dataesr/scientific-paragraphs-categorization huggingface dataset:
Extraction of individual parameters: the peakbagging function In what follows, we refer to the M n-1,2 , M n,0 , M n-1,3 , and M n,1 group of modes as a peakbagging order to avoid any confusion. Mode parameters can be fitted globally, by peakbagging order, or by pair. In the latter case, modes M n-1,2 , M n,0 or M n-1,3 , and M n,1 are then fitted together, respectively. The reader should note that the pair-fitting strategy is also suited to fitting any individual mode if the parameters of only one mode of a given pair is specified in the a2z input. The model used to compute the likelihood and posterior probability follows Eq. (  22 ). The initial value for the local S/N background term b is taken as 1 and set to vary between 10 -6 and 5. The minimal set of parameters that will be fitted by the code are mode frequencies, ν n, , heights, H n, (or amplitudes, A n, ), and FWHM, Γ n,.
Additional parameters that can be fitted are   2006, 2008 ), instead of s. Except for mode frequencies, every input parameter can be set to parametrise all modes, a whole order, a pair, or only one mode. In other words, it is possible to impose each mode of a given peakbagging order to have, for example, the same FWHM. If the user does not provide a fitting window size, the data to fit are restricted to an adaptive fitting window: only PSD elements within [ν -, ν + ] are considered, with: ν -= ν low - ν up -ν low d , ν + = ν up + ν up -ν low d , (28) where ν low and ν up are respectively the minimal and maximal guess frequencies of the modes to fit. The value of d is then determined by the considered method: if the fit is made by order, d = 3, if the fit is made by pair, d = 1. For modes above 800 µHz and if the fit is made by pair, another possibility offered by the code is to use the ∆ν value to constrain the fitting window.
The percentage of fits for which we find the true value to be in the 68% and 99. 7% credible intervals is therefore close to expectations, the standard deviation for the frequency of success being 3. 3% in the case of 200 draws following a binomial law of parameters (n draw = 200, p = 0. 68), and 0. 4% for (n draw = 200, p = 0. 997), with n draw being the number of draws. 5 Full results, Kepler light curves, and data analysis tools used to perform the benchmark can be accessed through the following repository: https://gitlab. com/sybreton/benchmark_peakbagging_ apollinaire When we consider the 2400 fitted frequencies at once, we find the uncertainties obtained with apollinaire to be conservative in this experiment. Indeed, 71. 5% of the fitted frequencies are in the 68% credible interval. With a standard deviation of 0.
GOLF solar PSD analysis To study the p-mode frequency shifts induced by the magnetic solar cycle,  Salabert et al. (2015)  performed an analysis of 69 one-year subseries of the GOLF instrument, spanning from 1996 April 11 to 2014 March 5, with 91. 25 days overlap. The modes were fitted using a MLE method  (Salabert et al. 2007). In order to compare the results from this method with apollinaire fits, we use the same MLE code to perform a similar analysis by considering subseries of 1-yr spanning from 1996 April 11 to 2020 July 6, with a 91. 25 days overlap 6. We remove five subseries that have a duty-cycle of below 90% and therefore consider 89 subseries. For each of these subseries, we fit modes = 0, 1, 2, 3. However, as = 3 have low signal-to-noise ratios in the GOLF data, we compare only the results obtained for mode = 0, 1, 2 in the following.
Conclusion In this paper, we introduced the apollinaire module, an opensource Python 3 package designed for helio-and asteroseismic disk-integrated peakbagging. The module implements a set of functions that are able to extract background, p-mode global pattern, and p-mode individual parameters through MCMC sampling implemented with emcee. The implementation of these functions is designed to provide a flexible framework. They can be used independently or combined depending on the task the user wants to achieve. We analysed data from the GOLF instrument and the Kepler LEGACY sample in order to compare apollinaire results with values available in the literature. We find good agreement between our results and the reference values. The discrepancies can be explained by differences in data calibrations, fitting strategies, and adopted models.
The code is in active development and the online repository is regularly updated. An online documentation of the package is available and includes several tutorials that should allow any interested user to quickly start working with apollinaire and use the different functions to analyse their own set of data. With the increasing number of TESS targets of asteroseismic interest and the preparation of the PLATO mission, we hope that apollinaire will become a widely used tool in the community. (Ambikasaran et al. 2015). The previous references should be cited together with apollinaire when using the module. Packaged version of apollinaire can be installed through pip and conda-forge (conda-forge community 2015). The source code and most recent versions of apollinaire can be found at https: //gitlab.
com/sybreton/apollinaire while the module documentation is hosted at https://apollinaire. readthedocs. io/. 4. After fitting the Universal pattern, the code provides an a2z file with the guesses for the even modes to be fitted by the peak bagging module. A first characterisation of those modes can be performed directly if we are not interested in the odd ones. 5. To fit the odd modes (see Fig. B. 1), the a2z guess file needs to be modified manually. The only way to do the fitting is global and not by order as the mixed modes cannot be assigned to any particular order. In the a2z guess file, we suggest to write the radial order n of each mixed mode starting by 100. The order is not important and these values are given only as an indication to the user that the corresponding modes are mixed modes.
