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Trackerless 3D Freehand Ultrasound Reconstruction Challenge 2024 - Train Dataset (Part 1)
<blockquote> <p><strong>This Challenge will be an open-ended challenge, and we welcome your submission. Please register your team via this <a title="https://forms.office.com/e/dPg47ktV7M" href="https://forms.office.com/e/dPg47ktV7M" target="_blank" rel="noopener">form</a>. You can submit the algorithm via this <a title="https://forms.office.com/e/QChhNkLYiu" href="https://forms.office.com/e/QChhNkLYiu" target="_blank" rel="noopener noreferrer">form</a> for TUS-REC2024 Challenge, and we will test your submitted docker on the test set.</strong></p> <p><strong>We are organising TUS-REC2025 at MICCAI2025. More information is available on the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC2025 challenge website</a> and <a href="https://github.com/QiLi111/TUS-REC2025-Challenge_baseline" target="_blank" rel="noopener">Baseline code repo</a>.</strong></p> </blockquote> <p><strong>This is the first part of the Challenge train dataset. <a href="../doi/10.5281/zenodo.11180795" target="_blank" rel="noopener">Link</a> to second part; <a href="../doi/10.5281/zenodo.11355499" target="_blank" rel="noopener">Link</a> to third part. <a href="../doi/10.5281/zenodo.12979481" target="_blank" rel="noopener">Link</a> to validation dataset.</strong></p> <p>Acquisition devices and config: The 2D US images were acquired using an Ultrasonix machine (BK, Europe) with a curvilinear probe (4DC7-3/40). The associated position information of each frame was recorded by an optical tracker (NDI Polaris Vicra, Northern Digital Inc., Canada). The acquired US frames were recorded at 20 fps, with an image size of 480×640, without speckle reduction. The frequency was set at 6MHz with a dynamic range of 83 dB, an overall gain of 48% and a depth of 9 cm. </p> <div> <p>Scanning protocol: Both left and right forearms of volunteers were scanned. For each forearm, the US probe moves in three different trajectories (straight line shape, "C" shape, and "S" shape), in a distal-to-proximal direction followed by a proximal-to-distal direction, with the US plane perpendicular of and parallel to the scanning direction. The train dataset contains 1200 scans in total, 24 scans associated with each subject.</p> <p>For detailed information please refer to the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/TUS-REC2024/" target="_blank" rel="noopener">Challenge website</a>. Baseline code is also provided, which can be found at this <a href="https://github.com/QiLi111/tus-rec-challenge_baseline" target="_blank" rel="noopener">repo</a>.</p> <p>Dataset structure: </p> </div> <div> <ul> <li> <p>The dataset contains 50 folders (one subject per folder), each with 24 scans. Each .h5 file corresponds to one scan, storing image and transformation of each frame within this scan. Key-value pairs in each .h5 file are explained below.</p> <ul> <li> <p>“frames” - All frames in the scan; with a shape of [N,H,W], where N refers to the number of frames in the scan, H and W denote the height and width of a frame. </p> </li> <li> <p>“tforms” - All transformations in the scan; with a shape of [N,4,4], where N is the number of frames in the scan, and the transformation matrix denotes the transformation from tracker tool space to camera space. </p> </li> <li> <p>Notations in the name of each .h5 file: “RH”: right arm; “LH”: left arm; “Per”: perpendicular; “Par”: parallel; “L”: straight line shape; “C”: C shape; “S”: S shape; “DtP”: distal-to-proximal direction; “PtD”: proximal-to-distal direction; For example, “RH_Per_L_DtP.h5” denotes a scan on the right forearm, with ultrasound probe perpendicular of the forearm sweeping along straight line, in distal-to-proximal direction.