dataesr/scientific-paragraphs-categorization huggingface dataset:
Data clean-up In this step, we remove from input SAXS profiles data values for which the expected value is not significantly different from zero. Let H 0 be the null hypothesis of a data point being purely noise-induced. Let H 1 be the alternative that it contains some signal. Then with a type-I error of , we can perform a one-sample one-sided t-test. Let Iðq i Þ be a mean intensity at momentum transfer q i , sðq i Þ the standard deviation and N the number of repetitions of the experiment. Then the t statistic is t ¼ Iðq i Þ sðq i Þ=N 1=2 ; ð1Þ and it has a Student t distribution with = N À 1 degrees of freedom. Since we are performing a large number of tests, we apply the Bonferroni correction by defining =M (M is the total number of points in the SAXS profile) and choose = 0. 05 by default.
In fact, two integrals would need to be computed numerically, equation (  17 ) and also the normalization constant of equation (  14 Luckily, as  Gibbs & MacKay (1997)  have pointed out, a Laplace approximation of this last integral is a very good approximation because hyperparameters are usually quite peaked around their most probable value. This approach is known as a type-II maximum likelihood (ML-II), pðIjS; NÞ ' pðIj Ĥ H; S; NÞ pð Ĥ HÞÁ Ĥ H; ð23Þ Á Ĥ H ¼ ð2Þ N p @ 2 E @H 2 ðI; Ĥ HÞ À1=2 ; ð24Þ EðI; HÞ ¼ À log pðIjH; S; NÞ À log pðHÞ: N p dimðHÞ is the number of parameters. Á Ĥ H is the phase space volume in which values of H are acceptable given D, and is usually small  (Rasmussen & Williams, 2006). This procedure has a considerable practical advantage, since optimization of the hyperparameters then does not need to be performed for each new IðqÞ but only once for this dataset.
Conclusion In this article we have developed SAXS Merge, a fully automated method for merging SAXS profiles in a robust and statistically principled way. It has been released as both a software package and a webserver, as described by  Spill et al. (2014). The required input consists only of the buffersubtracted profile files in a three-column format (q, intensity, standard deviation). YGS thanks Riccardo Pellarin for discussion about Bayesian scoring. MN acknowledges funding from the European Union (FP7-IDEAS-ERC 294809).
We study the formation of a supermassive black hole (SMBH) binary and the shrinking of the separation of the two holes to sub-pc scales starting from a realistic major merger between two gas-rich spiral galaxies with mass comparable to our Milky Way. The simulations, carried out with the Adaptive Mesh Refinement (AMR) code RAMSES, are capable of resolving separations as small as 0. 1 pc. The collision of the two galaxies produces a gravo-turbulent rotating nuclear disk with mass (∼ 10 9 M ) and size (∼ 60pc) in excellent agreement with previous SPH simulations with particle splitting that used a similar setup  (Mayer et al. 2007 ) but were limited to separations of a few parsecs. The AMR results confirm that the two black holes sink rapidly as a result of dynamical friction onto the gaseous background, reaching a separation of 1 pc in less than 10 7 yr.
More generally, confirmation of the results of SPH calculations on the effectiveness of the SMBH binary formation process in a gaseous environment is desirable. Due to the many scales involved, adaptive mesh refinement (AMR) simulations are ideally suited to the problem. In this paper, we used the RAMSES code  (Teyssier 2002)  to follow both the galaxy merger at large scale and the binary SMBHs merger at small separation. RAMSES is based on a Particle-Mesh N-body integrator for dark matter and stars and a second-order unsplit Godunov scheme for the gas. Its shock capturing abilities and low intrinsic numerical viscosity (we used the HLLC Riemann solver and the MinMod slope limiter) make it ideal to address the problem. It also features a multigrid Poisson solver that is both fast (of order N) and second-order accurate  (Guillet & Teyssier 2011) , this being another desirable feature for the problem at hand.
