dataesr/scientific-paragraphs-categorization huggingface dataset:
eu), a network of six European supercomputing centres. TVB cloud services are interlinked and make use of EBRAINS cloud services ( Fig. 1  ), which we briefly introduce in the following before focussing on the TVB services. Please see Table  3  for a glossary of technical terms and abbreviations. The 'Collaboratory' (Supplementary Note: The EBRAINS Collaboratory) provides online workspaces, called 'collabs', where research teams can exchange data and work together on documents, secured with access control to restrict usage to authorized users. 'Lab' provides JupyterLab instances for developing applications and running code in a protected environment that cannot be accessed by other users. Jupyter notebooks provide a programmatic interface to EBRAINS services, allow to execute live code and to link processing steps with visualized results and documentation.
Neuroimaging and brain modelling workflows that are used to analyse large data sets (like the UK Biobank or the Human Connectome Project data sets) require processing power and storage beyond what personal computers can offer. On EBRAINS a network of powerful supercomputers enables to scale computing resources to the needs of a project. Another key advantage of cloud-based research is the ability for interoperable and reusable sharing of data and software, which is an urgent need as there is typically not one individual researcher doing all the work from data acquisition, analysis, hypotheses generation, model building, validation, up to writing and publishing. Rather, it is getting increasingly common that multiple teams, with team members being potentially scattered all around the planet, work together in large projects that require ongoing interaction and synchronization of data and code.
Instead of frequently transmitting data sets via the internet and maintaining intricate software environments at multiple computing sites it is more efficient and practical to have a shared platform where teams can work together on datasets and run software in a common computing space. Problematically, sharing of and collaborative work on personal data raises privacy concerns: highly personal and detailed health data like MRI can be misused for malicious intents and must therefore be thoroughly protected, which is reflected in legislation like the General Data Protection Regulation (GDPR) of the European Union. With TVB on EBRAINS we created a software environment that globally implements state-of-the-art security mechanisms like encryption, access control and sandboxing to protect personal data, while at the same time workflows can be flexibly and reproducibly modified using containerized applications.
These globally implemented measures for data protection make it easier for individual researchers to protect confidential data and to comply with the law. An additional benefit of TVB on EBRAINS workflows is that mechanisms for data management, provenance tracking and reproducible research are directly embedded using DataLad  ( Halchenko et al. , 2021 ) , which enables explicit tracking of all inputs, codes and processing steps that produced a result in a manner similar to how GitHub (github. com) is used for source code management. Having reproducibility already "built-in" makes it not only easier for the scientist to understand and re-use their own complex workflows years later. More importantly, it makes it also easier for everyone else to understand and use a complex workflow or just individual steps thereof.
With simple commands a reviewer, a student, or another researcher can start the entire process or just individual steps and verify the consistency and correctness of the research, or use and adapt it for another problem, without necessarily needing domain knowledge about the used software, which helps to make workflows and results more robust and easier to review and reproduce. In the following we guide readers through the main components of TVB on EBRAINS, highlighting their main features and the respective advantages of cloud-based operation. Subsequently, we demonstrate an end-to-end use case example including the implemented mechanisms for reproducibility and provenance tracking (please see additional use cases in the Supplementary Material). We conclude the main part with a description of data protection mechanisms, the TVB on EBRAINS shared responsibility model, and a discussion.
Cloud service Publications The Virtual Brain  ( Ritter et al. , 2013 ; Sanz-Leon et al. , 2013 , 2015  ) TVB Image Processing Pipeline  ( Proix et al. , 2016 ; Schirner et al. , 2015a ) Fast_TVB ( Costa-Klein et al. , 2020 ; Schirner et al. , 2018 ; Shen et al. , 2019 ; Zimmermann et al. , 2018  ) Bayesian Virtual Epileptic Patient  ( Hashemi et al. , 2020 ; Jirsa et al. , 2017  ) TVB Mouse Brain  ( Melozzi et al. , 2019 ( Melozzi et al. , , 2017  ) TVB ready datasets  ( Aerts et al. , 2020 ( Aerts et al. , , 2018  ) INCF TVB training space  ( Matzke et al. , 2015 )  Fig. 1. TVB on EBRAINS cloud services. Human brain network modelling and neuroimaging require personal data applicable to data protection regulation. Encryption, sandboxing, and access control are used to protect personal data.
