dataesr/scientific-paragraphs-categorization huggingface dataset:
The combination of two main ingredients, i. e. , the Petviashvili scheme together with the conformal mapping technique, allowed us to propose a very efficient numerical scheme for the computation of solitary gravity waves of the full Euler equations in the water of finite depth  [10]. The proposed algorithm admits a very compact and elegant implementation in Matlab, for example. The resulting script is ready to use and it can be freely downloaded from the Matlab Central server  [8]. x y O y = -d y = η(x) u → -c as x → ±∞ O α β β = 0 β = -d x = α + X(α, β) y = β + Y (α, β) In the present study we perform further tests and validations of the new algorithm. Moreover, several important integral characteristics such as the mass, momentum, energy, etc. are derived in the conformal space and computed numerically to the high accuracy for a wide range of solitary waves.
Numerical results The validation of the proposed algorithm is done by comparing it with some existing approaches. One of the most used algorithms nowadays is the one of Tanaka  [60] , also based on the conformal mapping technique. Tanaka's solution is parametrized by the dimensionless parameter q c defined above. Let us take, for example, q c = 0. 87, which corresponds to a mild solitary wave. Tanaka's algorithm was implemented in Matlab 1  in its original version without any peculiar optimisations. The interval -30 ⩽ φ ⩽ 30 is discretised using 20, 001 points and the iterations are continued until the tolerance 10 -10 is achieved between two successive values of the Froude number: F n+1 -F n < ε 3 (= 10 -10 in our computation). This computation required 173 iterations which lasted 1, 269 s on our desktop computer.
One can see that the differences between the predicted  (Fenton)  and prescribed  (Petviashvili)  values start to appear after the ninth digit, which is in perfect agreement with the order of the asymptotic expansion. However, strictly speaking, the Fenton solution is valid only in the limit a d → 0. On the other hand, the Babenko equation can be used to compute much more nonlinear solitary wave to the arbitrary precision. To illustrate the last statement we implemented the Petviashvili scheme using the Multiprecision Computing (MC) Toolbox for Matlab  [29]. The transformations of the code needed to implement the arbitrary accuracy are really minimalistic. This constitutes one of the major advantages of the MC Toolbox. In these higher-precision experiments, we take a periodic φ-interval adapted for the increased accuracy (30 digits).
The convergence rate of the iterative process is lower for high amplitude solitary waves. The L ∞ -norm of the difference between two successive iterations, along with the residual error are plotted on Figure  5(b). One can notice that the convergence to the small wave is much faster, even if the process is still converging. This point could be probably improved in future studies applying, for example, the generalized Petviashvili iteration  [40]. Nevertheless, we have still a very good performance. For example, the computation of that large solitary wave to the standard 10 -15 accuracy requires only 1. 18 s (in terms of the CPU time) under Matlab. This timing is to be compared with Tanaka's results. For the sake of efficiency, we shall execute hereinafter the computations only within the standard double precision floating point arithmetics  [38] , unless the higher accuracy is explicitly required for some application.
Moreover, the proposed method is compared to the classical Tanaka algorithm which is currently widely used in the water wave community  [36, 14]. Our method outperforms the Tanaka algorithm in terms of the computational complexity (which results in much shorter CPU times) and the accuracy which is unlimited theoretically, but limited practically by the floating point arithmetics accuracy. The Matlab implementation is rather compact. The computational core is no longer than 50 lines of code. The script is freely available to download and to use for the scientific community through the Matlab Central File Exchange server  [8]. Concerning the perspectives, the next step will consist in the inclusion of the capillary effects  [19]. This new force introduces some inhomogeneous nonlinearities into the equations.
estimates Data-driven ET LC (6)   1870 & 1992" From 6 LSMs (LUCID) maps of 1870 and 1992 prescribed in each LUCID land surface model (LSM) to force the observation-based empirical models of ET derived here (Sect. 2. 3) and, with them, to perform several reconstructions of past and present-day ET (Fig. 1 ). Ê (F * v, P, SD, LD, SWE ) GLEAM" MPI" NTSG Secondly, we use the data simulated within LUCID to contrast the modelled historical changes in ET with the ones diagnosed here. The set of LUCID simulations used here were carried out by six atmospheric global circulation models (AGCMs) coupled to LSMs (Table  1 ). The modelling experiment includes two types of simulations, each with the same prescribed present-day sea-surface temperature/sea-ice coverage (from 1970 to 1999) and atmospheric CO 2 concentration (set to 375 ppm).
