dataesr/scientific-paragraphs-categorization huggingface dataset:
In PRISM, the controller was introduced to model high-level business logics when implementing user actions in applications as described in the BioTracs-MIMOSA application (manuscript submitted). (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A workflow is a process chain in which processes are connected through their input and output ports (Figure  3 ). It is a process containing sub-processes. It is then modelled using a hierarchical structure (Figure  4 ). The Workflow class therefore inherits from the Process class and is composed of a set of Process nodes. Compatible nodes can be chained in the workflow through their input and output ports. In practice, the configuration of workflows is challenging, in particular when the user wants to access and configure all subprocesses in a hierarchical manner.
; https://doi. org/10. 1101/2020. 02. 16. 951624 doi: bioRxiv preprint 8 were successfully used in academic and industrial projects involving large-scale clinical metabolomics data  [13]. The BioTracs-Mimosa application is one of them and is presented in a paper submitted in BMC bioinformatics. BioTracs-Mimosa provides ready-to-use workflows managed by a controller that a user can access and configure the workflows. After execution, the user can inspect workflows and their sub-processes (logs, internal configurations, results and result views). A 3-layer architecture scalable from the code to the project level System scalability is the ability to extend a system to new purposes. It relies on well-designed architectures in which the components can be safely upgraded without compromising the whole system.
That requires that components are loosely coupled with well-defined interfaces. Loose coupling reduces dependencies between components, i. e. each component must have a limited knowledge about the internal functioning the other ones. PRISM defines how resources are interfaced with processes. Any upgrade of a given node (process) in a workflow does not require upgrading the other processes as long as the adjacent nodes remain compatible. In addition, an essential property required to ensure system scalability is abstraction. Abstraction consists in identifying components that share common functions and factorizing these functions in more general (more abstract) components (Figure  5 ). For instance, in the library of BioTracs-Spectra (https://github. com/bioaster/biotracs-m-spectra), the NMR and MS spectra were modelled using the same mother class DataMatrix because they can both be represented as N-by-2 matrices, where N is the number of m/z and ppm ticks for MS and NMR, respectively.
The two columns represent the m/z (or ppm) axis and the intensity axis (see classes NMRSpectrum and MSSpectrum). Processes that are compatible with the DataMatrix class will be compatible with the NMRSpectrum and MSSpectrum classes by inheritance. Abstraction is therefore a powerful mean for code reusability. The main advantage of this architecture is to bring functionalities from the code to the business level, while ensuring the role and responsibility of all the stakeholders in the development process (e. g. developers, non-coder experts, clients). BioTracs is three-layer system in which core BioTracs libraries are specialized to create applications such as BioTracs-Spectra, BioTracs-Mimosa, BioTracs-Atlas for instance. (which was not certified by peer review) is the author/funder. All rights reserved.
Comparison with existing standardization systems Several solutions were developed in the past to improve the sharing and the standardization of the computational tools. These standardization systems apply at different levels: at the level of the management of workflows (KNIME, Snakemake, Airflow, etc. ), the packaging of applications (Docking systems, R Bioconductor), the standardization of the input/output data (file formats standardization), the standardization of the description of experiments (e. g. ISA tabs frameworks). The R Bioconductor framework is based on a package submission process to promote high-quality, well documented, and interoperable software in bioinformatics. For instance, R Bioconductor packaging promotes the use of common R data structures (S4 classes and methods) to easily operate with existing infrastructure.
One on the most recent approach that is gaining interest in bioinformatics is the use of docking systems to build containers (a. k. a. biocontainers). Containers allow to package applications in order to simplify their deployment and use without worrying about dependencies in heterogeneous platforms. This platform, supported by the ELIXIR consortium (https://biocontainers. pro), is not language specific and ensures analysis reproducibility. It does provide not guideline on the implementation of computational processes. PRISM is not devoted to replace docking systems and BioTracs processes could be "dockerized" to create containers to facilitate their sharing. Compared to R Bioconductor, BioTracs provides a more rigorous implementation architecture not only focused on data structure but also processes and workflows.
Workflow management systems such as KNIME and Snakemake allow encapsulating and chaining programs as building blocks to easily author, monitor and schedule workflows. They offer advanced management systems for cloud computing and analysis parallelization on clusters. BioTracs is, by construction, a system that allows chaining MATLAB algorithms and even external programs (R, Pyhton, Java) to build complex workflows. However, PRISM was not designed to be an advanced workflow management system. It does not define how to distribute and optimize the parallelization of workflows on cluster nodes. It is rather designed to standardize earlier, in the development process, the implementation of computational building blocks and provide a universal scheme to design complex workflows. It therefore allows to easily contribute to the development of computational applications independently of the way these applications could be packaged, deployed or executed (e.
