Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

4 image cytochrome dataset with per-shot spectra recorded on the Jungfrau 16M detector at SwissFEL in NeXus format

Cytochrome data recorded in 2019 on the Jungfrau 16M detector recorded at SwissFEL including per-shot spectra measure each SASE pulse. First a NeXus master file was created using scripts in cctbx.xfel: libtbx.python `libtbx.find_in_repositories xfel`/swissfel/jf16m_cxigeom2nexus.py unassembled_file=run_000795.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.3038 detector_distance=163.9 output_file=./run_000795.JF07T32V01_master_spectrum.h5 beam_file=run_000795.BSREAD.h5 include_spectra=True Then it was sliced down to 4 images that successfully index using the attached script, sliceit.py The file can be processed by DIALS using this command: dials.stills_process run_000795.JF07T32V01_master_spectrum_4img.h5 indexing.phil  Where the indexing phil refers to the attached mask and refined geometry file, both produced by DIALS.

opencc-by-4.0Aug 2022View details →

Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'

Here we publish the datasets associated with our publication ‘Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip’. 3D cultures of human induced pluripotent stem cells (hiPSCs) were imaged while undergoing definitive endoderm (DE) differentiation. Data were acquired either on the live 3D cultures at various time points during the 3 days DE differentiation, or after fixation and immunostaining, with the 3D cultures being fixed at regular 24 h intervals during differentiation, as to form a timeline. Images were recorded with a standard confocal microscope using a xy-resolution of 0.25 μm/px and a z-resolution of 1 μm/plane.

opencc-by-4.0Jun 2022View details →

Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset

The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types,  namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.

opencc-by-4.0May 2022View details →

Micro-CT Imaging Dataset on ex-vivo Ovine Functional Spinal Segments as Healthy, Injured and Treated with Cement Discoplasty

General information: - This dataset contains micro-CT images and mechanical test data from ovine functional spinal units (FSU).  - The micro-CT data was produced using a Bruker SkyScan 1172. The settings for the scans are given in the '.log' files in each folder.  - The compression testing was conducted on an MTS 858 Mini Bionix T/II. The settings for each test can be found in test '.txt' files. - In short, every FSU was mechanically tested in compression under different conditions. Before and after every test, the FSUs were scanned to ensure there was no damage   to the sample. More information can be found in the related publication:  - The mechanical testing data is arranged in folders with consecutive cycles. It is highly recommended to use the last three cycles for analysis.   Data set notation: - All the datasets are noted by Sheep number. Sh7 = Sheep 7; Sh8 = Sheep 8; Sh9 = Sheep 9. In the publication, the numbers were switched to 1,2,3 respectively. - files denoted with '_rec' contain the reconstruction of the projection images.  - 'Tested' or 'After test' files refers to the scan after mechanical testing.

opencc-by-4.0Sep 2022View details →

SUTO-Solar through-turbulence open image dataset

Database of solar small-area images disturbed by turbulent atmosphere. Together with short series of images, an MFBD-assisted recovered image is provided. Paper describing the dataset as well as utilized instrumentation can be found in the original  work at https://www.mdpi.com/1424-8220/22/20/7902

TrueFace: a Dataset for the Detection of Synthetic Face Images from Social Networks

TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram. Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.

opencc-by-4.0Dec 2021View details →

Podocyrtis Image dataset

Image dataset of the eight radiolarian species belonging to the middle Eocene genus of Podocyrtis. Images comes from ODP Leg 207, at the Demerara Rise outside the coast of Surinam and French Guyana, northwestern South America. These images were used for automatic image recognition by using convolutional neural networks.

opencc-by-4.0Sep 2022View details →

Datasets for Lorentz electron ptychography towards sub-nanometer resolution imaging of magnetic textures

These data sets are the raw experimental data used in a Letter titled, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit published on Nature Nanotechnology. The related paper should be cited whenever the datasets are used. Reference: Zhen Chen, Emrah Turgut, Yi Jiang, Kayla X. Nguyen, Matthew J. Stolt, Song Jin, Daniel C. Ralph, Gregory D. Fuchs, David A. Muller, Lorentz electron ptychography for imaging magnetic textures beyond the diffraction limit. Nature Nanotechnology, in press, https://doi.org/10.1038/s41565-022-01224-y (2022). The  file format is Matlab's *.mat file with version 7.3. The diffraction patterns are stored as the variable 'cbed'. Experimental conditions can be found in data_info.txt and the related paper.

Dataset for "Imaging of Small-Scale Heterogeneity and Absorption Using Adjoint Envelope Tomography: Results from Laboratory Experiments"

The codes for Monte-Carlo simulation, scripts used to calculate the misfit kernels, and the processed data of the laboratory experiment.