EBRAINS provides core cloud services: the 'Multilevel Human Brain Atlas' provides maps of structure, function, and connectivity in multiple reference spaces; 'Drive' for storing and sharing files; 'Wiki' and 'Office' to create workspaces and documents for collaborative research; 'Lab' for running live code in sandboxed JupyterLab instances; 'OpenShift' for service and resource management; 'HPC' are supercomputers for resource-intensive computations. All software components interact via RESTful APIs and use UNICORE for communication with supercomputers. Software components exist in the form of web GUIs, container images, Python notebooks, Python libraries and high-performance machine codes. Curated scientific results, input and output data can be loaded from and stored into the EBRAINS KnowledgeGraph using openMINDS-compliant metadata annotations to enable efficient and robust sharing and reproducible re-use.
TVB (thevirtualbrain. org ) is an open-source software for simulating and analysing brain network models, which describe the brain as a graph that is composed of nodes that represent brain areas and edges that represent physical connections between these areas (Supplementary Note: Brain simulation with TVB)  ( Ritter et al. , 2013 ; Sanz-Leon et al. , 2013 ). TVB can be directly used on EBRAINS from a web GUI ( Table  1  ), without the need to install further software or to have a specific operating system, computing environment or hardware. In addition, TVB can also be used as a Python library for programming in the EBRAINS Lab ( Fig. 1  ). Via these interfaces users can upload brain network models, configure, and run simulations, as well as postprocess and export results. TVB usage is introduced through Jupyter notebooks, explanatory videos, and technical documentation ( Table  1  ).
TVB Image Processing Pipeline Brain network modelling requires a description of the anatomical network that connects brain areas, called structural connectivity, which can be estimated from diffusion-weighted MRI data using the TVB Image Processing Pipeline. The pipeline takes anatomical, functional and diffusion MRI as input and provides as output structural connectivity, regionaverage functional MRI time series, functional connectivity, brain sur- providing a software product with a legal statement (license) that governs its use and redistribution metadata data that provides information (annotations) about other data metadata schema a definition how metadata is structured MRI magnetic resonance imaging neuromorphic systems electronic analogue circuits to mimic neuro-biological architectures ontology (information science) a way to organize data, information, knowledge by defining concepts, categories and their relationships openMINDS specifications for structuring metadata in neuroscience (github.
Multiscale Co-Simulation Multiscale Co-Simulation are two new Python toolboxes for simulating large-scale brain networks with TVB that interact with spiking networks in NEST  ( Gewaltig and Diesmann, 2007 ). The toolboxes provide interfaces to couple the two simulators by connecting the programmatic Python interface of TVB  ( Sanz-Leon et al. , 2013 )  with PyNEST  ( Eppler et al. , 2009 ) , a Python wrapper for NEST. Multiscale Co-Simulation can be downloaded as standalone container image or used on EBRAINS from Jupyter notebooks ( Table  1  ). The need for a high-performance environment is for multiscale cosimulations even more important than for single-scale simulations: instead of one resource-demanding simulator there are two and they need to be executed in parallel. Critically, the two simulators need to synchronize to exchange their respective inputs, which is costly because the latency of network interaction is often orders of magnitude higher than the time needed to compute these inputs.
