Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,152
datasets available to search
ShareScore release 0.9.0
Dataset results
9,152 results for “Networking”
FreeCiv games for the experiment on comparing Knowledge-Based Reinforcement Learning and Neural Networks in Strategic Games
<p>Dataset provides played FreeCiv games. The Tournament subset of them was played fully by two Artificial Intelligence (AI) agents against each other and one more computer player. The Human games subset was played by humans for demonstration/teaching purposes. The rest of the games were played 120 turns and stopped.</p>
Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends
<p>In this section, it was given that Annex Figures and Annex Tables related to the article "Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends" published in "Environmental and Climate Technologies" journal. </p>
SINS database - Node 1 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://kuleuvenadvise.github.io/SINS_database/">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Data 13 Bus Distribution Network
<p>Data for research paper Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City.</p> <p> </p> <p>Energies special issue intelligent transportation system for electric vehicles.</p>
Population estimation from mobile network traffic metadata
<p><em><strong>Please cite our paper if you publish material based on those datasets</strong></em></p> <blockquote> <p>G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). <a href="http://dx.doi.org/10.1109/TMC.2018.2871156">10.1109/TMC.2018.2871156</a></p> </blockquote> <p> </p> <p><strong>Abstract</strong></p> <p>Communication-enabled devices that are physically carried by individuals are today pervasive,<br> which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios.</p> <p><strong>Dataset Columns</strong></p> <p>This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html)</p> <p>1. <strong>grid_id</strong>: the coordinate of the grid can be retrieved with the shapefile of a given city<br> 2. <strong>date</strong>: format Y-M-D H:M:S<br> 4. <strong>landuse_label</strong>: the land use label has been computed by through method described in [2]<br> 5. <strong>population</strong>: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011<br> 6. <strong>estimation</strong>: Dynamics density population estimation (in person) as the result of the method described in [1]<br> 7. <strong>area</strong>: surface of the "grid id" considered in km^2<br> 8. <strong>geometry</strong>: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt)</p> <p><strong>Note</strong></p> <p>Due to legal constraints, we cannot share directly the original data from the Telecom Italia Big Data Challenge we used to build this dataset.</p> <p><strong>Easy access to this dataset with quilt</strong></p> <p>Install the dataset repository:</p> <p>$ quilt install vgauthier/DynamicPopEstimate</p> <p>Use the dataset with a Panda Dataframe</p> <p>>>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import pandas as pd<br> >>> df = pd.DataFrame(DynamicPopEstimate.rome())<br> <br> Use the dataset with a GeoPanda Dataframe<br> <br> >>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import geopandas as gpd<br> >>> df = gpd.DataFrame(DynamicPopEstimate.rome())</p> <p><strong>References</strong></p> <p>[1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016. </p> <p>[2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.<br> </p>
Videos: Computing on actin bundles network
<p>These are videos of experiments described in the paper </p> <p>Andrew Adamatzky, Florian Huber, Florian HuberJörg Schnauß. Computing on actin bundles network (March, 2019).</p> <p>Abstract </p> <p>Actin filaments are conductive to ionic currents, mechanical and voltage solitons. These travelling localisations can be utilised in making the actin network executing specific computing circuits. The propagation of localisations on a single actin filament is experimentally unfeasible, therefore we propose a `relaxed' version of the computing on actin networks by considering excitation waves propagating on actin bundles. We show that by using an arbitrary arrangement of electrodes it is possible to implement two-inputs-one-output circuits. Frequencies of the Boolean gates' detection in actin network match an overall distribution of gates discovered in living substrates.</p>
Reliable Many-to-Many Routing in Wireless Sensor Networks Using Ant Colony Optimisation
<p>Results files for testing of ACO protocol for many to many routing in wireless sensor networks. </p>
Initial abundances generator; allows choices of chemical mixture and network
<p>MESA work directory associated with an initial abundances generator; allows choices of chemical mixture and network</p> <p> </p>
Training material for analysis small RNA-seq data (Galaxy Training Network tutorial)
<p>The data provided here is part of the Galaxy Training Network tutorial for analysis of small RNA-seq (sRNA-seq) data using mirdeep2 and miranda. This dataset is provided by INRA (Le Rheu, France).</p>