Acknowledgements. The authors thanks the anonymous referee for constructive comments and precious suggestions that greatly enriched and completed the material of the paper. S. N. B. and R. A. G acknowledge the support from PLATO and GOLF CNES grants. J. B. acknowledges the support from CNES. V. D. acknowledges the support of the IAC (Tenerife, Spain). The authors are deeply grateful to G. R. Davies who originally designed the a2z data structures in collaboration with R. A. G. They also thank L. Bugnet, M. Delorme, A. Jiménez, S. Mathur, and V. Prat for fruitful discussions. A special thank to A. Finley for his precious suggestions concerning the name of the paper. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission Directorate.
We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability and easy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, and enabling tight integration with tools and services offered under the EBRAINS ecosystem.
Introduction A major shortcoming in computational approaches to neuroscience, such as modelling/simulation and data analysis, is the absence of a widely used and well-defined system for sharing code and data that would enable researchers to easily access the data resources employed in published studies and understand in detail the provenance of published results and figures. This impedes community-based, collaborative modelling efforts, reduces the utility of published models within the neuroscience community, and hinders the reproducibility of data analyses. Existing tools and services, such as ModelDB  (Hines et al. 2004) , Open Source Brain (OSB)  (Gleeson et al. 2012) , and NeuroMorpho. org (Ascoli et al. 2007) , make models, code and/or data available to the scientific community, with each attempting to fulfil a specific need.
For example, ModelDB is a repository of published models with code that allows users to replicate at least a single figure from the corresponding publication. It is not intended to tackle issues regarding access to experimental data, and therefore does not, in general, hold information about data used to tune, validate or simulate the model. Similarly, OSB is a model repository that focuses on model formats and simulator-independent model representations, but not on prioritising the data underlying the models. NeuroMorpho. org is a curated inventory of digitally reconstructed neurons associated with publications, but doesn't link with other data such as electrophysiological recordings or models of the reconstructed neurons. More general services such as Figshare  (Hahnel 2013) , Open Science Framework  (Foster and Deardorff 2017) , or  (Dillen et al.
2019 ) have much less structured records, with limited domain-specific metadata. Finally, applications/infrastructures specifically addressing reproducibility and transparency in scientific publishing (e. g. , eLife RDS, Galaxy, Repro Zip) require possibly demanding ad hoc installations, configurations and maintenance operations  (Konkol et al. 2020). EBRAINS Live Papers are interactive documents bringing together code, models, and data. They can be standalone publications, but in general complement published scientific articles. Interactivity is a prominent feature of the "Live Papers" with several integrated tools and services that allow users to download, visualise or simulate data, models and results presented in the corresponding publications. By bringing together the various resources underlying computational approaches in neuroscience publications (whether modelling/simulation or data analysis), they provide a more complete picture of the original researchers' workspace.
By virtue of being developed within the EBRAINS infrastructure, Live Papers take advantage of EBRAINS platforms and services for data and model integration and are able to effectively leverage the Knowledge Graph (KG) database for storing all information. The KG, being a graph-based database, interlinks all data units thereby readily offering a high degree of data provenance. Below we describe the various features and functionalities currently made available through the Live Papers platform, and also how authors can distribute their own resources by creating and publishing live papers. Our goal is to demonstrate to the scientific community the utility of building live papers to complement their publications, and to develop them not post facto, but rather as a tool to support their manuscript submissions by making available information that could assist reviewers in making a more informed evaluation  (Bailey et al.
EBRAINS Infrastructure An online platform for sharing digital resources and publishing interactive, scholarly documents places several requirements on the underlying infrastructure. Resources and documents must be persistent-reliably available over a long time period. Tools for visualising or re-using resources (models, code, data) should be available, reliable, give reproducible results, and be underpinned by adequate computational resources. Accurate and complete metadata must be available. EBRAINS is a new digital research infrastructure developed under the Human Brain Project and now on the European Strategy Forum on Research Infrastructures (ESFRI) roadmap. It aims to bring together an extensive range of data, alongside tools to help analyse and utilise these data. Among the key tools and services provided by EBRAINS are (i) long-term, archival-quality data storage, provided through an agreement with the Swiss National Supercomputing Centre; (ii) a state-of-the-art metadata store, the Knowledge Graph; (iii) a wide range of tools for modelling, simulation, and data analysis at different scales and levels of abstraction; (iv) facilitated access to high-performance computing systems; (v) collaborative workspaces (the Collaboratory) with fine-grained access control for the documents, code and data for a given project.
