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164 results for “image quality”
EO-derived water quality parameters using aerial imaging spectrometry for Lake Mulargia (Sardinia, Italy) (2020/09/24)
<p>This dataset contains Hyspex-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 24 September 2020. The acquisition was done by CGR Spa (Italy). Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632).</p>
OCTA image dataset with label annotation for quality assessment
<p>This dataset is publish by the research "<em>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</em>"</p> <p>Detail:</p> <p>OCTA image dataset with label annotation for quality assessment. sOCTA-3x3-10k: 10,480 3 × 3 mm<sup>2</sup> superficial vascular layer OCTA (sOCTA) images divided into three classes; sOCTA-6x6-14k: 14,042 6 × 6 mm<sup>2</sup> sOCTA images divided into three classes. </p> <p>GitHub: <a href="https://github.com/shanzha09/COIPS">https://github.com/shanzha09/COIPS</a></p> <p>These datasets are public available, if you use the dataset or our system in your research, please <strong>cite</strong> our paper: <em><code>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</code></em>.</p> <p>arXiv:<a href="https://arxiv.org/abs/2107.10476v1">https://arxiv.org/abs/2107.10476v1</a></p>
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
<p>Short Description:</p> <p>In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone.</p> <p>Code and More Details: https://github.com/lepotatoguy/aqi</p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
Open the record for dataset details and reuse information.
SiEUGreen - Data for 'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'
<p>Dataset used in the scientific publication <a href="https://zenodo.org/record/4671852">'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'</a>. The data includes chemical contents of tomatoes that was measured, images and scripts used in the paper. The scripts here aim to predict tomato quality parameters, sugar content, acidity, sugar acid ratio and lycopene, of automatically segmented tomato through hyperspectral image reconstruction from single RGB image. The same data can also be found at the <a href="https://github.com/ZJiangsan/TomatoQualityPredictionOnAutomaticallySegmentedTomato">Github repository</a>. The data collection and scientific paper was produced by SiEUGreen partners at Norwegian Institute of Bioeconomy Research (NIBIO).</p>
Performance Evaluation of Objective Quality Assessment Methods for Omnidirectional Images Under Emerging Compressions
<p>This dataset provides extensive data from large-scale subjective experiments, encompassing MOS values, eye-tracking data, and raw subjective scores, collected from two laboratories Brno University of Technology and Czech Technical University in Prague (BUT and CTU). This new dataset serves as a comprehensive foundation for future research endeavors into novel objective quality metrics for omnidirectional IQA (OIQA).</p> <p> </p> <p><strong>▷ </strong> contact: xsimka01@vut.cz</p>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
Asphalt concrete high-quality images
<p>Dataset containing high-quality (4000x6000 pixels) images of asphalt concrete AC16 specimens. The images can serve as the input for the digital microstructure recognition using the image processing. After transferring the geometry to the vector graphics, it can be further processed for the purposes of the numerical modeling. A controlled geometry simplification can facilitate finite element analysis due to NDOF reduction.</p> <p>Provided dataset was used for the asphalt concrete digital microstructure recognition within the National Science Center (in Polish: Narodowe Centrum Nauki) project MINIATURA 5, DEC-2021/05/X/ST8/00682. Financial support of the National Science Center (Poland) is kindly acknowledged.</p>
CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms
<p>The CID2013 Camera Image Database consists of real images taken by consumer cameras and mobile phones. It is developed to provide useful tool to allow researchers target more commercially relevant distortions when developing processes of objective image quality assessment algorithms.</p> <p>The CID2013 database consists of 480 evaluated images captured by 79 imaging devices (mobile phones, DSC, DSLR) in six Image Sets. Note that the actual number of images in the database is 474. In Image Set II, Device 6 is evaluated twice as we wanted to test inter-observer reliablity. The scores are later combined into a single MOS value as the two evaluations correlated strongly.</p> <p>If you use this database in your research, we kindly ask that you follow the copyright notice bellow and cite the following paper:</p> <p>Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 390-402, Jan. 2015. <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6975172">[pdf]</a></p> <p><strong>Method</strong></p> <p>The images are evaluated by 188 observers using Dynamic Reference (DR-ACR) method (explained below). A separate scale realignment ACR data consisting evaluations from 34 observers is also included that allows to combine the data from the six image sets</p> <p>In other respects the DR-ACR method resembles very much a basic Absolute Category Rating (ACR) method (ITU-R 500-11), except the observers saw a slideshow of all the other images in the test depicting the same scene before every evaluation (See DR_demo.mp4). By seeing the other images in the test setup as reference the observers were more aware of the total variation of quality represented within a single image set. This improved their evaluation as they didn’t need to save the far ends of the scale in case there would be even more better or worse image later on the experiment. The DR-ACR method is explained in detail in:</p> <p>Mikko Nuutinen, Toni Virtanen, Tuomas Leisti, Terhi Mustonen, Jenni Radun, Jukka Häkkinen (2014) A new method for evaluating the subjective image quality of photographs : dynamic reference Multimedia Tools and Applications 75: 4. 