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Mass spectrometry Imaging dataset for the study on fungicide application to tomato leaves - I
<p>The dataset uploaded here is in association to a manuscript in press by Ajith et al. titled, "Visualizing active fungicide formulation mobility in tomato leaves with Desorption Electrospray Ionisation Mass Spectrometry Imaging". This dataset contains .imzML format files of Mass Spectrometry Imaging data along with the zipped .ibd files for a fungicide application study with a commerical Azoxystrobin formulation. The files were generated with a DESI Imprint imaging method for a commercial pesticide formulation applied young tomato leaves after 2 hours, 24 hours, 56 hours and a week after application.</p> <table> <tbody> <tr> <td>File Name</td> <td>Time point</td> </tr> <tr> <td>DTIM_2h</td> <td>2h Adaxial Imprint</td> </tr> <tr> <td>DTIM_24h_1</td> <td>24h Adaxial Imprint</td> </tr> <tr> <td>DTIM_24h_2</td> <td>24h Adaxial Imprint</td> </tr> <tr> <td>DTIM_24h_3</td> <td>24h Adaxial Imprint</td> </tr> <tr> <td>DTIM_56h_1</td> <td>56h Adaxial Imprint</td> </tr> <tr> <td>DTIM_56h_2</td> <td>56h Adaxial Imprint</td> </tr> <tr> <td>DTIM_56h_3</td> <td>56h Adaxial Imprint</td> </tr> <tr> <td>DTIM_1week_1</td> <td>1 week Adaxial Imprint</td> </tr> <tr> <td>DTIM_1week_2</td> <td>1 week Adaxial Imprint</td> </tr> <tr> <td>DTIM_1week_3</td> <td>1 week Adaxial Imprint</td> </tr> <tr> <td>DTIM_48h_Abaxial</td> <td>48h Abaxial imprint</td> </tr> <tr> <td>DTIM_48h_Adaxial</td> <td>48h Adaxial Imprint</td> </tr> </tbody> </table> <p> </p>
Seatizen Atlas image dataset
<h1>Seatizen Atlas image dataset</h1> <p>This repository contains the resources and tools for accessing and utilizing the annotated images within the Seatizen Atlas dataset, as described in the paper <em><a href="https://www.nature.com/articles/s41597-024-04267-z">Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery</a></em>.</p> <h2>Download the Dataset</h2> <p>This annotated dataset is part of a bigger dataset composed of labeled and unlabeled images. To access information about the whole dataset, please visit the <a href="https://zenodo.org/record/11125847">Zenodo repository</a> and follow the download instructions provided.</p> <p>If you are interested in training AI models using this dataset, you can directly access the processed version on <a href="https://huggingface.co/datasets/lombardata/seatizen_atlas_image_dataset" target="_new" rel="noopener">Hugging Face</a>.<br>This version is already split into training, validation and test sets, and includes only the classes with more than 200 annotations for more robust model training.</p> <p>An example of a trained model based on this dataset is <a href="https://doi.org/10.57967/hf/2947">DinoVdeau.</a></p> <h2>Scientific Publication</h2> <p>If you use this dataset in your research, please consider citing the associated paper:</p> <pre>@article{contini2025seatizen,<br> title={Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery},<br> author={Contini, Matteo and Illien, Victor and Julien, Mohan and Ravitchandirane, Mervyn and Russias, Victor and Lazennec, Arthur and Chevrier, Thomas and Rintz, Cam Ly and Carpentier, L{\'e}anne and Gogendeau, Pierre and others},<br> journal={Scientific Data},<br> volume={12},<br> number={1},<br> pages={67},<br> year={2025},<br> publisher={Nature Publishing Group UK London}<br>}</pre> <p>For detailed information about the dataset and experimental results, please refer to the previous paper.</p> <h2>Overview</h2> <p>The Seatizen Atlas dataset includes 14,492 multilabel and 1,200 instance segmentation annotated images. These images are useful for training and evaluating AI models for marine biodiversity research. The annotations follow standards from the Global Coral Reef Monitoring Network (GCRMN).</p> <h3>Annotation Details</h3> <ul> <li>Annotation Types:</li> <li><strong>Multilabel Convention</strong>: Identifies all observed classes in an image.</li> <li><strong>Instance Segmentation</strong>: Highlights contours of each instance for each class.