dataesr/scientific-paragraphs-categorization huggingface dataset:
Website development and mobile applications For the active implementation of the designed CS protocol, a portal supporting multilingual content was developed. It is currently available in 6 languages: English, French, Greek, Italian, Turkish, and Spanish. For the website development all available options were considered and Drupal® was selected as the platform that could optimally satisfy the demanding requirements of the designed website. Flexibility, stability and an API framework that can accommodate almost all conceivable functionality were the key characteristics for choosing the specific Framework -CMS (Content Management System). The website uses the Bootstrap 3. x, the most popular HTML, CSS and JS framework for development of complex dynamic websites and web applications, providing an easy way to adapt a responsive web design layout to the final template.
All technologies involved in the development process are Open Source, based on the well-tested LAMP Platform (acronym for Linux, Apache, MySQL and PHP) and most used in WWW. For map display functionality Leaflet library and the Openlayers 3. x were used combined with the Google Maps API v. 3. An advanced user management system was developed, with three different groups of registered users: (a) divers, who can create, edit and delete content about observations, (b) country managers, being able to verify new user registrations, and (c) website administrators. Based on permissions set for those groups, each registered user has access to various features of the website. The demand of a complex observation registration form was covered by providing the user with the option to choose between different form displays when the user creates or edits an observation.
Field trials and preliminary data gathering Field trials for the implementation and optimization of the developed CS protocol took place in three countries: France (Marseille), Greece (National Marine Park of Zakynthos) and Turkey (Izmir). These included briefings to volunteer divers from local dive centres and associations, experimental dives for data collection followed by interviews, questionnaires and discussions, so as to come up with a more simplified list of requirements that clearly address the CIGESMED project objectives and make the data collection and reporting procedures as easy as possible for the participants. During these trials, the idea of developing an educational CS module to ensure a basic understanding of coralligenous bioherms and their associated communities also emerged, and was subsequently constructed.
Basic data and parameters This section integrates typical geographic and topographic data along with several easily assessed abiotic environmental parameters which can allow a preliminary description of the site, as well as the development of data series for sites receiving multiple visits (Table  Figure 1. User account registration form of CIGESMED for divers website. Observation form in the data submission platform of the CIGESMED for divers website. 1). The location of the dive is reported in the online observation form through an interactive map which allows the user either to visually recognize the site and place a waypoint, or manually enter coordinates. Observation ranks of the topographic features are presented with symbols taken from the CIGESMED protocol for monitoring coralligenous  (David et al.
Ecological observations: reporting biodiversity and threats This section of the CS platform focuses on: (a) the presence and relative abundance of typical conspicuous species of coralligenous assemblages and (b) the recording of pressures and imminent threats. A total of 23 taxa belonging to 7 taxonomic groups were included in the CS protocol (Table  2 ). The list is restricted to the minimum number of species possible, so as not to overwhelm citizen scientists with a large amount of information. The scope of this taxon list was not to be as comprehensive as possible but rather to include certain taxa that are characteristic of the various coralligenous facies, as well as a number of rare, protected and commercial species the reporting of which is o o Table  1. Basic information requested in the data submission platform of the on the CIGESMED for divers protocol (* compulsory field).
Data sharing policy and activities All currently available data reports of coralligenous formations, their conspicuous species and threats are visibly marked on the accumulative map of the CS website (Fig. 5 ). Submitted data are open to public view and pass through a quality control process by the CIGESMED CS team before being reposited to global biodiversity databases (e. g. GBIF). As the website is social media-friendly, it provides out-of-the-box widgets allowing users to easily share observations or pages through popular social media (e. g. Twitter, Facebook, and LinkedIn). Table  3. Reporting the intensity of pressures and threats on the CIGESMED for divers website.
Smartphone applications The "Citizen Science" sub-application of the LifeWatchGreece mobile app aggregates data from different CS initiatives in order to support a simple and quick view process, assisting sharing of the obtained information. It is available for Android platforms (Mobile/Tablet) and receives data in JSON format from CIGESMED for divers website in real time. The application showcases a simplified version of the observations submitted to the "CIGESMED for divers" website on a map. Users are given the option to select and view details for each observation, such as the list of reported species (Fig. 6 ). By selecting a species, users can view its distribution, based on the registered data, as well as taxonomic and ecological information from global biodiversity databases like GBIF and FishBase (Fig.
