dataesr/scientific-paragraphs-categorization huggingface dataset:
First, single events are simulated to evaluate the probability of a photon to be detected 8 http://www. mcs. anl. gov/research/projects/mpi on a detector element when emitted in a region of the model. Second, the simulator generates the true, scattered and random coincidence events using a deadtime model which incorporates these singles rates. As Monte-Carlo simulations, these steps can be split in any number of jobs lower than the number of independent random events. SORTEO produces sinograms or list-mode data. Although data correction and reconstruction are not included in VIP, the documentation refers to the STIR software  9  for both analytic and iterative reconstructions. Reconstruction examples will also be available in the portal. Object preparation for FIELD-II either samples scatterer positions and amplitudes from distributions of the model, or applies the scene transformation to existing scatterers.
B. Data provenance Simulations produce large amounts of diverse data, difficult to manipulate by non-expert users. To address this, provenance metadata is recorded during execution. Provenance is registered on-the-fly in an OPM-compliant RDF repository (Open Provenance Model  [45] ) tracking simulation component invocations, and the related consumed and produced data. The relations among files and parameters produced during a simulation are recorded as provenance graphs. Such a provenance model enables inference of new statements about the simulated data. For instance, rules can be applied to provenance paths to produce annotations describing simulation results and build a shared, structured database of simulated data from user activity. The scalability of semantic repositories is challenging since they contain information about all the data generated by the platform.
For instance, a single execution of the SORTEO core simulation workflow produces 15,000 triplets. To tackle this issue, we distinguish provenance metadata from the annotations of simulated data. Provenance metadata is generic, technical, and volatile whereas simulated data annotations represent only a few statements with a long-term added-value. Consequently, a dedicated short-term semantic repository is deployed to store provenance metadata of each simulation execution. Once generated, simulated data annotations are pushed to a long-term semantic repository. If necessary, the short-term provenance repository can then be cleaned-up.
C. Storage VIP stores files on the EGI data management system. Files are distributed on storage resources at the different sites supporting the biomed virtual organization. These resources expose a homogeneous interface through which files can be listed and transferred. A central logical file catalog provides a common indexing space for these files. 3. 5 PB are available for storage, among which 2 PB are currently used. Although storage availability is not ensured (availability usually ranges from 80% to 95%), replication is possible by linking several physical files to the same logical name. Availability of critical files (e. g. application workflows) is thus ensured. The platform integrates its own local data manager, where critical files are replicated and accessed when EGI storage is down.
YFP fluorescence was detected with an FITC 530/30-nm band-pass filter, and red spectrum auto-fluorescence (RSA) from living protoplasts (due to chlorophyll) was detected with a 670-nm long-pass filter in the PcrCP channel. The forward scatter and side light scatter detectors were set to 130 and 250 V, respectively. For each sample, 2 Â 10 4 protoplasts were analysed and gated according to YFP and RSA values. The gate boundaries were defined using negative controls (protoplasts that were transformed with a target plasmid alone). Data were analysed by FlowJo (Tree Star Inc. , OR). Cell survival assay. The CHO-K1 cell line was used to seed a 96-well plate at a density of 2 Â 10 5 cells per well. The next day, varying amounts of TALEN expression vectors and a constant amount of green fluorescent protein (GFP)encoding plasmids were used to transfect the cells using Polyfect transfection reagent in accordance with the manufacturer's protocol (Qiagen).
Technical description of the water isotopic scheme used in iLOVECLIM The iLOVECLIM model is a code fork of the LOVECLIM-1. 2 climate model extensively described in  Goosse et al. (2010). From the original model, we retain the atmospheric (ECBilt), oceanic (CLIO), vegetation (VECODE) and landsurface (LBM) components and developed a complete, conservative, water isotope cycle through all cited components. With regards to water isotopes, the main development lies in the atmospheric component in which evaporation, condensation and existence of different phases (liquid and solid) all affect the isotopic conditions of the water isotopes. Hence, in the following I first describe extensively the method used to trace the water isotopes in ECBilt and only briefly their treatment in other components afterwards.
