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11,506 results for “Dynamics”
Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models
<p>Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme 'pulses' against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time-limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology, and do not consider biological effects as a basis of MHW severity. The reemergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across 1) three thermal tolerance adaptive strategies, 2) interannual temperature variation, and 3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration-high magnitude) and chronic (long duration-low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs. </p>
Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction.
<p>The dataset consists of third part of data for the same paper " Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction." Data were uploaded separately due to the limited space available for uploading which is f 50.00 GB and since i have more I could not upload them together.</p> <p>The data are Illumina raw reads obtained from vesicle fractions of 20%, isolated from Coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from EVs 20% fraction metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial organisms involved in the production of EVs and packaging of DNA into those EVs in our annual sampling and the types of genes that were packaged in vessicles isolated from the Coastal Mediterranean seawaters (Functional annotation of genes packaged in vesicles).</p> <p> The data are labeled as "EVs_20_DEC_1.fastq" which denotes the vesicle 20% Optiprep fraction for sample collected in December, with "1" indicating the forward read, while "EVs_20_DEC_2.fastq" represents the corresponding reverse read. This naming scheme is consistent across all vesicle fraction samples in the dataset.</p>
Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction
<p>The data consists second part of data for the same paper " Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction." Data were uploaded separately due to the limited space available for uploading.</p> <p>The data are Illumina raw reads obtained from vesicle fractions of 25%, isolated from coastal Mediterranean seawaters in Alicante, Spain, over the course of one year. Illumina DNA prep kit (IDT, Ref. 20026930) was used following the manufacturer's protocol to generate and sequence the libraries for the obtained DNA samples from EVs fractions metagenomes. Subsequently, these libraries were sequenced using HiSeq X technology (150 PE, 1 lane) by two different facilities: Macrogen in Seoul, Republic of Korea, and the Genomics Unit of the Center for Genomic Regulation (CRG) in Barcelona, Spain. The dataset was used in order to ascertain the main microbial organisms involved in the production of EVs and packaging of DNA into those EVs in our annual sampling and the types of genes that were packaged in vessicles isolated from the Coastal Mediterranean seawaters (Functional annotation of genes packaged in vesicles).</p> <p> The data are labeled as "EVs_25_DEC_1.fastq" which denotes the vesicle 25% Optiprep fraction for sample collected in December, with "1" indicating the forward read, while "EVs_25_DEC_2.fastq" represents the corresponding reverse read. This naming scheme is consistent across all vesicle fraction samples in the dataset. </p>
Modeling the impacts of Antarctic Sea Ice Decline: Responses of Atmospheric Dynamics
Open the record for dataset details and reuse information.
Correlation-based Analysis of the Influence of Bound Constraint Handling Methods on Population Dynamics in Differential Evolution
<p>The dataset is based on the average values collected over 5 independent runs, considering the largest common number of<br>iterations for LSHADE algorithm coupled with ’sat’, ’midT’, ’midB’, ’unif’, ’beta’, ’mir’, ’tor’, ’expC_R’, ’expC_T’, ’expC_B’, ’vectR’, ’vectT’, ’vectB’, ’mahalanobis’ correction methods, on BBOB function f3, 4, 5, 16, 23, instance 1</p> <p>Plots for the averaged values are included for measures 'pop_size', 'best', 'error', 'prob_infeas', 'genMutatedComponent', 'genSuccessMutants','meanImprovements', 'varPop', 'avgF', 'avgCR', 'extension', 'shape', 'eccentricity', 'kl_unif'</p>
Data supporting: "Interaction of MRI Contrast Agent [Gd(DOTA)]− with Lipid Membranes: A Molecular Dynamics Study"
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numerical data to accompany "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations"
