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641 results for “data validation”
How can we biochemically validate protein function predictions with the deoxycytidine kinase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-1e5d-e272">How can we biochemically validate ProteinCartography with the deoxycytydine kinase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> on the deoxycytidine kinase (dCK) using human dCK as our input (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P27707/entry">P27707</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
Data for "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024)
<p>This archive contains the data and scripts needed to reproduce the analysis in "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024).</p> <p>Contents</p> <p>data/: Input data and posterior distributions of different model fits</p> <p>datasets/: Observational datasets</p> <p>figures/: Folder to save figures in</p> <p>fluxes/: Saved lightcurves for auxiliary plotting purposes</p> <p>archive_paper_plotter.ipynb: Example script to plot fitted eclipse maps</p> <p>eclipse_pixel_sampling.py: Script to fit eclipse map and test k-fold CV score</p> <p>paper_eclipse_suite.py: Script to use simulated or observational data to fit an eclipse map</p>
Rumsey Train and Validation Data for ICDAR'24 MapText Competition
<p>Data set of 2Kx2K image tiles cropped from maps of the <a href="https://davidrumsey.com">David Rumsey collection</a> for the <a href="https://rrc.cvc.uab.es/?ch=28">ICDAR'24 Competition on Historical Map Text Detection, Recognition, and Linking</a>.</p> <p>Annotations and images follow the format described at the competition website and can be evaluated using the official <a href="https://github.com/icdar-maptext/evaluation">evaluation repository</a> script.</p> <p><strong>Important</strong>: v1.1 fixes an image channel order error, superseding the prior version. v1.2 corrects group links among several annotations. v1.3 strips markup that inadvertently remained in some annotation transcriptions.</p> <table> <tbody> <tr> <td> </td> <td><strong>Train</strong></td> <td><strong>Validation</strong></td> </tr> <tr> <td>Annotations</td> <td><code>rumsey_train.json</code></td> <td><code>rumsey_val.json</code></td> </tr> <tr> <td>Images</td> <td><code>train.zip</code></td> <td><code>val.zip</code></td> </tr> <tr> <td>Files</td> <td><code>rumsey/train/*.png</code></td> <td><code>rumsey/val/*.png</code></td> </tr> <tr> <td>Tiles</td> <td>200</td> <td>40</td> </tr> <tr> <td>Map Sheets</td> <td>196</td> <td>40</td> </tr> <tr> <td>Words</td> <td>34,518</td> <td>5,544</td> </tr> <tr> <td>Label Groups</td> <td>21,205</td> <td>3,502</td> </tr> <tr> <td>Illegible Words</td> <td>1,870</td> <td>313</td> </tr> <tr> <td>Truncated Words</td> <td>3,582</td> <td>628</td> </tr> <tr> <td>Valid Words</td> <td>30,563</td> <td>4,860</td> </tr> </tbody> </table> <p> </p> <p><strong>Annotations</strong>: Copyright 2024 UMN Knowledge Computing Lab, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC-BY-NC-SA 4.0 International.</a><br><strong>Images</strong>: David Rumsey Map Collection, David Rumsey Map Center, Stanford Libraries. <a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC-BY-NC-SA 3.0 Unported</a>.</p>
Data used for validation of the Local Subtraction Approach
<p>This dataset contains the volume conductor models used in the evaluation of the local subtraction approach for the EEG and MEG forward problem, as detailed in the corresponding manuscript (Published in SIAM Journal on Scientific Computing: <a href="https://epubs.siam.org/doi/full/10.1137/23M1582874" target="_blank" rel="noopener">https://epubs.siam.org/doi/full/10.1137/23M1582874</a>).</p> <p> </p> <p>This first archive contains the four-layer sphere models, the EEG and MEG sensors, and the dipoles used in the validation studies against analytical solutions.</p> <p> </p> <p>The second archive contains the realistic volume conductor model we used for comparing the analytical and local subtraction approaches. </p> <p> </p> <p>Details on the creation and interpretation of the data are given in README files contained in the dataset.</p>
Training and validation data for MOF-801(Zr) with adsorbed water molecules.