Multivariate analysis and evapotranspiration reconstructions The goal of this study is to calculate LULCC-induced changes in ET. This implies that we develop a method that calculates ET given a specific land-cover map, at the global scale. The methodology we developed to achieve such calculations is presented in this section and summarized in Fig. 1. We use an implementation of Multivariate Adaptive Regression Splines (MARS) for Python (py-earth) to construct empirical models of ET based on the present-day data described in Sect. 2. 1. This technique builds an additive model of piecewise linear (hinge) functions of the predictor basis, capturing non-linear dependencies of a given variable to multiple explanatory variables  (Friedman, 1991). The coefficients of the basis are calculated in a two-step iterative computation that uses a least squares method to minimize the error between partial predictions and observations.
Further details on the MARS analysis and an evaluation of the predictive skill of the resulted ET models are described in Appendix C. Preindustrial (PI) and present-day (PD) reconstructions of ET are then computed by forcing each MARS-based ET model with the land-cover maps of 1870 and 1992 used by six LSMs in the LUCID simulations (Sect. 2. 2). Hence, we derive 18 different pairs of reconstructed ET that only differ in the choice of the F v values used in Eq. (  1 ) and the ET information deduced from each product. It should be noted that all reconstructions are carried out with the same environmental data used in the MARS computations (Sect. 2. 1) and the MODIS-based LAI of each land-cover class (L v ). Therefore, the diagnosed ET changes between PI and PD represent instantaneous responses to LULCC, and do not include any biogeophysical feedback involving a perturbation in downward radiation, precipitation or snow cover, nor in a biome-dependent LAI.
When the agricultural units (crops and pastures in this case) are allocated over natural grasslands as priority, the resulting change in forest area is comparatively low (as in the JSBACH case). The opposite case (favoured deforestation) occurred in the case of TES-SEL. Foliage density is a key parameter in both the simulated and diagnosed ET. As described in Sect. 2. 1, the present-day LAI as well as the changes in LAI from 1870 to 1992 are implicitly accounted for in the diagnosed ET through Eq. (  1 ). Yet, the change in LAI as diagnosed with our MODIS-based reconstructions is in itself an interesting result. Such an estimate is also important if we are to correctly interpret the diagnosed LULCC-induced changes in ET. Hence, based on the LUCID land-cover maps and the seasonally and geographically varying LAI values we derived from MODIS data for each of the four classes of vegetation (L v , see Appendix B), we computed the monthly distribution of LAI for 1870 and 1992 as L(yr) = v F v (yr) L v.
Our results show that these sensitivities are mainly related to the products' present-day crop ET estimates; we therefore highlight the necessity of revisiting how data sets treat crops. The large differences in LAI shown by the LUCID models, and the associated uncertainties in simulated ET, reiterate previous findings and point out the need for in-depth evaluations of the vegetation phenology simulated in LSMs (e. g. ,  Richardson et al. , 2012; Anav et al. , 2013). Including irrigation is crucial to proper assessments of past land-use-induced ET changes  (Gordon et al. , 2005). Although not explicitly accounted for in this study, irrigation should be partially included in our ET reconstructions based on the MPI and NTSG products, given their FLUXNET data calibration. Regarding this aspect, it is noteworthy that the diagnosed ET based on these two products, despite the very different procedures used to derive them, show similar sensitivities to LULCC and, therefore, consistent spatial and seasonal patterns of ET change between the preindustrial period and the present.
