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99 results for “processing code”
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Data and code from "Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition"
<p>#### Data description<br> Data from large scale, long-term tree diversity experiment in southwestern France (<a href="https://sites.google.com/view/orpheeexperiment/home">ORPHEE</a>), additionally manipulating water contraint. Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper is found here:</p> <p>Maxwell TL, Augusto L, Tian Y, Wanek W & Fanin N (2023). Water availability is a stronger driver of soil microbial processing of organic nitrogen than tree species composition. <em>European Journal of Soil Science</em>. <a href="https://doi.org/10.1111/ejss.13350">https://doi.org/10.1111/ejss.13350</a></p> <p>#### Metadata<br> Soil sampling: July 2020<br> Maxwell_ShortComm_Data.csv data description</p> <p>ID: unique identifier per sample<br> Block: numbered 1-6. Blocks 1,3,6 are control (unirrigated), Blocks, 2,4,5 are irrigated<br> Plot: numbered plot according to the ORPHEE design. Plot 1 = BP, Plot 5 = PP, Plot 9 = BP_PP<br> Espece: species ID. BP = pure birch (<em>Betula pendula</em>), PP = pure pine (<em>Pinus pinaster</em>), BP_PP (50% mixed birch-pine)<br> Rep: sample replicate, 3 replicates per plot<br> Sample name: long unique identifier per sample. Concatenation of Block, Plot, and Espece<br> PD: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> AAU: gross free amino acid uptake rates (µg N g-1 d-1)<br> Cmicrobial_ug_g: microbial biomass carbon (µg C g-1)<br> Nmicrobial_ug_g: microbial biomass nitrogen (µg N g-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA_ugN_g: free amino acids (µg N g-1)<br> Moisture_percent: soil moisture percent (%)<br> N_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractable N (µg N g-1)<br> C_nonfumige_ug_g: nitrogen from non fumigated soils, i.e. extractableC (µg C g-1)</p>
Gaussian Process Model and Sensor Placement for Detroit Green Infrastructure: Datasets and Code
<ol> <li><strong>code.zip: </strong>Zip folder containing a folder titled "code" which holds: <ol> <li>csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features;</li> <li>csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period;</li> <li>csv file titled "newGIsites_AllData.csv" which contain the other 130 GI sites in Detroit and their design and physiographic features;</li> <li>csv file titled "Detroit_Data_MeanDesignFeatures.csv" which contain the design and physiographic features for all of Detroit;</li> <li>Jupyter notebook titled "GI_GP_SensorPlacement.ipynb" which provides the code for training the GP models and displaying the sensor placement results;</li> <li>a folder titled "MATLAB" which contains the following: <ol> <li>folder titled "SFO" which contains the SFO toolbox for the sensor placement work</li> <li>file titled "sensor_placement.mlx" that contains the code for the sensor placement work</li> <li>several .mat files created in Python for importing into Matlab for the sensor placement work: "constants_sigma.mat", "constants_coords.mat", "GInew_sigma.mat", "GInew_coords.mat", and "R1_sensor.mat" through "R6_sensor.mat"</li> <li>several .mat files created in Matalb for importing into Python for visualizing the results: "MI_DETselectedGI.mat" and "DETselectedGI.mat"</li> </ol> </li> </ol> </li> </ol>
Data and Code for "Why are generalists the 'winners' of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes"
<p><span>Data and R-based workflow for the study "Why are generalists the ‘winners’ of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes".</span></p>
Code to reproduce the analyses of "Multiple stressors alter greenhouse gas concentrations in streams through local and distal processes"
<p>Streams are significant contributors of greenhouse gases (GHG) to the atmosphere, and the increasing number of stressors degrading freshwaters may exacerbate this process, posing a threat to climatic stability. However, it is unclear whether the influence of multiple stressors on GHG concentrations in streams results from increases of in-situ metabolism (i.e., local processes) or from changes in upstream and terrestrial GHG production (i.e., distal processes). Here, we hypothesize that the mechanisms controlling multiple stressor effects vary between <span>carbon dioxide (</span>CO<sub>2</sub>) and <span>methane (</span>CH<sub>4</sub>), with the latter being more influenced by changes in local stream metabolism, and the former mainly responding to distal processes. To test this hypothesis, we measured stream metabolism and the concentrations of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) and CH<sub>4</sub> (<em>p</em>CH<sub>4</sub>) in 50 stream sites that encompass gradients of <span>nutrient enrichment, oxygen depletion, thermal stress, riparian degradation and discharge</span>. Our results indicate that these stressors had additive effects on stream metabolism and GHG concentrations, with stressor interactions explaining limited variance. Nutrient enrichment was associated with higher stream heterotrophy and <em>p</em>CO<sub>2</sub>, whereas <em>p</em>CH<sub>4</sub> increased with oxygen depletion and water temperature. Discharge was positively linked to primary production, respiration and heterotrophy but correlated negatively with <em>p</em>CO<sub>2.</sub> Our models indicate that CO<sub>2</sub>-equivalent concentrations can more than double in streams that experience high nutrient enrichment and oxygen depletion, as compared to those with oligotrophic and oxic conditions. Structural equation models revealed that the effects of nutrient enrichment and discharge on <em>p</em>CO<sub>2</sub> were related to distal processes rather than local metabolism. In contrast, <em>p</em>CH<sub>4</sub> responses to nutrient enrichment, discharge and temperature were related to both local metabolism and distal processes. Collectively, our study illustrates <span>potential climatic feedbacks resulting from freshwater degradation and </span>provides insight into the processes mediating stressor impacts on the production of GHG in streams.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data: Codes and processed data
