Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
469
datasets available to search
ShareScore release 0.9.0
Dataset results
469 results for “Image analysis”
Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis
<p>TEM images of Au nanoparticles of various sizes on ultra-thin carbon substrates and their corresponding labels for semantic segmentation. The images have a dataset label of "images" and the labels have a dataset label of "labels". </p>
VesselExpress: Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs
<p>This dataset contains raw, segmented and skeletonized 3D light-sheet microscopic image volumes of blood vessels of different organs which were processed by VesselExpress. Please find the software here: https://github.com/RUB-Bioinf/VesselExpress. For details on how to run and setup the software please watch our tutorial (https://youtu.be/a8GWVKJNh68).</p>
LMRG Image Analysis Study - FISH datasets
<p>Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D confocal fluorescence images of sub-diffraction punctate staining (fluorescence in situ hybridization (FISH) in <em>C. elegans</em>).</p> <p>See https://github.com/ABRFLMRG/image-analysis-study for more details.</p>
LMRG Image Analysis Study - nuclei datasets
<p>Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D widefield fluorescence images of nuclei.</p> <p>See https://github.com/ABRFLMRG/image-analysis-study for more details.</p>
Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"
<p>Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"</p> <p> </p> <p>Please find below an explanation for the <strong>files </strong>in this repository:</p> <p><br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong></p> <p>Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset"</p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>03_ExampleMeasurement.zip</strong></p> <p>One measurement file and a corresponding scatterplot</p> <p><strong>04_Dataset_load.zip</strong></p> <p>The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv".</p> <p><strong>05_RF_training</strong></p> <p>Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv").</p> <p><strong>07_pytranskit</strong></p> <p>Scripts for training and evaluating CDT-PLDA classifier</p> <p> </p> <p> </p>
Imaging mass cytometry analysis of brain tissues with CNS immune-related adverse events during anti-PD-1 cancer immunotherapy
<p><span>Dataset accompanying the manuscript "Anti-PD-1 cancer immunotherapy induces CNS immune-related adverse events by Spleen tyrosine kinase activation in microglia".</span></p> <p><span>The Metadata.xls file includes the metadata, the raw image data is saved as .txt file, the segmented cellular expression data is available as csv files. </span></p>
Sample data for "Live Cell Fluorescence Microscopy – An End-to-End Workflow for High-Throughput Image and Data Analysis"
<p>This repository contains:</p> <ul> <li> <p>Sample data for the "Live Cell Fluorescence Microscopy – From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky & Malinsky. The paper describes the preparation of live yeast cell samples for microscopy, the subsequent semi-automatic analysis of the microscopy images using our custom-written Fiji macros, and automatic processing of the output (Results table) from the image analys using custom-written R scripts. The data provided here are real experimental data from two publications of our group: Zahumensky et al., 2022 and Vesela et al., 2023</p> </li> <li> <p>"Results tables" from the Fiji based analysis</p> </li> <li> <p>Outputs of the processing of these Results tables using our R scripts, in the form of summary tables, graphs, and statistical analyses</p> </li> </ul>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
<p>Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.</p> <p>The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user's system or to familiarize oneself with the pipeline.</p>
An Image-Based Gamut Analysis of Translucent Digital Ceramic Prints for Coloured Photovoltaic Modules: Supplementary Data
<p>Colouring the frontglass of PV modules via digital ceramic printing aids in concealing the PV when integrated into existing building façades as BIPV, while admitting sufficient light to produce electricity. This promotes the visual acceptance and adoption of PV as a source of renewable energy in urban environments. The effective colour of the PV laminate is a combination of the transparent colour on glass and the colour of the PV cells. This colour should ideally match the architect’s visual expectations in terms of fidelity, but also in terms of relative PV efficiency as a function of print density. In practice, these requirements are often contradictory, particularly for vivid colours, and the visual results may deviate significantly. This paper presents an objective analysis of how colours appear on PV frontglass laminated with a PV module, using an image-based colour acquisition process. Given a set of 1044 nominal colours uniformly distributed in the RGB colour space, each printed in 10 opacities, we quantify the range of effective colours observed when printed on glass and combined with PV, and their deviation from the nominals. Our results confirm that the effective colour gamuts are significantly constrainted and skewed, depending on the ink volume and glass finish used for printing. In particular, blue-magenta hues cannot be reliably rendered with this process. These insights can serve as guidelines for selecting target colours for BIPV that can be well approximated in practice.</p>
