dataesr/scientific-paragraphs-categorization huggingface dataset:
2013 (Gramfort et al. , 2014)) , FieldTrip  (Oostenveld et al. 2011 ) (see Table  S1 ). More recently, some software packages have been developed to cover additional aspects of the neuroimaging workflow. For instance, nibabel  (Brett et al. 2020 ) to read and write images in many formats, the Advanced Normalization Tools (ANTs) for image registration and segmentation, or Nilearn  (Abraham et al. 2014 ) for statistical analysis and visualization. Workflow engines conveniently connect between the building blocks and determine how the steps are executed in the computational environment. Solutions range from general-purpose scripting (e. g. , Bash or Python) to neuroimaging-specific libraries (e. g. , NiPype;  Gorgolewski et al. 2011). Researchers have all these tools (and others) at their disposal to "mix-and-match" in their workflow.
2021; Clayson et al. 2021) , PET  (Nørgaard et al. 2020 ) and diffusion MRI  (Schilling et al. 2021). There are two crucial aspects of the high analytical variability and its effect on results in neuroimaging. First, when high analytical variability (that potentially affects results) is combined with partial reporting or with incentives to find significant effects, it can alarmingly undermine the reliability and reproducibility of results. Second, even in the apparently ideal scenario in which the researcher performs a single pre-registered valid analysis and reports it fully and transparently, it is still likely that the results are not robust to arbitrary analytical choices. Therefore, new tools are needed to allow researchers to perform a "multiverse analysis" (section 5. 4), where multiple data workflows are used on the same dataset and all the results are reported and their agreement or convergence discussed.
It also increases the likelihood of undetected errors that may remain even after running tests on the code. The first and foremost strategy available to maximize the transparency of research methods is openly sharing the code with the minimal restrictions possible (see section 6. 2; Barnes 2010;  Gorgolewski and Poldrack 2016). Complementarily, version control systems, such as Git  (Blischak, Davenport, and Wilson 2016 , see Table  S1 ), are the most basic and effective tool to track how software is developed, and to collaboratively produce code. Beyond open-sourcing the code, software tools implement further transparency strategies by thoroughly documenting their tools and by supporting implementations with scientific publications  (Barnes 2010; Gorgolewski and Poldrack 2016).
An additional and relevant premise of standardized workflows is transparency -tools must be transparent not only in their implementation, but also in their reporting. For example, fMRIPrep produces visual reports with the double goal of assessing the quality of results, and also providing the researcher with a resource to comprehensively understand every step of the workflow. In addition, the report includes a text description which comprehensively describes each major step in the pipeline, including the exact software version and principle citation. This text, referred to as the "citation boilerplate", is released under a public domain license, and therefore can be included verbatim in researcher's manuscripts, facilitating accurate reporting and proper referencing of academic software. A final relevant aspect towards transparency is the comprehensive documentation of pipelines.
In most cases, standardized workflows preprocess datasets in a fully automated manner, taking a BIDS dataset as input and outputting data that is ready for subsequent analysis with little manual intervention. Importantly, such workflows are typically designed to be as robust as possible to diverse input data (e. g. , with varying parameters or sampling distant populations), a challenge that is facilitated by data standardization (i. e. , BIDS). Additionally, workflows must be portable, enabling users to execute them in a wide variety of environments. A key technology in this endeavor is containers-such as Docker and Apptainer/Singularity-which facilitate packaging specific versions of heterogeneous dependencies while ensuring cross-platform compatibility (e. g. , high-performance computing clusters, desktop, or cloud services).
The BIDS apps framework (section 4. 1) leverages containers by standardizing input parameters to make it trivially easy to execute a wide variety of standardized workflows on BIDS datasets. An example of a higher-level combination of workflows is found in  Esteban et al. (2020) , which describes an MRI research protocol using MRIQC and fMRIPrep. Finally, recent efforts to standardize the outputs of workflows (BIDS Derivatives), further enhances the interoperability of workflows, by ensuring their outputs are compatible with subsequent analysis.