<p>We employ MACE 0.3.5 (github.com/acesuit/mace) to train an ML potential to the extended XYZ file `data.xyz`, which contains atomic geometries and potential energy and force labels. The system is MOF-801(Zr) at various water loadings. Data was generated in an active learning fashion using psiflow (github.com/molmod/psiflow).</p>
RUSH3D - 2P validation data
<p>Here are the synchronized data pair from a standard two-photon microscope and RUSH3D (a sort of light field microscope). </p> <p>TP_xxx.tif files are from 2p</p> <p>realign_RUSH3D.tif files are from RUSH3D</p>
ProbeSpec validation data
<p>Arb data used for validation of ProbeSpec. This data set is a sub-set of the data used for the environmental microarray published in Krausfeld <em>et al</em><em>. </em>(2017).</p>
Test and validation data for Robbie: A Batch Processing Work-flow for the Detection of Radio Transients and Variables
<p>Robbie: a general work-flow for the detection and characterization of radio variability and transient events in the image domain.<br> Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach.<br> Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility.<br> In the description paper we describe the methodology behind Robbie, and demonstrate its use on real and simulated data.</p> <p>This repository contains the observed and simulated data that was used in the description paper.</p> <p> </p>
The supplementary data for validation of paper <The effect of the biogenic isoprene emission around the megacity on ozone pollution in Beijing>
<p>The supplementary data of paper <<strong>The effect of the biogenic isoprene emission around the megacity on ozone pollution in Beijing>. The zip bag includes the data of four figures in paper with the format of Netcdf, and the more detail could be got by connecting with Hui Wang(wanghui6@mail.bnu.edu.cn)</strong></p>
Building Brain Invaders: EEG data of an experimental validation
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 25 subjects testing the <em>Brain Invaders </em>(Congedo, 2011), a visual P300 Brain-Computer Interface inspired by the famous vintage video game <em>Space Invaders</em> (Taito, Tokyo, Japan). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. EEG data were recorded by 16 electrodes in an experiment that took place in the GIPSA-lab, Grenoble, France, in 2012 (Van Veen, 2013 and Congedo, 2013). A full description of the experiment is available <a href="https://hal.archives-ouvertes.fr/hal-02126068">https://hal.archives-ouvertes.fr/hal-02126068</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2012-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2012-GIPSA</a>. The ID of this dataset is <em>BI.EEG.2012-GIPSA</em>.</p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02126068">https://hal.archives-ouvertes.fr/hal-02126068</a></p> <p> </p> <p><strong><em>Principal Investigator</em>:</strong> B.Sc. Gijsbrecht Franciscus Petrus van Veen</p> <p> </p> <p><strong><em>Technical Supervisors</em>: </strong>Ph.D. Alexandre Barachant, Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>BI.EEG.2012-GIPSA</em></p>
Raw data on the validation of the Polish language version of THE BRIEF HEPATITIS C KNOWLEDGE SCALE
<p>Raw data on the validation of the Polish language version of THE BRIEF HEPATITIS C KNOWLEDGE SCALE.</p>
Data file with manuscript titled 'A Structurally Validated Sequence Alignment of 497 Human Protein Kinase Domains'
<p>The files used in different analysis reported in the manuscript titled - 'A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains' are shared at two locations. Following is a brief description of these files.</p> <p>Location - https://github.com/DunbrackLab/Kinases<br> 1. HMM profile files - HMM files for each of the nine groups computed separately labeled as Groupname.hmm, like AGC.hmm<br> 2. HMM profile file - HMM file computed from the full alignment including all the sequences - Human-PK.hmm<br> 3. Score files - HMM scores of each kinase sequence against all the groupwise HMMs both for iteration1 (HMM-iter1-scores-tables.txt) and iteration2 (HMM-iter1-scores-tables.txt)<br> 4. Jalview session file - Kinase alignment with sequences colored by secondary structure information from PDB file if the structure is known; or predicted secondary structure if the experimental structure is not known. The file could be opened in Jalview - kinases-PDB-SSPred.jvp</p> <p>Location - https://zenodo.org/record/3445533<br> 1. The file contains list of residue pairs aligned in pairwise structural alignments of 272 human protein kinases which were used as a benchmark in the study. The alignments were created by FATCAT and optimized by SE program.</p>
Fig. 1 in New data on the ichthyosaur Platypterygius hercynicus and its implications for the validity of the genus
Fig. 1. Location of MHNH 2010.4 in Saint−Jouin, France.