Given that the preindustrial ET reconstructions were calculated using present-day data of the environmental drivers (precipitation, radiation and snow cover) and, implicitly, with current atmospheric CO 2 concentrations, the estimated ET changes do not consider any feedback that involves these drivers. This assumption could be particularly important for precipitation. Previous results from LUCID have shown that precipitation responds to LULCC synchronously with the changes in ET, hence amplifying the impacts on ET when atmospheric feedbacks are accounted for  (Boisier et al. , 2012). The set of diagnosed ET presented here were derived with empirical multivariate models of ET. It is important to recall that the ET products used to derive these models were themselves obtained with empirical or semi-empirical methods.
Boisier et al. : Land-use-induced evapotranspiration changes estimated from observations Appendix A: Evaluation of gridded products of evapotranspiration A comparison of the ET products used in this study is presented in Appendix Fig. A1. The three data sets agree fairly well on the spatial distribution of climatological  annual ET, characterized by values above 1000 mm yr -1 in some tropical areas and very low rates in dry regions (Appendix Fig. A1a ). The zonal mean of annual ET, depicted in the right-hand panels of Appendix Fig. A1a  (solid lines), show similar patterns for the three products, with values below 500 mm yr -1 in the extratropics and a maximum of around 1000 mm yr -1 near the Equator. In contrast to the climatological ET, large discrepancies between the products can be seen in the year-to-year variability of ET.
The standard deviation (SD) of annual ET time series  were calculated locally and averaged zonally (dashed lines on the right panels of Appendix Fig. A1a ), revealing large differences in the magnitude of the interannual variability shown by the products. Near the Equator, the mean SD ranges from around 25 mm yr -1 (MPI) to around 100 mm yr -1 (GLEAM). The spatial distribution of SD is not homogeneous between the products either (not shown). Given the differences in the temporal variability of ET shown by the three data sets, we considered it pertinentregarding the use of these products in this study-to evaluate how ET relates at interannual timescales with some key drivers of ET. In particular, we studied the role of precipitation (P ) and downward radiation (R D ). The correlation coefficient between annual time series of ET and P (ρ E,P ) and between ET and R D (ρ E,R ) tend to oppose each other when compared geographically, that is, in areas where one pair are positively correlated, the other pair are negatively correlated, and vice versa.
Preliminary tests showed that the predictive performance of the constructed models is not improved by using larger samples in the training data. In order to evaluate the predictive skill of the MARS-based ET models, we reconstructed ET with each model and the complete predictor data set, and we compared them to the actual ET. Appendix Fig. C1  shows the scatter diagrams between the observed ET (product) and the reconstructed ones. Considering the full record (i. e. , collecting together all the pixel-month values from 1984 to 2006), the reconstructed ET explains about 90, 91 and 95 % of the observed ET variance in the case of GLEAM, NTSG and MPI, respectively. The typical errors (MAE) are, in the same order, 8. 2, 6. 9 and 5. 3 mm month -1. These errors are reduced by a factor ∼ 2, when the climatological monthly values are considered, indicating that an important fraction of the errors occurs at the interannual timescale.
This is somehow expected since the input data set record is much larger in space (about 11 000 pixels) than in time (276 months). The skill of MARS models to predict the spatial and seasonal distribution of ET is reasonably good despite the relative simplicity of the set of explanatory variables used and their independence with respect to the ET data sets. The environmental data adopted here are in most cases different to those originally used to derive the ET products (Table  1 ). Yet, the typical error of the reconstructed ET climatologies, ranging from ∼2. 9 mm month -1 (MPI) to ∼4. 1 mm month -1 (GLEAM), remains lower but of the same order than the diagnosed changes in ET in regions of important land-cover perturbations (see Fig. 6 ). Besides the predictive skills of the MARS ET models derived here, an important fraction of the resulting errors in the ET reconstructions should be attributable to the omission of key drivers of ET originally accounted for in a given product, such as the soil moisture in the case of GLEAM.
As described in Sect. 2. 1, the present-day LAI as well as the changes in LAI from 1870 to 1992 are implicitly accounted for in the diagnosed ET through Eq. (1). Yet, the change in LAI as diagnosed with our MODIS-based reconstructions is in itself an interesting result. Such an estimate is also important if we are to correctly interpret the diagnosed LULCC-induced changes in ET. Hence, based on the LUCID land-cover maps and the seasonally and geographically varying LAI values we derived from MODIS data for each of the four classes of vegetation (L v , see Appendix B), we computed the monthly distribution of LAI for 1870 and 1992 as.
