dataesr/scientific-paragraphs-categorization huggingface dataset:
sph. umich. edu/csg/abecasis/Metal). Reaction time genome-wide association analysis. The GWAS of reaction time from the UK Biobank sample was performed using the BGENIE v1. 2 analysis package (https://jmarchini. org/bgenie/). A linear SNP association model was tested which accounted for genotype uncertainty. Reaction time was adjusted for the following covariates: age, sex, genotyping batch, genotyping array, assessment centre, and 40 principal components. Genomic risk loci characterization using FUMA. Genomic risk loci were defined from the SNP-based association results, using FUnctional Mapping and Annotation of genetic associations (FUMA)  23. Firstly, independent significant SNPs were identified using the SNP2GENE function and defined as SNPs with a P-value of ≤5 × 10 -8 and independent of other genome wide significant SNPs at r 2 < 0.
A CADD score  75  , RegulomeDB score  76  , and 15-core chromatin states  [77] [78] [79]  were obtained for each SNP. eQTL information was obtained from the following databases: GTEx (http://www. gtexportal. org/home/), BRAINEAC (http:// www. braineac. org/), Blood eQTL Browser (http://genenetwork. nl/ bloodeqtlbrowser/), and BIOS QTL browser (http://genenetwork. nl/ biosqtlbrowser/). Functionally-annotated SNPs were then mapped to genes based on physical position on the genome, eQTL associations (all tissues) and chromatin interaction mapping (all tissues). Intergenic SNPs were mapped to the two closest up-and down-stream genes which can result in their being assigned to multiple genes. Gene-set analysis implemented in FUMA. In order to test whether the polygenic signal measured in each of the GWASs clustered in specific biological pathways, a competitive gene-set analysis was performed.
The goal of this analysis was to test if, in 30 broad tissue types and 53 specific tissues, tissuespecific differential expression levels were predictive of the association of a gene with general cognitive function and reaction time. Tissue types were taken from the GTEx v6 RNA-seq database  85  with expression values being log2 transformed with a pseudocount of 1 after winsorising at 50, with the average expression value being taken from each tissue. Multiple testing was controlled for using a Bonferroni correction.
Acknowledgements This research was conducted in  The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology , funded by the  Biotechnology and Biological Sciences Research Council and Medical Research Council  ( MR/K026992/1 ). This research was conducted using the  UK Biobank Resource (Application Nos. 10279  and  4844 ). The Neurology Working Group within the Cohorts for Heart and Aging Research in Genomic Epidemiology is partly supported by grants from the  National Institute on Aging  ( R01 AG033193 ,  U01 AG049505  and  U01 AG052409 ). Cohort-specific acknowledgements are in Supplementary Note 3.
Because CC involves long-term trends, the use of monthly climate normals is usually preferable  (Reinsborough, 2003). To determine seasonal temperatures and precipitations, this study used January-December monthly means from 1981 to 2010 from 32 weather stations around the country from Nigeria's Meteorological Agency. Data on soil types for the seven cocoaproducing states in Nigeria were obtained from the Food and Agricultural Organization (FAO). The FAO soil statistics include information about the major and minor soils in each location, as well as the slope and texture. In all, there exists four types of soil in the respective states, and all of them were used in the analysis. Farm-level data on NRh and its determinants were collected from 280 cocoa farmers spread over the various agro-ecological zones of the seven selected states -Cross River, Abia, Edo, Ondo, Ekiti, Oyo and Ogun (Figure  1 ).
Acknowledgments The fieldwork for the study was generously funded by the  African Economic Research Consortium (AERC)  through  Research Grant  No. :  RT10509. The insightful comments from three anonymous reviewers and the deputy editor of the journal are gratefully acknowledged. The authors specially thank the  German Federal Ministry of Education and Research (BMBF)  for the financial support through the  West African Science Service Center on CC and Adapted Land Use  (WASCAL), where the work was carried out. Note 1. The Harmattan season is peculiar to the West African subcontinent. It usually occurs between the month of December and early March and signifies the beginning of the dry season. It is often characterized by harsh weather conditions such as cold, dry and dust-laden wind, very low humidity and relatively lower temperatures.
