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4,578 results for “Assistance”
Data from: Assisted migration of cloud forest trees: Unearthing the effects of climatic transfer distance
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Suppression of reed canarygrass by assisted succession: A sixteen-year restoration experiment
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Polymer-assisted modification of metal-organic framework MIL-96 (Al): influence on particle size, crystal morphology and perfluorooctanoic acid (PFOA) removal
<p>Dataset supporting publication.</p> <p><strong>Polymer-assisted modification of metal-organic framework MIL-96 (Al): influence of HPAM concentration on particle size, crystal morphology and removal of harmful environmental pollutant PFOA</strong></p> <p>Chemosphere, <a href="https://doi.org/10.1016/j.chemosphere.2020.128072">https://doi.org/10.1016/j.chemosphere.2020.128072</a></p> <p>Preprint available from ChemRxiv, <a href="https://doi.org/10.26434/chemrxiv.12262010.v2">https://doi.org/10.26434/chemrxiv.12262010.v2</a></p> <p><strong>Abstract</strong></p> <p>A new synthesis method was developed to prepare an aluminum-based metal organic framework (MIL-96) with a larger particle size and different crystal habits. A low cost and water-soluble polymer, hydrolyzed polyacrylamide (HPAM), was added in varying quantities into the synthesis reaction to achieve >200% particle size enlargement with controlled crystal morphology. The modified adsorbent, MIL-96-RHPAM2, was systematically characterized by SEM, XRD, FTIR, BET and TGA-MS. Using activated carbon (AC) as a reference adsorbent, the effectiveness of MIL-96-RHPAM2 for perfluorooctanoic acid (PFOA) removal from water was examined. The study confirms stable morphology of hydrated MIL-96-RHPAM2 particles as well as a superior PFOA adsorption capacity (340 mg/g) despite its lower surface area, relative to standard MIL-96. MIL-96-RHPAM2 suffers from slow adsorption kinetics as the modification significantly blocks pore access. The strong adsorption of PFOA by MIL-96-RHPAM2 was associated with the formation of electrostatic bonds between the anionic carboxylate of PFOA and the amine functionality present in the HPAM backbone. Thus, the strongly held PFOA molecules in the pores of MIL-96-RHPAM2 were not easily desorbed even after eluted with a high ionic strength solvent (500 mM NaCl). Nevertheless, this simple HPAM addition strategy can still chart promising pathways to impart judicious control over adsorbent particle size and crystal shapes while the introduction of amine functionality onto the surface chemistry is simultaneously useful for enhanced PFOA removal from contaminated aqueous systems.</p>
Pandemic-related Attitudes, Stressors and Work Outcomes among Medical Assistants during the SARS-CoV-2 ("Coronavirus") Pandemic in Germany: a cross-sectional Study
<p>File type: SPSS file (.sav)</p> <p>Study type: Cross-sectional study</p> <p>Population: Medical assistants in Germany</p> <p>Study period: April 7th-April 14th, 2020</p> <p>Number of participants: 2150</p> <p>Research question: Investigation of pandemic-related attitudes, stressors and work outcomes among medical assistants during the SARS-CoV-2 (“Coronavirus”) pandemic</p> <p>Missing values: None (due to online survey) </p> <p>Original variables: v_982, v_1, v_2, v_3, v_5, v_6, v_7, v_13, v_14, v_21, v_22, v_23, v_24, v_26, v_27, v_28, v_29, v_31, v_32, v_33, v_40, v_41, v_42, v_43, v_46, v_47, v_48 v_49, v_52, v_57, Beruf_MFA</p> <p>All other variables were calculated from the original variables either by rescaling or dichotomization. </p>
Data from: Macro-detritivores assist resolving the dryland decomposition conundrum by engineering an underworld heaven for decomposers
<p>Litter decomposition in most terrestrial ecosystems is regulated by moisture-dependent microorganism activity, among other things. <span class="fontstyle01"><span>Decomposition models typically underestimate rates of plant litter decomposition in drylands, suggesting the existence of additional drivers of decomposition. Attempts to reveal these drivers have predominantly focused on abiotic degradation agents, alternative moisture sources,</span></span> and <span class="fontstyle01"><span>soil-litter mixing</span></span>. The role of burrowing animals in promoting decomposition has received less attention despite greatly contributing to plant litter transfer from the harsh desert surface to the moister and nutrient-rich environment belowground. Our goal was to explore how macro-detritivore burrows affect plant litter mineralization dynamics. We introduced <sup>13</sup>C-labeled litter belowground into (1) desert isopod (<i>Hemilepistus reaumuri</i>) burrows and (2) artificial burrows, and aboveground on top of (3) isopod fecal pellet mounds and (4) bare soil crust. We compared the litter mass loss between the four treatments and used cavity ring-down spectroscopy to reveal the <i>in situ</i> mineralization dynamics. No litter mineralization was evident during the dry summer months both above- and belowground. Following rain events, mineralization rates spiked in all four micro-environments, quickly diminishing aboveground while slowly waning belowground. Total litter mass loss was twofold higher below- than aboveground and was significantly higher in isopod burrows compared to artificial burrows. Our findings demonstrate that burrowing macro-detritivores promote litter decomposition in deserts by transferring organic matter to their burrows where favorable climatic conditions and a nutrient-enriched environment foster microbial activity. Thus, attempts to resolve the dryland decomposition conundrum should not be limited to exploring factors that allow decomposition under harsh desert surface climatic conditions, but focus on the role that animals play in facilitating decomposer-friendly environments to which they translocate plant litter.</p>
Dataset of paper: Supervised and Dynamic Neuro-Fuzzy Systems to Classify Physiological Responses in Robot-Assisted Neurorehabilitation (PLOS One)
<p>The data set contains number of user, user's physiological signals (Pulse, SCL, SCR, Respiration rate, Skin temperature), Label, Difficulty level from relax to stress. Label is codified from 1 to 5 corresponding to the Difficulty level.</p>
Quality Assisted Editor Demonstration
<p>This video shows the basic functionality of the Quality Assisted Editor</p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset 2
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into ten parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-04</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-05</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-06</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-07</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-08</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-09</li> </ol> <p>These tem parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>$ cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>$ tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 114 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into four parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> </ol> <p>These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 70 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Coordinate files from LipIDens: Simulation assisted interpretation of lipid densities in cryo-EM structures of membrane proteins.
<p>Coordinate files from the first and last frame of coarse-grained (CG) and atomistic (AT) molecular dynamics (MD) simulations used throughout the LipIDens pipeline.</p><p>CG simulations were run for HHAT, OTOP1, ELIC, MscS, TRPV6, ChRmine, Ste2, Connexin-50, NPC1 and the PAT complex. All CG simulations were run for 10 x 15 μs with the exception of NPC1 which was simulated for 10 x 30 μs.</p><p>AT simulations were run for HHAT (5 x 200 ns) and ELIC (3 x 200 ns) in apo configurations.</p><p><strong>File description:</strong></p><p>Directories for each protein are listed with the suffix CG or AT used to indicate the simulation resolution. </p><p>md_fit_firstframe_<i>X</i>.gro - GROMACS structure file for the first frame of replicate <i>X</i>. </p><p>md_fit_lastframe_<i>X</i>.gro - GROMACS structure file for the last frame of replicate <i>X</i>. </p>
Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria
<p>Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges.</p> <p>Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Efforts to mitigate losses along the diagnostic cascade were tracked. Participants with high likelihood on CAD4TB (≥80) who tested negative on a single spot GeneXpert were followed-up to assess TB status at six months.</p> <p>An experimental calibration method achieved a viable CAD threshold for testing. High-risk groups and key stakeholders were engaged. Operations evolved in real-time to fix problems. Incremental improvements in mean client volumes (128 to 140/day), target group inclusion (92% to 93%), on-site testing (84% to 86%), TB treatment initiation (87% to 91%), and TB treatment success (71% to 85%). Attention to those as highest risk boosted efficiency (the NNT declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added after follow-up among those with ≥ 80 CAD scores initially spot-sputum negative found 11 additional TB cases (6.3%) after 121 person-years of follow-up.