dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, we show the relative difference between a given code and such median in percentage. We note that, by definition of median, in the case an odd number of codes is available, there will always be a code that actually provides the median constraints, meaning its corresponding marker will be placed at 0%. The agreement between the outcomes is better than a few percent for the Intermediate case. In Fig. 2  we also show the relative difference between the full marginalised errors for the redshift dependent parameters [ln D A (z), ln H(z), ln f σ 8 (z)] produced by the six codes considered in the current comparison. Also for these set of parameters the difference among all the outcomes is of the order of 4%. Finally in Fig. 3  we also show the relative difference of the full marginalised projected parameters among six GC codes, for the non-flat ΛCDM + w 0 + w a + γ case, using the non-linear recipe up to k max = 0.
Results of the code comparison The codes used for the code comparison of the cosmic shear weak lensing are described in Sect. 4. 1, and include, in particular, seven codes: CCCP, CCCPy, CosmicFish, CosmoSIS, FisherMatica, STAFF, TotallySAF. One of them is publicly available, three of them are planned to be made available, and the features of all codes are summarised in Table  6. Computing the Fisher matrix for the different cases in Table  8  is conceptually simple using all the relevant formulae in Sects. 2 and 3, but there many steps to be implemented, and also many potentials errors that can hamper the implementation. Moreover, the critical role played by the numerical derivatives led us to compare the results from codes implementing different recipes. Among the codes described in Sect.
(  53 ). A more detailed explanation of the problems that may arise from the change of the parameter basis is presented in Sect. 4. 5. The STAFF code instead uses the semi-analytical approach described in Sect. 4. 3, where the numerical part of the derivatives is obtained following the method proposed by  Camera et al. (2017)  (see their Appendix A). It is also important to note that the four codes are completely independent of each other and use very different approaches. CosmicFish, STAFF, and TotallySAF implement a second order covariance given in Eq. (  137 ) and build the Fisher matrix as in Eq. (  143 ), while CosmoSIS considers a fourth order covariance given in Eq. (  138 ) and builds the Fisher matrix as in Eq. (  142 ). Moreover, the codes differ also in how they obtain the cosmological quantities needed to compute the observables used to construct the Fisher matrix.
Results of the code comparison In Fig. 5  we show the comparison of the four codes for the pessimistic and optimistic configurations (left and right panels respectively) for the combination of observables GC ph +WL+XC (GC ph ,WL) and the full combination GC s +GC ph +WL+XC (GC ph ,WL) described in Sect. 4. 4. 2. In all cases investigated here we use the non-linear corrections given by the 'TakaBird' formalism and marginalise over intrinsic alignment and galaxy bias nuisance parameters, included in the analysis as described in Sect. 3. 4. We find a good agreement between the four codes, with the relative difference on the errors on cosmological parameters on each pair of codes always within 10%. In addition to the results shown here, the comparison was performed using also a different neutrino mass ( m ν = 0.
Stability of the results As a final test, we performed several stability checks in order to ensure that the codes used in the code comparison give reliable results. One of the main challenges in the Fisher matrix approach is to consider an appropriate step for the derivatives of the observed matter power spectrum with respect to the parameters. In our analysis, we performed a systematic analysis considering, firstly the step in the derivatives with respect to the shape parameters ω m,0 , h, ω b,0 , n s , and, separately, we let the step for the derivatives vary with respect to ln D A (z) and ln H(z) only. 26 n Fig. 6  left panel, we show the FoMs when varying the step for the derivatives of the observed galaxy power spectrum with respect to the shape parameters for the w 0 w a flat projection case; we neglected to show all the other projection cases as they manifest a similar behaviour.
Both approaches have their pros and cons. The fully numerical approach is quite generic and does not require any intervention from the user if a different cosmological model is adopted (apart from changes in the Hubble rate expression), but it is prone to the instability of numerical derivatives. On the contrary, the 'semi-analytic' approach transforms almost all the derivatives into integrals (the only exception being those of the matter power spectrum). However, the user has to adjust the code to update the expressions for the derivatives of the Hubble parameter and the radial comoving distance each time a new cosmological model is considered. Of the seven codes participating to the challenge, three (CCCP, CCCPy, and STAFF) adopt the semi-analytic approach, three (CosmicFish, CosmoSIS, and TotallySAF) employ a fully numerical method, and a single code (FisherMathica) uses a mixed technique.