DGPS and photogrammetry measurements A terrestrial photogrammetric survey was carried out on 11 August 2016. We acquired 30 and 34 terrestrial photographs for the light and dark ogives, respectively. The photographs were taken using a Nikon Coolpix P7700 digital camera at a distance of ∼4 m, thereby covering a surface area of ∼5 m 2 for each ogive (Fig. 3 ). The images from the survey were processed using the Structure for Motion (SfM) algorithm implemented in the Agisoft Photoscan Professional version 1. 2. 0 software package  (Agisoft, 2014). In order to georeference the surface models and to geometrically correct the images, ten ground control points (GCPs) (0. 1 × 0. 1 m white crosses painted on pieces of wood) were set up for each sampled area. These GCPs, easily identifiable on the pictures, were measured using a geodetic DGPS.
The SfM stereo technique was then used to generate a dense point cloud of the glacier surface. All processing parameters were set to 'high quality' in the Agisoft Photoscan software. This dense point cloud was used to construct the DEMs. Photogrammetric restitutions were obtained with marker residuals of 0. 01 and 0. 03 m for the dark and the light ogives, respectively. A detailed description of the processing steps can be found in  Kraaijenbrink and others (2016)  or  Brun and others (2016). The positions of all GCPs used for the photogrammetric survey were measured with a Leica 1200 DGPS unit. Occupation times were 1 min with 1 s sampling and the number of visible satellites (GPS and GLONAS) was greater than seven. The distance between the fixed receiver and the mobile receiver was 2 km.
Surface roughness The aerodynamic roughness length z 0 is defined as the height above the ground at which a vertical wind profile drops to zero for a logarithmic profile  (Oerlemans, 2001; Greuell and Genthon, 2004 ). Here we estimate the effective roughness length z 0 and assess the impact on the energy balance of both ogives. Previous studies attempted to estimate z 0 using a microtopographic method (e. g. Lettau, 1969; Munro, 1989; Brock and others, 2006; Irvine-Fynn and others, 2014; Rounce and others, 2015). Traditional microtopographic methods rely on measurements of elevation along a transect. More recently, close-range accurate photogrammetry was used (Irvine-Fynn and others, 2014). Here we used DEMs with a grid size of 1 cm, determined from numerous terrestrial photographs using Agisoft Photoscan software.
s. l. This may explain part of the uncertainties in the calculated turbulent fluxes. It should be underlined that simulating snow and ice mass balance using detailed snowpack model is complex because of the high equifinality in the mass-balance calculation, i. e. the simulated mass balance may be accurate but for the wrong reasons (error compensation in the different components of the energy balance, e. g. Lafaysse and others, 2017). Snow/ice detailed modeling is generally affected by two sources of errors (i) uncertainties in the meteorological forcing data and (ii) errors and parameterizations in the physics of the snow/ice model itself. The use of another wind speed dataset or of another formulation of the turbulent fluxes will necessarily lead to different values of roughness length to minimize the SMB simulations errors.
ACKNOWLEDGEMENTS This study was funded by  Observatoire des Sciences de l'Univers de Grenoble (OSUG)  and  Institut des Sciences de l'Univers (INSU) in the framework of the French 'GLACIOCLIM (Les GLACiers comme Observatoire du CLIMat) ' project (https://glacioclim. osug. fr/). It was also funded by the  Labex OSUG@2020  ( ANR10LABx56 ),  ANR   JCJC MONISNOW  and  ANR 1-JS56-005-01  MONISNOW projects. We thank  Jonathan Rogge  and  Louis Debias  who made the mass balance and the roughness calculations. We are very grateful to  Michel Vallon  for his very useful comments. We are grateful to  H. Harder  for reviewing the English and to  Matthieu Lafaysse  for help with the SURFEX simulations.
DATA AVAILABILITY The input meteorological variables and the parameter lists used in the model are included as supplementary material to the paper. For reproducibility of the results, the version of SURFEX used in this work is tagged as 'MerDeGlace2017' on the SURFEX git repository (git. umr-cnrm. fr/git/Surfex_Git2. git). The full procedure and documentation to access this git repository can be found at https://opensource. cnrm-game-meteo. fr/ projects/snowtools_git/wiki. The files related to meteorological data and parameters are in Supplementary Material.
(2005)  used an empirical linear relationship between the surface elevation residuals and the backscattering coefficient residuals at a crossover points of the satellite tracks (data points where satellite tracks cross). A similar method had been described by  Wingham et al. (1998). Flament and Rémy (2012)  used a linear relationship between time series of the surface elevation and the all waveform parameters: the range, the backscattering coefficient, the leading edge width and the trailing edge slope (computed with the ICE-2 retracker;  Legresy et al. , 2005)  on the along tracks of the satellite. Both approaches are based on changes in the backscattering coefficient, which varies with time, reflecting changes in the snowpack properties  (Legresy and Remy, 1998; Lacroix et al. , 2007). A more precise understanding of the annual and interannual variations of the backscattering coefficient is a prerequisite for improving the estimation accuracy of the surface elevation trend over the AIS.
