dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The study was funded by the  European Commission 's  Copernicus Programme and EUMETSAT , contract number  EUM/CO/17/ 4600001980/EJK. We would like to recognize the time and effort done to collect the in situ measurements gathered in this study, which were collected by different research groups and for different areas across the globe. The final dataset was created from the following studies/projects: NOMAD 2015**, Valente et al. (2016), Loisel et al. (2018) (PANGEA), Plumes and blooms (funded by  NASA ), BOUSSOLE, funded by  ESA ,  CNRS ,  INSU , and IMEV, PEACETIME cruise ( Julia Uitz ,  Emmanuel Boss & Nils Haëntjens , contribution to the " PEACETIME-OC " project funded by  CNES) ,  NASA  NAAMES cruises, LIMNADES database, specifically Annelies Hommersom in Netherlands, financed by  NWO/SRON Programme Bureau Space Research , Estonian data collection and analysis were funded by the  Estonian Research Council  grants  SF01980009s11  and  PRG302 , Lake Superior river and estuary collected in the frame of the  NSERC  funding ( RGPIN-2019-06070  and  RGPIN-2014-03680 ), Lake Erie IOPs and AOPs were provided by  Dr.
The Gator program has been developed for computational spectroscopy and calculations of molecular properties using real and complex propagators at the correlated level of wave function theory. Currently, the focus lies on methods based on the algebraic diagrammatic construction (ADC) scheme up to the third order of perturbation theory. An auxiliary Fock matrix-driven implementation of the second-order ADC method for excitation energies has been realized with an underlying hybrid MPI/OpenMP parallelization scheme suitable for execution in high-performance computing cluster environments. With a modular and objectoriented program structure written in a Python/C++ layered fashion, Gator additionally enables time-efficient prototyping of novel scientific approaches, as well as interactive notebook-driven training of students in quantum chemistry.
The focus of the Gator program lies on single-reference methods, currently using the algebraic diagrammatic construction (ADC) scheme for the polarization propagator up to the third order in the fluctuation potential  [8] [9] [10] [11]  as implemented in the ADC connect (adcc) toolkit. 12 It had been inspired by the adcman module contained in Q-Chem  13, 14  but evolved into an independent, open-source hybrid Python/C++ module. Based on the capabilities of adcc, the Gator program enables the calculation of molecular properties using linear real and complex response functions. In addition, Møller-Plesset (MP) perturbation theory and ADC(2) have been implemented with efficient exploitation of high-performance computing (HPC) resources. This implementation is accomplished using a distributed auxiliary Fock matrix-driven tensor contraction scheme with hybrid MPI/OpenMP parallelization.
Overall, the Gator program is designed to provide a flexible platform for (i) high-performance ab initio quantum chemistry, (ii) scientific extensions (methods and algorithms) with relative ease and quick turnover time, and (iii) educational training with a high degree of student interactivity. The available features of Gator are described in more detail in the corresponding subsection. With partly different scientific objectives but an otherwise largely shared philosophy and intentions, similar program development efforts have recently been undertaken, for example: (i) the Psi4NumPy  15, 16  Python programming environment for facilitating the use of the self-consistent field (SCF) and post-Hartree-Fock (post-HF) kernels from the Psi4 program through NumPy 17 ; (ii) the PySCF program platform  18, 19  for Python-based SCF and post-HF electronic structure theory calculations of finite and periodic systems; (iii) the molsturm  20  quantum chemistry framework, which provides a contraction-based and basis function-independent SCF, integrating readily with existing integral libraries and post-HF codes; and (iv) the Dalton Project 21 providing a platform written in Python for the interoperability of quantum chemical software projects.
Any MO integral resulting from Equation (1) remains afterward on the node of its evaluation, being ready, for example, for the distributed calculation of the MP2 energy or ADC matrix-vector products. For the ADC part, Gator relies on contraction-based iterative algorithms for solving response and eigenvalue equations as has been described previously for adcc. 12 In such schemes, the ADC matrix M is never explicitly constructed, but instead, the secular and response equations are solved by calculating matrix-vector products with sets of trial vectors {b i }: σ i = Mb i : ð3Þ Formally, the ADC(2) vectors consist of a single-excitation part and a much larger double-excitation part, which can be folded into the space of single excitations. 25 We implemented this algorithm within the HPC-QC module of Gator as vectors in the single excitation space can be communicated between nodes at a negligible cost.
