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38,240 results for “Imaging”
Pl@ntNet-300K image dataset
<p>This paper presents a novel image dataset with high intrinsic ambiguity and a long-tailed distribution built from the database of Pl@ntNet citizen observatory. It consists of 306146 plant images covering 1081 species. We highlight two particular features of the dataset, inherent to the way the images are acquired and to the intrinsic diversity of plants morphology:</p> <p> (i) the dataset has a strong class imbalance, i.e. a few species account for most of the images, and,</p> <p> (ii) many species are visually similar, rendering identification difficult even for the expert eye.</p> <p> These two characteristics make the present dataset well suited for the evaluation of set-valued classification methods and algorithms. Therefore, we recommend two set-valued evaluation metrics associated with the dataset (macro-average top-k accuracy and macro-average average-k accuracy) and we provide baseline results established by training deep neural networks using the cross-entropy loss.</p> <p>A full description of the dataset as well as baseline experiments can be found in the following publication:</p> <p>"<a href="https://openreview.net/forum?id=eLYinD0TtIt">Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution</a>", Camille Garcin, Alexis Joly, Pierre Bonnet, Antoine Affouard, Jean-Christophe Lombardo, Mathias Chouet, Maximilien Servajean, Titouan Lorieul and Joseph Salmon, in Proc. of Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track, 2021.</p> <p> Please cite the above reference for any publication using the dataset.</p> <p>Utilities to load the data and train models with pytorch can be found here: <a href="https://github.com/plantnet/PlantNet-300K/">https://github.com/plantnet/PlantNet-300K/</a></p>
DeepOrchidSeries: A Sentinel-2 Dataset to inform convolutional SDMs with twelve-month Sentinel-2 image time-series, Orchid family
<p><strong>Deep Species Distribution Modelling from Sentinel-2 Image Time-series: a Global Scale Analysis on the Orchid Family</strong> </p> <ul> <li><strong><em>DeepOrchidSeries</em></strong> dataset gathers Sentinel-2 image time-series around geolocated orchid occurrences. Seasonal evolutions of the habitats are captured in the twelve-month RGB/IR time-series with 640x640m spatial resolution. It allows novel Species Distribution Models (SDMs) coupled with convolutional networks to take advantage of both spatial and temporal information.</li> <li>Our <strong>associated article</strong> is describing the modeling choices made to shape this ambitious dataset. It is submitted to <a href="https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence">https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence</a>. We believe such global data, methods and scripts are valuable to the conservation ecology community and especially deep-SDMs users. To our knowledge, no similar ready-to-use dataset is available. In the article, the dataset's temporal dimension is proven to significantly improve SDMs performances.</li> <li><strong><em>sen2patch</em></strong> is the gitlab project gathering the code to create such dataset. It is available at <a href="https://gitlab.inria.fr/jestopin/sen2patch">https://gitlab.inria.fr/jestopin/sen2patch</a>.