Find research datasets worth reusing

dMRI-RCNN Pre-Trained Weights - 1D b1000 10q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 10, b = 1000.

dMRI-RCNN Pre-Trained Weights - 3D b1000 10q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 10, b = 1000.

dMRI-RCNN Pre-Trained Weights - 3D b3000 10q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 10, b = 3000.

dMRI-RCNN Pre-Trained Weights - 1D b3000 10q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 10, b = 3000.

dMRI-RCNN Pre-Trained Weights - 3D b1000 30q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 30, b = 1000.

dMRI-RCNN Pre-Trained Weights - 3D b1000 6q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 6, b = 1000.

dMRI-RCNN Pre-Trained Weights - 1D b2000 10q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 10, b = 2000.

dMRI-RCNN Pre-Trained Weights - 3D 10q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 3D RCNN model, for q_in = 10 b = {1000, 2000, 3000}.

dMRI-RCNN Pre-Trained Weights - 1D b1000 6q

Pre-trained weights for <a href="http://github.com/m-lyon/dMRI-RCNN">dMRI-RCNN</a>. 1D RCNN model, for q_in = 6, b = 1000.

opencc-by-4.0Sep 2022View details →

Pre-trained Models for SMP Classification and Segmentation

This dataset provides access to pre-trained models that were used for SnowMicroPen profile classification and segmentation. The models were trained on a part of the MOSAiC SMP dataset, available on <a href="https://doi.pangaea.de/10.1594/PANGAEA.935554">https://doi.pangaea.de/10.1594/PANGAEA.935554</a>. The labeled training data consists mostly of profiles from leg three of the expedition (January - May 2020), some profiles from leg one and two, and no profiles from leg four. Please refer to the snowdragon GitHub repository (<a href="https://github.com/liellnima/snowdragon">https://github.com/liellnima/snowdragon</a>) to access the models' training code and be directed to current publications. The following trained models are available here (alphabetically ordered): <ul> <li>Artificial neural networks <ul> <li>Bi-directional long short-term memory (blstm.hdf5)</li> <li>Encoder-decoder (enc_dec.hdf5)</li> <li>Long short-term memory (lstm.hdf5)</li> </ul> </li> <li>Baseline <ul> <li>Majority vote classifier (baseline.model)</li> </ul> </li> <li>Semi-supervised models <ul> <li>Cluster-then-predict models: <ul> <li>Bayesian Gaussian mixture model (gmm.model)</li> <li>Bayesian mixture model (bmm.model)</li> <li>K-means clustering (kmeans.model)</li> </ul> </li> <li>Label propagation (label_spreading.model)</li> <li>Self-trained classifier (self_trainer.model)</li> </ul> </li> <li>Supervised models <ul> <li>Balanced random forest (rf_bal.model)</li> <li>Easy ensemble (easy_ensemble.model)</li> <li>K-nearest neighbors (knn.model)</li> <li>Random forest (rf.model)</li> <li>Support vector machines (svm.model)</li> </ul> </li> </ul> Loading Instructions: The models with the file-ending ".model" are pickeled Python objects and can be loaded with ``pickle.load(your_model.model)``. The random forest must be loaded with ``joblib.load(rf.model)``. All artificial neural networks are h5py.File objects (tf.keras models) and can be loaded with ``tf.keras.models.load_model(your_ann.model)``.

opencc-by-4.0May 2024View details →

Simulations dataset and pre-trained models of "Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory" Ph.D. project

Ph.D. project datasets and models release, Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory.

opencc-by-4.0Dec 2021View details →

MS2Query pre-trained embeddings and models

The models, embeddings, sqlite file with metadata and classifiers identifiers needed for running MS2Query (https://github.com/iomega/ms2query) MS2Query positive mode model and library. The library consists of the GNPS library, MassBank, MoNA and Brungs et al. 's library.  A bug with the compound classes was fixed. This version is the downloaded model with MS2Query version >= 1.5.3

opencc-by-4.0Sep 2021View details →

Towards safe human-to-robot handovers of unknown containers: pre-trained models and 3D hand keypoints annotations

This repository contains additional data to be used with the implementation of the real-to-simulation framework of the paper Towards safe human-to-robot handovers of unknown containers. The data include pre-trained models and annotations of the 3D hand poses for selected recordings from the public training and testing sets of <a href="http://corsmal.eecs.qmul.ac.uk/containers_manip.html">CORSMAL Container Manipulation (CCM) dataset</a>. The pre-trained models are used for classifying the filling type and filling level of a container. 3D hand poses are annotated as 21 keypoints based on the <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a> format.

ClinicalTrials.gov36/100

Serious Game Versus Online Course to Pre-train Medical Students on the Management of an Adult Cardiac Arrest.

ClinicalTrials.gov study NCT02758119. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →

opencc-by-4.0Aug 2020View details →

Pre-trained models for Vietnamese Natural Resources and Environment Domain

Two Pre-trained models for Vietnamese Natural Resources and Environment Domain: - fastText - Word2vec

opencc-by-4.0Oct 2023View details →

Pre-training and fine-tuning dataset for transformers consisting of basic blocks and their execution times (average, minimum, and maximum) along with the execution context of these blocks, for various Cortex processors M7, M4, A53, and A72.