7 ). Furthermore, species records are connected to the Micro-CT sub-application of LifeWatchGreece mobile app where users can explore relevant Micro-CT datasets (i. e. series of images using X-Ray micro-computed tomography) through images, videos and 3D models that can be virtually manipulated, dissected and rotated using online tools (see  Keklikoglou et al. 2016 , this special collection). Interface of the smartphone application showcasing information regarding Mediterranean coralligenous assemblages and species obtained from CIGESMED for divers website. This is a "Citizen Science" mobile sub-application of the LifeWatchGreece infrastructure.
2011). The increasingly important role that active volunteers can play in reporting of the presence and spread of marine allochthonous species has been recently pointed out  (Scyphers et al. 2014 , Zenetos et al. 2013) , along with the necessity for visible centralized data submission and dissemination platforms that can drive citizen participation. In this context, an array of established citizen-monitored sites can prove valuable to swift alien biodiversity records, especially if their online infrastructure is directly linked to relevant data aggregation channels (e. g. ELNAIS). Citizen-science approaches are generally acknowledged as a main pathway to assist the progress towards the upscaling of ecological data to the global level  (Edgar et al. 2016). In this context, LifeWatchGreece research infrastructure (ESFRI) provides the means, expertise and the continuous technical support for the maintenance and development of the electronic platform behind the pilot CIGESMED for divers CS project for the coralligenous habitats in the Mediterranean.
It also provides the electronic infrastructure needed for: (a) the data management in order to be harvested by the international aggregators, such as OBIS and GBIF; (b) the appropriate electronic infrastructure in order to analyze the data and interpret the results, in the form of the virtual laboratory RvLab  (Varsos et al. 2016, this special collection). Sharing data is one of the main objectives of CIGESMED. However, data assembled by non-experts should go through quality control before being used in scientific analyses or reposited to global biodiversity databases  (Arvanitidis et al. 2011 , Thiel et al. 2014). The list of organisms included in the present CS protocol include conspicuous species that are relatively easy to correctly identify. These include species that serve as ecosystem engineers and define coralligenous assemblages, such as coralline algae, sponges and gorgonians, as well as motile fauna that is also typical of this habitat type (e.
To this end, all participants will be fully acknowledged and in certain cases included as coauthors in resulting communications to scientific or general media. Divers with a high level of input, according to the online rewarding system, could further contribute as local collaborators supporting joined missions and monitoring activities. A series of joint activities organized by the CIGESMED CS researchers, with selected motivated diving associations and MPA management authorities have been scheduled in Greece (e. g. Korinthiakos Gulf and National Marine Park of Zakynthos), France (e. g. Atelier Bleu -CPIE Côte Provençale, Ailes Sportives Airbus Helicopters, Septentrion Environnement), and Turkey (e. g. Derin and Doğa Diving Center, Engin Sea and Nature Sports Center). Those include photo-sampling of permanent quadrats, recording of environmental data (e.
g. through the deployment of loggers) and recording of threats and impacts on a scheduled and systematic basis. Joint missions will further include training of the involved divers and demonstrating the proposed activities using the developed CS protocol and e-infrastructure. Furthermore, online photo-contests are to be periodically scheduled and high-quality photographs uploaded by users will be screened on the homepage of the website or could be used in future scientific and educational publications, always acknowledging the photographer. Combined with the CIGESMED for divers CS protocol, these activities, are expected to enhance the creation of long-term data series, increase public awareness and establish communication and knowledge exchange on a more trust-worth and permanent basis.
