Find research datasets worth reusing

Dataset of the "Unwrapping Non-Locality in the Image Transmission Through Turbid Media"

This post provides the dataset associated with the study titled "Unwrapping Non-Locality in Image Transmission Through Turbid Media." The "X_random.npy" file contains speckle image data with dimensions (150,000, 128, 128). The first axis spans the 150,000 images used in this study. The first 120,000 images are speckles corresponding to randomly generated images. The next 10,000 images (from 120,000 to 130,000) correspond to the first 10,000 images of the CelebA dataset. The remaining 20,000 (from 130,000 to 150,000) images consist of the first 10,000 images of the CIFAR and MNIST datasets, respectively. The ground truth images (SLM commanded images) are provided in "gt_random.npy", "gt_cifar.npy", and "gt_mnist.npy" for randomly generated images, CIFAR, and MNIST datasets, respectively. Each image in these datasets is scaled to a resolution of 128 by 128 pixels. The faces dataset are the 128 by 128 resized images of the CelebA dataset. A code example of the presented neural network model and its architecture implementation is given in "GAM on ImageNet data.py". This code reads the training dataset (speckle images) from the Ref [1] study ("x_train.npy") and rescales them to 128 by 128 pixels. The rescaled data, along with their corresponding resized SLM commanded images ("y_train.npy"), are used for training the GAM model (the model presented in the manuscript). This code then saves three files: "Trained GAM.h5," "samp_final_16_0.npy," and "samp_final_16_1.npy," which contain the trained model weights and biases and the sampling index of the model nodes, as described in the manuscript and commented on in the "GAM on ImageNet data.py" code. Code for testing this model is also provided in "test GAM.py." This file reads the testing dataset from the [1] study, specifically "x_test_cat.npy," "x_test_horse.npy," "x_test_punch.npy," and "x_test_parrot.npy" speckle images. It applies the trained model ("Trained GAM.h5," "samp_final_16_0.npy," and "samp_final_16_1.npy") to them. The code then calculates the structural similarity index (SSIM) to the ground truth files, namely "y_test_cat.npy," "y_test_horse.npy," "y_test_punch.npy," and "y_test_parrot.npy" for the "x_test_cat.npy," "x_test_horse.npy," "x_test_punch.npy," and "x_test_parrot.npy" data, respectively. <h3>Reference</h3> [1] Caramazza, P., Moran, O., Murray-Smith, R., & Faccio, D. (2019). Transmission of natural scene images through a multimode fibre. Nature Communications, 10(1), 2029.

Whole slide images of mouse liver serial sections - Test registration dataset

15 H&E serial section of mouse liver and small intestine. Sampled prepared in the <a href="https://www.epfl.ch/research/facilities/histology-core-facility/">EPFL histology core facility</a> by Nathalie Müller, Gian-Filippo Mancini, and Agnès Hautier. All slides where imaged with a VS200 Evident slide scanner from the<a href="http://biop.epfl.ch/"> EPFL BIOP imaging facility</a>.

Dataset: Beamr Imaging Ltd. (BMR) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Dataset: Planet Image International Limited (YIBO) Stock Performance