We are making public the dataset used for training CAWET, a tool for estimating the Worst-Case Execution Time (WCET) of basic blocks using the Transformer XL model. CAWET leverages the Transformer architecture for accurate WCET predictions, and its training involves two main phases: self-supervised pre-training and fine-tuning.CAWET undergoes a pre-training process on a substantial corpus of basic blocks to enable the Transformer to grasp the intricacies of the assembly language in focus. For this, we utilized CodeNet \cite{codenet}, a comprehensive collection of publicly submitted solutions to competitive programming challenges, comprising roughly 900,000 C programs. These programs were cross-compiled to the target architecture and subsequently disassembled using GNU binary utilities with objdump. The textual output from objdump, post a series of basic parsing operations (e.g., address extraction, separation of basic blocks), serves as the foundation for an extensive pre-training dataset. We employed this dataset to develop a vocabulary model utilizing sentence piece \cite{sentencepiece}. Following the completion of the sentence piece model's training, it becomes ready for use in tokenizing any binary programs written in the target instruction set.The fine-tuning phase of CAWET involves its adaptation to basic blocks along with their contextual information. Here, we used a varied and openly accessible collection of programs, namely, The Algorithms (accessible at: <a href="https://github.com/TheAlgorithms/C">https://github.com/TheAlgorithms/C</a>), MiBench \cite{mibench}, and Polybench \cite{polybench}.The provided zip file encompasses the following directories:Fine_Tuning: This includes four distinct files, each tailored for a specific processor: Cortex_M4, Cortex_M7, Cortex_A53, and Cortex_72. Each file encompasses the basic block under analysis (bbUA), the preceding 10 basic blocks executed prior to it, and timing information related to the bbUA (mean, min, max, normalization, etc.).Pre_Training: This comprises two extensive files, dataset_CortexA and dataset_CortexM, utilized for pre-training the transformers for the Masked Language Modeling Task (MLM). Additionally, it includes the sentence piece model and the necessary code to facilitate accurate tokenization.For additional information, please refer to the CAWET paper or contact us at <a href="mailto:ea_amalou@esi.dz">ea_amalou@esi.dz</a> Citation:@inproceedings{amalou2023cawet, title={CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers}, author={Amalou, Abderaouf N and Fromont, Elisa and Puaut, Isabelle}, booktitle={35th Euromicro Conference on Real-Time Systems (ECRTS 2023)}, year={2023}, organization={Schloss Dagstuhl-Leibniz-Zentrum f{\"u}r Informatik}} Bibliography:codenet@article{codenet2021, title={CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks}, author={Puri, Ruchir and Kung, David S and Janssen, Geert and Zhang, Wei and Domeniconi, Giacomo and Zolotov, Vladimir and Dolby, Julian and Chen, Jie and Choudhury, Mihir and Decker, Lindsey and others}, journal={arXiv preprint arXiv:2105.12655}, year={2021}}sentencepiece@article{sentencepiece2018, title={Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing}, author={Kudo, Taku and Richardson, John}, journal={arXiv preprint arXiv:1808.06226}, year={2018}}mibench@inproceedings{polybench2014, title={Understanding polybench/c 3.2 kernels}, author={Yuki, Tomofumi}, booktitle={International workshop on polyhedral compilation techniques (IMPACT)}, pages={1--5}, year={2014}}polybench:  @inproceedings{mibench, title={MiBench: A free, commercially representative embedded benchmark suite}, author={Guthaus, Matthew R and Ringenberg, Jeffrey S and Ernst, Dan and Austin, Todd M and Mudge, Trevor and Brown, Richard B}, booktitle={4th IEEE international workshop on workload characterization}, year={2001}}

opencc-by-4.0Nov 2023View details →

"An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model" train and test data

<ul><li>Model for the article "An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model".</li><li>The  .pth file is the pre-trained PtyNet-S model and the fine-tuned PtyNet-B model.</li><li>Please contact panxy@ihep.ac.cn if you have any questions.</li></ul>

opencc-by-4.0Mar 2024View details →

DCASE 2024 Task 9: Language-Queried Audio Source Separation | Pre-trained Weights for the Baseline System

== Descriptions == We trained the AudioSep [1] model using the <a href="../records/10887496">development set</a> (Clotho and augmented FSD50K datasets) for 200k steps with a batch size of 16 using one Nvidia A100 GPU (around 1 day). Model details can be found in the <a href="https://arxiv.org/abs/2308.05037">AudioSep paper</a>. Pre-trained weights for the baseline system: <ul> <li>audiosep_16k,baseline,step=200000.ckpt</li> </ul> Baseline codebase: <ul> <li>GitHub: <a href="https://github.com/Audio-AGI/dcase2024_task9_baseline">https://github.com/Audio-AGI/dcase2024_task9_baseline</a></li> </ul> == Reference == [1] Liu X, Kong Q, Zhao Y, et al. Separate anything you describe. arXiv:2308.05037, 2023. == Contact == Xubo Liu, xubo.liu@surrey.ac.uk

opencc-by-4.0Apr 2024View details →

Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)

This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).