https://doi. org/10. 1007/s00267-013-0171-0 • Boero F, Belmonte G, Bracale R, Fraschetti S, Piraino S, Zampardi S (2013) A salp bloom (Tunicata, Thaliacea) along the Apulian coast and in the Otranto Channel between March-May 2013. F1000Research 2: 1-6. https://doi. org/10. 12688/ f1000research. 2-181. v1 • Boero F, Putti M, Trainito E, Prontera E, Piraino S, Shiganova T (2009) First records of Mnemiopsis leidyi (Ctenophora) from the Ligurian, Thyrrhenian and Ionian Seas (Western Mediterranean) and first record of Phyllorhiza punctata (Cnidaria) from the Western Mediterranean. Aquatic Invasions 4 (4): 675-680. https://doi. org/10. 3391/ ai. 2009. 4. 4. 13 • Bonney R, Shirk JL, Phillips TB, Wiggins A, Ballard HL, Miller-Rushing AJ, Parrish JK (2014) Next Steps for Citizen Science. Science 343 (6178): 1436-1437.
Acknowledgements We are grateful to all citizen scientists and dive centres for participating in field trials (dives), discussions and questionnaire-filling during the preparatory phase of the project; from  France  ( Clément Aldebert ,  Dorian Guillemain ,  Eric Vastine ,  Etienne Pont ,  Gisèle Chatelat ,  Jacky Dubar ,  Joëlle Fossati ,  Frédéric Sonnerat ,  Frédéric Zuberer ,  Laurent Vanbostal ,  Marielle Ferré ,  Marina Caillaud ,  Associations "Little Nemo" and "Atelier Bleu"), Greece  ( Kostas Konstantinidis ,  Pavlos Koutsis ,  Dennis Mohr ,  Fanis Nikoloudakis ,  Divers Paradise Diving Centre, Nero Sport Diving Centre  and the rangers of the  National Marine Park of Zakynthos)  and  Turkey  ( Mert Palaoğlu ,  Bülent Şelli Derin  and  Doğa Diving Center). We also thank diving clubs for their willingness to participate to the CIGESMED for divers CS initiative, including joined missions for monitoring coralligenous sites on a stable basis ( Thessaloniki Dive Club, "Tethys" Association of Recreational Scuba Divers, and "OCEANOS" Association of Achaia Divers ).
This however poses a real challenge due to the magnitude and fine grain structure in the variability of landscape configurations in mountain regions  (topography, aspect, local climate, soils, etc. ). Observation and monitoring tools have allowed major progress in LUCC analysis and characterization for the last two decades. Based on existing GIS databases of LUCC and/or remote sensing data, they are now routinely applied given their capacity to produce consistent long-term Earth observation data from local to global scales  (Giri et al. , 2013 ; Hansen and Loveland, 2012 ; Hubert-Moy et al. , 2012 ; Smith and Wyatt, 2007 ; Strahler et al. , 2006). One of the main challenges of space observation of LUCC change is to perform time series observation (i) to optimize the analysis at different scales (local, regional, global)  (Lambin and Geist, 2008) , (ii) to explore landscape dynamics and to highlight the main drivers of changes  (Bürgi et al.
, 2004 ) (iii) to investigate current and future trends and to produce decision-support tools for policy makers  (Vacquié, 2015 ; Dodane et al. , 2014 ; Lakes and Kim, 2012; Houet et al. , 2010; Antrop, 2005 Antrop, , 2004)). Moreover remotesensing observations and monitoring of LUCC (using GIS databases) are now more routinely used for ecological assessment in environmental science, including biodiversity or conservation questions  (Ayanu et al. , 2012; Nagendra, 2001; Turner et al. , 2003; Wang et al. , 2010). The starting point of a LUCC study using observation and monitoring tools is to define typology, grain, and data relevant to the focal research question and landscape, subject to constraints of data availability  (Bousquet et al. , 2013). Existing data on LUCC are based on targeted methodological choices such as the typology used for a specific study and geographical scales  (Balestrat et al.
, 2011). Our focus on a mountain region requires fine resolution mapping due to the diversity and grain of the landscape. Therefore, the use of CORINE Land Cover database, with a 25 ha grain resolution, was not appropriate for our study to capture landscape heterogeneity. In the present study, the production of a finegrain LUCC database was required, and particular attention was needed regarding the adequacy between existing data and our objectives  (Bousquet et al. , 2013). For example, at a fine grain resolution, the BD Topo (IGN) is the most exhaustive database covering the whole French national territory ; the Urban Atlas was developed to study the density of urban areas, with a very fine resolution of grain and typology for urban areas around the main European cities ; the "Registre Parcellaire Graphique" (national parcel data base) in agricultural areas reports crops in homogeneous group of fields, and has been updated yearly since 2002 but is not spatially exhaustive.