</li> </ul> <h2>List of Classes</h2> <h2>Algae</h2> <ol> <li>Algal Assemblage</li> <li>Algae Halimeda</li> <li>Algae Coralline</li> <li>Algae Turf</li> </ol> <h2>Coral</h2> <ol> <li>Acropora Branching</li> <li>Acropora Digitate</li> <li>Acropora Submassive</li> <li>Acropora Tabular</li> <li>Bleached Coral</li> <li>Dead Coral</li> <li>Gorgonian</li> <li>Living Coral</li> <li>Non-acropora Millepora</li> <li>Non-acropora Branching</li> <li>Non-acropora Encrusting</li> <li>Non-acropora Foliose</li> <li>Non-acropora Massive</li> <li>Non-acropora Coral Free</li> <li>Non-acropora Submassive</li> </ol> <h2>Seagrass</h2> <ol> <li>Syringodium Isoetifolium</li> <li>Thalassodendron Ciliatum</li> </ol> <h2>Habitat</h2> <ol> <li>Rock</li> <li>Rubble</li> <li>Sand</li> </ol> <h2>Other Organisms</h2> <ol> <li>Thorny Starfish</li> <li>Sea Anemone</li> <li>Ascidians</li> <li>Giant Clam</li> <li>Fish</li> <li>Other Starfish</li> <li>Sea Cucumber</li> <li>Sea Urchin</li> <li>Sponges</li> <li>Turtle</li> </ol> <h2>Custom Classes</h2> <ol> <li>Blurred</li> <li>Homo Sapiens</li> <li>Human Object</li> <li>Trample</li> <li>Useless</li> <li>Waste</li> </ol> <p>These classes reflect the biodiversity and variety of habitats captured in the Seatizen Atlas dataset, providing valuable resources for training AI models in marine biodiversity research.</p> <h2>Usage Notes</h2> <p>The annotated images are available for non-commercial use. Users are requested to cite the related publication in any resulting works. A GitHub repository has been set up to facilitate data reuse and sharing: <a href="https://github.com/SeatizenDOI">GitHub Repository</a>.</p> <h2>Code Availability</h2> <p>All related codes for data processing, downloading, and AI model training can be found in the following GitHub repositories:</p> <ul> <li><a href="https://github.com/SeatizenDOI/plancha-workflow">Plancha Workflow</a></li> <li><a href="https://github.com/SeatizenDOI/zenodo-tools">Zenodo Tools</a></li> <li><a href="https://github.com/SeatizenDOI/DinoVdeau">DinoVdeau Model</a></li> </ul> <h2>Acknowledgements</h2> <p>This dataset and associated research have been supported by several organizations, including the Seychelles Islands Foundation, Réserve Naturelle Marine de la Réunion, and Monaco Explorations, among others.</p> <p>For any questions or collaboration inquiries, please contact <a href="mailto:seatizen.ifremer@gmail.com">seatizen.ifremer@gmail.com</a>.</p>
WiFiCam Dataset | Through-Wall Imaging based on WiFi Channel State Information
<p><strong>Through-Wall Imaging based on WiFi Channel State Information</strong></p> <p>This repository contains the <strong>WiFiCam dataset</strong> for through-wall imaging based on WiFi channel state information proposed in [1]. The corresponding source code repository is located at: <a href="https://github.com/StrohmayerJ/wificam">https://github.com/StrohmayerJ/wificam</a></p> <p>The demo video (demo.mp4) showcases the through-wall imaging capabilities of our approach. </p> <p> </p> <p><strong>Dataset Structure</strong></p> <p>/wificam</p> <p>├── j3</p> <p> └── 320 <-- 320x240 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 92108.png <-- 320x240 RGB image</p> <p> └── 92112.png</p> <p> └── ...</p> <p> └── 640 <-- 640x480 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 154.png <-- 640x480 RGB image</p> <p> └── 155.png</p> <p> └── ...</p> <p>├── statistics320.csv <-- per-channel means and standard deviations for 320x240 images</p> <p>├── statistics640.csv <-- per-channel means and standard deviations for 640x480 images</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] J. Strohmayer, R. Sterzinger, C. Stippel and M. Kampel, "Through-Wall Imaging Based On WiFi Channel State Information," <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 4000-4006, doi: 10.1109/ICIP51287.2024.10647775.</p> <p>BibTeX:</p> <pre>@INPROCEEDINGS{10647775, author={Strohmayer, Julian and Sterzinger, Rafael and Stippel, Christian and Kampel, Martin}, booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, title={Through-Wall Imaging Based On WiFi Channel State Information}, year={2024}, volume={}, number={}, pages={4000-4006}, doi={10.1109/ICIP51287.2024.10647775}}</pre>
