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20230120-MTOA: Carrying fewer tasks does not affect the accuracy of resource-restricted agents but improves the prosperity and equitability of agent societies.
This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 202300120-MTOA<br><br>Experiment design: Agents specialize through expressing their preferences.<br><br>Experiment setting: Agents are trained with respect to different tasks and then coordinate upon acting on them. Each time they disagree, one agent adapts its knowledge with respect to the current task.<br><br>Hypotheses: The higher is the number of carried out tasks, the higher the average accuracy is.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20230120-MTOA">https://sake.re/20230120-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br>
20230505-MTOA: Multitasking agents improve their average and maximum accuracy when tasks overlap.
This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20230505-MTOA<br><br>Experiment design: Agents specialize by accepting or not to play a task.<br><br>Experiment setting: Agents are trained with respect to different tasks and then coordinate upon acting on them. Each time they disagree, one agent adapts its knowledge with respect to the current task.<br><br>Hypotheses: Agents will improve their accuracy more on tasks they choose to play.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20230505-MTOA">https://sake.re/20230505-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br>
20230110-MTOA: Carrying fewer tasks is detrimental to the accuracy of agents with un-restricted resources.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20230110-MTOA<br><br>Experiment design: Agents specialize by focusing on specific tasks.<br><br>Experiment setting: Agents are trained with respect to different tasks and then coordinate upon acting on them. Each time they disagree, one agent adapts its knowledge with respect to the current task.<br><br>Hypotheses: The higher is the number of carried out tasks, the higher the average accuracy is.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20230110-MTOA">https://sake.re/202300110-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br></p>
20220109-MTOA: Tackling one task affects the accuracy of an agent on another task.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20220109-MTOA<br><br>Experiment design: Improving knowledge on task1 by carrying out additional tasks.<br><br>Experiment setting: A population of agents evolves its knowledge while keeping their setup constant.<br><br>Hypotheses: knowledge acquired while carrying out one task improves the accuracy of an agent on another task.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20220109-MTOA">https://sake.re/20220109-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br></p>
Data supporting: "Interaction of MRI Contrast Agent [Gd(DOTA)]− with Lipid Membranes: A Molecular Dynamics Study"
Open the record for dataset details and reuse information.
Synchonization in an audience of clapping agents
<p>Supporting material for the article "Emerging Synchrony in Applauding Audiences: Formal Analysis and Specification", in ISoLA 2024.</p>
20231120-MTOA: Single-tasking agent populations cannot achieve equitable task exploration.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20231120-MTOA<br><br>Experiment design: Single-tasking agents are periodically replaced by new, naive ones. The new agents are trained by previously existing agents. The selection of the teaching agents is based on different selection criteria. These include: (a) low/high acquired compensation, (b) low/high success rate, (c) random selection.<br><br>Hypotheses: Favoring the reproduction of agents with the lowest success rate will improve the maximum accuracy and re-balance the tasks representation.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20231120-MTOA">https://sake.re/20231120-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br></p>
