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ShareScore release 0.9.0
Dataset results
52 results for “training and test dataset”
UmamiIP_dataset_train_test_threshold_pepname-20231025
<p>The dataset used for UmamiIP(http://tastepeptides-meta.com/Umami_IP).</p>
Dataset for training and testing the SCFNN model
<p>train_test_data.tar.gz contains the DFT calculation results of water cubic box under various strength of external electric field. This dataset is used to train and test the SCFNN model.</p> <p>HirshfeldMullikenCharges.tar.gz contains the DFT calculation results of Hirshfeld and Mulliken charges for the same water box. This dataset is used to train the 4G-HDNN model.</p>
2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 7/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains a sample of the input data for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</p>
Training and test datasets for dual-layer network model
<p>Training and test datasets for dual-layer network model</p>
Training and testing dataset for CNN
<p>This file includs the training and testing datasets for the CNN classifier for the Edge-computing embeded Malaria DNA diagnostic study. </p>
Test Dataset for running Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network
<p>Test Dataset for running Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network</p>
Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - images
<p>The image dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available.</p> <p>After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset. </p>
Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - videos
<p>The video dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available.</p> <p>After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset. </p>
Training and test dataset for Receiver functions for quality classification and inversion
<p> </p> <p>traindata_inv.dat file contains the 60,000 1D crust layered-model and synthetic receiver functions, as well as surface wave dispersion. </p> <p> </p> <p>First row: the name of synthetic models, </p> <p>Second row: Receiver functions with 300 sample points (-5~25 s)</p> <p>Third row: Surface wave dispersion data with 16 sample points (4~32 s with 2 s interval)</p> <p>Fourth row: Shear wave velocities of 60-layer-model, and the thickness from the first layer to the last layer are</p> <pre>0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0</pre> <p> </p>
Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - videos
<p>The video dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available.</p> <p>After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset. </p>
Datasets for training and for testing ligand-based reverse screening to predict drug targets.
<p>Bioactivity data were obtained from the ChEMBL (version 25) and the Reaxys<sup>®</sup> (version 03.2019) databases for training/screening and testing target prediction of small molecule drug by reverse screening. A short extract of the raw ChEMBL data for training is given in <strong>Supplementary Table 1 </strong>(Supp_Table_1.xlsx) to show three lines corresponding to an active, an inactive and an ‘gray area’ datapoints, respectively. Processed data are deposited here. The screening set file contains (CHEMBL-screeningset.csv), for each active compound, the standardized SMILES, the ChEMBLID, the number of experimental target(s) and their UniProt identifier(s). The test set file (Reaxys-testset-with300-SMILES.csv) contains, for each 364201 active compound, the ReaxysID, the number of experimental target(s) and the UniProt identifier(s). For Reaxys users, the chemical structure can be obtained through bulk request on the website. Access to www.reaxys.com and to Reaxys data can be obtained by contacting Elsevier directly. The first 300 entries display the standardized SMILES so that every reader can reproduce the results obtained by the reverse screening exercise.</p> <p>Further datasets can be obtained at www.swisstargetprediction.ch/download.php</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.