</li> <li><strong><em>DeepOrchidSeries.csv</em></strong> contains all occurrences-level information. <ul> <li>We advice to load it with: <pre><code class="language-python">import pandas as pd df = pd.read_csv("path/to/DeepOrchidSeries.csv", sep=';') df.columns ['gbifid', 'canonical_name', 'decimallatitude', 'decimallongitude', 'speciesKey', 'cell_index', 'bot_country', 'bot_code', 'lvl2_code', 'continent_code']</code></pre> <ul> <li>'gbifid' is the occurrences GBIF ID</li> <li>'canonical_name', is the species canonical name</li> <li>'decimallatitude', 'decimallongitude' are the species coordinates in decimal degrees</li> <li>'speciesKey' is the species GBIF unique identifier</li> <li>'cell_index' is a unique cell ID in a 0.0025° lon/lat grid partitioning the Earth (used to stratify train/val/test set by geographic blocks)</li> <li>'bot_country', 'bot_code', 'lvl2_code', 'continent_code' are geographic subdivisions defined in <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a> (code and string for WGSRPD level 1, the botanical countries)</li> </ul> </li> </ul> </li> <li> <p>Initial <a href="https://www.gbif.org/">GBIF</a> query DOI is <a href="http://https://doi.org/10.15468/dl.4bijtu">https://doi.org/10.15468/dl.4bijtu</a> (26 August 2019).</p> </li> <li><strong><em>DeepOrchidSeries.tar</em></strong> file contains the satellite image time-series and is available at <a href="https://lab.plantnet.org/deeporchidseries/">https://lab.plantnet.org/deeporchidseries/</a> <ul> <li><em>.tar</em> archive measure 286 GB and extends to 432 GB once decompressed.</li> <li>Image time-series relative tree paths are constructed from the occurrences unique GBIF IDs.</li> <li>For a given occurence <em>gbifid</em>, matching patches are located in: <em>final_dataset_by_gbifid/gbifid[-2:]/gbifid[-4:-2]</em>, <em>i.e.</em> in a first folder named with the <em>gbifid</em> last two numbers and a subfolder with the previous two ones. Example: the time-series files matching occurrence 2236837714 are located at <em>final_dataset_by_gbifid/14/77/</em>. </li> <li>Image time-series are composed of twelve 16 bits RGB <em>.png</em> and twelve 16 bits IR <em>.png</em> files containing data identical to the original L1C products, no lossy compression was made. There are one RGB and one IR .png file per month.</li> <li>Patches from month MM/YYYY of occurrence <em>gbifid</em> are named<em> </em><em>RGB_YYYY_MM_gbifid_.png</em> and <em>IR0_YYYY_MM_gbifid_.png</em>.</li> </ul> </li> <li><em><strong>models.zip</strong></em> is the archive containing the four PyTorch models weights described in our article and<strong><em> </em></strong><em><strong>inception_env.py</strong></em> the used Inception V3 architecture. <em><strong>index.json</strong></em> contains the dictionnary linking the models class indexes from 0 to 14128 with our labels <em>speciesKey</em>: {"class_index":speciesKey}.</li> </ul> <p> </p> <ul> <li><strong>ACKNOWLEDGMENTS</strong>: We warmly thank Alexander Zizka et al. for providing us the geographically and taxonomically curated set of Orchids occurrences. This dataset contains modified Copernicus Sentinel data and Copernicus Service information (2018). Sentinel-2 MSI data used were available at no cost from ESA Sentinels Scientific Data Hub.</li> </ul>
Annotations to direct and indirect image rotation estimation methods of orthopedic X-ray images
<p>The annotation file contains labels for AP wrist images of the MURA dataset on the center line of the radius bone. The annotations are stored in json format. For each annotated image file of the MURA dataset an entry is provided with the coordinates of the start and end point of the radius' center line.</p>
In situ Bragg Coherent X-ray Diffraction Imaging of Corrosion in a Co-Fe alloy microcrystal
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "<em>In situ</em> Bragg coherent X-ray diffraction imaging of corrosion in a Co–Fe alloy microcrystal" published in CrystEngComm, 24(7), 1334-1343, on 18/01/2021. </p> <p><a href="https://doi.org/10.1107/S1600577520016264">https://doi.org/10.1107/S1600577520016264</a></p>
Twitter dataset of flood-related images for September 2021, Thailand and June/July 2021, Nepal floods