Dataset: Varex Imaging Corporation (VREX) Stock Performance

A Fundus Image Dataset for RDD-Net in Glaucoma diagnosis

我们为 RDD-Net 提供了眼底图像数据集,包括 4 种不同的医疗数据集。 此数据集基于 REFUGE2[1] 数据集、RIM-ONE[2] 数据集、ORIGA[3] 数据集和 Harvard[4] 数据集。我们感谢 [1-4] 名作者所付出的努力。 Refuge2 数据集详细信息 <table> <tbody> <tr> <td>断层</td> <td>样本数量</td> </tr> <tr> <td>火车</td> <td>1200</td> </tr> <tr> <td>瓦尔</td> <td>400</td> </tr> <tr> <td>测试</td> <td>400</td> </tr> </tbody> </table> 超越高峰 <table> <tbody> <tr> <td>领域</td> <td>每个领域案例(训练/测试)</td> </tr> <tr> <td>避难所2</td> <td>1400/600</td> </tr> <tr> <td>RIM-ONE</td> <td>339/146</td> </tr> <tr> <td>奧利加</td> <td>454/196</td> </tr> <tr> <td>學術</td> <td>1234/310</td> </tr> </tbody> </table> [1] Fang, H. 等:REFUGE2 挑战:青光眼缉多维分析与评估的宝库。收录于:arXiv 预印本 arXiv:2202.08994 (2022) [2] Fumero,F.,等人:RIM-ONE:用于视神经评估的开放视网膜图像数据库,在:基于计算机的医疗系统国际研讨会-CBMS,第 1-6 页(2011 年) [3] Zhuo, Z. 等:Origa-light:用于青光眼分析和 研究的在线视网膜图像数据库,载于:IEEE Eng.在医学领域和 Bio。社会。第3065-3068页 (2010) [4] Ahn, JM 等人: 使用眼底照相检测晚期和早期青光眼的深度学习模型。在:PloS one 13(11),e0207982 (2018) 如果您发现该框架提出的研究有用,请考虑按以下方式引用该论文: <pre><code> title={RDD-Net: Randomized Joint Data-Feature Augmentation and Deep-Shallow Feature Fusion Networks for Automated Diagnosis of Glaucoma}, author={Tang, Yilin and Zhang, Min and Feng, Jun}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024}, year={2024} </code></pre>

Dataset: QT Imaging Holdings, Inc. (QTI) Stock Performance

Dataset: Nano-X Imaging Ltd. (NNOX) Stock Performance

The ORBIT (Object Recognition for Blind Image Training)-India Dataset

<div> The ORBIT (Object Recognition for Blind Image Training) -India Dataset is a collection of 105,243 images of 76 commonly used objects, collected by 12 individuals in India who are blind or have low vision. This dataset is an "Indian subset" of the original ORBIT dataset [1, 2], which was collected in the UK and Canada. In contrast to the ORBIT dataset, which was created in a Global North, Western, and English-speaking context, the ORBIT-India dataset features images taken in a low-resource, non-English-speaking, Global South context, a home to 90% of the world’s population of people with blindness. Since it is easier for blind or low-vision individuals to gather high-quality data by recording videos, this dataset, like the ORBIT dataset, contains images (each sized 224x224) derived from 587 videos. These videos were taken by our data collectors from various parts of India using the Find My Things [3] Android app. Each data collector was asked to record eight videos of at least 10 objects of their choice.  </div> <div> Collected between July and November 2023, this dataset represents a set of objects commonly used by people who are blind or have low vision in India, including earphones, talking watches, toothbrushes, and typical Indian household items like a belan (rolling pin), and a steel glass. These videos were taken in various settings of the data collectors' homes and workspaces using the Find My Things Android app.  </div> <div> The image dataset is stored in the ‘Dataset’ folder, organized by folders assigned to each data collector (P1, P2, ...P12) who collected them. Each collector's folder includes sub-folders named with the object labels as provided by our data collectors. Within each object folder, there are two subfolders: ‘clean’ for images taken on clean surfaces and ‘clutter’ for images taken in cluttered environments where the objects are typically found. The annotations are saved inside a  ‘Annotations’ folder containing a JSON file per video (e.g., P1--coffee mug--clean--231220_084852_coffee mug_224.json) that contains keys corresponding to all frames/images in that video (e.g., "P1--coffee mug--clean--231220_084852_coffee mug_224--000001.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, "P1--coffee mug--clean--231220_084852_coffee mug_224--000002.jpeg": {"object_not_present_issue": false, "pii_present_issue": false}, ...). The ‘object_not_present_issue’ key is True if the object is not present in the image, and the ‘pii_present_issue’ key is True, if there is a personally identifiable information (PII) present in the image. Note, all PII present in the images has been blurred to protect the identity and privacy of our data collectors. This dataset version was created by cropping images originally sized at 1080 × 1920; therefore, an unscaled version of the dataset will follow soon.  </div> <div> This project was funded by the Engineering and Physical Sciences Research Council (EPSRC) Industrial ICASE Award with Microsoft Research UK Ltd. as the Industrial Project Partner. We would like to acknowledge and express our gratitude to our data collectors for their efforts and time invested in carefully collecting videos to build this dataset for their community. The dataset is designed for developing few-shot learning algorithms, aiming to support researchers and developers in advancing object-recognition systems. We are excited to share this dataset and would love to hear from you if and how you use this dataset. Please feel free to reach out if you have any questions, comments or suggestions.  </div> <div> REFERENCES:  </div> <div> <ol> <li> Daniela Massiceti, Lida Theodorou, Luisa Zintgraf, Matthew Tobias Harris, Simone Stumpf, Cecily Morrison, Edward Cutrell, and Katja Hofmann. 2021. ORBIT: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision. DOI: <a href="https://doi.org/10.25383/city.14294597" target="_blank" rel="noreferrer noopener">https://doi.org/10.25383/city.14294597</a> </li> <li> microsoft/ORBIT-Dataset. <a href="https://github.com/microsoft/ORBIT-Dataset" target="_blank" rel="noreferrer noopener">https://github.com/microsoft/ORBIT-Dataset</a>   </li> <li> Linda Yilin Wen, Cecily Morrison, Martin Grayson, Rita Faia Marques, Daniela Massiceti, Camilla Longden, and Edward Cutrell. 2024. Find My Things: Personalized Accessibility through Teachable AI for People who are Blind or Low Vision. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems (CHI EA '24). Association for Computing Machinery, New York, NY, USA, Article 403, 1–6. <a href="https://doi.org/10.1145/3613905.3648641" target="_blank" rel="noreferrer noopener">https://doi.org/10.1145/3613905.3648641</a>  </li> </ol> </div>

