dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Reuter et al. 2009; Grosse et al. 2012). Selection of the volcanoes for the database was based on the Smithsonian Institution's Global Volcanism Program (GVP) compilation of active and potentially active volcanoes of the world  (Siebert et al. 2010). The GVP catalogue lists 1, 545 volcanoes worldwide (Fig. 1 ). Of these, entries with one or more of the following features were discarded from our database: (1) volcanoes not covered by the SRTM DEM dataset above latitudes 60°N and 56°S (n =80); (2) submarine and subglacial volcanoes (n =145); (3) monogenetic cones, domes, and shields (n =345); (4) volcanoes with no discernible positive topographies or with mostly negative topographies (i. e. , calderas, maars, and lava fields; n =148); (5) volcanoes with no discernible topographic boundaries (n = 47); and (6) volcanoes with basal widths <2 km (arbitrary limit established to discard edifices that are too small to accurately analyze with the 90-m SRTM DEM resolution; n =53).
The remaining volcanoes are included in our database. They are classified in the GVP database as stratovolcano(es), complex volcano, compound volcano, somma volcano(es), shield volcano(es), pyroclastic shield, lava cone, lava dome(s), or unknown. Some volcanoes listed as 'stratovolcanoes' or 'complex volcano' contain clearly separate edifices; in these cases we obtained data for the main edifice(s) and sometimes also for the whole massif. Conversely, in a few cases two edifices forming a twin massif are listed separately as 'stratovolcano'; in these cases we have considered each edifice as well as the whole massif. We have also included seven postcaldera edifices that are listed as 'caldera' in the GVP database. In total, our database has 759 entries (Fig. 1 ).
In a first stage, a main cost function is applied that considers only convexity and aspect, and in a second stage three alternative functions can be applied (useful in complex cases) which also consider slope, elevation, and/or radial distance; for details see  Euillades et al. (2013). In practice, for each volcano of the database we cut a portion of the SRTM DEM containing the edifice and selected a central pixel. With this input NETVOLC calculated edifice outlines for each volcano using the main cost function. Unsatisfactory results (e. g. , outlines along slope-breaks within edifices at high gradients and outlines following slope-breaks on neighboring landforms), identified visually, were re-processed applying an alternative cost function selected by the user; particularly problematic cases were very flat shields, edifices that are part of a mountainous chain with complicated topography, and cones with very smooth concave-up profiles that merge almost completely with the surroundings.
Also to keep in mind is that for volcanic islands where the edifice continues underwater, only the subaerial part of the edifice is considered, since the SRTM DEM does not contain bathymetric data. This is the case for 218 edifices in our database; of these, 102 partially continue underwater and 116 totally continue underwater. The size parameters for these edifices will be underestimations. In addition to the basal outlines, well-defined summit craters or calderas of 104 edifices were manually delineated (we do not attempt to distinguish between 'crater' and 'caldera', hereon we use the term 'crater' indistinctly). This was done in order to mask out the crater when calculating edifice flank slopes, but also enabled the computation of crater-specific morphometric parameters. Manual delineation was quite straightforward, since only craters with clear topographic breaks were considered.
Database design The database is presented as an Excel spreadsheet (Online Resource 1). Data are in columns and each volcano entry occupies a row. The database consists of a main worksheet 'Parameters' and four additional worksheets containing arrays of data. The main worksheet is ordered as follows: -Columns A to D contain data taken from the GVP database: volcano number, name, geographical location, and type. For entries that differ from the GVP compilation as explained in Section "DEM source and volcano selection", we have added the appropriate name and/or type between brackets, and the situation is stated in column E. The four additional worksheets contain arrays of ellipticity and irregularity values of elevation contours, average slopes of height intervals, and slope frequencies. In all of these worksheets, columns A to C are the same as for the main worksheet, containing number, name, and location.