</p> <p>Iterative adaptation in response to performance metrics foster feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD scores can identify subclinical TB and those at risk of progression to bacteriologically-confirmed TB disease in the near term.</p> <p>Policy makers, donors, and community advocates are hesitant to invest in the steep infrastructure costs for mobile digital chest x-ray and GeneXpert MTB/RIF (dCXR/GXP) laboratories without a better understanding of how to maximize and sustain their impact. It is rarely possible to conduct the months of local CAD calibration recommended by experts via costly universal testing with a reference standard.4,9 Stakeholder needs and resource limitations require a more rapid and cost-conscious means of setting a sustainable algorithm. Viable, field-robust methodologies are needed, and optimization strategies informed by routine field findings were lacking. A precise assessment of the contribution of routine mobile TB screening has been challenging because few authors fully disaggregate losses along the diagnostic cascade or track TB treatment outcomes. Publication bias has limited access to results of active case finding pilots with suboptimal risk group targeting, community engagement, yield, or treatment outcomes.10–14 Evaluations (and scrutiny) of routine data are needed that make the demands, constraints, costs and choices facing implementers more explicit.</p>
Environmental DNA reflects spatial distribution of a rare turtle in a lentic wetland assisted colonisation site
<p>Conservation translocations require robust post-release monitoring to evaluate their success, which can be challenging to implement and maintain. Monitoring techniques that can account for the dispersal and cryptic nature of translocated animals are necessary to provide critical information on persistence and distribution. In this study, we developed a highly sensitive environmental DNA (eDNA) assay specific to the Critically Endangered western swamp turtle (<em>Pseudemydura umbrina</em>), a species currently undergoing trials of assisted colonisation. Actively filtering sufficient volumes of water in lentic systems is difficult due to high concentrations of clogging particulates, therefore we assessed the viability of passive sampling in a controlled environment by submerging filter membranes and directly extracting DNA. Active sampling detected <em>P. umbrina</em> with a 97.6% detection rate, whereas passive sampling resulted in an 8.3% detection rate. We then used a fine-scale eDNA sampling design and radio tracked translocated <em>P. umbrina</em> at the assisted colonisation wetland to investigate eDNA dispersal and spatial monitoring resolution. We detected <em>P. umbrina</em> at 42% (7 / 17) of eDNA sample sites, and the probability of a positive eDNA detection was negatively associated with the distance of <em>P. umbrina</em> from the sampling site, indicating limited eDNA dispersal from the source. Systems with low natural mixing and limited eDNA dispersal provide an opportunity for high resolution spatial and temporal monitoring via targeted eDNA approaches. This is beneficial for monitoring rare species in these systems, as such high-resolution results can provide insights on species presence, distribution, and microhabitat use.</p>
Data from: Use of the lung flute ECO to assist in sputum collection for tuberculosis testing: a randomized crossover trial
<p>The Lung Flute ECO, a self-powered, low cost, oscillatory positive expiratory pressure (OPEP) device, assisted people with presumptive tuberculosis to produce an adequate sputum volume for diagnostic testing and was well-tolerated.</p>
Data for: Brown juice assisted ensiling of straw and press cake for enhanced biogas production and nutrient availability in digestates
<p>Data for: <span>Brown juice assisted ensiling of straw and press cake for enhanced biogas production and nutrient availability in digestates (https://doi.org/10.1016/j.eti.2023.103248)</span></p>
DNA large fragment deleting by compact, sequence-motif-free and specific TaqTth-hpRNA assisted with the microhomology-mediated end joining pathway