2367-2391 Dec.</p> <p>Database contains consumer camera images and their subjective evaluations in mean opinion score (MOS), sharpness, graininess, lightness and color saturation scales. It includes the complete raw data and background information from the naïve observers used to evaluate the images. Subjects’ vision was controlled for the near visual acuity, near contrast vision (near F.A.C.T.) and color vision (Farnsworth D15) before the participation. They received movie tickets as a reward. Outlier removal is made for mean opinion score (MOS) evaluations using ITU-R 500-11 recommendations to ease out the implementation of the database.</p> <p><strong>Material</strong></p> <p>The images in CID2013 are intended to represent typical photographs that consumers might capture with their cameras. The photographed scenes were based partly on the Photospace approach described by I3A (CPIQ Initiative Phase 1 White Paper: Fundamentals and review of considered test methods, I3A, 2007) The I3A CPIQ project has migrated under IEEE.</p> <p><strong>The test environment</strong></p> <p>The room has been covered with medium gray curtains to diffuse the ambient illumination. Fluorescent lights (5800K) were positioned behind the monitors and reflected from the back wall covered with grey curtain to create dim and uniform ambient illumination in the room. The light hitting the monitors measured below 20 lx. The subject’s viewing distance (approximately 80 cm) was controlled by a line hanging from the ceiling, and they were instructed to keep their forehead steady next to the line. Because of the display size, images were scaled to a size of 1600 x 1200 pixels using the bicubic interpolation method. Eizo ColorEdge CG241W, with 1920x1200 pixel resolution, monitors in was calibrated to sRGB having target values of: 80 cd/m2, 6500K and gamma 2.2 using EyeOne Pro calibrator (X-rite co.).</p> <p> </p> <p>-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------</p> <p>Copyright (c) 2014 The University of Helsinki<br> All rights reserved.</p> <p>Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirely appear in all copies of this database, and the original source of this database,Visual Cognition research group (www.helsinki.fi/psychology/groups/visualcognition/index.htm) and the Institute of Behavioral Science (www.helsinki.fi/ibs/index.html) at the University Helsinki (www.helsinki.fi/university/), is acknowledged in any publication that reports research using this database. Individual videos and images may not be used outside the scope of this database (e.g. in marketing purposes) without prior permission.</p> <p>The database and our paper are to be cited in the bibliography as:</p> <p>-----------------------------------------------------------------------------<br> Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, 2014, In press.<br> -----------------------------------------------------------------------------</p> <p>LIMITATION OF LIABILITY</p> <p>UNIVERSITY OF HELSINKI SHALL IN NO CASE BE LIABLE IN CONTRACT, TORT OR OTHERWISE FOR ANY LOSS OF REVENUE, PROFIT, BUSINESS OR GOODWILL OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL OR PUNITIVE COST, DAMAGES OR EXPENSE OF ANY KIND HOWEVER CAUSED OR HOWEVER ARISING UNDER OR IN CONNECTION WITH THE USE OF THIS DATABASE.</p> <p>THE UNIVERSITY OF HELSINKI SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF HELSINKI HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.</p> <p>THIS AGREEMENT SHALL BE CONSTRUED AND INTERPRETED IN ACCORDANCE WITH THE LAWS OF FINLAND, EXCLUDING ITS RULES FOR CHOICE OF LAW.</p> <p>-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------</p> <p> </p>
Perceived image quality of real time ray tracing in video games survey image archive
<p>These screenshots were used in my Bachelor’s Thesis Perceived image quality of real time ray tracing in video games survey. Images have been labeled with RT ON or RT OFF depending on whether the screenshot contains ray traced graphics or not. Original survey did not have these labels.<br> <br> Images 1-5 are from Control, Images 6-10 are from Shadow of the Tomb Raider, Images 11-15 are from Cyberpunk 2077 and Images 16-20 are from Metro Exodus and Metro Exodus Enhanced Edition.</p>
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
<p>Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high- quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5).</p> <p>Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’.</p> <p>Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified.</p> <p> </p> <p><strong>Funding Declaration:</strong></p> <p>This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment: SPIE, 2022.</li> <li>Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.</li> <li>Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.</li> <li>Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE, 2021; p. 1962-1965.</li> <li>Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050</li> <li>Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for Low-dose Computed Tomography Denoising. IEEE Access 2022.</li> <li>Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.</li> <li>Kim W, Lee J, Kang M, Kim JS, Choi J-H. Wavelet subband-specific learning for low-dose computed tomography denoising. PloS one 2022;17(9):e0274308.</li> <li>Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021;48(10):5727-5742. doi: 10.1002/mp.15161</li> <li>Kim B, Shim H, Baek J. Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021;71:102065. doi: 10.1016/j.media.2021.102065</li> <li>Wagner F, Thies M, Gu M, Huang Y, Pechmann S, Patwari M, Ploner S, Aust O, Uderhardt S, Schett G, Christiansen S, Maier A. Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography. Med Phys 2022;49(8):5107-5120. doi: 10.1002/mp.15718</li> <li>Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation. Med Phys 2022. doi: 10.1002/mp.15885</li> <li>Kim S, Ahn J, Kim B, Kim C, Baek J. Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med Phys 2022;49(9):6253-6277. doi: 10.1002/mp.15884</li> <li>Bier B, Berger M, Maier A, Kachelrieß M, Ritschl L, Müller K, Choi JH, Fahrig R. Scatter correction using a primary modulator on a clinical angiography Carm CT system. Med Phys 2017;44(9):e125-e137.</li> <li>Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A. Learning-based occupational x-ray scatter estimation. Phys Med Biol 2022;67(7). doi: 10.1088/1361-6560/ac58dc</li> <li>Roser P, Birkhold A, Preuhs A, Syben C, Felsner L, Hoppe E, Strobel N, Kowarschik M, Fahrig R, Maier A. X-Ray Scatter Estimation Using Deep Splines. IEEE Trans Med Imaging 2021;40(9):2272-2283. doi: 10.1109/TMI.2021.3074712</li> <li>Maier J, Nitschke M, Choi JH, Gold G, Fahrig R, Eskofier BM, Maier A. Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Trans Biomed Eng 2022;69(5):1608-1619. doi: 10.1109/TBME.2021.3123673</li> <li>Choi JH, Maier A, Keil A, Pal S, McWalter EJ, Beaupré GS, Gold GE, Fahrig R. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. II. Experiment. Med Phys 2014;41(6Part1):061902.</li> <li>Choi JH, Fahrig R, Keil A, Besier TF, Pal S, McWalter EJ, Beaupré GS, Maier A. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. Part I. Numerical modelbased optimization. Med Phys 2013;40(9):091905.</li> <li>Berger M, Muller K, Aichert A, Unberath M, Thies J, Choi JH, Fahrig R, Maier A. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys 2016;43(3):1235-1248. doi: 10.1118/1.4941012</li> <li>Ko Y, Moon S, Baek J, Shim H. Rigid and non-rigid motion artifact reduction in X-ray CT using attention module. Med Image Anal 2021;67:101883. doi: 10.1016/j.media.2020.101883</li> <li>Preuhs A, Manhart M, Roser P, Hoppe E, Huang Y, Psychogios M, Kowarschik M, Maier A. Appearance Learning for Image-Based Motion Estimation in Tomography. IEEE Trans Med Imaging 2020;39(11):3667-3678. doi: 10.1109/TMI.2020.3002695</li> </ol>
Sentinel-2 Wind Turbine Images with Spin and Spin Quality Labels
<p>Reproduction Data for training a classifier on the Sentinel-2 Band Offset for motion detection.</p>
image classification dataset on carbon fiber reinforcement quality control
<p>Image classification dataset on carbon fiber quality control.</p> <p>To represent a practical quality control problem, a dataset was generated using carbon plain weave with a grammage of 200g/m². Pieces of the weave, measuring 300x300 mm², were cut using a CNC cutter table. Two such pieces were stacked, with a binder applied between them for shape stability after the forming process. The formed stacks were then scanned using a high-resolution camera mounted on a robotic arm, resulting in 500 images of the textiles' surfaces in three-dimensional shape. The images were then cropped to 341x384 pixels patches and transformed to grayscale.</p> <p>Each patch was classified into one of three classes: normal textile, gap, or fold. Images that were blurred, out of focus, or had bad contrast were sorted out. The dataset has not yet been released, and will be available upon the acceptance of the document.</p>
A Dataset Containing Tiny Vehicle Images Collected in Low Quality Imaging Conditions
<p>This dataset contains 4800 tiny and low resolution vehicle images collected in low lighting and different weather conditions. The vehicles in the images are grouped in six classes: Bike, Car, Juggernaut, Minibus, Pickup, and Truck. For each class, there are 800 vehicle images with 100 × 100 pixels and 96 dpi resolution.</p> <p>The peer-reviewed data descriptor for this dataset has been published in MDPI Sustainability - an open access journal, and can be accessed here: <a href="https://doi.org/10.3390/su152316292">https://doi.org/10.3390/su152316292</a>. Please cite this when using the dataset.</p>
AAPM TG 236 Report 236: Recommendations on volume-image-based treatment planning, dosimetry, and quality management for HDR intracavitary brachytherapy: Part I breast
<p>This is a zipped file for Appendix A.1 DVH validation using reference DCIOM datasets for AAPM TG report 236: Part I. Intracavitary breast brachytherapy. One can download and unzip the file. These are CT images and a structure file in DICOM format to validate the DVH information in your HDR brachytherapy treatment planning system either Varian BrachyVision or Elekta Oncentra. One can import all the DICOM files into your brachytherapy TPS and compute the dose. One can follow the instruction described in detail in Appendix A.1 of the AAPM TG 236: Part I report.</p>
A Randomized, Controlled Trial to Evaluate CT Image Quality Lumentin® 44
ClinicalTrials.gov study NCT03326518. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Free Living Food Waste Management and Diet Quality Improvement Using Smart Intervention and Food Image Application
ClinicalTrials.gov study NCT05061888. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy
Open the record for dataset details and reuse information.
A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality
Open the record for dataset details and reuse information.
Setaria viridis images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Setaria viridis</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</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.