Database analysis We here make a general analysis of the database; more detailed analyses, including classification schemes or regional comparisons, are not the objective of this paper. Table  1  lists statistics of the main morphometric parameters for all entries, grouped by their underwater extent (whether they continue underwater or not), and grouped into four morphological types, obtained by simplifying the GVP classification: (1) stratovolcano, (2) stratovolcanoes, (3) complex/compound/ somma volcano, and (4) [pyroclastic] shield(s) volcano(es). Parameter histograms and statistics considering all entries are shown in Fig. 3 ; Fig. 4  consists of X -Y plots showing correlations between parameters, in which the data are discriminated by type and underwater extent; and Fig. 5  shows box-plots of parameters for all entries and grouped according to the four main morphological types.
Crater parameters Only relatively large craters were delineated and thus the crater data in our database is incomplete; statistics provided below should not be considered representative of the entire population of summit craters. Crater widths range between 0. 6 to 11 km; the median is 2. 2 km. Crater depths range from under 100 to 860 m; the median is 240 m. Crater volumes range from less than 0. 1 to over 10 km 3 , with a median of 0. 5 km 3. The crater depth/crater width ratio (D C /W C ) is very variable, from 0. 01 to 0. 29, with a median of 0. 11. The ratio tends to decrease with increasing crater size (width and volume). Crater widths correlate with edifice basal and summit region widths, but there is no correlation between crater depth and edifice height. The D C /W C ratio roughly correlates with the H /W B ratio, i.
5 ). This transition is similar to the morphometric evolution of arc volcanoes from 'cones' to 'sub-cones' to 'massifs' proposed by  Grosse et al. (2009). However, overlap and spread are very large. In particular, the 'stratovolcano' group spans two-thirds of entries and contains edifices with very Values in the X-axes are the upper bin limits variable morphologies. Also, the 'stratovolcanoes' and 'complex/compound volcano' groups have very similar average values for most parameters. A more rigorous classification based on quantitative data is thus desirable; the database here presented could be used to this end. Curves of ellipticity index, irregularity index, and slopes vs. height The ei and ii values of successive elevation contours define two independent curves that together summarize edifice plan shape.
-Sakura-jima (Kyushu, Japan) has morphometric values of size, shape, and slope that are close to the average values of the whole database; the ei increases with height due to the presence of several summit region vents. -Kelut (Java, Indonesia) is an example of a very irregular edifice with high ii values caused by strong dissection; the W S /W B ratio is close to average, whereas the H/W B ratio is below average; slopes and ei increase towards the summit. -Rincón de la Vieja (Costa Rica) is an example of an elongated massif with several peaks in the summit region; it has a low H /W B ratio and a high W S /W B ratio; the ei is high and strongly increases towards the summit region; slopes are relatively low and increase with height. -Nemrut Dagi (Turkey) is an example of an edifice truncated by a very large crater (which is considered the summit region); it thus has a high W S /W B ratio and a low H/W B ratio; slopes are low and show an irregular trend with height; there are no closed elevation contours between the edifice and the crater outlines, so there are no ei and ii values.
Database access and future development In addition to the present contribution, we are working on the development of a website for online access of the database. The edifice and crater outlines will be uploaded, as well as plan and profile images and graphs for each volcano. We also plan to make available user-friendly versions of the NETVOLC and MORVOLC algorithms. Through the website, future developments and updates will be made available. Planned future work includes: addition of volcanoes to the database, eventually extending it to incorporate Pleistocene volcanoes listed in the GVP; supplementary databases of volcanoes not covered by the SRTM DEM using other DEMs; addition of underwater data using bathymetric DEMs; generation of new databases using higherresolution DEMs (e. g. , the soon to be released global TanDEM-X dataset); extension of the summit crater dataset using higher-resolution DEMs; and NETVOLC and MORVOLC updates, including the development of new morphometric parameters and of an algorithm for automatic delineation of summit craters.
