dataesr/scientific-paragraphs-categorization huggingface dataset:
CDE creates archives containing all the items required by an application to run on any recent Linux platform. CDE tracks all the files that interact with the application and creates the base archive. The only constraint regarding CDE is to create the archive on a platform running a Linux kernel from the same generation as those of the targeted computing elements. As a rule of thumb, a good way to ensure that the deployment will be successful is to create the CDE package from a system running Linux 2. 6. 32. Many HPC environments run this version, as it is the default kernel used by science-oriented Linux distribution, such as Scientific Linux and CentOS. CARE on the other hand presents more advanced features than CDE. CDE actually displays the same limit than a traditional binary run on a remote host: i.
e. the archive has to be generated on a platform running an old enough Linux kernel, to have a maximum compatibility with remote hosts. CARE lifts this constraint by emulating missing system calls on the remote environment. Thus, an application packaged on a recent release of the Linux kernel will successfully re-execute on an older kernel thanks to this emulation feature. Last but not least, CDE s development has been stalled over the last few years, whereas CARE was still actively developed over the last few months. CARE s developers are also very reactive when an eventual problem is detected in their piece of software. This makes CARE a reliable long-term choice for re-execution facilities. All that remains is to complete the package by adding specific customisations related to the integration of the application within an OpenMOLE workflow.
Define the problem to solve as an optimisation problem This model manipulates three parameters: • Population: number of ants in the model, • Evaporation-rate: controls the evaporation rate of the chemical, • Diffusion-rate: controls the diffusion rate of the chemical. Ants forage from three sources of food as represented in Figure  2 ). Each source is positioned at different distances from the ant colony. In this example, we want to search the best combination of the two parameters evaporation-rate and diffusion-rate which minimises the eating time of each food source. We will use OpenMOLE s embedded Evolutionary Algorithms features to perform this optimisation process. The first thing is to define a fitness function describing the optimisation problem. We build our fitness function by modifying the NetLogo Ants source code to store for each food source the first ticks indicating that this food source is empty, as shown in Listing 1: At the end of each simulation we return the values for the three objectives: • The simulation ticks indicating that source 1 is empty, • The simulation ticks indicating that source 2 is empty, • The simulation ticks indicating that source 3 is empty.
The combination of the three objectives indicates the quality of the parameters used to run the simulation. This situation is a multi-objective optimisation problem. In case there is a compromise between these three objectives, we will obtain a Pareto frontier at the end of the optimisation process. The code snippet in Listing 2 introduces several notions from OpenMOLE. First, the original model is wrapped in a NetLogoTask. It is of course not the case for all the different simulation frameworks. The two other main types of tasks are the ScalaTask, that executes inline Scala code, and the SystemExecTask, which runs any kind of application as it would be from a command line.
Getting the ant model to run in OpenMOLE The second notion to observe is the Hook displayHook associated with the main task. Tasks are mute pieces of software. They are not conceived to write files, display values, nor more generally present any side effects at all. The role of tasks is to compute some output data from their input data. That's what guaranties that their execution can be delegated to other machines. OpenMOLE introduces a mechanism called Hooks to save or display results generated on remote environments. Hooks are conceived to perform an action upon completion of the task they are attached to. In this example, we use a ToStringHook that displays the value of the task s outputs.
Scale up When the necessity comes to scale up and expand the exploration, OpenMOLE's environments come very handy to quickly distribute the workload of the workflow to a large computing environment such as the European Grid Infrastructure (EGI). The optimisation as we've done so far is not perfectly suited for this kind of remote environments. In this case, we'll use the Island model. Islands are better suited to exploit distributed computing resources than classical generational genetic algorithms. Islands of population evolve for a while on a remote node. When an island is finished, its final population is merged back into a global archive. A new island is then generated until the termination criterion is met: i. e. the total number of islands to generate has been reached. Listing 5 shows that implementing islands in the workflow leaves the script almost unchanged, save for the island and environment definition.