Application of BioTracs to a toy analysis workflow (TAW) We illustrate here, through a simple toy analysis workflow (TAW), how BioTracs can be used. Consider an initial dataset where rows and columns are observations and variables respectively. Each observation belongs to a group. The user is interested in performing a basic unsupervised analysis on this dataset to extract trends in the data, followed by a supervised analysis to extract features that discriminate each observation group. Let us build a simple analysis workflow to achieve this task (Figure  7 ). This workflow uses some basic statistical modules implemented in the core BioTracs and in the BioTracs-Atlas application devoted to machine learning. The first process is a FileImporter (FI) used to import a disk file as an in-memory resource file (DataFileSet) corresponding to the dataset.
The DataParser next parses the resource file to create the DataSet object. The Filter is used to remove variables having negligible variances (i. e. that can be considered constant) or having all their values under a predefined limit of quantitation. It namely prevents having ill-conditioned problems in the following numerical solvers. The DiffProcess performs differential analysis using Student t-test. The PCALearner process performs unsupervised analysis using principal component analysis. The PLSLearner process performs classification using partial least square discriminant analysis (PLS-DA)  [15, 16]. The PLSPredictor process receives the PLSLearnerResult and the initial training dataset as input resources to perform prediction. Also, a blind test dataset could be used to assess the learned model on a new set of observations.
The PartialDiffProcess can therefore assess group separations in the PCA or PLS score plots. Next, the user visualizes the analysis results using custom views generated and exported by the ViewExporter (VE) resources as disk files than can next be imported for further analyses. For traceability purposes, each process, as well as the encapsulating workflow, generate individual configuration files (config. params. xml) containing all the parameters used at each stage of the workflow as well as log files. TAW code file is available online on GitHub (https://github. com/bioaster/biotracs-papers). It provides three datasets for testing: the well-known Fisher's Iris flower dataset  [19] , the breast cancer Wisconsin dataset  [20, 21]  and an anonymized "metabo" dataset we constructed for classification analysis testing.
Conclusions We presented BioTracs, a transversal framework for the standardization and traceability of computational workflows. Fundamental concepts underlying BioTracs were presented. BioTracs derives from the PRISM architecture, which allows modeling and interfacing computational resources and processes to build complex workflows. It is designed to provide scalability and transparency from the code to the project level and demonstrated flexibility and robustness in large-scale metabolomics and clinical projects  [22]. It provides a framework in which computational scientists can implement simple building-block processes and next specialize them to fit their scientific use-cases. Today, BioTracs and related libraries are implemented in the object-oriented MATLAB environment. It can be extended to open object-oriented environments such as Python, C++ or java.
Acknowledgements The authors would to acknowledge  Frédéric BEQUET  for its advices during the development of BioTracs. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted February 19, 2020. ; https://doi. org/10. 1101/2020. 02. 16. 951624  doi: bioRxiv preprint.
We introduce a new software package for modeling the point-spread function (PSF) of astronomical images, called PIFF (PSFs In the Full FOV), which we apply to the first three years (known as Y3) of the Dark Energy Survey (DES) data. We describe the relevant details about the algorithms used by PIFF to model the PSF, including how the PSF model varies across the field of view (FOV). Diagnostic results show that the systematic errors from the PSF modeling are very small over the range of scales that are important for the DES Y3 weak lensing analysis. In particular, the systematic errors from the PSF modeling are significantly smaller than the corresponding results from the DES year one (Y1) analysis. We also briefly describe some planned improvements to PIFF that we expect to further reduce the modeling errors in future analyses.
Other recent cosmic shear experiments have similarly montioned PSF modeling errors as a significant source of systematic uncertainty  (Hamana et al. 2020; Hildebrandt et al. 2020). The Y1 PSF estimation used the software package PSFEX  (Bertin 2011). This package estimates the PSF at the location of each star using a linear combination of basis vectors. In our case, the basis used was simply the pixel values on a grid. The coefficients of this model are interpolated using a polynomial in the chip coordinates, x and y, across the area of each CCD image. This method worked very well and is quite robust. However,  Zuntz & Sheldon et al. , (2018)  detected some aspects that could potentially be improved for future DES analyses. First, the mean measured size of the PSF models showed a non-negligible offset from the mean measured size of the input stars.
• Code written in or easily accessible from Python. • Modular design to enable new (potentially non-linear) PSF models, outlier rejection algorithms, and interpolation schemes. The version of PIFF used for the DES Y3 WL analysis is 0. 2. 4, but PIFF has been in continuous development since the Y3 PSF models were finalized. In this paper, we demonstrate diagnostics for the DES Y3 PSF model based on version 0. 2. 4, but we will also point out various features that have already been improved upon in later versions. Throughout the paper, we will mention the choices used for the DES Y3 WL analysis where we discuss the various options enabled by PIFF. The complete configuration file that was used for this analysis is given in Appendix B. We give an overview of the procedure for building a PSF model for an exposure in §2.