20231220-MTOA: Multitasking agent populations can achieve equitable task exploration to the detriment of their agents' average and maximum accuracy.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20231220-MTOA<br><br>Experiment design: Agents improve task equitability by favoring the reproduction of agents with rare and conflicting knowledge.<br><br>Experiment setting: Each agent is initially trained on all tasks. The agents then carry all tasks. When they disagree the following take place:</p> <p>(a) The agent with the lower score will adapt its knowledge accordingly. If its memory limit is attained, the agent will try to forget knowledge.</p> <p>(b) The agent with the highest score will decide for both agents. If this agent's decision is correct, the agent will receive the points corresponding to both agents.</p> <p>Every 20000 games, 9 new agents are born and 9 agents are removed. Each new agent will be trained with examples provided by two parents.The first parent is selected either randomly, or based on their collected points/success rate (low success rate, high success rate, low points, high points).</p> <p>The second parent is selected based on its success rate with respect to the first parent (parent 2 is the the agent that agrees more often with parent 1).Agents undertake 3 tasks having a limited memory, enough for learning 1/3 tasks accurately (4 and 12 classes respectively)</p> <p><br>Hypotheses: Favoring the reproduction of agents with the lowest success rate (agents that agree less with their peers) will allow agent populations to equally explore all tasks. Hence, agent populations will become equally accurate on all tasks and thus improve their efficiency.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20231220-MTOA">https://sake.re/20231220-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br></p>
Data, models, and outputs for an agent-based hydro-economic modeling study in an intensively irrigated region of the U.S. High Plains
<p>In this dataset, we include all the models developed for the study "An integrated modeling approach to simulate human-crop-groundwater interactions in intensively irrigated regions", which is published in Environmental Modelling & Software (<a href="https://doi.org/10.1016/j.envsoft.2024.106120">https://doi.org/10.1016/j.envsoft.2024.106120</a>). Additionally, we provide all the data used in this study. Below, you will find a description of the contents of each file:</p> <ul> <li>abm_modflow.zip: This file includes the agent-based hydro-economic model (ABM-MODFLOW), including model inputs and outputs, Python post-processing scripts, and the Windows batch script for the integration process.</li> <li>modflow.zip: This file contains the standalone MODFLOW model files. Each folder includes files for individual simulation periods, starting with a steady-state model for the predevelopment period, followed by seven transient models.</li> <li>modflow_rs.zip: This file contains the MODFLOW-RS model files. Given that remote sensing data is provided for years from 1984 onwards, only models for the post-1980 simulation periods are included. For model files corresponding to years prior to 1980, refer to modflow.zip.</li> <li>Figurers.zip: This file includes all data and Python scripts used to produce the figures in the main text.</li> <li>Tables.zip: This file contains all tables and the associated data included in the main text.</li> <li>Supporting_Info_Figures: This file includes all data and Python scripts used to produce the figures in the Supporting Information.</li> <li>Supporting_Info_Tables: This file contains all tables and the associated data included in the Supporting Information.</li> <li>Supporting_Info_Videos: This file stores Videos S1 and S2 of the Supporting Information, displaying the historical development of irrigation wells and groundwater-fed irrigated lands in the study area from 1946 to 2018.</li> </ul>
Figure 6 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 6. Treatment Group with resveratrol standard after induced by Beta-Amyloid (10 x10).
Figure 3 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 3. Treatment group: 2- ME + Resveratrol isolated from Tempeh (10 x10).
Supplementary data for the paper: "Self-healing concrete with a bacteria-based or crystalline admixture as healing agent to prevent chloride ingress and corrosion in a marine environment'"