ECBilt-wiso: water isotopes tracking in the atmosphere ECBilt is the simplified component of the iLOVECLIM earth system model. It is a quasi-geostrophic atmospheric model with some additional correction terms, described in details in  Opsteegh et al. (1998)  and  Goosse et al. (2010). The atmosphere runs at a T21L3 resolution, that is approximately 5. 6 • resolution in latitude and longitude. Of main interest for the purpose of developing a water isotopic module is the water cycle dynamics. ECBilt contains a full description of the water cycle from the evaporation to precipitation through condensation. The vertical structure is on three levels with only one humid layer (troposphere) and two dry layers (stratosphere). A schematic representation of the water cycle in ECBilt is given in Fig. 1.
Dansgaard relationship Another well-known feature observed initially by  Dansgaard (1964)  is the local relationship between δ 18 O in precipitation and the mean annual temperature at the site. Using essentially high latitude sites (low mean annual temperatures) he found that the relationship was well approximated by the following linear approximation for mean annual values: δ 18 O precip. = 0. 69 T surf. -13. 9 (20) where T surf. is in • C. He also noted that the relationship was not linear anymore for annual mean temperature above 15 • C. The Dansgaard equation was used extensively for palaeotemperature evaluation from δ 18 O measurements in palaeoclimate proxy data. However, it is unlikely to always be stable through time  (Werner et al. , 2000). Using available data for δ 18 O in precipitation from the Global Network for Isotopes in Precipitation  (IAEA, 2006) , a linear fit on this larger dataset (not shown) can be computed, limiting also the temperature up to 15 • C.
Coloured dots are from the model simulation, coloured after their latitude, given on the scale to the right ; grey dots is the fitted second-order polynomial to the model data for temperature above -30 • C; magenta dots are constructed from the temperature data in iLOVECLIM, using the regression from  Dansgaard (1964) ; yellow dots are constructed from the temperature data in iLOVECLIM using a second order polynomial fitted to the GNIP dataset. R 2 values for the fit to the iLOVECLIM model is 0. 911 (grey dots); R 2 values for the fit to the GNIP data is 0. 977 (yellow dots). is modified by a source with higher δ 18 O content. Assuming that these anomalous datapoints are on a mixing line between the fitted polynomial and a source of moisture implies that the contaminating source has an isotopic signal of +5 to +10 per mil.
Again, it shows that the implementation of the water isotopes seems to function correctly in the atmospheric component. Comparing the modelled results with the amplitude derived from the GNIP database  (IAEA, 2006) , there is an overall good agreement between the two. Both observational data and simulation show an enhanced seasonal cycle in δ 18 O of precipitation over the continents and a dampened seasonal cycle over the oceans. There is also a continentality gradient observed both in model and observations, with a tendency to higher seasonal amplitude for higher continentality. The model even obtain a good representation of the minimum values over the ocean (around 0. 9 per mil over the tropical oceans) and a high amplitude for ice covered regions like Antarctica.
Similarly in the small regions where the sea-ice formation is dominant, the rejection of large amounts of salt into the surface oceanic waters with no similar 18 O counterpart may alter this relationship. Figure  5  present results from the simulation for the surface Atlantic data. The distribution within the δ 18 O-salinity space shows a good correlation between the two variables within the range simulated. Using a linear regression, we obtain a slope of 0. 43 to be compared to 0. 52 when the same linear regression is performed on modern observations  (Schmidt et al. , 1999)  of the GISS database (not shown). The agreement between the two slopes is excellent, showing our ability to simulate correctly both salinities and δ 18 O as described in the implementation. The modelled intercept is -14.
9 per mil while the one calculated on data is -17. 9. The end member of the modelling results are thus three per mil too high. Since the δ 18 O of precipitation is also positively biased around values of -20 per mil by about 5 per mil (see Fig. 3 ), the positive bias toward depleted δ 18 O values is expected to show also in the oceanic values. The fact that our slope is also underestimated by 0. 1 per mil (δ 18 O)/per mil (salinity) is also related to the same phenomenon. Linear fit to the model data is given in the equation in the bottom right box, with a R 2 coefficient of 0. 877. Performing the same analysis on Atlantic ocean data from the GISS database, we obtain a linear regression of 0. 52 × -17. 9 with a R 2 of 0. 81. The colour scale applied on modelled points is the given latitude of each point.