Conclusions The presented database is a systematic and comparable near-global database on the subaerial morphometry of composite volcanoes with basal widths greater than 2 km. The use of: (1) a single and continuous data source, the seamless SRTM DEM, (2) a single semiautomatic method, NETVOLC, for delimitation of volcano edifice boundaries, and (3) a single code for computation of parameters, MORVOLC, maximizes consistency, accuracy, and comparability throughout the database. The database contains 759 entries of Holocene, active and potentially active, composite volcanoes from around the World. The parameters thoroughly characterize the morphometry of each volcano edifice in terms of size, profile shape, plan shape, and slopes. Main drawbacks Fig. 6  Mean and median average profiles of ellipticity index (ei ), irregularity index (ii), and slope vs.
height considering edifices with at least five closed flank contours for the ei and ii profiles (n =597), and with at least ten slope bins for the slope profile (n =704) are the exclusion of small edifices (basal widths<2 km), the incompleteness of the summit crater dataset, and the lack of underwater data. These limitations are inherent of the data source resolution and coverage; higher-resolution and bathymetric DEMs are needed to resolve these issues. Most parameters show large variation suggesting a whole continuum of morphologies lacking clear separations (as previously pointed out by  Pike 1978). Analysis of the main GVP morphologic types indicates that the existing classification does not match the existing morphometric variability. The continuum in parameter values reflects the fact that volcano morphology is the result of a combination of several constructional, erosional, and structural processes that can vary between volcanoes and within a single edifice with time.
Nevertheless, detailed statistical analyses (such as clustering, e. g. ,  Hone et al. 2007)  of the morphometric parameters can potentially lead to the identification of processes and pave the way for a more rigorous quantitative classification of volcano morphology that is linked to the controlling We expect that the database will be useful as a research tool for volcanological studies at different scales including global, regional, local, and individual. It can be used for comparative studies and for quantitative and systematic classifications, and should also allow systematic planetary comparisons. The database will be maintained and updated through a website under preparation.
Ranges are 2-34 km basal widths, 0. 1-3. 10 km heights, and 0. 2-160 km 3 volumes. Basal width and especially volume values contain many outliers, with maximum values of 66 and 3,000 km 3 , respectively. Note that minimum values reflect the cutoff dictated by the SRTM DEM resolution; the smallest composite volcanoes (not included in the database) have basal widths of ∼1 km. Medians considering all entries are 8. 8 km basal width, 1. 02 km height, and 16 km 3 volume. Edifices that continue underwater have on average smaller subaerial sizes (Table.
-Column E lists problems or particularities that stood out during delineation of edifice outline and/or acquisition of morphometric parameters. -Column F indicates if a summit crater is considered, and column G contains observations about the crater, in par- ticular if the crater outline is used as the summit region; it also indicates identified craters that were not delineated, either because they are incomplete, unclear, or not on the summit (37 cases), or because they are too small (98 cases). -Column H indicates if the edifice continues underwater, either totally or partially. -Columns I and J give the latitude and longitude of the highest elevation pixel, as obtained from the SRTM DEM. -Columns K and L give the minimum and maximum elevations of the edifice; note that the maximum elevation is that of the SRTM DEM and thus is different than that given in the GVP database (it is generally lower because the SRTM DEM smoothes out the relief and no point elevations are considered).
Semantic image retrieval process The whole process of our semantic image retrieval approach is illustrated in figure  3. First, DICOM images 1 are stored in a cloud database (1). The ontology (2) is linked to this database via a keyword database (3): in the keyword database are stored image identifiers linked with keywords which are concepts taken from the ontology. Moreover image identifiers are also stored in the ontology as individuals that are instance of their associated image annotations. Two modules (4) and (  5 ) ensure the coherency between (1), (  2 ) and (3). Upon this knowledge infrastructure, the semantic image retrieval process runs as follows. First the system displays the subsumption hierarchy (the completed one from figure  1 ) computed from the ontology 1 Our prototype works on images stored into DICOM files.
An very interesting feature in this process is that the semantic part can be inserted  within a classical syntactic retrieval. Indeed, once the list of keywords is known (8), a keyword-based search engine can be run (  22 ) to retrieve images identifiers (18) from the keyword database (3). # Input Output R1 TBox T ABox A Query C All i ∈ A such that T , A |= C(i) R2 TBox T Query A Classification con- cept D All concepts C ∈ T such that C A D R3 TBox T Query A Classification con- cept D All concepts C ∈ T such that A C D.