ForTrunkDetV2 - Image dataset of visible and thermal annotated images for forest tree trunk detection (augmented version)

This dataset is an augmented version of an existing dataset (<a href="https://doi.org/10.5281/zenodo.5213824">10.5281/zenodo.5213824</a>), composed by visible and thermal images with trunk annotations. The images were acquired in three different portuguese forests and were captured by five different cameras: <ul> <li>GoPro Hero6</li> <li>Allied Mako G-125</li> <li>FLIR M232</li> <li>ZED Stereo</li> <li>OAK-D</li> </ul> The augmented images and their annotations are stored in an archive with the following structure: main_directory/ <ol> <li>annotations/ <ol> <li>pascalvoc/ <ul> <li>PascalVOC annotation files</li> <li>...</li> </ul> </li> <li>yolo/ <ul> <li>YOLO annotation files</li> <li>...</li> </ul> </li> </ol> </li> <li>images/ <ul> <li>Image files</li> <li>...</li> </ul> </li> </ol> Each annotation file links to its image by the file name, so if an image is named "img12345.jpg", its annotation files are named as "img12345.xml" (for PascalVOC format) and "img12345.txt" (for YOLO format). The dataset contains original and augmented images. The original images' names follow the pattern "img_******.jpg", where in the place of the asterisks are numbers. The remaining images are the augmented ones. 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.

Astur Apple image Dataset

Struture of file: 2022-v2-9classes-AsturApple-Balanced-SIZE224-train-dev-test.hdf5 ------------------------------------------------------------------------------------------- This file contains dataset of 6108 cider apple color images, 224x224 pixels to support the article "Transfer learning with convolutional neural networks for classification of cider apple varieties" results. -The images belong to nine apple classes: 'BLANQUINA' 'CARRIO' 'FLORINA' 'FUENTES' 'PRIETA' 'RAXAO' 'REINETA ENCARNADA' 'REINETA PINTA' 'REINETA ROJA DEL CANADA' -Full dataset is split in 4886 images for training, 611 for testing and 611 for validation. -Class labels are codified as one-hot enconding binary labels. (e.g. [0 0 0 0 0 1 0] ---> Reineta Pinta) -Training image set are store as tensor "trainX": (4168,224,224,3) -Training class labels "trainY": (4886,7) -Test image set tensor "testX": (611, 224,224,3) -Test image binary class labels "testY": (611, 7) -Validation image set tensor "devX": (611, 224,224,3) -Validation binary class labels "devY": (611, 7) * file was created with h5py module in Python.

opencc-by-4.0Dec 2023View details →

Raw images and processed datasets related to the journal article Robust Assessment of Post-Localisation Hardening Behaviour in Eurofer97 using Inverse Finite Element Methods

Open the record for dataset details and reuse information.

Baseline embeddings from the BBBC022 dataset used in "Semisupervised contrastive learning for bioactivity prediction using Cell Painting image data"

3 Baseline embeddings aclculated from the BBBC022 dataset. A self-supervised contrastive learning-based model, DINO and CellProfiler were used  to calculate the embeddings.

Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation and Damage Analysis

Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using scanning electron microscopy images collected from large areas (~1 mm²) of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure. https://doi.org/10.1016/j.matdes.2024.113031

opencc-zeroMay 2024View details →

dryad40/100

BIFROST: A method for registering diverse imaging datasets

The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, this is challenging because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas (FDA). We then develop a two-step pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

opencc-by-4.0Apr 2024View details →

ImageJ-processed Images from the BBBC022 dataset

Processed microscopy images from the Cell Painting dataset BBBC022. 5 fluorescence channel images converted to RGB images with ImageJ and resized to 224x224 pixels. The CSV files contain the metadata (b22_dataset.csv all data points, b22_dataset_mesh_nonans.csv only MeSH annotated data points).

opencc-by-4.0Jun 2023View details →

TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter

Update May 2024: Fixed a data type issue with "id" column that prevented twitter ids from rendering correctly. Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes). Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and human images (i) is correlated with the number of likes; and (ii) can be used to identify human images that served as inspiration for the gen-AI creations. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our analyses and findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images. Note that in accordance with the privacy and control policy of Twitter, NO raw content from Twitter is included in this dataset and users could and need to retrieve the original Twitter content used for analysis using the Twitter id. In addition, users who want to access Twitter data should consult and follow rules and regulations closely at the official Twitter developer policy at https://developer.twitter.com/en/developer-terms/policy.

Multienergy Fan Beam Computed Tomography Dataset of a Bird Chest Imaged with 3 Different X-ray Spectra

