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4,612 results for “labelled”
Labeled Images of Sand and Coins
<p>The dataset consists of images and associated binary masks, as well as augmented images and associated masks. Images depict sand and coins. Masks depict the coin (white) against a sand background (black).</p> <p>There are 132 augmented images and 132 images and associated labels in jpg format</p> <p>The principal purpose is to provide a dataset to help train a model or design an algorithm to segment the image from the coin background</p> <p> </p>
Dataset: A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern
<p>NORMAN Suspect List Exchange was used for the generation of the dataset. Datasets with clear label (LC or GC) were used. More specifically, we used S3 NORMANCT15, which contains a list of compounds that were detected in surface water from the Danube River in a pan-European collaborative trial employing both GC-HRMS and LC-HRMS. Moreover, the GC and LC target list were used by the following two institutes: National and Kapodistrian University of Athens (NKUA) and Helmholtz Centre for Environmental Research (UFZ). S21 UATHTARGETS is the LC target list of NKUA, S65 UATHTARGETSGC is the GC target list of NKUA and S53 UFZWANATARG contains the LC and GC target list of UFZ. Finally, two GC target lists (S51 WRIGCHRMS and S70 EISUSGCEIMS) were used. These lists contain GC substance lists and were provided by two Slovak institutes, the Water Research Institute (WRI) and Environmental Institute. The aforementioned compound lists were merged together to form a labelled dataset. The SMILES were used to calculate 1446 molecular descriptors. 1446 descriptors were produced by PaDEL-descriptor, logP was produced by JRgui and boiling point by USEPA ECOSAR.</p> <p>The dataset is used in the publication:</p> <p>"A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern" authored by</p> <p>Nikiforos Alygizakis, Vasileios Konstantakos, Grigoris Bouziotopoulos , Evangelos Kormentzas, Jaroslav Slobodnik and Nikolaos S. Thomaidis</p> <p>Github repository: https://github.com/nalygizakis/LCvsGC</p>
Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose models for Brightfield and Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional <strong>Training Dataset . Includes</strong> corresponding csv files with 'Quality Control' metrics(§) (model.zip).</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>)</p> <p><strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> . Training was done using a <a href="https://datascience.ch/renku/">Renku </a>environment (<a href="https://github.com/BIOP/renku-templates/tree/main/VNC-Napari-Fiji-Omero-CUDA11.4-cellpose-omnipose">renku template</a>).</p> <p> </p> <p><strong>Command Line Execution for the different trained models</strong></p> <p><strong>nuclei_from_bf: </strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_bf</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_dpc:</strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_dpc</strong>:</p> <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><em><strong>NOTE </strong></em>(§): We provide a notebook for Quality Control, which is an adaptation of the <a href="https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb">"Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic</a> .</p> <p><em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) in order to automatically generate the label images.</p> <p><strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.</p> <p> </p>
Labeled Images for Ulcerative Colitis (LIMUC) Dataset
<h1><strong>Dataset Details</strong></h1> <p>The LIMUC dataset compromises 11276 images from 564 patients and 1043 colonoscopy procedures, who underwent colonoscopy for ulcerative colitis between December 2011 and July 2019 at the Department of Gastroenterology in Marmara University School of Medicine. Two experienced gastroenterologists blindly reviewed and classified all images according to the Mayo endoscopic score (MES). Images that were differently labeled by two reviewers were also labeled by a third experienced reviewer independently without seeing their previous labels. The final MES for differently labeled images was determined using majority voting.</p> <p><br><strong>Mayo 0:</strong> 6105 (54.14%)<br><strong>Mayo 1:</strong> 3052 (27.70%)<br><strong>Mayo 2:</strong> 1254 (11.12%)<br><strong>Mayo 3:</strong> 865 (7.67%)</p> <p><strong>patient_based_classified_images:</strong> Images of each patient are separated according to Mayo classes. If a train-val-test splitting is to be made according to the ratios desired by the user, this folder should be used.</p> <p><strong>train_and_validation_sets:</strong> Train and validation sets used in the <a href="https://academic.oup.com/ibdjournal/advance-article-abstract/doi/10.1093/ibd/izac226/6830946">research paper</a>. Using the scripts in dataset's GitHub repository, same 10-fold can be generated for replicating the results.</p> <p><strong>test_set:</strong> Test set used for performance measurement in the research paper. For a fair performance comparisons, this should be used to report performances.