As our images are stored in the cloud, an interesting perspective of this work would be to benefit also from the computation capacities of the cloud by moving our reasoning computation there. Since a few years, some works adress this reasoning on the cloud problem  (Mutharaju et al. , 2010; Aslani and Haarslev, 2012). Another perspective of this work, to improve performances of the individuals retrieval reasoning, would be to use an ontological query answering approach. In this approach, the aim is to translate the query and the ontology in a classical relational database context so as to benefit from the perfomances of the existing optimized (relational) database management systems  (Calì et al. , 2009; Lenzerini, 2011). At last, another perspective of this work is related to the ranking of results.
Systems with bifurcations may experience abrupt irreversible and often unwanted shifts in their performance, called critical transitions. For many systems like climate, economy, ecosystems it is highly desirable to identify indicators serving as early warnings of such regime shifts. Several statistical measures were recently proposed as early warnings of critical transitions including increased variance, autocorrelation and skewness of experimental or model-generated data. The lack of automatized tool for model-based prediction of critical transitions led to designing DyGloSA -a MATLAB toolbox for dynamical global parameter sensitivity analysis (GPSA) of ordinary differential equations models. We suggest that the switch in dynamics of parameter sensitivities revealed by our toolbox is an early warning that a system is approaching a critical transition.
We illustrate the efficiency of our toolbox by analyzing several models with bifurcations and predicting the time periods when systems can still avoid going to a critical transition by manipulating certain parameter values, which is not detectable with the existing SA techniques. DyGloSA is based on the SBToolbox2 and contains functions, which compute dynamically the global sensitivity indices of the system by applying four main GPSA methods: eFAST, Sobol's ANOVA, PRCC and WALS. It includes parallelized versions of the functions enabling significant reduction of the computational time (up to 12 times). DyGloSA is freely available as a set of MATLAB scripts at http://bio. uni. lu/systems_biology/software/dyglosa. It requires installation of MATLAB (versions R2008b or later) and the Systems Biology Toolbox2 available at www.
Local and global PSA methods are distinguished, where local ones study the effect of a single parameter variation (in the close vicinity to its nominal value) on the variation of a model output and global PSA shows how the overall variability of the model output can be associated with the variations of model parameters (considering the whole parameter state space (PSS)). The MATLAB toolbox DyGloSA presented here allows the computation of dynamical global sensitivity indices (DGSIs) of ordinary differential equations (ODE) models and proposes that the switches in DGSIs behavior are early warnings of system's critical transitions. So far the usage of PSA methods for modelbased predictions of CSD was very limited  [21] , in our opinion, because there were no automatized tool for performing dynamical global PSA.
Overview of the design and implementation of DyGloSA toolbox DyGloSA is a MATLAB toolbox allowing the computation of dynamical GSIs of ODE models employing four GPSA methods  [33] : two variance-based methods, namely Sobol's analysis of variance (ANOVA)  [36, 37]  and extended Fourrier amplitude sensitivity test (eFAST)  [38] ; partial rank correlation coefficients (PRCC)  [39]  and weighted average of local sensitivities (WALS)  [40]. It is based on the respective functions for GPSA implemented in SB Toolbox2. For each of these GPSA methods DyGloSA includes a corresponding function for the computation of GSIs for parameters of the studied ODE model at each time step of the given time interval. Therefore the main input for DyGloSA functions is the ODE model given in SBML format. We briefly describe some additional inputs below, and the full description of the functions is included in the downloadable archive along with the toolbox and some examples.
Parallelized computation As biological models are usually stiff and large, and accurate estimation of the numerical solutions require large number of sampling points of the PSS, the computation of SIs can be time consuming (from several hours to several days, depending on the size of the model and configuration of the PC used for simulations). In order to reduce of the computational time, we have equally implemented parallelized versions of the DGPSA functions (to run the parallelized functions the installation of the MATLAB Parallel Computing Toolbox is required). Thus, depending on the version of MATLAB installed and on the technical characteristics of the computer, the application of the parallelized functions can reduce the computation time from 4 to 12 times, or even more when using grid computing.