</p> </li> </ul> </li> <li> <p>Calibration matrix: The calibration matrix was obtained using a pinhead-based method. The "scaling_from_pixel_to_mm" and "spatial_calibration_from_image_coordinate_system_to_tracking_tool_coordinate_system" are provided in the “calib_matrix.csv”. </p> </li> </ul> <div> <p><strong>Data Usage Policy:</strong></p> <ul> <li>The training and validation data provided may be utilized within the research scope of this challenge and in subsequent research-related publications. However, commercial use of the training and validation data is prohibited. In cases where the intended use is ambiguous, participants accessing the data are requested to abstain from further distribution or use outside the scope of this challenge.</li> <li>If you use our dataset in your publication, please cite the challenge paper and some of the following optional articles: <ul> <li>Challenge paper: <ul> <li><strong>Qi Li et al. "TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker." <em>arXiv preprint arXiv:<a title="https://arxiv.org/abs/2506.21765" href="https://doi.org/10.48550/arXiv.2506.21765" target="_blank" rel="noopener">2506.21765</a></em> (2025).</strong></li> </ul> </li> <li>Optional articles: <ul> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14.</a></li> </ul> </li> </ul> </li> </ul> </div> </div>
Videos during training and acquisition of awake Sheep MRI
<p>These videos are provided in support of Pluchot, C., Adriaensen, H., Parias, C. <em>et al.</em> Sheep (<em>Ovis aries</em>) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. <em>Behav Res</em> (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a> . One illustrates our technique to train sheep to lie down, while the other shows the acquisition of a T1-weighted image from an awake and unrestrained sheep.</p> <p>This Version 2 also includes the file Sheepvoice-V3.mp4, which contains footage of several training steps. </p>
Deep Reference Mining from Scholarly Literature in the Arts and Humanities - Pre-trained word embeddings
<p>Pre-trained word vectors of dimensionality 100 and 300 for the publication: Deep Reference Mining from Scholarly Literature in the Arts and Humanities, submitted to Frontiers in Digital Humanities.</p> <p>The corpus of scholarly publications from which these vectors were trained is under copyright, therefore we publish these vectors for reproducibility. Please refer to the publication's repository for further details: <a href="https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing">https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing</a>.</p> <p>These vectors were trained using Gensim 3.1.0. The corpus was preprocessed as follows:</p> <ol> <li>word tokenization with NLTK word_punct tokenizer.</li> <li>digits were converted into the $NUM$ token</li> <li>words less frequent than 5 times, for every document, were converted to the $UNK$ token</li> <li>vectors were trained using the function: Word2Vec(window=5, min_count=5, sg=1)</li> </ol>
Data and analysis supplement for: Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity.
<p>This submission provides the data and code for analyses reported in our publication.</p>
Gravity Spy Training Set
<p>We present an updated version of the training set used in the Gravity Spy citizen science project. This training set, curated further from the one discussed in detail <a href="https://www.sciencedirect.com/science/article/pii/S0020025518301634">here</a> and available here <a href="https://doi.org/10.5281/zenodo.1476156">10.5281/zenodo.1476156</a>, was utilized to train the convolutional neural network employed in the Gravity Spy project. We anticipate moving forward to release more labelled Gravity Spy data sets, including a data set containing the annotations provided by our citizen science volunteers.</p> <p><strong>Data Set Information</strong></p> <p>There are three files provided in this data set</p> <ul> <li><strong>trainingset_v1d1_metadata.csv</strong> <ul> <li>This file has many columns, <em>gravityspy_id, label, </em>and <em>sample_type.</em><em> gravityspy_id </em>is the unique 10 character hash given to every Gravity Spy sample. <em>label</em> is the string label of the sample. <em>sample_type </em>indicates whether this sample was used in the paper for testing training or validating the models. This is provided for those who would like to do direct comparisons to the network described in the paper.