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9,300 results for “detection”
RealDirt: waste detection dataset
<p>RealDirt is a waste detection dataset collected on a real waste recycling plant.</p> <p>It includes 2000 images and following classes:</p> <ol> <li>Plastic bottle</li> <li>Plastic bag</li> <li>Carton </li> </ol> <p>We hope that our dataset will provide researchers and machine learners in the sphere of waste detection with insightful and helpful dataset.</p> <p>Dataset was collected with IDS UI-5250 CP Rev.2 camera and 9mm Azure lens. </p>
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica
<p>This repository contains the waveform data used by Ho et al. (2024), along with all generated fine-tuned models and event catalogs. See the README file for a summary. The corresponding software packages are available on GitHub at <a href="https://github.com/jakewalter/easyQuake.git">https://github.com/jakewalter/easyQuake.git</a>, <a href="https://github.com/seisbench/seisbench">https://github.com/seisbench/seisbench</a>, and <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a>. See the README.md file at <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a> for additional details.</p>
AGS_apple_detection - Apple fruit images dataset for full image object detection
<p>This dataset correspond to full apple tree images (623) annotated for the task of object detection with its corresponding annotations in yolo format saved as txt files. The dataset was divided into test, train and validation<br><br>The data was collected in 2017 on 4 different apple varieties using a Samsung sm-a510F cell phone at two different resolutions: 2448 x 3264 px and 3096 x 4128 px in the orchards of Agroscope located in Wallis, Switzerland. </p>
Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection
<p>This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.</p>
Multiple DJI drone flights with thermal camera over Lithuanian forests in winter for wild boar detection
<p>5 Flight over multiple days in the evening time for better thermal conditions for boar detection.</p> <p>Flight were conducted with DJI thermal cameras filmed at the speed of about 5m/s. <br>Flights 1, 2, 4 and 5 were filmed at from 90m height with camera pointing straight down.<br>Flight 3 was filmed at 120m height.</p> <p> </p> <p>Link for Dataset download: <a title="Thermal imaging dataset over Lithuanian forests in winter" href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip" target="_blank" rel="noopener">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip</a> </p>
BotHawk: GitHub Bot Account Detection Dataset
<div> <p>The <strong>"BotHawk: GitHub Bot Account Detection Dataset"</strong> is a specialized resource developed for the research and development community focused on identifying and understanding bot accounts within the GitHub ecosystem. This comprehensive dataset offers an in-depth look into the behaviors, contributions, and patterns of bot accounts, distinguishing them from human user activities on GitHub.</p> <p>Designed to enhance the accuracy and efficiency of bot detection algorithms, the BotHawk Dataset encompasses a variety of features, including commit history, issue participation, pull request activities, and other interaction metrics specific to GitHub. It provides an invaluable foundation for developing machine learning models capable of distinguishing between bot-generated and human-generated activities, contributing to more secure and trustworthy interactions within the GitHub platform.</p> <p>This dataset not only serves as a tool for academic research in cybersecurity, machine learning, and software engineering but also offers practical applications for developers and platform administrators seeking to improve the health of their projects by filtering out bot-based noise and manipulation. The BotHawk dataset is instrumental in advancing our comprehension of bot dynamics within the GitHub ecosystem, supporting the broader objective of fostering a secure, transparent, and collaborative open-source community.</p> </div>
Dataset for the publication: Tactile Convolutional Networks for Online Slip and Rotation Detection
<p>Raw data recorded for the publication "Tactile Convolutional Networks for Online Slip and Rotation Detection" in rosbag format. Each .bag file contains recordings of two tactile sensors sampled with 1kHz as "sensor_msgs/Image" and a third channel for data labeling in the format "sr_robot_msgs/UBI0All". Detailed informations about the preprocessing and labeling are in the publication.</p>
MengxiaoZhao_Using sky-wave echoes information to extend HFSWR's maximum detection range
