranial shapes encompassing the largest possible diversity of extant and extinct archosaurs, and included selected lepidosaurs as an outgroup. In order to evaluate the influence of developmental heterochrony on the evolutionary trajectory towards modern birds, we furthermore investigated ontogenetic series of extant crocodilians and birds to assess associated brain shape developments while bracketing non-avian dinosaurs phylogenetically  28. PPC-SRµCT with optimised imaging protocols at beamlines BM05, ID17, and ID19 of the European Synchrotron Radiation Facility (ESRF) ensured reliable yet non-destructive visualisation of the endocasts in two specimens of Archaeopteryx and other fossil as well as extant archosaurs  18, 29, 30.
The data set was supplemented with conventional CT data representing a sufficiently wide selection of modern avian endocasts to resolve endocranial variation across the ecological and body size range occupied by present-day birds. Following qualitative characterisation, shape variation was quantified through two-dimensional landmarking in lateral view and shape changes were subsequently analysed with Principal Component Analysis (PCA). PCA identified dorsal endocast curvature, originating from brain axis flexure and relative telencephalic and cerebellar size, as the dominant shape variable in the data set. The C/D parameter, defined as the ratio of dorsal endocast length following the convex hull between the anteriormost tip of the cerebrum and the opisthion (C) over the linear distance between those two locations (D), was recovered as a straightforward geometric ratio conservatively capturing and quantifying endocast doming.
This renders the semilandmark set most appropriate for visualising and studying whole-endocast shape variation in the data set studied here. The dominant shape shift in the semilandmark set, reflected in PC1, can be reliably expressed as a ratio dividing the total distance of the dorsal margin of the endocast between the anteriormost point of the telencephalon and the dorsalmost point of the foramen magnum (C) over the shortest distance between these two points (D). The correlation between PC1 in semilandmark PCA and the novel geometrical ratio C/D was found to be substantial (88%, Extended Data Table  1 ), which reinforces employment of this ratio as a straightforward yet robust measure of the dominant endocast shape variability in our data set. The doming ratio C/D does not correlate with body mass (R 2 = 0.
The synchrotron data set was supplemented with an additional Crocodylus niloticus cranium visualised with a medical tomograph at the Centre Hospitalier Universitaire in Grenoble, France, and crania of one specimen of Paleosuchus palpebrosus and 146 adult Neornithes visualised using laboratory tomographical techniques at the Institute of Physiology and the Institute for Clinical and Experimental Medicine in Prague, Czech Republic. Additional cranial scanning data of 45 lepidosaurs, two crocodilians, and five Neornithes were accessed through DigiMorph (www. digimorph. com) and processed in tandem with the data obtained in the scope of this study. Material and data provenance as well as repositories curating the digital data are listed in Extended Data Table  1. Virtual three-dimensional endocasts were extracted from the reconstructed crania by adopting cranial bones as delimitating masks in VGStudio MAX 2.
4a ). Relative developmental age normalises absolute age (after oviposition) to homologuous developmental stages relative to hatchling (1) and the establishment of sexual maturity (2). As such, values between 0 and 1 reflect in ovo developmental stages, whereas values between 1 and 2 indicate hatchling, juvenile, and subadult to adult development (Extended Data Table  1 , Fig. 4b ). Statistical tests for the relations between doming ratio and biological indices were conducted in the R environment (Rstudio 1. 2. 1355). Normality of body mass and alert cranial angle (i. e. head posture) was assessed using Shapiro-Wilk testing. As at least one of the variables was found to disagree with the assumption of normality, Kendall rank correlation tests were performed. Towards comparing doming ratio and encephalisation quotient, the dataset was first subjected to a Bartlett test to assess homoscedasticity of the dataset.