Data integration and TVB-ready data Another advantage of cloud-based operation is that research results from different groups can be directly integrated into a central data record where they can be found and re-used by others. This functionality is provided by the EBRAINS KnowledgeGraph, an ontology-based graph data base where data sets are richly annotated with openMINDS metadata in order to ensure their interpretability in the future ( Table  1  ). The openMINDS metadata annotations define an exact classification of research inputs and outputs (for example, empirical recordings, software, articles, books, imaging coordinate systems, reference atlases, models, projects) against a scientific ontology or knowledge framework. To ensure data quality EBRAINS employs a team of expert curators who assist in creating and verifying that data format and metadata annotations fulfil state of the art practices for provenance tracking and data management with regard to long-term availability and interpretability of the results.
Data in the KnowledgeGraph is protected by the 'Human Data Gateway', which controls access to human datasets through regulatory compliant data use agreements and access policies. A first example of modelling results that were integrated into the KnowledgeGraph are TVB-ready connectivity data sets in BIDS format from tumour patients and matched control participants. The data set contains region-average fMRI time series, FC, and SC from 31 brain tumour patients before and after surgery, and 11 healthy controls  ( Aerts et al. , 2019 ). See Supplementary Methods: Data integration and TVB-ready data for more information.
End-to-end use case with reproducible brain model construction Upon introducing the individual components of TVB on EBRAINS we now exemplify how they may be combined. Additional use cases are described in the Supplementary Material, especially in the section 'Advanced use cases and training'. To get acquainted with TVB one may start by performing a few test simulations with TVB's default structural connectivity to learn usage of the web GUI and the Python interface; documentation and tutorials explain the steps ( Table  1  ). Visualizing the outputs for different parameter settings and fitting simulation results with empirical data (for example, using functional connectivity) helps to create an intuitive understanding of brain network model dynamics. Next, researchers may want to perform a more detailed analysis, for example, comparing individuals in patient versus control groups to study mechanisms of pathological versus healthy brain dynamics.
Here, the researchers can use the TVB Image Processing Pipeline to compute individual structural and functional connectivity from human MRI data. Estimating connectomes from MRI data consists of many complex steps, making it hard to explicitly track all the necessary provenance data to robustly reproduce a particular configuration of processing steps. Just a minor update of a dependency or an untracked renaming of a file can break the entire workflow and make a result not reproducible. The pipeline uses DataLad (datalad. org) to make its workflow reproducible in an actionable manner: all software and data are tracked in a way that the entire workflow or just individual steps can be easily re-run, archived, published and shared. With DataLad all data and code files are version-controlled and managed in a manner that is comparable to how software is managed with GitHub (github.
com), allowing to capture complex hierarchical project structures and all computational steps from raw data to final figures. When large cohorts are modelled users may find the speed of standard brain model implementations insufficient and switch to TVB's highperformance implementations, which allow fast execution and easy generation of high-performance codes for custom models with TVB's XMLbased modelling language RateML. To inform model parameters researchers may decide to include microstructural information from the EBRAINS Human Brain Atlas using the siibra interface  ( Wang, 2020 ). Or they may extend large-scale models to encompass finer scales using TVB Multiscale to study hypotheses about brain function that span spatial scales from individual point neurons over populations to whole brain models.
Discussion TVB cloud services were developed to lower the barriers to brain simulation and connectome analysis. They offer reproducible and protected workflows for collaborative computational neuroscience research. All codes are open source and available for download from EBRAINS and GitHub ( Table  1  ). Software is packaged in container images that can be directly used without the need to install dependencies. Several software and data components have been peer-reviewed, and results were published in academic journals ( Table  2  ). To enable actionable reproducibility the image processing workflow is equipped with tools for data management and provenance tracking. All computational steps, inputs and software are tracked, and each step can be easily rerun and verified with a simple set of commands.
, 2013 ). Like main TVB, the pipeline execution on the supercomputer can be controlled from the TVB web GUI without giving users direct access to the supercomputer. An orchestrator program on the supercomputer coordinates the execution of the container images and ensures that personal data is encrypted at all times, except for the duration of the processing and then only in the main memory of a sandboxed process ( Fig. 2  and Supplementary Note: TVB Image Processing Pipeline for more details). To make workflow processing reproducible the open source distributed data management solution DataLad (datalad. org; ( Halchenko et al. , 2021 ) ) was used for version control and provenance tracking: all files involved in a workflow (such as data, code and computational environment) are stored within nested directory trees, which allows to explicitly store the evolution of a data set from its raw state to the final result.
