dataesr/scientific-paragraphs-categorization huggingface dataset:
For the implementation of the proposed integrated methodology for the infrastructure risk to provide a common platform, the abstracted concept and methodology is coded in Python, which is coherent with the OpenQuake engine. NetworkX  (Hagberg et al. 2008 ) is one of the main Python packages used in coding to implement the infrastructure risk. In the following, we shortly present the different components of the module for the computation of infrastructure risk at the connectivity level in the OpenQuake engine. Exposure modelling Exposure models for infrastructure risk assessment contain the location and taxonomy linked with fragility/vulnerability class of the components, as well as their function within the system (i. e. , demand or source for nodes), and how they connect with each other (i.
In the case of utilities, demand nodes may be individual households or the end of the main/submains. In case of a lack of data, other nodes except supply nodes may also be assigned as demand nodes. Similarly, in the case of a transportation system, the demand nodes may be the traffic analysis zone from the survey, the centroid of the main settlements, the end of main arterial or sub-arterial streets, or a critical facility like a hospital, a fire-fighting station, or a school. Seismic hazard modelling OpenQuake already disposes a powerful comprehensive module for hazard analysis to compute various intensity measures (IMs), considering the cross spatial correlation. It is possible to run both scenario and event based probabilistic seismic hazard analysis suitable for infrastructure risk. Fragility, damage, and operationality assessment While defining the fragility model in OpenQuake, it is possible either through discrete function or cumulative lognormal distribution function.
For this, we utilized population data for each district provided by the census making the assumption that population is uniformly distributed across the area independently of the time throughout the year. We then abstracted demand nodes within each district and evaluated the mean connectivity loss for each district, considering only the demand nodes within that particular district. To estimate the potential effect on the population, we calculated the product of the mean connectivity loss and the population size of each district. The population map at the district level and the projection of the number of people affected with the complete loss of water supply according to CCL is shown in Fig. 9. From this figure, it can be seen that the affected population is greater in the southeastern part.
The main advantage of implementation is the already existing powerful strong seismic hazard capabilities, and database for fragility functions for buildings which will greatly aid in reaching the systemic risk considering the interdependencies. As of now, different stakeholders can now have the common platform from earthquake source modelling, risk analysis at the component level to risk analysis of infrastructures at the connectivity level analysis considering intra-dependencies. • To demonstrate the capabilities of the elaborated tool, we selected as a testbed, the water distribution system of Thessaloniki in Greece. We considered both scenario and event based probabilistic based analysis and computed the suitable performance metrics. The performance was checked at the city scale, district level, and nodal level, considering the system behavior.
Acknowledgements The present work has been done in the framework of Grant Agreement No. 813137  funded by the  European Commission ITN- Marie Sklodowska-Curie  project " New Challenges for Urban Engineering Seismology (URBASIS-EU )". Also, we would like to acknowledge the contributors of the SYNER-G project that was funded from the  European Community 's  7th Framework Program  under Grant No. 244061  from which many conceptual frameworks have been built upon.
Single-cell technologies have revolutionised biological research and applications. As they continue to evolve with multi-omics and spatial resolution, analysing single-cell datasets is becoming increasingly complex. For biologists lacking expert data analysis resources, the problem is even more crucial, even for the simplest single-cell transcriptomics datasets. We propose ShIVA, an interface for the analysis of single-cell RNA-seq and CITE-seq data specifically dedicated to biologists. Intuitive, iterative and documented by video tutorials, ShIVA allows biologists to follow a robust and reproducible analysis process, mostly based on the Seurat v4 R package, to fully explore and quantify their dataset, to produce useful figures and tables and to export their work to allow more complex analyses performed by experts.
Introduction In biology, single-cell RNA sequencing (scRNA-seq), and more generally singlecell genomics, have opened a new era. The scientific community has rapidly adopted those technologies as evidenced by their use in a growing number of high-impact biomedical publications  [1]. The rapid growth of new applications (scRNA-seq, CITEseq, scDNA-seq, spatial transcriptomics, scChIP-seq) is providing a large diversity of information about biological heterogeneity and dynamic processes in cell populations  [2]. A major hindrance to the meaningful use of these technologies is that they require both a very good knowledge of the underlying biology and an expert level in data analysis (bioinformatics and mathematics). The successful analysis of single-cell genomics datasets is therefore the result of a close collaboration between biologists and bioinformaticians.
In recent years, the bottleneck between generation of experimental data and their analysis has been getting worse  [3]. Until recently, the time required to produce datasets in the laboratory was greater than the time taken for bioinformatics analyses. Today, with the remarkable advances in biotechnologies, generating single-cell genomics datasets has become relatively easy and fast. On the other hand, time required for analysis has increased because of the large number and high complexity of analyses applied to those datasets. As a result, while research teams can produce large datasets quickly, they often have to wait long before analyzing them. A tool enabling biologists to execute first steps of data analysis autonomously and deeply explore the biology of their datasets would considerably speed up the process, and allow them to improve the knowledge they can infer from the exploration.