EyeOnWater training dataset for assessing the inclusion of water images
<h1>Training dataset</h1> <p>The EyeOnWater app is designed to assess the ocean's water quality using images captured by regular citizens. In order to have an extra helping hand in determining whether an image meets the criteria for inclusion in the app, the YOLOv8 model for image classification is employed. With the help of this model all uploaded pictures are assessed. If the model deems a water image unsuitable, it is excluded from the app's online database. In order to train this model a training dataset containing a large pool of different images is required. The dataset contains a total of 13,766 images, categorized into three distinct classes: “water_good,” “water_bad,” and “other.” The “water_good” class includes images that meet the requirements of EyeOnWater. The “water_bad” class comprises images of water that do not fulfill these requirements. Finally, the “other” class consists of miscellaneous images that users submitted, which do not depict water. This categorization enables precise filtering and analysis of images relevant to water quality assessment.</p>
FeM dataset – An iron ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p> <p> </p>
Resolution Enhancement of UWB Time-Reversal Microwave Imaging in Dispersive Environments (dataset)
<p>These files are the simulation data used to create the figures illustrated in the journal paper with the same title which has been accepted for publication as a regular paper in IEEE Transactions on Computational Imaging. Each filename indicates the figure number associated with the file. These files are text files. The column structure of each file is described in the README file.</p>
RAW SEM images mosaics dataset for the HRDIC strain localization study in shot peened Ni superalloy
<p>We used a FEI Magellan HR 400L FE-SEM with a theoretical resolution of ≤ 0.9 nm at <1kV and ≤ 0.8 nm at ≥ 5kV to take backscattered electron images of the fine, homogeneous distributed gold speckle pattern obtained by remodelling of a thin gold layer previously deposited on the polished sample surface. The images were obtained at a working distance of 3.5 mm, 5 kV and 0.8 nA beam current. Mosaics of 30x15 images were used to cover 950x420 µm<sup>2</sup>. Each image contains 2048 x 1768 pixels and has a horizontal field of view of 43 µm. The images were overlapped by 20% to enable easy stitching prior to the digital image correlation. We obtained 7 mosaics, one before tensile testing and 6 after each deformation step.</p> <p>This set of images at different strain steps is coupled with the EBSD data set in https://doi.org/10.5281/zenodo.4730184, the HRDIC strain maps in http://doi.org/10.5281/zenodo.4728016 and data visualisation scripts in http://doi.org/10.5281/zenodo.4727939</p> <p>0_def corresponds to the undeformed sample, while 1_def to 6_def were obtained after each deformation step.</p>
ForTrunkDet - Image dataset of visible and thermal annotated images for forest tree trunk detection
<p>Forest dataset composed by visible and thermal images with trunk annotations. The images were acquired in three different portuguese forests and were captured by four different cameras:</p> <ul> <li>GoPro Hero6</li> <li>Allied Mako G-125</li> <li>FLIR M232</li> <li>ZED Stereo</li> </ul> <p>The images and annotations are stored in two zip files:</p> <ul> <li>forest_dataset_original.zip - original dataset</li> <li>forest_dataset_augmented - augmented dataset</li> </ul> <p>Also, the subsets that were used to train, validate and test some deep learning models are available in three .TXT files (train.txt, val.txt and test.txt), where each file line corresponds to an image name.</p> <p> </p>
DATASET: A low elevation imaging radar using a non-uniform coplanar receiver array for E~region observations
<p>Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar 3-Dimensional (ICEBEAR-3D) dataset for validation of the receiver antenna array reconfiguration, Suppressed-Spherical Wave Harmonic Transform (Suppressed-SWHT), and proper geometry for vertical interferometry using the geocentral angle.</p>