<p>Twitter dataset related to flood events onsets in Thailand and Nepal, focused on September 26/27, 2022, June 16/17 2021 and July 01/02 2021. The dataset has been processed with a VisualCit pipeline in order to automatically filter a relevant subset of posts through automated image analysis, using deep learning techniques. The posts were then geolocated using the CIME algorithm. Additional information about the data collection and data processing are described in <a href="http://arxiv.org/abs/2202.12014">http://arxiv.org/abs/2202.12014</a></p>
Near-field images and cross-section of guided modes in a laser-inscribed double-tracks waveguide in TZN:Ag glass sample
<p><strong>Raw images were captured</strong> with a Thorlabs beam monitoring camera, while the waveguides were injected at 633 nm.<br> The fours cross-sections were computed from these raw images.<br> These files are new data from the co-authors among those presented in the review publication "Materials 2020, 13, 3846" (DOI: 10.3390/ma13173846.<br> <strong>Extracted, centered and scaled horizontal cross-sections are given in "Fig12-b-c_final.xlsx"</strong></p> <p>Sample name : TZN:Ag.</p> <p> </p>
Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines
<p>This is a dataset of nuclei (DAPI), and 3 multiplexed DNA imaging cycles to test and validate processing software packages, such as pyHiM (https://github.com/marcnol/pyHiM). This dataset was acquired in a nc14 Drosophila embryo.</p> <p>File contents:</p> <p>scan_001_RT27_001_ROI_converted_decon_ch00.tif barcode 27, fiducial <br> scan_001_RT27_001_ROI_converted_decon_ch01.tif barcode 27<br> scan_001_RT29_001_ROI_converted_decon_ch00.tif barcode 29, fiducial <br> scan_001_RT29_001_ROI_converted_decon_ch01.tif barcode 29 <br> scan_001_RT37_001_ROI_converted_decon_ch00.tif barcode 37, fiducial <br> scan_001_RT37_001_ROI_converted_decon_ch01.tif barcode 37 <br> scan_006_DAPI_001_ROI_converted_decon_ch00.tif DAPI <br> scan_006_DAPI_001_ROI_converted_decon_ch01.tif DAPI, fiducial <br> scan_006_DAPI_001_ROI_converted_decon_ch02.tif RNA</p> <p> </p> <p>To test this dataset please refer to <a href="https://github.com/marcnol/pyHiM">pyHiM documentation page</a>.</p>
NAAMES_IFCB_diatoms_image_features
<p>This text file (NAAMES_IFCB_diatoms_image_features.txt) contains data on all Imaging FlowCytobot (IFCB) images identified as diatoms (either individual cells or chains) during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES). Data are from the western North Atlantic, 2015-2018; see Behrenfeld et al. (2019) <em>Frontiers in Marine Science </em>for expedition details. The images were collected using an IFCB deployed onboard the ship and connected to the flowing sea-water system with intake at ~5 m depth. The images were classified and the data here identified as diatoms using a convolutional neural network with 90% accuracy. See https://github.com/emmettFC/selected-projects/blob/master/plankton_vision/README.md for details of the neural network development and testing. Data are used in the manuscript "Plankton Imagery Data Inform Satellite-Based Estimates of Diatom Carbon", Geophysical Research Letters, 49, e2022GL098076. https://doi. org/10.1029/2022GL098076. Additional data from the NAAMES expedition are available at https://seabass.gsfc.nasa.gov/naames.</p>
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol
<p><strong>Image 1.tif is a Brain slice</strong>. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.</p>
Bridging the gap between single nanoparticle imaging and global electrochemical response by correlative microscopy assisted by machine vision
<p>The data in this repository corresponds to experimental data: linear sweep voltammetry, optical movie and the database of the SEM images. They support the findings of a study discussed in the article by Godeffroy et al. published in Small Methods with the doi: http:/doi.org/10.1002/smtd.202200659. The data analysis to reproduce the results presented in the article has been carried out by homemade Python program routines also provided in this repository. The descirption of each routine is also provided in a text file.</p>
Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - images
<p>This resource contains images that are framegrabs from video recorded by submarine deployed by the MY Arctic Sunrise during their Antarctica expeditions. The first took place in 2018 and focused within the Gerlache Strait and along the western Antarctic Peninsula and the Antarctic Sound in January 2018. Dives were conducted beginning 19th to 27th January 2018. This resource supplement the images for “Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - data”</p>
Near infrared imaging data from brown rot decayed wood
<p>This dataset contains near infrared imaging data from the following publication: Belt, T.; Awais, M.; Mäkelä, M. (2022) Chemical characterization and visualization of progressive brown rot decay of wood by near infrared imaging and multivariate analysis. Frontiers in Plant Science 13:940745. DOI: 10.3389/fpls.2022.940745. Details on the samples, the decay test, and the image collection parameters can be found in the publication.</p> <p>The “Sample IDs and mass losses.cvs” file contains the sample ID and mass loss due to decay of each sample in the dataset. The “C puteana.mat” and “R. placenta.mat” files contain the near infrared imaging data of samples exposed to the fungus <em>Coniophora puteana</em> and the fungus <em>Rhodonia placent</em>a, respectively, organised into cell arrays of sample IDs and corresponding image files. To generate the image files, a region of interest of 551 x 384 pixels was selected from the raw image files to produce an image that contains the sample surrounded by background. The spectral data were then converted to reflectance and corrected using the calibration reflectance target values.</p>
Multiplexed DNA-FISH imaging dataset, drosophila embryos, nuclear cycles 11-14
<p>Multiplexed DNA-FISH imaging dataset from Drosophila embryos at nuclear cycles 11-14.</p> <p>Examples on how to load and use this dataset can be found at this <a href="https://github.com/NollmannLab/Goetz_etal">GitHub repository</a>.</p> <p><strong>Data processing details</strong></p> <p>Barcodes were segmented using a neural network (<a href="https://github.com/stardist/stardist"><em>stardist</em></a>) specifically trained for the detection of 3D diffraction limited spots produced by our microscope. To extract the position of the barcode with sub-pixel accuracy, a subsequent 3D Gaussian fit of the regions segmented by <em>stardist</em> was performed with Big-FISH (<a href="https://github.com/fish-quant/big-fish">https://github.com/fish-quant/big-fish</a>). Barcode localizations with intensities lower than 1.5 times that of the background were filtered out.</p> <p>Nuclei were segmented from projected DAPI images using <em><a href="https://github.com/stardist/stardist">stardist</a> </em>with a neural network trained for detection of nuclei from <em>Drosophila</em> embryos under our imaging conditions. Barcodes were then attributed to single nuclei by using the XY coordinates of the barcodes and the DAPI masks of the nuclei. Finally, pairwise distance matrices were calculated for each single nucleus.</p> <p><strong>Processed data in Figures</strong></p> <p>This new version of the dataset contains the raw data for each of the figures in the manuscript:</p> <p><strong>Associated publication</strong></p> <p><strong>Multiple parameters shape the 3D chromatin structure of single nuclei at the doc locus in </strong><em>Drosophila</em>.</p> <p>Markus Götz, Olivier Messina, Sergio Espinola, Jean-Bernard Fiche, Marcelo Nollmann</p> <p>Nature Communications (2022).</p>
Multiplexed fluorescence imaging based on cycles, raw and processed data.
