dataesr/scientific-paragraphs-categorization huggingface dataset:
This model allows the larval ecology to influence the optimal shell size, the rate of shell size evolution and the strength of pull toward the optima across our Terebridae data set. The optimal shell size value itself () has a value of 70 (±18) mm for planktotrophic and 21 (±7) mm for lecitotrophic species. Our results suggest that species with long-living pelagic larvae not only tend to be generally larger, but also have a wider shell size range than lecitotrophic species. The best-fitting model for depth distribution was a simple Brownian model (BM), which did not support any correlation between depth and larval development. Likewise, when coded as a discrete trait, there was no support for a correlation between shell size and depth distribution.
No Clear Drivers of Terebrid Diversification Potential key innovations such as venom apparatus, larval development, shell size, and depth distribution were examined in BiSSE using several models of trait evolution to determine potential drivers of terebrid diversification (Supplementary Table  S4  available on Dryad). Contrary to our expectations, for presence or absence of VG, the best-fit model had irreversible transition rates and equal speciation and extinction rates, suggesting the presence or absence of the VG does not impact the rate of diversification in the terebrids. For larval development, shell size, and depth, we recovered the same speciation, extinction, and transition rates for the two trait states considered, thus detecting no significant departure from the null model. These results do not identify any of the tested traits as drivers of diversification, suggesting that either additional traits and/or sampling of species is required, or terebrid diversification is not driven by a key innovation but Notes: Twelve anatomy types were defined by looking at the presence or absence of a proboscis, VG, SG, or APS, as well as looking at the type of marginal tooth.
The mosaic of habitat types in the Indo-West Pacific, a diversity hotspot for Terebridae as well as for other marine invertebrates, might have contributed to ecological release, as already suggested for other gastropod taxa  (Williams and Duda 2008). The lack of statistical support for this hypothesis from BiSSE modeling may be due to insufficient taxonomic coverage. In fact, simulation studies suggested that BiSSE modeling performs best with >300 terminal taxa  (Davis et al. 2013; Gamisch 2016). Despite the three-fold increase with respect to previous phylogenies, our data set still represents merely 26% of estimated Terebridae diversity. In addition, our sampling effort has been mostly concentrated on less known deep-water habitats, leading to a potential overrepresentation of deep-water species in our data set.
Larval Development Affects Terebrid Adult Shell Size Notably, for the first time we demonstrate that lecithotrophy has evolved at least 18 times in the Terebridae and there is a link between adult shell size and larval development. We corroborate in the terebrids, as in previous studies on other gastropod taxa, that larval development evolution trends are generally unidirectional, moving from planktotrophy to lecithotrophy  (Gould and Eldredge 1986; Rouse 2000; Collin et al. 2007 ). In the Terebridae, shell size appears to follow a complex history of diversification. Across our entire data set, the best-fitting model estimates with strong support, according to Akaike weights, VOL. 69 a different optimal size for the two divergent larval ecologies but with a higher strength of pull toward a size optimum in the lecithotrophic species.
evolutionary history, followed by several oscillations between 35 and 15 Ma, with the evolutionary rate still increasing toward the present (Supplementary Fig. S7available on Dryad). Our sample ranged in length from 10 mm (Partecosta trilineata) to 274 mm (Oxymeris maculata), with an average length of 61 mm, 104 species were classified as being >25 mm and 27 species ≤25 mm. Similarly, depth apparently underwent seven shifts in evolutionary rates that are summarized in the four groups outlined (Supplementary Fig. S8 availableon Dryad): 1) One shift for a subset of clade C including Terebra n. sp. aff. cumingii 1 (shallow), Terebra n. sp. aff. cumingii 2 (deep), Terebra n. sp. 27 (shallow) and Terebra cumingii (deep). 2) One shift for the subset of Clade E1, which is a shift to deep waters shared by Myurella brunneobandata, M.
pseudofortunei and M. n. sp. aff. Fortune. 3) Three shifts from shallow to deep for subsets of Clade E2, including, respectively, Punctoterebra teramachii and Punctoterebra baileyi, Punctoterebra polygyrata, P. trismacaria and P. textilis, P. sp. aff. textilis 1, and P. n. sp. aff. trismacaria 1. 4. The last two shifts are in the E5B clade for the species Myurellopsis joserosadoi and Myurellopsis guphilae were both shifts to deep waters (Supplementary Fig. S8 available on Dryad).
