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979 results for “Image Dataset”
Swahili Image Captioning Dataset
<p>The SwaFlickr8k dataset is an extension of the well-known Flickr8k dataset, specifically designed for image captioning tasks. It includes a collection of images and corresponding captions written in Swahili. With 8,091 unique images and 40,455 captions, this dataset provides a valuable resource for research and development in the field of image understanding and language processing, particularly in the context of Swahili language.</p>
Monospecific Mediterranean Pollen Images Dataset
<p>Monospecific Mediterranean Pollen Images Dataset. It contains 19579 labeled images of different pollen taxa from different monospecific samples. Ideal for Object Detection tasks.</p> <p> </p> <p>Specifically, we have the following pollen types:</p> <ul> <li>Chenopodiaceae</li> <li>Cupressaceae</li> <li><em>Olea</em></li> <li><em>Pinus</em></li> <li>Urticaceae</li> <li><em>Causarina</em></li> <li>Palmaceae</li> <li><em>Plantago</em></li> <li><em>Platanus</em></li> <li>Poaceae</li> <li><em>Rumex</em></li> </ul> <p> </p> <p>This work is funded by the European project LIFEWATCH-2019-11-UMA-01-BD (‘EnBiC2-Lab - Environmental and Biodiversity Climate Change Lab’). A. Picornell was supported by a postdoctoral grant financed by the Ministry of Economic Transformation, Industry, Knowledge and Universities of the Junta de Andalucía (POSTDOC_21_00056).</p>
Multiplexed Staining Dataset - OMAP 5 - Liver-Lanthanides-conjugated antibodies and C60-secondary ion mass spectrometry imaging
<p>This dataset contains images of multiplexed antibody panel on a human pediatric liver section including the nuclear marker and antibodies conjugated with lanthanides tags. The dataset is one example of serial experiments of multiplexed antibody staining and imaging. The antibody panel targets the major cell types and tissue structures in the liver tissue. Data acquisition was performed using single multiplexing imaging by C60-secondary ion mass spectrometry.</p> <p> </p>
OPTICAL COHERENCE TOMOGRAPHY (OCT) IMAGE DATASET OF RADIATION DERMATITIS
<p><strong>Optical Coherence Tomography (OCT) Image dataset of radiation dermatitis </strong></p> <p><strong>Citing the Dataset</strong></p> <p>The dataset is released under a Creative Commons Attribution license, so please cite the dataset if it is used in your work in any form. Published academic papers should use the academic paper citation for our paper. Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <p>Photiou C., Cloconi C. & Strouthos I. Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study. <em>J Digit Imaging. Inform. med.</em> (2024). https://doi.org/10.1007/s10278-024-01241-4</p> <p><em>Personal use citation</em></p> <p>Include a link to this Zenodo page - 10.5281/zenodo.8238140</p> <p><strong>ACKNOWLEDGMENT</strong></p> <p>This research is funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programs, Coordination and Development.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at photiou.christos@ucy.ac.cy.</p> <p><strong>Dataset Description</strong></p> <p>This dataset consists of Optical Coherence Tomography (OCT) images from 22 head and neck cancer patients undergoing radiotherapy. Specifically, this dataset includes OCT images of five stages of Acute Radiation Dermatitis (ARD), labelled by an expert oncologist as Grade 0 (0), Grade 1 (1), Grade 2a (2), Grade 2b (3) and Grade 3 (4). Twenty-two head and neck cancer patients who were scheduled to receive radiation therapy at the German Oncology Center (GOC) in Limassol, Cyprus, participated in this proof-of-concept trial. The trial has received bioethics approval from the Cyprus National Bioethics Committee (Cyprus National Bioethics Committee 2020/61) and informed consent was collected. Patients under the age of 18 or with disabilities, expectant women, those who had recently undergone radiation therapy in the same area, and patients with autoimmune diseases were excluded from the study. After informed consent, the irradiated side of the neck of the subjects, was imaged with OCT. The imaging was performed with a swept-source OCT system (Santec IVS300), with a center wavelength of 1300 nm, an axial resolution of 12 micrometers in tissue, and an A-scan rate of 40 kHz. Six images were acquired at 1 cm intervals, covering the region from the mandibular angle to the clavicle. Imaging was repeated prior to every radiation therapy session, twice per week, until the conclusion of the therapy, resulting in a dataset of 1487 images. During each visit, the patient's ARD grade, at each of the imaging sites, was determined and recorded by a senior oncologist.