<p><span>A DNA editing tool TaqTth-hpRNA was developed in this study, composed of a compact recombinant TaqTth nuclease (832 aa) and a simple hairpin-RNA guiding probe (hpRNA). <em>In vitro</em> biochemical studies showed the TaqTth-hpRNA efficiently cleaves artificially synthesized ssDNA without stringent sequence motif like PAM. It can also cleave the genomic DNA of <em>E. coli</em> with ~80% efficiency. The TaqTth-hpRNA cleavage of genomic DNA in mammalian cells generated products with large fragment deletions mediated by the microhomology-mediated end joining (MMEJ) pathway. In addition, the cleavage was sensitive to mismatches in targeted regions, which was applied to specific damage of the <em>APP<sup>lon</sup></em> mutation in Alzheimer’s disease without disrupting the <em>APP<sup>wt</sup></em> locus. It is worth mentioning that the <em>APP<sup>lon</sup></em> sequence has only one base difference from that of <em>APP<sup>wt</sup></em>. The characteristics of small size, no PAM requirement, high specificity, and large deletion products make the TaqTth-hpRNA a potential therapeutic strategy for treating autosomal dominant disorders in the future.</span></p>
Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score
<h2>ML-GPS: Machine Learning-Assisted Genetic Priority Score</h2> <p>This Zenodo repository contains data and code associated with the publication:</p> <p>Chen R, Duffy Á, Petrazzini BO, Vy HM, Stein D, Mort M, Park JK, Schlessinger A, Itan Y, Cooper DN, Jordan DM, Rocheleau G, Do R. Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score. Nat Commun. 2024 Oct 15;15(1):8891. doi: <a href="https://doi.org/10.1038/s41467-024-53333-y">10.1038/s41467-024-53333-y</a>.</p> <h3>Important notes</h3> <ul> <li>You can interactively view the top 10% of ML-GPS predictions without download at <a href="https://rstudio-connect.hpc.mssm.edu/mlgps/">https://rstudio-connect.hpc.mssm.edu/mlgps/</a>.</li> <li>For running Jupyter notebooks, please follow the instructions in the README of the GitHub repository at <a href="https://github.com/robchiral/ML-GPS">https://github.com/robchiral/ML-GPS</a>.</li> </ul> <h3>Repository contents</h3> <p>Files needed to train ML-GPS and ML-GPS DOE:</p> <ul> <li><strong>Files needed for Jupyter notebooks.zip</strong>: Data files required for preprocessing and training.</li> <li><strong>Jupyter notebooks.zip</strong>: Notebooks for cleaning data, training models, and generating predictions.</li> </ul> <h3>Other files:</h3> <ul> <li><strong>Predictions for all gene-phecode pairs.zip</strong>: ML-GPS and ML-GPS DOE scores for all analyzed gene-phecode pairs.</li> <li><strong>Summary statistics.zip</strong>: Genetic association summary statistics for all tested gene-phecode pairs.</li> </ul> <h3>Updated performance metrics</h3> <table> <tbody> <tr> <td><strong>Model</strong></td> <td><strong>Open Targets AUPRC</strong></td> <td><strong>SIDER AUPRC</strong></td> </tr> <tr> <td>ML-GPS (non-DOE)</td> <td>0.074</td> <td>0.080</td> </tr> <tr> <td>ML-GPS DOE (activator predictions)</td> <td>0.029</td> <td>0.042</td> </tr> <tr> <td>ML-GPS DOE (inhibitor predictions)</td> <td>0.067</td> <td>0.064</td> </tr> </tbody> </table> <h3>Zenodo versions</h3> <ul> <li><strong>Version 4: </strong>Updated notebooks and external data to use Open Targets 2024.9; summary statistics are unchanged</li> <li><strong>Version 3: </strong>Corrected error where DOE for rare and ultrarare variants was incorrectly incorporated</li> <li><strong>Version 2: </strong>Original release accompanying the publication</li> </ul>
Dataset for the study "Foundation Models as Assistive Tools in Hydrometeorology: Opportunities, Challenges, and Perspectives"
<p>This dataset contains the materials, files, and codes used in the study "Foundation Models as Assistive Tools in Hydrometeorology: Opportunities, Challenges, and Perspectives". </p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
Tailoring the Phase in Nanoscale MoTe2 Grown by Barrier-Assisted Chemical Vapor Deposition (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Tailoring the Phase in Nanoscale MoTe2 Grown by Barrier-Assisted Chemical Vapor Deposition</em>" by <a href="https://pubs.acs.org/doi/abs/10.1021/acs.cgd.1c00130">C. Martella et al., <em>Cryst. Growth Des.</em> 2021, 21, 5, 2970–2976</a></p>
CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
<p>Abstract— Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at outdoor large scale scenes. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars’ unique features. Based on the unique scan pattern of Livox lidars, we propose an automatic lidarcamera calibration method that will work in uncontrolled scenes. The long depth detection range also benefit a more accurate mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (LOAM) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on GitHub. (https://github.com/ISEE-Technology/CamVox)</p> <p>This contains our dataset in SUSTech campus with loop closure (CamVox.bag) and the Lidar-camera Synchronization ARM(stm32) code (synchronization.zip ).</p>
ScienceDex guides
Understand access before you commit
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.