dataesr/scientific-paragraphs-categorization huggingface dataset:
Next, the cells were permeabilized and then incubated with an anti-rat Cy3conjugated (red, Jackson ImmunoResearch Laboratory, Cat. #712-165-150) secondary antibody to label the internalized population of receptors. The cells were mounted with Mowiol and imaged on an epifluorescent microscope equipped for optical sectioning (AxioImager Z1 Zeiss microscope equipped for optical sectioning Apotome, Zeiss). Internalization was assessed in at least three independent experiments for each condition. The fluorescence intensity (pixels) is measured on each channel (CY3 values: endocytosis; CF350: membrane) in a specific location (ROI) using the Image J software (NIH). The ratio between the fluorescence intensity value of the CF350 channel and the CY3 channel is calculated. One ROI per neuron was collected, and colocalization analysis was measured based on the data collected in 3 independent experiments (at least 6 to 20 neurons/condition).
Pyramidal neurons and interneurons have been distinguished by their location (stratum pyramidale versus stratum oriens/stratum radiatum) and their electrophysiological characteristics (firing rate, resting potential)  (Pelkey et al. , 2017). For all recording conditions, only cells with access resistance < 18 MΩ and a change of resistance < 25% over the course of the experiment were analyzed. Data were filtered with a Hum Bug (Quest Scientific, Canada), digitized at 2 kHz (Digidata 1444A, Molecular Devices, Sunnyvale, CA, USA), and acquired using Clampex 10 software (Molecular Devices). Activation of mGlu3 receptor by LCCG-1 (10 µM) induces an inward current in CA3 pyramidal cells (PCs) in the presence of TTX (1 µM), D-AP5 (40 µM) and picrotoxin (100 µM)  (Ster et al. , 2011). Methacholine (500 nM; 10-20 min) induces synaptic theta activity in CA3 pyramidal cells of hippocampal slice cultures  (Fischer et al.
| Statistical analysis and sample sizes Statistical analyses were carried out with the program R, version 4. 0. 3 (R Core Team, 2021). All statistical tests reported in this study are two-tailed. The statistical units were the values obtained from the different study animals, details on sample sizes below. We tested for litter size effects on (a) pup body mass on postnatal day 1 (n = 87 litters with a total of 662 pups, from 51 breeding pairs) and (b) the increase in body mass from postnatal day 1 to day 20 (n = 34 litters with a total of 263 pups, from 28 breeding pairs), using a larger dataset from our mound-building mouse breeding facility database. For these analyses, averaged values over all pups per litter were used. We fitted linear mixed-effects models (LMM) using the R package lme4  (Bates et al.
, 2015)  including the identity of the parental pair as a random intercept factor. Response variables were right skewed, resulting in nonnormally distributed model residuals. Thus, data were log[x] transformed, leading to the adjustment of model residuals to a normal distribution, which we verified by normal probability plots. For each of our focal females (n = 35 individuals from 15 litters), we calculated, within their litter of origin, the body mass ranking relative to their (male and female) siblings, using the body masses determined on postnatal day 9 (i. e. , at the youngest age when it was possible to individually mark the animals with black hair dye, i. e. , in a low invasive way). This proportional ranking ranged between 0 (lightest pup) and 1 (heaviest pup) and was independent of differences in litter size, thus could be used as predictors within the same multifactorial model (see also below).
, 2017). We assessed individual-based associations between the 10 different parameters of oxidative status by a multivariate LMM using the R package MCMCglmm  (Hadfield, 2010) , including litter identity and cage identity as random intercept factors. Pair-wise correlation coefficients between the different parameters of oxidative status were calculated based on the among-individual variance matrix provided by this model  (Houslay & Wilson, 2017). Coming back to the main questions of the study, we tested in a first step the effects of the females' original litter size (covariate), their proportional within-litter body mass ranking on postnatal day 9 (earliest time of individual marking; covariate), and their exploration tendency (PCA score; covariate) on the different parameters of oxidative status (8-OHdG, PCC, CAT, GPx and SOD in liver and skeletal muscle tissue, respectively) by LMMs  (Bates et al.
