Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity

Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity ' Rigoni et al. 2023, Neurobiology of Disease, doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.nbd.2023.106382" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.nbd.2023.106382</a> Dataset description Data\data2publish\sub- : 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs, Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal  Data\data2publish\EA_info.xlsx: number of epileptiform activities automatically detected for each animal at d28 and d29 Data\data2publish\derivatives\eeg_preprocessing: results of the script A_EEG_preprocessing.m, for each animal and session Data\data2publish\derivatives\elec_layout: different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4 Data\data2publish\derivatives\network_metrics_wpli: results of network analyses (script C_network_analyses.m) Data\data2publish\derivatives\wpli: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit Code for analyses available here:  <a href="https://github.com/IsottaR/ir_mice_project_Zenodo">https://github.com/IsottaR/ir_mice_project_Zenodo </a> Abbreviations: EEG= electroencephalography

opencc-by-4.0Dec 2023View details →

Dataset of the paper "Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow"

Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow

opencc-by-4.0Jan 2024View details →

Indoor climate projections at 90 workplaces in the Upper Rhine Valley modelled by artificial neural networks

The uploaded files contain the modelled indoor temperature (Ti) and physiologically equivalent temperature (PETi) data at 90 different workplaces in the Upper Rhine Valley, presented in the article "Climate projections of human thermal comfort for indoor workplaces" by Sulzer and Christen (2024), <a href="https://doi.org/10.1007/s10584-024-03685-7">https://doi.org/10.1007/s10584-024-03685-7</a>. The different csv files contain metadata to the different workplaces, the training data recorded in 2021 and 2022, the modelled data for the historical time period 1970-1999 using ERA5-Land data as input data, and for the future time period 2070-2099 using 22 different climate projections as input data.  In the file <a href="../api/records/8229253/draft/files/Workplaces_training_2021-2022.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_training_2021-2022.csv</a> you can find the measured data at the workplaces used for training of the models and in <a href="../api/records/8229253/draft/files/Workplaces_metadata.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_metadata.csv</a> you can find some metadata about each workplace.

opencc-by-4.0Mar 2024View details →

Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus

Dataset accompanying the manuscript "Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus". This dataset is used to re-create all figure panels with underlying data in the manuscript.

opencc-by-4.0Mar 2024View details →

Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction

Open the record for dataset details and reuse information.

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).

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).

Data for DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection

Data used in the paper, including annotation files, graph embeddings from TransformerAE, and protein attributes for both human and mouse, and for cafa3 data. Extract and place them in the data folder.

Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution

This dataset contains the whistler-mode hiss waves obtained from the Van Allen Probes. It is accompanied by the manuscript "Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution".

opencc-by-4.0Nov 2024View details →

Supplementary data: Optimising centralisation and decentralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning

The success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e., stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. In this dataset, a comparison has been made between the cost performance of centralized, decentralized, and hybrid stock allocation policies in distribution networks for perishable products. The dataset comprises 100,000 realistic case studies generated through a Sobol quasi-random low discrepancy series.

Data sets used in "Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model"

The training, validation, and testing data sets for toluene, dodecane, and alpha-pinene models described in the manuscript. A link to the manuscript will be added here when it becomes available. All trajectories in the data sets were generated using GECKO-A. The source code for using the data sets can be found at https://github.com/NCAR/gecko-ml

opencc-zeroJan 2022View details →

Pre-trained neural network model for pruning example code

Pre-trained neural network models for example codes of neural network pruning. Example pruning codes are published in "https://github.com/FujitsuResearch/automatic_pruning".

opencc-by-4.0Apr 2021View details →

OA eBook Usage Data Exchange Network Business Model Canvas

This initial business model canvas was created to inform technical and governance roadmap discussions pertaining to the data exchange elements of the Developing a Data Trust for Open Access Ebook Usage project.

A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 2

Models used as part of the paper "A Probabilistic Framework for Mutation Testing in Deep Neural Networks ?" submitted to the journal Information and Software Technology Replication package using the data is available at https://github.com/FlowSs/PM

A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 3

A Probabilistic Framework for Mutation Testing in Deep Neural Networks - Models archive Part 1

opencc-zeroJun 2022View details →

dryad32/100

Data from: A stochastic generative model for citation networks among academic papers

We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.

opencc-by-4.0Jun 2022View details →

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Preprocessed Datasets

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains the preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the raw datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

opencc-by-4.0Jun 2022View details →

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - USPS

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from USPS. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "usps_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.