Find research datasets worth reusing

Supporting data for "The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows"

Open the record for dataset details and reuse information.

opencc-by-4.0Feb 2024View details →

Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach

This dataset is experimental results from a research topic of Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach.

Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle's 2015 study: "The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception"

Data for the study "Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle’s 2015 study: “The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception”" Contents consist of curated EEG data for statistical analysis, statistical analysis workflow and code and Matlab code for the behavioural flash fusion task.

Datasets associated with the manuscript Protein Condensate Atlas from predictive models of heteromolecular condensate composition

Datasets associated with the manuscript "Protein Condensate Atlas from predictive models of heteromolecular condensate composition".

NGII Data Set for Black Ice Traffic Accident Prediction

<a href="../api/records/10863284/draft/files/NGII%20Data%20Set%20for%20Black%20Ice%20Traffic%20Accident%20Prediction.zip/content" target="_blank" rel="noopener noreferrer">Title: NGII Data Set for Black Ice Traffic Accident Prediction</a> This dataset has been processed for scholarly purposes, utilizing data provided by the National Geographic Information Institute of Korea. <Reference> National Geographic Information Institute. (n.d.). National Land Information Platform. Retrieved from https://map.ngii.go.kr/ms/map/NlipMap.do

Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling

This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling".  The code to generate the models and reproduce the results of the comparative study in the above paper is available on this  <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a> Supplementary data: - The trained_models folder contains the results of the trained models. - The folder data contains data needed for for the comparative study for the year 2019 in the paper above.  This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.

To enhance CO2 saturation prediction from seismic data by joint use of attenuation and elastic properties: A machine learning approach

Wang and Zhao submitted for GJI

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.

The data and notebook for "Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence"

The data and Jupyter notebook for "Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence." Fig_6_code_implementation.ipynb This notebook presents implementation of our closure model in python to predict turbulent transport for all $(r, \mathrm{Pr})$ in Fig. 6(a). GSF_r_Pr_scan__Shear_eq_3.h5 This file contains data output from the $(r, \mathrm{Pr})$-scan of the closure model, obtained using "Fig_6_code_implementation.ipynb".  The h5 data file can be simply read using the following lines of code: <code>import h5py</code> <code>hf=h5py.File('~/GSF_r_Pr_scan__Shear_eq_3.h5', 'r')</code> <code>ux_uy = hf['ux_uy/ux_uy/ux_uy'][()]</code> <code>ux_th = hf['ux_th/ux_th/ux_th'][()]</code> <code>Pr_exp = np.linspace(0.02, 7, 28)</code> <code>Pr_array = 10**(-Pr_exp) #These are the values of Pr for which the transport is computed.</code> <code>r_exp = np.linspace(0.02, 5, 20)</code> <code>r_array  = 10**(-r_exp)  #These are the values of  r for which the transport is computed.</code>   <blockquote> Authors: B. Tripathi Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA ORCID : 0000-0002-4723-2170 Email : btripathi@wisc.edu A.J. Barker Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK ORCID : 0000-0003-4397-7332 Email : A.J.Barker@leeds.ac.uk A.E. Fraser Department of Applied Mathematics, University of Colorado, Boulder, Colorado 80309, USA Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder, Colorado 80309, USA Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Colorado 80309, USA ORCID : 0000-0003-4323-2082 P.W. Terry Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA ORCID : 0000-0002-4981-9637 E.G. Zweibel Department of Physics, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA Department of Astronomy, University of Wisconsin--Madison, Madison, Wisconsin 53706, USA ORCID : 0000-0003-4821-713X </blockquote>

Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes

This data set is created for a purpose of publication "Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.

Figure for "A quick battery charging curve prediction by artificial neural network"

Open the record for dataset details and reuse information.

Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques

Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center. Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority.  The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.

