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979 results for “image dataset”
Dataset used in "SynQuant: an automatic tool to quantify synapses from microscopy images"
<p>All data used in the SynQuant paper,</p> <ul> <li>Synthetic data with label</li> <li>Real data with annotations</li> <li>The code that is used to generate the synthetic data.</li> </ul> <p> </p>
TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection
<p>This dataset is from the paper "TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection", which has been accepted by the WACV 2024 CV4EO Workshop.</p>
Dataset for Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging
<p>In this repository, we provide all the datasets that are needed to reproduce the analysis conducted in the paper entitled "Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging."</p> <p><br> codes: This folder contains Python codes to run the forward and inverse modeling. Install the following packages.<br> notebook, fenics, numpy, pandas, matplotlib, scipy, numdifftools, and lmfit for inverse modeling (needs to be run on Linux).<br> data: This directory contains data used in the inverse modeling.<br> gif: This directory contains GIF movies of the upward infiltration experiments.</p> <p>readme.xlsx: This file explains which data are used for each figure in the paper.</p>
Aruzz22.5K: An Image Dataset of Rice Varieties
<p>This extensive dataset presents a meticulously curated collection of low-resolution images showcasing 20 well-established rice varieties native to diverse regions of Bangladesh. The rice samples were carefully gathered from both rural areas and local marketplaces, ensuring a comprehensive and varied representation. Serving as a visual compendium, the dataset provides a thorough exploration of the distinct characteristics of these rice varieties, facilitating precise classification.</p> <p><strong>#Dataset Composition#</strong><br>The dataset encompasses <strong>20 distinct classes</strong>, encompassing "1_Subol_Lota","2_Bashmoti","3_Ganjiya","4_Shampakatari","5_Katarivog","6_BR28","7_BR29","8_Paijam","9_Bashful","10_Lal_Aush","11_Jirashail","12_Gutisharna","13_Red_Cargo","14_Najirshail","15_Katari_Polao","16_Lal_Bi"roi,"17_Chinigura_Polao","18_Amon","19_Shorna5","20_Lal_Binni". In total, the dataset comprises <strong>4,500 original</strong> JPG images and <strong>22,500 augmented</strong> images.</p> <p><strong>#Image Capture and Dataset Organization#</strong><br>These images were captured using an iPhone 11 camera with a 5x zoom feature. Each image capturing these rice varieties was diligently taken between October 18 and November 29, 2023. To facilitate efficient data management and organization, the dataset is structured into two variants: Original images and Augmented images. Each variant is systematically categorized into 20 distinct sub-directories, each corresponding to a specific rice variety.</p> <p><strong>#Original and Augmented Image Dataset#</strong><br>The primary and the augmented image set comprises 4,500 JPG images, uniformly sized at 512 × 512 pixels. To address the substantial image volume requirements of deep learning models for machine vision, data augmentation techniques were implemented. Transformations such as rotation (90° left, 90° right, 180° left) and flip were applied, generating an additional set of augmented images in every class, totaling 22,500 augmented images. </p> <p><strong>#Dataset Storage and Access#</strong><br>The datasets are stored in two distinct folders namely <strong>'1_TRAIN'</strong> and <strong>'2_VALID'.</strong> Both zip files contain 20 sub-folders representing a unique rice variety, namely 1_Subol_Lota, 2_Bashmoti, 3_Ganjiya, 4_Shampakatari, 5_Katarivog, 6_BR28, 7_BR29, 8_Paijam, 9_Bashful, 10_Lal_Aush, 11_Jirashail, 12_Gutisharna, 13_Red_Cargo,14_Najirshail, 15_Katari_Polao, 16_Lal_Biroi, 17_Chinigura_Polao, 18_Amon, 19_Shorna5, 20_Lal_Binni.</p>
Dataset for the paper: "StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation", CVPR 2024
<h1>Three benchmark datasets for non-bijective image-to-image translation</h1> <h3><strong>PlanIGN</strong></h3> <blockquote> <p>This dataset was constructed from the French National Mapping Agency (IGN), comprising 1900 aerial images (ortho-imagery) at 3m spatial resolution and two versions of their corresponding maps -- one with toponyms and one without toponyms (_TU). We divided them into training (1000 images) and testing (900 images). In our experiment, we use trainA & trainB, testA & testB_TU for training and testing, respectively. </p> </blockquote> <h3><strong>Google_mismatch</strong></h3> <blockquote> <p>We created non-bijective datasets from the maps dataset by seperating the samples with highways from those without. We excluded all satellite images (trainA) featuring highways and subsampled maps (trainB) with varying proportions of highways from 0% to 65%. For the test set, we selected 898 pairs without highways.