<div> <p>Supplementary data for the paper: "Self-healing concrete with a bacteria-based or crystalline admixture as healing agent to prevent chloride ingress and corrosion in a marine environment"<br><br>Open data concerning experimental work. Bacteria-based agents (BAS) and crystalline admixtures (CA) are explored as healing agents to enhance chloride resistance and prevent corrosion. Healing of 100 µm and 300 µm wide cracks was investigated, in combination with two conditioning methods. Either the samples were subjected to wet/dry cycles for 3 months before exposure (“healed”), or they were directly exposed to artificial seawater after crack creation (“unhealed”). </p> </div> <h2>Files</h2>
Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures (raw data)
<p>This data set includes raw data supporting the paper "<strong>Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures</strong>".</p> <p><strong>The files include the following data</strong>:</p> <p>1. MTT assay results used for calculation of <strong><em>EC</em><sub>50</sub> values</strong> of tested compounds and their combinations in cancer cells;</p> <p>2. The data used for calculating <strong>combination index</strong> in order to evaluate synergistic activity;</p> <p>3. The raw data from compound activity evaluation in <strong>3D tumor spheroid assay</strong>;</p> <p>4. The data from <strong>compound and hyperthermia </strong>effect evaluation in cancer cells.</p>
set of exploration data and parameters for h24/5ad agent based model
<p>Different set of data used for exploration, used for reproductibility, updated with HigherProp parameters</p>
Leveraging Multi-Agent Systems and Decentralised Autonomous Organisations for Tax Credit Tracking: A Case Study of the Superbonus 110% in Italy
<p>The present dataset was obtained as output of the simulation of Secure Fiscal Credit Model (SCFM) demonstrator. The software, inspectable at the link in the repository url, allows to simulate the workflow and payments involved in the execution of Italian Superbonus 110% fiscal credits model. Two different Scenarios were tested, parameters are reported in the following table.</p> <table> <tbody> <tr> <td> </td> <td><strong>GC payment</strong></td> <td><strong>Tech Asseveration</strong></td> <td><strong>Open</strong></td> <td><strong>Anticipation</strong></td> <td><strong>SAL1</strong></td> <td><strong>SAL2</strong></td> <td><strong>EOW</strong></td> <td><strong>Tech fee</strong></td> </tr> <tr> <td><strong>Scenario 1</strong></td> <td>50%</td> <td>50%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> <tr> <td><strong>Scenario 2</strong></td> <td>25%</td> <td>25%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> </tbody> </table> <p>File named with S1 prefix refer to Scenario 1, while those with S2 prefix refer to Scenario 2.</p>
Lambda-cyhalothrin-driven mortality of pest control agents - IRAC insecticide-coated glass vials test
<p>The dataset contains 24 h mortality data from the IRAC-recognised insecticide-coated glass vials test on lambda-cyhalothrin toxicity to pest control agents tested in 5 European countries. Individuals of each tested species were exposed to five insecticide doses, relative to the Recommended Field Dose (RFD), namely 0.8% RFD, 4% RFD, 20% RFD, 100% RFD, and 200% RFD, plus acetone control. RFD was assumed at 7.5 g a.i./ha as this value is most relevant to the crops covered by the study. <strong>The dataset was used to generate Species Sensitivity Distributions published in the article "Species Sensitivity Distribution (SSD) profiles towards λ-cyhalothrin for key ecosystem service provider (ESP) species across five European countries representing different pedoclimatic zones", STOTEN 2024, which should be quoted if the data are used for any purpose: https://doi.org/10.1016/j.scitotenv.2024.176412. </strong></p> <p>The research was funded by the European Union Horizon 2020 Research and Innovation Program “EcoStack” (grant agreement no. 773554). </p> <p> </p>
AAU_Multi_Agent_UVDAR_Dataset