<p>This is the numerical data to accompany the paper "Strong asymmetry in near-fault ground velocity during an oblique strike-slip earthquake revealed by waveform particle motions and dynamic rupture simulations". Please refer to the README.txt file for information about the individual datasets and archive files. </p>
Data for "Magic Angle Spinning Solid-State 13C Photochemically Induced Dynamic Nuclear Polarization by a Synthetic Donor–Chromophore–Acceptor System at 9.4 T"
<p>NMR data and photo-CIDNP-enhanced NMR data for "Magic Angle Spinning Solid-State 13C Photochemically Induced Dynamic Nuclear Polarization by a Synthetic Donor–Chromophore–Acceptor System at 9.4 T".</p> <p>All data but those relative to the spectra in Figure S13 are provided in Bruker format. For the low field experiments (Figure S13), raw free induction decay NMR data are provided, together with a processing script written in Wolfram Mathematica.</p>
Traits mediate environmental responses of benthic ciliates in dynamic coastal habitats
<p><strong>Aim:</strong> Understanding how species' traits mediate environmental responses provides a mechanistic perspective on community assembly processes. Although traits that influence the response of multicellular organisms to environments have been studied, the identification of such traits in unicellular organisms, like ciliates, is underexplored. Since ciliates are dominate animals at coastal sandy sediments and contribute significantly to interstitial community respiration, identifying links between their occurrence and sandy-coast characteristics is crucial for conserving biodiversity and protecting coastal habitats.</p> <p><strong>Location:</strong> Coasts of China.</p> <p><strong>Methods:</strong> We used Hierarchical Modeling of Species Communities, a trait-based joint species distribution model, to analyze ciliate community data collected from 344 sites belonging to 31 beaches across the Chinese coastline.</p> <p><strong>Results:</strong> We found that physical variables play the most important role in determining ciliate occurrence. The beach index which combines the tidal range, sediment grain size and beach slope of beach selected species based on body size as well as feeding type, while sediment grain size constrained different mobility types, with swimming species favoring fine sandy sediments. The significant contribution of phylogeny in explaining the residual variation among species responses indicates that there are phylogenetically conserved but unmeasured traits too that influence species' responses. Species richness of benthic ciliates was higher in dissipative beaches with high primary productivity.</p> <p><strong>Main conclusions:</strong> Based on our research, ciliate occurrence in coastal beaches is mediated by their traits which are filtered by local environmental variables. The beach index is essential in predicting distribution patterns and species richness of benthic ciliates. Nevertheless, further research is needed to identify additional traits that will enhance the accuracy of predicting ciliate niches. Our study provides insights into the mechanisms driving ciliate community assembly processes and has implications for the conservation of biodiversity in sandy coastal habitats.</p>
Analysis Data, "Strain dynamics of contaminating bacteria modulate the yield of ethanol biorefineries"
<p>This package contains datasets in `Rdata` format underlying analyses presented in the study "Strain dynamics of contaminating bacteria modulate the yield of ethanol biorefineries", first available as a preprint on February 08, 2021:</p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.02.07.430133v1.article-info">https://www.biorxiv.org/content/10.1101/2021.02.07.430133v1.article-info</a></p>
Dataset associated with "Constraints on the subsecond modulation of striatal dynamics by physiological dopamine signaling"
<p>This repository contains behavioral data, dLight photometry measurements, smoothed spiking data from recorded striatal neurons, and miniscope recordings. Text files with further explanation are provided to assist with replication of the main analyses. Matlab code to perform key analyses can be found in the accompanying GitHub repository (https://github.com/sotmasman/Dopamine-constraints)</p>
Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion</em>”</strong> in Optics Express (doi.org/10.1364/OE.521702)<em>. </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage.</p> <p>We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>10050, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>CS50-28, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Mix, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated mixed sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Dark, 10 us.mat</p> </td> <td> <p>Background interference intensity from a custom setup.</p> </td> <td> <p>Na=2048, Nb=5, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Concentrated 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated mixed sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute mixed sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data for the Thorlabs OCT measurements.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis functions.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Custom_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the custom setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_dilute.py</p> </td> <td> <p>The script is for running analysis of raw dilute measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Dataset for Engineering multifunctional dynamic hydrogel for biomedical and tissue regenerative applications
<p><span>Hydrogels have emerged in various biomedical applications, including tissue engineering and medical devices, due to their ability to imitate the natural extracellular matrix (ECM) of tissues. However, conventional static hydrogels lack the ability to dynamically respond to changes in their surroundings to withstand the robust changes of the biophysical microenvironment and to trigger on-demand functionality such as drug release and mechanical change. In contrast, multifunctional dynamic hydrogels can adapt and respond to external stimuli and have drawn great attention in recent studies. It is realized that the integration of nanomaterials into dynamic hydrogels provides numerous functionalities for a great variety of biomedical applications that cannot be achieved by conventional hydrogels. This review article provides a comprehensive overview of recent advances in designing and fabricating dynamic hydrogels for biomedical applications. We describe different types of dynamic hydrogels based on breakable and reversible covalent bonds as well as noncovalent interactions. These mechanisms are described in detail as a useful reference for designing crosslinking strategies that strongly influence the mechanical properties of the hydrogels. We also discuss the use of dynamic hydrogels and their potential benefits. This review further explores different biomedical applications of dynamic nanocomposite hydrogels, including their use in drug delivery, tissue engineering, bioadhesives, wound healing, cancer treatment, and mechanistic study, as well as multiple-scale biomedical applications. Finally, we discuss the challenges and future perspectives of dynamic hydrogels in the field of biomedical engineering, including the integration of diverse technologies.</span></p>
Molcular Dynamics Trajectories for GPR156 PG No Cholesterol
<p>Note the cholesterol between the dimers were not modelled in these trajectories. Obsolete.</p> <p> </p> <p>This is not the Molecular Dynamics Data for 10.1038/s41594-024-01224-7 for publication at</p> <p>Shin, J., Park, J., Jeong, J. <em>et al.</em> Constitutive activation mechanism of a class C GPCR. <em>Nat Struct Mol Biol</em> (2024). https://doi.org/10.1038/s41594-024-01224-7</p> <p>This folder contains the PDB format file ("Topology") and the XTC format file (Trajectories). The timestep in this strided trajectory is 0.5 ns per frame. Periodic boundary condition (pbc) can be restored using VMD's standard pbc commands.</p> <p>But please cite us if you still find this data useful!</p>
BaTiO3 coarse-grained molecular dynamics simulations
<p>This repository contains the simulation results for BaTiO3 using coarse-grained molecular dynamics package <a title="Feram" href="https://loto.sourceforge.net/feram/" target="_blank" rel="noopener">Feram</a>.</p> <p>The files (1: data.avg, 2: *.csv) use the space-separated format or comma-separated format.</p> <p>(1) data.avg columns:<br>T: temperature in Kelvin<br>Ex Ey Ez: external_E_field along x,y,z in V/Angstrom.<br>exx eyy ezz eyz ezx exy: strain tensor<br>ux uy uz: dipole displacements in Angstrom<br>uxux uyuy uzuz uyuz uzux uxuy: cross-terms of dipole displacements in Angstrom^2<br>dk: dipo_kinetic in eV/u.c.<br>lr: long_range in eV/u.c.<br>dEf: dipole_E_field in V/Angstrom<br>unhar: unharmonic in eV/u.c.<br>s_ho: homo_strain in eV/u.c.<br>c_ho: homo_coupling in eV/u.c.<br>s_inho: inho_strain in eV/u.c.<br>c_inho: inho_coupling in eV/u.c.<br>etot: total energy in eV/u.c.<br>HNP: H_Nose_Poincare in eV/u.c.<br>e2: e2<br>dkt: dipo_kinetic_true in eV/u.c.<br>ak: acuou_kinetic in eV/u.c.<br>sr: short_range in eV/u.c.<br>mod: inho_modulation in eV/u.c.<br>px py pz: px py pz<br>ppx ppy ppz ppyz ppzx ppxy: ppx ppy ppz ppyz ppzx ppxy<br>mx my mz: <ux>, <uy>, <uz> in Angstrom<br>amx amy amz: <|ux|>, <|uy|>, <|uz|> in Angstrom</p> <p>(2) *.csv contains header in each file.</p> <p>(3) avg2csv.ipynb contains script to convert files.</p>