</li> <li>Additional columns contain some metadata information about the glitch <ul> <li><em>event_time,ifo,peak_time,peak_time_ns,start_time,start_time_ns,duration,search,process_id,event_id,peak_frequency,central_freq,bandwidth,channel,amplitude,snr,confidence,chisq,chisq_dof,param_one_name,param_one_value,</em></li> </ul> </li> </ul> </li> <li><em><strong>trainingsetv1d1.h5</strong></em> <ul> <li>This file contains the exact arrays used in the paper for every Gravity Spy sample. Each Gravity Spy sample is defined by four different images with varying temporal duration, <em>0.5, 1.0, 2.0, and 4.0</em> second, respectively. This also determines the naming conventions of the PNGs: <em>interferometer_gravityspyid_spectrogram_duration.png (e.g. H1_Fv3p6eROvA_spectrogram_0.5.png, H1_Fv3p6eROvA_spectrogram_1.0.png, H1_Fv3p6eROvA_spectrogram_2.0.png, H1_Fv3p6eROvA_spectrogram_4.0.png</em>).</li> <li>This file contains all the information needed for each sample in the Gravity Spy dataset (i.e. the label, the sample type of the sample, the unique id of the sample, and the image data for that sample. <ul> <li>/1080Lines/validation/xUEyaWr34c Group<br> /1080Lines/validation/xUEyaWr34c/0.5.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/1.0.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/2.0.png Dataset {1, 140, 170}<br> /1080Lines/validation/xUEyaWr34c/4.0.png Dataset {1, 140, 170}</li> </ul> </li> </ul> </li> <li><strong>trainingsetv1d1.tar.gz</strong> <ul> <li>Contains the raw PNGs of the Gravity Spy training set.</li> <li>The structure of the folder is <em>/"label"/"sample_type"/"pngs"</em></li> </ul> </li> </ul> <p><strong>Data Set Parsing Information</strong></p> <p>To read and crop out the plot axis and labels of the provided PNGs, the following small python code using scikit-image should work.</p> <p>from skimage import io</p> <p>image_data = io.imread("filename_of_image")</p> <p>x=[66, 532]; y=[105, 671]</p> <p>image_data = image_data[x[0]:x[1], y[0]:y[1], :3]</p>
Training images for the MADE site
<p>Five training images that represent spatial hydraulic conductivity [log10 (cm/s)] of the MAcro Dispersion Experiment (MADE) site in Mississippi, USA. The .mat file contains five matrices each of them representing a training image called:</p> <p>TIlog10K_chanStrebelleRoyane = highly conductive channels in an homogeneous matrix [Strebelle, 2002; Ronayne et al., 2010; Linde et al., 2015].</p> <p>TIlog10K_herten = model based on a mapping study at the Herten site in Germany [Bayer et al., 2011; Comunian et al., 2011; Linde et al., 2015b] featuring representative alluvial deposit structures.</p> <p>TIlog10K_lito = model based on lithological borehole data collected at the MADE site [Bianchi and Zheng, 2016].</p> <p>TIlog10K_multiG = multi-Gaussian field adapted from Bianchi et al., [2011].</p> <p>TIlog10K_outc = model based on a mapping study of a MADE outcrop [Rehfeldt et al., 1992; Linde et al., 2015].</p>
OATH Training Dataset
<p>Data from: http://tid.uio.no/plasma/oath/</p> <p>with regard to paper: <a href="https://doi.org/10.1029/2018JA025274">https://doi.org/10.1029/2018JA025274</a></p> <p> </p>
Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data
<p>This is a release of the Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data.<br> <br> ERuDIte contains over 11,000 training resources on data science including courses (MOOCs), video tutorials, conference talks, and other materials. The metadata of these resources is described uniformly using schema.org. In addition, we use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology (DSEO), which we developed to further describe the contents of the training resources. Resource relevance and tags are curated by experts to ensure high quality. Finally, we map the references to people and organizations in the learning resource metadata to entities in DBpedia, DBLP, and ORCID, thus embedding our collection in the web of linked data. Our collection is continually growing. We hope that ERuDIte will provide a framework to foster open linked educational resources on the web.<br> <br> Distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/)</p>