<p>Codes and processed data to reproduce the analysis discussed in: </p> <p>Wegmann <em>et Al.</em>,<strong> CellSIUS provides sensitive and specific detection of rare cell<br> populations from complex single cell RNA-seq data</strong>, Genome Biology 2019 (Accepted)<br> </p>
Measurements of Ice Nucleating Particles in Beijing, China - Data and processing code
<p>Dataset needed to replicate findings published in the journal article "Measurements of Ice Nucleating Particles in Beijing, China, published in the Journal of Geophysical Research. The dataset contains the following:</p> <p>1. Data files containing raw data from a Continuous Flow Diffusion Chamber - Ice Activation Spectrometer (CFDC-IAS), in comma-delimited format).</p> <p>2. Data and processing files for analysis of backward air trajectories as an Igor Pro 8 packed experiment package file. Igor Pro is available from www.wavemetrics.com and a free 30-day trial version can be used to export data to other formats.</p> <p>3. Data and processing files for analysis of CFDC, APS and meteorological data, including data in the form of waves, as part of an Igor Pro 8 packed experiment package.</p>
Thermal Comfort and Perception of Different Materials for Tabletops: Datasets, Processing Code, and Supplementary Tables.
<p>In this data repository, data related to the study <em>Thermal Comfort and Perception of Different Materials for Tabletops </em> is deposited, and includes datasets, processing code, and supplementary tables.</p> <p> </p>
Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System
<p>This excel file complements the <a href="https://doi.org/10.5281/zenodo.1488652">EU database of processing factors for pesticide residues</a> by providing the mapping between processed food/feed listed in the database and the FoodEx2 coding system.</p>
Files and code for English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19
<p><span lang="EN-GB">Automated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. </span></p> <p><span lang="EN-GB">Here we present an NLP toolbox comprising COVID-19-related dictionaries and annotated corpora in English as well as useful code and workflows for their update and use. The dictionaries contain terms referring to the COVID-19 disease, the SARS-CoV-2 virus, its variants and common mutations, respectively. They were used together with the EasyNER NLP tool to extract and annotate all 764 398 abstracts in the CORD-19 dataset, creating a very large silver standard corpus (named Lund-Annotated-CORD-19 corpus). This was complemented with a small gold standard corpus consisting of PubMed abstracts manually annotated for key entity classes such as disease, virus, symptom, protein/gene, cell type, chemical and species terms. </span></p> <p><span lang="EN-GB">The toolbox can support various text analysis tasks related to COVID-19 such as named entity recognition and co-mention analysis. A preliminary version of the toolbox, which was released early in the pandemic, was</span><span lang="EN-GB"> for example already used to create a COVID-19 knowledge graph and study the evolution and variation of COVID-19-related terminology. In addition, the toolbox can be applied in the development of other NLP tools, for example to train and evaluate large language models.</span></p> <p><span lang="EN-GB">When using the toolbox, please cite this record and the associated article.</span></p> <p> </p> <p> </p>
Post‐processed data and analysis codes for the research "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"
<p>[Earth's Future] Oh et al. "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"</p> <p>1. Information for Raw datasets<br>- The data of eight global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) can be accessed at https://esgf-node.llnl.gov/search/cmip6/, <br> and can also be accessed in Eyring et al. (2016). <br>- The NOAA OISST high resolution dataset can be obtained in Reynolds et al. (2007) or via https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. <br>- The five ocean mask dataset can be obtained from https://reccap2-ocean.github.io/regions/. </p> <p>2. Information for Software<br>- The raw data in this study were analyzed using Fortran 90, R version 4.0.3, and Grads version 2.2.1.<br>- The Fortran 90 can be accessed at https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html#fortran. <br>- The R version 4.0.3 is available from https://cran.r-project.org/bin/windows/base/old/4.0.3/. <br>- The Grads version 2.2.1 can be downloaded from http://cola.gmu.edu/grads/downloads.php.</p> <p>3. Information for Post-Processed data and Codes used in this work.<br>Please find each folder and the relevant post-processed dataset and codes.</p>
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"