Vehicle trajectory data in simulation network
<p>The project will use vehicle trajectory data generated from simulation platform. The simulation network was built in VISSIM containing a four-leg intersection with left-turn, through, and right-turn movements. The trajectory data were generated based on various traffic demand levels. The data set contains second-by-second vehicle speed and location. Details of the data set are explained as:</p> <p>Column 1 (NO): Number (Number/Index of the vehicle)<br> Column 2 (SimSec): Simulation second (Simulation time [s]) [s]<br> Column 3 (Lane\Link\No): Lane\Link\Number (Unique number of the link or connector)<br> Column 4 (Lane\Index): Lane\Index (Unique number of the lane)<br> Column 5 (Speed): Speed (Speed at the end of the time step) [km/h]<br> Column 6 (Pos): Position (Distance on the link from the beginning of the link or connector) [m]</p>
Data and code for training neural network parameterizations from an near-global aqua-planet simulation
<p>This commit contains the code, coarse-grained data, processed training data, neural network models, and coupled NN-GCM simulations. It can be extracted by running</p> <pre><code>tar xzf <archive></code></pre> <p>While this archive contains code (it is slightly out of date). This is the up-to-date code: <a href="https://zenodo.org/record/3248586">https://zenodo.org/record/3248586</a></p> <p>Move the "nn", "debiased", and "data" folders from this archive into that code directory.</p> <p> </p> <p> </p>
Videos for "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground"
<p>Supplementary videos for the paper "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground" by S. Scher and G. Messori, Geoscientific Model Development 2019</p>
Wind Technology Network data
<p>This dataset contains nodes and edges of the wind technology diffusion network inferred in the paper:</p> <p> </p> <p>S. Halleck-Vega, A. Mandel & K. Millock, (2018). "Accelerating diffusion of climate-friendly technologies: A network perspective." Ecological Economics, Vol.152, pp 235-245.</p>
Machine Learning and Network Analyses Reveals Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics
<p>Supplementary information for the thesis chapter: Machine Learning and Network Analyses Reveals Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics</p>
Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6),<a href="http://dx.doi.org/10.1093/gigascience/giy057"> http://dx.doi.org/10.1093/gigascience/giy057</a>). </p> <p>The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.</p>
Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ultrafine Particle Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains over 2 and a half years (04/2012-12/2014, >36 Mio samples) worth of ultra-fine particle (UFP) concentration measurements collected by a mobile senor network. The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><strong>Hardware:</strong></p> <ul> <li><strong>Ultrafine particle sensor</strong>: MiniDiSC (see also: Martin Fierz et al. Design, Calibration, and Field Performance of a Miniature Diffusion Size Classifier. Aerosol Science and Technology, Volume 45, 2011.)</li> <li><strong>GPS receiver</strong>: u-blox EVK-6p<br> </li> </ul> <p><strong>Sensor Data<br> ------------------</strong><br> <strong>ufp_data</strong><strong>*.csv column format:</strong></p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Number of particles [#/ccm]</li> <li>Average particle diameter [nm]</li> <li>LDSA: lung deposited surface area [um2 /cm3]</li> </ol> <p><strong>Data quality:</strong><br> The data has been post-processed by performing a periodic null-offset calibration and filtering samples during malfunction.</p> <p><strong>High-Resolution Maps<br> --------------------------------</strong></p> <p>The data has been used to create high-resolution ultrafine particle concentration maps. Four maps, which show the seasonal average particle concentration over seasonal periods, can be found in ufp_seasonal_maps_201204_201304.csv.</p> <p><strong>ufp_map*.csv column format:</strong></p> <ol> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>Estimated number of particles [#/ccm]</li> </ol> <p><strong>Map quality</strong></p> <p>Please have a look at the papers in References 1. and 2. (Hasenfratz et al. 2014 and 2015) for a detailed evaluation of the maps.</p> <p><strong>References<br> ----------------</strong><br> The dataset has been used and is described in more detail in the following publications:</p> <ol> <li>David Hasenfratz et al.<em> Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps.</em> IEEE International Conference on Pervasive Computing and Communications (PerCom). Budapest, Hungary, March 2014. Best Paper Award. </li> <li>David Hasenfratz et al. <em>Deriving High-Resolution Urban Air Pollution Maps Using Mobile Sensor Nodes. </em>Pervasive and Mobile Computing. Elsevier, 2015. </li> <li>David Hasenfratz et al. <em>Demo Abstract: Health-Optimal Routing in Urban Areas.</em> ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). Seattle, USA, April 2015.</li> <li>Michael Müller et al. <em>Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network. </em>Atmospheric Environment. Elsevier, 2016.</li> </ol> <p>For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