</p> <p>Dataset<br>The data consists of two items: (1) the excel file 'Description.xlsx' with the patient information and (2) the zip file 'Dataset.zip' containing the images, as described below.</p> <p>1) Description.xlsx<br>This excel file contains patient information such as age, habits, etc, in the sheet 'Patient_Info'. The sheet 'Image_Info' contains the information for each image, such as the patient number (1-22), week number, visit number (usually one or two visits per week), image number (six images per visit with some exceptions), and classification (0-4). </p> <p>2) Dataset.zip <br>This zip file contains the OCT images. Each patient's folder has sub-folders corresponding to each week, within which there are sub-folders corresponding to each visit, which contain the image folders. Each image folder contains two excel files: OCT Data (demodulated and logarithmic intensity image) and Raw Data (resampled interferometric data). </p> <p> </p>
PTX-498: A multi-center pneumothorax segmentation chest X-ray image dataset
<p>Pneumothorax is a common medical emergency defined as the abnormal collection of air in the pleural space between the lung and chest wall. Its typical symptoms include chest pain and dyspnea, leading to oxygen deficiency or even life-threatening in severe cases. Therefore, an efficient and automatic pneumothorax diagnosis algorithm would be useful in many clinical scenarios. Recently, deep learning methods have achieved impressive progress in medical image segmentation tasks. However, a large-scale dataset is one of the critical components for the success of deep learning. On the other hand, there are few public chest X-ray images with pneumothorax.</p> <p>To stimulate the researchers' interest in the pneumothorax diagnosis algorithm, <strong>we released a new data set PTX-498 here. It contains 498 chest X-ray images of pneumothorax collected from three hospitals, and each image contains pixel-level annotations.</strong> All images were resized to 1024×1024. The raw image intensity was clipped according to the window width and level inside the dicom tag and then normalized to 0 to 255. The contours of the pneumothorax area were labelled by two senior radiologists using ITK-SNAP. The dataset was anonymized and every record related to patients' privacy was removed. Only the image data and the corresponding labels were included in PTX-498.</p> <p><strong>Please use the latest v2-fix version which removes duplicate images and uses the window width and level from the original dicom tag for normalization.</strong></p> <p><strong>Citation: If you are interested in this dataset and applying it in your research, please cite the following article.</strong><br> Paper link: https://doi.org/10.1016/j.neucom.2021.05.029<br> Cite this article as Yunpeng Wang, Kang Wang, Xueqing Peng, Lili Shi, Jing Sun, Shibao Zheng, Fei Shan, Weiya Shi, Lei Liu*. DeepSDM: Boundary-aware pneumothorax segmentation in chest X-ray images [J]. Neurocomputing, 2021, 454: 201-211.</p> <div> <div class="gtx-trans-icon"> </div> </div>
Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets
<p>Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either <em>inner guard cell walls</em> or <em>whole_stomata</em> (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.</p>
Dataset for demonstration of quantitative label-free imaging with phase and polarization
<p>The QLIPP_Reconstruction_Resources_20x.zip file contains raw images of mouse brain slice and anisotropic glass target acquired with QLIPP. The file also contains the configuration files to reconstruct the phase, retardance, and orientation from this data with the recOrder pipeline. The tutorial slides for using this dataset for reconstruction can be found here (10.5281/zenodo.5135889).</p> <p> </p> <p>v1.1.0: Upload two zip files for automated testing of recOrder and waveOrder repositories.</p> <p>v1.2.0: add pycromanager dataset for testing the reader and converter in waveOrder.</p> <p>v1.3.0: reduce the size of recOrder test dataset</p> <p>v 1.4.0: add datasets for new data schema defined for recOrder 0.4.0 </p> <p>v1.5.0: add a dataset that shows images of an embryo </p>
