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.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “image diversity”
Receptor exocytosis imaged with high temporal resolution for diverse receptor cargos
<p>Cells perceive and interact with their environment in part through the expression, activation, and regulation of receptors on their plasma membrane. These receptors are dynamically trafficked from the plasma membrane in a process called endocytosis and delivered to the plasma membrane via exocytosis. Different receptors take diverse routes through the cell before being delivered via exocytosis. The data in this project focuses on 3 prototypical plasma membrane receptors - the B2 adrenergic receptor, the µ opioid receptor, and the transferrin receptor. Using a pH-sensitive green fluorescent protein variant, we visualized these receptors in cells as they recycled to the plasma membrane. We subsequently hand-labeled a subset of the data in order to build an automated image analysis method that could be used to detect receptor exocytosis across diverse imaging conditions. This repository contains our primary microscopy data from these studies as well as the labeling for use in supervised machine learning.</p> <p>These data support <a href="http://arxiv.org/abs/2106.07623">Evans et al 2021</a> and subsequent publications.</p> <p><strong>Data Collection</strong><br> TIFF image stacks were collected using a Nikon Eclipse TiE Inverted Microscope using TIRF illumination with a solid state 488nm laser through a Nikon 60x/1.49NA TIRF objective and captured using an Andor iXon 897+ EMCCD camera. The camera was windowed to a 300x300 pixel view and images were collected with a 18.5ms exposures (~54Hz). Images were collected across two days, with two coverslips of each condition collected on day 1, and one coverslip collected on day 2.</p> <p><strong>DNA Constructs</strong><br> The 3 cargos imaged in these data are the transferrin receptor (TfR), the B2-adrenergic receptor (B2AR, B2), and the µ opioid receptor (MOR). Constructs encoding these receptors, tagged extracellularly with the ph-sensitive GFP variant Superecliptic pHluorin (SpH, <a href="https://www.cell.com/biophysj/fulltext/S0006-3495(00)76468-X">Sankaranarayanan et al. 2000</a>, have been previously described in <a href="http://www.nature.com/articles/nn1679">Yudowski et al. 2006</a> for B2AR, <a href="https://www.jneurosci.org/content/30/35/11703">Yu et al. 2010</a> for MOR, and <a href="https://www.molbiolcell.org/doi/10.1091/mbc.e08-08-0892">Yudowski et al. 2009</a> for TfR.</p> <p><strong>Cell Culture</strong><br> HEK293 cells were cultured in DMEM High Glucose (Hyclone) supplemented with 10% Heat Inactivated FBS (Gibco). Cells expressing B2 and MOR were stably selected from transient transfection using G418. Cells expressing TfR were transfected 3 days before the experiments presented here using Effectene following manufacturers' instructions. Before imaging, cells were transferred to 25mm diameter #1.5 glass coverslips (Electron Microscopy Sciences). Two days after plating, experiments began.</p> <p><strong>Imaging conditions</strong><br> Cells were imaged in L-15 minimal media supplemented with 1% FBS. For MOR and B2, cells were imaged for 1 minute at ~0.16Hz without perturbation. Then agonist was added (10µM DAMGO for MOR, 10µM isoproterenol for B2) to the media and cells were imaged for 5 minutes to ensure that receptors clustered and internalized. After internalization, cells were bleached with 100% laser power for 1 minute and then imaged at 54Hz to visualize exocytic events. exocytosis was captured for up to 20 minutes after initial treatment, one cell at a time. For TfR, a single frame was taken before bleaching to show receptor expression levels and then cells were bleached and imaged as described above.</p> <p><strong>Data blinding</strong><br> After collection, files were renamed as described in <em>map.md</em>. All metadata files and internalization imaging were separated into the 2 "extras" folders. The exocytosis movies were 'scrambled' to hide cargo identity using the included <em>scrambler.py</em> file. <em>OPP_scramble.log</em> described the mapping of scrambled filenames to the original imaging.</p> <p><strong>Human labeling</strong><br> A subset of the images (22, with roughly equal representation across cargos) were hand labeled for exocytic events. Images were viewed in FIJI <a href="https://www.nature.com/articles/nmeth.2019">Schindelin et al. 2012</a> nad played back at 0.5x. When exocytic events were identified by eye, the playback was paused and the appearance of an event was found through manual advancing of the frames of the movie. The event was labeled using the Cell Counter plugin. Each movie was watched twice to identify as many events as possible. Labeled events are saved a <em><movie-name>-ZYW-1.xml</em> in this dataset.</p> <p><strong>Data organization</strong><br> All exocytic event movies and any matching human labeling are included in this base directory. All internalization movies and all metadata for all movies are included in the Extras folder for the day that movie was recorded. Coverslip and cargo identity are listed in <em>map.md</em> and the ground truth for cargo identity is in <em>OPP_scramble.log</em></p>
Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols
<p>A database of organ dose coefficients using MC simulations for a clinically representative virtual population of adult and pediatric XCAT patient models over an expanded set of 21 exam protocols in digital tomosynthesis. </p>
BIFROST: A method for registering diverse imaging datasets
<p>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.</p>
Figure 7-13. Habitus images. 7 in Diversity and distribution of the scarab beetle tribe Phanaeini in the northern states of the Brazilian Northeast (Coleoptera: Scarabaeidae: Scarabaeinae)
Figure 7-13. Habitus images. 7) Coprophanaeus cyanescens (Olsoufieff, 1924) male specimen from Maranguape, São Benedito, Sanctuario da Nossa Senhora de Penha (CE). 8) Coprophanaeus acrisius (Macleay, 1819) male specimen from Santa Quiteria de Maranhão, Fazenda Rodiador (MA). 9) Coprophanaeus dardanus (Macleay, 1819) male specimen from Baturité, Uirapuru (CE). 10) Coprophanaeus (Metallophanaeus) pertyi (Olsoufieff, 1924) male specimen from Maranguape, Tabatinga (CE). 11) Diabroctis mimas (Linnaeus, 1767) male specimen from Maranguape, Piraponga (CE). 12-13) Phanaeus melibaeus Blanchard, 1843 male specimen from Santa Quiteria de Maranhão, Fazenda Rodiador (MA). Photographs by M.P.T. Gillett.
VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation
<p> VegAnn - Vegetation Annotation - dataset, a collection of 3795 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. </p>
BIFROST: A method for registering diverse imaging datasets
Open the record for dataset details and reuse information.
Data from: Robust mosquito species identification from diverse body and wing images using deep learning
Open the record for dataset details and reuse information.
Image 13 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 13. Industrialization in scrubland. The most important habitat for arachnids
Image 12 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 12. Waste land development: cultivation and farming
Image 11a. H in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 11a. H. phipsoni in a scrubland under a stone
Image 10. H in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 10. H. xanthopus - burrow of this fossorial species
Image 7 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 7. Hillslopes with boulders
Image 8 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 8. Eucalyptus plantation
Image 5 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 5. Black soil in mango orchard
Image 1 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 1. Loam on hilltop
Image 4 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 4. Grassy hilltop with stones
Image 3 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 3. Red and black soil in cropland
Image 2 in Diversity of scorpion fauna of Saswad-Jejuri, Pune District, Maharashtra, western India
Image 2. Scrubland with stones
Animal Diversity Web: ADW Traits & image objects from Quaardvark Tool
This morphometry and life history dataset for size characteristics of birds is from undergraduate students who contributed to the Animal Diversity Web. Traits and images are harvested using ADW__s Quaardvark data download tool.<p></p>
A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids from four different clones
<p>This dataset is presented in the paper <strong><span>A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids from four different clones</span></strong></p> <p> </p> <p>This dataset encompasses two sources of data:</p> <ol> <li>A comma-separated values ('CSV') file. This file serves as a key to our dataset with one image per row. Each image is represented by its image identifier ('img_id') with the format [org_id]_[clone]_d[imaging_day]_[imaging_lab]. For each image, the CSV file also specifies the organoid size for convenience. Alternatively, the organoid size can be calculated using the ground truth organoid segmentation (org_segGT). </li> <li>For each row of the CSV file, we provide the image and org_segGT. For Lab A, the images are in JPEG format. For lab B, the images are in TIF format. Org_segGT is a manually created binary 2D NumPy array with the same size as the image (1024 x 768 for lab A, 1388 x 1040 for lab B). A value of 1 in org_segGT at position (x, y) means that the same position (x, y) in the corresponding image is covered by the organoid. The image file and the org_segGT file have the following format: [img_id].[jpg|tif] and [img_id].npy. For day 12, organoids were imaged before and after embedding from 96-well plates in 12-well plates, allowing the investigation of well-specific optical properties.</li> </ol> <p>For segmentation and growth monitoring using this dataset, please see <a href="https://github.com/deiluca/robust_monitoring_organoid_growth">https://github.com/deiluca/robust_monitoring_organoid_growth</a>.</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.