dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research was funded under the  'Open Research Area (ORA)  for the Social Sciences' programme by the  Economic and Social Research Council  (No. ES/K006169/1 ), the  Agence Nationale de la Recherche  ( ANR, France) ,  Deutsche Forschungsgemeinschaft (DFG, Germany)  and the  Nederlands Organisatie voor Wetenschappelijk Onderzoek (NWO, Netherlands). We would like to thank the practitioners interviewed for this research for their valuable help and comments on earlier drafts of this article. We would also like to thank the referees who provided very helpful and constructive comments. The authors, however, take responsibility for the views expressed in this article.
Octodon degu polygon distribution was obtained from Nature Server  (Patterson et al. , 2005) , at 1:1,000,000 scale. Tree species distributions (Lithrea caustica and Acacia caven) were extracted from vector layers at 1:50,000 scale from the most complete vegetation and land cover classes map for Chile provided by the Chilean Forestry Services Database at 1:50,000 resolution. We were not able to find distribution maps for the tarantula Grammostola mollicoma, so we excluded this flagship species from analysis. We identified areas where the distributions of all 7 species overlapped using vector overlay of the polygons. Polygons drawn at large scales are likely to include some spaces that are errors of commission: the species are not really present within the whole polygon. Thus, using the 1:50,000 scale from the tree data to determine overlaps should reduce errors of commission.
We checked whether the 7-species overlapped area coincided with protected areas. National protected area boundaries were obtained from the World Database on Protected Areas (WDPA IUCN) and private protected areas boundaries from Nú ñez-Ávila et al. (2013). In order to identify the potential reintroduction and conservation habitat suitable for guanaco and puma, land cover of native forest, scrublands and vegetation types were obtained from the vectorial land cover map provided by Chilean Forestry Services Database at 1:50,000 resolution CONAF (2011). Polygons of espinal (Acacia caven silvopastoral savanna), native sclerophyllous forest and scrubland were considered suitable habitat for guanacos, which are generalists that used to live throughout Chile, including central Chile, until around 500 years ago when they were locally extirpated through overhunting.
Big data opens new possibilities in the field of quantitative understanding of complex social systems. Empirical data, for instance, may be retrieved from opinion pools or elections in the case of opinion dynamics or market analysis in the case of financial or economic models. An entire chapter (ch. 13) of the Helbing's book  [90]  is devoted to the determination of the model parameters from empirical data: once the model parameters are evaluated usually with the method of least squares, a sensitivity analysis should be carried out; a decomposition with respect to explanatory variables can be done using a regression analysis in connection with a factor analysis or a ranking regression analysis. Another suggestion that is worth mentioning is the possibility to empirically individuate the subsystem to use in a network by means of the cluster analysis  [90].
Archaeological data The archaeological data are the fruit of more than 50 years (38 field campaigns) of excavation at Tel Dan since 1966  (35, 40, 87, 88)  and are summarized as a supplementary text (see the Supplementary Materials and tables S2 to S4). Estimates of population and population density are based on the measured area of habitation gleaned from excavations and surveys of the site carried out since 1966. All these estimates assume a contemporaneity of occupation of architectural units throughout the site based on the homogeneity of material culture in the identified stratigraphic horizons.
Statistical analyses All archaeological and botanical data were analyzed using the software package PAST version 2. 17c. A regular interpolation (20 years) was first applied to the entire data set. Pollen data were investigated using cluster analysis (paired group as algorithm and correlation as similarity measure; fig. S1 ). Cluster analysis (descending type) was used to compute the lengths of tree branches, using branches as ecological distances between groups of taxa. Neighbor joining, an alternative process for hierarchical cluster analysis, was also calculated for the hygrophiloushydrophilous components, using correlation as similarity measure and final branch as root (fig. S1 ). Each cluster was summed to create pollen-derived vegetation patterns. A PCA was run to test the ordination of terrestrial ecosystems by assessing major changes in the pollen-derived vegetation patterns (Fig.
The most common way to use geographic information is to make maps. With the ever growing amount of Volunteered Geographic Information (VGI), we have the opportunity to make many maps, but only automatic cartography (generalisation, stylisation, text placement) can handle such an amount of data with very frequent updates. This chapter reviews the recent proposals to adapt the current techniques for automatic cartography to VGI as the source data, focusing on the production of topographic base maps. The review includes methods to assess quality and the level of detail, which is necessary to handle data heterogeneity. The paper also describes automatic techniques to generalise, harmonise and render VGI.