<p>1. for Figure 2,3,4</p> <p>a. 'Ground Attention.mat' —— The data of ground wave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> Attenuation: 501*4 —— the ground wave attenuation for four different frequency<br> ]</p> <p>b. 'Skywave Attention.mat' —— The data of skywave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> attenuation: 34*4 —— the skywave attenuation for four different frequency<br> ]</p> <p>c. 'Path attenuation of 5Mhz.mat' —— four paths' attenuation of 5Mhz<br> [<br> L —— Ground distance<br> path1 —— the attenuation of path 1<br> path23 —— the attenuation of path 2&3<br> path4 —— the attenuation of path4<br> ]</p> <p><br> 2. for Figure 7 - simulation result</p> <p>a. 'simulation_echoes data.mat' —— the data of simulation targets' echoes</p> <p>b. 'Figure7_data.mat' —— the simulation results<br> [<br> data —— Doppler*Range<br> Doppler —— the axis of Doppler <br> Range —— the axisof Range <br> ]</p> <p>3. for Figure 8,9,10 - actual data processing results. We give 5 batches of echoes' data and the final result of Figure 9.</p> <p>a. 'TCDat1.mat','TCDat2.mat','TCDat3.mat','TCDat4.mat','TCDat5.mat',<br> —— the echoes' data</p> <p>b. 'Figure9_data.mat' —— the final result of Figure 9. <br> [<br> data ——Doppler*Range<br> axist_Doppler —— the axisof Doppler<br> axist_Range —— the axisof Range<br> counT —— the number of detections<br> mTgt —— the parameters of detections<br> ]</p> <p>4. 'parameters.txt' —— the parameters of simulation data and actual data</p> <p><br> </p>
ScriptNet: ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD)
<p>This dataset contains the training and test set for the ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD).</p> <p>A newly created freely available real world dataset consisting of 2035 annotated document page images that are collected from 9 different archives and form the basis of cBAD. Two competition tracks test different characteristics of the methods submitted. Track A [Simple Documents] is published with annotated text regions and tests therefore a method's quality of text line segmentation. The more challenging Track B [Complex Documents] provides only the page area. Hence, baseline detection algorithms need to correctly locate text lines in the presence of marginalia, tables, and noise.</p> <p>The dataset comprises images with additional PAGE XMLs. The PAGE XMLs contain text regions and baseline annotations.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/5/</p> <p>Version 3 is the version of the cBad competition</p> <p>Version 4 contains also the page region and in case of a double-page the page split as separator.</p>
Jamendo Corpus for Singing Voice Detection
<p>This is a public corpus of 93 creative-commons licensed music pieces annotated<br> by voice (sung or spoken) and no-voice.</p>
Detecting edge effects of geese grazing at the boundary of woodland and grassland
<p>The presence of geese on different areas of lawn was estimated by the length of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating their density on areas of land (Owen, 1971). However, we found it difficult to distinguish individual defecation events as the dropping tend to break apart as they are released. Therefore, we measured the total length of dropping in an area. Geese dropping are more or less cylindrical and we consider a measure related to the volume of droppings is more reliable than a count of their number.</p> <p>Observations were conducted in July 2014 and March and April 2015 at Meise Botanic Garden, Meise, Belgium. Rectangular plots were laid out perpendicular to a woodland-lawn boundary on sections of a Botanic Garden frequently used by geese. These plots are detailed in file DroppingsPlots.csv. The sites for these plots were chosen because they were well separated from each other; were away from other trees and faced different directions. The plots were marked out using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m<sup>2</sup> square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimeter with a ruler.</p> <p>The results are found in file DroppingsMeasurements.csv.</p> <p>The columns of this file are as follows</p> <p>Plot - The identifying number given to the plot</p> <p>X - The distance parallel to the woodland-lawn boundary</p> <p>Y - The distance from the woodland-lawn boundary</p> <p>Length - The total length in centimeters of the dropping found in a 1m<sup>2</sup> quadrat</p> <p>Prunella - coverage of <em>Prunella vulgaris</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Renoncule - coverage of <em>Ranunculus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Bellis - coverage of <em>Bellis perennis</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Lotus - coverage of <em>Lotus</em> sp. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Glechoma - coverage of <em>Glechoma hederacea</em> L. in the 1m<sup>2</sup> quadrat on the DAFOR Scale (dominant 5, abundant 4, frequent 3, occasional 2, rare 1, absent 0)</p> <p>Four species of geese are present in the Botanic Garden and may have contributed droppings to the observations. These species are <em>Alopochen aegyptiaca</em> (L. 1766) (Egyptian geese), <em>Branta canadensis</em> (L. 1758) (Canada geese), <em>Anser anser</em> (L. 1758) (greylag geese) and <em>Branta leucopsis</em> (Bechstein, 1803) (barnacle geese).</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>
Augmented dataset of rumours and non-rumours for rumour detection
<p> </p> <p>This data set contains a collection of Twitter rumours and non-rumours during six real-world events: 1) 2013 Boston marathon bombings, 2) 2014 Ottawa shooting, 3) 2014 Sydney siege, 4) 2015 Charlie Hebdo Attack, 5) 2014 Ferguson unrest, and 6) 2015 Germanwings plane crash</p> <p>The data set is an augmented data set of the PHEME dataset of rumours and non-rumours based on two data sets: the PHEME data [2] (downloaded via <a href="https://figshare.com/articles/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification/6392078">https://figshare.com/articles/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification/6392078</a>), and the CrisisLexT26 data [3] (downloaded via <a href="https://github.com/sajao/CrisisLex/tree/master/data/CrisisLexT26/2013_Boston_bombings">https://github.com/sajao/CrisisLex/tree/master/data/CrisisLexT26/2013_Boston_bombings</a>).</p> <p> </p> <p><strong>PHEME-Aug v2.0 (aug-rnr-data_filtered.tar.bz2 and aur-rnr-data_full.tar.bz2) </strong>contains augmented data for all six events.</p> <p><strong>aug-rnr-data_full.tar.bz2</strong> contains source tweets and replies without temporal filtering. Please refer to [1] for details about temporal filtering. The statistics are as follows:</p> <p>* 2013 Boston marathon bombings: 392 rumours and 784 non-rumours </p> <p>* 2014 Ottawa shooting: 1,047 rumours and 2,072 non-rumours </p> <p>* 2014 Sydney siege: 1,764 rumours and 3,530 non-rumours </p> <p>* 2015 Charlie Hebdo Attack: 1,225 rumours and 2,450 non-rumours </p> <p>* 2014 Ferguson unrest: 737 rumours and 1,476 non-rumours </p> <p>* 2015 Germanwings plane crash: 502 rumours and 604 non-rumours </p> <p> </p> <p><strong>aug-rnr-data_filtered.tar.bz2 </strong>contains source tweets, replies, and retweets after temporal filtering and deduplication. Please refer to [1] for details. The statistics are as follows:</p> <p>* 2013 Boston marathon bombings: 323 rumours and 645 non-rumours </p> <p>* 2014 Ottawa shooting: 713 rumours and 1,420 non-rumours </p> <p>* 2014 Sydney siege: 1,134 rumours and 2,262 non-rumours </p> <p>* 2015 Charlie Hebdo Attack: 812 rumours and 1,673 non-rumours </p> <p>* 2014 Ferguson unrest: 471 rumours and 949 non-rumours </p> <p>* 2015 Germanwings plane crash: 375 rumours and 402 non-rumours </p> <p> </p> <p>The data structure follows the format of the PHEME data [2]. Each event has a directory, with two subfolders, rumours and non-rumours. These two folders have folders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet. Also each folder contains ‘aug_complete.csv’ and ‘reference.csv'.</p> <p>'aug_complete.csv' file contains the metadata (tweet ID, tweet text, timestamp, and rumour label) of augmented tweets before deduplication and filtering tweets without context (i.e., replies).</p> <p>'reference.csv' file contains manually annotated reference tweets [2, 3].</p> <p> </p> <p><strong>If you use our augmented data (PHEME-Aug v2.0), please also cite:</strong></p> <p>[1] Han S., Gao, J., Ciravegna, F. (2019). "Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection", The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), Vancouver, Canada, 27-30 August, 2019</p> <p>==============================================================================================</p> <p>[2] Kochkina, E., Liakata, M., & Zubiaga, A. (2018). All-in-one: Multi-task Learning for Rumour Verification. COLING.</p> <p>[3] Olteanu, A., Vieweg, S., & Castillo, C. (2015, February). What to expect when the unexpected happens: Social media communications across crises. In <em>Proceedings of the 18th ACM conference on computer supported cooperative work & social computing</em> (pp. 994-1009). ACM</p>
Data and code from: Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras.
