dataesr/scientific-paragraphs-categorization huggingface dataset:
Recent works and technologies should allow to address this issue in the near future (see reference  [53]  for example). Likewise, the properties of the building facades, which are currently deduced from another source of information (e. g. type or age of the building, location of services), could also be provided through open data portals or community databases as OpenStreetMap. Indeed, there is a general movement toward open data access  [54]. For environmental assessment, it induces new challenges as, for example, the necessity to develop data processing techniques to capture, document and collect data coming from various providers. GIS tools are once again well adapted to solve this issue. Moreover, the calculation of sound levels requires significant computing resources. Nevertheless, the computational burden can be addressed by optimizing the implementation of the computational core, e.
Concerning the noise emission, the main simplifications relate to the description of the type and the age of the road pavement, the stopping and starting road sections, the vehicles kinematic, as well as the distribution between traffics of light and heavy vehicles over the various daily time periods, due to the lack of related data. Nevertheless, the majority of the roads in built-up area consists of non draining (i. e. non-porous) surfaces, these latter being mostly used for road segments with vehicle speed over 70 or even 90 km/h. Other types of surfaces (e. g. rubberized asphalts) are therefore not considered. This missing information in terms of input data also affects the sound propagation modelling since land use data are often defined using another geographical reference frame as other dataset which make difficult to merge them with each other.
In practice, the value of the power level W S and of the parameter are fixed at 94 dB(A) and 0. 03 dB(A) respectively. The values of the distance d min is chosen on a given range [d max /5, d max /4, d max /2, d max ]. All these parameters seems to give relevant results, but can be modified depending on the urban configuration. The proposed prediction method is implemented in an open source GIS ecosystem. As described in the figure  6 , the general framework consists of three main modules, written in JAVA: the NoiseModelling plugin, the H2 database 5 with its H2GIS extension 6 [59] and the OrbisGIS platform  [60]. NoiseModelling is organized in 2 modules. The NoiseModelling core contains the main algorithms for both the sound levels and noise maps calculations. The first part evaluates the noise emission related to the road and rail traffic, as detailed in section 3.
2. The second part computes the noise propagation from each point source to potential receivers, according to the simplified standard-based model described in section 3. 3, by taking as inputs: • a set of sound sources defined by a geometry (lines or points) and an associated emission power in the form of a third octave band spectrum within the frequency range from 100 Hz until 5 kHz; • an array of buildings as a 2D geometry (polygons), without information requirement concerning neither the buildings height nor the topography; • a list of parameters such as the order of reflection n ref and the walls absorption α vert (Eq. (  10 )), the order of diffraction n dif (Eqs. ( 8 )-(  9 )), or else the maximum distance of propagation d max. The NoiseModelling SQL functions module exposes the core processes as a set of SQL functions.
These functions are implemented on top of the Relational Database Management System (RDMS) H2 and its spatial extension called H2GIS  [59] , and take thus benefits from optimised spatial functions and indexes. The SQL functions are loaded by the H2GIS database that is used by the OrbisGIS platform to store the data and to perform spatial analysis. The data model that stores and queries the geographic features (geometry and attributes) uses the OGC (Open Geospatial Consortium) simple feature specification   61, 62]. The communication between the database and the GIS tools is carried out from Java Database Connectivity (JDBC) API. The JDBC API provides standardized methods to query and to update data in a database. As a consequence, the H2GIS noise SQL functions could be executed in another context e.
g. with a command line script in Python using Psycopg or in an Extract Transform Tool like Pentaho Kettle that supports JDBC connection 7. Inside the OrbisGIS ecosystem, the noise functions are manipulated both from a web service or a Graphical User Interface (GUI). When NoiseModelling is used as a web service, it is integrated as standalone module that communicates with the database and the client (that sends the requests) through OGC standards and JDBC protocol (figure  7 ). To interact with the web service, the user must use a client application that supports the WPS specification as QGIS 8 offers. On the GUI side, NoiseModelling is managed from a SQL console that enables to execute SQL requests and to build complex scripts. This console also proposes numerous additional features for helping the user with code formatting or writing for example.
The GUI of OrbisGIS is based on the same architecture as the web service except that it runs on a local server, invisible to the end-user. The processing chain takes profit of the GIS capabilities in terms of database access 9 , geometrical calculations (H2GIS, OrbisWPS) and map rendering, cartography (OrbisMAP). As the main component of the OrbisGIS ecosystem, H2GIS and its spatial functions allow to handle numerous tasks such as: • producing a continuous noise map as iso-levels based on the colour code defined in the standard NF S31-130; • computing the population noise exposure by means of a spatial statistical analysis with external data 10.