| RESULTS 3. 1 | Effects of litter size and within- litter body mass ranking on growth 3. 1. 1 | Litter size effects The analysis of a larger dataset from our breeding stock (n = 87 and 34 litters, respectively) revealed that the mean body mass on postnatal day 1 (LMM: F 1,74 = 13. 55, marginal R 2 = 0. 141, β = -0. 350 ± 0. 095 SE, p < 0. 001) as well as the pre-weaning growth from postnatal day 1 to day 20 (F 1,31 = 7. 91, marginal R 2 = 0. 189, β = -0. 440 ± 0. 156 SE, p = 0. 008) were significantly and negatively associated with litter size. That is, mound-building mouse pups from smaller litters were on average heavier shortly after birth and showed a higher pre-weaning growth. When only considering growth from postnatal days 32 to 43 (weaning was at day 28), litter size also showed a significant and negative effect indicating a lower growth in juveniles from larger litters during the first few weeks after weaning (F 1,32 = 4.
| Consistent individual differences in exploration tendency We quantified individual differences in exploration tendency of the 35 focal females for which we measured parameters of oxidative status later on. PCAs based on the three behavioral parameters recorded during open field and novel object tests, and separately calculated for T 1 (tests on postnatal day 32) and for T 2 (postnatal day 43), revealed a first axis explaining 48. 4% (T 1 ) and 51. 7% (T 2 ) of the variation of the data (eigenvalues T 1 : 1. 45; T 2 : 1. 55), respectively. The eigenvalues of all further axes were <1, and thus these axes were not considered in our analysis. The distance covered in the open field arena (loadings, T 1 : +0. 594, T 2 : +0. 276) and the time spent climbing and exploring the novel object (T 1 : +0.
ACKNOWLEDGMENTS We are grateful to O. Jean and to F. Robles Guerrero for their help with the data collection. The study was financially supported by a grant from the Institut Fédératif de la Recherche Biomédicale IFRB (USPN) to HGR (IFRB-2019). VJ was funded by the two Brazilian agencies, the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). MR was funded by a grant from the Ecole Doctorale Galilée of the Université Sorbonne Paris Nord.
| Sociodemographic, clinical, and neuroimaging data We considered age, sex, available cardiovascular risk factors (hypertension (HTN), coronary artery disease (CAD), diabetes mellitus (DM), atrial fibrillation (AF), history of smoking and prior stroke), and the mRS-derived functional outcome (7-point score: 0 = no detectable symptoms, 2 = slight disability, 3 = moderate disability, 6 = death). Data on age, sex, and comorbidities were acquired during hospitalization, and functional outcome was recorded between day 60 and day 190 post-stroke. Neuroimaging scans, more specifically diffusion-weighted images (DWI), were collected during the acute hospital stay (in the majority of cases in the first 48 h, c. f. , Supporting Information for a description of imaging parameters). DWIderived stroke lesion segmentations were generated via a previously validated ensemble of 3-dimensional convolutional neural networks  (Wu et al.
| Data and code availability The authors agree to make the data available to any researcher for the express purposes of reproducing the here presented results and with the explicit permission for data sharing by individual sites' institutional review boards. The Harvard-Oxford atlas can be downloaded here: https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/Atlases. Bayesian analyses were implemented in Python 3. 7 (predominantly relying on packages: nilearn  [Abraham et al. , 2014]  and pymc3  [Salvatier et al. , 2016] ).
| Strength and limitations The current work has several strengths and limitations: First, we had access to a large stroke database, that, due to its multicenter character, may be representative of a common stroke patient population. In combination with our comprehensive Bayesian modeling and crossvalidation scheme, these factors may lay the foundation for a successful subsequent generalization to new stroke samples and individual patients. However, patients were relatively mildly affected by their strokes ($73% with favorable outcomes) and had overall fairly small stroke lesions. One might hypothesize that this small average stroke lesion volume could be due to a high fraction of patients experiencing strokes due to small artery occlusion. However, only 13. 9% of our patient sample were categorized as small artery occlusion strokes, which is lower than for example reported in registry-based studies (e.