We describe the options in PIFF for the PSF surface brightness model, interpolation schemes, and how it finds the overall solution in §3-5. We describe the DES Y3 data in §6 and show our tests of the PSF solution on these data in §7. In §8, we discuss some potential future improvements to PIFF cur-rently in development, and we conclude in §9. Detailed instructions for using PIFF can be found in the online code documentation 2.
Gaussian processes The GPInterp class in PIFF implements a Gaussian process regression  (Rasmussen & Williams 2006)  on the parameter vectors pi. Gaussian process regression, also known as "Kriging," assumes that the parameter vector p at every location is drawn from a multidimensional Gaussian random field across the (u, v) space. It requires an estimate of the spatial covariance function of p, commonly referred to as the kernel. The interpolation estimate at an arbitrary location (u, v) is the minimum variance unbiased estimate from the Gaussian distribution at that location conditioned on the values pi measured at all the PSF stars. The GPInterp class is implemented using the TREEGP module 8. It can use a number of possible kernels from SCIKIT-LEARN to define the covariance matrix along with a few custom options, which we use in PIFF.
The default kernel is the so-called "squared   3 ) the residuals from a second order polynomial fit on each CCD, and (4) the residuals from an anisotropic Gaussian process fit over the whole FOV. Each panel shows "whiskers" whose length is proportional to the ellipticity of the PSF, and whose orientation matches the orientation of longest axis of the PSF shape. In both cases, the residuals are shown only for a set of reserve stars, which were not used to fit the PSF model. exponential" or "radial basis function" kernel, known as RBF in SCIKIT-LEARN. The TREEGP module has several methods for how to optimize the kernel's hyper-parameters, which are available in PIFF via the optimizer parameter: (i) likelihood is the traditional maximum likelihood optimization. It finds the hyper-parameters that maximize the likelihood of the Gaussian process solution.
Output files Once the iteration has converged, PIFF writes the final solution into an output FITS file. The file format is rather complicated, using many HDUs (header/data units) to store the various kinds of information in a modular way. For instance each Model class and each Interp class stores different kinds of information, so each uses one or more HDUs in a class-specific way. Users do not need to know anything about this file format however, since the piff Python module has code to read the output file and reconstruct a PSF object that can compute the correct PSF profile at an arbitrary location. PIFF is designed to serve the roles both of solving for the PSF solution and of using that solution for further analysis. In addition to the FITS file containing the final PSF solution, one can also choose to have PIFF generate a number of diagnostic output files.
The full solution to the world coordinate system (WCS) is implemented by the PIXMAPPY 13  software package. We used this WCS solution for each exposure rather than the simpler TAN-PV WCS solutions stored in the FITS files. Since PIFF models the PSF in sky coordinates, the astrometric effects of the tree rings and other distortion effects are accounted for by transforming the image data of each star to sky coordinates. The led to a significant improvement in the PSF shapes compared to using the native FITS-based WCS solutions.
SUMMARY We have presented a new software package for PSF estimation of astronomical images, called PIFF, which was developed primarily for the DES Y3 WL analysis. The PIFF PSF models were tested on the Y3 data and show significantly smaller residuals than had been seen in the Y1 data. Most notably, the ρ1 statistic is more than an order of magnitude smaller at large scales than what was found for the Y1 PSF model. This had been one of the most significant sources of systematic uncertainty in the Y1 cosmic shear analysis. Development of PIFF is still very active. We have described in §8 several potential improvements we hope to include in the DES Y6 analysis. In addition, as part of the Dark Energy Science Collaboration (DESC) for the Vera Rubin Observatory Legacy Survey of Space and Time (LSST), we have been testing PIFF on simulated LSST images.
DATA AVAILABILITY The PIFF software is publicly available 18 under an open source license. This paper describes version 1. 0 of the software, although most of the tests were made with PSF models produced by version 0. 2. 4. Installation instructions are found on the website, and we welcome feature requests and bug reports from users. The code we used to run PIFF on DES images, produce the PSF catalogs, and create many of the plots is also publicly available 19. Catalogs of the PSF measurements on the reserve stars will be made available as part of the DES Y3 coordinated release 20. Of course, some values needed to be set differently for each exposure and CCD, so these were specified on the command line when we ran the piffify executable for each image. The other parameters shown here were the same for all exposures.
ACKNOWLEDGEMENTS This paper has gone through internal review by the DES collaboration. We thank the internal reviewers for their very helpful suggestions for improving the paper. We also thank  Robert Lupton  for a very careful and helpful review as referee for this paper. M. Jarvis  and  G. Bernstein  are partially supported by the  US Department of Energy  grant  DE-SC0007901  and funds from the  University of Pennsylvania. This document was prepared by the DES collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. This work was based in part on observations at  Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory , which is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation.