</p> <h1><strong>Suggested Metrics</strong></h1> <p>Since there are imbalances and ordinality among classes (Mayo-0, Mayo-1, Mayo-2, Mayo-3), quadratic weighted kappa (QWK) can be used as the main performance metric. The QWK is one of the commonly used statistics for the assessment of agreement on an ordinal scale and it is one of the best singular performance metrics for this problem regarding class imbalances. Mean absolute error (MAE), macro F1 score, or macro accuracy can be used as alternative performance metrics.</p> <h1><strong>LIMUC Code Repository</strong></h1> <p>Many scripts for preprocessing, splitting, training, and validating the dataset are provided in <a href="https://github.com/GorkemP/labeled-images-for-ulcerative-colitis">this</a> GitHub repository.</p> <h1><strong>Terms and Conditions</strong></h1> <p>The LIMUC dataset is licensed under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. This license permits unrestricted use, distribution, and reproduction in any medium, provided that proper attribution is given to the original creators. This ensures that the dataset can be used for both <strong>research and commercial applications</strong> while maintaining transparency and acknowledgment of the contributors.</p> <p>For more details about the license, please refer to: <a>Creative Commons Attribution 4.0 International License</a>.</p> <p>Regarding the questions, please contact <em><strong>polatgorkem@gmail.com.</strong></em></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>
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems
<p>This is the dataset created for the paper, "EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems" (https://arxiv.org/abs/2109.04919).</p> <p>EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset (https://github.com/budzianowski/multiwoz). It contains more than 11K task-oriented dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. There are 7 emotion labels, which are adapted from the OCC emotion models.</p> <p>For data format and label definition, please refer to README.md. </p>
SupportingDataset Identification and Quantification of Within-Burst Dynamics in Singly-Labeled Single-Molecule Fluorescence Lifetime Experiments
<p>The Jupyter notebooks and resulting files used to demonstrate divisor-based mpH<sup>2</sup>MM. The analysis is demonstrated with both simulations and analyses of alpha-synuclein.</p> <ol> <li> <p><em><strong>Notebooks.zip</strong></em>:* Zip file containing the Jupyter notebooks for producing, analyzing and visualizing the simulated photon trajectories. Note: this folder contains all code needed to reproduce simulations. All other files related to the simulations are produced by one of the notebooks in this trajectory. However, as simulations can take a long time, the various results files are included in this repository so that notebooks can be run from intermediate steps.</p> <ol> <li> <p><strong>1-PIFE-pybromo-sims.ipynb</strong> : The code for producing simulated diffusion trajectories and photon-HDF5 files of two-state systems undergoing transition dynamics (the results of this notebook are stored in the sub-folder <em>PyBroMo_photonHDF5</em>)</p> </li> <li> <p><strong>2-PIFE-mpH2MM-sim-[lifetime components].ipynb </strong>: Notebooks performing divisor-based mpH<sup>2</sup>MM on simulated datasets for a given combination of lifetime states. (these notebooks store files that are contained in the sub-folder <em>H2MMresults</em>)</p> </li> <li> <p><strong>3-PIFE-mpH2MM-compiled-plots.ipynb</strong> : Jupyter notebook for producing figures comparing all results globally</p> </li> <li> <p><strong>532nm_IRF_19-10-2021.csb</strong>: the file containing the experimental IRF used in the simulations</p> </li> </ol> </li> <li> <p><em><strong>PyBroMo_photonHDF5.zip</strong></em>:* Zip file containing the simulated results of <em>1-PIFE-pybromo-sims</em> notebook as photon-HDF5 files (1 file per transition rate/lifetime combination)</p> </li> <li> <p><strong>PIFE-sim-dynamicmix_[lifetime components]_result.hdf5</strong>: special HDF5 files containing the results of each notebook in <em>Notebooks</em>, which are used by <em>3-PIFE-mpH2MM-compiled-plots</em></p> </li> <li> <p><strong>PIFE-mpH2MM-alpha-syn-vFinal.ipynb</strong>: divisor-based mpH<sup>2</sup>MM analysis of alpha-synuclein smPIFE data</p> </li> <li> <p><strong>H2MM-Lifetime_example.ipynb</strong>: A demonstration of divisor-based mpH<sup>2</sup>MM using nsALEX-smFRET data. This method could potentially demonstrate states differentiated in lifetimes independently of potential changes in E & S.</p> </li> <li> <p><strong>Template_ltH2MM.ipynb</strong>: An easy-to-follow implementation of divisor-based mpH<sup>2</sup>MM demonstrated on a single alpha-synuclein experimental data acquisition file. This can be used for learning how to implement and analyze single dye fluorescence lifetime data with mpH<sup>2</sup>MM</p> </li> </ol> <p> </p> <p>* For running these notebooks, generally, all files in <em>PyBroMo_photonHDF5.zip</em> should be placed into a single directory (i.e., the files in <em>Notebooks</em>.<em>zip</em> should be placed into the same directory as the files in <em>PyBroMo_photonHDF5</em>.<em>zip</em>) as the notebooks are set to read in files from their current directory.</p>
Data: Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography.