Use of NoiseModelling from the GUI NoiseModelling is available in OrbisGIS as a set of SQL functions. These functions are encapsulated into SQL scripts and executed from the SQL console GUI illustrated at figure 8. The database access allows to interact with both the input and output data. 10  Figure  9  describes the general data flow processing. Firstly, the data must be loaded from OrbisGIS and, secondly, they are processed by the NoiseModelling functions. Then, the results stored in a H2GIS database are queried by the OrbisGIS tools to create thematic maps (style rendering). At the end, the data and their styling could be shared using geospatial standards (GeoJSON to export raw data, Symbology Encoding format to share map styles). For demonstration purpose, a single approach to both perform a noise map and compute noise exposure is presented hereinafter following three steps: Each step is based on the use of SQL queries and functions that are fully documented in the NoiseModelling documentation 11.
Step 1: Compute sound sources from traffic data To compute the sound sources, a table that represents the road network must be available in the OrbisGIS platform. The table  3  shows the required input values for each geometry of the road network data and for the same reference periods (see section 5. 2. 1). Note that the geometries must be duplicated to give sound level for each driving direction. Then the beginning and end-z values are extracted from the road geometry and its 2D length is calculated thanks to the H2GIS spatial function BR_EvalSource, by executing the SQL query given by the Script 1 in Appendix A that creates the table roads_src_global. A new table is next produced that contains, for each geometry, a noise emission value expressed in dB(A) for light and heavy vehicles. The spectrum distribution is computed using the BR_SpectrumRepartition function that expects 3 parameters: • a third octave band frequency among the values from 100 to 5000 Hz; • an integer value, to set the category of the road surface ('0' for porous pavements and '1' for non-porous pavement); • a noise emission value in dB(A).
As depicted in figure  10 , the BR_TriGrid function produces a constrained Delaunay triangulation by executing the Script 3 given in Appendix A. In addition to its geometry, each triangle is defined by 5 values stored in a table with the following columns: • TRI_ID, unique identifier of a mesh (i. e. a triangle); • W_V1, sound energy for the receiver at the first vertex of the mesh; • W_V2, sound energy for the receiver at the second vertex of the mesh; • W_V3, sound energy for the receiver at the third vertex of the mesh; • CELL_ID, unique identifier for a cell if the computational domain is subdivided (default is 0; see section 3. 4). The output triangles are then merged to compute a noise contour map. The process, presented in the SQL Script 4 in Appendix A, takes advantage of the SQL language and of the spatial functions ST_TriangleContouring, ST_Union, ST_Accum and ST_Explode.
Step 3: Estimate population exposure The population exposure is the most common indicator used to put in perspective a noise map and the distribution of the population. These exposure rates correspond to the number of inhabitants living in dwellings exposed to equivalent noise levels (i. e. L den ) within standard value ranges. It could be calculated from all noise level categories or from a level exceeding a value, according to the most exposed facade (in this case, receiver points are placed at a distance of 1 m in front of the facade), as recommended in the french methodological guide of CERTU  [64]. The spatial analysis functions available in OrbisGIS allow to compute the number of inhabitants for each noise level categories. For example, the SQL Script 5 in Appendix A details the method, using two input tables: the contouring noise map and the location of inhabitants provided by the national institute of statistics and economic studies (INSEE).
The three acoustic indicators are then used to calculate the day-evening-night noise indicator L den as recommended by the Directive 2002/49/EC to assess noise 12 The traffic data includes, for each reference periods, the required values listed in the Table  3. Peer-reviewed version available at ISPRS Int. J. Geo-Inf. 2019, 8, 130; doi:10. 3390/ijgi8030130 annoyance, which is calculated for each frequency band j by summing the contributions over the three acoustic time 13 , that is: L j den = 10 log 10 1 24 12 × 10 L j d 10 + 4 × 10 L j e +5 10 + 8 × 10 L j n +10 10. ( 12 ) Table  5. Breakdown of "acoustic" reference periods from "traffic" reference periods. The sound levels are thus calculated for each traffic reference period, that is L NOH , L MRH , L DOH and L ERH , and then reconstructed for the acoustic reference periods for the L j den calculations.
Geographical data The geographical data (topography, roads) are issued from the BD TOPO R database provided by the National Institute of Geographic and Forestry Information (IGN 14 ). Note that there is no noise barrier in the study area. Road pavement Due to the lack of information concerning the nature of road pavements, some simplifications in comparison to the standard method  [34]  are considered in terms of type and age of the road pavement. Because porous surfaces are mostly used for road segments with vehicle speed over 70 or even 90 km/h, only non-porous pavements are considered, which represent the majority of the roads in built-up area. In addition, the age of pavement is fixed at 10 years, which is, here again, a relevant hypothesis. Population Statistical population units are given from INSEE databases with Commune boundaries 15.