DATA AVAILABILITY STATEMENT The authors agree to make the data available to any researcher for the express purposes of reproducing the here presented results and with the explicit permission for data sharing by individual sites' institutional review boards. The Harvard-Oxford atlas can be downloaded here: https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/Atlases. Bayesian analyses were implemented in Python 3. 7 (predominantly relying on packages: nilearn and pymc3).
Wind Simulations Results We used the wind simulations results data to produce two different representations in the Paraview (V5. 5. 0, kitware, Clifton Park, NY, USA) software environment. The first one was a 1. 5 m-high section (see in Figure  3b ) used to control the tactile and sound rendering in the VR scene (presented in Section 4. 2), and the second one was a streamlines visualization (see in Figure  3b ) used to create the wind-flow animation in the Unity3D context (detailed in Section 4. 3).
Tukey's post-hoc test was performed to compare all the conditions of the categorical variable representation. The statistical analysis was conducted using RStudio software (V2021. 9. 2. 382, RStudio Team, Boston, MA, USA), with the function lme of the nlme package (V3. The glht (multcomp package (V1. 4-18)) function was used for pairwise comparisons and the shapiro. test (stats package (V4. 1. 2)) function for the assumption of normality.
Ideally, SOPs describe the entire data collection procedure (e. g. , recruitment, performing the experiment, data storage, preprocessing, quality control analyses), in sufficient detail for a reader to conduct the experiment themselves with minimal supervision. SOPs may begin as an outline with vague descriptions, and then become more detailed over time. For example, if a session is lost due to a button box signal failure, an image of its correct settings could be added to the SOPs. At the end of the project, its SOPs should be released along with its publications and datasets, to provide a source of answers for the detailed procedural information that may be needed for experiment replication or dataset reuse, but are not included in typical publications. Many resources can assist with experiment planning and SOP creation.
2. and 2. 4; for power estimation tools see Table  S1 ). A second type of pilot analysis is to establish tests for effects not of direct interest, but suitable for controls. As discussed further in section 2. 4, positive control analyses involve strong, well-understood effects that must be present in a valid dataset. It is worth mentioning that well structured and documented openly available datasets (see section 4) could also serve for analysis piloting though they would lack the test for potential site specific technical issues. Simulations could also be used to ensure that the planned analysis is doable and valid.
Pre-registration could be performed following the piloting stage (section 2. 1), but studies can be pre-registered irrespective of whether they include a piloting stage or not. There are many online registries where researchers can pre-register their study. The three most frequently used platforms are: (1) The Open Science Framework (OSF), a platform that can also be used to share additional information about the study/project (such as data and code), with multiple templates and forms for different types of pre-registration, in addition to extensive resources about pre-registration and other open science practices; (2) Aspredicted. org, a simplified form for pre-registration  (Simmons, Nelson, and Simonsohn 2021) ; and (3) Clinicaltrials. gov, which is used for registration of clinical trials in the U.
The informed consent should include a statement about the research goals, inform about the experimental procedures, its expected duration, potential risks, who can participate in a study, and potential conflicts of interest if they exist. It should also inform participants that participation is voluntary, and that the consent can be withdrawn at any time during the experiment without a disadvantage for the participant. If limited intervals for consent withdrawal exist, they should be clearly stated. Furthermore, it should also include a statement that the participant understood the information provided and agreed to participate. Clinical research may require adherence to additional, country specific regulations. Since 2014, founded under ReproNim project umbrella, the Open Brain Consent project (see Table  S1 ) provides examples and templates translated to multiple languages to help researchers prepare consent forms, including the recent development of an EU GDPR-compliant template (The Open Brain Consent working group 2021).