FUNDING This work was supported by funding from the  Camille and Henry Dreyfus Teacher-Scholar Award, National Academy of Sciences Arab American   Frontiers Fellowship Grant ,  The Alfred P. Sloan Foundation  and  NSF  awards [ CHE-1247550  and  CHE-1228921  to M. H. ], and in part by the  CONOTAX  project funded by the  French National Research Agency  [grant number  ANR-13-JSV7-0013-01 ], and by the  Russian Science Foundation  grant [ RSF 16-14-10118  to  A. E. F]. Access  to ship time on the  French Oceanographic Fleet  is gratefully acknowledged; the Taiwan and South China Sea cruises were supported by bilateral cooperation research funding from the  Taiwan Ministry of Science and Technology  ( MOST 102-2923-B-002-001-MY3 ,  PI Wei-Jen Chen ) and the  French National Research Agency  ( ANR 12-ISV7-0005-01 ,  PI Sarah Samadi ).
Additional materials were used from fieldwork expeditions organized in collaboration with the  Smithsonian Tropical Research Institute  in Panama in 2006-2008 with funding to M. H. ( NSF-CHE 0610202 ). All expeditions operated under the regulations then in force in the countries in question and satisfy the conditions set by the  Nagoya Protocol  for access to genetic resources. The participation of A. E. F. was supported by the grant No. 16-14-10118  from the  Russian Science Foundation  (principal investigator Yu I. Kantor). M. V. M. was supported by a  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 748902. J. G. was supported partially by  CUNY Graduate Research Fellowship. ACKNOWLEDGMENTS The authors thank  Philippe Bouchet ,  Philippe Maestrati ,  Virginie Héros ,  Barbara Buge , and  Julien Brisset  for their help in curating the vouchers.
Virtual Observatory tools are specifically designed for astronomical data, but they can be adapted to work with geospatial data by providing existing Geographical Information System tools with Simple Application Messaging Protocol interface. Open source QGIS package was chosen as a platform for this. The Simple Application Messaging Protocol interface was made with Python plug-ins. Geospatial data were exposed to Virtual European Solar and Planetary Access via a dedicated German Astronomical Virtual Observatory Data Center Helper Suite server and several tables exposing existing Open Geospatial Consortium-compliant planetary services were published to Virtual European Solar and Planetary Access providing a variety of geospatial data types: vector data and spectral cube rasters, as well as Open Geospatial Consortium Web Map Service of planetary maps.
Plain Language Summary Geospatial Science and Astronomy are closely related fields which nevertheless use different tools, standards, interfaces, formats, and protocols. Heterogeneous Astronomical data are accessed routinely via standard system largely known as Virtual Observatory, whereas in Planetary Science, capabilities of cross-mission and cross-archive data discovery are lacking. The present work tries to bridge these two communities by leveraging on commonalities in data types and tools while exposing Geospatial data sets to the Virtual Observatory and creating necessary interfaces in Geographical Information Systems.
Introduction Solar System exploration through planetary missions generates many disjoint data sets-some are rasters originating from imaging experiments; others contain vector features obtained from point measurements or derived from imaging experiments. These data sets include geospatial as well as nonsurface and are made available via core digital archives accessible through Planetary Spatial Data Infrastructure  (Laura et al. , 2017) , developed primarily by the Mapping and Planetary Spatial Infrastructure Team  (Naß et al. , 2017). However, the Planetary Spatial Data Infrastructure is in its infancy and is driven by the core data services such as the International Planetary Data Alliance, the Planetary Science Archive (PSA), and the Planetary Data System (PDS), which provide the backbone of the data and standards  (Laura et al.
, 2017). Data access to these archives is provided by FTP and HTTP access, with data discovery by implicit tools such as the Planetary Image Atlas at PDS imaging node (https://pds-imaging. jpl. nasa. gov/), Orbital Data Explorer of the PDS Geosciences node (http://ode. rsl. wustl. edu/), PDS Planetary Image Locator Tool (https://pilot. wr. usgs. gov/), and the PSA search interface (https://archives. esac. esa. int/psa). Programmatic access to PSA and Data ARchive and Transmission System was enabled with International Planetary Data Alliance Planetary Data Access Protocol. However, this protocol has a limited set of search parameters, so the Europlanet begun development of a more flexible protocol EPN-TAP, derived from IVOA's TAP protocol to fulfill the needs of the planetary science community  (Erard, Cecconi, Le Sidaner, Berthier, Henry, Chauvin, et al.