</p> </blockquote> <h3><strong>BRATS_mismatch</strong></h3> <blockquote> <p>We used two modalities from Brats2018 -- T1 and FLAIR. We selected transverse slices from the 60° to 100°. Each scan was classified as tumorous if more than 1% of its pixels were labelled as such and as healthy if it contained no tumor pixels. We provide "generate_mismatched_datasets.py" so users can generate datasets with varying proportions of tumorous samples during training. In our default seeting, we have 800 training samples with source images (T1) being healthy and target images (FLAIR) comprising 60% tumorous scans. The test set contains 335 paired scans of healthy brains.</p> </blockquote> <p>Please cite this paper if you want to use our datasets:</p> <pre><code>@inproceedings{wu2024stegogan, title={StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation}, author={Wu, Sidi and Chenn Yizi and Mermet, Samuel and Hurni, Lorenz and Schindler, Konrad and Gonthier, Nicolas and Landrieu, Loic}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2024} } </code></pre> <p>In addition, you should cite the following paper if you use Google_mismatch dataset:</p> <pre><code>@inproceedings{isola2017image, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2017} }</code></pre> <p>And cite the following papers if you use Brats_mismatch dataset:</p> <pre><code>@article{menze2014multimodal, title={The multimodal brain tumor image segmentation benchmark (BRATS)}, author={Menze, Bjoern H and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, <br>Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and others}, journal={IEEE transactions on medical imaging}, volume={34}, number={10}, pages={1993--2024}, year={2014} } @article{bakas2017brats17, title={Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features}, author={Bakas, Spyridon and Akbari, Hamed and Sotiras, Aristeidis and Bilello, Michel and Rozycki, Martin and Kirby, <br>Justin S and Freymann, John B and Farahani, Keyvan and Davatzikos, Christos}, journal={Scientific data}, volume={4}, number={1}, pages={1--13}, year={2017} } @article{bakas2018ibrats17, title={Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge}, author={Bakas, Spyridon and Reyes, Mauricio and Jakab, Andras and Bauer, Stefan and Rempfler, Markus and Crimi, <br>Alessandro and Shinohara, Russell Takeshi and Berger, Christoph and Ha, Sung Min and Rozycki, Martin and others}, journal={arXiv preprint arXiv:1811.02629}, year={2018} }</code></pre> <p> </p>
single-cell imaging datasets associated with JupyterLab notebooks
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DepthMars Dataset for Semantic Segmentation of the Martian Surface from Rover Images
<p>This dataset is resulted from a research article, "DepthFormer: Depth-Enhanced Transformer Network for Semantic Segmentation of the Martian Surface from Rover Images", which includes surface images on Mars collected by the Zhurong rover along its traverse, depth images generated from stereo images, and corresponding manually labeled images.</p>
Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain
<p>This dataset gathers synthetic-yet-highly-realistic T2-weighted magnetic resonance images (MRI) of the fetal brain based on the latest development of our prototype Fetal Brain magnetic resonance Acquisition Numerical phantom that now simulates local heterogeneities within white matter tissues throughout maturation (FaBiAN v2.0).<br>This dataset is associated with the following paper:</p> <p><strong>- Lajous H. et al. (2024) A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain.</strong> Submitted to Nature Scientific Data, Pre-print available https://doi.org/10.1101/2024.04.08.588566</p> <p>We propose this unique, extensive fetal MRI dataset of simulated standard clinical fast spin echo sequences in both healthy and pathological neurodevelopmental trajectories to address data scarcity in this sensitive population, and therefore support the continuous endeavor of the community to develop advanced post-processing methods as well as cutting-edge artificial intelligence models. Automated brain tissue annotations of the two-dimensional, low-resolution series as well as super-resolution (SR) reconstructions of the fetal brain volumes are also included.</p> <p><strong>Work using any of these data should cite the following references:</strong></p> <ul> <li>Lajous, H. et al. A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data (2024), https://doi.org/10.1101/2024.04.08.588566</li> <li>Lajous, H. et al. Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Zenodo (2024). 10.5281/zenodo.10940427</li> <li>Lajous, H., le Boeuf Fló, A., Esteban, O. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v2.0 (2.0). Zenodo (2023), 10.5281/zenodo.5471094</li> </ul> <p>This work was supported by the Swiss National Science Foundation through grant 182602, and by the ProTechno Foundation. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).</p> <p>Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2024.</p> <p>Note: <em>Terms of use for the original cohort (</em>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI, 2021<em>) are for research and education purposes only.</em></p>
Biospeckle images dataset - SEEDS