Data set related to the manuscript "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model". XYZ files giving the particles and bulk positions in the lattices are also provided.</p>
Climate and ice sheet dynamics in Patagonia throughout Marine Isotope Stages 3 and 2
<p>This dataset contains the modelled ice thickness over Patagonia during the global Last Glacial Maximum at 4 km resolution by using SICOPOLIS forced by the results from PMIP models. It also contains the modelled output of the transient simulations performed at 8 km resolution forced by MPI-ESM1-2-LR and different cores.</p>
Data from: Seabird presence and seasonality influence nutrient dynamics of atoll habitats
<p>In this study, we investigate nutrient dynamics linked to seabird presence and seasonality. We compare seabird colonies of different taxa nesting on separate islands of Farquhar Atoll, Seychelles. We first quantify guano nutrient input and guano nitrogen isotopes for each seabird species. We assess the transfer of seabird-derived nutrients in terrestrial and marine habitats by comparing nitrogen isotope values in soil, plants, and seagrass on each seabird island and a control island, during the wet and dry seasons. We then assess the influence of seabirds by comparing nutrient levels in soil, plants, and seagrass on each seabird island and a control island, during the wet and dry seasons. For plants and seagrass, we also assess and compare nutrient ratios and carbon stable isotope ratios on each seabird island and a control island, during the wet and dry seasons.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
Simulations of PKA RIα Homodimer Reveal cAMP-coupled Conformational Dynamics of Each Protomer and the Dimer Interface with Functional Implications
<p>Protein kinase A (PKA) is a ubiquitous cAMP-dependent enzyme in mammalian tissues. The inactive PKA holoenzyme disassociates into a homodimer of regulatory (R) subunits and two active catalytic (C) subunits upon cAMP binding to two tandem domains (termed <span>CBD-A</span> <span>and</span> <span>CBD</span>-<span>B</span>) in R subunits. The release of cAMP facilitates reassociation of R and C subunits<span>,</span> resetting PKA to its basal state. The cAMP-mediated structural changes in the activation-termination cycle <span>remain</span> <span>partially</span> <span>understood</span>. The multimeric states of PKA complicate the issue and are particularly <span>less</span> <span>studied</span>. Therefore, we computationally investigate the conformational dynamics of PKA <span>RI</span>a homodimer in different cAMP-bound states. The absence of cAMP in two CBDs affect differently the <span>conformational</span> <span>dynamics</span> of protomers. Moreover, <span>such</span> disparate <span>responses</span> are extended to the dimer interface <span>constituted</span> <span>by</span> <span>the</span> <span>N</span>-<span>terminal</span> <span>helical</span> <span>sub-domains</span> termed N3A motifs. T<span>he removal of cAMP from CBD-A induces large-scale structure</span> changes <span>of individual </span>R subunits <span>towards the holoenzyme state,</span> consist with previous simulations of a single R subunit. <span>Meanwhile</span> <span>it</span> <span>keeps the structural heterogeneity of the </span>N3A-N3A' <span>dimer interface observed in the fully bound state.</span> By contrast, the removal of cAMP from CBD-B does not affect <span>individual </span>R subunits but alters the conformational space of the N3A-N3A' dimer interface. The cAMP-coupled s<span>tructural</span> <span>changes</span> <span>of</span> <span>each</span> <span>protomer</span> <span>and</span> conserved conformational space of <span>the</span> N3A-N3A' <span>dimer</span> <span>interface</span> are essential for t<span>he</span> <span>transition</span> <span>between</span> the fully cAMP-bound R<sub>2</sub> homodimer and the R<sub>2</sub>C<sub>2</sub> holoenzyme as suggested by their crystal structures. Our work provides structural insights into <span>the</span> regulatory mechanism of cAMP in PKA signaling. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.