<p>This data set includes all the raw data collected for the following article: "Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography."</p>
YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines
<p><strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> </p> <p><strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%. </p> <p> </p>
Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography
<p>This repository contains the raw K-space data and all MATLAB (The MathWorks, Natick, MA) code used for reconstruction, simulations, data analysis, and figure generation used in the article titled "Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography".</p> <p>## Referencing</p> <p>If you use any of the data or the code provided here, please cite:</p> <p>1. Woods JG, Schauman SS, Chiew M, Chappell MA, Okell TW. Time-encoded pseudo-continuous arterial spin labeling: Increasing SNR in ASL dynamic angiography. Magnetic Resonance in Medicine. 2023; doi:10.1002/mrm.29491<br> 2. This Zenodo repository: Joseph G. Woods, S. Sophie Schauman, Mark Chiew, Michael A. Chappell & Thomas W. Okell. (2022). Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6791097</p> <p> </p> <p>If you use the reconstruction code provided here, please also cite:</p> <p>1. Fessler JA, Sutton BP. Nonuniform fast fourier transforms using min-max interpolation. IEEE Transactions on Signal Processing. 2003;51(2):560-574. doi:10.1109/TSP.2002.807005<br> 2. Fessler JA. Michigan Image Reconstruction Toolbox. https://web.eecs.umich.edu/~fessler/code/. Accessed February 26, 2018.<br> 3. Schauman SS. Accelerated_TEASL. https://github.com/SophieSchau/Accelerated_TEASL. Accessed October 19, 2020.<br> 4. Chiew M. MR Linear Encoding Operators. https://users.fmrib.ox.ac.uk/~mchiew/Tools.html. Accessed March 30, 2020.</p> <p>## Notes for use:</p> <p>All data processing is performed in MATLAB (tested with 2021a and 2021b on macOS 10.14 (Mojave) and 12 (Monterey))</p> <p>The "code/" and "data/" folders should be placed within the same folder. The necessary file paths within each script in "code/" will be set up automatically.</p> <p>**Note**: the file paths use "/", so would need to be manually changed for use on Windows, which uses "\" in file paths.</p> <p>- For performing full reconstruction of in vivo K-space data, **run reconalldata.m**.<br> - All code provided<br> - Uses:<br> 1. Jeff Fessler's MIRT: http://web.eecs.umich.edu/~fessler/irt/fessler.tgz<br> 2. Mark Chiew's: xfm_NUFFT.m, etc: https://users.fmrib.ox.ac.uk/~mchiew/Tools.html<br> 3. Sophie Schauman's Accelerated_TEASL: https://github.com/SophieSchau/Accelerated_TEASL</p> <p>- For plotting in-vivo data, **run figures_invivo.m**.<br> - FSL's read_avw to read in NIFTIs (but can replace this with MATLAB's in-built niftiread instead).<br> - (Install FSL from here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation)<br> - All other code provided</p> <p>- For performing simulation and plotting results, **run figures_simulation.m**.<br> - All code provided</p> <p>- Location of specific Figure generation code:<br> - Figure 1: no code - manually made.<br> - Figure 2: figures_invivo.m<br> - Figure 3: figures_simulation.m<br> - Figure 4: figures_invivo.m<br> - Figure 5: figures_invivo.m<br> - Figure 6: figures_invivo.m<br> - Figure 7: figures_invivo.m<br> - Figure 8: figures_invivo.m<br> - Figure 9: figures_simulation.m<br> - Figure 10: figures_invivo.m<br> - Supporting Information Figure S1: figures_simulation.m<br> - Supporting Information Figure S2: figures_invivo.m<br> - Supporting Information Figure S3: figures_invivo.m<br> - Supporting Information Figure S4: figures_invivo.m<br> - Supporting Information Figure S5: figures_invivo.m<br> - Supporting Information Figure S6: figures_invivo.m<br> - Supporting Information Figure S7: figures_invivo.m<br> - Supporting Information Figure S8: figures_invivo.m<br> - Supporting Information Figure S9: figures_invivo.m<br> - Supporting Information Figure S10: figures_simulation.m</p>
Labeled data and models for COVID-19 vaccine related tweets with stance, location, and topics
<p>The dataset contains Tweet IDs along with the location and tweet timestamp. The tweets are labeled based on motivating/demotivating status, stance towards the COVID-19 vaccine, and topic in the tweet text. To comply with Twitter guidelines, we removed the tweet texts and author information. You can use Hydrator API to hydrate the tweets.</p> <p>The repository also contains the machine-learning models for topic modeling, de/motivation classifier, and stance detection from the tweets.</p>
VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling
<p>This repository contains the simulation code, in vivo data, preprocessing code, and analysis code used in the Magnetic Resonance in Medicine article titled "VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling" (<a href="https://doi.org/10.1002/mrm.29159">https://doi.org/10.1002/mrm.29159</a>). Please cite this article and this repository (<a href="https://doi.org/10.5281/zenodo.6870683">https://doi.org/10.5281/zenodo.6870683</a>) if you use any of this code or data in your work.</p> <p>Software requirements: BASH, FSL6, and MATLAB (tested with MATLAB versions 2021a and 2021b; requires the Optimization Toolbox and the Statistics and Machine Learning Toolbox). Preprocessing steps tested on macOS 12.4 (Monterey) and 10.14 (Mojave).</p> <p>This updated version (V2.0) simplifies and streamlines the setup of command line and MATLAB paths, making it easier to re-run the preprocessing and analysis steps.</p>
Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'
<p>Here we publish the datasets associated with our publication ‘Label-free imaging of 3D pluripotent stem cell differentiation dynamics<br> on chip’. 3D cultures of human induced pluripotent stem cells (hiPSCs) were imaged while undergoing definitive endoderm (DE) differentiation.<br> Data were acquired either on the live 3D cultures at various time points during the 3 days DE differentiation, or after fixation and immunostaining, with the 3D cultures being fixed at regular 24 h intervals during differentiation, as to form a timeline. Images were recorded with a standard confocal microscope using a xy-resolution of 0.25 μm/px and a z-resolution of 1 μm/plane.</p>
OME-Zarr 3D hiPSCs with labels & measurements, 2x2 field of views
<p>These are 2 small OME-Zarr files of the data from <a href="https://doi.org/10.5281/zenodo.7057076">10.5281/zenodo.7057076</a>.</p> <p>They have been processed using <a href="https://pypi.org/project/fractal-client/">fractal-client</a> 0.2.1, <a href="https://pypi.org/project/fractal-server/0.1.2/">fractal-server</a> 0.1.4 and <a href="https://pypi.org/project/fractal-tasks-core/">fractal-tasks-core</a> 0.1.9 using this workflow: <a href="https://github.com/fractal-analytics-platform/fractal/tree/main/examples/08_cardio_2x2_dataset_processing_zenodo">https://github.com/fractal-analytics-platform/fractal/tree/main/examples/08_cardio_2x2_dataset_processing_zenodo</a></p> <p>Both Zarr files are Zip-compressed to allow easier upload & download from Zenodo. </p> <p>20200812-CardiomyocyteDifferentiation14-Cycle1.zarr contains 3 3D channels and a table with regions of interest for the 4 field of views contained in this data.</p> <p>20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr contains the same 3 channels, but as maximum intensity projections. It contains nuclear segmentation through cellpose. It also contains 2 tables: The region of interests like in the 3D data, as well as measurements performed with <a href="https://github.com/haesleinhuepf/napari-skimage-regionprops">napari-skimage-regionprops</a>.</p> <p>The tables are stored in the OME-Zarr file according to the <a href="https://github.com/ome/ngff/pull/64">proposed OME-NGFF</a> table spec in AnnData.</p> <p>The 3 channels are:</p> <p>- 0: DAPI, nuclear stain</p> <p>- 1: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog</p> <p>- 2: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1</p> <p> </p> <p>This updated version now passes the ngff schema validation. A version with 3D segmentation is available here: <a href="https://zenodo.org/record/7144919">https://zenodo.org/record/7144919</a></p>
Text-fig. 2. Type specimen of Palmocarpon cretaceum MIQUEL, 1853 (V 21763 in the NHM, London). a: Shows the front (for comparison with Text-fig. 1), b: shows the reverse, with the label glued to it. in The Type Of Palmocarpon Cretaceum Miq., 1853 Described From The Cretaceous Of The Sint-Pietersberg, The Netherlands, Is An Eocene Nypa Burtinii (Brongn.) Ettingsh., 1879, Most Likely From The Brussels Area, Belgium
Text-fig. 2. Type specimen of Palmocarpon cretaceum MIQUEL, 1853 (V 21763 in the NHM, London). a: Shows the front (for comparison with Text-fig. 1), b: shows the reverse, with the label glued to it.
