dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion SCALPEL3 could be further improved by optimizing the flattening step, so as to ensure optimal blocksparsity of the resulting denormalized databases automatically. Besides, optimizing the cluster design to separate storage from computation as well as using YARN instead of Mesos to manage resources could help to improve its performance further by lowering data access times. Finally, using Apache ORC  [2]  instead of Parquet could also lead to further performance improvements. Parquet was initially chosen over ORC because of better integration with Spark. ORC is now well-integrated in it and has been reported to have better performances and a higher compression factor on non-nested data. Caillol, Anthony Du, Sébastien Dumontier, Mehdi Gabbas, Claude Gissot, Moussa Laanani, Mickaël Lechapelier, Clémence Martin, Anke Neumann, Cédric Pulrulczyk, Jérémie Rudant, Omar Sow, Kévin Vu Saintonge, Alain Weill, and for Polytechnique: Qing Chen, Agathe Guilloux, Anastasiia Nitavskyi, Yiyang Yu.
Acknowledgments We thank the engineers who worked on this project at some point:  Firas Ben Sassi ,  Prosper Burq ,  Philip Deegan ,  Daniel De Paula e Silva ,  Angel Francisco Orta ,  Xristos Giatsidis ,  Sathiya Prabhu Kumar. We also thank the people from CNAM or Polytechnique who were or are currently involved in the Polytechnique-CNAM partnership, namely, for CNAM :  Muhammad Abdallah ,  Aurélie Bannay , Hélène.
: from scalpel. stats. patients import distribution_by_gender_age_bucket from scalpel. core. cohort_flow import CohortFlow flow = CohortFlow([base_population, exposed_subjects, final_cohort]) for cohort in flow. steps:figure = plt. figure(figsize=(8,4. 5)) distribution_by_gender_age_bucket(cohort=cohort, figure=figure)B List of SNDS databases currently denormalized. Database Contents DCIR Outpatients reimbursement data PMSI Hospital discharges MCO Acute ward MCO CE Acute ward outpatients treatment SSR Rehabilitation SSR CE Rehabilitation outpatients treatment HAD Home-to-home care IR IMB R Long term chronic diseases IR BEN R Patients socio-demographic information.
VERIFICATION AND VALIDATION OF SEPARATED EFFECTS Based on the previous step, verification and validation of separated effects test cases have been identified for each physical phenomenon with a specific care for those with a limited level of maturity but a high contribution. After explaining the methodology, the selected test cases for the target flow are detailed. It has to be noted that the V&V process relies also on the code verification performed by the Ansys Fluent developers' team, which is not detailed in this paper.
Turbulent flow around 3x3 rods with an obstacle (Krull et al. , 2012) In this test case, an isothermal longitudinal flow of p-cymene ( ρ = 850. 79 kg. m and μ = 7. 61. 10 Pa. s) is evolving around 3x3 rods organized in a square lattice. The computational domain is represented in Fig. 4 (a). The distance between two rods is equal to 13 mm. The main pipe is 978 mm high and has a diameter equal to 54 mm. A spacer grid, displayed in Fig. 4  (c), which supports the rods is placed in the first third of the computational domain and constitutes an obstacle for the flow. and distributions from the Helmholtz Zentrum Dresden-Rossendorf (HZDR-Germany) are available to validate the Fluent simulations. Simulation results obtained with Ansys CFX 14. 0  (Krull et al. , 2012)  allow a code-to-code comparison as part of the code verification process  (Oberkampf and Roy, 2010).
924 factor). For plane 4, simulations predict larger variations of the velocity especially for the positive x values, which is not the case with the CFX velocity profiles. The experimental results measured in planes 2 and 4 are not symmetrical. Nevertheless, Fluent simulations do not reproduce this asymmetrical behavior. To have a quantitative comparison of the results, the mean velocity, the maximum, minimum and amplitude values are extracted from the profiles for x position between -22 mm and 22 mm. Table  3  presents the results for plane 2 H2, Table  4  for plane 2 H3, Table  5 plane 4 H2  and Table 6 plane 4 H3. These quantities of interest transcript quite well the behavior of the different velocity profiles. Thus, for each position, the mean value prediction is improved by the mesh refinement with Fluent.
