dataesr/scientific-paragraphs-categorization huggingface dataset:
The work represents a collaboration with the Mammal Diversity Database (https://www. mamma ldive rsity. org; Burgin et al. , 2018;  versions on Zenodo: https://doi. org/10. 5281/zenodo. 4139722), an authoritative effort to address and curate the global taxonomy of mammals conducted under the auspices of the American Society of   1 ). This translates to 916, 998, and 946 species names (1227 species names across all three sources combined) in the HMW, CMW, and MDD, respectively, which are absent from the IUCN Red List website, as well as 557 species names within the IUCN taxonomy that are absent from the HMW, CMW, and MDD (total of 1784 differences in species names across sources). The range maps described here therefore provide much complementary material to those available through IUCN, and vice-versa.
We provide the georeferenced range maps as shapefiles with species-level metadata and source information for non-commercial use. Maps are available for bulk download at https://doi. org/ 10. 48600/ mol-7r3j-8066 (HMW), https://doi. org/10. 48600/ molzzrs-q778 (CMW) and https://doi. org/10. 48600/ mol-48vz-p413 (MDD), or all three sources can be accessed from https://mol. org/ datas ets/?dt=range &sg=Mammals. Maps for individual species can be viewed and downloaded, along with other species-level spatial data, on Map of Life's species pages (https://mol. org/species).
| MATERIAL S AND ME THODS Range maps were generated by digitally interpreting the maps in the source materials (starting with the HMW), georeferencing them using coastlines, rivers and other geographical features and converting the georeferenced images to vector shapefiles, before quality control and assembling all the species from each source taxonomy into a unified product. To harmonise the HMW and CMW maps, we retained any maps from HMW in which there were no taxonomic or range updates and repeated the digitisation steps with the CMW as source material for any differences. For the MDD dataset, we generated maps for any differences using the HMW or CMW maps as a base along with information from alternative sources, such as published literature. For the range maps with HMW as the main source, we imaged the maps from each volume, which were then georeferenced using the World Geodetic System projection (WGS84; EPSG: 4326) against the coastlines and political boundaries provided by the Database for Global Administrative Areas v3.
6 (GADM; Global Administrative Areas, 2018). Additional spatial data such as rivers and waterbodies were used to determine more exact locations for ground control point placement. For each image, we used a minimum of 10 ground control points that were approximately evenly spaced to ensure constant warping during transformation. The georeferenced images were converted to GeoTIFFs with cell size equivalent to the resolution of the image using a seconddegree polynomial-type transformation with a nearest-neighbour resampling method. These rasters were converted to shapefiles by manually tracing range edges using either the Add Polygon Feature tool in QGIS (QGIS. org, 2020) or the Create Features panel in ArcMap and ArcGIS Pro. In cases where a species range consisted of geographically distinct areas, multipolygons were created.
Validation below). Once validated, the total number of features in a volume was checked prior to merging. To ensure no geometries were lost during downstream processes, the combined shapefiles were disaggregated so that all geometries were separate before getting clipped, as described above. This clipped file was then merged back together, dissolved by scientific name, and exported as the combined geopackage consisting of range maps for all species. The number of individual features were checked again at the end of the process to confirm that no geometries were lost. Once a volume was completed, we carried out a second quality control to ensure all species in the volume were included, species names were spelled correctly, and that the geometry of each species was valid. Geometry issues were first fixed automatically using the Geospatial Data Abstraction Library (GDAL; GDAL/OGR contributors, 2020) and then using the Check Validity tool in QGIS (QGIS.
| Harmonisation between data sources To create the CMW dataset, range maps for each species in the CMW were compared to those in the HMW volumes. The majority of species did not exhibit range map changes from the HMW to the CMW, but 515 species required some degree of further edits (see the Supplementary Material). These edits fell into one of five categories (Table  2 ): (1) species that needed to be digitally interpreted from the maps published in the CMW, because they were newly described since the HMW (new species); (2) species that needed to be digitally interpreted directly from the CMW maps because their range changed substantially from the range in the HMW (range changes); (3) species from the HMW that had been taxonomically split into multiple species in the CMW (splits); (4) species in the HMW that had been taxonomically merged into a single species in the CMW (lumps) and (  5 ) species in the HMW that had changes to the scientific name in the CMW taxonomy but no change in geographic range (name changes).