Summary This dataset is a collection of X-ray projection data of a biological imaging phantom (a bird chest) imaged in an X-ray microtomography scanner, using three different X-ray spectra. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters, as well as photographs and example reconstructions. The dataset is designed for use in algorithm development for multienergy computed tomography.   Description Sample Information The sample is the chest of a common quail (Coturnix coturnix) bird obtained frozen from a local supermarket. The chest section of the frozen bird was removed using a handsaw, and left to melt and settle in a sample holder before imaging. Scanner The measurement data were acquired using an X-ray microtomography scanner in the University of Helsinki Micro-CT Laboratory. The scanner uses cone beam geometry and it is equipped with an end-window tube with a tungsten target. Scan Settings The dataset consists of three consecutive scans made using identical geometry but different X-ray spectra and detector exposure times. For each scan, 720 X-ray projections were acquired using an angle increment of 0.5 degrees. Multiple frames were averaged for each projection in order to increase signal-to-noise ratio. The scan geometry and the energy-specific settings are summarized in the following two tables. Table 1. Imaging geometry used for collecting the data. <table> <tbody> <tr> <td>Parameter</td> <td>Value</td> </tr> <tr> <td>Focus-center distance</td> <td>252 mm</td> </tr> <tr> <td>Focus-detector distance</td> <td>420 mm</td> </tr> <tr> <td>Geometric magnification</td> <td>5/2</td> </tr> <tr> <td>Detector pixel size</td> <td>0.200 mm</td> </tr> <tr> <td>Effective pixel size</td> <td>0.120 mm</td> </tr> <tr> <td>Projection size</td> <td>552 x 576 pixels</td> </tr> <tr> <td>Angular range</td> <td>360'</td> </tr> <tr> <td>#projections</td> <td>720</td> </tr> </tbody> </table> Table 2. Energy-specific settings used for collecting the data. <table> <tbody> <tr> <td>Energy label</td> <td>U (kV)</td> <td>Filtration</td> <td>I (μA)</td> <td>Exposure time (ms)</td> <td>Frame averaging</td> </tr> <tr> <td>E1</td> <td>50</td> <td>None</td> <td>300</td> <td>125</td> <td>4</td> </tr> <tr> <td>E2</td> <td>80</td> <td>1 mm Al</td> <td>180</td> <td>125</td> <td>4</td> </tr> <tr> <td>E3</td> <td>120</td> <td>0.5 mm Cu</td> <td>120</td> <td>250</td> <td>4</td> </tr> </tbody> </table> Data Post-Processing Before the scans were made, a dark current image and flat-field image were acquired for each scan setting. During the scans, dark current subtraction and flat-field correction were automatically applied to the X-ray projections by the measurement software. Data Contents This dataset contains the following files: <ul> <li>The raw projection data (.tif format) for each scan and a metadata file (.txt format) describing the measurement setup, with formatting that is both human-readable and machine-readable.</li> <li>Pre-created 2D sinograms for each energy level. The sinograms have been created from the central plane of the cone beam, which reduces to fan beam geometry. The sinograms are stored in Matlab's .mat file format in data structures which also contain metadata on the measurement.</li> <li>Photographs taken during the measurement process.</li> <li>Example filtered backprojection (FBP) reconstructions of the central plane of the phantom for each energy. The reconstructions were computed using the  Phoenix datos|x CT software provided with the microtomography scanner</li> </ul>   Research Group This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>) in collaboration with the Computational Physics and Inverse Problems research group at the University of Eastern Finland, Finland (<a href="https://sites.uef.fi/inverse">https://sites.uef.fi/inverse</a>) and the X-ray Laboratory at the Department of Physics at the University of Helsinki, Finland (<a href="https://www.helsinki.fi/en/researchgroups/x-ray-laboratory">https://www.helsinki.fi/en/researchgroups/x-ray-laboratory</a>).   Previous Use This dataset has been used in the following publications: Jussi Toivanen, Alexander Meaney, Samuli Siltanen, Ville Kolehmainen. Joint reconstruction in low dose multi-energy CT. Inverse Problems and Imaging, 2020, 14(4): 607-629. doi: <a href="https://doi.org/10.3934/ipi.2020028" target="_blank" rel="noopener">10.3934/ipi.2020028</a>. E. Cueva, A. Meaney, S. Siltanen, M. J. Ehrhardt. Synergistic multi-spectral CT reconstruction with directional total variation. Philos Trans A Math Phys Eng Sci. 2021 Aug 23;379(2204):20200198. doi: <a href="https://doi.org/10.1098/rsta.2020.0198">10.1098/rsta.2020.0198</a>.   Additional Links To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected by the Inverse Problems research group, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>.   Acknowledgements We wish to thank laboratory engineer Heikki Suhonen for his guidance and assistance in conducting the measurements.   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

Cone-Beam Computed Tomography Dataset of a Walnut Imaged at 4 Different Dose Levels

Summary This dataset is a collection of X-ray projection images of a walnut imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.   Description Sample Information The sample is a walnut in its shell. For the scanning process double-sided tape was used to attach the sample to a plastic tube placed into the rotation stage. Scanner The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). Scan Settings The dataset consists of four different scans of the same sample. For each scan 360 X-ray projections were acquired using an angle increment of 1 degrees, with one additional frame taken at the end to estimate sample movement. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were: <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> Data Post-Processing Before the scans, two correction images were acquired for each scan setting. A dark current image was created by averaging 255 images taken with the X-ray source off. A flat-field image was created by averaging 255 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. Data Format The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable. Notes Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.   Research Group This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www.helsinki.fi/en/researchgroups/inverse-problems">https://www.helsinki.fi/en/researchgroups/inverse-problems</a>.   Additional Links To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox">https://se.mathworks.com/matlabcentral/fileexchange/74417-heltomo-helsinki-tomography-toolbox</a>. Please note that this is a an entirely separate dataset from the Walnut datasets accessible at <a href="../record/1254206">https://zenodo.org/record/1254206</a> and <a href="https://doi.org/10.5281/zenodo.6986012">https://doi.org/10.5281/zenodo.6986012</a>, although both datasets have been created by the same research group.   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.