The 18SIB04 QuantumPascal EMPIR project aims for development of photon-based standards that can replace primary standards of the SI unit of pressure, the Pascal. In this project, four partners simulated the pressure-induced deformation of a given Fabry-Pérot cavity, using various versions of two types of software, COMSOL Multiphysics ® and ANSYS Workbench. It was demonstrated that, for a given geometry and set of material parameters, simulations of the deformation could be performed by the various partners with such small discrepancies that methodological mistakes of the simulation procedures will solely contribute to a sub-ppm uncertainty in the assessments of refractivity of N2.
Software used To ensure that the simulation results were not software dependent the software used in the simulations were, at the start of the project, not only chosen by the partners themselves, it was also emphasised that not all partners should use the same type or version of software. Three partners, A, B, and D, utilized various versions of COMSOL Multiphysics ® , viz. the versions 5. 3a/5. 5, 5. 4, and 5. 5, respectively, together with the structural mechanics module, while two of the partners, B and C, used ANSYS Workbench -Academic Student 2019 R2 with a static structural toolbox (partner B thus used both types of software).
Set-up of the cavity To create the 3D model of the cavity the partners A and B used the built-in design module in COMSOL Multiphysics ®. Partner C utilized the DesignModeler Ansys Workbench module to import CAD file created in Autodesk fusion 360. Partner D employed the CAD import module to import 3D models originally created in Autodesk Inventor Professional 2020. Both the material of the cavity spacer and the mirrors were modelled using a linear-elastic model. The cavity spacer and the mirrors were considered to be unified, which means that they were seen and treated as one joint solid object. To simulate the application of a pressure onto the cavity, a boundary load was applied to all external surfaces. To decrease computational work, the partners B, C, and D, utilized (three) symmetry planes and simulated solely 1/8 of the cavity.
Consensus As was alluded to above, the simulations performed by the various partners, utilizing dissimilar software and set-up strategies, did finally, for a given geometry and set of material parameters, provide a relative 95% ( ) confidence interval of ± 3 × 10 -16 Pa -1. For the FP cavity addressed, this corresponds, for N2, to a relative uncertainty in the assessed refractivity solely of 0. 1 ppm, which, for the pertinent QuantumPascal EMPIR project, is considered adequate.
SUMMARY AND CONCLUSION The ability of four partners of the 18SIB04 QuantumPascal EMPIR project to simulate, by FEM-simulations, the pressure-induced cavity deformation of a given FP cavity has been assessed. When setting up the simulations, it was found that considerable care must be taken regarding the input parameters and boundary conditions since any minor inaccuracies in these could lead to non-negligible errors in the simulations. In the absence of methodological inconsistencies, and for a given geometry and set of material parameters, adequate agreement between the simulations made by the various partners was reached. It was found that the use of different types of software did not significantly affect the results. On the other hand, a certain dependence on the number of mesh elements used was found.
Data analysis We have conducted a re-used of qualitative data  (Germain & Chabaud, 2006). It "consists of re-examining one or more sets of qualitative data, with a view to examining research questions that are different from those contained in the original study"  (Thorne, 2004 (Thorne, , p. 1006)). Our data were derived from an early study on the link between HRM and innovation in SMEs. The interviews conducted by one of the authors of this article highlighted the manager's predominant role in the evolution of the organization toward an alternative form that clearly differs from the other two SMEs studied. Indeed, values seemed decisive for managers, guiding them in their choices and in the construction of their organization. The leader wants to operationalize them and put in place tools that are consistent with them.
So we decided to conduct a supra-analysis that "transcends the focus of the primary study from which the data was derived, examining new empirical, theoretical or methodological questions"  (Heaton, 2004, p. 34). In the context of the supra-analysis, we identified the potential of the data that emerged from an empirical problem and have actually turned out as a gap in the literature. The new analysis is in keeping with the dynamics of the initial research project, following what  Heaton (2004)  recommends and was conducted by the researcher who carried out the initial research, which facilitates data reprocessing. To re-use qualitative data, two themes in the initial interview guides are analyzed for each population: organization, values, and environment; human resources management. All interviews were recorded and fully transcribed.