Soil physical properties play an important role in estimating soil water and energy fluxes. Many hydrological and land surface models (LSMs) use soil texture maps to infer these properties. Here, we investigate the impact of soil texture on soil water fluxes and storage at different scales using the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) LSM, forced by several complex or globally uniform soil texture maps. At the point scale, the model shows a realistic sensitivity of runoff processes and soil moisture to soil texture and reveals that loamy textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets, although important differences can be found at the regional scale, particularly in areas where the different maps disagree on the prevalence of clay soils.
(2000)  and 30 cm for  Zobler (1986) ; the FAO/UNESCO Soil Map of the World was updated as the Harmonized World Soil Database (HSWD), produced at 30 arc-sec by including new regional and national soil information  (Nachtergaele et al. , 2010; Batjes, 2016) ; the soil texture map of the 1 km SoilGrids database  (Hengl et al. , 2014) , recently updated at 250 m  (Hengl et al. , 2017) , is not independent of the above FAO/UNESCO global soil maps, but also relies on a large number of national and international soil profile databases combined with automated spatial prediction models. Both the HSWD and SoilGrids soil texture maps are available at seven depths ranging from 0 cm to 2 m. Most studies concluding that soil texture exerts an important impact on soil hydrology were conducted at small to medium scales, either through site measurements (e.
Simulation protocol We performed nine global-scale simulations with OR-CHIDEE (tag 2. 0), using different soil texture maps and climatic forcing datasets (Table  1 ). The analysed period is 1980-2010, following a 20-year warm-up since 1960 to provide accurate initial conditions. Atmospheric forcing datasets being known to exert a first-order influence on LSM results  (Guo et al. , 2006; Yin et al. , 2018) , we used two different datasets to drive our simulations, to compare the related uncertainties to the ones coming from the different soil texture maps. Both datasets were constructed at a 0. 5 • resolution by downscaling and bias-correcting an atmospheric reanalysis. All simulations but one use the GSWP3-v1 meteorological dataset  (Kim, 2017) , with a 3-hourly time step and based on the 20th Century Reanalysis (20CR;  Compo et al.
, 2011). In contrast, simulation EXP1 uses the 6-hourly CRU-NCEP-v7 meteorological dataset  (Wei et al. , 2014) , based on the NCEP/NCAR reanalysis  (Kalnay et al. , 1996)  and extended beyond 1957-1996 in near-real time. Both meteorological datasets were selected for the offline CMIP6 simulations  (van den Hurk et al. , 2016). The three simulations EXP2 to EXP4 rely on complex soil texture maps to define the dominant texture class of each 0. 5 • grid cell (Fig. 1 ): the 1 • map of  Zobler (1986)  originally contained five soil textural classes, but is simplified by ORCHIDEE into three USDA texture classes (Sandy Loam, Loam, and Clay Loam); the 5 arc-min map of  Reynolds et al. (2000)  uses the USDA classification and we used directly the 30 cm map; the third map was upscaled from the 1 km Soil-Grids map at the 0 cm depth  (Hengl et al.
Evaluation datasets To assess the realism of our simulations, we use three different datasets. Jung et al. (2010)  constructed a series of global 1 • evapotranspiration maps at the monthly time step from 1982 to 2008 by interpolating in situ eddy-covariance measurements from the FLUXNET network using machine learning algorithms and ancillary geospatial information (land surface remote sensing and meteorology). GLEAM  (Martens et al. , 2017)  is another series of global evapotranspiration maps, provided by the 0. 25 • resolution and the daily time step over 1980-2015. They strongly rely on remote-sensing datasets (radiation, precipitation, temperatures, surface soil moisture, vegetation optical depth, snow water equivalents) used as input to an evapotranspiration model based on  Priestley and Taylor (1972).
Discussion and conclusions Using the ORCHIDEE LSM and different soil texture maps, we found that the model shows a realistic sensitivity of surface runoff, drainage and soil moisture to soil texture compared to experimental and field studies  (Rawls et al. , 1993; Osman, 2013). These sensitivities lead to higher simulated evapotranspiration and lower total runoff for medium texhttps://doi. org/10. 5194/hess-24-3753-2020 Hydrol. Earth Syst. Sci. , 24, 3753-3774, 2020 tures, which are discernable against other sources of variability when sorting the 12 USDA texture classes based on their median diameter. Apart from some areas which exhibit important differences in evapotranspiration, often attributed to the Clay texture class, the three complex soil texture maps tested here lead to similar water budgets at all scales, and the large uncertainties in observation-based products and climate forcing datasets make it impossible to conclude which map gives the best simulation.