Deep convolutional neural network for owl vocal identification
<p>This repository contains all the code and data necessary to replicate the results presented in Ruff et al. 2019, "Automated identification of avian vocalizations with deep convolutional neural networks", and is published in support of that manuscript. The folder includes several Python scripts, our trained convolutional neural network (CNN), and a set of 164,210 spectrogram images that were reviewed to generate CNN performance metrics. We include the CNN's predicted class scores for the test images as well as the set of labels assigned to the same images by experienced human technicians. The published article can be found here: <a href="https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125">https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125</a></p> <p>As presented, the CNN is designed to accept grayscale PNG images at 500x129 resolution and will generate a set of seven class scores for each image. Class scores are the softmax activation from the final (seven unit) fully-connected layer of the CNN. Scores are bounded between 0 and 1 and sum to 1 for each image. This means target classes are implicitly treated as mutually exclusive (i.e., each image belongs to exactly one class), although in reality some images contain calls from >1 target species.</p> <p>The different scripts and their functions are as follows:<br> - Code used to construct and train the CNN is in Owl_CNN_train_model.py<br> - Code to generate spectrograms with randomized parameters based on tagged calls in audio files is in Owl_CNN_generate_training_data.py<br> - Code to generate random spectrograms from a set of audio files (used to generate training data for the Noise class) can be generated with Owl_CNN_make_noise_data.py<br> - Code used to process raw audio files, including segmenting them into 12 s clips, generating spectrograms, and generating class scores using a pre-trained CNN is in Owl_CNN_process_audio.py<br> - Code to generate class scores for an existing set of spectrogram images using a pre-trained CNN are in Owl_CNN_process_images.py</p> <p>Our seven target classes are as follows:<br> AEAC - Northern saw-whet owl, Aegolius acadicus.<br> BUVI - Great horned owl, Bubo virginianus.<br> GLGN - Northern pygmy-owl, Glaucidium gnoma.<br> MEKE - Western screech-owl, Megascops kennicottii.<br> STOC - (Northern) spotted owl, Strix occidentalis caurina.<br> STVA - Barred owl, Strix varia.<br> Noise - Catch-all for any clip that did not contain vocalizations of at least one of the six owl species listed above.</p> <p>The CNN was trained for 100 epochs and saved only after epochs in which validation loss improved. Loss was measured as categorical cross-entropy. The CNN was last saved at epoch 97 with reported metrics:<br> Training loss = 0.218<br> Training accuracy = 0.972<br> Validation loss = 0.165<br> Validation accuracy = 0.987</p> <p>Although this code has been tested and works on our system, we make no guarantee that it will work for others without modification. Created using Python version 2.7.14, TensorFlow version 1.2.1, Keras version 2.2, and SoX version 14.4. Code was developed by Bharath Padmaraju, Zack Ruff, and Chris Sullivan. Questions and comments may be directed to zjruff at gmail dot com.</p> <p>Zack Ruff<br> 15 July 2019</p>
REAVER Vascular Networks Fluorescent Image Dataset
<p><strong>Fluorescent Images of Vessel Networks from Various Murine Tissues</strong></p> <p> </p> <p><strong>Purpose</strong>: Image dataset of vascular networks with a diverse range of vessel architectures. Dataset is used to evaluate performance of several image processing programs (AngioQuant<sup>1</sup>, AngioTool<sup>2</sup>, RAVE<sup>3</sup>, REAVER). Manual analysis from ImageJ is used as ground truth to compare other programs against.</p> <ul> <li><strong>Labeling</strong>: IB4-Lectin with Alexa Flour 647</li> <li><strong>Modality</strong>: Confocal Microscope Nikon 80i CLSM</li> <li><strong>Objective</strong>: Mixture of 20x and 60x objective images</li> <li><strong>Image Format</strong>: Images originally acquired in Nikon IDS format, converted to 8-bit greyscale TIFs found in “_Original_Images” folder.</li> <li><strong>Questions</strong>: Email <a href="mailto:bac7wj@virginia.edu">bac7wj@virginia.edu</a> for inquiries.</li> </ul> <p> </p> <p><strong>External Links</strong></p> <ol> <li><strong>Manuscript</strong>:</li> <li><strong>Code repository: </strong><a href="https://github.com/bacorliss/REAVER_public">https://github.com/bacorliss/REAVER_public</a> for code to analyze this data (MATLAB 2019a).</li> </ol> <p> </p> <p><strong>Dataset Summary:</strong></p> <p>Each image folder contains 36 images. For each image:</p> <ol> <li>The first channel (red) is the segmented image with values of 0 or 255 (false or true).