<h1>AAU_Multi_Agent_UVDAR_Dataset</h1> <h2>## Introduction </h2> <p>The aim of the dataset is to collect relative position measurements (with the origin in the body of the vehicle) between a set of UAVs with precise groundtruthing through a motion capture system for evaluations.</p> <p>This dataset was recorded in the [Dronehall](https://www.aau.at/en/research/research-profile/the-drone-hall-at-the-university-of-klagenfurt/) of the University Klagenfurt, with three autonomous [F450](https://github.com/ctu-mrs/mrs_uav_system?tab=readme-ov-file#unmanned-aerial-vehicles) unmanned aerial vehicles (UAVs) from [Multi-robot Systems (MRS) group](http://mrs.felk.cvut.cz/) of the [Czech Technical University in Prague](https://www.cvut.cz/en).</p> <p>The UAVs were equipped with the [UVDAR system](https://github.com/ctu-mrs/uvdar_core), a planar scanning LIDAR sensor (RPLIDAR A3), a laser range finder (Garmin LidarLite V3) and a Pixhawk 6C flight controller.<br>The UVDAR system is a visual mutual relative localization (position and orientation) system for cooperating micro-scale UAVs based on ultraviolet-sensitive cameras and blinking ultraviolet markers.<br>Note: the UVDAR system was configured to provide relative position information only, thus the estimated orientation is wrong and set to identity! <br>To assess the measurements obtained from the UVDAR system, a [relative pose evaluation python package] was implemented.</p> <p>To evaluate the measurments recorded, execute the [eval.sh](./tools/eval.sh) script in the `tools` folder, which creates a folder `eval` in the root of that directory with various plots and statistics for each sequence listed below. Note that the package needs to be installed first, see below. </p> <p>A hybrid dataset with a synthetic Gaussian measurement noise for the UVDAR measurement can be generated with the [add_noise.sh](./tools/add_noise.sh) script.</p> <p>Each sequence contains the recored bag file in the root, the ground truth pose of the UAVs in a CSV file, and image of the 3D trajectory and a subfolder `hybrid` (syntehthic UVDAR measurmeents with a standard deviation of 0.3 m for the position and 0.1 rad for the orientation). Different synthetic datasets can be generated on demand by running [add_synthetic_meas](./tools/add_synthetic_meas.sh) in the `tools` folder. </p> <h2>## Setup</h2> <p><code>```</code><br><code>pip install --upgrade cnspy-relative-pose-evaluation</code><br><code>```</code></p> <h2>## Sequences</h2> <pre><code>| Name | # | Notes | Trajectory length [m] | D [s] | UAVs | |---------------------------|----|----------------------------------------------------------------------------------------------------------------------------|-----------------------|-------|--------------| | static_test | 3 | Agents standing still on the ground. Agents were facing each other | {4.3, 0.65, 0.31} | 86 | {10, 11, 12} | | one_takesoff_two_onground | 3 | Agents on the ground, one hovering and later manually controlled | {0.33, 21.5, 3.5} | 222 | {10, 11, 12} | | two_takeoff_land | 2 | Agents (10+12) hovering, one lands earlier (battery low) | {4.7, 3.6} m | 89 | {10, 12} | | two_yaxis_looped | 2 | Agents (10+12) were facing each other, they hovered for a while, and started moving in their y-axis. One landed mid flight | {27.1, 16.3} | 25 | {10, 12} | | two_yaxis_looped2 | 2 | Agents (10+12) were facing each other and started moving in their y-axis. | {26.4, 17.7} | 149 | {10, 12} | | two_octagon | 2 | Agents were facing each other and performed a octagon | {44.3, 41.6} | 171 | {10, 11} | | two_spiral_2to3m | 2 | Spiral from 2m to 3m (one loop), drones facing each other | {45.6, 42.4} | 158 | {10, 11} | | two_spiral_2to4m | 2 | Spirals from 2m to 4m (two loops), drones facing each other, one drone landed mid flight. | {33.8, 45.7} | 188 | {10, 11} | | two_octagon_one90deg | 2 | Agents were facing each other 90 deg shifted and performed an octagon | {40, 36.7} | 154 | {10, 11} | | two_loiter_one90deg | 2 | Agents facing each other at 2m height, while loitering | {37.1, 35.3} | 197 | {10, 11} | | two_yaxis_one_hovers | 3 | Two agents along y-axis, one hovers, one landed mid flight | {19.9, 14.5, 7.3} | 133 | {10, 11, 12} | | two_spiral_one_hovers | 3 | Two agents facing each other and spiraled from 2m to 4m (2 loops), one hovers | {64.1, 62.4, 18.6} | 216 | {10, 11, 12} | | loiter_spiral_yaxis | 3 | One spirals from 2m to 4m, one loiters, one along the y-axis (landed mid flight) | {55.7, 55.1, 20.1} | 219 | {10, 11, 12} |</code></pre> <h2> ## Contained topics:</h2> <p><br><code>```<br>topics: /uav10/data_handler/uvdar_fcu 775 msgs : mrs_msgs/PoseWithCovarianceArrayStamped<br> /uav10/hector_mapping/slam_out_pose 1103 