Acknowledgements The authors thank  C. Berruyer  for her assistance during experiments and acknowledge  M. Kundrát  ( Pavol Jozek Safarik University, Kosice, Slovakia ) for his role in the origin of the initial project. D. Berthet  ( Musée des Confluences, Lyon, France), Miroslav Procházka (Krokodýlí ZOO, Protivín, Czech Republic ),  S. Martin ,  E. Fernandez ,  A. Tomas  and  A. Soler  ( La Ferme aux Crocodiles, Pierrelatte, France ),  E. Woessner ,  C. Dubois  and  B. Chenet  ( Safari de Peaugres, Peaugres, France ),  S. Chapman  ( Natural History Museum, London, England ),  P. Candegabe  ( Museum d'Histoire Naturelle, Grenoble, France ),  T. Loeb  ( Echirolles, France ),  L. Viriot ,  C. Charles  and  B. Thivichon-Prince  ( ENS, Lyon, France ),  R. David  ( Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany ),  B.
Data Analysis and Information Delivery Support Observing Enterprises The great lakes observing system (GLOS) is 1 of 11 Regional Associations in the integrated ocean observing system (IOOS). As certified regional information coordination entities, IOOS regions play a crucial role in coordinating their science and technology communities around the data and information priorities of resource managers and policy makers. In a geography as large as the Great Lakes, there are many academic, government and private entities that work in monitoring and science, and GLOS serves a role that is more focused on data management and aggregation services. Limited resources have also influenced the scope of GLOS' role in the region. Like other IOOS regions, GLOS provides direct resources to the sustained operations of local observing systems but in a more limited capacity.
Similarly, GLOS has provided grants to partners for the development of models and information products or developed data tools and products directly, but typically as discrete projects that require further investment for sustained operations and maintenance. The experience in the governance and management of GLOS provides a case study for why diversifying revenue is inherently important to sustaining observing and data management systems. Moreover, it has become critical for GLOS in a competitive market; one where organizations with similar names and broad missions can make it difficult to identify the best group for the job. GLOS is positioning itself to stay true to its mission while including non-traditional partners to achieve better results. The small size and 501c3 status of GLOS allows a less traditional approach to seeking partners and funding.
Crowdsource and private fundraising As a function of its mission, GLOS provides financial support to an extensive network of over 35 nearshore buoys, gliders, and AUVs operating primarily in the nearshore areas of the Great Lakes, from Lake Superior to Lake Ontario. An opportunity was presented when NOAA's Coastal Storms Program dedicated focus to the Great Lakes region and was looking to make smart investments in addressing critical information gaps related to coastal storms issues. Over the three-year period of working with the Coastal Storms Program 5  , GLOS worked with the National Weather Service to support the capitalization of four new, privately operated, nearshore buoys placed in areas of critical importance for beach safety and shipping stakeholders. The agreements with the buoy operators was clear that funding would support the capitalization of the buoys but a financial plan (outside of federal grant funding) for sustained operations and maintenance of the buoys needed to be in place and supported by the operators.
Multi-sector stakeholder agreements As expensive buoys outlive their initial funding programs, GLOS is engaging public utilities to take on the ongoing expenses, leaving them with a high-quality observation instrument and GLOS with continued shared data and equipment utilization. The most successful demonstration of this is the service agreement between GLOS and the City of Cleveland, Ohio which involves a multi-sector partnership to support the operations of three buoys that provide critical water quality information for the city's drinking water system that serves over 1. 5 million residents in the Cleveland area. During the summer of 2006, over a twelve-day period, the City of Cleveland's water department was inundated with complaints from their customers about the taste, odor and color of the water.
Areas of low oxygen and decomposing organic matter, also known as hypoxia zones, were moving toward the city's water intakes. As Cleveland's information needs grew, NOAA's Great Lakes Environmental Research Lab and GLOS worked together to consider options for transitioning existing hypoxia research efforts at GLERL toward developing a more operational monitoring capacity for the city. Building from the process utilized with the Coastal Storms Program, GLOS used competitive grant funding to support the capitalization of an additional monitoring buoy to expand monitoring coverage in Lake Erie. Then in 2016, Cleveland Water entered into a service agreement with GLOS where Cleveland Water provides funds annually to GLOS to manage the annual buoy deployments, data collection and delivery. The operations of the buoy are carried out by LimnoTech, a private engineering firm, with a portfolio of supporting science and engineering projects across the region, and in Lake Erie in particular.