Vector-LabPics dataset for images of materials in vessels in the chemistry lab
<p><strong>LabPics 2: A newer and larger a version (But harder to use) can be found here: <a href="../record/4736111"> https://zenodo.org/record/4736111</a></strong></p> <p>The Vector-LabPics V1 dataset contains 2187 images of chemical experiments with materials within mostly transparent vessels in various laboratory settings and in everyday conditions such as beverage handling. Each image in the dataset has an annotation of the region of each material phase and its type. In addition, the region of each vessel and its labels, parts, and corks are also marked.</p> <p>For more details see:</p> <p><a href="https://pubs.acs.org/doi/10.1021/acscentsci.0c00460">https://pubs.acs.org/doi/10.1021/acscentsci.0c00460</a></p> <p> </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support.</p> <p>Images of the dataset were taken from images and videos shared on Youtube and Instagram, Twitter and Tumblr channels and other contributors; we do not have copyright for the images. Any commercial or none academic use of the images depends on acquiring permission from the owner of the images. Note that the name of each image contains the image source. For any non-academic use of the images, please contact their sources for permission. We like to thank the following channels for sharing the images used in this dataset.</p> <p>We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@<a href="https://twitter.com/SuperScienceGrl?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor">SuperScienceGrl</a> Twitter), <a href="https://cen.acs.org/sections/chemistry-in-pictures.html">Chemical and Engineering Science chemistry in pictures</a>, YouTube channels dedicated to chemistry experiments: <a href="https://www.youtube.com/channel/UCIgKGGJkt1MrNmhq3vRibYA">NurdRage</a>, <a href="https://www.youtube.com/user/TheRedNile">NileRed</a>, <a href="https://www.youtube.com/user/DougsLab">DougsLab</a>, <a href="https://www.youtube.com/channel/UCsJHe4uMbquncMpe1PiLa2A/videos">ChemPlayer</a>, and <a href="https://www.youtube.com/user/koen2all">Koen2All</a>. Additional sources for images include Instagram channels <a href="https://www.instagram.com/chemistrylover_/">chemistrylover_</a>(Joana Kulizic),<a href="https://www.instagram.com/chemistry.shz/?hl=en">Chemistry.shz</a> (Dr.Shakerizadeh-shirazi), <a href="https://www.instagram.com/ministryofchemistry/?hl=en">MinistryOfChemistry</a>, <a href="https://www.instagram.com/chemistryandme/?hl=en">Chemistry And Me</a>,<a href="https://www.instagram.com/explore/tags/chemistrylifestyle/?hl=en"> ChemistryLifeStyle</a>, <a href="https://www.instagram.com/explore/tags/vacuumdistillation/?hl=en">vacuum_distillation</a>, and <a href="https://docs.google.com/document/d/16QkXuIesB80gDONX-YVNFP4C6Mmj-14ZDrTmLrnNfms/edit#organic_chemistry_lab">Organic_Chemistry_Lab</a>. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images from C&EN's Chemistry in Pictures (<a href="http://cen.chempics.org/">cen.chempics.org</a>) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.</p>
UBC2019 - A dataset of subjective image quality of head-mounted displays
<p>This is the dataset described in the following publication:</p> <p>"A subjective method for evaluating foveated image quality in HMDs", V. Thirumalai, J. Ribera, J. Xiang, J. Zhang, M. Azimi, J. Kamali, P. Nasiopoulos, <em>Society of Information Display, Display Week</em> - May 2020, San Francisco, CA</p>
Counted Biopores dataset used in 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p>Counted biopores dataset used in the article: 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'</p> <p>Originally collected as part of a field trial at the University of Bonn in 2012, described in the following paper:</p> <p>Eusun Han, Timo Kautz, Ute Perkons, Marcel Lüsebrink, Ralf Pude, and Ulrich Köpke.Quantification of soil biopore density after perennial fodder cropping.Plant and Soil, 394(1-2):73–85, sep 2015. ISSN 15735036. doi:10.1007/s11104- 015- 2488- 3</p>