This is due to the fact that image receivers are most of the time hidden behind another wall which implies no specular reflection on this wall. Moreover, the number of image receivers is related to the reflection order without collision test. secondly by collecting the population data, and thirdly by assigning the population data to the habitable surface 17 of buildings  [67]. These 3 scenarios are automated within a parametrised geoprocessing script that load, process and build the map, thanks to the spatial SQL functions available in H2GIS and OrbisGIS renderer. In the following, the analysis rests only upon the results for the City of Nantes, which is a part of Nantes Métropole, with an estimated total population of 280 920 inhabitants in 2008. A first clustering is given in table 6 on the basis of the statistical analysis of all scenarios that ranks the number of inhabitants within sound level classes and that informs thus about the percentage of population per noise level range.
The identification of residential buildings is achieved on the basis of the IGN's topographical database that does not take into account business and offices outside the activities areas, what must probably lead to an overestimate of the number of residential buildings and consequently of the impacted population. Nonetheless, a few developments are still necessary in order to improve the proposed framework. The sound emission is, for now, simplified and neglects the surface type and ageing in the rolling noise contribution, as well as accelerating, decelerating and stopping road sections. In addition, the modelling of railway noise sources that, for now, only deals with the case of tramways must be enlarged on the consideration of trains. Regarding the noise forecasting method, recent developments addresses the consideration of the topography, the ground effects and the vertical diffraction over the horizontal edges of the buildings by extending the implementation from 2D to 3D modelling  [69].
It must however be noted that these upgrades would have a little impact on the application presented in this paper. Finally, another area of development would concern the use of open data. Since a few years, many geographical data sources are indeed made available in open data such as OpenStreetMap (OSM). Updated regularly by voluntary contributors and available on a larger territory with the same data structure, OSM offers potential for generalizing noise map production. In addition, data access conditions (by means of dedicated APIs) are well suited to the evolution of the GIS domain, where tools are controlled remotely via services and communicate with each other using standards. This is the case of NoiseModelling which is developed to be integrated into a remote service. However, before producing noise maps on-demand from open data, several methodological issues will have to be resolved : data quality, data model, architecture.
preprints. org) | NOT PEER-REVIEWED | Posted: 11 February 2019 Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 11 February 2019 doi:10. 20944/preprints201902. 0087. v1 Peer 5H2 website: http://h2database. com/. 6 H2GIS website: http://www. h2gis. org/. Preprints (www. -reviewed version available at ISPRS Int. J. Geo-Inf. 2019, 8, 130; doi:10. 3390/ijgi8030130 [.
(www. preprints. org) | NOT PEER-REVIEWED | Posted: 11 February 2019 Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 11 February 2019 doi:10. 20944/preprints201902. 0087. v1 Peer 11NoiseModelling documentation website: https://github. com/Ifsttar/NoiseModelling/wiki. Preprints -reviewed version available at ISPRS Int. J. Geo-Inf. 2019, 8, 130; doi:10. 3390/ijgi8030130.
org/especiesamenazadas_osohormiguero. htm), Argentina; Proyecto Hormigueros (https://www. cunaguaro. co/proyecto-hormigueros), Colombia; Programa Tatu-bola (http://tatubola. org. br/), Brazil; Armadillos de Chile (http://www. armadilloschile. cl/), Chile; The Sloth Conservation Foundation (https://slothconservation. com/), United Kingdom; and Projeto Tatu-canastra (www. icasconservation. org. br/o), Brazil. Nonetheless, relatively few species are considered in those programs (such as Myrmecophaga tridactyla, Priodontes maximus, Cyclopes sp. and Tolypeutes tricinctus). Most xenarthran data are unpublished or have been published in the grey literature, including reports, Master and PhD theses in different languages, with low accessibility to the broad scientific community. In an effort to gather this dispersed and unavailable data in one place, we compiled published and unpublished data on xenarthrans, from the years 1855 to 2018, including occurrence and quantitative data.
A total of 24,847 unique georeferenced records were assembled, ranging from the northern frontier of the Neotropics (Mexico, southern USA, and Caribbean regions), through to the austral south of South America (Chile, Argentina, Uruguay, and Paraguay). Neotropical Xenarthrans is by far the largest dataset ever assembled with respect to armadillos, sloths, and anteaters. This work is part of the Neotropical, Atlantic, Brazil, and Amazonia series initiative, the objective of which is to make data on the biodiversity of these regions publicly available. Until now, the following data-papers of these series have been published: Atlantic Frugivory  (Bello et al. 2017) , Atlantic Camtraps  (Lima et al. 2017) , Atlantic Small Mammals  (Bovendorp et al. 2017) , Atlantic Bats  (Muylaert et al. 2017) , Atlantic Birds  (Hasui et al.