The expressions for the ADC(2) matrix-vector product in Equation (3) have thus also been derived and implemented with the use of the auxiliary Fock matrix-driven MO transformation. For smaller systems, the computational cost of the auxiliary Fock matrix-driven approach is not beneficial, and hence, both variants of integral transformation and calculations of σ-vectors are made available in the Gator program by VeloxChem. 26 With AO integral screening based on a combination of the Cauchy-Schwarz inequality and the density, the calculation of auxiliary Fock matrices scales somewhat better than N 3. As there are a total of N 2 pair indices (i, j), this results in a total scaling of less than N 5 for the auxiliary Fock matrix-driven integral transformation in Equation (1).
| Modular structure of Gator The Gator program consists of three major modules as illustrated in Figure  1 : (i) HPC-QC for MP2 and ADC(2) calculations in HPC environments, exploiting the Fock matrix-driven integral transformation outlined above; (ii) Respondo for the evaluation of real and complex linear response functions; and (iii) ADC connect (adcc)  12  for excited-state calculations based on the ADC scheme up to the third order. The Hartree-Fock reference state and the required one-and twoelectron integrals are provided by the VeloxChem  26  program. The modules of Gator are developed in a two-layered fashion. The upper layer is written in Python and includes the management of the hardware resources, input/output handling, result processing, and high-level parts of some algorithms. Numerical array operations are carried out using NumPy  17  and SciPy.
| Implementation and efficiency of HPC-QC In addition to the standard implementations of MP2 and ADC(2) available within the adcc module, we have implemented new and highly parallel versions of MP2 and ADC(2) that exploit the auxiliary Fock matrix-driven MO transformation within the HPC-QC module. VeloxChem is responsible for the construction of the auxiliary Fock matrices, including the underlying evaluation of two-electron integrals on the AO basis using a hybrid MPI/OpenMP parallelized module for efficient execution on HPC clusters. The module accepts, depending on memory limits, an arbitrary number of density matrices in a batch and handles those in parallel. With a single evaluation of the set of two-electron integrals, the corresponding complete batch of auxiliary Fock matrices is constructed.
| Capabilities Calculations using the ADC(2), 8 ADC(2)-x,  9  and ADC(3) 10 methods can be performed using the adcc module for shared-memory architectures. For spectrum calculations in the soft X-ray region, the core valence separation (CVS) is F I G U R E 1 1] [32]  All excited state and transition properties implemented in adcc are also available in the Gator program. Absorption cross sections and C 6 dispersion coefficients can be obtained through the complex polarization propagator (CPP) approach,  [33] [34] [35] [36] [37] [38]  implemented in the Respondo library. Calculations with explicit solvents are currently available using the polarizable embedding (PE) model  [39] [40] [41]  through the CPPE library  42  as interfaced with VeloxChem. MP2 energies and ADC(2) excitation energies can additionally be obtained by employing the HPC-QC module using the distributed aggregated memory on HPC clusters.
| HOW TO USE GATOR To illustrate the modular, low-barrier nature of our Python-based environment, we outline the calculation of the X-ray absorption spectrum of water in Figure  3. This calculation was performed with the Gator program in a Jupyter notebook,  43  which is an open, web-based interactive environment of increasing popularity. By dividing the code into multiple blocks, Jupyter enables the calculation of individual code sections, keeping results in local memory. This simplifies rewriting and recomputing code snippets, such that the user does not need to recalculate all quantities after a modification is performed. This approach allows for an interactive path to programming, analysis, and education. In Gator, we take advantage of these concepts by combining ab initio computational spectroscopy with intuitive analysis and plotting routines through NumPy  17  and Matplotlib.