Finally, the super-Lagrangian refinement strategy we have used in this project allows in principle to reach smaller separations than possible with an SPH code in a realistic galaxy merger at a reasonable computational cost. The paper is organized as follows. In section 2, we describe the hydrodynamical simulations we have performed. Results on SMBHs pairing and dynamical friction due to gas are then presented in section 3. Finally, section 4 is devoted to discussion and conclusions.
HYDRODYNAMICAL SIMULATION PARAMETERS We use the AMR hydrodynamical code RAMSES  (Teyssier 2002)  to model the evolution of a galactic major merger in which each galaxy hosts a SMBH at its center. The galactic model, inspired by the one from  Mayer & Kazantzidis (2008) , consists in a NFW isotropic dark matter halo, a rotating exponential stellar disc, a Hernquist bulge, a thin rotating exponential gas disc and a single motionless and nonaccreting SMBH particle placed at the center of the galaxy. The dark matter halo has a virial rotation velocity v200 = 138 km/s (M200 = 8. 7 × 10 11 M ), a concentration parameter c = 9. 0, a dimensionless spin parameter λ = 0. 05 and is made of N dm ∼ 7 × 10 6 particles. The disk has a mass M d = 0. 05 × M200, a scale length R d = 3. 6 kpc, a thickness H d = 0.
ACKNOWLEDGMENTS This work was granted access to the HPC resources of C. C. R. T. under the allocation 2010(1)-SAP2191 made by  GENCI  ( Grand Équipement National de Calcul Intensif ). The authors are grateful to  P. Madau  for fruitful discussions, D. C. acknowledges support from the  Astrosim program  of the  European Science Foundation  (grant  2009-2277 ) and L. M. acknowledges support from a grant of the  Swiss National Science Foundation (SNSF).
The CHIMERE code organisation Apart from the preprocessing, i. e. the preparation of the forcing, the CHIMERE model is split into two main parts: an initialisation phase and the model integration phase (Fig. 2 ). The initialisation phase consists in reading all input parameters as well as the preparation of the initial meteorological and chemical field. The model integration phase is split into three stages: -An hourly time step that corresponds to the provision of forcings, i. e. meteorological fields, emission fluxes and chemical boundary conditions. -A user's defined coarse time step "nphour", corresponding to the time interpolation of "physical" parameters, such as wind, temperature, reactions rates, etc. In parallel, to optimise the time simulation and prevent issues associated to the violation of the Courant-Friedrich-Levy (CFL) criteria, a "physical" time step is dynamically estimated in the meteorological preprocessor, taking into account horizontal and vertical winds and deep convective updraft, as presented in Fig.
The GlobCover LC is a global land cover map at 10 arc second (300 m) resolution  (Bicheron et al. , 2011). It contains 22 global land cover classes defined within the UN Land Cover Classification System (LCCS). GlobCover database is based on the ENVISAT satellite mission's MERIS sensor (Medium Resolution Image Spectrometer) level 1B data acquired in full resolution (FR) mode with a spatial resolution of 300 m. GlobCover LC was derived from an automatic and regionally-tuned classification of a time series of MERIS FR composites covering the period December 2004-June 2006. The global land cover NetCDF files are provided along with the CHIMERE distribution. The nine CHIMERE land use types are described in Table 2. The correspondence table between the database land use types and the CHIMERE land use types is provided with the model.
, 2011)  projects, and an interface with OsloCTM2  (Sovde et al. , 2008)  was also developed for the CityZen project. For the specific case of mineral dust, the GOCART (Goddard Fig. 7. Treatment of meteorological fields; two options are available: (i) using a meteorological dataset restricted to the mean parameters (u, v, T , q, P , precipitation) and (ii) using a complete meteorological dataset that includes turbulent parameters. Chemistry Aerosol Radiation and Transport)  (Ginoux et al. , 2001)  monthly average fields are also available. The current version of the model includes in its name list a series of flags in order to define which model is to be used for the following three groups of species: gases, dust aerosols, and nondust aerosols. For each of them either climatological or timevarying fields can be used.