Counted Nodules dataset used in 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p>Counted Nodules dataset used in the article: 'RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'</p>
Datasets and images of publication: Additive manufacturing for self-healing soft robots
<p>This entry contains the images and data used for the publication: Additive manufacturing for self-healing soft robots (DOI: 10.1089/soro.2019.0081). The datasets are named after the image they refer to and are available under the CC-BYSA 4.0 International license.</p>
Dataset: Whole blood count, used in: "AIDeveloper: deep learning image classification in life science and beyond"
<p>Real-time deformability cytometry (RT-DC) data of whole blood measurements.<br> Data was used to train and validate a neural net to perform a blood count based on brightfield images of RT-DC.</p> <p>01_Model: Contains the final model as well as an AIDeveloper meta-file that allows to reproduce the training procedure. The metafile preciesely defines which dataset was used for training and which for validation as well as all parameters that were set in AIDeveloper.</p> <p>The following folders contain data that was used for training (and validation):</p> <ul> <li>Cambr</li> <li>KIK</li> <li>20190306_DextranBlood_AI_DataSet</li> <li>Gs_Blood_Train</li> </ul> <p>Testing data is stored on figshare:<br> https://figshare.com/articles/Krater_et_al_2020_Data_zip/9902636</p>
AIDER (Aerial Image Dataset for Emergency Response Applications)
<p><strong>AIDER </strong>(<strong>A</strong>erial <strong>I</strong>mage <strong>D</strong>ataset for <strong>E</strong>mergency <strong>R</strong>esponse applications): The dataset construction involved manually collecting all images for four disaster events, namely Fire/Smoke, Flood, Collapsed Building/Rubble, and Traffic Accidents, as well as one class for the Normal case.</p> <p>The aerial images for the disaster events were collected through various online sources (e.g. google images, bing images, youtube, news agencies web sites, etc.) using the keywords ”Aerial View” or ”UAV” or”Drone” and an event such as Fire”,”Earthquake”,”Highway accident”, etc. Images are initially of different sizes but are standardized prior to training. All images where manually inspected to first contain the event that was of interested and then to have the event centered at the image so that any geometric transformations during augmentation would not remove it from the image view. During the data collection process the various disaster events were captured with different resolutions and under various condition with regards to illumination and viewpoint. Finally, to replicate real world scenarios the dataset is imbalanced in the sense that it contains more images from the Normal class.</p> <p>This subset includes around 500 images for each disaster class and over 4000 images for the normal class. This makes it an imbalanced classification problem.</p> <p>It is advised to further enhance the dataset that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p>
Transmission Electron Microscopy Dataset for Image Deblurring
<p>The dataset consists of images corrupted by motion blur together with corresponding high-quality images from two different samples, one of thin sectioned kidney tissue and one of a calibration grid. The data was collected using a MiniTEM microscope (Vironova AB). The motion corrupted images are created by moving the sample under the microscope. Each low-quality (motion blurry) imaging sequence has corresponding high-quality images (captured by stopping the microscope at each position in the sequence). The high-quality frames have a size of 2048 x 2048 pixels with an overlap of 50% between adjacent frames. The low-quality (motion blurry) frames are captured with a size of 1024x1024 with the same motion direction (approximately vertically upwards). All images were captured at a field of view of 32μm, and with a per image exposure time of 15ms and stored as 16 bit tiff files. Both samples are imaged with the same settings and have four imaging sequences each.