Introduction Maps are now everywhere, from the Web to smartphones, and are no longer limited to paper maps for hiking or routing. But most of the maps provided to the general public are not good maps, so they are not as effective as they could be. Whether they are static or dynamic (i. e. pan and zoom allowed), on paper or on screens of variable sizes, good maps are maps where every feature is legible, and where the user can easily understand the geography behind the map and the message of the map. Making good maps manually requires cartographic skills. However, when the amount of data is huge, for instance with the world OpenStreetMap (OSM) dataset, mapmaking has to be automated. Automating mapmaking entails two steps to obtain a legible topographic map out of a geographic database: selecting the data and the styles to be used to portray them, and refining the content in order to reach a legible map, which is complex when scale decreases, as the space in which to put the map symbols and the text reduces.
These steps require the automation of three main processes: map generalisation (the simplification and abstraction of map objects when scale decreases), text placement, and cartographic symbolisation or stylisation. How to optimally automate such processes is still a research question, but, in recent years, maps have been more and more often produced through complete or partial automation. The traditional actors of automated mapmaking are the national or regional mapping agencies, the private map editors and the GIS software vendors. These actors have been used to making their maps out of traditional geographic databases, but what happens if the source data are partly or totally derived from Volunteered Geographic Information (VGI)? VGI is geographic information, and past studies on its quality  (Girres and Touya, 2010; Haklay, 2010)  have shown that it was satisfactory for many uses, but quite heterogeneous.
Why Are Traditional Automated Mapping Processes Not Fully Adapted to VGI? Traditional automated mapping processes have been developed to process authoritative datasets, or at least datasets with consistent and homogeneous specifications, which is clearly not the case when VGI is used as (one of) the map source(s). The first problem is that VGI datasets suffer from level of detail (LoD) heterogeneities. For instance, there is no LoD specification in OSM, which allows contributors a great deal of freedom in capturing either detailed features (e. g. the cadastral LoD buildings from Figure  1 ) or less detailed features (e. g. the rough built-up areas or lake outlines in Figure  1 ) depending partly on their skills but mostly on the data source, as precise GPS tracks allow more precision than low-resolution satellite imagery.
This heterogeneity leads to LoD inconsistencies, i. e. some very detailed features and some less detailed features might coexist on a map and share spatial relations (Figure  1 ). Maps produced by National Mapping Agencies (NMAs), on the other hand, are based on datasets with strict specifications, where all features share the same geometrical resolution or granularity, whether they belong to the same theme or not. Thus the processes used to automate the production of such high-quality maps are not capable of handling the inconsistencies shown in Figure  1. The main characteristic of VGI compared to traditional authoritative datasets is the heterogeneity of quality, with very-good-quality contributions and very-bad-quality ones. This is true for most types of VGI: for OSM first and Fig. 1 : Examples of LoD inconsistency in OSM.
On the left, the rough built-up areas/forest limits intersect detailed buildings; on the right, detailed footpaths lie on the surface of a roughly digitised lake. ©OpenStreetMap contributors. foremost, as shown in seminal studies by  Girres and Touya (2010)  and  Haklay (2010) , but also for photo sharing platforms such as Flickr  (Zielstra and Hochmair, 2013) , or even for hiking route sharing platforms  (Ivanovic et al. , 2015). Data quality varies from theme to theme, but also from feature to feature in the same theme  (Girres and Touya, 2010). This is really different from authoritative datasets, where data quality is homogeneous, and cartography processes are developed in adaptation to this known quality. Among the quality indicators that can be heterogeneous with VGI, the most significant components are positional accuracy, thematic accuracy, completeness and logical consistency: • Positional accuracy heterogeneity is, of course, a problem because it can increase the symbol overlap problems faced when creating a small scale map.
Traditional NMA maps cover the classic themes of topographic maps, or road maps, and most automated mapmaking processes focus on roads, buildings, hydrography, relief or vegetation. VGI has a broader range of contributed geographic features; even OSM, which started as a free alternative to topographic maps, has been extended to cover amenities, shops or addresses. Thus an automated process to make maps with VGI needs to handle unusual themes as well as classic road and building datasets. Another particularity of VGI is the broader range of scales used to describe the world, from world views that range from very small scales (smaller than 1: 100 000 000 scale) to very large scales. For instance, OSM suggests the capture of zebra crossings or traffic signals that can only be displayed at very large scales.
Some projects even extend the OSM framework to indoor mapping  (Goetz and Zipf, 2011). In contrast, traditional automated mapmaking targets a small number of fixed scales  (Duchêne et al. , 2014) , and, even when the maps are displayed in online tools, the number of scales available is often limited by the number of scales available for paper maps  (Dumont et al. , 2016). In addition to the issue of the large range of scales in VGI, it should be noted that most of the automated processes were never developed for large scales that large and for small scales that small (e. g. the smallest scale produced by the French NMA only covers the whole French territory, excluding overseas territories). Regarding symbology and stylisation, the automated processes are strongly related to the data and semantics.