METADATA domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae  (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this datapaper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay.
Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i. e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset.
E. Description To organize the dataset into the Neotropical realm, we selected all the countries and islands indicated in the literature as the Neotropical area and merged all polygons into a single shapefile. Our study extent includes the southern USA, Mexico, Central America, the Caribbean islands and South America (Figure  1 ). The dataset is comprised of 42,528 records (Figure  1 ; Table  1 ), including 40,794 (95. 92%) with taxonomic certainty down to species-level identification, and 1,734 (4. 08%) records of six genera with taxonomic uncertainty, with the majority of those records belonging to Dasypus sp. (1,359 records, 78. 37%). In some cases, we taxonomically corrected some of those uncertain records, based on the distribution, on available literature, or on evidence material sent to us by the authors and after consulting specialists (more details, Table  2 ).
When correction was not possible, we opted to maintain only the genus. A special case includes the genus Cyclopes sp. (240 records): at the beginning of this study, the genus was monospecific, Cyclopes didactylus. In the middle of the process, the taxon was subjected to a taxonomic review, which split the previously single species into seven  (Miranda et al. 2017). To avoid misinterpretation of the data, we decided to maintain Cyclopes sp. The two armadillo families appear most frequently in terms of both the number of records and species; Dasypodidae is responsible for the most records (n=14,790), followed by Chlamyphoridae (n=13,887), and they are represented by seven (two subspecies) and 13 species, respectively (Figures  3 4 5 ). Two of these species are the most common in the dataset (Figure  2 ): Dasypus novemcinctus (Dasypodidae, n=11,588) and Euphractus sexcinctus (Chlamyphoridae, n=7,325), both of which are species with wide distributions (Figures  3  and 4 , respectively).
Only one species, Cyclopes sp. , represents the family Cyclopedidae (Figure  9 ; see the recent taxonomic review above). The other anteater species, Myrmecophagidae, is the third-most numerous, (n=11,349), represented by its three species Myrmecophaga didactyla (n=5,941), Tamandua tetradactyla (n=5,089), and Tamandua mexicana (n=319). The two sloth families are the least numerous, mainly the Megalonychidae, with only 414 records (Figure  7 ). Bradypus variegatus, from the family Bradypodidae, is the most-common sloth species, with 962 records (Figure  8 ). Bradypus pygmaeus, also from the family Bradypodidae, is the least-common species (n=12), both among sloth species and the dataset as a whole, reflecting its restricted insular distribution (Figure  8 ). Approximately 99. 94% of the records are from native species, and only 0.
42%), especially when used in conjunction with other methods (Table  3 ). But even when used as the sole sampling method, camera trapping is still the most-frequently method (32. 4%). Roadkill is the second most-successful method, accountable for 14. 6% of the records. Active search is mostly used in combination with other methods, followed by interview. Other methods include line transect, vestiges, museum collections, telemetry, bibliographical survey, live trapping, sand plots, track plots, car monitoring, and opportunistic encounters (Table  3 ). About 83. 90% of the records correspond to presence-only data, 10. 02% to presence-absence data, and 6. 08% to quantitative data. According to our database, studies on Xenarthra have been increasing in recent years (Figure  10 ). Most of the data (< 10,000 records) were collected between 2010 and 2014, and this is the major period for most species (Figure  11 ).
The raster files corresponding to temperature and precipitation values were obtained from the WorldClim database 1. 4 (http://www. worldclim. org/version1). The altitude of most records is below 1,000 m, with a peak around lowland regions (between 200 and 300 m; Fig. 12 ). The average annual temperature of the sampling sites varies mainly between 20 and 27° C, and few sampling records show temperatures below this range (which is expected for the neotropical region; Fig. 12 ). Lastly, annual precipitation varies mostly between 1,500 and 2,000 mm, and few sampling sites show values above 2,000 mm. Elevation data were obtained from the digital elevation map from the CGIARCSI database version v4. 1 (http://www. cgiar-csi. org/data/srtm-90m-digital-elevation-database-v4-1). The digital elevation model has 1 km of spatial resolution and is available in a unique file in ".
Data Compilation We created a collaborative network of researchers who had collected and published data on xenarthran occurrence and community composition in the Neotropics and invited everyone to contribute to this NEOTROPICAL XENARTHRANS data-paper (i. e. , their data would be added to our dataset). In addition, we collected occurrence data from the following museums and institutions: Museu Nacional/Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil After the first round of data compilation, we performed a systematic review of published literature on occurrence records and abundance of Xenarthra in the Neotropics using the following electronic databases: Web of Science (http://scientific. thomson. com/isi/), Science Direct (https://www. sciencedirect. com/), PubMed (https://www.