<p>This dataset was created from a larger acquisition in order to provide an example of reasonnable size, as a companion data set to the F1000Research paper preprint DOIXXX.</p> <ul> <li>The original raw data including metadata files are included in <strong>Microscope_Output.zip.</strong></li> <li><strong>Experiment.json</strong> and<strong> channelnames.txt </strong>are the ones generated by the acquisition software. They are the only files needed when starting from one of the processed data set below.</li> <li>The deconvolution obtained with the commercial software Microvolution is also provided in <strong>bu_deconvolution.zip.</strong> To start from Step 1(Extended Depth of Field) instead of Step 0 (deconvolution), unzip this file in your output directory and rename the folder bu_deconvolution to out.</li> <li>The extended field of view 2D images created from step 0 to step 2, provided for convenince in <strong>edfonly.zip</strong></li> <li>The final files generated by trhe Multiplex processor, including the segmentation mask , are provided in<strong> finaloutput.zip</strong>. These files can be used in a specific analysis software.</li> </ul> <p> </p>
Thermal Bridges on Building Rooftops - Hyperspectral (RGB + Thermal + Height) drone images of Karlsruhe, Germany, with thermal bridge annotations
<p><strong>Overview:</strong></p> <p>The dataset of <strong>Thermal Bridges on Building Rooftops (TBBR dataset)</strong> consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000x4000 pixels, aligned, and cropped to <strong>2680x3370</strong> to remove empty borders. See the "Usage" section below for details about the stored formats made available here.</p> <p>The raw images for our dataset were recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p>The dataset contains <strong>926 images</strong> with a total of <strong>6,927 annotations</strong> of thermal bridges on rooftops, split into train and test subsets with 723 (5,614) and 203 (1,313) images (annotations), respectively. The annotations only include thermal bridges that are visually identifiable with the human eye. Because of the aforementioned image overlap, each thermal bridge is annotated multiple times from different angles.</p> <p>For the annotation of the thermal images the image processing program <em>VGG Image Annotator </em>from the Visual Geometry Group, version 2.0.10, was used. The thermal bridge annotations are outlined with polygon shapes. These polygon lines were placed as close as possible but outside the area of significant temperature increase. If a detected thermal bridge was partially covered by another building component located in the foreground, the thermal bridge was also marked across the covering in case of minor coverings. Adjacent thermal bridges, which affect different rooftop components, were annotated separately. For example, a window with poor insulation of the window reveal located in the area of a poorly insulated roof is annotated individually. There is no overlap between annotated areas. While each image contains annotations, they also include thermal bridges present that are not annotated.</p> <p><strong>Usage:</strong></p> <p>Each compressed archive file represents one of the six flight paths. For the related publication the final path (Flug1_105Media) was used as a hold-out test sample. The archives contain Numpy files (one per image) of shape (2680, 3370, 5), where the final dimension is the colour channel in the format [B, G, R, Thermal, Height].</p> <p>Archives were compressed using <a href="https://facebook.github.io/zstd/">ZStandard</a> compression. They can be decompressed in a terminal by running e.g.</p> <pre><code class="language-bash">tar -I zstd -xvf Flug1_105Media.tar.zst</code></pre> <p>these will be decompressed into the file structure:</p> <pre><code>images/ └── Flug1_105Media/ └── DJI_0004_R.npy └── DJI_0006_R.npy └── ...