Houborg et al. (2007)  developed an image-based method for LAI and CHL l map-ping which included automatic parameterization of a combined leaf optics-CRTM model (PROSPECT-ACRM). The method identifies bare soil and dense vegetation fields, and the spectral signatures of these fields are then used to constrain the model inversion for class-specific parameterization. Very good results were obtained for LAI (rmse = 0. 4-0. 7) and leaf-scale CHL (rmse = 5-9 µg cm -2 ) when applied at a regional scale  (Houborg et al. , 2007; Houborg and Boegh, 2008; Houborg and Anderson, 2009) , and even better results were achieved when applied to field-scale image data with 1 m spatial resolution (rmse = 0. 25 for LAI and 4. 4 µg cm -2 for CHL) due to the efficient model parameterization scheme  (Houborg et al.
Overview The five landscapes are presented in Sect. 2. Satellite and field data are described in the Methods Sects. 3. 1 and 3. 2, the SVIs are presented in Sect. 3. 3, and the REGFLEC approach is described in Sect. 3. 4. The Results section presents first landscape-scale variations in terms of observed vegetation seasonality and LAI measurements at the time of SPOT satellite passage (Sect. 4. 1). The variations in CHL l and N l field measurements of different crop types and landscapes are then presented (Sect. 4. 2), and the observed within-canopy   (Peel et al. , 2007). The climate zones reflect the agricultural land use patterns of the selected landscape sites, which are dominated by grassland in the Netherlands (NL) and Scotland (UK), grain crops, maize and potatoes in Denmark (DK) and Poland (PL), and maize, vegetables and fruit production in Italy (IT).
Satellite data Multi-spectral high spatial resolution satellite image data representing radiance in the green (0. 5-0. 59 µm), red (0. 61-0. 68 µm) and NIR (0. 79-0. 89 µm) spectra, as measured by the HRG (high resolution geometry) and HRVIR (high resolution visible and infra-red) sensors aboard the SPOT-4 and SPOT-5 satellites were acquired within the NitroEurope project and used in this study (Table  1 ). The acquired satellite data were processed to Level 2A which includes standard geometric corrections. The georeferencing quality was assessed by overlaying the satellite images by ESRI's Streetmap Premium Europe Tele Atlas data set, and minor deviations were adjusted by shifting the image coordinates in the X-Y directions. One satellite image is available for each landscape for the period 31 May-21 July 2008, and one additional satellite image is available for the DK site during an intensive measurement campaign for 19 April 2009.
Image data are available at a 10 m spatial resolution for most sites, except for the UK and the Danish site in 2008 (DK08), where images are available with 20 m resolution. All satellite images were atmospherically corrected using data on aerosol optical depth, ozone and atmospheric precipitable water content from the MODIS and AIRS/AMSU sensors aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Atmospheric data were acquired as close as possible in time to the acquisition of the SPOT data (Table  1 ). Surface reflectance is calculated considering directional multiple scattering using the 6SV1 atmospheric radiative transfer model  (Kotchenova et al. , 2006) , which is included in the REGFLEC tool. REGFLEC results and SVIs were extracted for 3 × 3 pixels centered at each field plot for comparison with field measurements.
Field data In each study landscape, field measurements of LAI and SPAD meter indices (related to CHL l and N l ) or N l were made in 7-22 fields over 1-2 days within 4-10 days of the relevant satellite image acquisitions, to provide field reference data for evaluating the REGFLEC simulations. Field  measurements were conducted in a total of 93 homogeneous field plots (Table  2 ) within the five landscapes. Each field plot is represented by two sub-areas of each 3 m × 3 m located within a 10 m × 10 m region. Field plots were geographically referenced with an accuracy of 0. 5 m using GPS (Trimble Geo XT, Trimble, USA). Plots were generally located in different fields, however, at the Italian site five plots were located within a large experimental maize field exposed to different stress treatments.
LAI LAI was measured with the LAI-2000 instrument (LAI-2000, LiCor, USA) which uses canopy transmission data measured along a transect. Despite being an indirect estimate of LAI, measurements from the LAI-2000 instrument were shown to be in very good agreement with destructively sampled LAI of maize, wheat and barley until the senescent phase sets in (eg. Boegh et al. , 2004). In this study, measurements were conducted in the beginning or middle of the growing season (Table  1 ) where LAI estimation uncertainty is low. Using data published in  Boegh et al. (2004) , the LAI-2000 instrument explained 96 % of direct LAI estimates in the vegetative period, and the LAI-2000 estimation uncertainty, assessed as the relative standard error, was found to be 21 %. LAI was measured in two neighbouring plots of each field (Sect.