Source interpretation and georeferencing of new species was performed using the same methods as described above for the HMW. For splits, if the split occurred along clear geographical boundaries such as island populations, then the polygons of the original shapefiles were manually separated into child species. However, if the sum of child species was not equal to the range map of the parent species, then each child species was digitally interpreted from the CMW map. The same rule also applied for lumping multiple child species in HMW into a single CMW species. For name changes, the HMW-based shapefiles were simply renamed. The entire CMW dataset was combined into a single geopackage as described above. Once conversion of shapefiles from the HMW to the CMW was complete, we then harmonised the CMW shapefiles to the MDD taxonomy to create the MDD dataset.
The CMW maps were altered in the same way as described above, except using primary taxonomic descriptions to guide the modifications. For newly described species, range maps were derived directly from the source articles. If sources only presented the geocoordinates of a type specimen (type locality), then a range map was generated by buffering the point with a radius of 50 km. If range maps or type localities were not present in source materials, then range maps were hand-drawn following maps from the CMW as well as maps drawn by experts in support of the Red List assessment effort by IUCN (IUCN, 2020; necessary for 63 species or ~1% of the total), followed by error-checking. In all cases, the source materials used to generate each map are recorded in the species-level citation provided in the metadata of the shapefile for each species (see 'Usage notes' section below).
Details of all differences between the HMW and CMW, and the CMW and MDD are presented in the supplementary material. Two species in the CMW, HMW and MDD have no range maps as knowledge of their range is too limited (Nycticeius aenobarbus and Phoniscus aerosus). Two species have no range map in the HMW, but they have range maps within the CMW and MDD (Biswamoyopterus laoensis and Ctenomys dorsalis). One species has a range map in the MDD, but not in the CMW or HMW (Makalata obscura). Three species that feature in the HMW were considered extinct in the CMW and so were excluded from this dataset and the MDD (Juscelinomys candango, Pipistrellus sturdeei and Tonatia saurophila). In all three datasets, two species considered extinct in the wild are retained as their maps are published (Elaphurus davidianus and Oryx dammah).
| TECHNIC AL VALIDATION The HMW maps were quality controlled by published volume. 10%-30% of images per volume were randomly sampled for manual inspection by a team-member other than the individual that had carried out the source interpretation and georeferencing. Images were checked for: (1) appropriate transformations and that the World Geodetic System (WGS84) was the reference coordinate system; (2) that each geometry accurately reflected the information in the raster and that no geometries were lost; (3) all geometries were valid; (4) no features were duplicated and (5) that metadata was complete and without translation errors in the scientific name. If over 15% of sampled species were reported to have errors, then the entire volume of species was checked again, otherwise the dataset was cleared for further processing.
, 2012). The data provided has no restriction on the non-commercial use and revision of maps. This might include the updating of maps to an alternative or new taxonomy, or spatial refinements, for example, to allow higher precision coastline and river information. The presented data will undoubtedly require further updates and improvements. We expect a range of such improvements to be provided through Map of Life, where new datasets will also support users with version-control. In order to support this larger vision for transparently and openly improved expert maps, a new tool, soon to be released, will provide an interactive environment for experts to evaluate and improve range maps (https://mol. org/exper t-range -mappe r-demo/welcome). The new toolset will allow range maps to be updated in a transparent and traceable manner, with all decisions directly attributable.
Mammalogists. With regard to the suite of range maps developed by experts as part of Red List assessments of the International Union for the Conservation of Nature (IUCN; https://www. iucnr edlist. org/resou rces/spati al-data-download), the datasets presented in our data paper provide additional range information for many mammal species. The maps available on the IUCN Red List website represent F I G U R E 1 Range maps georeferenced from the Handbook of the Mammals of the World (HMW) for four South American species, (a) Alouatta caraya, (b) Pteronura brasiliensis, (c) Hydrochoerus hydrochaeris and (d) Panthera onca. Different maps species show different levels of precision (i. e. , how roughly it approximates the edge of the species' range and how finely they demarcate different populations) and so will differ in their accuracy at different grain sizes fewer extant species (5829 species, including 20 species labelled as 'Possibly Extinct'; IUCN, 2021, downloaded 2021-06-03) than presented here (HMW = 6253 species, CMW = 6431 species, MDD = 6362 species; Table.