-pc_fieldsplit_gkb_delay 5 -pc_fieldsplit_gkb_tol 1e-5 -pc_fieldsplit_gkb_nu nu The inversion of M is done using a direct solver: The reference solutions are obtained by solving the original augmented system  (11)  for the corresponding right-hand side received from code_aster, using the LU decomposition from the MUltifrontal Massively Parallel Solvers (MUMPS). In the convergence plot captions, we display the corresponding backward error estimates. However, due to the bad conditioning of the matrix, the backward error estimate can deteriorate. The workaround implemented in code aster is the double Lagrangian approach described in  [23]. The number of Lagrangian DOF doubles and increases the computational cost dramatically. To avoid this approach, as will be detailed in the sequel, we will encapsulate the direct LU decomposition in a Flexible GMRES (FGMRES) solver with a sufficiently small tolerance.
Discussion on ν Theorem 1 provides the optimal choice of ν to make the number of iterations in Algorithm 1 independent of the mesh size of the finite element discretization. In general, we are not able to compute λ 1 and λ n in Theorem 1 which would allow us to obtain a more precise estimate of ν for a given η. For the smallest three test problems above, we are however able to do so using MATLAB. Hence, we can compare our recommendation to the optimal value ν η , say, for some η. Let us use η = 1, i. e. κ( Ã) ≤ √ 2. Table  3  shows that the choice of ν = W 1 leads to smaller values than would be necessary for Theorem 1 and η = 1. Indeed, with the optimal ν η=1 a constant iteration number of 8 is attained for Prob. 2 and 3 instead of 12 and 14 respectively for the chosen ν. In Table  4 , we present a short study of possible choices for ν and Prob.
While any of the previous cited hyperreduction method could have been used, we choose the ECM for its ability to easily derive an approximation for the assembling of a boundary term only, the regular distribution of the reduced integration points on this boundary, and the fact that the approximated reduced operators conserve the spectral properties of their high-fidelity counterpart. We also illustrate that using a nonintrusive approach allows to reduce a problem without knowing precisely the model, thanks to the underlying regularization property of reduced order methods. Indeed, using the finite element software Z-set  [22] , for which we know precisely the models, we were able to construct a ROM solving the same model and obtained very accurate results. However, the models and solvers in the commercial software Abaqus  [23]  are proprietary, and using the same ROM leads to less accurate results, but still within engineering accuracy.
Nonintrusive reduced order modeling library This work has been implemented in an in-house nonintrusive reduced order modeling library developed at Safran, see  [21]. Thanks to the nonintrusive implementation, the high-fidelity snapshots can be generated by a commercial software. In our case, we use the commercial versions of Abaqus  [23]  and Z-set  [22]. The nonintrusivity is gained from having coded routines to read solutions and meshes from the used finite element software into an in-house format, and having recoded all the finite element assembling routines in our library. Then, considering nonparametrized variability is now readily available: we can, at the beginning of the online stage, for a new variability, assemble the reduced loading terms using these available assembling routines.
Simple test case with convection only using Z-set We consider a very simple test case consisting of a square mesh with N = 676 degrees of freedom, a uniform initial condition of 100 • C, 100 time step for a duration of 1000 s, and a convection boundary condition with h = 1000 J s -1 m -2 K -1 and T 1,e oscillating between  100 and 1000 • C with a period of 600 s. A HFM solution is computed using Z-set, then a ROM is computed on the same configuration for validation purposes. The training data contains 100 snapshots. The POD selects 11 modes with POD = 10 -6 , and ECM is not computed here since radiation is not considered. The durations for the offline and online stages are given in Table  1 , where "Remaining" refers to all the remaining operations including the reading of the snapshots and the operations required for the nonintrusive approach and where "compute online loading" refers to the computation of the reduced external forces vectors and the extraction of the sequences of coefficients h (i) (t) and T (i) 1,e (t) from the input files, see "Nonintrusive reduced order modeling library" section.