Conclusion In this paper, we have shown the features and capabilities of the OpenMOLE scientific workflow engine. The light was put on two main components of OpenMOLE: its Domain Specific Language and the set of distributed environments it can address. The DSL is an elegant and simple way to describe scientific workflows from any field of study. The described workflows can then be executed on a wide range of distributed computing environments including the most popular job schedulers and grid middlewares. The DSL and computing environment were then applied to a real-life Ant simulation model. We showed how to describe a multi-objective optimisation problem in OpenMOLE, in order to optimise a particular parameter from the model. The resulting workload was delegated to the European Grid Infrastructure (EGI).
Water scarcity is one of the main factors limiting agricultural development in semi-arid areas. Remote sensing has long been used as an input for crop water balance monitoring. The increasing availability of high resolution high repetitivity remote sensing (forthcoming Sentinel-2 mission) offers an unprecedented opportunity to improve this monitoring. In this study, regional crop water consumption was estimated with the SAMIR software (SAtellite Monitoring of IRrigation) using the FAO-56 dual crop coefficient water balance model fed with high resolution NDVI image time series providing estimates of both the actual basal crop coefficient and the vegetation fraction cover. Three time series of SPOT5 images have been acquired over an irrigated area in central Tunisia along with a SPOT4 time series acquired in the frame of the SPOT4-Take5 experiment, which occurred during the first half of 2013.
In this frame, SPOT4 images at 20 meters resolution were acquired every 5th day from 3 rd February to 18 th June 2013 over the Kairouan plain, but only 14 dates were cloud free among the 28 images acquired (Figure  2 ). The SPOT4 series was corrected using the Multi-sensor Atmospheric Correction and Cloud Screening (MACCS) algorithm taking into account both temporal and spectral approaches for retrieving the aerosol optical thickness (AOT)  [40]. Monthly irrigation volumes used for validation were obtained at the scale of each GDA irrigated sector. It was assumed that these data were trustworthy since these entities manage a collective well equipped with a meter, providing the water to the plots inside the perimeter. However, some plots outside the official perimeter also benefit from this water and in the frame of the acquisition of our validation data, they were delineated with the help of the irrigation manager.
Spatialization of ET and Irrigation. SAMIR was run over the whole irrigated plain using the image time series for the three seasons to compute spatially distributed estimates of irrigation depths. Water balance components, including irrigation, are computed for each pixel. Land use information is required for each pixel since many parameters of the model are crop specific. This is the case for the root zone parameters, the irrigation rules and the fc(NDVI) and Kcb(NDVI) relations. Land cover maps were available for 2008-2009  [44] , 2011-2012  [45]  and 2012-2013  [46] ; they include eight, six and six land use classes, respectively. These classifications were obtained by applying a multi temporal decision tree which allows the identification of crop types on the basis of NDVI thresholds derived from ground truth datasets.
The SAMIR parameters required for each land use class were taken either from the previous calibration step (i. e. , for cereals) or from bibliographic data since there was no calibration data for market gardening and fruit trees classes. The climatic forcing was considered homogeneous over the plain and meteorological data was taken from the sole station present in the area. Finally, the irrigation parameterization for crops other than cereals was defined from our knowledge of the farmers' practices, while considering the main differences in irrigation practices between classes (e. g. , aspersion for cereals, drip irrigation for orchards). In order to validate the SAMIR estimates, irrigation depths were cumulated to monthly values at the scale of irrigation sectors (GDA) and compared to available official irrigation volumes gathered through our ground survey.
For full vegetation cover, fc was assumed to be 1 and Kcb was taken as Kcb-mid in the FAO paper 56, while NDVI was also extracted from the images. For olive trees, the same method was applied to estimate the NDVI-fc relation. For Kcb, a relationship between fc and Kcb was obtained from a previous calibration achieved on experimental data for irrigated olive trees in the Haouz plain in Morocco (not shown here) which was considered more representative of the Merguellil area than using bibliographic data. In absence of a detailed soil map, soil properties (θfc, θwp, REW, Ze, Zsoil, Dif er, Dif rd ) were considered homogeneous in the study area and the parameters were taken from calibration at plot scale (Section 3. 2). Crop specific parameters (Zrmax and p) were set to calibrated values for cereals and taken from FAO paper 56  [16]  for the other land use classes (Table  3 ).