Global richness maps of terrestrial mammals generated by all species with digitisable range maps in (a) the Handbook of the Mammals of the World (HMW), (b) the Illustrated Checklist of the Mammals of the World (CMW) and (c) the CMW harmonised to the latest taxonomy from the Mammal Diversity Database (MDD), as well as the difference in species richness between (d) the HMW minus the CMW, (e) the HMW minus the MDD and (f) the CMW minus the MDD. Detailed regions (sub-Saharan Africa) for each map of difference are presented to the right, with main differences attributed to the inclusion (HMW, CMW) or exclusion (MDD) of ungulate species from Groves.
org), a scalable platform for the integration and use of global species occurrence and environmental data. The aim of MOL is to support effective and global biodiversity monitoring, research, education, and decision-making through the provision of tools and information products addressing global species distributions and their dynamics. MOL is deployed on the Google Cloud Platform, which provides the ability to work with managed, scalable services. The data is hosted within a PostgreSQL database with custom API built for both species information and mapping, which are driven by the need for various unique services that users require. Individual mammal species maps are publicly available for visualisation and download (e. g. , https://mol. org/speci es/map/Chaet odi-pus_nelsoni) under the 'Detailed Map' tab for any given species, along F I G U R E 3 Comparisons with expert maps contributed to IUCN assessment.
(a and c) Richness patterns and absolute differences with the Mammal Diversity Database (MDD) calculated as MDD minus IUCN (for details see Figure 2). (b and d) Map differences for the antelope Kobus kob in West and Central Africa. In the IUCN source (b), it is treated as a single species and includes information on the type of distribution and seasonality, but it is separated into four species in the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW) (d)with other sources of spatial information. Each species is represented by a multipolygon that includes dataset-and species-level metadata, as well as the citation information. For convenience, in order to aid the download of large numbers of species, multiple species' shapefiles have also been packaged by taxonomic ranks (order and class).
Summary of taxonomic and geographical differences of extant, non-domesticated mammal species recognised in each taxonomic source: The Handbook of the Mammals of the World (HMW), the Illustrated Checklist of the Mammals of the World (CMW), and version 1. 2 of the Mammal Diversity Database (MDD). All species listed as domesticated and extinct have been removed from this summary. Note that multiple categories can apply to a single taxonomic revision.
DATA AVA I L A B I L I T Y S TAT E M E N T All range maps for the three taxonomic sources are openly available for non-commercial use through https://mol. org/datasets or at species-level at https://mol. org/species, or for bulk download at https://doi. org/10. 48600/mol-7r3j-8066 (HMW), https://doi. org/10. 48600/mol-zzrs-q778 (CMW) and https://doi. org/10. 48600/ mol-48vz-p413 (MDD).
F Global richness maps of terrestrial mammals generated by all species with digitisable range maps in (a) the Handbook of the Mammals of the World (HMW), (b) the Illustrated Checklist of the Mammals of the World (CMW) and (c) the CMW harmonised to the latest taxonomy from the Mammal Diversity Database (MDD), as well as the difference in species richness between (d) the HMW minus the CMW, (e) the HMW minus the MDD and (f) the CMW minus the MDD. Detailed regions (sub-Saharan Africa) for each map of difference are presented to the right, with main differences attributed to the inclusion (HMW, CMW) or exclusion (MDD) of ungulate species from Groves.
TA B L E 1 Total number of extant, non-domesticated mammal species recognised in each taxonomic source: The Handbook of the Mammals of the World (HMW), the Checklist of the Mammals of the World (CMW), and version 1. 2 of the Mammal Diversity Database (MDD). All species listed as domesticated and extinct have been removed from this summary HMW CMW MDD v1. 2 Species 6253 6431 6362 Genera 1289 1310 1306 Orders 27 27 27 Species lacking georeferenced maps 5 3 2.
