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.
52
datasets available to search
ShareScore release 0.9.0
Dataset results
52 results for “training and test dataset”
Training and Test Datasets, Pretrained Weights and Predictions for HIDRA3
<div>HIDRA3 is a state-of-the-art deep neural model for multi-point sea-level prediction based on past sea level observations and future tidal and geophysical forecasts. Published data contain HIDRA3 pretrained weights, predictions for all 50 ensembles, geophysical training and evaluation data and SSH observations from Koper (Slovenia).</div> <div> </div> <div>The structure of the data is described in README.md.</div>
ZeroCostDL4Mic - CARE (3D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CARE (3D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D paired microscopy images (fluorescence) of low and high signal-to-noise ratio</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP)</p> <p><strong>Microscope</strong>: Structured Illumination Microscopy (SIM) with a 60x 1.42 NA objective </p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP</p> <p><strong>File format</strong>: .tif (32-bit)</p> <p><strong>Image size</strong>: 1024x1024x33 (Pixel size: x,y: 40 nm, z: 125 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Noise2Void (3D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 63x 1.4 NA objective </p> <p><strong>Cell type</strong>: A2780 ovarian carcinoma cells, transiently expressing Lifeact-RFP</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512x512x13 (Pixel size: x,y: 195 nm , z: 500 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Kaukonen <em>et al.</em> 2017, Nature Protocol DOI: 10.1038/nprot.2017.107</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - Stardist example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Stardist example training and test dataset</p> <p>(see <a href="http://https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) and corresponding masks</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM LifeAct-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Johanna Jukkala<sup>1,2</sup> and Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - pix2pix example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - pix2pix example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP cells</p> <p><strong>File format</strong>: .png (RGB images)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation(s)</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku</p> <p>3) ORCID: 0000-0002-9286-920X</p> <p><br> <strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - YoloV2 example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - YoloV2 example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 2D grayscale .png images with corresponding bounding box annotations in .xml PASCAL Voc format. </p> <p><strong>Microscopy data type</strong>: Phase contrast microscopy data (brightfield)</p> <p><strong>Microscope</strong>: Inverted Zeiss Axio zoom widefield microscope equipped with an AxioCam MRm camera, an EL Plan-Neofluar 20 × /0.5 NA objective (Carl Zeiss), with a heated chamber (37 °C) and a CO2 controller (5%). </p> <p><strong>Cell type</strong>: MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts. </p> <p><strong>File format</strong>: .png (8-bit)</p> <p><strong>Image size</strong>: 1388 x 1040 px (323 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup>, Lucas von Chamier<sup>4,5</sup></p> <p><strong>Contact email</strong>: <a href="mailto:lucas.chamier.13@ucl.ac.uk">lucas.chamier.13@ucl.ac.uk</a> and guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation(s)</strong>:<strong> </strong></p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku</p> <p>3) ORCID: 0000-0002-9286-920X</p> <p>4) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK</p> <p>5) ORCID: 0000-0002-9243-912X</p> <p> </p> <p><strong>Associated publications</strong>: Jacquemet et al 2016. DOI: 10.1038/ncomms13297</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p> <p> </p>
ZeroCostDL4Mic - CycleGAN example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CycleGAN example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy. </p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 100x 1.4 NA objective. SRRF reconstructions were performed using the latest generation of SRRF and 200 frames.</p> <p><strong>Cell type</strong>: U2OS cells</p> <p><strong>File format</strong>: .png (8-bit)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 62.5 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation(s)</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku</p> <p>3) ORCID: 0000-0002-9286-920X</p> <p> </p> <p><strong>Associated publications</strong>: Jacquemet et al 2020, Journal of Cell Science DOI: https://doi.org/10.1242/jcs.240713</p> <p> </p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
Dataset containing images for training and testing deep learning image recognition models
<p>The dataset contains a training and a test subsets of images. Each image belongs to one out of 10 categories of animals</p>
Train and Test Dataset for a Neural Network for the task of RSA Segmentation in the context of a Master thesis 2024
<p>The data corresponds to the work of the master thesis "3D Segmentation of Plant Roots from MRI Images for Enhanced Automated Root Tracing using Deep Neural Networks" by Daniel Weißen from the 24/07/2024</p> <p>train_data.tar<br>- contains the training and validation data used for the DNN<br> - data_gmm contains the soil noise data based on a Gaussian Mixture Model<br> - data_FS contains the soil noise data based on the Fourier Synthesis approach described in the thesis</p> <p>test_data.tar<br>- contains the test data which is manually labeled</p> <p>segmentations.tar<br>- contains the segmentations of the trained SwinUNETR-V2+ on the real test MRIs<br> - fs_model_output: contains the Fourier Synthesis trained NNs segmentation<br> - fmm_model_output: contains the GMM trained NNs segmentation<br> - threshold_output: contains the Dice optimized threshold labeling</p> <p>FS_checkpoint.ckpt, GMM_checkpoint.ckpt<br>- contains the checkpoint for the trained SwinUNETR-V2+ on the Fourier synthesis, and GMM soil noise respectively</p>
2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 1/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 4/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 5/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 6/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 3/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 2/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
README Installation Instructions Train and Test Datasets
Open the record for dataset details and reuse information.
Training and Testing Datasets for skin colour characterisation
<p>The document includes three training datasets and a testing dataset for image-based skin colour characterisation. The three training datasets are respectively listed in the worksheets named FSCD, SSCC, CCSG, and the testing dataset is in the "Testing" worksheet. Each dataset contains a group of CIE XYZ tristimulus values measured using a CM-2600d spectrophotometer and the corresponding RGB values extracted from the captured RAW and JPG images.</p>
Training and Test Datasets for HIDRA2
<p>HIDRA2 is a state-of-the-art deep neural model for sea-level prediction based on past sea level observations and future tidal and atmospheric forecasts. Training and evaluation of the model was performed on the published datasets.</p>
Training and test dataset for CNN-LSTM model for GW waveform extraction
<p>This repository contains training and test samples corresponding to the paper, 'Extraction of binary black hole gravitational wave signals from detector data using deep learning', Chatterjee et al., Phys. Rev. D <strong>104</strong>, 064046 (2021).</p>
ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)
<p><strong>Name</strong>: ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)</p> <p>(see <a href="http://https//github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) and corresponding masks</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM LifeAct-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Johanna Jukkala<sup>1,2</sup> and Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong> : </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</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.