The types of geographic objects presented in these different LUCC databases are not necessarily consistent with each other (not the same scale of analysis or the same grain of elements). In addition, the territories covered are not necessarily continuous nor complete. For all these reasons, the use of these LUCC databases which provide heterogeneous information requires a merging exercise to ensure consistency between the indicated information and to provide a coherent database. In addition, to complement and refine information available from existing GIS data we used very high spatial resolution remote sensing data. The main challenge in creating our own database by a multi-source merging method (existing GIS data and remote sensing data) lies in the consistency of spatial scales / temporal scales / territory element contents  (Warnock and Griffiths, 2015 ; Mathian et al.
5 With this background in mind, the present study focused on developing an appropriate land cover typology and in adopting grain and spatial extent relevant for the time scale of interest, i. e. the last two decades. More specifically, we addressed the following three issues : • Producing a cartographic database to characterize the land cover dynamics of the Grenoble urban region. We produced detailed maps for the years 1998, 2003 and 2009 by combining existing a high resolution remote sensing dataset and public information available under Geographic Information System (GIS). • Analyzing the observed changes over the 1998-2009 period, with particular emphasis on trajectories, frequencies and intensities of changes. • Defining a typology of land cover change at the municipality scale, designed to provide scientists and decision-makers with a synthetic view and an overall understanding of the main land cover features and trends across the region.
Typology and scales 7 We designed a specific land cover typology with three nested levels comprising 5 classes at level 1, 16 classes at level 2 and 23 classes at level 3 (Table  1 ). This typology was established both from the existing literature  (Kias and Demel, 2003; Rameau et al. , 1997)  and from expert knowledge of existing national databases (Institut Géographique National, BDTOPO Version 2. 1, BD ORTHO, Institut Forestier National, BD Foret). The maps produced can be used at the 1:15,000 scale, with a minimal area unit of 0. 01 ha (100 m²), although interpretation should be restricted to 0. 2 ha for homogeneity across classes over the entire study area and because the location accuracy ranges from 5 to 10 m. These maps were produced at three  dates, 1998, 2003 and 2009 , to cover 11 years of landscape change, depending on available IGN data.
The dataset we used to produce land cover maps was based on existing vector and raster data (Table  2  and 3 ): • Urban Atlas: provides pan-European comparable land use and land cover data for Large Urban Zones with more than 100,000 inhabitants as defined by the Urban Audit. 2009, 2003, 1998 2006,2007,2008,2009,2010 2006  Patterns Mapping procedure 9 In order to produce multi-timescale maps of land cover at the 1 : 15,000 scale, we developed an object-oriented, semi-automatic, multi-source procedure which was applied for the year 2009, because the full raster and vector dataset was only available for this date. 10 Because the study area is far too extensive for a complete manual digitizing process to be practical at the required resolution  (Hengl et Rossiter 2003) , we used existing LUCC databases presented in table  3.
By combining these databases, all the requested types of land cover classes we wanted to map were informed (elements of typology used, table  3 ), but the databases are not spatially exhaustive. Consequently, we chose to merge all the relevant landscape elements for our study from existing LUCC databases and to complete the database using remote sensing data classification and finally photo-interpretation to complete and validate the analysis. The processing flow-chart is illustrated in Figure  2  and can be summarised as follows. Patterns of landscape change in a rapidly urbanizing mountain region Cybergeo : European Journal of Geography , Cartographie, Imagerie, SIG | 2016 First, we collected, assembled, cut, projected and cleaned the heterogeneous dataset that proposes different spatial, temporal and qualitative information (i.
e. preprocessing, figure  2 ). Second, we processed through segmentation and classification of 2010 RapidEye satellite images and of the vector dataset (which was previously normalized and merged). Third, we conducted a manual photo-interpretation of the 2009 orthophotoplan to refine and correct the classification. Segmentation and classification processing was performed using the eCognition Developer 8. 7 © software  (Baatz M. et al. , 2004). Object edges were identified from vector data input (Table  3 ). Then a multi-resolution segmentation was based on spectral, texture and context-dependent criteria; this allowed us to capture objects that were not identified in the first phase  (Baatz and Schäpe, 2000 ; Benz et al. , 2004). This process produces precise polygons that were classified according to spectral differences to match the typological classification presented in table  1.