However, this physical scale shows up at different angular multipoles, depending on the redshift bin considered. Therefore, a step ε optimised to capture at best the response of C i j ( ) to Ω b,0 at i = j = 1 and = k BAO,0 r(z = 0), may itself (and indeed typically will) lead to potentially significant numerical errors for a different combination of i j and mode. As a possible solution to this issue,  Camera et al. (2017)  proposed an alternative method, first implemented in the CCCP code (see Sect. 4. 1). Let us consider a parameter x: for each combination of i j and -in our example, the generic function f (x)we sample the x-line in parameter space in, say, 15 x i points around the fiducial value x 0 (including x 0 ). In the hypothesis that the neighbourhood is small enough, we interpolate the x i values with a straight line.
The outputs of the Boltzmann code may be subject to a significant numerical noise for small steps: in the calculation of the power spectrum P δδ (k) this problem was noted at a step-size below 10 -5 using CAMB and 10 -4 with CLASS. The fact that the outputs have a different sensitivity depending on the parameter does not ease the determination of an optimal step. An adaptive scheme can then be used but at the price of a significant increase of the computational time. This issue is extremely relevant for parameters that are not well constrained by the probe in use; this is because poorly constrained parameters lead to elements of the Fisher matrix that have small numerical values, and therefore lead to complications when inverting the matrix. We therefore find that this problem is most significant when using the observational probes separately, while the combination of these probes has a weaker dependence on the choice of the step size.
Conclusions In this paper we have presented the validation of Fisher-matrix forecasts for the Euclid satellite, as a result of activity carried out by the inter-science task force for forecasting, joining expertise from different science working groups (SWGs) of the Euclid Collaboration. In particular, we have included in this analysis consideration of Euclid primary probes, spectroscopic galaxy clustering and weak lensing cosmic shear, as well as the cross-correlation between WL and photometric galaxy clustering. Our target was to provide reliable numerical codes for each probe, which can be then used for a variety of forecasts and specifications -including applications beyond the scope of this paper. We also provide full documentation and tools that will allow any new code owner to perform their own validation At the start of this work, some numerical codes were already available, although they were providing different results, both in amplitude and in orientation.
Acknowledgements Authors' contributions: All authors have significantly contributed to this publication and/or made multi-year essential contributions to the  Euclid  project which made this work possible. The paper-specific main contributions of the lead authors are as follows: Coordinators and corresponding contacts:  Thomas Kitching ,  Valeria Pettorino ,  Ariel Sánchez. Galaxy Clustering Task:  Ariel G. Sánchez ,  Domenico Sapone ,  Carmelita Carbone ,  Santiago Casas ,  Katarina Markovic ,  Alkistis Pourtsidou ,  Safir Yahia-Cherif ,  Elisabetta Majerotto ,  Victoria Yankelevich. Weak Lensing Task: Vincenzo F. Cardone ,  Santiago Casas ,  Stefano Camera ,  Matteo Martinelli ,  Isaac Tutusaus ,  Thomas Kitching ,  Martin Kilbinger. Cross-correlations Task:  Matteo Martinelli ,  Isaac Tutusaus ,  Alain Blanchard ,  Martin Kunz ,  Fabien Lacasa ,  Vincenzo F.
Stefano Camera  is supported by the  Italian Ministry of Education, University and Research (MIUR)  through  Rita Levi Montalcini  project ' PROMETHEUS -Probing and Relating Observables with Multi-wavelength Experiments To Help Enlightening the Universe's Structure ', and by the  'Departments of Excellence   2018-2022 ' Grant awarded by  MIUR  ( L. 232/2016 ). Carmelita Carbone acknowledges financial support from the  European Research Council  through the  Darklight Advanced Research Grant  (n. 291521), and from the  MIUR PRIN 2015 'Cosmology and Fundamental Physics : illuminating the  Dark Universe with Euclid'. Santiago Casas  and  Safir Yahia-Cherif  acknowledge support from french space agency CNES. Sebastien Clesse  aknowledges support from the  Belgian Fund for Research F. R. S-FNRS.