Acknowledgements. We thank the  LUCID community  for providing the model data and support. We also thank  Sonia Seneviratne  and two anonymous reviewers for their fruitful comments and suggestions. We acknowledge  NTSG (University of Montana ),  Diego Miralles  and  Martin Jung  for making available the ET products used here. The MODIS MCD12Q1 product was obtained through the online Data Pool at the  NASA Land Processes Distributed Active Archive Center (LP DAAC ),  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota  (https://lpdaac. usgs. gov/data_access). The reprocessed LAI data was obtained from the  Beijing Normal University College of Global Change  and Earth System Science. Surface radiation data were obtained from the  NASA Langley Research Center Atmospheric Sciences Data Center NASA/GEWEX SRB  Project.
The  AVHRR/SMMR/SSM/I SWE product (NSIDC-0271 ) was obtained from the  National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC). Precipitation data are available at the GPCC Homepage: http://gpcc. dwd. de. This study was partially funded by the  EU-FP7  project  AMAZALERT  and the  Chilean National Commission for Scientific and Technological Research (CONICYT). Edited by:  K. Stahl.
Translational research in Life-Science nowadays leverages e-Science platforms to analyse and produce huge amounts of data. With the unprecedented growth of Life-Science data repositories, identifying relevant data for analysis becomes increasingly difficult. The instrumentation of e-Science platforms with provenance tracking techniques provide useful information from a data analysis process design or debugging perspective. However raw provenance traces are too massive and too generic to facilitate the scientific interpretation of data. In this paper, we propose an integrated approach in which Life-Science knowledge is (i) captured through domain ontologies and linked to Life-Science data analysis tools, and (ii) propagated through rules to produced data, in order to constitute human-tractable experiment summaries.
However, many different "standards" have emerged especially when linking data from different sub-disciplines. Data deluge in Life Sciences is not only a matter of volume but also a matter of diversity  [3, 4]  as both structural heterogeneity (incompatible formats) and semantic heterogeneity (multiple terminologies and conceptualizations) are common. To face the data deluge and facilitate resources sharing, scientists increasingly use e-Science platforms  [5]  dedicated to Life Sciences in order to capture raw data and transform it into well-documented data sets of interest for future exploration. Collaborative e-Science platforms are typically used to perform in-silico experiments, share the resources involved, and produce new valuable data (e. g. to evaluate a data analysis procedure onto several open databases, or to quantitatively compare several data analysis procedures through a common reference database).
The Artifact is thus linked to an instance of the AValue OPM class through the avalue property. Finally, a content is associated to the value through the OPM content property. This content finally gives the logical file name (LFN) of the sinogram, a URI locating the data on the EGI grid infrastructure. Data might be later on downloaded through a dedicated data transfer interface. The use of the OPM ontology leads to verbose provenance annotations. Indeed, more than 14 RDF statements (timestamping has not been represented) are necessary to represent a single data item production in the Sorteo workflow. This is mainly due to the reification of all dependencies, leading to complex paths between provenance entities (we consider here only a single data production). Finally, OPM statements illustrated above represent technical information such as the location of produced files in a distributed computing infrastructure, the name of the processing tools involved in simulation experiments, or time-stamping.
They represent precise and fine-grained information, beneficial when inspecting logs of medical imaging simulations, however, they do not convey any domain-specific information such as simulation modality or high-level parameters, useful for medical imaging experts. Needs for concise and domain-specific provenance. Although precise provenance statements are definitely necessary for technical workflow refinement or debugging, the size, the fine granularity of provenance and its lack of links with domain-specific concepts makes it unmanageable from a scientist perspective, possibly running workflows on large input datasets. As an example, a single run of the Sorteo workflow leads to a large OPM technical provenance graph composed by 4523 nodes and 15154 edges. To address this issue, we propose to distinguish between two levels of provenance information.