| NUMERICAL EXAMPLES Within the electric dipole approximation, the absorption cross section of a randomly oriented molecular system is obtained from the isotropic average of the complex polarizability  33, 35, 37  : σ ω ð Þ= ω cϵ 0 Im α ω ð Þ ½ , ð4Þ F I G U R E 3 Gator notebook mode: The notebook input for the calculation of the X-ray absorption spectrum of water (upper left) and program output (lower left), focusing only on information for the requested five excited states. The resulting spectrum plot is shown on the right F I G U R E 4 Gator program input file for performing the core valence separation-complex polarization propagator-algebraic diagrammatic construction (CVS-CPP-ADC(  2 )) spectrum calculation shown in Figure  3  allowing for the calculation of an absorption spectrum at arbitrary energy intervals by solving the associated linear response equations over a range of frequencies.
| GATOR FOR TRAINING AND EDUCATION The Gator program is a convenient platform for undergraduate training and education in computational and theoretical chemistry with students ranging from the introductory bachelor to the advanced master level. It offers insight into the underlying general theory of quantum chemistry and the adopted electronic structure theory methods with direct and concrete access to the intermediate quantities (tensor elements and wave functions) that are addressed in the workflow of quantum chemical calculations. This is accomplished by interfacing the Gator program with the Jupyter notebook as described above. This allows the student to steer, control, and analyze the course of a desired quantum chemical calculation in a modular manner. Students who are initially not familiar with programming are naturally motivated to overcome barriers involved with entering the field of theory and program development.
With basic knowledge of Python programming, it is thus possible for students to design and implement key algorithms such as, for example, the SCF F I G U R E 6 UV/vis spectra of noradrenaline in vacuum and solution. Continuous lines show the convoluted stick spectra, obtained from a Lorentzian function with γ = 0. 124 eV. Dots depict the one-photon absorption cross section σ(ω) computed with complex polarization propagator (CPP) optimization of electron densities or the response theory calculations of excitation energies. Following this philosophy, it is possible to incorporate such practical exercises into existing courses, which has been successfully demonstrated already by the Psi4 16 community for example. The deepened algorithmic understanding gained by the students will be advantageous when entering the research level in this field.
| OUTLOOK The Gator platform will undergo development following several major lines: (i) The HPC-QC module will be extended to include implementations for the calculation of transition and excited-state properties, as well as the inclusion of external one-particle potentials exploiting the intermediate state representation. 62 (ii) The complex linear response function will be implemented in the HPC-QC module. (iii) The capabilities of the Respondo library will be further extended by including higher-order response functions to allow for simulations of advanced linear and nonlinear spectroscopies. (v) Modular libraries for environment models other than the PE model are under development. (vi) The engine of the HPC-QC module, that is, the efficient construction of the large number of auxiliary Fock matrices on the AO basis, will be adapted for execution on heterogeneous cluster nodes with varying combinations of central processing units and graphics processing units.
ACKNOWLEDGMENTS Financial support is acknowledged from the  European Commission  in the form of the ITN titled " Computational Spectroscopy in Natural Sciences and Engineering (COSINE) " (Grant No. 765739 ), the  European Research Council  under the  Horizon 2020 program  (Grant No. 810367 ), the  Swedish Research Council  (Grant Nos. 2018-4343 ,  2017-00356 , and  2017-06419 ), the  Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences  (Grant No. GSC220 ), and the  Swedish e-Science Research Centre , as well as a fellowship from  Heidelberg University  for P. N. to become a visiting professor at the  Interdisciplinary Center for Scientific Computing , and computational resources were provided by the  Swedish National Infrastructure for Computing. Fruitful discussions with  Evgeny Epifanovsky  and  Anna I.
These properties also make the use of modelling techniques that rely on a periodic structure, sometimes used for plain line, infeasible. S&C instead feature two discrete wheel transition areas over a limited distance. This means that more detailed structural modelling is called for in S&C, but also that it is more feasible given the limited track distance covered. Even though there is a lack of track modelling surveys for S&C, there are papers that compare different track model formulations. Kassa and Nielsen compare a 7 degrees of freedom (dof) co-running track model in MBS software Gensys and a modal superposition structural track model with up to 500 modes in DIFF3D in  [10]. Good agreement in wheel-rail contact forces is obtained for the lower frequency content, while discrepancies are observed in the rail transition areas in the switch and crossing panels.