High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma

This deposition contains only training dataset of ORCHID database. The validation and test dataset related to the same study can be found at DOI: 10.5281/zenodo.12646943.

VOnet codes and organoid single-plane image datasets

Code information that composes VONet. VO and RO image datasets used to verify the performance of VONet.

Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 06 (SARS-CoV-2 Omicron B.1.1.529; BA.2)

Dataset 06 comprises 164 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Omicron B.1.1.529; BA.2) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 06 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).

Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 04 (SARS-CoV-2 Beta B.1.351)

Dataset 04 comprises 132 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Beta B.1.351) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 04 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).

Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 02 (SARS-CoV-2 Italy-INMI1)

Dataset 02 comprises 154 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Italy-INMI1) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 02 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).

opencc-by-4.0Jan 2018View details →

ENTICE VM image analysis and optimised fragmentation frequently built images dataset

As part of the evaluation of ENTICE VM image analysis and optimised fragmentation services we have implemented a simulation environment which analyses online software package repositories (e.g. ones offered by the maintainers of the Ubuntu and Debian Linux distributions) and deduces decomposition options as well as expected fragment sizes based on metadata acquired from these repositories. This dataset contains the collected recipes for several frequently built Ubuntu Linux based VMIs (e.g., LAMP, LAPP, LEMP, LLMP, LYME, MEAN/MERN, LTM, etc.) and  the calculated fragments and their relations. The dataset is used to analyse and evaluate the behaviour of the fragmentation services. The dataset is in compressed LRZIP format.

(05)-Strobl2018A-DS0001 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(05)-Strobl2018A-DS0001 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(05)-Strobl2018A-DS0003 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(05)-Strobl2018A-DS0003 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(05)-Strobl2018A-DS0002 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(05)-Strobl2018A-DS0002 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

MSD-I: Million Song Dataset with Images for Multimodal Genre Classification

The Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/) is a collection of metadata and precomputed audio features for 1 million songs. Along with this dataset, a dataset with annotations of 15 top-level genres with a single label per song was released. In our work, we combine the CD2c version of this genre datase (http://www.tagtraum.com/msd_genre_datasets.html) with a collection of album cover images.  The final dataset contains 30,713 tracks from the MSD and their related album cover images, each annotated with a unique genre label among 15 classes. Based on an initial analysis on the images, we identified that this set of tracks is associated to 16,753 albums, yielding an average of 1.8 songs per album. We randomly divide the dataset into three parts: 70% for training, 15% for validation, and 15% for test, with no artist and album overlap across these sets. This is crucial to avoid possible overfitting, as the classifier may learn to predict the artist instead of the genre.    Content: MSD-I dataset (mapping, metadata, annotations and links to images) Data splits and feature vectors for TISMIR single-label classification experiments  These data can be used together with the Tartarus deep learning python module https://github.com/sergiooramas/tartarus.   Scientific References: Please cite the following paper if using MSD-I dataset or Tartarus software. Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).