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.
544
datasets available to search
ShareScore release 0.9.0
Dataset results
544 results for “HeLa cells”
Additional Data - Phototoxicity induced in living HeLa cells by focused femtosecond laser pulses
<p>Nonlinear optical microscopy is a powerful label-free imaging technology, providing biochem-ical and structural information in living cells and tissues. A possible drawback is photodamageinduced by high-power ultrashort laser pulses. Here we present an experimental study on thou-sands of HeLa cells, to characterize the damage induced by focused femtosecond near-infraredlaser pulses as a function of laser power, scanning speed and exposure time, in both wide-field andpoint-scanning illumination configurations. Our data-driven approach offers an interpretation ofthe underlying damage mechanisms and provides a predictive model that estimates its probabilityand extension and a safety limit for the working conditions in nonlinear optical microscopy. Inparticular, we demonstrate that cells can withstand high temperatures for a short amount of time,while they die if exposed for longer times to mild temperatures. It is thus better to illuminatethe samples with high irradiances: thanks to the nonlinear imaging mechanism, much strongersignals will be generated, enabling fast imaging and thus avoiding sample photodamage.</p>
Automatic labelling of HeLa "Kyoto" cells using Deep Learning tools
<p><strong>Name</strong>: Automatic labelling of HeLa “Kyoto” cells using Deep Learning tools</p> <p><strong>Data type</strong>: Microscopy images from the dataset “<strong>HeLa “Kyoto” cells under the scope</strong>”, Brightfield (BF), Digital Phase Contrast (DPC, either “raw” or “square-rooted”), Tubulin and H2B fluorescent channel, paired with their corresponding nuclei or cell/cyto label images.</p> <p><strong>Labels images</strong>: Labels images were generated using the script <em>“prepare_trainingDataset_cellpose.ijm</em>”.</p> <p>Briefly, for 5 defined time-points (1,10,50,100,150), channels of interest were duplicated, resaved and :</p> <p>- nuclei label images were obtained using <a href="https://github.com/stardist/stardist">StarDist</a> on H2B channel</p> <p>- cell label images were obtained using <a href="https://github.com/MouseLand/cellpose">Cellpose</a> on Tubulin and H2B channels</p> <p>A quick visual inspection of the resulting label images concluded that they were satisfying enough, despite certainly not being perfect.</p> <p>Notes :</p> <p>- This labelling strategy:</p> <p>o will not produce 100% accurate labels, but they might be more reproducible than labels generated by humans and are (definitely) much faster to obtain.</p> <p>o is <strong>NOT a recommended way of generating labels images</strong>, but for educational purposes.</p> <p>- The fluorescent channels are part of the dataset to ease the process of review of the labels and are NOT used for training. We generated the labels from the fluorescent channels to later predict labels from the BF or DPC channels only. As such, the fluorescent channels should not be “reused” with our labels during training.</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 540x540 (Pixel size: 0.299 nm)</p> <p> </p> <p><strong><em>NOTE</em></strong>: This dataset uses the “HeLa “Kyoto” cells under the scope” dataset (<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) to automatically generate annotations</p> <p><strong><em>NOTE</em></strong>: This dataset was used to train cellpose models in the following Zenodo entry <a href="https://doi.org/10.5281/zenodo.6140111">https://doi.org/10.5281/zenodo.6140111</a></p>
HeLa "Kyoto" cells under the scope
<p><strong>Name</strong>: HeLa “Kyoto” cells under the scope</p> <p><strong>Microscope</strong>: Perkin Elmer Operetta microscope with a 20x N.A. 0.8 objective and an Andor Zyla 5.5 camera.</p> <p><strong>Microscopy data type</strong>: The time-lapse datasets were acquired every 15 minutes, for 60 hours. From the individual plan images (channels, time-points, field of view exported by the PerkinElmer software Harmony) multi-dimension images were generated using the <a href="https://github.com/BIOP/ijp-operetta-importer/releases/tag/Operetta_Importer-0.1.21">Operetta_Importer-0.1.21</a> with a downscaling of 4. </p> <p>Channel 1 : Low Contrast DPC (Digital Phase Contrast)</p> <p>Channel 2 : High Contrast DPC</p> <p>Channel 3 : Brightfield</p> <p>Channel 4 : EGFP-α-tubulin</p> <p>Channel 5 : mCherry-H2B</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 540x540 (Pixel size: 0.299 nm), 5c, 1z , 240t</p> <p> </p> <p><strong>Cell type</strong>: HeLa “Kyoto” cells, expressing EGFP-α-tubulin and mCherry-H2B ( <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3839080/">Schmitz <em>et al</em>, 2010</a> )</p> <p><strong>Protocol</strong>: Cells were resuspended in <strong>Imaging media</strong> and were seeded in a microscopy grade 96 wells plate ( <a href="https://catalyse-erm.epfl.ch/erd-client/app/secure/sourcesearch/results?p=1&channel=SpendDirectorCatalog">CellCarrier Ultra 96</a>, Perkin Elmer). The day after seeding, and for 60 hours, images were acquired in 3 wells, in 25 different fields of view, every 15 minutes.</p> <p><strong>Imaging media</strong>: DMEM red-phenol-free media (FluoroBrite™ DMEM, Gibco) complemented with Fetal Calf Serum and Glutamax.</p> <p> </p> <p><strong>NOTE: </strong>This dataset was used to automatically generate label images in the following Zenodo entry: <strong> <a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a></strong></p> <p><strong>NOTE: </strong>This dataset was used to train the cellpose models in the following Zenodo entry:<strong> <a href="https://doi.org/10.5281/zenodo.6140111">https://doi.org/10.5281/zenodo.6140111</a></strong></p>
Benchmark FIB SEM and Airyscan data of HeLa cells
<p>Sample volume electron microscopy dataset cropped from EMPIAR-10819 and fluorescence dataset from BioImage Archive S-BSST707 for the CLEM-Reg paper.</p>
Multiple Nuclei HeLa cell ground truth images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.