Data files for In situ training of feedforward and recurrent convolutional memristor networks
<p>MATLAB data files for the manuscript "<em>In situ training of feedforward and recurrent convolutional memristor networks" </em>published on Nature Machine Intelligence, 2019.</p> <p>The MATLAB data file "exp_mnist.mat" consists of all experimental data on implementing the convolutional neural network with the 1-transistor 1-memristor array that is used for plotting the Figure 1 and 2 of the manuscript.</p> <p>The MATLAB data file "exp_mnistsequence.mat" consists of all experimental data on implementing the convolutional long short-term memory network on the 1-transistor 1-memristor array that is used for plotting the Figure 3 and 4 of the manuscript.</p> <p>The code that generated these data files are provided by the link within the manuscript. Alternatively, the code can be accessed via <a href="https://github.com/zhongruiwang/memristorCNN">https://github.com/zhongruiwang/memristorCNN</a>.</p>
Dataset: Diamonds from Hadley's ggplot2 for Galaxy training
<p>Sample dataset created from https://doi.org/10.5281/zenodo.3522106 by selecting carat,price,color,clarity and cut columns only. In addition color and clarity are factors with integer values so we can reuse the dataset directly with an existing workflow (taught in Galaxy 101 for everyone).</p>
Training Machine-Learned Density Functionals on Band Gaps
<p>This dataset contains atomic structures and molecular orbitals (computed with DFT using the PBE functional) for the systems studied in the paper "<a href="https://doi.org/10.1021/acs.jctc.4c00999">Training Machine-Learned Density Functionals on Band Gaps</a>." The <a href="https://github.com/mir-group/CiderPress">CiderPress code</a> can be used to analyze the data.</p> <p>Please see the README.md file for more details on the dataset, and refer to the original paper for details on methodology and funding acknowledgments.</p>
Case studies related to the manuscript Tuning Trains Speed in Railway Scheduling
<p>This dataset is dedicated to the case studies related to the manuscript <strong>Tuning Trains Speed in Railway Scheduling</strong> by Étienne André, published in the proceedings of the 25th International Conference on Formal Engineering Methods (ICFEM 2024).</p> <p>See README.md for more information.</p>
From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers
<p>This dataset contains the raw data from the evaluation instruments and accompanies the manuscript: "From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers". It contains all trainee and advisor interviews from 2018 - 2022, as well as a select few partner interviews. It also contains pre and post-annual trainee survey data and the codebook to decipher the survey data. Rubric criteria and scores are included for trainees enrolled in the R2R Communication Skills course. The R script contains the statistical analyses reported in the manuscript and code used to generate figures.</p>
Fuτure - dataset for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons
<h1> Data description</h1> <h2>MC Simulation</h2> <p><br>The <strong>Fuτure</strong> dataset is intended for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons. The dataset is generated with Pythia 8, with the full detector simulation being performed by Geant4 with the CLIC-like detector setup CLICdet (CLIC_o3_v14) setup. Events are reconstructed using the Marlin reconstruction framework and interfaced with Key4HEP. Particle candidates in the reconstructed events are reconstructed using the PandoraPF algorithm.</p> <p>In this version of the dataset no γγ -> hadrons background is included.</p> <h2>Samples</h2> <p><br>This dataset contains e+e- samples with Z->ττ, ZH,H->ττ and Z->qq events, with approximately 2 million events simulated in each category.</p> <p>The following processes e+e- were simulated with Pythia 8 at sqrt(s) = 380 GeV:</p> <ul> <li>p8_ee_qq_ecm380 [Z -> qq events]</li> <li>p8_ee_ZH_Htautau [ZH -> Ztautau]</li> <li>p8_ee_Z_Ztautau_ecm380 [ZH -> Ztautau]</li> </ul> <p>The .root files from the MC simulation chain are eventually processed by the software found in <a href="https://github.com/HEP-KBFI/ml-tau-en-reg">Github</a> in order to create flat ntuples as the final product.