At the end of this semi-automatic extraction process, a photo-interpretation and manual digitize correction was performed to ensure a precise matching between the map produced and the IGN orthophotoplan dataset. This   3 ) helped us in the photo-interpretation process. All types of changes were taken into account. The photo-interpretation process for detecting and updating land cover changes was conducted at a 1:10,000 resolution. Lastly, all three maps were independently assessed by a photo-interpreter who had not been involved in initial map production  (Bariou, 1978; Bie and Beckett, 1973). For all specific details about the mapping procedure please refer to  Lefebvre (2014). The final map is evaluated with a Global Precision mapping higher than 95%. Landscape dynamics analysis  15  We analyzed resulting maps in three steps: • First we analyzed area and percentage of land cover change between 1998 and 2009, per landscape type (at the three levels of the typology), per study site sector.
The analysis was conducted by analyzing the GIS database using the ArcGis (version 10. 2, ESRI Inc. ) software. • Second, we then designed a dynamic typology of municipalities to produce a synthetic map of land cover changes, aggregating fine-scale patterns to a scale more relevant to stakeholders and decision-makers. This classification step aimed to determine the dominant land cover types for all municipalities in 1998 and the major trends observed over the 1998-2009 period. We quantified for each municipality the land cover transitions over the 1998-2009 period using the GIS database in ArcGis (version 10. 2, ESRI Inc. ) software. • Third, the landscape dynamics analysis identified some characteristic trends of changes across the study area. We analyzed it for a selection of seven municipalities that represent an overview of land cover change types and dynamics in different parts of the study site.
We refined the major trends changes in spatial patterns of land cover using two main landscape metrics (table 4): the Landscape Shape Index (LSI) and the Shannon Diversity Index (SHDI). LSI and SHDI provide a good representation of landscape configuration and diversity  (Cushman et al. , 2008; Sheeren et al. , 2014). Of the multiple landscape metrics tested, we chose to present these two which adequately capture landscape patterns and their changes over the observation period. We computed the LSI and SHDI metrics using the Fragstats software  (McGarigal et al. , 2012). (Cushman et al. , 2008).
Discussion Quantifying artificialization at different scales In general, quantitative analyses of landscape dynamics make use of existing cartographic databases. These data sources are either used "as is" or after improvements tailored to fit the needs of the project at hand. In Europe, Corine Land Cover (CLC) from the EEA (  2009 ) is used most frequently as primary data  (Díaz-Palacios-Sisternes et al. , 2014; Feranec et al. , 2010 Feranec et al. , , 2007;; Guérois, 2003) , or combined with remote sensing data  (Tapiador and Casanova, 2003 ; Pekkarinen et al. , 2009). The CLC database has already been used to analyze urbanization and its major patterns from European to regional scales, for the years  1990, 2000 and 2006. This general analysis allows us to put our fine scale study and its spatial trends in a larger perspective  : Feranec et al.
(2010)  showed that France is one of countries with the most distinct and extensive urbanizing process (+6. 8% during the 1990-2000 period) along with Germany, Spain, the Netherlands, Italy and Portugal. The artificialization process in Europe is marked essentially by residential, industrial and commercial area growth. In France expansion has however started to slow down from +4. 8% between 1990 and 2000, to +3% between 2000 and 2006. The same trend is relevant at the regional scale for our study area, where according to CLC artificialization in the Rhône-Alpes NUTS2 region was +3. 7% between 1990 to 2000 and +3% for the 2000-2006 period, the latter mostly due to the growth of industrial and commercial areas. A finer database than CLC, around 1 ha resolution, could be necessary to discern patterns of urban sprawl in most French territories  (Aguejdad et al.