It is also assumed that two-noded beam elements with uniform properties can represent the crossing behaviour in similar frequency ranges, especially in the vertical direction that is of greatest interest at the crossing transition. In this study, the rail nodes are constrained such that they cannot move longitudinally nor rotate about the longitudinal axis. The rail nodes are thus free to displace laterally and vertically, but rail roll is not accounted for. The sleeper nodes are free to displace in the vertical plane through each sleeper but constrained otherwise. Full details on the model properties and discretisation are given in the Appendix. The model is also available for download  [3]. It is provided as an input deck for the commercial FE software Abaqus. The bushing properties and nodal constraints in the model are parameterised via a Matlab script.
Co-simulation between finite elements and VOCO (Univ-Eiffel) This track model entry concerns Co-simulation using FE code Ansys and MBS code VOCO. The Co-simulation methodology has been used in a preceding study  [25]  and its principle and implementation is presented below. The co-simulation is non-intrusive and could be performed with other FE solvers than Ansys. It uses the same coupling scheme as VOCO's own track models. A comparable co-simulation procedure has also been used with other MBS packages  [26].
To more accurately represent (1) with a modal formulation, complex modes accounting for the non-proportional damping would have to be computed, but this is not supported in Simpack linear flextrack at the time of writing. To implement a track structure with non-proportional damping in Simpack, the non-linear flextrack option would need to be employed. Here the linear flextrack option was chosen for its comparatively lower computational effort and ease of modelling. To generate the modal track model from the common FE model the following steps were performed in software in analogy with the steps outlined above. • A 25-m plain line track extension was added in front of the turnout to allow for the initialisation of simulations with the vehicle outside of the main turnout structure. • A modal analysis was performed in Abaqus using the base FE model to extract the undamped modes up to 2 kHz.
Appendix. Description of common FE model The investigations in this paper use a finite element model of a 60E1-760-1:15 turnout as a common base. The model has been generated using a turnout model script developed within the Shift2Rail research program and the projects In2Track [33] and In2Track2  [34]. The purpose of this script is to allow for the creation of turnout models of different sizes and properties. The models are by necessity rather generic and are intended to be representative but not exact representations of any specific turnout design. The script takes the following parameters as input • Turnout radius and sleeper spacing • Mass and area moment of inertia for rails and sleepers • Stiffness and damping properties for rail pads, ballast, and check rail fastenings • Finite element discretisation in terms of typical element lengths and nodal degrees of freedom The script generates models of standard right-hand turnouts with constant curvature and a tangential entry into the switch.
1e ). The largest scaffolds correspond to the 17 macrochromosomes arms of the karyotype of N. forsteri. We also assembled all ten microchromosomes into single scaffolds (Supplementary Information). We constructed a comprehensive multi-tissue de novo transcriptome assembly (BUSCO score of over 98% core vertebrate genes) using RNA extracted from the same individual lungfish. For annotation of protein-coding genes, we combined evidence from transcript alignments and homology-based gene prediction. This resulted in 31,120 high-fidelity gene models. We assessed the completeness of the genome assembly using the predicted gene set and the BUSCO pipeline, detecting 91. 4% of core vertebrate genes (233 genes) and 90. 9% of vertebrate conserved genes (2,586 genes) (Supplementary Table  2 ). We predicted 17,095 noncoding RNAs (ncRNAs), including 1,042 transfer RNAs (tRNAs), 1,771 ribosomal RNAs (rRNAs) and 3,974 microRNAs (Supplementary Table  3 , Supplementary Information).
Phylogeny of lungfish, coelacanth and tetrapods Phylogenetic relationships among coelacanths, lungfishes and tetrapods have been debated  4, 5, 11. We used Bayesian phylogenomics (Fig. 1 ) with 697 one-to-one orthologues for 10 vertebrates, with a complex mixture model that can overcome long-branch attraction artefacts  4  and also used noncoding conserved genomic elements (96,601 aligned sites) (Extended Data Fig. 3a ). Both datasets unequivocally support lungfish  4, 5  as the closest living relatives of land vertebrates, with which they shared a last common ancestor around 420 Ma (Extended Data Fig. 3b ).