<p>Detection data from American black bears and code used in "Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras" published in the Journal of Applied Ecology (Evans & Rittenhouse 2018). Unmarked detection data was collected using remote cameras in northwest Connecticut in 2014, and individual detection data was determined from unique genotypes obtained from non-invasive hair snares constructed at camera sampling locations.</p> <p>EN14.rds contains detection data as an R list:</p> <p>$y (num): J (sites) x K (occasions) matrix containing detection counts</p> <p>$X (int): 2 x J matrix of site coordinates</p> <p>$xlims (num): bounding x-coordinates</p> <p>$ylims (num): bounding y-coordinates</p> <p>$M (int): upper bound for super population of individuals</p> <p>$nTraps (int): number of sampling sites (J)</p> <p>$nReps (int): number of MCMC interations</p> <p>$forest (num): vector of site-specific covariates</p> <p>$mark (int): K x I matrix storing site numbers at which individual (i) was detected on occasion k</p> <p>FullModel.R provides functions used to fit constant density models to unmarked detections incorporating covariates of detection probability.</p> <p>partialID.R provides functions and code used to estimate density from mixtures of marked and unmarked detection data</p> <p>VariableDensity.R provides functions and code used to fit variable density models to unmarked detection data incorporating spatial covariates of density.</p> <p> </p>
TensorFlow models for CK object detection
<p>Tarball containing the yolo model for the tensorflow object detection program in CK repositories</p> <p> </p>
Maps related to the detection of abrupt changes in NDVI approximated phenological cycles of Donana marshes for 2007-2016
<p>Monitoring of abrupt changes among annual vegetation cycles of consequent years in Protected Areas is valuable for the recognition of patterns, which represent the reaction of the biomes to external factors, such as changes in the meteorological conditions (e.g. the precipitation regime), human intervention or extreme events (e.g. fire). It is an indicator of the primary production of the area and other relevant functions of the ecosystem. The BFAST, Breaks For Additive Seasonal and Trend, approach can be used for monitoring changes, since it is globally applicable and able to analyze each pixel individually without the need to set thresholds for detecting changes within time series. Thus, BFAST is applied for the detection of abrupt trend changes in NDVI time series in the case of Doñana marshes, as a proxy to phenological metrics per pixel.</p> <p>BFAST outputs are used to generate: (i) a raster with the time of all detected abrupt changes per pixel (filename: “All_break_times_2007_to_2016.tif”), (ii) a raster with the total number of detected abrupt changes per pixel (filename: “Marshes_maximum_number_of_breaks_2007_to_2016.tif”), (iv) a raster with the time for which the biggest change is detected per pixel has the (filename: “Marshes_maximum_break_time_2007_to_2016.tif”).</p> <p>The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.docx” included in the zip containing the dataset.</p>
Web robot detection - Server logs
<p>This dataset contains server logs from the search engine of the library and information center of the Aristotle University of Thessaloniki in Greece (<a href="http://search.lib.auth.gr/">http://search.lib.auth.gr/</a>). The search engine enables users to check the availability of books and other written works, and search for digitized material and scientific publications. The server logs obtained span an entire month, from March 1st to March 31 2018 and consist of 4,091,155 requests with an average of 131,973 requests per day and a standard deviation of 36,996.7 requests. In total, there are requests from 27,061 unique IP addresses and 3,441 unique user-agent strings. The server logs are in JSON format and they are anonymized by masking the last 6 digits of the IP address and by hashing the last part of the URLs requested (after last /). The dataset also contains the processed form of the server logs as a labelled dataset of log entries grouped into sessions along with their extracted features (simple semantic features). We make this dataset publicly available, the first one in this domain, in order to provide a common ground for testing web robot detection methods, as well as other methods that analyze server logs.<br> <br> </p>