LimnoTech leverages the buoy operations work to support other clients including LEEDCo. , a wind-energy development company that has also contracted with LimnoTech for the operations of another buoy in the area to assist with wind farm siting and planning. This multi-sector partnership serves as a model for GLOS in structuring other types of service-based agreements to support its data management and aggregation operations. Acknowledging the limitation of United States federal funding to support observations at regional and local level, it is important to consider the appropriate role and creative options for federal and local government, the private sector, and others to work together to meet important monitoring needs.
Data service based business models As the management of GLOS has matured and evolved over time, it has become clear that data aggregation, integration, and service delivery are core to the organization's value proposition and are, arguably, a more appropriate function of its mission to prioritize resources toward given existing funding limitations at the Federal level. GLOS has several examples of partner agreements with both federal agencies and non-federal partners where data services, rather than direct support for observations, are the primary focus of the scope of work. A recent example is the current Cooperative Agreement between GLOS and the United States Geographic Survey (USGS) to support improved coordination around various bathymetric and lake-bottom habitat mapping efforts in the region.
While the BMW provides an effective regional coordinating mechanism, its progress has been hindered by the absence of dedicated capacity to coordinate meetings and outreach, create and manage web content, and carry out other activities. Although regional coordination on the issue of lake bottom mapping is increasing, essential pre-requisites to strategic investment in new data acquisition are missing. Through the Cooperative Agreement with GLOS, USGS is committing resources to support a full-time employee to support coordination of data providers around the theme of bottom mapping, conduct a regional data inventory and needs assessment, draft lake floor mapping standards for the Great Lakes, and build the data infrastructure needed to discover, archive, and serve data to the public. The USGS views GLOS as uniquely qualified to undertake this role.
As the regional association for the Great Lakes within the United States integrated ocean observing system (IOOS) that is overseen by an inter-agency governing council, GLOS has a neutral posture that will allow them to work productively with a variety of agencies in the United States and Canada as well as non-federal partners including academic institutions and state agencies. GLOS is also able to leverage its existing infrastructure to support data assembly, quality control, discovery and access services for multiple data types and has a well-visited web service for data visualization and download. This agreement serves as an example of how the role GLOS serves to the Great Lakes region is evolving with a greater emphasis toward that of a data assembly center (DAC) or regional information coordination entity (RICE).
This is a good thing, as it is precisely the purpose and presumed benefit of the Certification process required through the Integrated Coastal and Ocean Observation System Act of 2009 (2009 ICOOS Act). 7 However, as this becomes a more viable area for growth, it undoubtedly has implications for the overall management and business model of the organization. These types of data services are growing in demand, and as GLOS builds its reputation and competencies as a certified regional information coordination entity, it can respond more often to requests for these services. GLOS is evolving its business model in this area where basic operations can be implemented as part of its mission, but cost recovery is required to grow and expand services. The ICOOS Act mandates the establishment of a national integrated system of ocean, coastal, and Great Lakes observing systems coordinated at the federal level.
In this paper we present the latest refinements brought to the DARDAR-CLOUD product, which contains ice cloud microphysical properties retrieved from the cloud radar and lidar measurements from the A-Train mission. Based on a large dataset of in situ ice cloud measurements, the parameterizations used in the microphysical model of the algorithm -i. e. the normalized particle size distribution, the mass-size relationship, and the parameterization of the a priori value of the normalized number concentration as a function of temperature -were assessed and refined to better fit the measurements, keeping the same formalism as proposed in DARDAR basis papers. Additionally, in regions where lidar measurements are available, the lidar ratio retrieved for ice clouds is shown to be well constrained by the lidar-radar synergy.