Quality assurance (QA). Tracking QC decisions will also enable identifying structured failures and artifacts that require not just dropping affected datasets, but rather taking corrective actions to preempt propagation to additional datasets. When a mishap occurs, the experimenters should investigate its cause, and if possible, change the SOPs (section 2. 1) and related materials to reduce the chance of it happening again. For example, if many participants report confusion about task instructions, the training procedure and experimenter script could be altered. Automated checks and reports can be very effective, such as real-time monitoring of participant motion during data collection  (Heunis et al. 2020) , or validating that image parameters are as expected before storage (e. g. , with XNAT  (Marcus et al.
Positive control analyses. A final aspect of ensuring high-standards of the research workflow is the incorporation of positive control analyses: analyses included not because they are of interest for the scientific questions, but because they provide evidence that the dataset is of sufficient quality to conduct the analyses of interest, and that the analysis is valid. Ideally, positive control analyses focus on strong, well-established effects that must be present if the dataset is valid. For example, with task fMRI designs, button pressing, which should be associated with contralateral motor activation, is often a convenient target for positive control analysis. In MEG and EEG, participants can be asked to blink their eyes, open their mouths, or clench their jaws, and the recordings checked for the associated artifacts.
2014). Additionally, initiatives, such as the OpenBCI, offer open EEG hardware and tools for biosensing and brain computer interfacing through continuous community driven development. As we have mentioned, very little is known on how the variability of data acquisition parameters affect downstream comparability of results. The EEGManyLabs project  (Pavlov et al. 2021)  will provide a comprehensive dataset in this regard, as many labs with different equipment try to replicate the same studies. Given the large variations across different vendors for all neuroimaging modalities, which often cannot be overcome, it is crucial to report all data acquisition parameters in a comprehensive and standardized manner to make potential differences in data acquisition across studies and sites transparent (for a discussion of reporting guidelines see section 6.
Stimulus presentation and behavior Several actively maintained programs for stimulus presentation and response logging are available. Open source software includes PsychoPy  (Peirce et al. 2019)  in Python and Psychtoolbox  (Brainard 1997; Pelli 1997; Kleiner, Brainard, and Pelli 2007)   Using open stimuli and presentation software generally increases the likelihood a dataset will be useful to others, and its results reproducible. Although desirable, it is not always possible to use fully open stimuli, particularly in the case of commercial movies, audio plays, and image databases. This should also be considered when planning studies. Stimuli, presentation scripts, behavioral tests and related material should be shared whenever possible (see DuPre, Hanke, and Poline 2019 for a list of datasets sharing naturalistic stimuli and section 6).
For example, BIDS converters make it easier to convert data into BIDS format (e. g. , MNE-BIDS  (Appelhoff, Sanderson, et al. 2019)  for MEG and EEG, dcm2bids, ReproNim's HeuDiConv  (Halchenko, Goncalves, et al. 2021)  and ReproIn (Visconti di Oleggio Castello et al. 2020) for MRI and PET2BIDS for PET; see many more on Table  S1 ). The BIDS validator can help researchers make sure their dataset is BIDS-valid following conversion. Once data are in BIDS, users need tools that ease interaction with the data. Two commonly used software packages are PyBIDS  (Yarkoni et al. 2019) , and BIDS-Matlab  (Gau et al. 2022) (ref). These tools facilitate useful dataset queries-such as how many participants are part of a dataset or what tasks were performed-as well as programmatically retrieving specific files-such as all functional runs for a specific subject.
Finally, BIDS apps are containerized analysis pipelines that use full BIDS datasets as their input and produce derivative data  (Gorgolewski et al. 2017). Examples of BIDS apps include MRIQC  (Esteban et al. 2017) , for MRI quality control, fMRIPrep  (Esteban et al. 2019)  for fMRI preprocessing, and PyMVPA  (Hanke et al. 2009)  for statistical learning analyses of large datasets (see more at Table  S1 ). BIDS is a community-led standard and thus, open and inclusive. The BIDS specification is the result of the ongoing collaboration, shared knowledge, discussion, and consensus through the email discussion list, shared Google docs, and GitHub. Questions are also answered on the Neurostars forum and the Brainhack Mattermost channel. BIDS has a well-specified governance structure where everybody is welcome to participate, and the BIDS Starter Kit is a growing resource intended to simplify the learning process for newcomers.