</code></pre> <p>Corresponding annotations are provided in the COCO JSON format. There is one file for training (Flug1_100Media - Flug1_104Media blocks) and one for test (Flug1_105Media block). They contain a single class (thermal bridge) and expect the folder structure shown below.</p> <p>Note: The annotation files contain <em>relative</em> paths to numpy files, in case of problems please convert to <em>absolute</em> paths (i.e. insert the containing directory before each file path in the JSON annotation files).</p> <p>We provide the <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet"><strong>TBBRDet software</strong></a> which includes a dataloader and dataset inspection tools which make use of the <a href="https://github.com/facebookresearch/detectron2">Detectron2</a> and <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> libraries.</p> <p>We recommend the following folder structure for use:</p> <pre><code>├── train/ │ ├── Flug1_100-104Media_coco.json │ └── images/ │ ├── Flug1_100Media/ │ │ ├── DJI_XXXX_R.npy │ │ └── ... │ ├── ... │ └── Flug1_104Media/ │ ├── DJI_XXXX_R.npy │ └── ... └── test/ ├── Flug1_105Media_coco.json └── images/ └── Flug1_105Media/ ├── DJI_XXXX_R.npy └── ...</code></pre> <p><strong>Metadata:</strong></p> <p>The experimental metadata was structured with the <strong>Spatio Temporal Asset Catalog (STAC)</strong> specification family. This specification provides a standardized way for describing geospatial assets. It defines related JSON object types of Item, Catalog, and Catalog, extending on Collection as the basis.</p> <p>One STAC Collection JSON object provides information about the recorded images and the environmental conditions during recordings. It also contains information about the overall bounding box of the entire area in which images were recorded.</p> <p>This object links to related STAC Item JSON objects containing information about the recorded city blocks and the cameras. The objects for the city blocks contain the GeoJSON geometry of the respective block and the<br> corresponding bounding box. The objects containing the camera information are based on an existing STAC extension for camera related metadata.</p> <p>Metadata of the archived NumPy files for each image was structured using the <strong>Data Package</strong> schema from the <strong>Frictionless Standards</strong>. This standard describes a collection of data files. Therefore, metadata about all containerized NumPy files of the six flight paths (Flug1_100Media - Flug1_104Media blocks and Flug1_105Media block) is provided within a JSON-based file.</p> <p>Note that <strong>camera1</strong> corresponds to the <strong>RGB camera</strong> and <strong>camera2</strong> the <strong>thermal</strong>.</p> <p><strong>FAIR Digital Objects:</strong></p> <p>All files are represented in a standardized way as <strong>FAIR Digital Objects<br> (FAIR DOs)</strong> to enable machine actionable decisions on the data in spirit of<br> the FAIR principles.</p> <p><strong>Persistent Identifier (PID):</strong></p> <p>Persistent Identifiers (PIDs) are resolvable with the <a href="https://hdl.handle.net/">Handle.Net Registry (HNR)</a>.</p> <table> <thead> <tr> <th scope="col">File</th> <th scope="col">Persistent Identifier (PID)</th> </tr> </thead> <tbody> <tr> <td>Flug1_100-104Media_coco.json</td> <td>21.11152/6ea60288-d895-414e-80c0-26c9fdd662b2</td> </tr> <tr> <td>Flug1_105Media_coco.json</td> <td>21.11152/58d43ddc-5e29-4980-8675-ae579b50a1e2</td> </tr> <tr> <td>Flug1_100.tar.zst</td> <td>21.11152/6858a0b5-cc60-40e9-afef-8c2dd8b35e8e</td> </tr> <tr> <td>Flug1_101.tar.zst</td> <td>21.11152/e670f510-7e00-4d3a-9b90-3bac7a7c069e</td> </tr> <tr> <td>Flug1_102.tar.zst</td> <td>21.11152/3ab9f444-05f6-445e-a691-62fae4021bea</td> </tr> <tr> <td>Flug1_103.tar.zst</td> <td>21.11152/365fd8cf-8e86-41b8-9d0e-b816fdd01d29</td> </tr> <tr> <td>Flug1_104.tar.zst</td> <td>21.11152/041a6111-644a-4617-afb3-3c421a88e8e3</td> </tr> <tr> <td>Flug1_105.tar.zst</td> <td>21.11152/f48bf4e7-3879-4216-8f64-45a060b8f658</td> </tr> <tr> <td>Flug1_100-105_frictionless_standards.json</td> <td>21.11152/7b58b