Live Paper Resources The live papers allow for diverse types of resources to be presented, with practically no limitations. For computational modelling studies, we have found that the most common resources being distributed comprise the following: • Electrophysiological Recordings Data obtained from experimental recordings often lay the foundations for modeling studies. These recordings can be obtained from a variety of different experimental techniques and protocols, such as intracellular recordings via sharp microelectrodes or patch clamp setup, calcium or dye imaging studies, and so on. The Live Paper platform provides for listing and access of all kinds of experimental data. A typical characteristic of experimental data is that they are often recorded as time series. The platform integrates an online visualiser for such data, as seen in Fig.
• Model Source Code One of the fundamental requirements to ensure reproducibility of a computational study is access to the original source code used by the authors to arrive at the reported results. It is almost always non-trivial to reconstruct a model from scratch solely based on the model description provided in publications. Without access to the model source code, all inferences and conclusions have to be evaluated at face value without any scientific appraisal. This has encouraged most scientific publishers to ensure that the source code underlying modeling studies be made publicly accessible, or at least available on request. Live papers allow authors to distribute the source code for their models. As shown in Figs. 2  and 3 , they also enable importing and linking to corresponding entries in other neuroscience repositories such as ModelDB, OSB and Bio-Models, in addition to the EBRAINS Knowledge Graph.
Additionally, live papers are closely integrated with the EBRAINS Model Catalog and KG. This permits authors to link their models to corresponding entries in the model catalog, which then provides additional information on the Fig. 1  The electrophysiology resource section embeds an interactive visualiser for each electrophysiology data file. This enables users to visualise and to interact with the data live from within the document 1 3 models, including outcomes of any validation tests they might have undertaken using the EBRAINS Validation Framework. • Jupyter Notebooks Jupyter Notebook is a free, open-source interactive web application that supports several programming languages. Over the past few years, notebooks have become the foremost choice for researchers to demonstrate their computational work.
It allows them to aggregate software code, simulated outputs, documentation and other relevant resources into a single document  (Perkel 2018). A highlight of Jupyter notebooks is their ability to capture output, and thereby act as the equivalent of laboratory notebooks for computational researchers. A Jupyter notebook basically creates a snapshot of the actions of the original authors and the outcomes they witnessed, and in principle should enable any other user to follow the same footsteps to arrive at the same results. Well designed Jupyter notebooks can also serve as documentation of a study for the better understanding of reviewers and readers. The Live Paper platform allows authors to add Jupyter Notebooks, and enables users to launch these within the EBRAINS Collaboratory by simply clicking on each of them.
Provenance Tracking The EBRAINS KG is a multi-modal metadata store that aggregates information from various fields of brain research into a single interconnected network of data. It integrates experimental data, models, software and other related Fig. 3  Based on the specified filters, data from the specified neuroscience repository, here ModelDB, are retrieved and tabulated. Authors are provided with relevant metadata for these entries, and are able to add these items to the live paper 1 3 resources into a graph based database, and links these data units by their relationships to each other. This enables identifying all relevant resources associated with a particular research object. For example, for a given electrophysiological recording stored in the KG, it would be possible to retrieve all metadata associated with that recording, such as information on species, cell type, data modality, and additionally other linked research objects such as experimental protocols, software, and models that have employed the given recording in their development or validation.
The KG also allows for connecting datasets to relevant software tools that can help with analysing and visualising the contained data. The Live Paper platform stores all the information provided within the live papers in the KG. While creating the live paper, users have the option of selecting existing KG resources (e. g. models, experimental recordings) or, alternatively, specifying new resource units not currently available in the KG. We encourage the former approach, and accordingly guide users to tools/processes for registering.
Issuing DOIs The EBRAINS platform issues Digital Object Identifiers (DOIs) for curated research objects that are published through its Data and Knowledge Services, including experimental datasets and Live Papers. Before publication, live papers go through a quality control process; described in the following section. Issuance of a DOI further assists the citation of published data and models, thereby incentivising authors to publish and share such resources, and ensuring that they are duly acknowledged when these resources are reused.