3. 2), with each LAI estimate being based on four light transmission measurements along a 3 m transect. If the LAI estimates of the two transects varied, a third transect (a third plot) was included. In a few cases at the grassland sites in NL, up to four transects were included due to high spatial data variability. In all fields, the average LAI is used to represent the field plot. In the UK landscape, LAI was estimated using light transmission measurements along a 10 m transect. Based on the 2-4 sets of LAI estimates in each field, the averaged relative uncertainty (sd/mean) is found to be ≈ 10 %, except for the UK grassland plots where LAI is very low (≈ 1) and the relative uncertainty is higher (median 34. 9 %). In Denmark, UK, the Netherlands, Poland and Italy, the averaged uncertainties for LAI measurements are 7.
Nitrogen was estimated using a CHNS-O Elemental Analyzer (CE Instruments, UK). Leaf areas were measured using a scanner (EPSON Expression 1680 Professional, Seiko Epson Corporation, US), and specific weights estimated for the same leaf samples were used to convert the mass-based chlorophyll and leaf N concentrations to leaf area based CHL l and N l. CHL l was found to be exponentially related to SPAD values (r 2 = 0. 73-0. 93), as also shown in other studies, while N l was linearly related to the SPAD indices (r 2 = 0. 62-0. 89). Due to the close similarity of SPAD-CHL l calibration curves for all vegetation types (Fig. 3a ), it was decided to use one single calibration curve for all crops. The resulting calibration curve fit all data quite well (r 2 = 0. 87), including the few data that were available for artichokes, tomatoes and alfalfa, however the standard error of estimates is quite high (23.
For N l , species-specific SPAD meter calibration curves are needed (Fig. 3b ). Based on the data shown in Fig. 3b , the relative standard error of N l estimates is 12. 9 %. The better estimates for N l than CHL l may be due to the larger samples for N l estimation (69) than for CHL l estimation (30) and the use of species-specific SPAD-N l calibration curves. Combining the SPAD-CHL l and SPAD-N l calibration curves, the species-specific nitrogen partitioning is clearly illustrated (Fig. 3c ). The CHL l -N l relationships (Fig. 3c ) were used to convert REGFLEC CHL l simulations to N l. In each field plot, 30-70 SPAD meter measurements were conducted depending on the variability of the data. In order to assess the possible impact of vertical CHL l variability on the total chlorophyll content of the canopy, measurements were conducted on green leaves at five heights in the canopy (this was not always possible for the NL grass fields, due to low canopy heights and narrow leaves).
At each level, two measurements were conducted on the same leaf to identify deviating data caused by erroneous data resulting from measurement on veins or, for small grass leaves, insufficient leaf cover of the sensor. If one of the paired measurements approached zero, and the other did not, the lower measurement was discarded. Canopy chlorophyll (CHL c ) and canopy nitrogen (N c ) contents are computed by multiplication of LAI and the E. Boegh et al. : Remote sensing of LAI, chlorophyll and leaf nitrogen pools averaged leaf-scale measurements, ie. CHL c = LAI × CHL l , (1) N c = LAI × N l , (2) where CHL l is the average CHL l measured at five height levels in the canopy, and N l is the corresponding average N l. Since the variation in CHL l and N l with height is not all due to measurement uncertainty, but related to vertical data structure, relative measurement uncertainties ( l ) for CHL l and N l are calculated by propagating absolute data uncertainty (σ ) at each measurement level: l = 1 5 (σ 2 (0.
Spectral vegetation indices Five different SVIs were calculated from each of the six satellite images in order to assess field data consistency and evaluate REGFLEC performance. The Simplified Ratio (SR) and the Normalized Difference Vegetation index (NDVI) were the earliest SVIs to be developed, and are frequently used indices. They are calculated as SR = ρ NIR /ρ red , (5) NDVI = (ρ NIR -ρ red ) / (ρ NIR + ρ red ) , (6) where ρ is spectral surface reflectance. Despite inherent normalization of NDVI to reduce soil background and atmospheric sensitivity of SR, the NDVI remains sensitive to soil reflectance. A soil-adjusted vegetation index (SAVI) was developed, which uses a soil-adjustment factor to shift the origin of the NIR-red spectral space and accounts for first-order soil-vegetation interactions and differential NIR and red radiative transfer through a canopy  (Huete, 1988).