, 2014). IVOA standards create an interoperability environment allowing for transfer of resulting data and making them available to the client  (Quinn et al. , 2004). Many of the VO and Planetary Spatial Data Infrastructure aspects are similar, as both communities operate on spatially enabled data, perform transformations in spherical coordinates, image warping, and reprojection, and spectroscopic analysis. However, there exists a gap in tools and capabilities since Geospatial data are generally plotted on an oblate spheroid with major and minor radii, while in Astronomy, the data are plotted on an imaginary Sky Sphere with no radius. This fundamental difference means that direct transformations between astronomical and geospatial coordinate systems are impossible. The adaptation of Astronomical data standards and software to read and display geospatial data is an area of ongoing research  (Erard et al.
, 2017). In this paper we present a system of QGIS plug-ins, web interfaces, and data discovery services that allows geospatial data discovery via European Virtual Observatory architecture and integration of QGIS into VESPA software suite. The VO framework can be adapted for use in planetary science by creation of standards and services providing discovery and access to planetary geospatial data that also adhere to Terrestrial Open Geospatial Standards  (Hare, 2011; Hare et al. , 2018)  used by Geographical Information Systems (GIS). Currently, these standards are being integrated into the European VO by Surfaces Task of the EuroPlanet Research Initiative H2020 Virtual European Solar and Planetary Access (VESPA)  (Erard, Cecconi, Le Sidaner, Berthier, Henry, Chauvin, et al. , 2014). VESPA is an activity reaching into all science aspects of the Solar System studies, aiming to set up a distributed collaborative system based on IVOA standards and to provide a uniform interface to query all Solar System data together at once  (Erard et al.
, 2017). Such an interface would enable scientific interdisciplinary interoperability, for example, to study interaction between heliophysics and planetary science  (Hughes et al. , 2018). Various utilities for data mining and analysis, such as Tool for OPerations on Catalogues And Tables (TOP-CAT), Aladin, and Centre d'Analyse Scientifique de Spectres Instrumentaux et Synthétiques (CASSIS), compose the VESPA suite of tools. The modular design allows different teams to focus on development of software specific to their field, while interoperability is provided via the Simple Application Messaging Protocol (SAMP) interface  (Erard et al. , 2017). A simplified relational diagram of the VESPA architecture is shown in Figure  1   (top). This paper presents IVOA-centric approach, in which the data from the core data archives such as PDS and PSA are exposed to the community by publication to VESPA, while at the same time, geospatial tools were enabled to connect to the VO software framework-thus bridging the gap between Astronomical and Planetary software.
The data and metadata to populate EPN-TAP tables were obtained from the Core Data Archives and their derived services and catalogs such as the Orbital Data Explorer and the Planet-Server (http://access. planetserver. eu/). The EPN-TAP tables were published to EPN-VESPA DaCHS Server, which was registered with the IVOA RofR to allow its discovery through the main VESPA interface. The interoperability of GIS with the VO software suite is ensured through QGIS plug-ins enabling SAMP, the interoperability protocol of the Virtual Observatory, allowing it to receive the data catalogs sent from a VO data mining client such as TOPCAT or TAPHandle. The tables provide subgranule access through a link to ad-hoc web interfaces that provide capabilities such as accessing spectra at a point and forwarding this data to other VO applications via SAMP.
GAVO DaCHS and the IVOA RofR Data publication to VESPA can be done by means of the German Astronomical Virtual Observatory (GAVO) Data Center Helper Suite (DaCHS). GAVO DaCHS is a Python server designed to provide VO-compliant access to data through a variety of IVOA standard protocols  (Demleitner et al. , 2014)  of which we only use EuroPlaNet Table Access Protocol (EPN-TAP)  (Erard, Cecconi, Le Sidaner, Berthier, Henry, Molinaro et al. , 2014)  version 2. 0.