Rawdata of a bibliometric analysis of liver fibrosis magnetic resonance imaging research from 1998 to 2017
<p>The details of all literature downloaded from WoSCC as plain-text files including the full records.</p>
Confocal imaging raw data files of autophagy analysis in optn and p62 zebrafish mutants during Mycobacterium marinum infection
<p>Association of fluorescent Mycobacterium marinum bacteria with Ubiquitin immunolabelling and GFP-Lc3 signal in zebrafish larvae carrying mutations in the selective autophagy receptors optnineurin and p62. Data deposited are Leica LIF files belonging bioRxiv 415463; doi: https://doi.org/10.1101/415463</p>
Microscope Image Analysis Course Sept 2109 -- Image Data siRNA Screen
<p>Contains 380 Fluorescence images of DAPI stained HeLa nuclei, acquired in 42 wells of a 384 well plate. individual wells were treated with siRNA according to the layout file. Images were acquired with an Olympus ScanR system at 10x magnification, maetadata is given in the experiment_descriptor.xml file.</p>
2 experiments of water infiltration investigated with an image analysis method
<p>suplementary material for experiments described in the following article:</p> <p>B. Belfort, S. Weill, M. Fahs and F. Lehmann. 2019. Laboratory Experiments of Drainage, Imbibition and Infiltration under Artificial Rainfall Characterized by Image Analysis Method and Numerical Simulations. Water MDPI Journal.</p>
Dataset for image analysis of LCDV susceptibility in sea bream
<p>This dataset comprises the photographs used to carry out the genetic estimates of LCDV susceptibility in sea bream. The numbers 1-499 correspond to batch1 and 500-100 to batch2</p>
Computer Vision-Based Image Analysis
<p>Computer Vision-Based Image Analysis Tool is an innovative tool analysing the colour change of the foods in the fridge to correlate this information to the food loss and waste at household level. This tool deals with colour measurement from the 2D image of 3D fresh fruit and vegetable. The system takes the pictures of the selected fruit or vegetable, analyses the image and detects the colour-based deterioration, if any. The deterioration is calculated as area percentage. Finally, this information is correlated to the weight of these fruits or vegetables. This technology will also be adapted to the meat supply chain to evaluate the meat quality according to their colour for further processing.</p>
Preliminary data and analysis from ultrafast calcium or voltage imaging recordings at 8-bits resolution using a Kinetix camera
<p><span>This dataset was obtained from brain slices of the mouse. Data are from transversal hippocampal slices from </span><span>30-40 postnatal days old C57Bl6 mice (of both genders), stained with the Ca<sup>2+</sup> indicator Fluo-4 AM; or from layer-5 pyramidal neurons loaded intracellularly either with the Ca<sup>2+</sup> indicator Oregon Green BAPTA-5N or with the voltage sensitive dye JPW1114. <span> </span>Details are in the Read_me file. This dataset cannot be used for publications without permission of the contact person.</span></p>
Introduction to Bioimage Analysis using QuPath (supporting image)
<p>Example image supporting the workshop "Introduction to Bioimage Analysis using QuPath", in-person at HMS.</p>
Content analysis of a sample of images about climate change on Twitter
<p>We carried out a content analysis of images (photographs, illustrations and graphics) posted on Twitter, during five randomly selected weeks between 28 November 2019 and 29 November 2020. The random process of selecting five weeks, performed using the website random.org, yielded the following weeks: 3, 11, 22, 32 and 45. These weeks correspond to the following dates:</p> <p>Week 3: from 11 to 17 November 2019</p> <p>Week 11: from 6 to 12 January 2020</p> <p>Week 22: from 23 to 29 March 2020</p> <p>Week 32: from 8 to 14 June 2020</p> <p>Week 45: from 7 to 13 September 2020</p> <p>The sample was selected using the Twitter API (twitter.com) by selecting the “top tweets” that included photos or videos and were posted during the periods mentioned. The sample was chosen on 30 January 2021. We considered that the time interval between the tweet dates and the date the sample was chosen allowed enough time for each image to reach its full interaction potential.