This is an encouraging result considering the fact that (i) the calibration dataset is minimal, and (ii) the calibration protocol affects a limited number of parameters for a limited number of LU classes. Progress in parameterization would require ideally crop specific information, e. g. , flux measurements on vegetables of trees, or at least irrigation volumes collected at plot scale so as to be crop specific. However, even with the current irrigation volumes at perimeter scale, it might be possible to achieve a global calibration of some parameters. Although there would be too many degrees of freedom to correctly calibrate all parameters with such aggregated data, we could focus on irrigation practices which are much uncertain though quite sensitive.
Acknowledgments We are grateful to the  CNES  for supplying image time series in the frame of the  SPOT4-Take5  experiment, and also for helping us acquiring SPOT5 images thanks to several ISIS actions. This work was also permitted with the collaboration of the  CRDA (Centre Regional de Développement Agricole) of Kairouan , the  GDAs of Mlelssa, Karma  and  Ben Salem  and  Mr Abderazak Selmi  from  CTV-Chebika  who provided irrigation data. We also thank the  Tunisian Ministry of Research  for granting students.
For example, the widely used ZINC database (containing >13 million molecules) enables to rapidly identify drug-like molecules based on structural or pharmacophoric features  (2). Several recent web servers performing binding site prediction (e. g. FPocket (3), GalaxySite (4), etc. ), de novo drug design (e. g. e-LEAD3  (5) ) or docking of a few ligands at time into a protein target (e. g. SwissDock  (6) , CovalentDock  (7) , etc. ) have been reported. Yet, docking is not a trivial task and its performance strongly depends on the algorithms and scoring functions used, and on the definition of the binding site  (8) (9) (10) (11) (12) (13). Very few online structure-based virtual screening services have been reported thus far. The platform iScreen  (14)  performs virtual screening for over 20 000 traditional Chinese medicine compounds.
DOCK Blaster  (15)  and the recently developed istar (16) perform large-scale screening using ZINC and protein-ligand docking. Here we present the new web server MTiOpenScreen dedicated to small molecule docking and virtual screening. MTiOpenScreen offers users the possibility to screen in one run up to 5000 small molecules selected in different databases or up to 10 000 molecules selected among the 150 000 compounds ready to dock provided at MTIOpen-Screen. The web server MTiOpenScreen includes two services, MTiAutoDock and MTiOpenScreen. MTiAutoDock allows to dock compounds into a binding site defined by the user or blind docking using AutoDock 4. 2  (17)  and MTiOpenScreen performs automated virtual screening using docking with AutoDock Vina  (18). MTiOpenScreen is unique as providing original valuable starting collec-Nucleic Acids Research, 2015, Vol.
43, Web Server issue W449 tions for screening. Two in-house prepared drug-like chemical libraries containing compounds from the PubChem BioAssay Database  (19)  are provided for screening, one called Diverse-lib containing diverse molecules and the other, iPPI-lib, a collection enriched in putative inhibitors of protein-protein interactions (PPI). Thus, MTiOpenScreen enables researchers to run virtual screening computations on different chemical libraries ready for docking for traditional or more challenging protein targets like PPI, the latter representing a new class of promising therapeutic targets  (20) (21) (22) (23).
Output MTiAutoDock and MTiOpenScreen provide several outputs. MTiAutoDock returns an interactive page allowing access to browse the 3D protein structure with the bestscored pose for each of the 10 docked ligands. The user can download the 10 best-scored poses for the 10 ligands docked by AutoDock. The predicted binding energies of the 10 best-scored poses for the 10 docked ligands can also be retrieved. MTiOpenScreen returns an interactive page allowing access to browse the 3D structure of protein with the bestscored pose of the 100 top ranked ligands (see Figure  2 ). The 3D visualization of the poses relies on a javascript Protein Viewer PV. The user can download the three best scored poses of the 1000 top ranked compounds among the 10 000 compounds screened with AutoDock Vina. The predicted binding energies of the three best-scored poses of the 1000 top ranked compounds and physico-chemical properties of ligands can also be retrieved.