Rad52 or Rfa1 labeled with 3HA or 6HA WB images
<p>WB images of S. cerevisiae cells expressing Rad52 or Rfa1 labelled with 3HA or 6HA. Anti-HA or Anti-PGK1 antibodies were used and a secondary goat-anti-mouse+HRP.</p>
Sample dataset to accompany Hamilton, Chang, Lee, & Chang. Semi-automated anatomical labeling and inter-subject warping of high-density intracranial recording electrodes in electrocorticography
<p>This dataset accompanies the following paper: <br> Hamilton, Chang, Lee, and Chang. Semi-automated anatomical labeling and inter-subject warping of <br> high-density intracranial recording electrodes in electrocorticography</p> <p>This data includes an anonymized and de-identified CT and T1 MRI scan, plus all intermediate and final files<br> produced by the img_pipe software for testing and instructional purposes. This subject had a right hemisphere implantation including high density grids, strip electrodes, and depth electrodes.</p> <p>img_pipe software and installation instructions can be found at http://github.com/changlabucsf/img_pipe</p> <p>If you wish to follow along yourself, we recommend creating a new subject in your Freesurfer $SUBJECTS_DIR, <br> then copy the acpc and CT directories from this dataset into that new subject directory. </p> <p>The electrode montage is provided in test_subj_montage.txt and describes the type of electrodes implanted<br> (grid, strip, or depth) and their general location. </p>
Android Malware Dataset with VirusTotal Labels
<p>This dataset contains labels of 2.47 million Android apk hashes extracted from VirusTotal reports.</p> <p>The dataset was used in the experiments of our publication titled <a href="https://www.mdpi.com/1424-8220/21/16/5671" rel="nofollow">An Analysis of Android Malware Classification Services<br></a><br>The csv of the labels that was extracted from the VirusTotal reports is provided in <code>labeling_dataset.csv.gz</code> . A cell's value of <code>-1</code> is used whenever there was no result from the<br>engine for the given apk file hash value. The column names are provided in <code>cols_labeling_dataset.csv</code>.</p> <p>Note</p> <p><code>-1</code> is a <code>string</code> and not an <code>integer</code></p> <h2>If you use information from this repo, please cite our paper</h2> <p>Rashed M, Suarez-Tangil G. An Analysis of Android Malware Classification Services. Sensors. 2021; 21(16):5671. <a href="https://doi.org/10.3390/s21165671" rel="nofollow">https://doi.org/10.3390/s21165671</a></p> <h2>BibTeX</h2> <p><code>@Article</code>{s21165671,<br>AUTHOR = {Rashed, Mohammed and Suarez-Tangil, Guillermo},<br>TITLE = {An Analysis of Android Malware Classification Services},<br>JOURNAL = {Sensors},<br>VOLUME = {21},<br>YEAR = {2021},<br>NUMBER = {16},<br>ARTICLE-NUMBER = {5671},<br>URL = {<a href="https://www.mdpi.com/1424-8220/21/16/5671%7D,%5C" rel="nofollow">https://www.mdpi.com/1424-8220/21/16/5671},\</a><br>ISSN = {1424-8220},<br>DOI = {10.3390/s21165671}<br>}</p> <h2>Required Software</h2> <p><code>gzip</code></p> <ul> <li>Debian-based Linux: you may install it using the following command <code>apt-get install gzip</code></li> <li>MacOS: <code>gzip</code> is pre-installed</li> <li>Windows: you may download <code>gzip</code> from <a href="http://gnuwin32.sourceforge.net/packages/gzip.htm" rel="nofollow">http://gnuwin32.sourceforge.net/packages/gzip.htm</a></li> </ul> <h2>How to use the file?</h2> <p>There are two ways to use the file:</p> <ol> <li>Extract the gzip file and then you will have a csv output file. For that you need to install gzip and then extracting .csv.gz. The user may use the command <code>gunzip labelingDataset.csv.gz</code></li> <li>Extract information from the zipped file directly (following the same logic of <a href="https://androzoo.uni.lu/lists" rel="nofollow">AndroZoo's csv</a>):<br>To extract the first column and save to a file called <code>list_of_selected_sha256</code>, run the following command:<br><code>zcat labelingDataset.csv.gz | cut -d',' -f1 > list_of_selected_sha256</code><br>To obtain rows of apk hashes that were first seen after the 1st of May, 2016, run this command:<br><code>zcat labeling_dataset.csv.gz | grep -v ',snaggamea' | awk -F, '{if ( $2 >= "2016-05" ) {print} }'</code></li> </ol>