Statistical modeling and advanced analysis Analysis of neuroimaging data is particularly heterogeneous and prone to excessive analytical flexibility and underspecified reporting. Whereas preprocessing is ideally performed once per dataset, there is often a large number of types of analyses that may be used with the preprocessed data. In MRI and fNIRS, for example, analyses range from multi-stage general linear models (GLMs), multivariable decoding analyses, to anatomical and functional connectivity, and more. In PET, analyses consist of region-wise averaging, although voxel-wise approaches are gaining popularity, followed by kinetic modeling and subsequent statistical analyses, which can be GLM or more advanced, such as latent variable models. In MEG and EEG, the broad variety includes analyses such as evoked response potentials, power spectral density, source reconstructions, time-frequency, connectivity, advanced statistics and more.
Each type of analysis also has a wide variety of subtypes, parameters, and statistical models that can be specified, and the form of that specification varies across the dozens of analysis packages that implement each type of analysis. Data analysis reporting is made more transparent when sharing code that relies on open-source software. A prime example is SPM  (Flandin and Friston 2008) , which has been open source since its inception in 1991. Additional widely used open-source tools for data analysis are FSL and AFNI for MRI, and some examples of reproducible pipelines for MEG and EEG developed based on each of the following software: EEGLAB  (Pernet, Martinez-Cancino, et al. 2020 ) Fieldtrip  (Andersen 2018b; M. Meyer et al. 2021; Popov, Oostenveld, and Schoffelen 2018) , Brainstorm  (Niso et al.
2019; Tadel et al. 2019) , SPM  (Henson et al. 2019 ) and MNE-Python  (Andersen 2018a; van Vliet et al. 2018; Jas et al. 2018 ) (see  Niso et al. (2022)  for a detailed review on main EEG and MEG open toolboxes and reproducible pipelines). Reproducibility is also improved when relying on modular and well-documented software such as Nilearn, which offers versatile methods to perform advanced analyses of fMRI data, from GLM to connectomic and machine learning  (Abraham et al. , 2014). Ideally, a single analysis script is made creating a report, from signal extraction, data analysis, and reproducing all figures. An additional challenge for the reproducibility of analysis workflows is the representation of statistical models across distinct implementations of analysis software. For example, GLM approaches to analyze fMRI time series are prevalent and supported by all of the major statistical packages (e.
g. , AFNI, SPM, FSL, Nilearn). However, specifying equivalent models across packages is non-trivial and requires time consuming package specific model specification  (Pernet 2014) , which obfuscates details of the statistical model, exacerbates variability across pipelines, and makes it difficult to perform multiverse analyses (see section 5. 4). The BIDS Stats Model (BIDS-SM, see Table  S1 ) specification has been proposed as a implementation-independent representation of fMRI GLM models; BIDS-SM describes the inputs, steps, and specification details of GLM-type analyses, and encodes them in a machine readable JSON format. The PyBIDS library provides tooling to facilitate reading BIDS-SM, and FitLins  (Markiewicz, De La Vega, et al. 2021 ) is a reference workflow that fits BIDS-SM using AFNI or Nilearn.
The transformative potential of BIDS-SM is showcased by Neuroscout (de la  Vega et al. 2022) , a turnkey platform for fast and flexible neuroimaging analysis. Neuroscout provides a user-friendly web application for creating BIDS-SM on a curated set of public neuroimaging datasets, and leverages FitLins to fit statistical models in a fully reproducible and portable workflow. By standardizing the entire process of statistical modeling, users can formally specify a hypothesis and produce statistical results in a matter of minutes, while simultaneously ensuring a fully reproducible and transparent analysis that can be readily disseminated to the scientific community.
2016; Simonsohn, Simmons, and Nelson 2015; Patel, Burford, and Ioannidis 2015) , there are not yet mature "classic multiverse analysis" tools for high-dimensional data like in neuroimaging. Explorable Multiverse Analyses is an R-tool that allows the readers to explore different statistical approaches in a paper  (Dragicevic et al. 2019). Other tools, such as the Python-based Boba  (Liu et al. 2021) , aim to facilitate multiverse analyses by allowing users to specify the shared and the varying parts of the code only once and by providing useful visualizations of the pipelines and results. However, these tools currently fit simpler analyses and datasets compared to the ones common in neuroimaging. In neuroimaging, recent progress has been made in creating infrastructure for multiverse analysis in fMRI, based on the C-PAC tool (see section 5.