Furthermore, atmospheric state data and solar and sensor view angle geometries must be specified. Following atmospheric correction of satellite data (using the 6SV1 model), the ACRM-PROSPECT model is first run in forward mode to build lookup tables representing relationships between spectral reflectance, CHL l and LAI. This includes model generated curves of LAI as a function of ρ NIR , NDVI and GNDVI, in addition to curves of CHL l as a function of ρ green. The lookup tables (LUTs) are built using a wide parameter space representative of a full range of soil and vegetation parameters  (Houborg and Anderson, 2009). REGFLEC then identifies bare soil pixels or low vegetation pixels (LAI < 0. 5) and estimates a single soil reflectance parameter for each of these by running the model in LUT-based inverse mode for a limited number of averaged spectral reflectance values representing each soil class.
NDVI NDVI LAI LAI CHL l CHL l CHL l N l N l N l CHL c CHL c N c N c mean sd mean sd mean sd1 sd2 mean sd1 sd2 mean sd1 mean sd1 (-) (-) (-) (-) (mg m -2 ) (mg m -2 ) (mg m -2 ) (g m -2 ) (g m -2 ) (g m -2 ) (mg m -2 ) (mg m -2 ) (g m -2 ) (g m -2 ) DK08 0. Soil maps (1:1. 100. 000) from the European Soil Database (ESDB) of the European Soil Data Center (http://eusoils. jrc. ec. europa. eu/) were used as base soil maps for all landscapes except for Denmark, where a more detailed map comprising three classes (instead of two classes in the ESDB map) was available. For the UK site, it was observed that the CORINE category "peat bogs" was not well represented by the European soil map. The high organic content of these soils is likely to influence soil reflectance, and it was therefore decided to add the higher spatial resolution (100 m) CORINE "peat bogs" class as an additional soil class to the ESDB soil map.
Acknowledgements. This study is a contribution to the  EU Integrated Project NitroEurope  (www. nitroeurope. eu),which was funded by the  European Commission  (project  17841-2 ), and to the project " Remote sensing of leaf nutrition and its incorporation for biochemical and environmental modelling of crop photosynthesis and evapotranspiration ", funded by the  Danish National Research Council  (project  23-04-00156 ). The study is also affiliated to the project " Ecosystems Surface Exchange of Greenhouse Gases in an Environment of Changing Anthropogenic and Climatic Forcing " (ECOCLIM), financed by the  Danish Strategic Research Council  (project  10-093901 ). Financial support by  Spot Image , now  Astrium GeoInformation Services , to acquire SPOT satellite images is gratefully acknowledged.
Point cloud generation The most important and difficult part of the processing chain was the point cloud generation. The state of the art and several experiments lead us to select several applications that seemed appropriate. Among those we have eliminated, there is 123D Catch (Autodesk), which, after a test, provided a very noisy and not dense enough point cloud. We suppose that it has to be related to the size of the stone blocks. A block of a headstone can reach about 2 m x 1 m and is therefore larger than architectural objects modeled in the study of  (Kersten & Lindstaedt, 2012). The open source software Photosynth (by Microsoft) solution coupled to PhotoSynth Toolkit (by H. Astre) seems to be the most appropriate tool for our needs and for the conditions of acquisition of steles. Introduced first by  (Pomaska, 2009)  for the purpose of generating relative oriented images and of allowing navigation in 3D, this application has been improved to generate also point clouds based on images.
PhotoSynth and PhotoSynth Toolkit: Only a few steps require user intervention when using PhotoSynth. This is of course an advantage in terms of ease of use and processing time, but it arises the question of the generated product quality. The black box of this solution does not enable the mastering of the calculation operations. This disadvantage is hardly acceptable for scientific work. From the perspective of the user, the work begins first with the processing of images (in PhotoSynth), after transferring the data to the web service. (Pomaska, 2009)  assumes that the software has been built from the Bundler core. PhotoSynth takes into account the EXIF (Exchangeable Image File) data and enables therefore the use of photographs taken with different cameras and focal lengths. The raw point cloud produced this way requires to be densified.