Habitats labelled data
<p><span>This dataset has been created within the EU H2020 Natural Intelligence project (ID </span><span> 101016970</span><span>).</span></p> <p><span>The dataset contains labeled pictures of typical and early warning species of four different habitats, divided into four subfolders. </span></p> <p><span>Each subfolder contains:</span></p> <p><span>1) species data images (.jpg files)</span></p> <p><span>2) .txt files for each image containing the labelling information. The first value indicates the species (defined in the .yaml file), the remaining values describe the box vertices.</span></p> <p><span>3) a single .yaml file containing the parameter used in the labelling and detection AI training code:</span></p> <p><span> - path to the dataset root directory</span></p> <p><span> - path to the the train images directory</span></p> <p><span> - path to the validation images directory</span></p> <p><span> - path to the test images directory</span></p> <p><span> - number of classes (species)</span></p> <p><span> </span></p> <p><span>The four subfolders are named after the four habitats, i.e.,</span></p> <p><span>- </span><span>Dunes. This refers to EU habitats 2110 and 2120 in Italian dunes. The included species are:</span></p> <p><span> o </span><span>Achillea maritima – typical species</span></p> <p><span> o </span><span>Calamagrotis arenaria – typical species</span></p> <p><span> o </span><span>Carpobrotus acinaciformis – invasive species</span></p> <p><span> o </span><span>Eryngium maritimum – typical species</span></p> <p><span> o </span><span>Pancratium maritimum – typical species</span></p> <p><span> o </span><span>Thinopyrum junceum – typical species</span></p> <p><span>- </span><span>Grasslands. This refers to EU habitat 6210* in the Italian Central Apennines. The included species are:</span></p> <p><span> o </span><span>Asphodelus macrocarpus – early warning species</span></p> <p><span> o </span><span>Dactylorhiza sambucina – typical species</span></p> <p><span> o </span><span>Orchis morio – typical species</span></p> <p><span>- </span><span>Forests. This refers to EU habitat 9210* in the Italian Apennines. The included species are:</span></p> <p><span> o </span><span>Anemonoides nemorosa – typical species</span></p> <p><span> o </span><span>Corydalis cava – typical species</span></p> <p><span> o </span><span>Doronicum columnae – early warning species</span></p> <p><span> o </span><span>Anemonoides ranunculoides – typical species</span></p> <p><span>- </span><span>Screes. This refers to EU habitat 8110 and 8120 in the Italian Alps. The included species are:</span></p> <p><span> o </span>Cerastium sp.<span> – typical species</span></p> <p><span> o </span><span>Luzula alpino-pilosa – early warning species</span></p> <p><span> o </span><span>Saxifraga – typical species</span></p> <p><span> o </span><span>Ranunculus glacialis – typical species</span></p> <p><span> o </span><span>Geum reptans – typical species</span></p> <p><span> o </span><span>Papaver alpinum – typical species</span></p> <p><span> </span></p> <p><span>Researchers from a variety of disciplines can benefit from using this dataset because of its multidisciplinary scope. Botanists could evaluate the accuracy of this data as well as the habitat's conditions using the plant images that the robot captured, robotic engineers could test their AI algorithms for identifying and classifying different species using these data. The code used to label the images can be found here: <a href="https://github.com/ivangrov/ModifiedOpenLabelling">https://github.com/ivangrov/ModifiedOpenLabelling</a></span></p> <p><span> </span></p>
Assessment of Fair Trade education programs in France: data 2022 from the control group and those from the two experimental fields (the Fair Generation scheme and the Fair Trade Universities Label)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></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.