Martin Kunz  and  Fabien Lacasa  acknowledge financial support from the  Swiss National Science Foundation. Eric Linder acknowledges support by  NASA  ROSES grant  12-EUCLID12-0004. Katarina Markovic  carried out some of the work at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ), and acknowledges support from the  UK Science & Technology Facilities Council  through grant  ST/N000668/1 , and from the  UK Space Agency  through grant  ST/N00180X/1. Matteo Martinelli  acknowledges support from the  D-ITP  consortium, a program of the NWO that is funded by the  OCW. Alkistis Pourtsidou is a  UK Research and Innovation Future Leaders Fellow  and also acknowledges support from the  UK Science & Technology Facilities Council  through grant  ST/S000437/1.
Domenico Sapone  acknowledges financial support from the  Fondecyt Regular  project number  1200171. Isaac Tutusaus  acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through grant  ESP2017-89838-C3-1-R , and the  H2020  programme of the  European Commission  through grant  776247. Victoria Yankelevich  acknowledges financial support by the  Deutsche Forschungsgemeinschaft  through the  Transregio 33 'The Dark Universe' , the  International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne  and the  Bonn-Cologne Graduate School for Physics and Astronomy. Thomas D. Kitching  acknowledges support from a  Royal Society University Research Fellowship. Stéphane Ilić  acknowledges financial support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)/ERC Grant  Agreement No.
Finally, the original implementation has also the V CKM = V CKM and V PMNS = V PMNS equalities built in, which is again not justified (see appendix B). For all those reasons, we decided on designing a fresh, more general, implementation, that is also publicly released on the FeynRules model database  1. In order to facilitate the usage of our FeynRules implementation, we document it further in appendix C, where we provide information on the new physics mass-eigenstates supplementing the SM field content, the free model parameters and their relation to all the other (internal) parameters. We have then made use of FeynRules to generate CalcHep  [41]  model files and a UFO  [42]  version of the model  [43] , so that we could employ MicrOMEGAs (version 5. 0. 8) [40]  for the computation of the predictions relevant for our dark matter study, and MG5 aMC (version 2.
In addition, we have relied on HiggsBounds (version 4. 3. 1) [52]  and HiggsSignals (version 1. 4. 0) [53]  to verify the compatibility of the ALRSM Higgs sector with data, with the H 0 0 field being associated with the SM Higgs boson. We have used the PySLHA package  [54]  to read the input values for the model parameters that we encode under the SLHA format  [55] , and to integrate the various employed programmes into a single framework. Using our interfacing, we performed a random scan of the model parameter space following the Metropolis-Hastings technique. We have fixed the SM parameters to their Particle Data Group (PDG) values  [56] , chosen the V CKM and V PMNS matrices to be equal to their SM counterparts, and varied the remaining 15 parameters as described in table  2. The SU (2) R coupling g R is allowed to vary within the [0.
Acknowledgments The authors are grateful to  S. Chakraborti  and  P. Poulose  and for enlightening discussions in the early stages of this work, and to  J. Araz  for useful comments and suggestions. Parts of our numerical calculations have been performed using the High Performance Computing (HPC) server managed by  Calcul Québec and Compute Canada. The work of MF and Ö Ö has been partly supported by  NSERC  through the grant number  SAP105354 , and the work of BF has been partly supported by  French  state funds managed by the  Agency Nationale de la Recherche (ANR) , in the context of the  LABEX ILP  ( ANR-11-INDEX-0004-02 ,  ANR-10-LAB-63 ). Ö Ö thanks the  University of Southampton , where part of this work was completed, for their hospitality.