Principle of the interface ShIVA is a software allowing any biologist to apply a correct and validated analytic process that corresponds to current state-of-the-art, relying mostly on functions from the Seurat v4 R package that was developed by single-cell data analysis experts  [4]. With this tool, non-experts (biologist researchers, PhD students or engineers) can load single-cell sequencing data (scRNA-seq or CITE-seq), apply the main quality control steps, execute normalization and dimensionality reduction, perform clustering, identify marker genes and their main functions, and finally visualize the data interactively to execute supervised and non-supervised scientific analyses. ShIVA has been designed specifically to guide biologists through the analysis workflow, offering them to visualize, quantify and explore the data at each stage before making decisions affecting downstream analysis.
Overview of main features ShIVA covers all the functions described in Figure  2. It is able to process data from scRNA-seq and CITE-seq experiments, including samples multiplexed with "hashtag" oligonucleotides (HTO). The workflow in ShIVA is performed through a succession of interdependent modules. Activating a module is allowed only if the mandatory previous steps have been correctly performed, helping the user to grasp the rigorous analysis process to follow. In each module, the user visualizes the relevant data, makes decision on the required analysis parameters and evaluates their impact through direct observation of the result. When the result of an analysis is not optimal, the user can easily step backward in the previous modules to modify the parameters and re-run the corresponding analysis.
Advantages of ShIVA Several tools already exist to manage and visualize single-cell datasets (  [5] ,  [6] ,  [7] , BBrowser, CellxGene, SeqGeq. ). To our knowledge, no freely available solution offers the combination of high level of simplicity and deep data exploration ShIVA provides. Existing user-friendly solutions are limited in the ability for user to control, compare and explore the analysis results. Compared to others tools, the main advantages of ShIVA are (i) user experience orientation, allowing a non-expert to easily understand and control each step of the analysis, (ii) modularity, allowing to easily follow the rigorous workflow of analysis, (iii) interactivity and iterability, allowing to acquire a better understanding of the underlying biology of the dataset, (iv) exportability and reproducibility, allowing to easily share and push forward the analysis with expert collaborators.
Example of analysis with ShIVA We tested ShIVA on a public dataset of human peripheral blood mononuclear cells (PBMC) from 8 donors (Figure  3A , as described in  [8]. Samples were stained with barcoded anti-CD45 hashtag antibodies prior to capture for 10x Genomics 3'v2 scRNA-seq. We downloaded gene expression and hashtag count matrices from the Seurat tutorial website (https://satijalab. org/seurat/articles/hashing_vignette. html) and combined both matrices into a Seurat object in R. The object was then loaded into ShIVA for analysis, with log normalization of the gene expression count matrix. We performed hashtag count matrix normalization and automatic demultiplexing to identify samples from the eight donors, and excluded doublets and negative events from further analysis (Figure  3B ).
For higher resolution analysis of those myeloid cell clusters, we created a new object containing only cells from clusters 1, 8 and 9, and repeated the computation of 2,000 highly variable genes, PCA, UMAP embedding and clustering. That re-analysis revealed several sub-clusters of CD14+ monocytes, but did not reveal additional sub-clusters of FCGR3A+ monocytes or CD1C+ conventional dendritic cells (cDC) (Figure  3H ). The exploratory analysis of that dataset in ShIVA took approximately 2 hours from loading the dataset to exporting all figure plots in vectorized pdf format.
Discussion In conclusion, ShIVA offers a user-friendly and user-oriented interface dedicated to the analysis of single-cell RNA-seq and single-cell CITE-seq data with an emphasis on ease of use, application of state-of-the-art methods, and reproducibility. ShIVA supports cell hashing analysis and provides great flexibility in visualization, whether by dimensionality reduction maps, boxplots, violin plots, histograms, density plots, or count tables. ShIVA is designed to give non-experts the power of state-of-theart algorithms, allowing them to gain a deep understanding of their dataset while preparing for an optimized collaboration with data analyst experts in subsequent extensive analyses. A complete series of tutorial videos are available on YouTube (https://www. youtube. com/channel/UCJJ3Svi8AY6XGx4Y9r9G3Iw).