Python functions -- cross-validation methods from a data-driven perspective
<p>This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation (SP-CV), dissimilarity quantification by adversarial validation (AVD), and dissimilarity-adaptive cross-validation (DA-CV). The description of how to run codes is in Readme.txt. The descriptions of functions are in functions.docx.</p>
Model and Data for the T&C-CROP Validation Paper: T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C,v1.5): Model formulation and validation.
<p>Here included is the code used to run T&C-CROP as used for the GMD paper submission alongside with the necessary weather data and raw field data used as part of the validation exercise. </p> <p> </p>
Script and data from: The best of two worlds: toward large-scale monitoring of biodiversity combining metabarcoding and optimised parataxonomic validation.
<h2>Description</h2> <div> <p>Zenodo linked to : Penel, B., Meynard, C.N., Benoit, L., Bourdonné, A., Clamens, A., Soldati, L., Migeon, A., Chapuis, M.-P., Piry, S., Kergoat, G. and Haran, J. (2025), The best of two worlds: toward large-scale monitoring of biodiversity combining COI metabarcoding and optimized parataxonomic validation. Ecography, 2025: e07699. <a href="https://doi.org/10.1111/ecog.07699">https://doi.org/10.1111/ecog.07699</a></p> <div> <div> <div> <div> <p><strong>Publication abstract </strong></p> </div> </div> </div> <p>In a context of unprecedented insect decline, it is critical to have reliable monitoring tools to measure species diversity and their dynamic at large-scales. High-throughput DNA-based identification methods, and particularly metabarcoding, were proposed as an effective way to reach this aim. However, these identification methods are subject to multiple technical limitations, resulting in unavoidable false-positive and false-negative species detection. Moreover, metabarcoding does not allow a reliable estimation of species abundance in a given sample, which is key to document and detect population declines or range shifts at large scales. To overcome these obstacles, we propose here a Human-Assisted Molecular Identification (HAMI) approach, a framework based on a combination of metabarcoding and image-based parataxonomic validation of outputs and recording of abundance. We assessed the advantages of using HAMI over the exclusive use of a metabarcoding approach by examining 492 mixed beetle samples from a biodiversity monitoring initiative conducted throughout France. On average, 23% of the species are missed when relying exclusively on metabarcoding, this percent being consistently higher in species-rich samples. Importantly, on average, 20% of the species identified by molecular-only approaches correspond to false positives linked to cross-sample contaminations or mis-identified barcode sequences in databases. The combination of molecular methodologies and parataxonomic validation in HAMI significantly reduces the intrinsic biases of metabarcoding and recovers reliable abundance data. This approach also enables users to engage in a virtuous circle of database improvement through the identification of specimens associated with missing or incorrectly assigned barcodes. As such, HAMI fills an important gap in the toolbox available for fast and reliable biodiversity monitoring at large scales.</p> <div> <h4><strong>File description: </strong></h4> <h4>MiSeq raw sequences of the COI barcode from 492 Coleoptera field samples :</h4> <div>The Raw_sequencage_data ZIP directory contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Coleoptera field samples in duplicate using the MiSeq platform GenSeq (ISEM - University of Montpellier)</div> <div> </div> <div>The HAMI_data_script_results_R zip directory contains Rmarkdown script files (.Rmd and .html) and associated data used to analyse the systemic errors of the metabarcoding approach (N= 492 Coleoptera field samples).</div> <div> </div> <div>The HAMI_pipeline zip directory contains all the codes associated with the HAMI pipeline, as well as a ReadMe file and a test data set.</div> <div> </div> <div>The Residual_chimera.zip directory contains lists of MOTUs associated to residual chimeric sequences that were not filtered using FROGS pipeline but secondarily detected with the <em>de novo</em> approach implemented in HAMI pipeline with ‘isBimeraDenovo’ R function from DADA2 v1.28.0. It contains two distinct files according to the two sequencing runs.