</p> <p>The dataset contains the raw image files as well as a partitioning into training, validation and testing. For these images, five low-quality images have been registered to each high-quality image. For the five registered images the intersection of all is cropped and stored. 1 of the 4 imaging sequences are chosen as the test set and the last part of another of the imaging sequences as a validation set. The rest is put in the training set.</p> <p><em><strong>Folder Structures:</strong></em></p> <ul> <li><strong>Raw data:</strong> <ul> <li>Raw data is the unprocessed data and each sample folder contains 4 image sequences. In each of these folders low-quality (motion blurry) images are stored in folder “Low” and corresponding high-quality images are stored in “GT”</li> </ul> </li> <li><strong>TrainValTest:</strong> <ul> <li>TrainValTest consist of data where the low-quality frames have been registered to the high-quality frames and divided into a training, validation and test set.</li> <li>Each of the Train, Val, Test folders contains 3 subfolders. “Low” contains folders names the same as the files in “GT” where each folder contains five low-quality (motion blurry) images, registered the that corresponding high-quality image. “GT” contains the corresponding high-quality images down sampled to the same spatial size as the low-quality images. “GT_hr” contains the same images as “GT” but not down sampled.</li> </ul> </li> </ul>
Dataset containing laser speckle-contrast images
<p>Dataset contains laser speckle-contrast images of human skin under various physiological tests (controlled respiration test, breath holding test, venous occlusion test).</p>
Dataset for: Searching for Imaging Biomarkers of Psychotic Dysconnectivity
<p>This dataset contains features used in analyses for the following manuscript:</p> <p>Rodrigue, AL, et al. (2021). Searching for Imaging Biomarkers of Psychotic Dysconnectivity. <em>Biological Psychiatry: Cognitive Neuroscience and Neuroimaging,</em> in press.</p> <p>Contents:</p> <p><br> - 4 Demographic csv files. Each dataset has a csv for covariates of interest- Age, Sex, and Site (BSNIP1 only)<br> site coding: 1=Hartford,CT, 2=Baltimore,MD<br> DIAG coding: SZ=Schizophrenia, SAD=Schizoaffective Disorder, BPP=Bipolar Disorder I with Psychosis, MDD=Major Depressive Disorder with Psychosis,OTH=Other Psychotic Disorder</p> <p>- 16 feature csv files. Each dataset has a .csv for raw and residualized DTI and rsfMRI features<br> ISMMS_DTI_raw_Features.csv<br> Olin_DTI_raw_Features.csv<br> BSNIP1_DTI_raw_Features.csv<br> BSNIP2_DTI_raw_Features.csv<br> ISMMS_rsfMRI_raw_Features.csv<br> Olin_rsfMRI_raw_Features.csv<br> BSNIP1_rsfMRI_raw_Features.csv<br> BSNIP2_rsfMRI_raw_Features.csv</p> <p>Residualized (age, sex, site (BNIP1 only))<br> ISMMS_DTI_res_Features.csv<br> Olin_DTI_res_Features.csv<br> BSNIP1_DTI_res_Features.csv<br> BSNIP2_DTI_res_Features.csv<br> ISMMS_rsfMRI_res_Features.csv<br> Olin_rsfMRI_res_Features.csv<br> BSNIP1_rsfMRI_res_Features.csv<br> BSNIP2_rsfMRI_res_Features.csv</p>
PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset
<p>The PICCOLO White-Light and Narrow-Band Imaging Colonoscopic dataset comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion.</p>
Dataset used for making conclusions in article Gesture-controlled image management for operating room: A randomized crossover study.
<p>Dataset used for article titled:</p> <p>Gesture-controlled image management for operating room: A randomized crossover study.</p>
Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using a variety of interpolation and reconstruction methods.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (reconstruction_goblet_ID_0_of_1.hdf5).</li> <li>The Python script which was used to create the database (reconstruction_goblet.py).</li> <li>Auxillary Python scripts needed to run the simulations (optim_reconstructions.py, it_reconstruction.py, interp_reconstructions.py, gamp_reconstructions.py, and utils.py).</li> <li>MD5 and SHA256 checksums of the database and Python script files (reconstruction_goblet.MD5SUMS, reconstruction_goblet.SHA256SUMS).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </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.