Simple test case with radiation only using Z-set We consider again a very simple test case consisting of the same square mesh as the previous section, with N = 676 degrees of freedom, a uniform initial condition of 1000 • C, 100 time step for a duration of 1000 s, and a convection boundary condition with σ = 3. 9683 × 10 -8 J s -1 m -2 K -4 and T 2,e oscillating between 100 and 1000 • C with a period of 600 s. A HFM solution is computed using Z-set, then a ROM is computed on the same configuration again for validation purposes. The training data contains 100 snapshots. The POD selects 9 modes with POD = 10 -6 , and ECM selects 19 integration points with ECM = 10 -6. The durations for the offline and online stages are given in Table  2. The HFM duration is 5. 87 s, leading to a speedup of 37 in this case.
Industrial application In this section, we apply our nonintrusive reduced order strategy on an industrial study of the high-pressure compressor presented in "Introduction" section, where we recall that the number of degrees of freedom is N = 4785. Various temporal scenarios of convection coefficients h (i) and external temperatures T (i) 1,e for each of the 39 surfaces (i)  are considered, see Fig. 6. The physical duration of the simulation is now t f = 600 s, and Abaqus is chosen to compute the snapshots. We use the high-fidelity data from the scenarios 1 to 7 (defined in from Fig. 6 ) in the offline stage, while the accuracy of the ROM will be tested at the variability of scenario 8. We precise that the variability of our model is not parametrized by T min , T max and τ : these are parameters for a precomputing step handled by the in-house 1D nodal network code, which generates the nonparametrized time series h (i) (t) and T (i) 1,e (t), 1 ≤ i ≤ 39, which are the input of our problem.
Even though we used the commercial version of Z-set in our nonintrusive framework, we have the source of the code at our disposal, and we have checked that our reduced model, in the online stage, implements the same model as the one in Z-set. Abaqus being a commercial code, the exact implementation of the solvers and models is proprietary and not known. However the accuracy of the ROM using Abaqus generated snapshot is still within engineering tolerances, even in the context of the nonparametrized variability detailed in "Industrial application" section. This robustness with respect to the model is provided by the a posteriori reduced order modeling technique, where regularity features are transported from the snapshots to the modes. In  [32] , a reduced order model is constructed from Large Eddy Simulations snapshots, but a resolution of the Navier-Stokes equations with no turbulence model is carried out in the online stage, with accurate results.
Output/product -All products generated by an intervention, including scientific or non-scientific knowledge (including publication, report, database, etc. ), methods, processes, professional or academic training, expertise, technology, networks, etc. Plausibility -The plausibility of an intervention impact pathway is hinged on the relevance and soundness of the proposed actions in relation to the hypotheses put forward and the collective knowledge of the intervention context. The plausibility of the proposed impact pathway is strengthened by the collective elucidation of these hypotheses, striving to avoid 'miracle hypotheses', as well as by the elucidation of the processes and causal links to generate the desirable outcomes. Strategy -A set of coordinated resources and actions to overcome obstacles and/or leverage opportunities in a systemic way to achieve a goal.
ImpresS ex ante The problem tree is a tool often used by CIRAD researchers and project development officers. There are several guides to serve its implementation and facilitation. A problem tree may be drawn up/visualized using PowerPoint, other software, or the online ImpresS tool 6. The problem tree is used to initiate the elucidation work and the strategic choices of the intervention team, and to begin outlining what the intervention will focus on. This problem tree should be seen as an intermediate object. It is not necessarily intended for use in documents that will bedisseminated to different audiences at a later stage (e. g. donors, project documents). It can however be reused in subsequent ImpresS ex ante steps to check the overall consistency, to iteratively refine the intervention definition, and to maintain a systemic vision.