3b5-75eb-4417-ac4d-abe025e159f6</td> </tr> <tr> <td>Flug1_collection_stac_spec.json</td> <td>21.11152/ba370aa3-6422-428c-9ff7-c2ef429df603</td> </tr> <tr> <td>Flug1_100_stac_spec.json</td> <td>21.11152/09cb76fc-b8cb-4116-a22a-68c5bdfa77b0</td> </tr> <tr> <td>Flug1_101_stac_spec.json</td> <td>21.11152/24a55398-b96b-43dd-b0fb-cd8ce302c7ce</td> </tr> <tr> <td>Flug1_102_stac_spec.json</td> <td>21.11152/721234ac-4b5a-4d02-9944-82a08ef2db35</td> </tr> <tr> <td>Flug1_103_stac_spec.json</td> <td>21.11152/ebaeb5bc-0514-47c9-bcd2-98f0253843d8</td> </tr> <tr> <td>Flug1_104_stac_spec.json</td> <td>21.11152/9854677c-77c5-4a0b-916b-57dd9ec20198</td> </tr> <tr> <td>Flug1_105_stac_spec.json</td> <td>21.11152/cfd0fc0e-f5ea-464e-a57f-28e882924860</td> </tr> <tr> <td>Flug1_camera1_stac-spec.json</td> <td>21.11152/976fcf28-f924-4a21-b53d-5d054ad8198d</td> </tr> <tr> <td>Flug1_camera2_stac-spec.json</td> <td>21.11152/37833c54-1d36-42e4-858d-831447122863</td> </tr> </tbody> </table>
Refinements for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation
<p>Here we present the final crystal reconstructions and analysis scripts for the paper titled "Refinement for Bragg coherent X-ray diffraction imaging: Electron backscatter diffraction alignment and strain field computation" published in Journal of Applied Crystallography, 55, 2022. Please see the README file for more information.</p>
Datasets for "Single-molecule and super-resolved imaging deciphers membrane behaviour of onco-immunogenic CCR5"
<p><strong>Flow cytometry</strong></p> <p>Modality / instrument: <em>Flow cytometer</em> <em>(CytoFLEX LX, Beckman Coulter)</em></p> <p>File format:<em> FCS + XIT (CytExpert, Beckman Coulter).</em></p> <p>Samples and acquisitions:</p> <p>Fluorescent fusions in live Chinese Hamster ovary (CHO) cells.</p> <table> <tbody> <tr> <td> <p><em>File</em></p> </td> <td> <p><em>Cell line</em></p> </td> <td> <p><em>Runs</em></p> </td> <td> <p><em>Cells counted</em></p> </td> </tr> <tr> <td> <p>CONTROL.fcs</p> </td> <td> <p>CHO wild-type</p> </td> <td> <p>1</p> </td> <td> <p>7000</p> </td> </tr> <tr> <td> <p>GFP-CCR5.fcs</p> </td> <td> <p>CHO-GFP-CCR5</p> </td> <td> <p>1</p> </td> <td> <p>7000</p> </td> </tr> <tr> <td> <p>Exp_20220916_1_GFP.xit</p> </td> <td> <p>N/A - metadata</p> </td> </tr> </tbody> </table> <p>Approx. size 6 MB</p> <p> </p> <p><strong>PaTCH microscopy images</strong></p> <p>Imaging modality / instrument: <em>Brightfield</em> + <em>PaTCH fluorescence microscopy</em></p> <p>Image format:<em> OME TIFF (16 bit) + MicroManager metadata files</em></p> <p>Microscope settings:</p> <p><em>488 nm triggered excitation; split red/green detection, cropped to green (GFP) channel only; 10 ms/frame laser exposure; 13.5 ms/frame-to-frame; 53 nm/px. Photometrics Prime95b CMOS.</em></p> <p>Samples and acquisitions:</p> <p>Fluorescent fusions of GFP-CCR5 receptor in live CHO cells imaged with and without 100 nM CCL5 ligand. Each subfolder corresponds to a field of view and contains one brightfield and one PaTCH acquisition of the same cell.</p> <table> <tbody> <tr> <td> <p>Folder</p> </td> <td> <p>Condition</p> </td> <td> <p>Fields of view</p> </td> </tr> <tr> <td> <p>AC6 CONTROL sc</p> </td> <td> <p>CCL5-</p> </td> <td> <p>11</p> </td> </tr> <tr> <td> <p>AC6 CCL5 sc</p> </td> <td> <p>CCL5+ (100 nM)</p> </td> <td> <p>10</p> </td> </tr> </tbody> </table> <p>Approx. size before compression: 14 GB</p> <p> </p> <p><strong>Structured illumination microscopy - volumetric stacks</strong></p> <p>Imaging modality / instrument: <em>SIM fluorescence microscopy (custom setup at NPL based on Olympus IX71)</em></p> <p>Image format:<em> OME TIFF (16 bit) with intrinsic metadata (voxel size)</em></p> <p>Microscope settings: <em>638 nm excitation; 60x/1.3 NA; Flash 4.0, Hamamatsu Photonics. For additional details see the reference below (Hunter et al, bioRxiv).