), locate its position (if several values are maxima, take the position of the first one) and select the column containing it (column M1); (iii) rank elements of this column and find the rank corresponding to the row with similar elements (rows K4 and K6 of our example); (iv) assign the above rank to all elements of the row. Modified so, the IARs of the rows with similar elements better represent their ranks in terms of the whole dataset, then ARs. The choice of the first global maximum at the step (ii) might slightly influence the final result; however the comparison of the elements of rows having similar AR values with any of global maxima will improve the result of average ranking because it will successfully distinguish low raw values from high raw values and therefore successfully update the average ranks.
Global PSA of systems with multiple stability In order to illustrate the efficiency of DyGloSA in the early detection of system's critical transitions, several models were studied, including a model of the GATA1-PU. 1 gene regulatory circuit  [41] , a three-species food chain model  [42, 43] , an apoptotic core model  [44]  and a model for immune cell interactions  [45]  which analysis is considered in details in the following sections. All the studied models are available in the downloadable package archive in the SBmodel format. GATA1 -PU. 1 gene regulatory circuit. The model describes a system containing mutual inhibition of two transcription factors, GATA1 and PU. 1 and thus represents a bistable genetic toggle switch, previously published in  [46]. The simple model without auto-regulation was considered here, where model states X and Y are defined by the relative activity levels of GATA1 and PU.
Availability and Future Directions DyGloSA is freely available for download at http://bio. uni. lu/ systems_biology/software/dyglosa as a. zip archive which includes the toolbox functions together with the complete description, examples and installation guidelines. DyGloSA requires the installation of MATLAB (versions R2008b or later) and the Systems Biology Toolbox2 and SBPD available at www. sbtoolbox2. org. To use the parallelized functions, MATLAB Parallel Computing Toolbox has to be installed. DyGloSA can be run on Windows and Linux systems, both -32 and -64 bits. Since the computation of sensitivity indices is seriously affected by the PSS sampling method, this fact can define the directions of DyGloSA development. We plan to implement other sampling methods, like quasi Monte Carlo sampling, which could improve the estimation of sensitivity indices for ANOVA and WALS methods.
Description of the CHIMERE model CHIMERE is a 3-D chemistry-transport model developed to simulate gaseous and particulate chemistry at regional scale  (Bessagnet et al. , 2004; Menut et al. , 2013). In this study, the horizontal grid extends from 43. 40 • N to 63. 20 • N in latitude and from 18. 70 • E to 57. 30 • E in longitude, with a 30 km resolution. The vertical grid includes 15 levels from surface to 500 hPa (altitude of about 6 km for this specific episode). The aerosol module takes into account nine species (sulphate, nitrate, ammonium, primary organic carbon (OC), black carbon (BC), secondary organic aerosols (SOA), sea salt, dust and water) distributed in eight bins ranging from 40 nm to 10 µm (in diameter). Microphysical processes influencing aerosol population, such as nucleation of sulphuric acid, coagulation, adsorption/desorption, wet and dry deposition and scavenging, are taken into account.
Description of the WRF model and its offline coupling with CHIMERE The version 3. 1 of WRF  (Skamarock et al. , 2001)  has been used for a domain covering eastern Europe with the same horizontal resolution as CHIMERE (30 km), and with 27 vertical levels (from 40 m to about 20 km). The configuration, similar to that in  Péré et al. (2011) , includes the WRF singlemoment five-class scheme of  Hong et al. (2006)  for the microphysics module, the Kain-Fritsch cumulus parametrization  (Kain, 2004) , the NOAH land surface module of  Chen and Dudhia (2001)  and the Yonsei University planetary boundary layer scheme  (Hong et al. , 2006; Hong, 2007). It should be noted that interactions between aerosols and clouds (such as activation of particles in cloud condensation nuclei) are not represented in this configuration of WRF.