Performance of MTiOpenScreen In order to validate our implementation of AutoDock and Vina software packages, we assessed the performance of MTiOpenScreen on several classes of important therapeutic protein targets: enzymes (kinases, serine protease, acetylcholinesterase), GPCR, nuclear receptors and PPI. Docking accuracy of blind and binding site docking with MTiAutoDock has been validated on 20 crystal structures of protein-drug complexes taken from the DrugPort database available at EMBL-EBI (http://www. ebi. ac. uk/ thornton-srv/databases/drugport/). We have selected only complexes with high quality crystal structures verified using the software VHELIBS  (26). In addition seven proteinligand complexes with protein targets involved in PPI have been used for docking accuracy validation. Interestingly, the top scored pose of blind docking performed with MTi-AutoDock identified the right binding pocket for 24 out of the 27 proteins assessed (results shown in Supplementary Figure  S1  in Supplementary data).
Chemical compound collections preparation We prepared two electronic drug-like chemical libraries: a diverse chemical compound collection (Diverse-lib) and a focused chemical compound collection (iPPI-lib), the latter is tuned to target PPIs. We downloaded 12 chemical libraries from the PubChem BioAssay Database  (19) , assembling 3 574 650 molecules. After removing the redundant molecules, we employed an in-house developed 'soft' drug-like filter using the FAF-Drugs3 web server  (24)  to remove molecules with undesired physico-chemical properties (drug-likeness parameters used are given in Supplementary data). High-risk toxic groups and PAINS  (29)  as defined in FAF-Drugs3 were removed (more information on toxic groups and PAINS can be seen at http://fafdrugs2. mti. univparis-diderot. fr/groups.
372 PubChem molecules. We then used our in-house PPI-HitProfiler  (31)  to select PPI-friendly compounds. The program PPI-HitProfiler is based on a machine-learning model that was previously trained on 66 chemically diverse low MW inhibitors of PPI. The model uses a decision tree to select putative inhibitors of PPI by combining two properties, a shape descriptor and the unsaturation index (i. e. a property that can be related to a critical number of multiple bonds), and outputs the selected compounds into a focused chemical library. The re-  The 3D structures of the two predefined collections Diverse-lib and iPPI-lib were generated using the freely available web-server Frog2  (25). The procedure was launched keeping a maximum of one stereoisomer per compound without generating multiple ring conformations.
Preparation of data for performance assessment All protein structures used to validate MTiOpenDocking and MTiOpenScreening were uploaded in PDB format. The PDB structures were cleaned and preprocessed automatically: all HETATMs were removed and hydrogen atoms were added to the structure using MGLTools  (17). The grid center and dimensions of binding sites were defined using AutoDockTools4  (17)  and then uploaded to the web server interface of MTiOpenDocking and MTiOpenScreening. Ligands used to validate docking with MTiOpenDocking were extracted from PDB files (the list of PDB IDs is given in Supplementary data). Active compounds of FXa and VEGFR2 used to validate screening with MTiOpenScreening were taken from the PubChem BioAssay Database (see.
Due to the temporal and spatial limitations of the in situ pCO 2 data, the satellite data can help estimate the air-sea CO 2 fluxes over the entire Baltic Sea. The satellite data have a higher spatial coverage of the Baltic Sea, allowing estimation of the pCO 2 from in situ data using the SOMLO method. Chierici et al. (2009)  demonstrate that, in the North Atlantic Ocean, SST, Chl a, and MLD contributed significantly to the estimation of pCO 2 from a linear relationship. Based on this idea, we applied these parameters to the Baltic Sea, adding two other parameters, i. e. net primary production (NPP) and coloured dissolved organic matter (CDOM), that provide information about the biological activity occurring in summer. From this, we develop pCO 2 algorithms applicable to the Baltic Sea using in situ pCO 2 values; remotely sensed SST, Chl a, and CDOM; modelled MLD and NPP; and time.