EPN-TAP 2. 0 Mixin Accessing data on our server can be done with VO desktop tools such as TOPCAT  (Taylor, 2005)  and Aladin Sky Atlas  (Boch et al. , 2011)  and web-based interfaces such as Main VESPA interface and TapHandle  (Erard et al. , 2017) , as well as the local Astronomical Data Query Language (ADQL) and Table  Access  Protocol (TAP) form and HTTP GET services  (Demleitner et al. , 2014)  and a Python interface  (Graham et al. , 2014). EPN-TAP 2. 0 protocol defines names and metadata of the required columns. Compliance with this standard was ensured through an //epntap2 mixin. The mixin predefines Unified Content Descriptors  (Derriere et al. , 2005) , names, headers, descriptions, data types, and other metadata. The mixin validates the table and ensures that the required fields are created by default if not defined in a resource descriptor  (Demleitner et al.
QGIS QGIS (QGIS Development Team, 2016) is a free and open-source GIS. It provides a graphical user interface to the Geospatial Data Abstraction Library (GDAL/OGR contributors, 2018), supports Geographic Resources Analysis Support System  (GRASS Development Team, 2008)  and System for Automated Geoscientific Analyses  (Conrad et al. , 2015)  geoprocessing libraries, and makes extensive use of Python in user-contributed plug-ins  (Pejovi ć et al. , 2014). QGIS is a project of the Open Source Geospatial Foundation  (Goel et al. , 2015) , which is an umbrella organization for the development of open-source geospatial software. Since promotion of the OGC standards is one of the main goals of the Open Source Geospatial Foundation  (Song et al. , 2008) , we choose QGIS as the GIS contact point for interfacing with VESPA, and hence the point at which the gap is bridged into the geospatial community.
PlanetServer and Rasdaman PlanetServer (http://access. planetserver. eu/) is an open-source online tool for plotting hyperspectral data on planetary surfaces  (Marco Figuera et al. , 2018). It currently supplies projected and atmospherically corrected Compact Reconnaissance Imaging Spectrometer for Mars (CRISM)  (Murchie et al. , 2007)  coverages for Mars and projected Moon Mineralogy Mapper (M3)  (Green et al. , 2011)  coverages for Moon. The client side of the PlanetServer uses NASA's Web WorldWind  (Pirotti et al. , 2017)  as its visualization engine. The server side relies on the Rasdaman Raster Data Manager  (Baumann et al. , 2018)  to deliver hyperspectral data to the user by Web Coverage Processing Service (WCPS) requests  (Baumann, 2010). The NASA's Web WorldWind client obtains the basemap and topography from Web Map Service (WMS) and the polygons of coverage footprints from a JavaScript Object Notation (JSON) file supplied by an Apache Tomcat server (http://tomcat.
VO Tools Data on VO servers can be accessed from a variety of tools  (Demleitner et al. , 2015). A desktop and a web client were used to access data on our server during project development-TOPCAT and TapHandle. TOP-CAT is a desktop Java application for data discovery through TAP services, querying, table operations, and 10. 1029/2018EA000405 simple plotting. TOPCAT supports a variety of data formats such as VOTables, Flexible Image Transport System (FITS), and Structured Query Language (SQL) relational databases  (Taylor, 2005). TapHandle is a web interface that provides access to TAP services via ADQL and can pass data sets to connected applications through SAMP  (Michel et al. , 2014). Aladin Sky Atlas was used in this project for visualization of geospatial data in VO. Aladin is a desktop application written in Java, providing an interactive environment to explore and visualize astronomical data.
It can display FITS images of arbitrary size, display and play raster data cubes as a movie  (Boch et al. , 2008) , and display and generate Hierarchical Progressive Survey image surveys  (Allen et al. , 2016). There is a simple scripting language and support for SAMP  (Boch et al. , 2008). AladinLite is a web application programming interface with many of the features of Aladin desktop. AladinLite is written in HTML5 canvas and JavaScript which makes it embeddable on a web page  (Boch & Fernique, 2014). Much of the data we provide through our services are hyperspectral raster cubes. We chose CASSIS as a VO tool for working with large spectral surveys, since its developers are actively involved in VESPA  (Erard et al. , 2017). CASSIS supports the Simple Spectral Access Protocol for getting observational data, SAMP for interfacing with other VO applications and services, and TAP for linking with the Virtual Atomic and Molecular Database Center database  (Vastel et al.