Summary of taxonomic and geographical differences of extant, non-domesticated mammal species recognised in each taxonomic source: The Handbook of the Mammals of the World (HMW), the Illustrated Checklist of the Mammals of the World (CMW), and version 1. 2 of the Mammal Diversity Database (MDD). All species listed as domesticated and extinct have been removed from this summary. Note that multiple categories can apply to a single taxonomic revision Differences in CMW Differences in MDD from HMW v1. 2 from CMW Range changes 44 2 Name changes 146 66 Splits 94 species into 220 38 species into 97 species species Lumps 68 species into 32 210 species into 59 species species New species 95 20 and thus not directly tied to a specific spatial resolution, it is vital to recognise the spatial grain limitations of expert range map data.
Work is underway to include alien distribution data on Map of Life, incorporating information from the Global Register of Introduced Differences between range maps of extant, non-domesticated mammal species from the Mammal Diversity Database (MDD) version 1. 2 and range maps from the International Union for the Conservation of Nature (IUCN) Number of families with Species in MDD and not in Species in IUCN Order differences Name difference IUCN and not in MDD Afrosoricida 2 5 0 0 Artiodactyla 8 10 16 4 Carnivora 10 22 15 7 Chiroptera 12 81 137 31 Cingulata 1 1 2 1 Dasyuromorphia 1 1 8 2 Didelphimorphia 1 1 36 8 Diprotodontia 5 14 11 1 Eulipotyphla 3 27 65 7 Lagomorpha 2 1 14 1 Macroscelidea 1 2 1 0 Microbiotheria 1 0 2 0 Peramelemorphia 1 0 3 0 Pilosa 1 0 6 0 Primates 7 17 6 10 Rodentia 17 107 293 49 Pagad et al.
, 2018)s database (GRIIS;Pagad et al. , 2018)and the global Distribution of Alien Mammals database (DAMA; Biancolini et al. , 2021). While a different data type, this will enable timelier accounting for ongoing range dynamics. Clearly, a variety of different steps are needed to generate, keep up-to-date, and maintain a geospatial resource addressing the TA B L E 3.
The coregistration of single-look complex (SLC) SAR images for InSAR or offset tracking applications is often performed by using an accurate DEM and precise orbital information. However, in cold regions, such DEMs are rare over high-latitude areas or not up-to-date over fast melting glaciers for instance. To overcome this difficulty, we propose in this article a coregistration method preserving InSAR phase information that only requires a 3D point of reference instead of a full DEM. Developed in a Python toolbox called LabSAR, the proposed method only uses orbital information to coregister the images on the sphere centered on the Earth center passing by the ground control point (GCP). Thanks to the use of the orbital information, the so-called orbital fringes are compensated without having to estimate them.
All the other parameters are read directly from the auxiliary files. Image processing tools such as denoising or interferogram formation and visualization functions are also included in the toolbox. The toolbox is coded in Python in order to be easily used with any computer and updated when new sensors are launched. In order to evaluate LabSAR approach, we compared it to two state-of-the-art approaches: • SNAP: the Sentinel-1 ESA toolbox, which proposes two steps coregistration based on ground control points and offset tracking. • EFIDIR: a displacement estimation toolbox, which uses a DEM-based geolocalization of SAR pixels. The three methods are described in Section 2. The comparison is performed over the Chamonix Mont Blanc test site, in the western European Alps. Two study areas, presented in Section 3, are selected: • The city of Chamonix, which exhibits many point-like scatterers with a high degree of coherence.
LabSAR LabSAR is a platform coded in Python that offers coregistration for SAR images from different satellites such as TerraSAR-X (TSX), TanDEM-X (TDX), PAZ and Cosmo-SkyMed images in X-band, Sentinel-1 images in C-band, and ALOS PALSAR images in L-band, as long as the images are synthesized in zero-Doppler configuration. LabSAR works with only a limited set of auxiliary data. In stripmap mode, these parameters are the satellite trajectory (time, position, and velocity), the pixel spacing in azimuth and range, equivalent to the pulse repetition frequency (PRF) and range sampling frequency (RSF) in the time domain, and the time of the first sample in range t 0. Auxiliary data are extracted from each sensor auxiliary files, thus LabSAR can be used for all the aforementioned sensors. The first step in LabSAR approach is to compute a Virtual Geographic Coordinate Grid (VGCG) corresponding to the main image I m (r, a), on which the secondary images are going to be coregistered.