<p>Processed data and code for reproducing the main results and figures of the paper "<strong>Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions</strong>".</p> <p>We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019).</p> <p>Reference<br> A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, <strong>‘‘</strong>Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms<strong>’’</strong> <em>in proceedings of the</em> <em>ACM/IEEE Design, Automation, and Test in Europe Conference (DATE)</em>, Florence, Italy, March 25-29, 2019. </p>
Data and code from: Geomorphological processes shape plant community traits in the Arctic
<p><strong>Aim</strong></p> <p>Geomorphological processes profoundly affect plant establishment and distributions, but their influence on functional traits is insufficiently understood. Here, we unveil trait-geomorphology relationships in Arctic plant communities.</p> <p> </p> <p><strong>Location</strong></p> <p>High-Arctic Svalbard, low-Arctic Greenland, and sub-Arctic Fennoscandia.</p> <p> </p> <p><strong>Time period</strong></p> <p>2011-2018</p> <p> </p> <p><strong>Major taxa studied</strong></p> <p>Vascular plants</p> <p> </p> <p><strong>Methods</strong></p> <p>We collected field-quantified data on vegetation, geomorphological processes, microclimate, and soil properties from 5280 plots and 200 species across the three Arctic regions. We combined these data with database trait records to relate local plant community trait composition to dominant geomorphological processes of the Arctic, namely cryoturbation, deflation, fluvial processes, and solifluction. We investigated the relationship between plant functional traits and geomorphological processes using hierarchical generalised additive modelling.</p> <p> </p> <p><strong>Results</strong></p> <p>Our results demonstrate that community-level traits are related to geomorphological processes, with cryoturbation most strongly influencing both structural and leaf economic traits. These results were consistent across regions, suggesting a coherent biome-level trait response to geomorphological processes.</p> <p> </p> <p><strong>Main conclusions</strong></p> <p>The results indicate that geomorphological processes shape plant community traits in the Arctic. We provide empirical evidence for the existence of generalisable relationships between plant functional traits and geomorphological processes. The results indicate that the relationships are consistent across these three distinct tundra regions and that geomorphological processes should be considered in future investigations of functional traits.</p> <p> </p> <p>Kemppinen, Niittynen, Happonen, le Roux, Aalto, Hjort, Maliniemi, Karjalainen, Rautakoski & Luoto. Provisionally accepted. Geomorphological processes shape plant community traits in the Arctic. Global Ecology and Biogeography.</p> <p>These are the data and code from Kemppinen et al. (Provisionally accepted).</p>
Experimental Datasets and Processing Codes for the Semantic PHD Filter
<p>The water bottle detection dataset and measurement model dataset for the paper titled "The Semantic PHD Filter for Multi-class Target Tracking: From Theory to Practice" by Jun Chen, Zhanteng Xie and Philip Dames, and the paper titled "Experimental Datasets and Processing Codes for the Semantic PHD Filter" by Zhanteng Xie, Jun Chen and Philip Dames</p> <p><strong>1. Detection dataset: </strong></p> <p>Size: <br> Total: 4870 images<br> Training: 4000 images<br> Validation: 870 images</p> <p>Bottle Classes: Aquafina, Deer, Kirkland, Nestle</p> <p>Format: PASCAL VOC, Darknet</p> <p>Folder Structure:<br> - Annotations: containing the xml label files in PASCAL VOC format<br> - ImageSets: containing the training index files <br> - JPEGImages: containing the image data in jpg format<br> - Labels: containing the txt label files in Darknet format</p> <p><strong>2. Measurement model dataset:</strong></p> <p>Format: ROSBAG</p> <p>Duration: 19:59s (1199s)</p> <p>Topics:<br> /darknet_ros/detection_image 3543 msgs : sensor_msgs/Image<br> /map 1 msg : nav_msgs/OccupancyGrid<br> /sphd_measurements 3585 msgs : sphd_msgs/SPHDMeasurements<br> /tf 142727 msgs : tf2_msgs/TFMessage<br> /tf_static 1 msg : tf2_msgs/TFMessage</p> <p>Message Types:<br> nav_msgs/OccupancyGrid<br> sensor_msgs/Image<br> sphd_msgs/SPHDMeasurements<br> tf2_msgs/TFMessage</p> <p> </p> <p><strong>3. Processing codes:</strong></p> <p>Detection processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066045<br> GitHub: https://github.com/TempleRAIL/yolov3_bottle_detector<br> <br> Measurement model processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066050<br> GitHub: https://github.com/TempleRAIL/sphd_sensor_models</p>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> 📓</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (🚀 RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED)</li> </ol> <p>Read README.md file for more details</p>
Data and Code for "A lasting impact of serotonergic psychedelics on visual processing and behavior"
<p>Processed data and analysis code for "<span><span>A lasting impact of serotonergic psychedelics on visual processing and behavior". <span>https://doi.org/10.1101/2024.07.03.601959 </span></span></span></p>
Data and Code Supplement to: "Processes of change in a randomized clinical trial of Radically Open Dialectical Behavior Therapy (RO DBT) for adults with treatment refractory depression"
<p>Dataset to support secondary analyses reported in "Processes of change in a randomized clinical trial of Radically Open Dialectical Behavior Therapy (RO DBT) for adults with treatment refractory depression" in the Journal of Consulting and Clinical Psychology</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.