Biological materials Biopsy material for DNA and RNA isolation was obtained from a juvenile Australian lungfish (N. fosteri) imported from Australia (CITES permit no. : PWS 2017-AU-000242). Owing to the immature status of the gonad, the sex could not be determined. The same specimen was used for genome sequencing (muscle), construction of the Hi-C library (spleen) and transcriptome sequencing of brain, gonad and liver. The second set of reads was generated from lungfish embryos (embryonic stage 52, GenBank accession numbers SRR6297462-6297470)  36. Embryos were bred and collected under permit ARA 2009. 039 at Macquarie University.
The final repeat-masking track covered 28. 7% of the 1. 2 Tb. We then processed with an all-against-all alignment with repeat masking in place, yielding five billion alignments. On the basis of these alignments, we derived read qualities at 100-bp resolution, highlighting low sequencing quality regions in the reads. Using the alignments and the read qualities structural weaknesses (chimeric breaks, high-noise regions and other sequencing artefacts) in the reads were repaired (Supplementary Methods, Extended Data Fig. 10 ). Repaired reads were then used for a new round of alignments, again with repeat masking, in place. After alignment, the default MARVEL assembly pipeline proceeded as shown in the included examples of the source distribution (Extended Data Fig. 1 ). For the current MARVEL source code repository, see https://github.
2. 8. 4 (k-mer = 25), SPAdes v. 3. 13. 3  45  (k-mer = 55), SPAdes (k-mer = 75) and Trans-Abyss v. 2. 0. 1  46  (k-mer = 32). The four different assemblies were then merged using the OrthoFuser module  47, 48  implemented in ORP. Completeness of the de novo-assembled transcriptome was assessed with BUSCO v. 3  49  using core vertebrate genes and Vertebrata genes (vertebrata_odb9 database) in the gVolante webserver  50. For reference-guided assembly, all reads were aligned to the N. forsteri genome (each sample independently) using the program HISAT2 v. 2. 1. 0 51 (maximum intron length set to 3 Mb). The resulting mapping files were parsed by StringTie v. 1. 3. 6  52  and transcripts reconstructed from each aligned sample were merged in a single consensus. gtf file.
Repeats and transposable elements annotation Neoceratodus forsteri repeat sequences were predicted using Repeat-Masker (v. 4. 0. 7) with default transposable element Dfam database and a de novo repeat library constructed using RepeatModeler (v. 1. 0. 10), including the RECON (v. 1. 0. 8), RepeatScout (v. 1. 0. 5) and rmblast (v. 2. 6. 0), with default parameters. Transposable elements not classified by RepeatModeler were analysed using PASTEC (https:// urgi. versailles. inra. fr/Tools/) and DeepTE  53. Repeat sequences of A. mexicanum (AmexG_v3. 0. 0, https://www. axolotl-omics. org/) were predicted using the same approach. Repetitive sequences of Anolis carolinensis (GenBank accession GCA_000090745. 2), Xenopus tropicalis (GCA_000004195. 4), Rhinatrema bivittatum (GCA_901001135. 1), Latimeria chalumnae (GCA_000325985.
2), Lepisosteus oculatus (GCA_000242695. 1), Danio rerio (GCA_000002035. 4) and Amblyraja radiata (GCF_010909815. 1)) were identified using Dfam TE Tools Container (https://github. com/Dfam-consortium/TETools) including RepeatModeler (v. 2. 0. 1) and RepeatMasker (v. 4. 1. 0). To further examine the remaining intergenic sequences, we predicted repetitive sequences again using the same workflow on the genome hard-masked with repeats already predicted by RepeatMasker.
Kimura distance-based distribution analysis and transposable-element-composition principal component analysis Kimura substitution levels between the repeat consensus to its copies were calculated using a utility script calcDivergenceFromAlign. pl bundled in RepeatMasker. Repeat landscape plots were produced with the R script nf_all_age_plot. R and nf_am_rb_age_plots. R, using the divsum output from calcDivergenceFromAlign. pl. Principal component analysis on repetitive element composition was performed in R (v. 3. 6) using factoextra package (v. 1. 0. 6). Repetitive element compositions (SINE, LINE, DNA, LTR and unknown) were calculated from the predicted libraries. Repetitive element copies were filtered by the 80/80 rule (equal or longer than 80 bp, equal or more than 80 per cent identity compared with the consensus sequence).