</div> <div> </div> <div>The NUMTS_filtered.zip directory contains lists of MOTUs that were excluded of the final dataset according to the NUMTS filtering. File xxx_pseudogene_f1_deteled.csv corresponds to MOTUs that were excluded according to the first filtrering step based on DNA sequencing. File xxx_pseudogene_f2_deteled.csv corresponds to merged MOTUs that were excluded according to the second filter based on occurrence and percentage of identity. This folder contains files for the two sequencing runs.</div> </div> </div> </div>
Data set: Evidence for the validity of the Attitudes toward Sexual Behaviours Scale (ASBS) with Canadian young people
Open the record for dataset details and reuse information.
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models
<p>This repository contains data and models used in the following paper.</p> <p>Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. <a href="https://www.nature.com/articles/s42256-024-00809-7">Generative AI for designing and validating easily synthesizable and structurally novel antibiotics</a>. <em>Nature Machine Intelligence, </em>2024.</p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/SyntheMol">SyntheMol</a> code. More details about how to use the data and models with the code are available <a href="https://github.com/swansonk14/SyntheMol/tree/main/docs">here</a>.</p> <p>The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1).</p> <p>Data</p> <p> 1_training_data: The <em>Acinetobacter baumannii</em> inhibition data used to train antibiotic property prediction models.</p> <p> 2_chembl: Known antibiotic and antibacterial molecules from <a href="https://www.ebi.ac.uk/chembl/">ChEMBL</a>, which are used to compute the novelty of generated antibiotic candidates.</p> <p> 4_real_space: Data files and statistics for the <a href="https://enamine.net/compound-collections/real-compounds/real-space-navigator">Enamine REAL Space</a>. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022).</p> <p> 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP.</p> <p> 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict <em>A. baumannii</em> inhibition.</p> <p> 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine.</p> <p>The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.</p>
Data used, summary and codes: A validation standard for Area of Habitat maps for terrestrial birds and mammals
<p>Birds_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Birds .</p> <p>Mammals_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Mammals.</p> <p>Column_Names_Index_AOH_Metedata: Description of column names of Birds_AOH_Metadata and Mammals_AOH_Metadata</p> <p>Point_localities_ebird: Latitude and longitude of points per species for birds downloaded from eBird used to validate the AOH maps. </p> <p>Point_localities_gbif: Latitude and longitude of points per species for mammals downloaded from GBIF used to validate the AOH maps. </p> <p>Logistic_Regression_Codes_AOH_Validation: R script for logistic modelling.</p> <p>Point_Validation_Codes_AOH_Validation: R script for point validation of AOH maps.</p> <p>Sample_AOH_Maps: Few sample AOH maps which were validated.</p> <p> </p>
Data for the practical exercises in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset contains all the data that is required to carry out the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The dataset includes data for three different case studies: The Asturias Central Area (Spain), the Ariège Valley (France) and Marqués de Comillas (Mexico). For the Asturias Central Area and the Ariège Valley, the dataset includes Land Use Cover (LUC) maps for several years of reference as well as data (simulation outputs, model drivers) for different modelling exercises. For Marqués de Comillas, the dataset includes a LUC map and a set of reference points used to validate it.</p> <p>The dataset includes a readme file listing all the files it contains and auxiliary files describing the data. For further information on the study area and the files used in the practical exercises, users are referred to Chapter 1 of the book.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.