Cartoon images dataset for total variation parameter learning
<p>This dataset contains test and training image data for results presented in:</p> <p>Bredies K., Chenchene E., Hosseini A. A hybrid proximal generalized conditional gradient method and application to total variation parameter learning. 2022. <a href="https://arxiv.org/abs/2211.00997">https://arxiv.org/abs/2211.00997</a></p> <p>Also, see the <a href="https://github.com/TraDE-OPT/TV-parameter-learning">GitHub repository</a> for the implementation.</p> <p>Training and test sets have been downloaded from <a href="https://pixabay.com/">Pixabay</a> with permission.</p> <p> </p>
ICE-LAPSE image dataset
<p>########################################################</p> <p>Image dataset acquired within the project "ICE-LAPSE: Analysis of Antarctic benthos dynamics by using non-destructive monitoring devices and permanent stations'', PNRA 2013/AZ1.16, funded by the Italian National Antarctic Program.</p> <p>The dataset is in support of the publication entitled:</p> <p>Long-term Automated Visual Monitoring of Antarctic Benthic Fauna</p> <p>Simone Marini 1,2∗ , Federico Bonofiglio 1 , Lorenzo P. Corgnati 1 ,<br> Andrea Bordone 3, Stefano Schiaparelli 4 , Andrea Peirano 3</p> <p>1 National Research Council of Italy (CNR), Institute of Marine Sciences, La Spezia, 19132, Italy,<br> 2 Stazione Zoologica Anton Dohrn, Naples, 80121, Italy<br> 3 ENEA-Marine Environment Research Centre, La Spezia, 19132, Italy<br> 4 DISTAV, Università di Genova, Genova, 16132, Italy</p> <p>∗E-mail: simone.marini@sp.ismar.cnr.it.</p> <p> </p> <p>Related Scientific Publications:</p> <p>Marini, S., Bonofiglio, F., Corgnati, L. P., Bordone, A., Schiaparelli, S., & Peirano, A. (2022). Long-term automated visual monitoring of Antarctic benthic fauna. <em>Methods in Ecology and Evolution</em>, 00, 1– 19. <a href="https://doi.org/10.1111/2041-210X.13898">https://doi.org/10.1111/2041-210X.13898</a></p> <p> </p> <p>########################################################</p> <p>The dataset consists of the following files:</p> <p>- Raw_Images.tgz contains the images in CR2 format<br> - JPG_Images.tgz contains the images in JPG format<br> - 640x427_Images.tgz contains the images in JPG format, with resolution 640x427 Pixels<br> - ICE-LAPSE_Tags.tgz contains<br> - the textual_Tags folder,<br> - the Tagged_Images folder,<br> - the SpeciesColorLabel image representing the legend for the tagged images contained in the Tagged_Images folder<br> - the SpeciesDistribution.csv spreadsheet listing the species for each image</p> <p>- iFDO_Dataset.tgz contains the images renamed according to the iFDO standard</p> <p>- Deployment in Tethys bay - Mario Zucchelli Station- 2015_GUARD1-PNRA-2015_iFDO.yaml contains the iFDO metadata information.</p>
Magnetic Resonance Imaging Copper Sulfate Dataset
<p>The data has been produced by the Institut für Mikrostrukturtechnik (IMT) at Karlsruher Institut für Technologie (KIT). This dataset represents the DICOM (Digital Imaging and Communications in Medicine) files, which belong to one MRI (Magnetic Resonance Imaging) study and contain a series of images that have been measured with different protocols. The samples shown by the images are tubes, which contain different concentrations of CuSO4. The DICOM file headers have metadata tags, which embody additional information about the study and the particular series.</p>
Multispectral Spectral Imaging dataset for use in Heritage Science
<p>The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. </p> <p>This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation. </p> <p>We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. </p> <p>If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. </p> <p>Other Data sets available <a href="https://zenodo.org/record/7319696#.Y3NuOXbP2Uk">Here</a></p> <p>E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a> </p> <p>Object Paradata; </p> <ul> <li><strong>Postcard – c. Early 1900's </strong></li> <li><strong>Language – Eng. </strong></li> <li><strong>Materials – colour print on card, metallic leafing. </strong></li> <li><strong>Front transcription - </strong></li> <li><strong> ‘Greetings’ </strong></li> <li><strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong></li> <li><strong>Object Dimensions – 138mm X 88mm </strong></li> </ul> <p>The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015.