(  33 ): E i = nveg v=1 A veg FL veg EF i,veg. (33) The emissions are then binned into the specified model grid. The temporal and horizontal resolutions of the fire emissions depend on the resolution of the different parameters. The area burned parameter is estimated from the global observations of fire activity and areas burned at a daily and 1 km resolution from the MODIS instrument  (Giglio et al. , 2010) , coupled to the SEVIRI/METEOSAT observations  (Roberts and Wooster, 2008)  for the regions covered (Europe and Africa) to allow the evaluation of a diurnal cycle. An example is presented in Fig. 11  for 8 May 2012. Depending on the fire location, a specific vegetation burned is attributed using the USGS (US Geological Survey) land use database (at 1 km resolution) and the corresponding fuel load (or carbon content) is evaluated from simulations by the ORCHIDEE (Organising Carbon and Hydrology in Dynamic Ecosystems) vegetation and carbon cycle model  (Krinner et al.
Chemical preprocessor The complete chemical mechanism used by CHIMERE is built using a suit of scripts and programs called chemprep. CHIMERE offers the option to include different gasphase and aerosol chemical mechanisms. The originality in CHIMERE is that these chemical mechanisms are written in ASCII format, and, thus, do not need to be compiled. The user can easily change some reactions or add new ones. When the model is launched, the availability of chemistry input files corresponding to the specific simulation is checked. If the data already exist, the run continues. If not, a preprocessor script will create the data directory. The strength of this approach is that the user may easily create very particular chemical schemes. All chemical parts being independent, the user may choose to have gas chemistry or not, aerosols chemistry or not (and the number of bins), sea salt, dust, secondary organic aerosols, persistent organic pollutants, tracers or not.
, 2005) , except in the Spanish part of the domain where emissions were provided by the Spanish Ministry of Environment and Rural and Marine Affairs for 2006. Original emissions were spatially and temporally disaggregated and adapted to the simulated domain by taking into account the land use information of GLCF (Global Land Cover Facility, http://change. gsfc. nasa. gov/create. html). In the case of TNO emissions, only the total amount for each EMEP grid cell was available. The temporal disaggregation of these totals is normally performed in the CHIMERE emission processor considering the SNAP activity. As we did not have this information for the metals coming from the TNO database, we used a SNAP disaggregation similar to PM 2. 5 particles for those species. We also used a temporal profile similar to PM fine particles.
Ion beam therapy offers the possibility of excellent dose localization for treatment of malignant tumours, minimizing radiation damage in normal tissue, while maximizing cell killing within the tumour. However, as the underlying dependent physical, chemical and biological processes are too complex to treat them on a purely analytical level, most of our current and future understanding will rely on computer simulations, based on mathematical equations, algorithms and last, but not least, on the available atomic and molecular data. The viability of the simulated output and the success of any computer simulation will be determined by these data, which are treated as the input variables in each computer simulation performed. The radiation research community lacks a complete database for the cross sections of all the different processes involved in ion beam induced damage: ionization and excitation cross sections for ions with liquid water and biological molecules, all the possible electron -medium interactions, dielectric response data, electron attachment to biomolecules etc.
A recent project exploits MBN-Explorer to focus on the thermo-mechanical pathways of DNA damage as a consequence of irradiation in ion beam cancer therapy  [2, 3]. However, such simulations are strongly dependent upon the quality of the input data. At a recent workshop organized by the COST Action Nano-IBCT in Vienna February 24-26, 2012 the need for a new database for the data on the processes relevant for the interaction of ionizing radiation with organic molecules became obvious. It was decided that this database entitled RADAM should exploit the experience of the VAMDC project [http://www. vamdc. eu/], which is devoted to the creation of an interoperable e-Infrastructure for the exchange of atomic and molecular data. The RADAM database could be extended with the data from simulated outputs (using a database network model), to compare the capabilities of different computer codes.
The general structure of the RADAM Database is to consist of five principal areas: Ionic Interactions, Electron/Positron Interactions, Photonic Interactions, Multiscale RADAM phenomena and Radiobiological phenomena, see figure  1. Each of the aforementioned areas will be represented by at least one database node, where the corresponding information will be stored. Data stored at all the nodes should be accessed through a single user portal (RADAM DB portal), which will provide the user interface for creating database queries using a unified query language for all the databases. This query language is currently the VAMDC SQL subset 2 (VSS2), but might be extended in the future as necessities arise. The RADAM DB nodes will be also connected to the VAMDC portal in order to provide the possibility for users to search for data compatibility with other VAMDC databases.