) and which actors are peripheral or dependent on others. It is focused on the relationships between actors rather than on their individual features  (Durland & Fredericks, 2005). In social network analyses, mathematical tools may be used to analyze relations between actors. Data is collected through surveys, interviews, or focus groups. Mapping networks of actors is the first stage in social network analysis. In the ImpresS approach, the ambition is to gain insight into relationships between the different actors in innovation, as well as their role in this process. A map is thus drawn up where the actors are represented by nodes and between-actor relationships by links between these nodes. Actors may be specific individuals acting within an organization or, more generally, organizations themselves.
Acknowledgements The editorial team would like to sincerely thank the following people for their contributions to the design, writing and proofreading of this second version of the ImpresS ex ante Methodological Guide:  Sélim Louafi ,  Damien Conaré  and  Sylvain Perret , as well as the  ImpresS methodological committee  for their constructive contributions towards improving the approach and its description. An approach for building ex ante impact pathways in development-oriented research Glossary.
A hackathon is a type of software development lifecycle model featuring problem-focused development via intensive, time-limited, self-organized group activities, typically involving programmers and various types of collaborators  7. The hackathon methodology has been shown to be productive in a variety of biomedical fields, including rehabilitative healthcare  8  , biological data science  9  , neuroscience  10  , computeraided differential diagnosis  11  , stroke detection  12  , standards specification in systems and synthetic biology  13  , data science for knowledge discovery in medicine  14  , medical device innovation  15  , enrichment of biodiversity data  16  , and teaching genomics  17. BH15 was held in Nagasaki, Japan, over the period of September 14 th to 18 th 2015, and was hosted by the National Bioscience Database Center (NBDC,  18  ) and the Database Center for Life Science (DBCLS,  19  ) to promote interoperability of life sciences databases in Japan.
Life sciences data Genotypes and phenotypes Variation graph construction. In the context of the Global Alliance for Genomics and Health (GA4GH,  21  ) there is a challenge to build genomic variation graphs (VG). A genomic variation graph represents all "common" genetic variation, providing a means to stably name and canonically identify each variant. At BH15, we modeled such graphs using RDF semantics. Taking the 1000 Genomes project phase 3 Variant Call Format (VCF) files and the GRCh37 human reference genome we built a variant graph using the VG tool  22. Such a VG graph corresponds to just fewer than 2 billion triples. It was loaded inside 67 minutes on a server from 2013 that had 64 AMD X86_64 cores, 256 GB ram, and 3 TB of consumer-quality SSD storage without specific tuning. The SPARQL database disk footprint with indexes was 49 GB, i.
e. double the disk space consumed by the raw VG tool files. This shows that a modern SPARQL database does not require exorbitant resources to be able to index and load a variant graph of interest to the medical community. We also demonstrated that a number of queries on the graph executed within reasonable times. This work was contributed to the VG development team and incorporated into the VG release 1. 4. 0. At BH15, the standard API developed by the core API team of the GA4GH was implemented as a service running on top of a SPARQL endpoint.
Variant call transformation. VCF is a standard for text files that store gene sequence variations and is used for large-scale genotyping and DNA sequencing projects. Converting a single high-throughput sequence dataset, e. g. a VCF file but more so a very large database such as the Ensembl Variation Database, into RDF results in a number of triples that may be unmanageable for a small bioinformatics lab, even if backed by current hardware. However, data can also be considered in a more dynamic way, if we abstract the concept of data generation to, for instance, some bioinformatics analysis or pipeline, where new data can be generated on the fly as a result of some computation over existing information or files. To this end we prototyped a real-time system to transform VCF into RDF and query it by SPARQL.
With JRuby we could use the original samtools/htsjdk libraries for manipulating VCF, BED, SAM/BAM files. With this approach, we could quickly prototype our solution and defer the development of proper Java libraries sharable by alternative approaches and/or applications. Our approach allows generating virtual endpoints over multiple VCF files, combining the simplicity of native file formats with the power of the SPARQL language, significantly improving the way we link and query heterogeneous information. An implementation of such a system was conceived during the 1 st RDF summit in 2014 at the DBCLS in Tokyo and further developed during BH15. The system 23 was based on de facto standard frameworks, such as Ruby RDF  24  and OpenSesame 25 , which facilitate the generation and transformation of RDF based data and the processing of SPARQL algebra and queries.