</li> <li>The second channel (green) is the skeleton image with values of 0 or 255 (false or true).</li> <li>The third channel (blue) is empty except for the Manual images where the third channel contains the original raw image.</li> </ol> <p> </p> <p><strong>Subfolders</strong></p> <ol> <li><strong>_Original_Images</strong>: contains raw input images.</li> <li><strong>AngioQuant_Auto</strong>: contains output images from automated analysis in AngioQuant.</li> <li><strong>AngioTool_Auto</strong>: contains output images from automated analysis in AngioTool.</li> <li><strong>ImageJ_Auto</strong>: contains output images from automated analysis in ImageJ.</li> <li><strong>ImageJ_Manual</strong>: contains output images from manual analysis in ImageJ.</li> <li><strong>RAVE_Auto</strong>: contains output images from automated analysis in RAVE.</li> <li><strong>REAVER_Auto</strong>: contains output images from automated analysis in REAVER.</li> </ol> <p> </p> <p><strong>Image Metadata and Output data</strong></p> <p>Each image folder has a .mat file called “Results.mat” containing the results of analysis in the form of the following variables all of which are 1x36 arrays (one entry for each image) unless specified otherwise:</p> <ol> <li><strong>branchpoint_RC</strong>: A 1x36 struct containing the row-column values for each branchpoint in the i<sup>th</sup> image (when organized in alphabetic order which is the order given everywhere else); Effectively the same as “BranchpointsByName.mat”</li> <li><strong>mean_diameter</strong>: The mean diameter of vessels in the image</li> <li><strong>num_branchpts</strong>: The number of branchpoints in the image</li> <li><strong>threshold_false_neg</strong>: The number of false negative pixels – a pixel is a false negative if the program has it as “false” and the manual image has the pixel as “true”</li> <li><strong>threshold_false_pos</strong>: The number of false positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “false”</li> <li><strong>threshold_true_neg</strong>: The number of true negative pixels – a pixel is a true negative if the program has it as “false” and the manual image has the pixel as “false”</li> <li><strong>threshold_true_pos</strong>: The number of true positive pixels – a pixel is a false positive if the program has it as “true” and the manual image has the pixel as “true”</li> <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li> <li><strong>vessel_area</strong>: The number of “true” pixels in the segmented image</li> <li><strong>vessel_length</strong>: The number of “true” pixels in the skeleton image</li> </ol> <p> </p> <p><strong>Dataset Output Data</strong></p> <p>The file “image_quantification.csv” in the base folder contains the aggregated results from each image folder. Each row contains the results for a given (Program, Image) pair. The columns are described below:</p> <ol> <li><strong>Program</strong>: Designates the program used to calculate the data for that row</li> <li><strong>Tissue_Type</strong>: Gives the tissue type for the image</li> <li><strong>Image_Name</strong>: Gives the specific name of the given image</li> <li><strong>Vessel_Length</strong>: The number of “true” pixels in the skeleton image</li> <li><strong>Vessel_Area</strong>: The number of “true” pixels in the segmented image</li> <li><strong>Mean_Diameter</strong>: The mean diameter of vessels in the image</li> <li><strong>Num_Branchpoints</strong>: The number of branchpoints in the image</li> <li><strong>Sensitivity</strong>: (Number of True Positive pixels) / (Number of True Positive pixels + Number of False Negative pixels)</li> <li><strong>Specificity</strong>: (Number of True Negative pixels) / (Number of True Negative pixels + Number of False Positive pixels)</li> <li><strong>Accuracy</strong>: (Number of True Positive pixels + Number of True Negative pixels) / (Total number of pixels)</li> <li><strong>umppix</strong>: The length of the edge of one pixel in micrometers</li> <li><strong>pix_dim</strong>: The edge length in pixels of the square image</li> </ol> <p> </p> <p><strong>References</strong></p> <p>1. Niemisto, A., Dunmire, V., Yli-Harja, O., Wei Zhang & Shmulevich, I. Robust quantification of in vitro angiogenesis through image analysis. <em>IEEE Trans. Med. Imaging</em> <strong>24</strong>, 549–553 (2005).</p> <p>2. Zudaire, E., Gambardella, L., Kurcz, C. & Vermeren, S. A Computational Tool for Quantitative Analysis of Vascular Networks. <em>PLOS ONE</em> <strong>6</strong>, e27385 (2011).</p> <p>3. Seaman, M. E., Peirce, S. M. & Kelly, K. Rapid Analysis of Vessel Elements (RAVE): A Tool for Studying Physiologic, Pathologic and Tumor Angiogenesis. <em>PLoS ONE</em> <strong>6</strong>, e20807 (2011).</p>
Original Data of Paper: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis
<p>Paper title: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis. Our paper was submitted to PeerJ recently. This data file is the original data of this study which contains all the original data involved in this work.</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.