</p> <p>This folder contains:</p> <ul> <li>Images captured using a <a href="https://photography.phaseone.com/xf-camera-system/">PhaseOne XF Multispectral Camera System.</a> <ul> <li>Image filenames are arranged as postacards_postcardmsi-<strong>Postcard</strong>- <strong>(Wavelength No.)(Filter)</strong>_*sequence order number*_R.tif where wavelength number is the nominal central illumination wavelength in nm (365, 385, 410, 420, 450, 480, 510, 550, 600, 630, 640, 660, 740, 850, 940), Filter is the colour of the long-pass filter (N - no filter, I - Infrared filter, G - Green filter, R - Red filter) and sequence order number is a count from 0001 denoting the order in which the image was acquired)</li> <li>Complementary flats for each of the object images, used typically to process even illumination distribution, captured of white, flat, smooth, non-chemically processed imaging standard flat paper with the same naming convention as above. </li> </ul> </li> <li>postcard_postcardmsi-Postcard.json - Metadata read out collected from MS camera system</li> <li>Truecolour RGB reference image</li> </ul>
Imaging dataset for use in Heritage Science
<p>The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. </p> <p>This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation. </p> <p>We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. </p> <p>If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. </p> <p>Other Data sets available <a href="https://zenodo.org/record/7319696#.Y3NuOXbP2Uk">Here</a></p> <p>E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a> </p> <p>Object Paradata; </p> <ul> <li><strong>Postcard – c. Early 1900's </strong></li> <li><strong>Language – Eng. </strong></li> <li><strong>Materials – colour print on card, metallic leafing. </strong></li> <li><strong>Front transcription - </strong></li> <li><strong> ‘Greetings’ </strong></li> <li><strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong></li> <li><strong>Object Dimensions – 138mm X 88mm </strong></li> </ul> <p>The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015.</p> <p>This folder contains:</p> <ul> <li>Photographs at various conditions</li> <li>Flats at matching conditions</li> <li>Metadata stored within image file</li> </ul> <p>Reading file names: </p> <p>(Flat or postcard)<strong>-exp</strong>(exposure value). format</p> <p>*.Jpg - compressed JPEG readout, saved straight from camera</p> <p>*.tiff - non-compressed TIFF readout- saved straight from camera</p> <p>Exposure values (time {sec}): 1/<strong>30</strong>, 1/<strong>60</strong>, 1/<strong>125</strong>, 1/<strong>200</strong>, 1/<strong>400</strong></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 2 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the second part of 14 parts of the full dataset (2/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 3 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the third part of 14 parts of the full dataset (3/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 20ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 12 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the twelfth part of 14 parts of the full dataset (12/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 5 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fifth part of 14 parts of the full dataset (5/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 4 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fourth part of 14 parts of the full dataset (4/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 25ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each of simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 11 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the eleventh part of 14 parts of the full dataset (11/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 25ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </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.