2;  Li et al. 2021). Ongoing efforts to formalize machine-readable standards for statistical models (BIDS-SM) and pipelines to estimate them (e. g. , FitLins;  Markiewicz, De La Vega, et al. 2021) , and their integration with datasets using platforms such as brainlife. io (Avesani et al. , 2019) , could facilitate the development of multiverse tools. In order to make sense of a multiverse analysis, one needs methods to test for convergence across results of diverse analysis pipelines with the same data. Such a method for fMRI image-based meta-analysis was recently used in NARPS  (Botvinik-Nezer et al. 2020)  as well as in subsequent projects  (Bowring, Nichols, and Maumet 2021). Another simple statistical approach to a multiverse analysis was presented with PET data  (Nørgaard, Ozenne, et al.
For data to be interoperable and reusable, it should be organized following an accepted standard, such as BIDS (section 4. 1) and with at least a minimal set of metadata. Free data-sharing platforms are available for publicly sharing neuroimaging data, such as OpenNeuro  (Markiewicz, Gorgolewski, et al. 2021) , brainlife  (Avesani et al. 2019)   S1 ). Data could also be shared on institutional and funder archives such as the National Institute of Mental Health Data Archive (NDA); on dedicated repositories, such as the the Cambridge Centre for Ageing and Neuroscience, Cam-CAN  (Shafto et al. 2014; Taylor et al. 2017)  or The Open MEG Archive, OMEGA  (Niso et al. 2016) ; or on generic archives that are not neuroscience or neuroimaging specific, such as figshare, GitHub, the Open Science Framework, and Zenodo.
If allowed by the law and participants' consent (see section 2. 3), data sharing can be made open, or at least public. Once curated and archived, data can further benefit the individual researcher, for example by adding them to the scientific literature in the form of data descriptors. Such an article type is not intended to communicate findings or interpretations but rather to provide detailed information about how the dataset was collected, what it includes and how it could be used, along with the shared data. In addition, an "open science badge" for data sharing is available in an increasing number of scientific journals  (Kidwell et al. 2016 ) and some prizes are also available as a recognition for such efforts (e. g. , OHBM's Data Sharing prize).
2021 ). It should be noted that sharing an imperfect code is still much better than not sharing at all  (Barnes 2010; Gorgolewski and Poldrack 2016). While GUIs are a great and interactive way to learn to analyze data, one has to pay special attention to properly report the steps followed, as clicks are more difficult to be reported and reproduced than code  (Pernet and Poline 2015). Some toolboxes using GUIs keep track of data 'history' and 'provenance' (e. g. , Brainlife, Brainstorm, EEGLAB, FSL's Feat tool) facilitating this task. Efforts are being put to improve these features. To ensure long-term preservation of the shared code, we suggest using version control systems such as Git, and social coding platforms such as GitHub in combination with an archival database for assigning permanent DOI to code served for research, for instance, Zenodo  (Troupin et al.
Yet, these projects are far from being open or community-developed as they must be centrally governed and mandated by the directives of the research plan. As a result of the paucity of community-oriented standards, archives and software methods, sharing highly-processed neuroimaging data is still the frontier of reproducible science. One community-open archive for highly processed neuroimaging data derivatives is NeuroVault  (Gorgolewski et al. 2015). The archive accepts brain statistical maps derived from fMRI with the goal of being reused for meta-analytic studies. Data upload is open to researchers world-wide and the archive can accept brain maps submitted using most major formats but preferably using the NeuroImaging Data Model (NIDM; see section 4. 2). Another interesting example for automated and standardized composition and sharing of derived data is TemplateFlow  (Ciric et al.
2021) , which provides an open and distributed framework for establishment, access, management, and vetting of reference anatomies and atlases. Another general neuroimaging platform, which allows lowering the barrier to sharing highly processed data derivatives, is Brainlife. Brainlife provides methods for publishing derivatives integrated with the data-processing applications used for generating results from the data via easy-to-use web interfaces for data-upload, processing, and publishing. Different licenses can be selected when publishing a record, allowing reuse of data and derivatives to other researchers (see sources that support the selection of an appropriate license listed in Table  S1 ). Sharing lighter-weight data products such tables and figures is easier using generic repositories under, for example, CC-BY license.