msgs : geometry_msgs/PoseStamped <br> /uav10/hw_api/distance_sensor 7332 msgs : sensor_msgs/Range <br> /uav10/mavros/extended_state 86 msgs : mavros_msgs/ExtendedState <br> /uav10/mavros/imu/data_raw 8635 msgs : sensor_msgs/Imu <br> /uav10/mavros/imu/mag 7452 msgs : sensor_msgs/MagneticField <br> /uav10/mavros/imu/static_pressure 1605 msgs : sensor_msgs/FluidPressure <br> /uav10/mavros/state 8629 msgs : mavros_msgs/State <br> /uav10/vrpn_client/raw_pose 10396 msgs : geometry_msgs/PoseStamped <br> /uav11/data_handler/uvdar_fcu 728 msgs : mrs_msgs/PoseWithCovarianceArrayStamped<br> /uav11/hector_mapping/slam_out_pose 1109 msgs : geometry_msgs/PoseStamped <br> /uav11/hw_api/distance_sensor 7331 msgs : sensor_msgs/Range <br> /uav11/mavros/extended_state 86 msgs : mavros_msgs/ExtendedState <br> /uav11/mavros/imu/data_raw 8642 msgs : sensor_msgs/Imu <br> /uav11/mavros/imu/mag 7510 msgs : sensor_msgs/MagneticField <br> /uav11/mavros/imu/static_pressure 1606 msgs : sensor_msgs/FluidPressure <br> /uav11/mavros/state 8642 msgs : mavros_msgs/State <br> /uav11/vrpn_client/raw_pose 10402 msgs : geometry_msgs/PoseStamped <br> /uav12/data_handler/uvdar_fcu 2180 msgs : mrs_msgs/PoseWithCovarianceArrayStamped<br> /uav12/hector_mapping/slam_out_pose 1173 msgs : geometry_msgs/PoseStamped <br> /uav12/hw_api/distance_sensor 7357 msgs : sensor_msgs/Range <br> /uav12/mavros/extended_state 87 msgs : mavros_msgs/ExtendedState <br> /uav12/mavros/imu/data_raw 8666 msgs : sensor_msgs/Imu <br> /uav12/mavros/imu/mag 7479 msgs : sensor_msgs/MagneticField <br> /uav12/mavros/imu/static_pressure 1616 msgs : sensor_msgs/FluidPressure <br> /uav12/mavros/state 8659 msgs : mavros_msgs/State <br> /uav12/vrpn_client/raw_pose 1868 msgs : geometry_msgs/PoseStamped <br>types: geometry_msgs/PoseStamped [d3812c3cbc69362b77dc0b19b345f8f5]<br> mavros_msgs/ExtendedState [ae780b1800fe17b917369d21b90058bd]<br> mavros_msgs/State [65cd0a9fff993b062b91e354554ec7e9]<br> mrs_msgs/PoseWithCovarianceArrayStamped [3dc32bc5b82667712bb3f8fdf00477a4]<br> sensor_msgs/FluidPressure [804dc5cea1c5306d6a2eb80b9833befe]<br> sensor_msgs/Imu [6a62c6daae103f4ff57a132d6f95cec2]<br> sensor_msgs/MagneticField [2f3b0b43eed0c9501de0fa3ff89a45aa]<br> sensor_msgs/Range [c005c34273dc426c67a020a87bc24148]</code><br><code>```</code></p> <h2><br>## Credit</h2> <p>This code was written by the maintainer with the [Control of Networked System (CNS)](https://www.aau.at/en/smart-systems-technologies/control-of-networked-systems/), University of Klagenfurt, Klagenfurt, Austria.<br>The dataset was recorded in collaboration with the [Multi-robot Systems (MRS) group](http://mrs.felk.cvut.cz/) of the [Czech Technical University in Prague](https://www.cvut.cz/en).</p> <p>* [Roland Jung](https://www.aau.at/team/jung-roland/)<br>* [Jiří Horyna ](https://mrs.fel.cvut.cz/members/phdstudents/jiri-horyna)<br>* [Martin Saska](https://mrs.fel.cvut.cz/members/martin-saska)<br>* [Stephan Weiss](https://www.aau.at/team/weiss-stephan-michael/)</p> <h2>## License</h2> <p>This software is made available to the public to use (_source-available_), licensed under the terms of the BSD-2-Clause-License with no commercial use allowed, the full terms of which are made available in the `LICENSE` file. No license in patents is granted.</p> <h3>Usage for academic purposes</h3> <p>If you use this software in an academic research setting, please cite the<br>corresponding [academic paper] and consult the `LICENSE` file for a detailed explanation.</p> <p><code>```latex</code><br><code>@inproceedings{jung_uvdarbdataset_2024,</code><br><code> author = {Roland Jung, Jiri Horyna, Martin Saska, and Stephan Weiss},</code><br><code> journal = {tbd},</code><br><code> doi = {tbd},</code><br><code> title = {tbd},</code><br><code> year = {2024},</code><br><code>}</code><br><code>```</code></p>
Figure 8 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 8. Treatment Group with resveratrol tempeh + 2-ME after induced by Beta-Amyloid (10 x10).
Figure 5 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 5. Treatment group: 2- ME (10 x10).
Figure 9. Treatment Group with 2 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 9. Treatment Group with 2-ME after induced by Beta-Amyloid (10 x10).
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.