Altimetric observations Radar altimeters data were acquired by ENVISAT launched on March 2002 by the European Space Agency (ESA). Acquisitions are simultaneous at the S and Ku bands, every 330 m along track on a 35-day repeat cycle orbit from September 2002 to October 2010 (the end of its repeat cycle orbit). The S band sensor failed after 5 years of measurements. The satellite footprint has typically a 5 km radius and no data were acquired above 81. 5 • S due to its orbit maximum inclination. The range gate resolution is about 94 and 47 cm at the S and Ku bands, respectively. To ensure a long and homogeneous time series of post-ENVISAT missions and to complement the Ocean Surface Topography Mission (OSTM)/Jason  (Steunou et al. , 2015) , the SARAL/AltiKa was launched on 25 February 2013 by a joint CNES-ISRO (Centre National d'Etudes Spatiales -Indian Space Research Organisation) mission, on the same 35-day repeat cycle orbit as ENVISAT.
On March 2016, SARAL/AltiKa orbit was shifted onto a new orbit. Unlike classical Ku band radar altimeter, the SARAL/AltiKa altimeter operates at the Ka band (37 GHz ∼ 0. 8 cm) and has a range gate resolution of 30 cm. The ICE-2 retracking process was applied to the Ka, Ku and S band waveforms, allowing estimation of the range, the backscattering coefficient (σ 0 ), the leading edge width and the trailing edge slope. The difference between the Ka and Ku bands and between the Ka and S bands is up to a factor of 2. 7 and 11. 6, respectively, which results in different sensitivity to the surface and the subsurface characteristics. The ENVISAT and AltiKa datasets used in this study were averaged at a 1 km scale on the ENVISAT nominal orbit. We processed 84 cycles of the backscattering coefficient from October 2002 until September 2010 for the Ku band and 55 cycles from October 2002 until December 2007 for the S band.
Temporal variations of the surface elevation with respect to the backscattering coefficient Figure  7  shows the spatial distribution of temporal variations of the estimated surface elevation residuals with respect to σ 0 residuals at the Ku band, hereafter denoted dh/ dσ 0. The surface elevation was indirectly estimated from the retracked range (computed with the ICE-2 retracker) at each data point and was corrected for atmospheric errors. dh and dσ 0 were derived by subtracting the mean value from the time series of the elevation and backscatter, respectively. dh/ dσ 0 represents the correlation gradient or the slope at each data points over the AIS. Negative values of dh/ dσ 0 indicate that surface elevation decreases when σ 0 increases, implying that temporal variations in σ 0 are due to changes in the deep snowpack properties, i.
To further investigate this point, we used ERA-Interim reanalysis wind speed data supplied by ECMWF (European Centre For Medium-Range Weather Forecasts) on the period 2002 to 2010, corresponding to that of the Ku band. Equation (  1 ) is used to compute the seasonal characteristics of the wind speed by replacing σ 0 with the wind speed. A visual inspection shows a high spatial coherence of the seasonal amplitude of the wind speed (Fig. 10a ) patterns with the date of maximum σ 0 over the seasonal cycle at the Ku band (Fig. 2b ). Wind speed average (8. 2 ± 1. 6 m s -1 ) and seasonal amplitude (1. 7 ± 0. 4 m s -1 ) are higher in the WP than in the SP (6. 6 ± 1. 58 m s -1 and 1. 0 ± 0. 3 m s -1 , respectively). The striking similarity in the spatial distribution of the seasonal amplitude of σ 0 at the Ka band (Fig.
Acknowledgements. This work is a contribution to the  ASUMA (improving the Accuracy of the SUrface Mass balance of Antarctica)  project funded by the  Agence Nationale de la Recherche , contract  ANR-14-CE01-0001-01. ENVISAT and AltiKa data were provided by the  Center for Topographic studies of the Oceans and Hydrosphere (CTOH)  at  LEGOS  and are available at http://ctoh. legos. obs-mip. fr/,last access: 23 May 2018. The authors would like to thank  Etienne Berthier  and  Jessica Klar  from LEGOS for their helpful comments and suggestions. We are grateful to the anonymous reviewers and the editor, whose comments significantly improved the manuscript. Edited by:  Robert Arthern Reviewed  by: two anonymous referees.