This allows authors to retain rights on the figures they created, and others to re-use their figures freely, either in other publications or for educational purposes. Additional material, such as slide presentations, or supporting content, should be shared using accessible formats (e. g. , image files, pdf, PowerPoint slides, Markdown, jupyter notebooks, etc) via online repositories (e. g. , GitHub or Bitbucket), or shareable links (e. g. , Google Drive, Dropbox) or institutional platforms, with appropriate licenses to indicate how the work could be reused. Whenever possible, using platforms that ensure long-term preservation is recommended (e. g. , Zenodo, FigShare, OSF).
Publication of scientific results Scientific papers are currently the most important means for disseminating research results. However, they should also be written with reproducibility in mind. Guidelines to improve reproducibility can support the writing. The OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), has been promoting best practices, including open science. Recommendations from the committees for MRI  (Nichols et al. 2017 ) and MEG and EEG  (Pernet, Garrido, et al. 2020)  provide guidance on what to report. Other recent community efforts also led to guidelines for PET  (Knudsen et al. 2020 ) and EEG reporting (e. g. , Agreed Reporting Template for EEG Methodology -International Standard (ARTEM-IS)  (Styles et al. 2021) ). One tool that could help authors follow these guidelines while writing their report are their web-based apps (see Table  S1 ).
For the data description and preprocessing aspects, some tools (pyBIDS, bids-matlab) or pipelines (fMRIPrep) can also generate reports automatically, and/or method templates are provided (see section 5. 2). Exact description of methods is mandatory for reproducibility alongside detailed quantitative reporting of results, and reporting of all results is necessary for replicability. In recent years, it has become very common in neuroimaging to publish papers as preprints, on servers such as bioRxiv, medRxiv, PsyArXiv or OSF (see Table  S1 ), prior to peer review in scientific journals. Preprints are publicly available, expedite the process of releasing new findings, and also, importantly, allow authors to get feedback on their paper from a broader audience prior to final publication  (Moshontz et al.
is an open-source, community-developed, general purpose tool for managing and version controlling digital files in a decentralized manner. It tracks data of any type or size in a scalable, Git-repository-based overlay structure, called the dataset (practically, a structure of folders and files). DataLad allows tracking data and metadata files stored on local devices as well as remote or cloud infrastructure. DataLad can retrieve public data from major providers such as OpenNeuro, the Canadian Open Neuroscience Platform, the International Neuroimaging Data-sharing Initiative, the Healthy Brain Network Serial Scanning Initiative, Data sharing for Collaborative Research in Computational Neuroscience, the Human Connectome Project's open access dataset (Van Essen et al. 2013), and many more. Beyond public data, with appropriate permissions or authentication, it can retrieve data from web-based storage providers including major cloud storage services, and local and remote paths.
Introducing Biophysical Parameters in FluoSim FluoSim is an interactive simulator of membrane protein dynamics for fluorescence live-cell and super-resolution imaging (SRI) techniques  (Lagardère et al. , 2020). The program calculates in real time the localization and intensity of thousands of independent molecules in 2D cellular geometries, providing simulated data directly comparable to actual experiments. FluoSim requires several inputs: (1) a realistic cellular geometry defined from a microscopy image, comprising potential subcompartments with specific trapping properties; (2) a given (F) Simulated NLGN1 trajectories based on the diffusion coefficients obtained from uPAINT and binding coefficients deduced from FRAP experiments (621 trajectories). The diffusion color code in logarithmic scale applies to both E and F panels.
DISCUSSION In summary, we provide here a detailed description of the surface trafficking of NLGN1 in the dendritic membrane by interpreting fluorescence live-cell and super-resolution imaging experiments using a quantitative computer software, FluoSim  (Lagardère et al. , 2020). The advantage of this correlative approach is that different imaging paradigms can be modeled using a small set of dynamic and photophysical parameters. Interestingly, each technique is used to feed the program with critical parameters that are not easily accessible with other imaging methods. We give a schematic overview of our strategy to estimate one by one the parameters entered in the simulator (Supplementary Figure  7 ). Specifically, single molecule tracking (uPAINT) provides precise mean values of NLGN1 diffusion coefficients inside and outside synapses, but because of the short duration of the trajectories, fails to capture the long-term residence time of NLGN1 in PSDs.