CLDF Dataset derived from List's "Sample Size and Cognate Detection" from 2014
<p>Cite the source of the dataset as:</p> <blockquote> <p>List, Johann-Mattis (2014): Investigating the impact of sample size on cognate detection. Journal of Language Relationship. 11. 91-102. DOI: https://doi.org/10.31826/jlr-2014-110111</p> </blockquote>
CLASSIC and OE02 Cell Detections
<p>This dataset consists of cell detections, taken from digital whole slide images from two datasts. The method of obtaining these cell detections as well as the sources of the datasets are described in more detail below. </p> <p>For both datasets, the 2D coordinates of the nuclear centroid locations and cell features were extracted using the HeteroGenius MIM Cell-Analysis Add-On (HeteroGenius, Leeds, UK). The model used was a UNet-based cell detector and classifier trained on over 50,000 manually annotated HE-stained cells. 12 nuclear features were extracted. These consisted of length (micrometers), elongation, angle, and the probabiltiy of the clel being one of the following 9 cell types: tumour cell, lymphoycte, granulocyte, plasma cell, fibroblast, smooth muscle cell, endothelial cell, normal epithelium, or other. </p> <p>One dataset consisted of cell detections from 950 haematoxylin eosin (HE) stained 3mm tissue microarray (TMA) cores from the resection specimen of gastric cancer patients from the CLASSIC trial (Noh et al., 2014). Manual annotations were made of different tissue classes for the purpose of supervised node classification using a graph neural network. These ground truth tissue classes can be found in the column "class" within the csv files. The tissue classes identified were cancer, lymphocyte aggregates, muscle, and stroma. For cells without a class, these were labelled as "notype". Ony 260 TMA cores contained these tissue annotations. A list of these files can be found in the file 'annotated_classic_cores.csv'</p> <p>The second dataset consisted of cell detections from 45 HE-stained endoscopic biopsies from oesophageal cancer patients from the OE02 trial (Girling et al. 2002). The ground truth target classes in this dataset were "tumour" and "not tumour". Exact annotations of the tumour areas were available from a previous study (Hale et al., 2016) and non-tumour areas were annotated manuyally for a seperate study. </p> <p> </p> <p>Noh, S. H., Park, S. R., Yang, H.-K., Chung, H. C., Chung, I.-J., Kim, S.-W., Kim, H.-H., Choi, J.-H., Kim, H.-K., Yu, W., Lee, J. I., Shin, D. B., Ji, J., Chen, J.-S., Lim, Y., Ha, S., & Bang, Y.-J. (2014). Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): 5-year follow-up of an open-label, randomised phase 3 trial. The Lancet Oncology, 15(12), 1389–1396. https://doi.org/10.1016/s1470-2045(14)70473-5</p> <p>Girling, D. J., Bancewicz, J., Clark, P. I., Smith, D. B., Donnelly, R. J., Fayers, P. M., Weeden, S., Girling, D. J., Hutchinson, T., Harvey, A., & Lyddiard, J. (2002). Surgical resection with or without preoperative chemotherapy in oesophageal cancer: A randomised controlled trial. Lancet, 359(9319), 1727–1733. https://doi.org/10.1016/S0140-6736(02)08651-8</p> <p>Hale, M. D., Nankivell, M., Hutchins, G. G., Stenning, S. P., Langley, R. E., Mueller, W., West, N. P., Wright, A. I., Treanor, D., Hewitt, L. C., Allum, W. H., Cunningham, D., Hayden, J. D., & Grabsch, H. I. (2016). Biopsy proportion of tumour predicts pathological tumour response and benefit from chemotherapy in resectable oesophageal carcinoma - Results from the UK MRC OE02 trial. Oncotarget, 7(47), 77565–77575. https://doi.org/10.18632/oncotarget.12723</p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </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.