<p>This is a data set that contains <strong>labelled HeLa cell images</strong>, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background. Similar ground truth have been published for this data set, but in this case, multiple nuclei have been labelled, whilst previous ones only focused on the central cell (https://doi.org/10.5281/zenodo.3874949)</p> <p>Details of the imaging, preparation and segmentation have been published in:</p> <ul> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> <strong>2019</strong>, <em>5</em>(9), 75; <a href="https://doi.org/10.3390/jimaging5090075">https://doi.org/10.3390/jimaging5090075</a></li> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, <strong>2020</strong>; <a href="https://doi.org/10.1371/journal.pone.0230605">https://doi.org/10.1371/journal.pone.0230605</a></li> <li> <p>Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells,<em> J. Imaging</em> <strong>2021</strong>, <em>7</em>(6), 93; <a href="https://doi.org/10.3390/jimaging7060093">https://doi.org/10.3390/jimaging7060093</a></p> </li> </ul> <ul> <li>The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.</li> </ul>
Oneat division model for Hela cells
<p>Trained models for hela cells for the bright field channel for locating mitosis events. Provided is an example dataset for bright field image and its corresponding model that can be used using the notebook here: https://github.com/Kapoorlabs-CAPED/CAPED-AI-oneat</p> <p> </p> <p>Made by oneat software, pip install oneat for training of such datasets.</p> <p> </p> <p>Original data published by Romain Guiet at https://zenodo.org/record/6139958#.YmAh1NpBxhG</p>
High-throughput poly(A) length measurement of HeLa and NIH 3T3 cells using TAIL-seq with MiSeq
<p>This dataset contains the full raw data directory from Illumina MiSeq generated for Chang et al. (2014, DOI: 10.1016/j.molcel.2014.02.007). Please refer to the original paper and its supplementary materials for further details.</p>
uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells
<p>This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.</p>
3D time-course iSIM dataset HeLa cells MitoTracker Red CMXRos
<p>2020-03-17</p> <p>Microscopy dataset:</p> <p>- Type: Fluorescence, 3D, time-course, iSIM Visitech, 100x Silicon NA 1.35 (65 nm x 65 nm x 300 nm xyz pixel sizes), 20s time interval</p> <p>- Cell type: HeLa, labelled with MitoTracker Red CMXRos</p> <p> </p> <p>Romain F. Laine, r.laine@ucl.ac.uk, MRC-LMCB, UCL, London, UK</p>
Fluorescence Microscopy Images of Hela Cell infected with Plasmodium Berghei parasite expressing mCherry in cytoplasm
<p>The purpose of our experiments was to delve into the liver stage development of the P. berghei parasite and examine the host-parasite interactions using HeLa cells. This research is primarily focused on in vitro analysis and does not extend to in vivo applications. Our study investigated the integration of fluorescent microscopy with artificial intelligence to <br>track and predict the developmental milestones of Plasmodium liver stage development. </p> <p>This is the dataset used in our study.</p>
TestDataset for Oneat networks for detection and prediction of dividing Hela cells for different imaging modalities
<p>Here we present the tif files to be used to test the Oneat networks for detection and prediction of division events for Hela cells imaged under different imaging modalities and presented originally here: https://zenodo.org/record/6139958#.Yjcjl3rMJD8</p>
Training dataset for HeLa "Kyoto" cells under the scope
<p>The Patches folder contains the training patches with csv files of cells 2 steps before the division and 2 steps in time after the division for 5 channels of the Kyoto Hela cells, the Oneat folder contains the json file of the cell categories and the map of XYTHW locations and its corresponding integer labels. The dataset is intended to be used for training oneat networks for these cells in different imaging modalities.</p> <p> </p> <p>Training patches with labels for 5 channels of hela cells: trainX, trainY = 64, 64, tminus = 2, tplus = 2, whole image was normalized using percentile based normalization pre patch making process. Patches made without segmentation image as input so height and width = 1 in yolo training labels.</p> <p>Made by oneat software, pip install oneat for training of such datasets.</p> <p> </p> <p>Original data published by Romain Guiet at https://zenodo.org/record/6139958#.YmAh1NpBxhG</p>
STING OPS: HeLa cGAMP (6 hours) Secondary Screen Single-Cell Features
<div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, 6 hours post-cGAMP, with or without rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> <p> </p> </div>
STING OPS: HeLa Unstimulated Secondary Screen Single-Cell Features
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, unstimulated, without rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> <p> </p> </div> <p> </p> </div>
STING OPS: HeLa Unstimulated Secondary Screen Single-Cell Features (Background-Subtracted)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, unstimulated, with rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> <p> </p> </div> <p> </p> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 2/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 2/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 5/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 5/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa cGAMP (4 hours) Secondary Screen Single-Cell Features
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa secondary screen, 4 hours post-cGAMP, with or without rolling ball background subtraction.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/SEC_HeLa.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 1/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 1/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
STING OPS: HeLa Genome-wide Screen Single-Cell Features (Part 3/5)
<div> <div> <p><strong>Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening</strong></p> <p>Single-cell features and coordinates for HeLa genome-wide screen, Zenodo dataset part 3/5.</p> <p>Images available at gs://opspublic-east1/STINGOpticalPooledScreen/Images/GW*.</p> <p>README for additional image information is at gs://opspublic-east1/STINGOpticalPooledScreen/STING_README.</p> </div> </div>
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.