Metadata and Data annotation Metadata and data annotation induce consistency and facilitate data replication and reuse. It improves the clarity of the dataset, the ability for collaborators to understand the conditions in which the data were collected, and the ability to effectively share and reuse them. Commonly, metadata files are data dictionaries that map key terms from an agreed-upon vocabulary to data values that contain detailed and standardized information about the key terms. For example, a key called "SampleFrequency" might map to a numerical value, or a key "TaskDescription" might map to a free-form text that describes the task used in a specific experiment. The BIDS standard has proposed a consistent metadata structure in its specification along with a set of specification terms and tags.
2021; Robbins, Truong, Appelhoff, et al. 2021 ). Drawing on a set of hierarchical vocabulary structures (the HED base schema) and application rules, the HED standard allows for both human-and machine readability, validation, and search of annotations across studies. HED is furthermore fully integrated with the BIDS standard (see section 4. 1), and can be extended by researcher supplied schemas. Additionally, the Neuroimaging Data Model  (Maumet et al. 2016; NIDM; Keator et al. 2013)  effort aims to build a core structure for neuroscience datasets to improve searching across publicly-available datasets. The initiative also provides tools to create and use NIDM documents from BIDS datasets  (Appelhoff, Bates, et al. 2019). To effectively describe neuroscience data, well-developed community-driven vocabularies are needed.
NIDM is built using semantic web techniques and builds off the PROV (provenance) vocabulary (L. Moreau et al. 2015). Moreover, the NIDM-Terms effort has begun to collect and extend sets of community-developed controlled vocabularies and techniques for associating concepts with selected study variables of publicly-available neuroimaging datasets (i. e. , OpenNeuro, ABIDE, ADHD200, and CoRR). This keeps a registry of the domain-relevant vocabularies and concepts used to annotate datasets, further facilitating concept reuse, and improved inter-dataset search. The NIDM team has developed a JavaScript web application, as well as Python-based command line annotation tools, that allow researchers to annotate their BIDS structured datasets and single tabular files (e. g. , csv and tsv spreadsheets), and export both BIDS sidecar file JSON-formatted data dictionaries, NIDM JSON-LD data dictionaries, and NIDM semantic web documents.
Currently, the NIDM-Terms annotation tools allow researchers to associate their study variables with concepts available in the Cognitive Atlas  (Poldrack et al. 2011) , the InterLex information resource, and those in the canonical NIDM terminology/ontology as well as encourage them to add descriptive information to improve the clarity of their variables. Such an effort harmonizes and improves the consistency of neuroimaging data and thus makes querying across neuroimaging datasets more efficient.
DataLad implements this decentralized data management functionality in order to ensure streamlined access to tracked data regardless of hosting service, and to expose datasets for easy access on repository hosting structure. It separates management of file content from lean metadata management by tracking pointers to the services that host managed files (i. e. , local infrastructure, remote hosting services, or multiple storage solutions at once). Using these pointers, it enables streamlined on-demand file retrieval in uniquely identified versions from the registered source. Importantly, data retrieval works via streamlined commands regardless of where the data are hosted. Information about DataLad can be found in the DataLad Handbook  (Wagner et al. 2021 , see Table  S1 ). Entire computing environments could be efficiently managed in DataLad using datalad-container extension (K.
Meyer et al. 2021 ) developed in collaboration between DataLad and ReproNim projects. Brainlife. io is another open science project that allows data management. Brainlife. io is a free and open community-oriented, non-commercial cloud platform that provides web services to support reproducible data management and analysis. Brainlife. io tracks data provenance automatically for the users. As data are analyzed using the Graphical User Interfaces (GUI) and the platform's data processing applications, provenance metadata information is automatically generated and stored associated with the data derivatives. The users do not have to manually save data versions, the platform does that automatically and it allows visualizing data provenance graphs. DataLad and Brainlife. io are synergistic but not overlapping projects that address different user bases and needs.