LoD or Scale? In cartography, the scale of a map is the ratio of the length of an object on the map by the length of the same object on the ground. But scale is also somehow related to map usage, and is then a proxy for map content. Maps around the scale of 1:25k are mainly used for hiking and contain information readable at this scale and useful for this purpose (e. g. footpaths, contour lines etc. ); maps with a scale smaller than 1:500k are mainly used for road trips, and highlight the map themes related to roads. In contrast, it is too complex to assign a scale to VGI features, but here we consider the scale of a feature as the scale of the map at which this feature would be legible and legitimate. LoD is a vaguer notion, which can be considered as the translation of map scale to geographic databases for which the scale is not fixed.
Several factors affect the level of detail of geographic features: • geometric resolution, i. e. the minimum distance between two vertices of the geometry, as an analogy with image resolution. • geometric precision, i. e. the difference between the position in the database and the position in reality. • granularity, i. e. the size of the smallest details in a geometry, such as the protrusions in the church in Figure  2  (left). • semantic resolution, i. e. the amount of details in the semantic information attached to the geometric feature. • conceptual schema, i. e. how much the ground truth information is abstracted; for instance, a wood abstracted by individual trees is more detailed than one abstracted by a polygon feature. Thus it is difficult to infer LoD as a numerical value as one would for scale, so often categories are used, such as the LoD for 3D city models  (Biljecki et al.
Multiple Criteria Decision Method We stated in Section 3. 1 that LoD can be affected by a combination of five factors, all of which can be measured in a geographic dataset but are hardly comparable or can hardly be added. Multi-criteria decision methods are computational techniques that allow decision-making based on several criteria in those cases where a simple numerical value such as a mean is not a valid solution  (Roy, 2005). Touya and Brando-Escobar (2013)  propose a multi-criteria decision method to classify VGI features into LoD categories from street to country level. The method was improved by integrating elements from the scale equivalency in  Touya and Reimer (2015). Some automatic results from the improved method are presented in Figure  4.
Current Generalisation in OpenStreetMap Map generalisation is a complex process that simplifies and abstracts geographic information to produce a legible map at a given (smaller) scale. The problem of map generalisation automation has attracted research proposals for many years (see for instance  Burghardt et al. , 2014; Mackaness et al. , 2007) , and some mapping agencies are now able to use research results to produce maps with partial or total automation  (Duchêne et al. , 2014). One of  the remaining challenges of automated generalisation research is to extend the current processes to make maps with VGI or maps that combine authoritative and user generated information. If we look at the default maps available from OSM, there is almost no generalisation operation carried out on them. This is partly due to the philosophy of the OSM portal, which aims to show the content of the dataset rather than to display the best map possible.
#buildings { [zoom >= 13] { polygon-fill: @building-low-zoom; polygon-clip: false; } [zoom >= 15] { line-color: @building-line; polygon-fill: @building-fill; line-width:. 75; line-clip: false; } } Besides these minimal selection operations, there are very few proposals dedicated to the issues of generalising VGI at present  (Sester et al. , 2014). Klammer (2013)  proposed some solutions for tile-based maps such as OSM, with each tile being generalised separately, but potential problems at tile junctions are not handled: generalisation often requires an analysis of the neighbouring objects, which is not possible at the edge of the tiles. Schmid and Janetzek (2013)  proposed to generalise the OSM road network at small scales on-thefly using important placenames in the dataset. However, most of the issues remain unsolved: how can we deal with the broad range of scales in generalisation processes, with the diversity of themes or with the heterogeneities in quality and LoD? The next two subsections address issues related to the range of scales and the diversity of themes with the generalisation of complex airports and railways from OSM.
For the examples in Figure  12 , a clearing would be introduced around the group of buildings, and the bus stop would be moved to the closest road. We call this operation to artificially increase the LoD through probable spatial relations LoD harmonisation  (Touya and Baley, in press ). However, there is no clue in the data as to the real shape of the clearing required in Figure  12 : we only know that there must be one. This makes harmonisation tend more towards caricature and schematic mapping than towards realistic mapping. The map does not present real and precise shapes to the reader, but rather presents very probable spatial relations. The next section briefly describes some harmonisation operations and shows some results of their implementation on OSM data, while Section 5. 3 discusses the problem of automatically chaining these harmonisation operations on a complete large scale map.
Quality Assessment Taking into Account Crowdsourced Ground Truth Data As mentioned in Section 2, automatic mapmaking processes require some consistency in data quality, or some kind of assessment of this quality if consistency is not achievable, which is the case with VGI. This section describes a study to assess the quality of OSM features, using ground truth data. In many studies, OSM is usually used as a proxy for VGI data; this study is not an exception, as OSM is a prime source of vector-encoded GI that can be directly used in cartographic processes. However, any effort in mapmaking using VGI data should expand its horizons to include other sources as well. Today, VGI comes from different sources and in many flavours, such as toponyms, GPS tracks, geotagged photographs, synchronous micro-blogging, social networking content, blogs, gaming spaces, sensor measurements, etc.