</em></p> <p>Samples and acquisitions:</p> <p>Dylight 650-MC-5 labeled CCR5 receptor in fixed CHO-CCR5 cells, imaged with and without 100 nM CCL5 ligand. Each acquisition is of a unique field of view and contains one SIM reconstruction as an XYZ volumetric stack. ‘Basal membrane’ acquisitions consist of 5 slices at 200 nm z-intervals across the range of the basal membrane. ‘Whole cell' acquisitions are made up of 7 slices with 500 nm z-interval ranging from just below the basal membrane to just above the apical membrane. </p> <table> <tbody> <tr> <td>Folder</td> <td>Subfolder/condition</td> <td>Fields of view</td> </tr> <tr> <td>Basal membrane</td> <td>CCL5-</td> <td>5</td> </tr> <tr> <td>Basal membrane</td> <td>CCL5+ (100 nM)</td> <td>6</td> </tr> <tr> <td>Whole cells</td> <td>CCL5-</td> <td>5</td> </tr> <tr> <td>Whole cells</td> <td>CCL5+ (100 nM)</td> <td>8</td> </tr> </tbody> </table> <p>Approx. size before compression: 300 MB</p>
COCO, LVIS, Open Images V4 classes mapping
<p>This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.</p> <p>COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes.</p> <p>We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using <a href="https://wordnet.princeton.edu/">wordnet</a></p> <p>This repository contains the following files:</p> <ul> <li><em>coco_classes_map.txt</em>, contains the mapping for the 80 coco classes</li> <li><em>lvis_classes_map.txt</em>, contains the mapping for the 1460 coco classes</li> <li><em>openimages_classes_map.txt</em>, contains the mapping for the 601 coco classes</li> <li><em>classname_hyperset_definition.csv</em>, contains the final set of 1460 classes, their definition and hierarchy</li> <li><em>all-classnames.xlsx</em>, contains a side-by-side view of all classes considered</li> </ul> <p>This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet [Zhang et al. 2021] (trained on COCO dataset), Mask R-CNN [He et al. 2017] (trained on LVIS), and a <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4).</p> <p>This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form:</p> <blockquote> <pre>@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} } </pre> </blockquote> <p> </p> <p> </p> <p><em><strong>References:</strong></em></p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. <em>MMM 2022. Lecture Notes in Computer Science</em>, vol 13142. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-98355-0_52">https://doi.org/10.1007/978-3-030-98355-0_52</a></p> <p>[Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. <em>Journal of Imaging</em>, <em>7</em>(5), p.76.</p> <p>[Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In <em>Proceedings of the IEEE/CVF conference on computer vision and pattern recognition</em> (pp. 5356-5364).</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In <em>Proceedings of the IEEE international conference on computer vision</em> (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. <em>International Journal of Computer Vision</em>, <em>128</em>(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In <em>European conference on computer vision</em> (pp. 740-755). Springer, Cham.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em> (pp. 8514-8523).</p> <p> </p>
Ultrafast imaging recordings from the axon initial segment of neocortical layer-5 pyramidal neurons.
<p>This dataset contains imaging and whole-cell electrophysiological recordings from neocortical layer-5 pyramidal neuron from brain slices of the mouse.</p> <p>Electrophysiological recordings (at 20 kHz) are from the soma. Imaging data (10 kHz) are from lines along the axon initial segment (distal>proximal) with 500 nm pixel resolution. These correspond to:</p> <ul> <li>Sodium imaging (Figures 1 and S6).</li> <li>Voltage imaging (Figures 2,4,5,S4,S7)</li> <li>Calcium imaging (Figures 3,S3,S8).</li> </ul> <p>This dataset is used in the paper available online:</p> <p>Filipis L, Blömer LA, Montnach J, De Waard M, Canepari M. Nav1.2 and BK channels interaction shapes the action potential in the axon initial segment. bioRxiv, 2022. doi: 10.1101/2022.04.12.488116.</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.