The same holds true for 3D visualization of neural morphologies and model data and metadata access. In addition to the user-friendliness of the Live Papers webpage, the insertion of online resources is made even easier thanks to an ad hoc developed search engine that is able to query and fetch content data, based on keywords inserted by the users, from several online scientific repositories (e. g. , ModelDB, OSB, EBRAINS KG). Differently from other applications, the Live Papers web interface is extremely lightweight, in that it consists in an easy-to-maintain JavaScript-based web frontend; hence, no specific installation and configuration are required to authors, publishers and developers. At the same time, being part of the EBRAINS Research Infrastructure, the Live Papers take advantage of the rich ensemble of tools and services it offers: 1) Jupyter notebooks can be created and configured in the EBRAINS Collaboratory environment and linked to the Live Papers; 2) the Live Papers platform is integrated with the BlueNaas simulator engine (https:// ebrai ns-cls-inter active.
github. io/ online-use-cases. html#/ single_ cell_ insil ico_ exper iments) via a web-socket communication channel; with a little programming effort a NEURON model can be run without any specific installation and the results shown in the user's browser; 3) if needed and duly justified, authors can request dedicated HPC resources available in the EBRAINS framework for running demanding operations/simulations; 4) long-term data repositories are offered to the Live Papers creators. Thanks to this tight integration, Live Papers delegate the burden of software development and maintenance to external services keeping the interface easily accessible and maintainable. In addition, Live paper documents are built separate from the article manuscript, thereby allowing authors to follow the traditional approach to manuscript preparation, while being able to work in parallel on the supplementary live paper document.
Data Availability The EBRAINS Live Papers platform is entirely opensource, with the code being available on GitHub (https:// github. com/ appuk uttan-shail esh/ ebrai ns-live-papers). This includes both the live paper viewer and the live paper builder tools. All live paper resources are publicly accessible, via the live paper platform, without requiring any registration or authentication. The use of resources hosted in each live paper is governed by the license specified by the authors of that specific live paper.
In many cases, the sensitivity is also constricted by low cfDNA input rather than due to technical limitations, and could thus be improved by increasing cfDNA input. When comparing the HYTEC-seq and the Oncomine Pan-Cancer Cell-Free Assay for ctDNA detection in patient samples, variants detected by both methods showed a high degree of correlation in terms of VAF (Spearman correlation coefficient = 0. 98, p < 0. 001) (Fig. 5 ). The only major difference was a high-frequency hotspot TP53 variant (chr17:7577111, VAF = 31. 6%) that was filtered out by the Oncomine Variants filter in the Ion Reporter software, probably because it has not been recorded in the Oncomine database (ref https:// www. oncom ine. org/ resou rce/ login. html). Additionally, several variants (mostly with VAF < 1%) were exclusively called by either HYTEC-seq or the Oncomine Pan-Cancer Cell-Free Assay.
Samples. To validate the HYTEC-Seq method, we sequenced 50-ng control samples from the Multiplex I cfDNA Reference Standard Set (Horizon Discovery). We made a 0. 5% control by diluting one part 5% control in nine parts of the negative control from the kit. We also sequenced duplicate 20 ng and 50 ng samples of fragmented leukocyte DNA spiked with 5%, 1%, 0. 5%, 0. 1%, and 0% (negative control) of fragmented cell line DNA from four cell lines: AsPC-1 (RRID: CVCL_0152), BxPc-3 (RRID: CVCL_0186), HCT-116 (RRID: CVCL_0291), and HT-29 (RRID: CVCL_0320). These cell lines were purchased from the European Collection of Authenticated Cell Cultures (ECACC) and chosen because they reportedly contain mutations in the genes KRAS (AsPc-1, HCT-116), SMAD4 (AsPc-1, HT-29), TP53 (AsPc-1, BxPc-3), and RNF43 (AsPc-1).
Bioinformatic processing of sequencing data. Supplementary Fig. S7  presents an overview of the bioinformatic pipeline for analysis of the HYTEC sequencing data. Signal processing, base calling, quality control, adapter trimming, sample barcode de-multiplexing, and alignment were performed with Ion Torrent Suite Software version 5. 12, using the default settings, except for the blind calibration. The sequencing coverage and quality statistics for each sample are summarized in Supplementary Table  S7. On-target reads were extracted from aligned BAM files, and the forward and reverse reads were separated and converted to FASTQ format using the samtools software (versions 1. 8 and 1. 10)  18. Remaining adapter sequences (duplex part) were removed, and the molecular tag sequence was extracted using a locally developed Python script called TagXtractor (https:// github.
com/ oddmu ndn/ TagXt ractor). Subsequently, the reads were aligned to the reference genome using BWA-MEM (version 0. 7. 17)  19  , producing aligned and sorted BAM files. These BAM files were processed using a Python script called SSCScreator (https:// github. com/ oddmu ndn/ SSCSc reator), version 1. 3, which utilized both molecular tag and genome alignment position to collapse reads into single-strand consensus sequences (SSCS). The SSCS included only variants that were present in the majority (> 70%) of reads derived from the same original cfDNA molecule. Subsequently, the forward and reverse SSCS files were combined and aligned to the reference genome, again using BWA-MEM. For the Oncomine sequencing data, the raw data were first processed using the Ion Torrent Suite Software version 5.