</p> <p>The searches carried out using the Twitter API were as follows:</p> <p>1. “climate change” since:2019-11-25 until:2019-11-17 filter:media</p> <p>2. “climate change” since:2020-01-12 until:2020-01-06 filter:media</p> <p>3. “climate change” since:2020-03-29 until:2020-03-23 filter:media</p> <p>4. “climate change” since:2020-06-14 until:2020-06-08 filter:media</p> <p>5. “climate change” since:2019-09-13 until:2019-09-07 filter:media</p> <p>Each of these searches yielded a result of between 90 and 100 tweets. The results were saved on a spreadsheet and all of the fixed images were selected (photographs, graphs, illustrations, etc.). When several images appeared on the same post, we considered each one of them independently. Besides the images, we saved the following information for each tweet: date, user, number of likes, number of retweets, number of comments and text associated with each tweet. The interactions (number of likes, number of retweets and number of comments) were considered indicators of interest in the content of the message and therefore an indication of the potential of that image (along with the text associated) to foster public involvement in climate change.</p> <p>Of the 419 total images included in the initial sample, 39 contained text only (the image showed only a sign, press cutting or similar), so these were excluded, leaving a final sample (n) of 380 images.</p> <p><em>Coding</em></p> <p>After putting the selected images in chronological order in a database, we developed the codebook based on examples from previous studies. To classify the types of images, we used the classification system proposed by O’Neill (2017) for the most common images in traditional media:</p> <p>- Identifiable people: i.e. politicians, businesspeople and celebrities.</p> <p>- Non-identifiable people.</p> <p>- Impacts of climate change: i.e. episodes of extreme weather, ice melting, desertification and endangered animal species.</p> <p>- Energy, emissions and pollution: i.e. factory smokestacks, renewable energy sources and traffic.</p> <p>- Protests: i.e. demonstrations and other protest actions.</p> <p>- Scientific images: i.e. graphics on greenhouse gas emissions and maps of global warming.</p> <p>- Other images.</p> <p>Basing our work on the principles outlined by Climate Visuals (2018), we propose seven factors that lend effectiveness to images as a means to foster climate change engagement:</p> <p>- Showing real people, avoiding staged images. Images that show people expressing identifiable emotions are especially effective. Politicians, due to their low credibility and the fact that they’re perceived as not being authentic, are not very effective.</p> <p>- Telling stories. Images that tell a story by themselves, especially the newest ones, tend to be more effective at fostering public involvement.</p> <p>- Showing the causes of climate change on the appropriate scale. For example, showing a gridlocked motorway could be more effective than showing a single driver. Images that show individual behaviour (such as eating meat) can trigger defensive reactions and may not be effective.</p> <p>- Showing powerful climate impacts. For example, floods and the effects of extreme weather, which can have a huge emotional impact.</p> <p>- Showing solutions. The levels of involvement and the ideology determine the response to the images. However, images that show “solutions” to climate change tend to generate positive emotions.</p> <p>- Establishing local connections. It’s a good idea to use images that connect climate change with a local environment. However, at the same time, they should connect with the problem on a global level.</p> <p>- Showing people who are directly affected. Although images of protests tend to generate scepticism among most observers, protests by people who are directly affected by climate change are usually perceived as more authentic and emotionally moving.</p> <p> </p> <p> </p>
Images, data, and statistical analysis scripts for review article on cover crop roots