Data availability. Data can be access on request on the CTOH and AVISO websites. ENVISAT datasets at (http://ctoh. legos. obs-mip. fr/products, last access: 23 May 2018) and SARAL/AltiKa datasets are downloadable from AVISO website (https://aviso-data-center. cnes. fr/\T1\textbackslash#altika_LG. htm, last access: 23 May 2018).
A secondary motivation was the need for a public SPH code that is not primarily focused on cosmology, as in the highly successful Gadget code  (Springel et al. , 2001; Springel, 2005). The needs of different communities produce rather different outcomes in the code. For cosmology, the main focus is on simulating the gravitational collapse of dark matter in large volumes of the universe, with gas having only a secondary effect. This is reflected in the ability of the public Gadget-2 code to scale to exceedingly large numbers of dark matter particles, yet neglecting elements of the core SPH algorithm that are essential for stellar and planetary problems, such as the Morris &  Monaghan (1997)  artificial viscosity switch (c. f. the debate between  Bauer & Springel 2012 and Price 2012b) , the ability to use a spatially variable gravitational force softening  (Bate & Burkert, 1997; Price & Monaghan, 2007)  or any kind of artificial conductivity, necessary for the correct treatment of contact discontinuities  (Chow & Monaghan, 1997; Price & Monaghan, 2005; Rosswog & Price, 2007; Price, 2008).
Gasoline  (Wadsley et al. , 2004 ) is another code that has been widely and successfully used for galaxy formation simulations, with its successor, Gasoline 2  (Wadsley et al. , 2017) , recently publicly released. Hubber et al. (2011)  have developed Seren with similar goals to Phantom, focused on star cluster simulations. Seren thus presents more advanced N -body algorithms com-pared to what is in Phantom but does not yet include magnetic fields, dust or H 2 chemistry. A third motivation was the need to distinguish between the 'high performance' code used for 3D simulations from simpler codes used to develop and test algorithms, such as our already-public ndspmhd code  (Price, 2012a). Phantom is designed to 'take what works and make it fast', rather than containing options for every possible variation on the SPH algorithm.
The arm potentials include some of the more complicated profiles. The first is the potential of  Cox & Gómez (2002) , which is a relatively straightforward superposition of three sinusoidal-based spiral components to damp the potential "troughs" in the inter-arm minima. The other spiral potential is from  Pichardo et al. (2003) , and is more complicated. Here the arms are constructed from a superposition of oblate spheroids whose loci are placed along a standard logarithmic spiral. As the force from this potential is computationally expensive it is prudent to pre-compute a grid of potential/force and read it at run time. The python code to generate the appropriate grid files is distributed with the code. Finally, the bar components: We include the bar potentials of  Dehnen (2000a) ,  Wada & Koda (2001) , the "S" shaped bar of  Vogt & Letelier (2011) , both biaxial and triaxial versions provided in  Long & Murali (1992) , and the boxy-bulge bar of  Wang et al.
Equations of non-ideal MHD We assume the strong coupling or 'heavy ion' approximation (see e. g. Wardle & Ng, 1999; Shu et al. , 2006; Pandey & Wardle, 2008) , which neglects ion pressure and assumes ρ i ρ n where the subscripts i and n refer to the ionised and neutral fluids, respectively. In this case, (167) contains three additional terms in the form d dt  (195)  where σ e is the electrical conductivity, n e is the number density of electrons, e is the charge on an electron and PASA (2018) doi:10. 1017/pas. 2018. xxx γ AD is the collisional coupling constant between ions and neutrals  (Pandey & Wardle, 2008). We write this in the form d dt B ρ nimhd = 1 ρ ∇ × J σ e + J × B en e + (J × B) × B γ AD ρ i , B ρ nimhd = 1 ρ ∇ × η O J + η H J × B + η AD (J × B) × B , ( 196 ) where B is the unit vector in the direction of B such that η O , η AD and η Hall are the coefficients for resistive and ambipolar diffusion and the Hall effect, respectively, each with dimensions of area per unit time.