Recombinant AP-NLGN1 biotinylated by BirA ER was sparsely labeled using a low concentration of STAR635P-conjugated mSA (1 nM). A four-color laser bench (405/488/561 nm lines, 100 mW each; Roper Scientific, Evry, France and 1 W 647 nm line, MPB Communications Inc. , Pointe-Claire, QC, Canada) is connected through an optical fiber to the Total Internal Reflection Fluorescence (TIRF) illumination arm of the microscope. Laser power was controlled through an acousto-optical tunable filter (AOTF) driven by the Metamorph software (Molecular Devices, San Jose, CA, United States). STAR635P was excited with the 647 nm laser line (∼2 mW at the objective front lens), through a four-band beam splitter (BS R405/488/561/635, Semrock, Rochester, NY, United States). Samples were imaged by oblique laser illumination, allowing the excitation of individual mSA-STAR635P molecules bound to the cell surface, without illuminating probes in solution.
Fluorescence was collected on an EMCCD camera with 16 µm pixel size (Evolve, Roper Scientific, Evry, France), using a FF01-676/29 nm emission filter (Semrock, Rochester, NY, United States). Stacks of 2,000 consecutive frames were obtained from each cell with an integration time of 20 ms. Images were analyzed using PALM-Tracer, a program running on Metamorph and based on wavelet segmentation for molecule localization and simulated annealing algorithms for tracking (generously provided by J. B. Sibarita, Bordeaux)  (Izeddin et al. , 2012). This program allows for the tracking of localized molecules through successive images. Trajectories longer than 10 frames (200 ms) were selected. The diffusion coefficient, D, was calculated for each trajectory, from linear fits of the first 4 points of the mean square displacement (MSD) function versus time.
Description of FluoSim Algorithm and Parameters A thorough description of the FluoSim algorithm together with a detailed user manual have been previously published  (Lagardère et al. , 2020). We give below a general outline of the software and the important parameters used in each simulation mode (SPT, STORM, and FRAP). The contour of a 48 µm-long dendritic segment containing 23 PSDs was drawn in Metamorph using an image of a 14 DIV neuron expressing Homer1c-DsRed, and saved as a region file. This region was imported in FluoSim and randomly populated with NLGN1 molecules (1,500-25,000 copies depending on the experiment to model). Those molecules are kept within the dendrite boundaries by introducing rebound conditions. An individual molecule is characterized by its 2D coordinates x and y over time t, and its intensity.
ACKNOWLEDGMENTS We thank  T. Dresbach ,  P. Scheiffele , and  A. Ting  for the generous gift of plasmids;  A. Toledo  for uPAINT experiments;  I. Chamma  for FRAP experiments;  B. Tessier ,  M. Munier , and  C. McKenzie  for molecular biology;  R. Sterling  and  J. Girard  for logistics;  C. Breillat ,  N. Retailleau ,  E. Verdier , and  N. Chevrier  from the Cell culture facility of the Institute;  C. Lemoigne  for probe production;  C. Butler ,  A. Kechkar , and  J. B. Sibarita  for the kind gift of the PALM-Tracer and WAVE-Tracer programs; and  M. Letellier  for sharing reference images.
FUNDING This work received funding from  Fondation pour la Recherche Médicale  (" Equipe FRM "  DEQ20160334916 ),  Agence Nationale de la Recherche  (grants  « SynAdh  »  ANR-13-BSV4-0005-01  and  « Synthesyn  »  ANR-17-CE16-0028-01 ),  ERA-NET Neuron "Synpathy"  ( ANR-15-NEUR-0007-04 ),  Investissements d'Avenir Labex BRAIN (« SynOptoGenesis »   ANR-10-LABX-43 ),  Conseil Régional Nouvelle Aquitaine (« SiMoDyn ») , and  French Ministry of Research  (Ph. D. fellowship to ML).