Acknowledgments We gratefully acknowledge the  Swiss National Supercomputing center CSCS  for supporting this project by providing computing time through the  Interactive Computing E-Infrastructure (ICEI)  on the  Supercomputer Piz Daint of the Fenix Infrastructure  (projects ich10, ich12) and the  Gauss Centre for Supercomputing e. V. ( www. gauss-centre. eu ) for supporting this project by providing computing time through the  John von Neumann Institute for Computing (NIC)  on the  GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC). We acknowledge support by  H2020 Research and Innovation Action grants Human Brain  Project  SGA2 785907 ,  SGA3 945539 ,  ICEI 800858 ,  VirtualBrainCloud 826421  and  ERC 683049 ;  Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative.
Data availability The datasets generated and analysed during the current study are available in the EBRAINS KnowledgeGraph repository, search. kg. ebrains. eu. KnowledgeGraph is a DOI-minting repository where EBRAINS data and software services are indexed and referenced. To share personal data amongst researchers subject to national or international data protection laws (e. g. , the General Data Protection Regulation of the European Union) protected environments and workflows as well as a shared responsibility model were created (Supplementary Methods: Data protection in the TVB on EBRAINS cloud).
Code availability All software codes presented in this article have open-source licenses and can be downloaded from GitHub ( Table  1  ). The EBRAINS database service KnowledgeGraph (search. kg. ebrains. eu) is a DOI-minting repository where all data and software services are indexed and archived. Source codes are deployed as cloud services that can be used on ebrains. eu and as standalone download versions that can be pulled as container images from Docker Hub ( Table  1  ).
apps. hbp. eu Collab (TVB-Multiscale) wiki. ebrains. eu/bin/view/Collabs/the-virtual-brain-multiscale Collab (Parallel CoSimulation) wiki. ebrains. eu/bin/view/Collabs/co-simulation-tvb-and-nest-high-computer Source code (TVB-Multiscale) github. com/the-virtual-brain/tvb-multiscale Source code (Parallel CoSimulation) github. com/multiscale-cosim/TVB-NEST Container image (TVB-Multiscale) hub. docker. com/r/thevirtualbrain/tvb-nest TVB-HPC Collab Automatic code generation wiki. ebrains. eu/bin/view/Collabs/rateml-tvb/ Source code github. com/the-virtual-brain/tvb-root Fast_TVB Collab Parallelized simulation (multithreading) wiki. ebrains. eu/bin/view/Collabs/fast-tvb Source code github. com/BrainModes/fast_tvb Container image hub. docker. com/r/thevirtualbrain/fast_tvb Bayesian Virtual Epileptic Patient Collab Epilepsy modelling wiki.
ebrains. eu/bin/view/Collabs/bayesian-virtual-epileptic-patient Source code github. com/ins-amu/BVEP TVB Mouse Brains Collabs Mouse brain simulation wiki. ebrains. eu/bin/view/Collabs/tvb-mouse-brains wiki. ebrains. eu/bin/view/Collabs/mouse-stroke-brain-network-model/ TVB-ready dataset kg. ebrains. eu/search/instances/Dataset/a696ccc7-e742-4301-8b43-d6814f3e5a44 SC, FC, and fMRI from tumour patients and controls openMINDS metadata for TVB-ready data Collab Metadata in JSON-LD format wiki. ebrains. eu/bin/view/Collabs/openminds-metadata-for-tvb-ready-data openMINDS schema github. com/HumanBrainProject/openMINDS TVB atlas adapter Collab Brain atlas wiki. ebrains. eu/bin/view/Collabs/sga3-d1-1-showcase-1 Source code github. com/FZJ-INM1-BDA/siibra-python; github. com/FZJ-INM1-BDA/siibra-api Visualizer brainsimulation.