Cross sections for dissociative electron attachment The Nano-Bio-Physics group at the University of Innsbruck has established a database node containing cross sections for dissociative electron attachment (DEA) processes in the gas phase. The node is called "Innsbruck DEA Database" (IDEADB) and is fully compatible with VAMDC standards version 11. 12 (the current, production release) and 12. 07 (the forthcoming release). Additionally, a simple web interface was developed in order to allow easy inspection and comparison of data (http://ideadb. uibk. ac. at) IDEADB currently contains gas phase measurements of the electron energy dependence of cross sections for DEA processes to various molecules, some of which are biomolecules. For each process, the originating species and resulting anion fragments are described in as much detail as possible (e.
g. InChI for molecules where possible) and the electron energy resolution used for the measurement is given along with comments taken from the original publication. Since all data is taken from publications and DOIs are given for each process, the data can be cited easily and the additional information given by a full publication can be accessed. For further investigations of data, each cross section can be exported to an ASCII file. The system is based on the Python implementation of the Node Software developed by the VAMDC project. It uses nginx as a reverse proxy and gunicorn as an application server to power the Django framework. Since the amount of data is rather small, the VAMDC performance goals can be met easily. The system is hosted on dedicated hardware at the University of Innsbruck and therefore well connected to scientific networks.
Low energy (0-10 keV) electron and positron interaction Low energy (0-10 keV) electron and positron interaction cross sections and energy loss in biomolecular systems are being collated in a database prepared by G Garcia and co-workers at CSIC Madrid. Cross section data for electrons and positrons derived from the IAM-SCAR  [4, 5]  calculation procedure are stored together with a critical compilation of available theoretical and experimental data. This node is connected with the Centre for Antimatter-Matter Studies (CAMS, Australia) and the Laboratory for Atomic Collisional Processes of Institute of Physics University of Belgrade (see subsection 2. 2. 2).
Electron/positron interactions with biomolecules Electron/positron interactions with biomolecules node is created in Belgrade at the Laboratory for Atomic Collisional Processes of Institute of Physics University of Belgrade, IPB ,(mail. ipb. ac. rs/~centar3/acp. html). Primarily data measured at the own laboratory will be stored in the node. Those include absolute electron differential (in angle and energy) cross sections for elastic scattering and excitation of biomolecules and molecules of biological interest, but will also include targets such as metal atoms. Secondary data sets will be assembled by collating calculated data for the same targetsmeasured at IPB. All data are in compliance with VAMDC standards and the xsams scheme for the collisional processes. Data sets are presented as 3D surfaces (cross sections versus electron impact energy and scattering angle).
Interaction of ionizing particles with all constituents of DNA In an ongoing research program at PTB, cross section data for the interaction of ionizing particles with all constituents of the DNA or of model molecules for these building blocks (such as tetrahydrofuran for deoxyribose or pyrimidine for the nucleic bases cytosine and thymine) are determined. For electrons with kinetic energies between 20 eV and 1 keV, total scattering cross sections as well as single differential elastic and double differential inelastic cross sections are determined, where the experimental apparatus covers a range of scattering angles between 15° and 135°  [8]. The experimental data are extrapolated to forward and backward scattering angles via phenomenological models that are supported by theoretical calculations based on, for example, the modified independentatom model  [9].
Multiscale RADAM phenomena The Multiscale RADAM phenomena node has been created in Frankfurt in the Meso-Bio-Nano Science group of Prof. Dr. Andrey Solov'yov (fias. uni-frankfurt. de/mbn). The node will store experimental and theoretical data on RADAM related phenomena such as stopping power, diffusion coefficients, Bragg peak characteristics, thermo-mechanical damage, beams defocusing/spreading, complex dielectric functions, molecular fragmentation cross sections, chemical reaction rates. See subsection 4 in the appendix for details. The initial version of Multiscale RADAM phenomena node was created in collaboration with the group of Prof. Bratislav Marinković at Institute of Physics, Belgrade. Currently the node stores experimental data on the linear energy transfer for carbon ions with different initial energies in water.