</p> <h2><br>Features</h2> <p><br>The basis of the ntuples are the particle flow (PF) candidates from PandoraPF. Each PF candidate has four momenta, charge and particle label (electron / muon / photon / charged hadron / neutral hadron). The PF candidates in a given event are clustered into jets using generalized kt algorithm for ee collisions, with parameters p=-1 and R=0.4. The minimum pT is set to be 0 GeV for both generator level jets and reconstructed jets. The dataset contains the four momenta of the jets, with the PF candidates in the jets with the above listed properties.</p> <p>Additionally, a set of variables describing the tau lifetime are calculated using the software in <a href="https://github.com/HEP-KBFI/ml-tau-en-reg">Github</a>. As tau lifetime is very short, these variables are sensitive to true tau decays. In the calculation of these lifetime variables, we use a linear approximation.</p> <p>In summary, the features found in the flat ntuples are:</p> <p> </p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>reco_cand_p4s</td> <td>4-momenta per particle in the reco jet.</td> </tr> <tr> <td>reco_cand_charge</td> <td>Charge per particle in the jet.</td> </tr> <tr> <td>reco_cand_pdg</td> <td>PDGid per particle in the jet.</td> </tr> <tr> <td>reco_jet_p4s</td> <td>RecoJet 4-momenta.</td> </tr> <tr> <td>reco_cand_dz</td> <td>Longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dz_err</td> <td>Uncertainty of the longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dxy</td> <td>Transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dxy_err</td> <td>Uncertainty of the transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>gen_jet_p4s</td> <td>GenJet 4-momenta. Matched with RecoJet within a cone of radius dR < 0.3.</td> </tr> <tr> <td>gen_jet_tau_decaymode</td> <td>Decay mode of the associated genTau. Jets that have associated leptonically decaying taus are removed, so there are no DM=16 jets. If no GenTau can be matched to GenJet within dR < 0.4, a fill value is used.</td> </tr> <tr> <td>gen_jet_tau_p4s</td> <td>Visible 4-momenta of the genTau. If no GenTau can be matched to GenJet within dR<0.4, a fill value is used.</td> </tr> </tbody> </table> <p>The ground truth is based on stable particles at the generator level, before detector simulation. These particles are clustered into generator-level jets and are matched to generator-level τ leptons as well as reconstructed jets. In order for a generator-level jet to be matched to generator-level τ lepton, the τ lepton needs to be inside a cone of dR = 0.4. The same applies for the reconstructed jet, with the requirement on dR being set to dR = 0.3. For each reconstructed jet, we define three target values related to τ lepton reconstruction:</p> <ul> <li> a binary flag <strong>isTau</strong> if it was matched to a generator-level hadronically decaying τ lepton. <strong>gen_jet_tau_decaymode</strong> of value -1 indicates no match to generator-level hadronically decaying τ.</li> <li> the categorical decay mode of the τ <strong>gen_jet_tau_decaymode</strong> in terms of the number of generator level charged and neutral hadrons. Possible <strong>gen_jet_tau_decaymode</strong> are {0, 1, . . . , 15}.</li> <li> if matched, the visible (neglecting neutrinos), reconstructable pT of the τ lepton. This is inferred from the <strong>gen_jet_tau_p4s</strong></li> </ul> <h2>Contents:</h2> <ul> <li>qq_test.parquet</li> <li>qq_train.parquet</li> <li>zh_test.parquet</li> <li>zh_train.parquet</li> <li>z_test.parquet</li> <li> z_train.parquet</li> <li>data_intro.ipynb</li> </ul> <h2>Dataset characteristics</h2> <p> </p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong># Jets</strong></td> <td><strong>Size</strong></td> </tr> <tr> <td>z_test.parquet</td> <td> <pre>870 843</pre> </td> <td>171 MB</td> </tr> <tr> <td>z_train.parquet</td> <td> <pre>3 483 369</pre> </td> <td>681 MB</td> </tr> <tr> <td>zh_test.parquet</td> <td> <pre>1 068 606</pre> </td> <td>213 MB</td> </tr> <tr> <td>zh_train.parquet</td> <td> <pre>4 274 423</pre> </td> <td>851 MB</td> </tr> <tr> <td>qq_test.parquet</td> <td> <pre>6 366 715</pre> </td> <td>1.4 GB</td> </tr> <tr> <td>qq_train.parquet</td> <td> <pre>25 466 858</pre> </td> <td>5.6 GB</td> </tr> </tbody> </table> <p>The dataset consists of 6 files of 8.9 GB in total.</p> <h2>How can you use these data?</h2> <p>The .parquet files can be directly loaded with the Awkward Array Python library.<br>An example how one might use the dataset and the features is given in <strong>data_intro.ipynb</strong></p>