Experiment summaries Services catalog Data catalog  The right part of the figure focuses on semantic data, and illustrates our approach to produce semantic experiment summaries. • First we rely on (i) semantically annotated input data (organ models), and (ii) semantically annotated services actually composed into simulation workflows ➊. • Then, domain-agnostic provenance ➋ is tracked on the fly at workflow runtime and represented through a standard model (OPM in the current implementation). • When the workflow successfully produced a simulated data, the set of available domain-specific production rules ➌ are applied. Each applicable rule involves semantic service annotations, and produces new domain-specific statements. • These new statements finally constitute the semantic experiment summaries and populate a dedicated catalog ➍.
g. Anatomical object, Pathological objects, Foreign body objects). The latter classes were extracted from existing ontologies such as FMA  [14]  (Foundational Model of Anatomy), RadLex  [15]  (Radiology Lexicon), MPATH  [16]  (Mouse pathology). OntoVIP also includes a detailed taxonomy of simulated data, i. e. data resulting of the execution of some medical image simulation software. This taxonomy involves three major semantic axes. The first is related to imaging modality (e. g. CT simulated data, MR  8  simulated data); the second makes a distinction between nonreconstructed data and reconstructed data (i. e. images); the former are further categorized into classes denoting the spatial or spatiotemporal organization of the data (e. g. , list-mode data, sinogram, set of signals, set of projection images); and finally the third distinguished between static simulated data and dynamic simulated data.
Simulated data are the result of the execution of some medical image simulator, i. e. software whose func-tion is to perform medical image simulation. Medical image simulators and medical image simulations are further categorized depending on imaging modalities (i. e. CT, MR, US 9 , PET 10 ). Medical image simulators are composed of simulator components addressing the different stages of a simulation: pre-processing (implemented by pre-processing simulator component), core simulation (implemented by core simulation simulator component) and post-processing (implemented by post-processing simulator component). OntoVIP models the relationships between simulated data and object models, and between simulated data and parameter sets or parameters; such relationships (derivedFromModel, derivedFromParameterSet, derivedFromParameter, respectively) are of key importance with regards to the domain-specific modeling of data lineage.
OntoVIP was developed based on OntoNeuroLOG, an ontology developed during the NeuroLOG project 11  for supporting the sharing of heterogeneous and distributed medical images and image processing tools in neuroimaging  [17, 18]. The OntoVIP ontology is used in the VIP software to support the annotation and querying of models, as well as the annotation and querying of simulated data and of the data processing actions that actually produced this data.
Semantic service annotation To complete the Knowledge Capture task on medical image simulation workflows, Semantic Services associate concepts of the OntoVIP ontology to the service descriptors composing simulation workflows. The field of Semantic Web Services aims at exploiting semantic web technologies to enhance service oriented architectures and thus e-Science workflow environments. Through a rich, formal and standard semantic description, benefits are expected both at workflow design-time, when discovering, composing and mediating services, and at workflow run-time, when linking back processed data to semantic service annotations. Our approach focuses on the latter to produce humantractable and informative enough semantic experiment summaries. Several initiatives have been targeting the standardization of semantic service description such as, for extended frameworks, OWL-S  [19] , WSMO  [20] , FLOWS  [21] , or for lighter approaches, SAWSDL  [22]  or WSMO-Lite  [23].
Although SAWSDL has been proposed by the W3C as a recommendation in 2007, no 9 Ultrasound. 10 Positron emission tomography. 11 NeuroLOG project: http://neurolog. unice. fr consensus clearly emerged, and OWL-S and SAWSDL provide good compromises for semantically annotating e-Science workflow components. We reused the OWL-S Profile ontology concepts to describe semantically the key processing steps of medical image simulation workflows, in terms of functionality, input and output parameters. These descriptions have been bridged to the OntoVIP domain ontology through refers-to properties. As an example, the "Lmf2RawSino" refers to the image-reconstructionsimulator-component OntoVIP class to describe its functionality, and refers to the PET-Sinogram class to describe the produced data through its output parameter.