Methods ShIVA is a Shiny Application built in an R framework (v. 4. 0. 3 ). ShIVA is deployed using Docker. ShIVA uses the Seurat package (v. 4. 0. 0,  [4] ) for the main analysis workflow. HTO demultiplexing is performed with MULTI-seq  [9]. Dimensionality reduction is performed using PCA (ade4 v. 1. 7. 16,  [10] ), t-SNE  [11]  and UMAP (v. 0. 2. 7,  [12] ). Clustering is performed through the methods proposed by Seurat among which the Leiden algorithm (v. 0. 3. 10,  [13] ). Visualization is performed using packages ggplot2 (v. 3. 3. 3), plotly (v. 4. 9. 3), pheatmap (v 1. 0. 12) and heatmaply (v. 1. 2. 1). Complete details on the other R packages used are available in the dockerfile provided on GitHub. Peripheral blood mononuclear cell (PBMC) samples from 8 human donors were stained with anti-CD45 hashtag antibodies, pooled and captured for 10x Genomics 3'v2 scRNA-seq and HTO library preparation, as described in  (Stoeckius et al.
, 2018). Gene expression reads were processed with cellranger count to generate the cell x gene UMI count matrix, HTO reads were processed with CITE-seq-Count to generate the cell x HTO UMI count matrix. Both count matrices were downloaded from the Seurat tutorial website (https://satijalab. org/seurat/articles/hashing_vignette. html), combined into a Seurat object in R, and analyzed in ShIVA. B. UMAP computed on normalized HTO counts showing the results of HTO-based sample demultiplexing in ShIVA. Each dot is a cell, colored according to HTO-based sample assignation. Doublets and Negative cells are excluded from further analysis. C. Scatter plot of number of genes detected (x-axis) and percentage of transcripts from mitochondrial genes (y-axis) used for quality controls. Each dot is a cell, colored according to quality control results; only TRUE cells are conserved for further analysis.
Acknowledgements We thank  Guillaume Voisinne  for his help in designing the basic architecture of the software. We thank  Bernard Malissen  for supporting the project through human resources. We would like to thank all our beta-testers from  Centre d'Immunologie de Marseille-Luminy ,  Institut de Neurobiologie de la Méditérranée, Institut de Biologie du Développement de Marseille, Centre de Recherche en Cancérologie de Marseille, Centre de Recherche en CardioVasculaire et Nutrition, Theories and Approaches of Genomic Complexity, Unité de biologie Moléculaire, Cellulaire et du Développement, Institut de Recherche Saint Louis, Institut de Biologie Paris-Seine, RESTORE , and  Institut de la Vision.
Data availability Code and documentation are available from the GitHub repository: https://github. com/CIML-bioinformatic/ShIVA Complete video tutorials are available on YouTube: https://www. youtube. com/channel/UCJJ3Svi8AY6XGx4Y9r9G3Iw Docker image is available on DockerHub: https://hub. docker. com/repository/docker/cb2m/shiva.
Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the Big-Mac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide.
Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available. Our ability to characterise brain connectivity has been greatly advanced by the scientific community's open access to big data. Big data can employ large cohorts to examine both inter-and intra-subject variability (in e. g. the UK Biobank 1 or Human Connectome Project 2 ), or aim to characterise a single brain in exquisite detail 3. Here we introduce "The BigMac Dataset" -Big data in a Macaque brain-a resource that combines in vivo MRI with extensive postmortem MRI and wholebrain, multi-contrast microscopy data in a single macaque brain.
Big-Mac consists of in vivo data acquired over multiple sessions, over 270 h postmortem MRI scanning, over 1000 h of microscopy data acquisition and several terabytes of raw data. The BigMac dataset combines multimodal data from both MRI and microscopy to explicitly address issues of sensitivity and specificity in MRI. MRI is a powerful non-invasive method that can inform on wholebrain structure and function, which can in turn be related to cognition, behaviour or medical outcomes. However, MRI also faces several limitations. In vivo signals are typically noisy and confounded by artefacts due to physiological effects and technical bottlenecks related to.
The latter is essential as only when combining data from the same brain can we be sure that we are not over generalising, or ignoring important intersubject variations. Few existing open datasets fulfil all these requirements. For example, existing data combining MRI and microscopy in the same tissue sample are often limited to small tissue sections  [4] [5] [6]  , a single microscopy contrast or minimal MRI data  7  , or compare data from different tissue samples, overlooking considerable between-brains variability. The BigMac dataset aims to address each of these goals, combining co-registered in vivo MRI, extensive postmortem MRI and whole-brain multi-contrast microscopy data in a single macaque brain. The BigMac dataset is interesting from both an anatomical and methodological viewpoint.
For those interested in microstructural neuroanatomy, the densely sampled multi-contrast microscopy can be used to examine both the myelo-and cyto-architecture in great detail and develop novel atlases or parcellations. For those interested in multi-scale neuroscience, BigMac provides whole brain multimodal data spanning four orders of magnitude with which we can link microscale cellular structures to macroscale features of brain organisation and function. For those interested in diffusion modelling, the comprehensively sampled diffusion MRI space-which is co-registered to microscopy data-can be used to drive protocol optimisation, advance computational modelling of the tissue microstructure or brain connectivity, and provide direct validation of many current and future diffusion MRI models and analysis methods  8, 9.