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).

opencc-by-4.0Feb 2024View details →

High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023

This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline.  <h2>1) Maize cover and yield seasonal composites</h2> The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at:  <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>. The land cover classification images contain 3 bands/variables: maizeProb, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; majorityClass, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and optimalClass, the categorical land cover class adjusted to agree with national statistics for expected maize area. The land cover classes map to the raster values as follows:  <div> <div> <pre>{ 1: 'maize', 2: 'nonmaize_annual', 3: 'nonmaize_perennial', 4: 'scrub_shrub_land', 5: 'forest', 6: 'flooded_vegetation', 7: 'water', 8: 'structure', 9: 'bare' }</pre> </div> </div> The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B. <h3>Maize yield</h3> The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>. Each of the images in the yield composites has 3 bands/variables also: maizeYield, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; maizeYield_majorityClass, predicted maize yield masked to the majority class land classification; and maizeYieldAdj_optimalClass, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics.  The dataset includes the same seasons as the classification product; see above for a description. <h2>2) End-to-end machine learning pipeline</h2> All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season).  The scripts needed to perform the pipeline are located in the <code>scripts</code> folder.  The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user.  Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed.  <h3>Google Cloud Storage and Earth Engine set-up</h3> Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the Objects section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>. <h3>Pipeline set-up</h3> In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction.  <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover. <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation.  <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>).  <h2>3) Input data into machine learning pipeline</h2> A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024. <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named districts_fc for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named aez_rwanda for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named impact_obs_lulc for use in the pipeline.</li> </ul> (The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.) <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul>

Disease trajectories in hospitalized COVID-19 patients are predicted by clinical and peripheral blood signatures representing distinct lung pathologies

COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology over the course of acute COVID-19 is necessary to understand its diverse pathogenesis and inform more precise and effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood biomarkers, and lung pathology in COVID-19 patients from the Brazilian Amazon. We identified core clinical and peripheral blood signatures differentiating disease progression between recovered patients from severe disease and fatal cases. Signatures were heterogenous among fatal cases yet clustered into two patient groups: “early death” (< 15 days of disease until death) and “late death” (> 15 days). Progression to early death was characterized systemically and in lung histopathology by rapid, intense endothelial and myeloid activation/chemoattraction and presence of thrombi, associated with SARS-CoV-2+ macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis and abundant SARS-CoV-2+ epithelial cells in post-mortem lung, with cytotoxicity, interferon and Th17 signatures only detectable in the peripheral blood 2 weeks into hospitalization. Progression to recovery was associated with higher lymphocyte counts, Th2 and anti-inflammatory-mediated responses. By integrating ante-mortem longitudinal systemic and spatial single-cell lung signatures, we defined an enhanced set of prognostic clinical parameters predicting disease outcome for guiding more precise and optimal treatments. Finally, this study represents a major advance in the investigation of acute respiratory infections by integrating serial clinical data and peripheral blood samples with histopathological and spatially-resolved single-cell analyses of post-mortem lung samples.

Finite element dataset and Artificial Neural Networks algorithms to predict the mechanical properties of innovative CLT

This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several Artificial Neural Networks algorithms with or without prior knowledge, the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.

Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) - code and data sets

This repository contains the scripts for the paper in revision to the AAPS J: Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS)  Authors: Niels Hendrickx, MSc, France Mentré, MD, PhD, Andreas Traschütz, MD, PhD, Cynthia Gagnon, PhD, Rebecca Schüle, MD, ARCA Study Group, EVIDENCE-RND consortium, Matthis Synofzik, MD, Emmanuelle Comets, PhD A simulated dataset (simulated_arsacs.csv) has been included in the repository to make the code executable as a standalone. Four main scripts have been provided in addition with the present Readme describing the files. The repository also includes 3 R objects and 2 folders which will be overwritten when the scripts are run, and are included as examples of the expected outputs. The main scripts are: - Script_imputation_selection.R: runs the covariate selection method. It uses a simulated dataset provided in the depot. The multiple imputation model is hardcoded as an input to the mice package to generate 10 imputed datasets, saved in current_directory/imputed_data_sets/df_arsacs_mi_i.csv. The script then runs the covariate selection method. The script prints out the list of selected covariates and returns a saemixObject containing the fit of the selected covariate model.  After the script executes, a list will be saved with the name of the selected covariates in the current directory (an example is included under the name "cov_matrix_model.RData" in the repository), the output of the selection, containing the whole history of runs will be saved under "final_covariate_model.RData", the list of selected covariate names will be saved under "list_covariates.RData". - source_mi.R: contains the functions used by Script_imputation_selection.R - script_bootstrap_indfit.R: This script loads "cov_matrix_model.RData" containing the matrix of covariate effects (used by saemix) and "list_covariates.RData", the list of covariates included, fits the model on the imputed data sets and computes its bootstrap distribution for each imputed data set (in the script, using only 20 samples for computation time, saved in current_directory/bootstrap/boot.arsacs.case.mi.i). It then computes the mean parameter and relative standard error of each parameter. It then computes the conditional distribution of each patient in each bootstrap samples and returns a data frame of individual predictions. The script will then plot 4 indivudal predictions.  - source_bootstrap.R: contains the functions used by script_bootstrap_indfit.R Both scripts need the saemix package to run, which we haven’t included in the repository as it is freely available on the CRAN (https://cran.r-project.org/web/packages/saemix/index.html). Additional libraries we make use of in the code (MICE, tidyverse, ggplot2) also need to be installed prior to execution.  The R code provided can be further customised to be adapted to different scenarios. For the code to run, it is preferable to unzip the whole folder and set the working directory to the source file location as the script uses the "bootstrap" and "imputed_data_sets" sub-folders To execute this code, assuming the required libraries are available in the local R installation, please open an R session and run: source("Script_imputation_selection.R") # for the covariate selection method (runtime: 3h on a  i7-8565U laptop) source("script_bootstrap_indfit.R") # to obtain individual trajectories (runtime: 1h on a  i7-8565U laptop)

Dataset for "Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision"

<h2>Overall</h2> A strawberry dataset for the paper "Qi Yang, Licheng Liu, Junxiong Zhou, Mary Rogers, Zhenong Jin, 2024. Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision, Computers and Electronics in Agriculture, 220, 108911. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compag.2024.108911" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compag.2024.108911</a>" <h2>Plant traits measurements</h2> The folder "measurement.zip" includes treatment-level and fruit-level ground truth data.  <h3>Treatment-level</h3> <pre><code>data_dryMatter_2022.csv data_dryMatter_2023.csv data_freshMatter_2022.csv data_freshMatter_2023.csv data_fruitNumber_2022.csv data_fruitNumber_2023.csv data_plantBiomass_2022.csv data_plantBiomass_2023.csv</code></pre> <h3>Fruit-level</h3> Fruit conditions with five classes, 1-5 represent Normal, Wizened, Malformed, Wizened & Malformed, and Overripe, respectively. <pre><code>data_size_freshWeight_condition_2022_0N.csv data_size_freshWeight_condition_2022_50N.csv data_size_freshWeight_condition_2022_100N.csv data_size_freshWeight_condition_2022_150N.csv</code></pre> Fruit size for tagged fruits <pre><code>data_taggedFruit_diameter_2022.csv data_taggedFruit_diameter_2023.csv data_taggedFruit_length_2022.csv data_taggedFruit_length_2023.csv</code></pre> Fresh yield and lifespan for tagged fruits (only available in experiment 2023) <pre><code>data_taggedFruit_freshMatter_2023.csv data_taggedFruit_lifespan_2023.csv</code></pre> <h3>Weather data</h3> <pre><code>weather_daily_2022.csv weather_daily_2023.csv</code></pre> <h2>Image data with label</h2> <h3>Object and phenology detection</h3> The folder "strawberry_img_random.zip" contains images and the corresponding JSON labels for object and phenological stages detection. <h3>Fruit size and decimal phenological stage</h3> The folder "strawberry_img_tagged.zip" contains images and the corresponding JSON labels for fruit size and decimal phenological stages detection. <pre><code>For example, "label": "small g, 8.84, 7.62, 0.4", This label means the fruit has an 8.84mm diameter and 7.62mm length, with the main stage being small green and the decimal stage being DS-4 </code></pre> <h3>Merge and split Data</h3> A Python script, "datasetProcessing.py", can be used to merge and split the image data into training and testing set. <h3>Pre-trained models</h3> models.zip   Data collector: Dr. Qi Yang, University of Minnesota, USA. Email: qiyang577@gmail.com All the files belong to Prof. Zhenong Jin, University of Minnesota, USA. Email: jinzn@umn.edu

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.