In most cases, the corresponding pixel locations are not integers and image resampling is necessary. 2) Selection of the image subset which covers the VGCG, by computing the minimum and maximum azimuth and range pixel position of all the points in the grid. 3) Resampling of the secondary subset. This is performed in two steps. First, the subset is oversampled using zero-padding by a factor 4. This Shannon oversampling preserves the direction of the phase by taking into account the sensor PRF. Moreover, during this oversampling, the deramping, necessary for spotlight or TOPS modes, can be performed. It requires complementary auxiliary data such as the minimum and maximum steering angles. After this Shannon oversampling, the oversampled subset is resampled over the pixel position of the main image by a bi-linear interpolation of the real and imaginary part followed by FIGURE 1 (A) Schematic view of the Virtual Geographic Coordinate Grid (VGCG) and the projection in the SAR image.
SNAP approach SNAP coregistration has been carried on using the Snappy, the Python API of SNAP. The coregistration steps are the followings: 1) The subsets extraction, for each image in the temporal stack. This subset is defined with the same pixel bounding box to have exactly the same subset for the main image as in LabSAR. 2) The stack creation, to apply the processing to all the images. 3) The coregistration, which is divided in two steps: a) The computation of the position of the main ground control points (GCPs) in the secondary images. An approximated position of the GCPs is first computed using geometrical information, and then refined using cross-correlation of patches around the GCPs. b) The warping which corresponds to the extrapolation of the offsets between main and secondary GCPs based on a polynomial function.
Data and study area To evaluate the performances of LabSAR, areas with steep relief in the French Alps have been chosen. For the interferometric evaluation, the test site is the city of Chamonix, which exhibits a good coherence. For the displacement estimation, the test site is the Bossons glacier. It is characterized by a steep slope and numerous crevasses, which can easily be tracked by offset tracking. The surface velocity of the glacier is on average of about 80 cm/ day and at the maximum of 2 m/day, leading to a low interferometric coherence. The images used are acquired by the satellite PAZ in stripmap mode. PAZ is a Spanish high-resolution X-band SAR sensor with a repeat cycle of 11 days. The spatial spacing is about 2 m in SAR geometry. The configuration of the satellite is very similar to TerraSAR-X configuration.
Mesh generation of the unit-cell To perform computational homogenization on woven composites, a finite element mesh of the composite unit-cell, which represents the smallest repetitive unit element of the microstructure, is required. Many works have been undertaken to deal with the microstructure generation of woven composites, leading to the development of several dedicated tools, such as WiseTex  (Verpoest and Lomov, 2005)  or TexGen  (Lin et al. , 2011). In the present work, a python script attached to the software TexGen is developed to automatically generate a finite element mesh of the woven unit-cell for a given set of geometrical parameters (p 7 , p 8 , p 9 , p 10 ). Figure  2  shows some examples of unit-cell's mesh generated by the developed python script and TexGen for arbitrary sets of parameters.
Non-intrusive PGD As summarized in Figure  4 , the estimation of the macrospcopic stiffness properties of woven composites depends on a numerical process involving homogenization techniques as well as the use of several software like TexGen and a finite element solver, Abaqus/Standard in the present case. Although, this numerical process is relatively quick and can easily be automated, a few dozen of minutes are still necessary to obtain a result. Such a computational cost is sufficiently important to hamper its use towards for simulation-based engineering, which may be limited in some way by the need to evaluate several configurations for different parameter values in order to achieve a satisfactory solution. This can make the design and the optimization process very time consuming. To overcome these limitations, it is proposed to use the concept of non-intrusive PGD to set up a multiparametric approximation based on precomputed solutions at certain points of the parametric space.