Besides qRT-PCR, genome analysis plays a crucial role in public health responses, including epidemiological efforts to track and contain the outbreak (see Tracking, epidemiology and evolution). The genome sequence of SARS-CoV-2 was rapidly determined and shared on GenBank (MN908947. 3). It is annotated based on sequence similarity to other coronaviruses. Nextgeneration sequencing (NGS) can be used to assess the genomic diversity of the virus. Regular sequencing from clinical cases is useful, for example, to monitor for mutations that might affect the qRT-PCR test (see CoVPipe, V-Pipe). To reliably derive intrahost diversity estimates from deep sequencing data is challenging since most variants occur at low frequencies in the virus population and amplification and sequencing errors confound their detection.
g. from human Bronchoalveolar lavage fluid (see VIRify). SARS-CoV-2 genomic traces in human faecal metagenomes from before the pandemic support the hypothesis of a possible presence of a most recent common ancestor of SARS-CoV-2 in the human population before the outbreak of the current pandemic, possibly in an inactive non-virulent form  [81]. Further, metagenomics helps to check sequence divergence as the virus could undergo mutation and recombination with other human coronaviruses. To help fight the COVID-19 pandemic, it is essential to make high-quality SARS-CoV-2 genome sequence data and metadata available through open databases either via a data-access agreement (e. g. GISAID, https://www. gisaid. org/) or without restrictions (e. g. GenBank, https://submit. ncbi. nlm. nih. gov/sarscov2/).
On GISAID (Global Initiative on Sharing All Influenza Data), laboratories around the world have shared viral genome sequence data with unprecedented speed (>71 000 SARS-CoV-2 genomic sequences on 23 July 2020). We encourage researchers to submit genome sequences to public databases that do not impose limitations on the sharing and use of the genomic sequences. NCBI offers a new streamlined submission process for SARS-CoV-2 data (https://ncbiinsights. ncbi. nlm. nih. gov/2020/04/09/sa rs-cov2-data-streamlined-submission-rapid-turnaround/). Several bioinformatics tools have been developed for the detection and annotation of SARS-CoV-2 genomes (see VADR, V-Pipe, VIRify, VBRC tools, VIRULIGN). Comparative genomics helps to detect differences to other coronaviruses, e. g. SARS-CoV-1, which might affect the functionality and pathogenesis of the virus.
PriSeT: Primer Search Tool PriSeT  [35]  is a software tool that identifies chemically suitable PCR primers in a reference data set. The reference data set can be a FASTA file of complete genomes or a set of short regions. It is optimized for metabarcoding experiments where species are identified from an environmental sample based on a barcodea relatively short region from the genome. The most frequently applied type of PCR for such experiments is the paired-end PCRtwo different primer sequences are chosen to be complementary to the template and located within an offset range. The region in between is the amplicon or barcode and will be matched against the reference database to resolve operational taxonomic units to organisms. The precise constraint ranges can be adjusted by the user. SARS-CoV-2 tests typically use mucus from the nose or throat that undergo a metabarcoding analysis.
For DNA amplification RT-PCR is applied, which has more stringent requirements for the primer sequences and the DNA product length. Figure  1  shows the approximate locations of in silico transcripts of 114 primer pairs computed by PriSeT on 19 SARS-CoV-2 genomes with recommended RT-PCR settings  [35]. The corresponding primer pairs have no co-occurrences in other coronaviruses. An additional online search on NCBI's GenBank confirmed that they also have no matches in any other sequences that are not assigned to SARS-CoV-2. A list of SARS-CoV-2specific primer pairs computed on 19 SARS-CoV-2 genomes is available on ResearchGate (https://www. researchgate. net/publi cation/340418344_Primer_pairs_for_detection_of_SARS-CoV-2_ via_RT-PCR). The computation of SARS-CoV-2-specific primers will help to design RT-PCR tests, since the resulting barcodes serve as unique identifiers for SARS-CoV-2 and avoid false-negative and falsepositive identifications.
The pipeline is designed for reproducibility and scalability in order to ensure reliable and fast data analysis of SARS-CoV-2 data. The workflow itself is implemented using Snakemake  [48] , which provides advanced job balancing and input/output control mechanisms, and uses conda  [28]  to provide well-defined and harmonized software environments. CoVPipe is available via GitLab under GPLv3: https://gitlab. com/RKIBioinformaticsPipelines/ ncov_minipipe.