<p>The image capture was performed in 4 experimental configurations as shown in Table. These different configurations are intended to emulate the conditions of frequent use in laser setups. We perform the captures with and without the use of a polarizer and a spectral filter. The exposure time of the camera for image capture was 67ms, achieving approximately 15 fps. These different configurations help to realize a more widely compatible enhancement method.</p> <p>Table: Experimental arrangements employed for acquiring the dataset.</p> <table> <tbody> <tr> <td>Code</td> <td>Polarizer</td> <td>Spectral filter</td> <td>CCD (Volt)</td> </tr> <tr> <td>P0_F0</td> <td>no</td> <td>no</td> <td>3 and 4</td> </tr> <tr> <td>P0_F1</td> <td>no</td> <td>yes</td> <td>3 and 4</td> </tr> <tr> <td>P1_F0</td> <td>yes</td> <td>no</td> <td>3 and 4</td> </tr> <tr> <td>P1_F1</td> <td>yes</td> <td>yes</td> <td>3 and 4</td> </tr> </tbody> </table> <p>All data were acquired in the Physics Laboratory of the Faculty of Exact and Natural Sciences at the National University of Asuncion.</p>
Hyperspectral imaging dataset
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AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error (Dataset)
<p>This upload contains the dataset used in <a href="https://arxiv.org/abs/2401.17879" target="_blank" rel="noopener">AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error</a>.</p> <p>Further details and instructions are provided in the <a href="https://github.com/jonasricker/aeroblade" target="_blank" rel="noopener">project's Github repository</a>.</p>
Dataset containing images for training and testing deep learning image recognition models
<p>The dataset contains a training and a test subsets of images. Each image belongs to one out of 10 categories of animals</p>
MosqVision-3K: A Balanced Multi-Source Dataset of 3,000 Annotated Images for Culex, Anopheles, and Aedes Mosquito Species Classification
<p><strong>Comprehensive Mosquito Species Image Dataset for Machine Learning</strong><br><strong>Description:</strong><br>This dataset is a meticulously curated collection of high-quality images featuring three major mosquito species: <strong>Culex</strong>, <strong>Anopheles</strong>, and <strong>Aedes</strong>. These species are significant vectors for transmitting vector-borne diseases such as malaria, dengue, and Zika. The dataset has been compiled to support research and development in entomology, vector-borne disease control, and image recognition.<br>With <strong>3,000 images in total</strong>, the dataset is structured to ensure a balanced representation of the three species, each having <strong>1,000 images</strong>. Images were sourced from four reputable platforms, including <strong>MosquitoAlert.com</strong>, <strong>Mendeley Data</strong>, <strong>IEEE DataPort</strong>, and the <strong>Dryad Digital Repository</strong>. These sources ensure a comprehensive and diverse representation of mosquito appearances, including variations in morphology, lighting conditions, and orientations.</p> <h2>The dataset is organized into directories for each species, making it easy to integrate into machine learning workflows for tasks like species identification and classification. The collection also includes metadata and annotations to enhance usability.</h2> <p><strong>Key Features:</strong></p> <ul> <li><strong>Species Represented:</strong> <ul> <li><em>Culex</em></li> <li><em>Anopheles</em></li> <li><em>Aedes</em></li> </ul> </li> <li><strong>Total Images:</strong> 3,000 (1,000 images per species)</li> <li><strong>Image Sources:</strong> <ul> <li><strong>MosquitoAlert.com</strong> (1,234 images)</li> <li><strong>Mendeley Data</strong> (876 images)</li> <li><strong>IEEE DataPort</strong> (748 images)</li> <li><strong>Dryad Digital Repository</strong> (600 images)</li> </ul> </li> </ul> <h2>- <strong>Image Annotations:</strong> Metadata and species labels are included for enhanced usability.</h2> <p><strong>Applications:</strong><br>This dataset is ideal for a variety of applications, including:</p> <ul> <li>Training machine learning models for mosquito species identification.</li> <li>Developing computer vision algorithms for pest control and public health.</li> </ul> <h2>- Enhancing vector control strategies to mitigate disease spread.</h2> <p><strong>Data Structure:</strong><br>The dataset is organized as follows:</p> <pre><code>Mosquito_Dataset/ ├── Anopheles/ │ ├── img_001<span>.jpg</span> │ ├── img_002<span>.jpg</span> │ └── ... ├── Aedes/ │ ├── img_001<span>.jpg</span> │ ├── img_002<span>.jpg</span> │ └── ... └── Culex/ ├── img_001<span>.jpg</span> ├── img_002<span>.jpg</span> └── ... </code></pre> <h2> </h2> <h2><strong>Acknowledgments:</strong><br>We acknowledge the following data sources for their contributions:</h2> <blockquote> <ul> <li>MosquitoAlert.com</li> <li>Mendeley Data</li> <li>IEEE DataPort</li> <li> Dryad Digital Repository</li> </ul> </blockquote>
The Light Field & Stereo (LFS) Image Dataset