We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the genelevel in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates, and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the setlevel to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared to other methods. Application to two datasets identified HIV-associated Interferon pathways in xenograft mice and pathways associated with Alzheimer's disease progression in humans.
Introduction Single-cell RNA sequencing (scRNA-seq) data have been used to understand the heterogeneity of cell type landscapes and to understand the variation of gene expression at the single-cell resolution across biological processes or treatments. Many of the data analysis methods originally designed for bulk RNA-seq data, including Differential Expression (DE) based gene set tests, have been re-applied to scRNA-seq data. These DE-based gene set tests are used to test whether a pre-constructed  [2]  set of genes is significantly differentially expressed between/among sample groups. In the past decade and a half, such gene set tests have been used to contextualize gene-level differential expression analyses and identify both important pathways and real biological mechanisms  [15, 21, 30, 14].
These gene set tests also improve statistical power and reduce spurious associations as compared to gene-level DE testing  [8, 12]. This further increases reproducibility across multiple experiments, which is often lower than desired due to biological and technical variability across different transcription profiling platforms  [8, 12]. Such DE-based gene set tests therefore constitute a routine step of performing differential expression (DE) based analyses, in both bulk RNA-seq and scRNA-seq datasets  [31, 7]. There are at least two different types of DE-based analyses for scRNA-seq data. The first type of DEbased analysis focuses on the difference between sample groups to identify genes or gene sets significantly associated with sample groups. Example comparisons include disease vs control, mutant vs wild-type, and before vs after treatment.
One such method is our previously published TWO-SIGMA  [38] , which has several useful features. First, TWO-SIGMA assumes that observed counts follow a Zero-Inflated Negative Binomial (ZINB) distribution, which provides a flexible approach to capture the zero-inflated and overdispersed counts often seen in scRNA-seq, but not in bulk RNA-seq, datasets. Available DE-based gene set tests originally developed for microarray and bulk RNA-seq, including GSEA  [34]  and related extensions sigPathway and fGSEA  [19] , CAMERA  [43] , and PAGE  [18] , all conduct gene-level analyses using either non-parametric rank-based methods or using distributional assumptions chosen to represent the features of microarray or bulk RNA-seq data. Misspecified gene-level models can lead to misleading inference at both the gene-level and set-level, taking the form of inflated type-I error or reduced power.
TWO-SIGMA's random effect modelling framework allows for efficient computation of within-sample correlation between cells in scRNA-seq data that has a large number of cells from the same biological sample. It is essential to discriminate between the two types of null hypotheses, competitive and self-contained, used in gene set testing. Competitive gene set tests evaluate significance by comparing the evidence of differential expression of a gene set to the evidence in a reference set of genes  [18, 36, 8, 28]. In contrast, self-contained tests are commonly used to compare the similarity of gene expression patterns in a gene set across different data sets  [13, 42] , not relative to other genes. Competitive tests use a battery of gene sets, for example from the Molecular Signatures Database  [34, 20] , to rank sets and analyze which are the most significantly associated with a given phenotype.
We introduce more of iDEA and MAST in the Methods section. Many other scRNA-seq-related gene set tests with different goals have been developed, including BAGSE  [17] , UniPath  [4] , PAGODA  [9] , SCENIC-AUcell  [1]. BAGSE is not a competitive test and has similar hypothesis as GSEA  [34] , consisting of a hybrid of the self-contained and competitive null hypothesis  [13, 6, 36] , PAGODA looks for coordinated variation and is not DE based, UniPath uses single-cell ATAC-seq (scATAC-seq) data in addition to scRNA-seq data, and AUcell in the SCENIC workflow is based on regulon activity. Overall, there is a need for methodological advancements to tailor gene set testing frameworks to scRNAseq data, and a need to evaluate the ability of methods designed for bulk data to provide statistically valid results when applied to scRNA-seq data.