The potential function is φ(q) = φ (q )dq. (218) The derivative of φ with respect to required in (  212 ) is also written in terms of a dimensionless function, i. e. ∂φ(r, ) ∂ ≡ 1 2 h(q), (219) where from differentiating (215) we have h(q) = -φ(q)q φ (q). (  220 ) Included in the Phantom source code is a Python script using the sympy library to solve (217), (  218 ) and (220) using symbolic integration to obtain the softening kernel from the density kernel. This makes it straightforward to obtain the otherwise laborious expressions needed for each kernel (expressions for the cubic spline kernel are in Appendix A of  Price & Monaghan 2007  and for the quintic spline in Appendix A of  Hubber et al. 2011). Figure  3  shows the functional form of the softening kernels for each of the kernels available in Phantom.
e. where the area element dS(x ) depends on the geometry and the chosen direction, given by M (x) ≡ x xmin ρ(x )dS(x )dx , (294) dS(x) =    1 Cartesian or cyl. /sph. along φ, θ or z, 2πx cylindrical along r, 4πx 2 spherical along r. (295) We solve (293) for each particle using Newton-Raphson iterations x = x - f (x) f (x) , (296) where f (x) = ρ(x)dS(x) M (x max ) , (297) iterating until |f (x)| < 10 -9. The Newton-Raphson iterations have second order convergence, but may fail in extreme cases. Therefore, if the root-finding has failed to converge after 30 iterations, we revert to a bisection method, which is only first order but guaranteed to converge. Stretch mapping is implemented in such a way that only the desired density function need be specified, either as through an analytic expression (implemented as a function call) or as a tabulated dataset.
Software Engineering No code is completely bug-free (experience is the name everyone gives to their mistakes; Wilde 1892). However, we have endeavoured to apply the principles of good software engineering to Phantom. These include: 1. a modular structure, 2. unit tests of important modules, 3. nightly builds, 4. automated nightly tests, 5. automated code maintenance scripts, 6. version control with git, 7. wiki documentation, and 8. a bug database and issue tracker. Together these simplify the maintenance, stability and usability of the code, meaning that Phantom can be used direct from the development repository without fear of regression, build failures or major bugs. Specific details of how the algorithms described in Section 2 are implemented are given in the Appendix. Details of the test suite are given in Appendix A.
Numerical tests Unless otherwise stated, we use the M 6 quintic spline kernel with h fac = 1. 0 by default, giving a mean neighbour number of 113 in 3D. Almost all of the test results are similar when adopting the cubic spline kernel with h fac = 1. 2 (requiring ≈ 58 neighbours), apart from the tests with the one-fluid dust method where the quintic is required. Since most of the algorithms used in Phantom have been extensively tested elsewhere, our aim is merely to demonstrate that the implementation in the code is correct, and to illustrate the typical results that should be achieved on these tests when run by the user. The input files used to run the entire test suite shown in the paper are available on the website, so it should be straightforward for even a novice user to reproduce our results.
There is no evidence that our simulations experience the numerical issues encountered by BK04 with their divergence cleaning schemes, suggesting that the problems they reported are primarily code dependent rather than being fundamental. The most probable reason our results are of the same quality as BK04's constrained transport result is our use of constrained divergence cleaning, PASA (2018) doi:10. 1017/pas. 2018. xxx Phantom 59 which guarantees that energy removed from the magnetic field is negative definite. A follow up study to examine this in more depth would be worthwhile. For our present purposes, we can conclude that our divergence cleaning algorithm is sufficiently robust -even in 'real world' problems -to be useful in practice (see also Section 6. 3).
Dust settling We perform the dust settling test from PL15 in order to directly compare the Phantom solutions to those produced in PL15 with the ndspmhd code. To simplify matters we do not consider rotation but simply adapt the 2D problem to 3D by using a thin Cartesian box (as for several of the MHD tests in Section 5. 6). Our setup follows PL15, considering a slice of a protoplanetary disc at R 0 = 50 au in the rz plane (corresponding to our x and y Cartesian coordinates, respectively) with density in the 'vertical' direction (y) given by ρ(y) = ρ 0 exp -y 2 2H 2 , ( 330 ) where we choose H/R 0 = 0. 05 giving H = 2. 5 au. We use an isothermal equation of state with sound speed c s ≡ HΩ where Ω ≡ GM/R 3 0 , corresponding to an orbital time t orb ≡ 2π/Ω ≈ 353 yrs. We adopt code units with a distance unit of 10 au, mass in solar masses and time units such that G = 1, giving an orbital time of ≈ 70.
Note that both Hydra and Gadget2 were both written primarily to solve galactic and cosmological models. The quantitive difference in results may be attributed to the improved SPH algorithms in Phantom compared to Hydra. The higher density in Hydra is consistent with the results in  Richardson et al. (2016) , who found higher densities in Hydra compared to the adaptive mesh refinement code Ramses  (Teyssier, 2002). It was determined that this was a result of a combination of the artificial viscosity, h min and the suppression of 'mixing' (which occurs when no thermal conductivity is applied).