<p>This dataset contains a collection of monocular light field raw images, stereo-paired images, and calibration files for both the light field camera and the stereo camera. It also includes rectified stereo and depth images as computed using the SGM stereo method, and these are further reprojected to the extrinsically and intrinsically calibrated light field camera frame. This dataset is intended to aid research in computational photography, computer vision, image processing, and related fields. The varied, static images have been captured indoors, aiming to minimize the size of the dataset while maximizing its variability.</p>
Multi-modality medical image dataset for medical image processing in Python lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="../records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a></li> <li>Data for the anonymization exercises: ultrasound (<em>identifiable_us.jpg</em>) dowloaded from <a href="https://www.flickr.com/photos/jcarter/2461223727">here</a>, and DICOM data (<em>our_sample_dicom.dcm</em>) shared for this course specifically by a colleague</li> <li>Histopathology data: histopathology slide images from <a href="https://openslide.org/">openslide</a> library samples in the freely distributable test data </li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Test dataset for "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset"
<p>The uploaded tar file contains anonymized and reduced test data for the paper "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset". (doi: https://doi.org/10.1101/2024.06.11.598306; https://github.com/mwess/miit)</p> <p>Dataset description:<br>- 9 serial histology sections with the following stains: (HES, HE, HES, HES, HES, MTS, IHC, IHC, HES)<br>- Sections are indexed in the following way (due to some sections not being part of this project): 1,2,3,6,7,8,9,10,11<br>- Each serial section contains: <br> - landmarks with matching labels across all sections.<br> - semi-manually generated tissue masks <br>- Section 2 contain spatial transcriptomics data and one annotation file in geojson format.<br>- Sections 6 and 7 contain imzml data that were generated with MALDI-MSI in positive ion mode (section 6) and negative ion mode (section 7) and additional histology annotations.<br>- MALDI-MSI is reduced. The positive ion data contains only intensities and spectra for spermine. The negative ion mode data contains only intensities and spectra for citrate and zinc.<br>- ST data contains only locations of spots and scalefactors. (I.e. no count data is included.). Barcode ids are randomly generated. <br>- In addition, for each ST spot histopathological annotations and GSEA scores for the Citrate-Spermine Secretion gene signature are provided.</p> <p>Abbreviations:</p> <p>- HES = Hematoxylin-Erythrosine-Saffron<br>- HE = Hematoxylin-Eosin<br>- MTS = Masson's Trichrome Staining<br>- IHC = Immunohistochemistry<br>- ST = Spatial Transcriptomics, here refers to Visium10X arrays.<br>- MALDI-MSI = Matrix-Assisted Laser Desorption Ionization - Mass Spectrometry Imaging.</p> <p> </p>
The Bangladesh Road Traffic Sign Dataset in Real-World Images for Traffic Sign Recognition
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Materials in Vessels Dataset, Annotated images of materials in transparent vessels for semantic segmentation
<p> Data set of materials in vessels<br> The handling of materials in glassware vessels is the main task in chemistry laboratory research as well as a large number of other activities. Visual recognition of the physical phase of the<br> materials is essential for many methods ranging from a simple task such as fill-level evaluation to the<br> identification of more complex properties such as solvation, precipitation, crystallization and phase<br> separation. To help train neural nets for this task, a new data set was created. The data set contains a<br> thousand images of materials, in different phases and involved in different chemical processes, in a<br> laboratory setting. Each pixel in each image is labeled according to several layers of classification, as<br> given below:</p> <p>a. Vessel/Background: For each pixel assign value of one if it is part of the vessel and zero otherwise.<br> This annotation was used as the ROI map for the valve filter method.</p> <p>b. Filled/Empty: This is similar to the above, but also distinguishes between the filled and empty<br> regions of the vessel. For each pixel, one of the following three values is assigned:0 (background); 1<br> (empty vessel); or 2 (filled vessel).</p> <p>c. Phase type: This is similar to the above but distinguishes between liquid and solid regions of the<br> filled vessel. For each pixel, one of the following four values: 0 (background); 1 (empty vessel); 2<br> (liquid); or 3 (solid).</p> <p>d. Fine-grained physical phase type: This is similar to the above but distinguishes between specific<br> classes of physical phase. For each pixel, one of 15 values is assigned: 1 (background); 2 (empty<br> vessel); 3 (liquid); 4 (liquid phase two, in the case where more than one phase of the liquid appears in<br> the vessel); 5 (suspension); 6 (emulsion); 7 (foam); 8 (solid); 9 (gel); 10 (powder); 11 (granular); 12<br> (bulk); 13 (solid-liquid mixture); 14 (solid phase two, in the case where more than one phase of solid<br> exists in the vessel): and 15 (vapor).