Finally, for those interested in machine-or deep-learning approaches, the BigMac dataset will support the development of novel algorithms which jointly model MRI and microscopy data  10, 11  , for example, to map quantitative imaging biomarkers to specific features of the tissue microstructure. This paper documents the first release of the BigMac dataset. The open data includes in vivo and postmortem MRI, as well as whole brain microscopy data from polarised light imaging  [12] [13] [14]  and myelin-stained histology  15  , both of which provide detailed information about tissue myeloarchitecture  16. Here we detail the multi-faceted data acquisition and curation, and conduct some of the first analyses which demonstrate the data quality and unique information or analyses afforded by BigMac.
Results First we provide an overview of the data included in BigMac. We then explore the BigMac data from various viewpoints including (i) how ultra-high angular resolution diffusion imaging affects connectivity estimates, (ii) how microscopy can be used to detail the myeloarchitecture of the brain, (iii) the quality of MRI-microscopy co-registration, (iv) voxelwise comparisons of MRI and microscopy metrics, and (v) a method for performing hybrid MRI-microscopy tractography. Here we primarily analyse the postmortem data in BigMac, where translating our work to the in vivo domain is the focus of future work.
Data summary Figure  1  gives an overview of the BigMac dataset, with further acquisition details provided in Table  1. Importantly, the BigMac dataset includes in vivo data combining behavioural data with diffusion MRI (1 mm isotropic resolution), task fMRI (2 mm isotropic), resting-state fMRI (2 mm isotropic) and structural MRI (0. 5 mm isotropic) acquired over multiple scan sessions  17, 18. This provides an excellent opportunity to link high-quality postmortem MRI and microscopy data (detailed structural connectivity) to in vivo data (structural and functional connectivity) within the same brain. Ex vivo, a comprehensive MRI dataset (~270 h scanning time) was acquired which includes high resolution (0. 3 mm isotropic) structural images and extensive diffusion MRI data at two spatial resolutions (0.
6 and 1 mm isotropic). The 0. 6 mm data includes 128 gradient directions at b = 4 ms/μm 2 , whilst the 1 mm data includes 250 gradient directions at b = 4 ms/μm 2 and 1000 gradient directions at b = 7 and 10 ms/μm 2 ('ultra-HARDI data'). This ultra-HARDI data is complemented by diffusion data with spherical tensor encoding at b-values of b = 4, 7, and 10 ms/μm 2 for advanced microstructural imaging. Images with negligible diffusion weighting (b ~0 ms/μm 2 ) and a T 1 map were also acquired. The postmortem MR data in BigMac serves two specific aims. First, the postmortem data acts as a crucial intermediary between in vivo diffusion MRI and postmortem microscopy: the postmortem and in vivo MRI are similar in nature (i. e. both are MRI signals) but image the tissue in different states (postmortem, fixed tissue versus in vivo), whereas the postmortem MRI and microscopy share a similar tissue state, though the signals are different.
The PLI and histology-stained slides were then imaged at high resolution (4 or 0. 28 μm/pixel) and co-registered to the MRI  19. Through this extensive, multimodal, multi-contrast acquisition, the BigMac dataset aims to provide a detailed characterisation of the macaque connectome, where the data and analysis tools are openly disseminated to the scientific community (c. f. Data and Code Availability).
The first data release includes PLI and myelin-stained histology (Gallyas); additional histological stains will be added to the BigMac resource as these data are acquired. iso isotropic; # number; DW diffusion-weighted; TE echo time; TR repetition time; 'linear' and 'spherical' indicate diffusion MRI acquired with linear and spherical tensor encoding. or fibre bundles which generally extend from the white matter and into the molecular layer. This pattern is observed in many but not all areas of the cerebellar cortex. To hypothesise on the origin of the birefringence in the molecular layer, we should consider the known architecture of the cerebellar cortex (Fig. 3b  right)  27. The granular layer (between the white matter and the molecular layer) contains cerebellar granule cells, some of the smallest but most numerous cells in the brain.
Co-registration of MRI and microscopy data Our ability to meaningfully compare MRI and microscopy data is greatly enhanced by having high quality multimodal data registration, like that provided in BigMac. However, co-registration of the BigMac MRI and microscopy data was highly challenging for a number of reasons. Firstly, the spatial resolution of the data spans up to 4 orders of scale, from microscopy images with sub-micrometre (histology) or micrometre (PLI) resolution to the ~millimetre MRI data. Secondly, the contrast between the MRI-microscopy images is substantially different and may highlight different tissue features. For example, in both the Gallyas and PLI images, neither the grey/white matter boundary nor outer tissue edges are consistently well defined. Thirdly, the thin tissue sections may be deformed during sectioning or microscopy preprocessing (e.