Rapid sample analysis is, therefore, of particular interest. The workflow poreCov is implemented in nextflow  [100]  for full parallelization of the workload and stable sample processing (see Figure  2 ). poreCov generates all necessary results and information before scientists continue to analyze their genomes or make them public on, e. g. GISAID or ENA / NCBI. The workflow carries out all necessary steps from basecalling to assembly depending on the user input, followed by lineage prediction of each genome using Pangolin (see below). Furthermore, read coverage plots are provided for each genome to assess the amplification quality of the multiplex PCR. In addition, poreCov includes a quick-time, tree-based analysis of the inputs against reference sequences using augur (https://gi thub. com/nextstrain/augur) and toytree (https://github.
com/ea ton-lab/toytree) for visualization. poreCov supports scientists in their SARS-CoV-2 research by reducing the time-consuming bioinformatic bottlenecks in processing dozens of SARS-CoV-2 sequencing runs. All tools are provided via 'containers' (pre-build and stored on docker hub) to generate a reproducible workflow in various working environments. poreCov is available on GitHub under GPLv3: https://github. com/replikation/poreCov.
VADR: SARS-CoV-2 genome annotation and validation VADR validates and annotates viral sequences based on models built from reference sequences  [85]. Coronavirus models, based on NCBI RefSeq  [73]  entries, including one for SARS-CoV-2 (NC_045512. 2), are available for analyzing coronavirus sequences. VADR computes an alignment of each incoming sequence against the RefSeq and uses it to map the RefSeq features, which include protein coding sequences (CDS), genes, mature peptides (mat_peptide) and structural RNA (stem_loop) features. The ORF1ab polyprotein CDS involves a programmed ribosomal frameshift, which VADR is capable of properly annotating. The tool identifies and outputs information about more than 40 types of problems with sequences, such as early stop codons in CDS, and has been in use by GenBank for screening and annotating incoming SARS-CoV-2 sequence submissions since March 2020.
VADR (v1. 1) includes heuristics for accelerating annotation and for dealing with stretches of ambiguous N nucleotides, that were specifically added for SARS-CoV-2 analysis. VADR helps advance SARS-CoV-2 research by standardizing the annotation of SARS-CoV-2 sequences deposited in GenBank and other databases and by allowing researchers to fully annotate and screen their sequences for errors due to misassembly or other problems.
A recent release of V-pipe addresses specifically the analysis of SARS-CoV-2 sequencing data. It uses the strain NC_045512 (GenBank: MN908947. 3) as the default for read mapping and reporting of genetic variants, and it includes several improvements, for example, for calling single-nucleotide variants. Also, V-pipe can generate a comprehensive and intuitive visualization of the detected genomic variation in the context of various annotations of the SARS-CoV-2 genome. This summary of the output can help to generate diagnostic reports based on viral genomic data. V-pipe is an SIB resource (https://www. sib. swiss/researchinfrastructure/database-software-tools/sib-resources) and available via GitHub under the Apache License 2. 0 (APLv2): https:// github. com/cbg-ethz/V-pipe. Users are supported through the website (https://cbg-ethz.
Haploflow: Multi-strain aware de novo assembly Viral infections often include multiple related viral strains  [113] , either due to co-infection or within-host evolution. These strains -haplotypes -may vary in phenotype due to certain, strain-specific genetic properties  [51]. It is not entirely clear yet whether SARS-CoV-2 has a tendency for multiple infections, though there are indications that co-infections with other Coronaviruses do occur  [59]. Most assemblers struggle with resolving complete viral haplotypes, even though these may be critical for the choice of therapy. Haploflow is a novel, de Bruijn graph-based assembler for the de novo, strain-resolved assembly of viruses that is able to rapidly resolve differences up to a basepair level between two viral strains. Haploflow will help advance Haploflow is available on GitHub under APLv2: https://github.
VIRify: Annotation of viruses in meta-omic data VIRify is a recently developed generic pipeline for the detection, annotation and taxonomic classification of viral and phage contigs in metagenomic and metatranscriptomic assemblies. This pipeline is part of the repertoire of analysis services offered by MGnify  [69]. VIRify's taxonomic classification relies on the detection of taxon-specific profile hidden Markov models (HMMs), built upon a set of 22,014 orthologous protein domains and referred to as ViPhOGs. Included in this profile HMM database are 139 models that serve as specific markers for taxa within the Coronaviridae family. Here, we show the applicability of VIRify on the assembly of a metatranscriptomic dataset from a human Bronchoalveolar lavage fluid. Within this assembly, a 29 kb contig was classified by VIRify as belonging to the Coronaviridae family (see Figure  3 ).