<br> The annotations are given as images of the size of the original image, where the pixel value is the<br> class number. The annotation of the vessel region (a) is used in the ROI input for the valve filter net .</p> <p>4.1. Validation/testing set<br> The data set is divided into training and testing sets. The testing set is itself divided into two subsets;<br> one contains images extracted from the same YouTube channels as the training set, and therefore was<br> taken under similar conditions as the training images. The second subset contains images extracted<br> from YouTube channels not included in the training set, and hence contains images taken under<br> different conditions from those used to train the net.</p> <p>4.2. Creating the data set<br> The creation of a large number of images with a variety of chemical processes and settings could have<br> been a daunting task. Luckily, several YouTube channels dedicated to chemical experiments exist<br> which offer high-quality footage of chemistry experiments. Thanks to these channels, including<br> NurdRage, NileRed, ChemPlayer, it was possible to collect a large number of high-quality images in a<br> short time. Pixel-wise annotation of these images was another challenging task, and was performed by<br> Alexandra Emanuel and Mor Bismuth.</p> <p>For more details see: <a href="https://arxiv.org/pdf/1708.08711.pdf">Setting attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels</a></p> <p>This dataset was first published in 2017.8</p> <p>For newer and Bigger datasets see</p> <p>https://zenodo.org/record/4736111#.YbG-RrtyZH4</p> <p>https://zenodo.org/record/3697452#.YbG-TLtyZH4</p> <p> </p>
Harnessing large-scale herbarium image datasets through representation learning (application images)
<p>Herbarium specimen images used in the manuscript <a href="https://www.frontiersin.org/articles/10.3389/fpls.2021.806407/full"><em>Harnessing large-scale herbarium image datasets through representation learning</em></a> for application tasks 2 and 3. These tasks were formulated to test the applicability of representations learned by three different neural networks trained on <a href="https://www.kaggle.com/c/herbarium-2021-fgvc8">the Half-Earth Challenge dataset</a>.</p> <p>All images are for specimens held by Royal Botanic Gardens, Kew. The images from Kew's HerbCat service, using URLs collated by <a href="https://www.idigbio.org/portal/search">iDigBio</a>. We first search iDigBio for the specimens we wanted, then downloaded the associated occurrence and image metadata files. We then used the URLs in the image metadata files to download the specimen images. The code used to download these images can be found in the <a href="https://doi.org/10.5281/zenodo.5776894">GitHub repository for the paper</a>.</p> <p>We searched iDigBio using these parameters:</p> <pre><code>[x] Must have media Genus: Dendrobium Institution Code: K Basis of Record: PreservedSpecimen</code></pre> <p> </p> <p>©<em> All images copyright of the Board of Trustees of the Royal Botanic Gardens, Kew.</em></p>
Dataset for Eye image effect in the context of pedestrian safety: a French questionnaire study
<p>Dataset for the paper Eye image effect in the context of pedestrian safety: a French questionnaire study</p> <p>Introduction: Human behavior is therefore influenced by the presence of others, which scientists also call ‘the audience effect’. The use of social control to produce more cooperative behaviors may positively influence road use and safety. This study uses an online questionnaire to test how eyes images affect the behavior of pedestrians when crossing a road.</p> <p>Material and methods: Different eyes images of men, women and a child with different facial expressions -neutral, friendly and angry- were presented to participants who were asked what they would feel by looking at these images before crossing a signalized road. Participants completed a questionnaire of 20 questions about pedestrian behaviors (PBQ). The questionnaire was received by 1,447 French participants, 610 of whom answered the entire questionnaire. 71% of participants were women, and the mean age was 35±14 years.</p> <p>Results: Eye images give individuals the feeling they are being observed at 33%, feared at 5% and surprised at 26%, and thus seem to indicate mixed results about avoiding crossing at the red light. The expressions shown in the eyes are also an important factor: feelings of being observed increased by about 10-15% whilst feelings of being scared or inhibited increased by about 5% as the expression changed from neutral to friendly to angry. No link was found between the results of our questionnaire and those of the Pedestrian Behavior Questionnaire (PBQ).</p> <p>Conclusion: This study shows that the use of eye images could reduce illegal crossings by pedestrians, and is thus of key interest as a practical road safety tool. However, the effect is limited and how to increase this nudge effect needs further consideration.</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.