Eleven years of training data for south foehn for two valleys in the Eastern Alps
<p>This south foehn training data is suited for machine learning purposes. </p> <p>It was created by applying objective foehn classification (OFC, Vergeiner 2004) on hourly data of various stations in the Eastern Alps in Austria. The two valleys Rhine and Inn and two intensities are available on a daily basis, where</p> <ul> <li>0.0 means no foehn on that day,</li> <li>0.5 means localised foehn on that day (at least one third and up to half the stations in the region responded to OFC),</li> <li>1.0 means widespread foehn on that day (more than half the stations in the region responded to OFC),</li> </ul> <p>provided for each valley individually.</p> <p>A paper, where the process of creation is described, is in preperation and will be linked as soon as it is reviewed. </p>
Dynamic load model for passenger trains based on dynamic train signature
<p>20 conventional load model trains based on dynamic signature envelope of 3,140 operating passenger trains in Europe</p> <ul> <li>axle distances in m and axle loads in kN for all model trains as txt-files</li> </ul>
RT-Trees: Evaluation and RGB training images with masks
<p>This is the RT-Trees dataset proposed and used in the paper titled, "Shadowsense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection From RGB-Thermal Drone Imagery", published at the <a href="https://openaccess.thecvf.com/content/WACV2024/html/Kapil_ShadowSense_Unsupervised_Domain_Adaptation_and_Feature_Fusion_for_Shadow-Agnostic_Tree_WACV_2024_paper.html">IEEE/CVF WACV 2024</a> conference. Due to the size of the dataset and Zenodo's 50GB limit, the dataset is partitioned into two separate uploads. This upload contains the evaluation splits (test & val), along with the labelled subset of RGB training images used for a supervised training experiment, and the much larger set of unlabelled RGB images used for fully-unsupervised training. </p> <p>The second upload includes the corresponding unlabelled thermal images used for unsupervised training. </p>
EyeOnWater training dataset for assessing the inclusion of water images
<h1>Training dataset</h1> <p>The EyeOnWater app is designed to assess the ocean's water quality using images captured by regular citizens. In order to have an extra helping hand in determining whether an image meets the criteria for inclusion in the app, the YOLOv8 model for image classification is employed. With the help of this model all uploaded pictures are assessed. If the model deems a water image unsuitable, it is excluded from the app's online database. In order to train this model a training dataset containing a large pool of different images is required. The dataset contains a total of 13,766 images, categorized into three distinct classes: “water_good,” “water_bad,” and “other.” The “water_good” class includes images that meet the requirements of EyeOnWater. The “water_bad” class comprises images of water that do not fulfill these requirements. Finally, the “other” class consists of miscellaneous images that users submitted, which do not depict water. This categorization enables precise filtering and analysis of images relevant to water quality assessment.</p>
FeM dataset – An iron ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p> <p> </p>
PhasAGE Training School 1 -Overview of bioinformatics tools for the life sciences & Classification and evolution of non-globular proteins- LECTUREs
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</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.