Software Libraries The support for SAMP in QGIS plug-ins was obtained through Astropy and for web applications through SAMPJS libraries. Astropy is an open-source Python package providing support for generalized containers for gridded and tabular data, SAMP interface, and input and output to a variety of VO formats such as FITS and VOTables. It also contains functions for transformations of celestial coordinates and time, World Coordinate System support, and unit conversions  (Robitaille et al. , 2013). SAMPJS is a JavaScript library for using the SAMP Web Profile (communication layer suitable for web clients running within a browser sandbox) from within web pages  (Fitzpatrick et al. , 2013). These software libraries were used to construct the bridge between VO and GIS.
q. rd Creator Wizard As q. rd is an Extensible Markup Language (XML) file with a complicated internal structure, manually writing it is time-consuming and prone to error. To simplify the process of creating and ingesting new table into DaCHS, we have developed a q. rd creator web appication. It was made available on our server (http://aux1. epn-vespa. jacobs-university. de/qrdcreator2/) as well as on github  (Minin et al. , 2018). It supports creation of all the columns required by the EPN-TAP 2. 0, most of the optional columns, and allows users to define their own. The wizard produces a q. rd for data supplied as Comma-Separated Values (CSV), the values entered in the form can be either the column names or Python expressions for computing the values. For each parameter defined in the EPN-TAP 2.
XML Parser To help collecting information from existing WMS providers such as the Astrogeology maps of the United States Geological Survey, we have created a WMS parser. The XML accessible via WMS GetCapabilities and WCS DescribeCoverage requests is parsed with Python using xml. dom. minidom module. The 10. 1029/2018EA000405 elements are found by their tag names, and data obtained from their child nodes are then used to generate a CSV table for ingestion into DaCHS. An example of XML parser is provided on GitHub  (Minin et al. , 2018).
Heterogeneous Data Set Publication A granule is one row of a table served by EPN-TAP. It provides one unit of data, usually a link to a single data set (e. g. , one image) located on other servers and services such as WMS and WCS, for example, for CRISM and M3 coverages on PlanetServer. We provide data sets of three basic types: vector data sets containing polygon features, web maps accessible via WMS interfaces, and coverages stored on Rasdaman and accessible as WCS and WCPS. For vector features, the geometry was stored in the s_region column, and user-defined additional fields were created to describe any parameters not defined in EPN-TAP 2. 0 protocol. For WMS, the field thumbnail_url provides a WMS request for a map of small scale, the field exter-nal_link contains a Uniform Resource Locator (URL) to a minimal JavaScript WMS web viewer for that map which allows zooming and panning; whereas the field access_url contains a link to the GetCapabilities request for the server acting as an endpoint for interfacing with QGIS.
Data Sets Made Available 4. 1. 1. Vector Data 4. 1. 1. 1. mars_craters This is a copy of the Robbins Crater database  (Robbins & Hynek, 2012). User-defined fields describe crater diameter, depth, its name, degradation state, crater, and ejecta morphology. Parameters c1min and c2min are equal to the c1max and c2max, respectively, and indicate the center of the crater. The footprint is stored in s_region field and is computed from crater diameter as a circle around the crater center.
pangaea_x This table demonstrates sharing of the terrestrial analog data. It consists of a drone survey of terrestrial volcanic planetary analog terrains in Lanzarote, Spain  (Rossi et al. , 2018; Unnithan et al. , 2017). It contains Space-Time Coordinates of areal photograph; s_region shows approximate field-of-view, access_url points to download link on Zenodo (https://zenodo. org/). User-defined fields provide camera orientations.
Web Map Services 4. 1. 2. 1. usgs_wms Listed here are various global mosaics for most planetary bodies in the Solar System from Astrogeology WMS Map Layers  (Hare et al. , 2014). Each granule represents a separate WMS layer. Granules are grouped by the name of the system (e. g. , maps of Tethys and Titan both have 'Saturn' as their granule_gid). The obs_id refers to the WMS layer name, granule_uid is the layer title, external_link points to a minimal JavaScript WMS web viewer for that map, access_url contains a link to the WMS GetCapabilities request serving as an endpoint for interfacing with QGIS. The WMS can be imported to QGIS by either copying the getCapabilities URL or via a VESPA plug-in  (Minin & Marmo, 2018).