<p>Images, data, and statistical analysis scripts for review article on cover crop roots.</p> <blockquote> <p><strong>Optimization of root traits to provide enhanced ecosystem services in agricultural systems: a focus on cover crops</strong> - [<a href="https://doi.org/10.1111/pce.14247">https://doi.org/10.1111/pce.14247</a>]</p> </blockquote> <ul> <li>Research site, planting, and growth <ul> <li>10/2020 - 04/26/2021 cover crop field trial. DDPSC FRS at Planthaven Farm, O'Fallon, MO 63366 (latitude 38.848240°, longitude -90.686640°). </li> <li>The field was tilled before sowing of cover crops. Seed for each cover crop were spread in using a push seed spreader and were lightly irrigated.</li> <li>Alfalfa (<em>Medicago sativa</em>), dundale pea (<em>Pisum sativum</em>), milkvetch (<em>Astragalus canadensis</em>, <em>Astragalus bisulcatus</em>), crimson clover (<em>Trifolium incarnatum</em>), hairy vetch (<em>Vicia villosa</em>), mustard (<em>Brassica junce</em>a var Mighty Mustard, var Kodiak), barley (<em>Hordeum vulgare</em>), wheat (<em>Triticum aestivum</em>, winter, spring), winter rye (<em>Secale cereale</em>), and triticale (× T<em>riticosecale</em> Wittmack).</li> </ul> </li> <li> <p>Field harvest measurements</p> <ul> <li> <p>Four canopy images were taken across each cover crop row using a Canon 5DS R camera. Images were taken from above each plot at 5ft height manually. Green color was thresholded from the canopy images in batch using OpenCV python script and the percent green cover calculated (Jupiter notebook).</p> </li> <li> <p>Five soil monoliths were excavated using a "shovelomics" approach with an average monolith size of 25.4cm x 25.4cm x 20 cm. The remaining four soil monoliths were destructively analyzed.</p> </li> <li> <p>One soil monolith was imaged using a Canon 50D DLSR camera in a photogrammetry shed. All photogrammetric analysis was conducted using Pix4D mapper software (Pix4D S.A. Prilly, Switzerland), and point cloud cleaning was conducted in CloudCompare V2. 10.2.</p> </li> <li> <p>Cover crop shoots from the remaining soil monoliths were cut and placed into a paper bag for dry biomass determination (60oC for 5 days). A cover crop shoot count was conducted for each monolith with each tiller considered as a shoot for the grasses (barley, wheat, triticale). After cover crop shoot harvesting, a photo was then taken of each soil monolith with remaining weed biomass. A weed score was assigned to each image by one trained researcher with a score 1 low weeds to 5 high weed presence.</p> </li> <li> <p>Soil monoliths were the soaked briefly in water and then the soil washed using a hose keeping the roots. Roots were then scanned on an Epson Expression 12000XL Photo Scanner with transparency unit. Images labeled with "_part" were samples with too many roots for scanning and so were separately weighed. Dry root biomass was taken for the scanned and unscanned roots separately. Root length was determined from images using software RhizoVision Explorer (https://doi.org/10.5281/zenodo.4095629), total root length was estimated using scanned root length and scanned dry biomass with unscanned root biomass.</p> </li> <li> <p>Along each cover crop plot a 10ft trench was dug using a Yanmar Excavator Vi020-6 perpendicular to the row with each trench fully bisecting the plot. Trench was one bucket wide (19 inches) and approximately 36 inches deep in the middle of the row. The five deepest roots that could be observed in the trench wall was measured manually with a tape measure for each cover crop. A garden trowel and shovel were used to excavate and confirm roots in trench wall.</p> </li> <li> <p>Data was analyzed using R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R). PCA analysis was conducted using the “FactoMineR” package (Husson <em>et al</em>. 2019) to explore the relationships between the traits within the dataset and clustered by family.</p> </li> </ul> </li> </ul> <p>Individual ZIP file contents:</p> <ul> <li><code><strong>2021PlantHavenCovercrop_CanopyImages.zip</strong></code> – Raw canopy images, processed percent green cover images, and Jupiter notebook python script (2021PlantHavenCovercrop_ImageBatchColorThreshold.ipynb).</li> <li><code><strong>2021PlantHavenCovercrop_RootFlatbedImages.zip</strong></code> – Raw flatbed root scans of cover crops and processed images using RhizoVision Explorer.</li> <li><code><strong>2021PlantHavenCovercrop_SoilMonolithWeedImages.zip</strong></code> – Images of soil monoliths after cover crop shoot biomass was removed.</li> <li><code><strong>2021PlantHavenCovercrop_dataprocessing.zip</strong></code> – R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R).</li> <li><code><strong>2021PlantHavenCovercrop_ShootPhotogrammetry.zip</strong></code> – 3D models of cover crop shoots from excavated soil monoliths. The .bin files can be opened using CloudCompare app.</li> </ul> <p> </p> <p> </p>
Accuracy of ultrasonography and magnetic resonance imaging for preoperative staging of cervical cancer – analysis of patients from the prospective study on total mesometrial resection.
<p>This is supplementary material to the manuscript published in the Diagnostics (MDPI) journal. </p> <p>Diagnostics 2021, 11, 1749</p> <p>https://doi.org/10.3390/diagnostics11101749</p> <p>Videos present examples of ultrasound examinations of patients with cervical cancer.</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.