All of these sources can either possibly offer valuable geographic information complementary to OSM data (e. g. Geonames can provide a supplementary dataset to the OSM places) Fig. 16 : 1) Detection of LoD inconsistencies (in this case a building intersecting the riverbank); 2) clusters of close buildings are created around the identified inconsistencies; 3) each cluster is harmonised as a whole to remove overlaps without creating new ones. ©OpenStreetMap contributors. or be used as quality assessment tools (e. g. through the use of geotagged photographs from photo-sharing repositories). This latter case is the focus of this section. Geotagged photographs are, in a sense, in-situ observations of the ground reality and thus, if properly used, can assess various quality factors of OSM data and improve the decisions in some of the cartographic processes analysed above.
Verify OSM Land Use / Land Cover The second case study for using geotagged photographs to evaluate a VGI dataset comes from the Land Use/Land Cover (LU/LC) domain. Here the challenge is to disambiguate inconsistencies regarding the actual LU/LC that arise from contradictory feature types that occur between different OSM layers, e. g. in the Landuse and the Natural OSM layers (a more thorough study can be found in Fig. 17:  A satellite image of a sample area in Paris (left) and the polygons of wooded areas (right) for the same area (©IGN, France). Fonte et al. , 2016). The LU/LC at each given point should be unambiguously retrieved: this requirement not only contributes to the overall quality of OSM and to the correct cartographic output but also enables the use of OSM data for the creation of LU/LC products.
Here again, overlaps between different and contradictory LU/LC feature types create inconsistencies that could possibly be disambiguated with the use of geotagged photographs. For example, Figure  19  (left) shows the overlap of a closed construction site (purple polygon) and a residential road (green line) in OSM (green dots represent the locations of Flickr photographs). Although the VGI elements co-exist in the same VGI source (i. e. in OSM), it is obvious that it is not possible for both layers to correctly denote the actual land use of the area. The use of geotagged images could provide the necessary information to clarify the mismatch. In Figure  19  (right), a Flickr photograph taken within the polygon clearly shows that the area has been turned into a construction site. Additionally, a valuable characteristic of the VGI datasets used is the time information they contain: using the individual timestamps of features, it is possible to analyse and understand the currency of each feature, which could be valuable in updating the overlapping features that have outdated information.
A number of tests can be applied in order to find inconsistencies in the OSM data between features from the same layer (e. g. two roads), or from different layers. Tests are based on consistency evaluation utilising topological relations that the data should satisfy, taking the data semantics captured by their attributes into account as well. In OSM, apart from the geometry capture, the existence of a plethora of tags provides a rich semantic dataset, and thus sophisticated topo-semantic relations can be explored. Here, we focus on POIs because they are more easily captured in photographs due to their dimensions. POIs that are problematic with regards to their position in comparison to other layers can be verified with Flickr images. If the Flickr images prove that the topo-semantic relation is correct, then no changes are made; otherwise the geometry (relative horizontal position) and/or the semantic information (Type tag) is updated according to the photograph.
Finally, the topo-semantic relations are re-evaluated. A case study was performed with OSM data that cover the broader Paris area  (Antoniou et al. , 2016). According to this study, in the area of interest there are 22,527 OSM POIs with two main attribute tags related to their identity: Name and Type. Topological relations of POIs against other thematic layers are examined based on a number of checks, and errors will be examined utilising Flickr photographs. For example, it is important to investigate the topological relationship between POIs and buildings, examining whether POIs should be situated inside or outside building polygons. Initially POIs are clipped with the convex hull of the area covered by buildings, resulting in 60136 points. A number of points (  21872 ) are situated inside the building polygons, 2338 (4%) are situated on the building boundaries and 35926 (60%) are situated outside.
Discussion and Guidelines for Using VGI in Symbol Specification Processes As for the other mapmaking processes, the first issue to address when using VGI in symbol specification processes is the adaptation of processes developed for consistent databases to the heterogeneity of VGI. This adaptation can be achieved by a characterisation of VGI features, i. e. its quality, semantics and LoD. But such characteristics of quality or LoD are no longer consistent on a given map theme, as each VGI feature might have its own quality or LoD. Thus a symbol specification for each map theme might not be possible with VGI. For the same map theme, for instance rivers, the symbol might be adapted to the quality, semantics and LoD of the features (e. g. darker shades of blue and wider symbols for rivers with more details/better quality).