Norman et al. (2013a ). The island is situated 4 km from the east coast of the larger island of Gotland. SST and pCO 2 are measured semi-continuously 4 m below the sea surface using a submersible autonomous moored instrument (SAMI) CO 2 sensor moored at a buoy 1 km southeast of the tower situate on the island. In addition, SST is also measured using a wave rider buoy (operated by the Finnish Meteorological Institute) at 0. 5 m depth situated approximately 4 km southeast of the tower. 2. Cargo ship: this data set derives from continuous measurements of the surface water pCO 2 made in the Baltic Sea using a fully automated measurement system deployed on a cargo ship. The Leibniz Institute for Baltic Sea Research, Warnemünde, Germany (IOW -Institut für Ostseeforschung Warnemünde), has made continuous measurements of pCO 2 at 5 m depth aboard the cargo vessel F innpartner.
This ship crosses between Lübeck and Helsinki at a 2-day interval, alternately crossing the eastern and western Gotland Sea  (Schneider and Kaitala, 2006; Schneider et al. , 2009). Data from F innpartner were acquired between July 2003 and December 2005. 3. The Swedish Meteorological and Hydrological Institute (SMHI) database Svenskt Havsarkiv (SHARK): pH (measured using the method of  Grasshoff et al. , 1999)  and total alkalinity (TA) (measured using potentiometric titration as described by  Grasshoff et al. , 1999)  are measured continuously at a monthly or semi-monthly resolution in the Baltic Sea at various stations. All measurements are made at a depth of 5 m depth. The uncertainty of the pH is ±0. 03 pH units and of the TA is ±5 %  (Wesslander et al. , 2009). pCO 2 is estimated from the pH, TA, salinity, and temperature measurements using the standard CO2SYS program  (Lewis and Wallace, 1998)  with the equilibrium constant from  Weiss (1974)  and  Merbach et al.
Remote sensing data The satellite data used in this study are from various sources. We use a monthly temporal resolution and a spatial resolution based on the lowest spatial resolution of our data sets, i. CDOM values come from MODIS-Aqua 4 km monthly average data. The CDOM index quantifies the deviation in the relationship between the CDOM and Chl a concentrations, where 1. 0 represents the mean relationship for  Morel and Gentili (2009)  case 1 waters, and values above or below 1. 0 indicate an excess or deficit, respectively, in CDOM relative to the mean relationship. The algorithm and its application are fully described by  Morel and Gentili (2009). In situ CDOM data (T. Kutser, personal communication, 2014)  give a lower correlation coefficient and a low average difference (R = 0. 48, MD = 2.
(1995). The EMIS data set comprises monthly average values from October 1997 to September 2008. The second data set, for 2009-2011, uses the Vertically Generalized Production Model (VGPM) of  Behrenfeld and Boss (2006)  as the standard algorithm. VGPM is a "chlorophyllbased" model that estimates net primary production from chlorophyll using a temperature-dependent description of chlorophyll-specific photosynthetic efficiency. For VGPM, net primary production is a function of chlorophyll, available light, and photosynthetic efficiency. VGPM uses MODIS-Aqua chlorophyll and temperature data, SeaWiFS photosynthetically active radiation (PAR) data, and estimates of the euphotic zone depth from a model developed by  Morel and Berthon (1989)   chard and Bolding, 2002). GETM simulates the most important hydrodynamic and thermodynamic processes in coastal and marine waters and includes flexible vertical and horizontal coordinate systems.
Data available The positions and values of all the in situ pCO 2 data are shown in Fig. 1. We use the spatial resolution of the parameter with the lowest resolution for the final data set chosen (i. e. CDOM). A monthly temporal resolution is used for this study. Rutgersson et al. (2008)  demonstrate that the agreement between SAMI sensor data and the ship data from F innpartner (near the mooring maximum of 23 km) is quite good. This good agreement is confirmed by the comparison between pCO 2 data from the SAMI sensor, the data surrounding the mooring (0. 2 • ), and the other data sets. These analyses give a good correlation factor of 0. 98. The in situ data are available mainly for the central basin, but the number of data for the Gulf of Bothnia is very low, coming from two SMHI stations.