Roadmap of the RADAM DB project. The roadmap of the RADAM DB project is presented in figure  2. In order to create the database it is necessary to define its principal logic structure. Working group leaders responsible for each database component have proposed structures for each of the topical areas and these are shown in the appendix. At the current stage of the development we will include only data, which has published in peerreviewed journals. However, in certain cases unpublished but thoroughly checked data can be also of high interest to the community. We plan to develop protocols for data verification at later stages of the project in collaboration with the SUP@VAMDC project (www. sup-vamdc. vamdc. org).
Conclusions and future plans The RADAM database being created within the framework of the COST Action Nano-IBCT will have a multi-node expandable infrastructure. Each node will be devoted to a certain RADAM data domain. Several nodes devoted to similar data domains can be maintained by different groups. A dedicated RADAM data portal for constructing queries to the nodes will be created. The RADAM portal will support all types of data stored at the nodes. The nodes will be also accessed by the queries from the VAMDC portal. In case of data compatibility with VAMDC XSAMS standards the reply to the VAMDC queries will be created. Further modifications of VAMDC SQL subset will include extensions towards broader coverage of RADAM data by VAMDC queries. Any group willing to provide data for RADAM DB or setting up a personal node is cordially invited to participate in the project.
Acknowledgements Parts of this work were supported by the  Fonds zur Förderung der wissenschaftlichen Forschung (FWF) ,  I978  and  22665 ,  Wien. AY, AS and BM acknowledge the support from bilateral project between  Germany  ( DAAD ID 54394840 ) and Serbia (# 680-00-00095/2012-09/9 ;  OI 171020 ). BM acknowledges the support from VAMDC. The authors also acknowledge the support and inspiration from  COST Action 1002 Nano-IBCT  under whose auspices the RADAM DB has been developed.
Experimental apparatus and design Larval stage (6 days post fertilisation). Locomotor activity was examined at 6 days post fertilisation (dpf) by recording larval swimming during a 1-hour period using ZebraLab software (ViewPoint Life Sciences, France; tracking parameters are described below). Larvae were placed into separate wells of a 24-well plate (BD Falcon GmBH, Germany) inside a ZebraBox (ViewPoint Life Sciences, France; Fig. S1 ). Fry were gently pipetted into the plate 1 hour before the experiment started. Multi-well plates were placed into the ZebraBox and larvae were allowed to habituate for 10 minutes before recording began. A schematic representation of the behavioural setup used at each life stage is provided in figure  S1. Juvenile stage (1 month). At one month the fish were individually placed in a small transparent tank (9.
These data were then exported into FastData Monitor, a software package developed in collaboration with ViewPoint Life Sciences. Several parameters were extracted to look at locomotion in greater detail including distance swum, swimming time and mean speed during one hour. The mean speed of swimming was calculated by dividing the total distance swum by the amount of time spent swimming (not the total length of the recording period); thus frames in which no movement was detected were excluded from this analysis. Fast swim events. Fast swim events were quantified by counting the number of events where the fish swim more than 5 mm in less than 12 seconds  [13]. The ZebraLab software was used to record locomotion during 5 minutes with an integration period of 3 seconds. The tracking parameters used were the same as those described above.
Data were exported into Excel (Microsoft) and the peak parameters automatically sorted using a self-designed macro (details available upon request). Surface and speed. The relative surface area of the fish was measured using the ''size'' extension of the ZebraLab software (ViewPoint), which allowed the surface area of the fish to be quantified in pixels. Pixel threshold was set at 117 in the transparent background mode for 1 month-old and 3 month-old fish. At 1 month, 1 pixel corresponded to 2. 23 mm 2 , and at 3 months to 3. 61 mm 2. The locomotion tracking parameters used in this experiment were identical to those described above. We did not include 6 dpf larvae in this test because the difference between individuals in relative pixel size was not significant at this stage. Surface data were exported and analysed with SigmaPlot 2.