Find research datasets worth reusing

opencc-by-4.0Jul 2020View details →

Dataset and Scripts for: RefPlantNLR: a comprehensive collection of experimentally validated plant NLRs (v.20200528_415)

RefPlantNLR v.20200528_415 See bioRxiv 2020.07.08.193961; doi: <a href="https://doi.org/10.1101/2020.07.08.193961">https://doi.org/10.1101/2020.07.08.193961</a> SUPPLEMENTAL DATA Table S1: Description of RefPlantNLR. Table S2: Plant orders represented in RefPlantNLR. Supplemental dataset 1: Amino acid sequences of RefPlantNLR entries (fasta format). This file contains 415 amino acid sequences. Supplemental dataset 2: CDS sequences of RefPlantNLR entries (fasta format). This file contains 400 CDS sequences. CDS sequences could not be retrieved for 15 RefPlantNLR entries. Supplemental dataset 3: Annotated genomic sequences of RefPlantNLR entries (GenBank flat file format). This file contains 329 genomic loci containing the gene models of 344 RefPlantNLR entries and 56 RefPlantNLR mRNA entries lacking genomic information. Supplemental dataset 4: InterProScan annotation of the RefPlantNLR amino acid sequences (GFF3 format). This file contains the InterProScan annotation of 415 amino acid sequences. Supplemental dataset 5: InterProScan annotation of the RefPlantNLR CDS sequences (GFF3 format). This file contains the InterProScan annotation of the 400 CDS sequences. Supplemental dataset 6: Amino acid sequences of the extracted RefPlantNLR NB-ARC domains (fasta format). This file contains 424 NB-ARC domain (SUPERFAMILY signature SSF52540) amino acid sequences belonging to 415 RefPlantNLR entries. Supplemental dataset 7: Amino acid sequences of the unique RefPlantNLR extracted NB-ARC domains (fasta format). This file contains 347 unique NB-ARC domain (SUPERFAMILY signature SSF52540) amino acid sequences. Supplemental dataset 8: Clustal Omega alignment of the unique RefPlantNLR extracted NB-ARC domains (PHYLIP format). This file contains the Clustal Omega alignment of 346 unique NB-ARC domains (SUPERFAMILY signature SSF52540) with all positions with less than 95% coverage removed. Pb1 was omitted from this alignment. Supplemental dataset 9: NB-ARC domain phylogeny of the RefPlantNLR entries using the Maximum likelihood method (Newick format). This file contains the phylogenetic analysis of the NB-ARC domain of the RefPlantNLR entries using the JTT method. Supplemental dataset 10: Amino acid sequences of the non-redundant RefPlantNLR entries (fasta format). This file contains 235 amino acid sequences representing the non-redundant RefPlantNLR entries at a 90% amino acid identity threshold per genus according to the NB-ARC domain. Supplemental dataset 11: Amino acid sequences of the NB-ARC domains of the non-redundant RefPlantNLR entries (fasta format). This file contains 241 amino acid sequences representing the extracted NB-ARC domains of the 235 non-redundant RefPlantNLR. Appendix S1: R script used to generate annotations and figures. Appendix S2: InterProScan descriptions used for generating annotations.

opencc-by-4.0Jun 2020View details →

Defect Prediction Tool Validation Dataset 2

This dataset is used to address the Research Questions in the study at Transactions on Software Engineering: Within-Project Defect Prediction of Infrastructure-as-Code using Product and Process Metrics. See also: https://github.com/stefanodallapalma/TSE-2020-05-0217. It provides * repositories.json - a list of repositories selected from open-source GitHub repositories based on the Ansible language. * fixing-commits.json - a list of defect-fixing commits extracted from those repositories. * fixed-files.json - a list of Ansible files fixed in those defect-fixing commits and respective bug-inducing commits. * failure-prone-files.json - a list of failure-prone files through the repository's commit history. * metrics.zip - csv files consisting of releases (set of files) and their IaC-oriented, delta and process metrics extracted from each analyzed repository * projects.zip - for each analyzed project, it contains the data (models, performance, and results of Recursive Feature Elimination) used to answer the Research Questions. Context Infrastructure-as-code (IaC) is the DevOps strategy that allows management and provisioning of infrastructure through the definition of machine-readable files and automation around them, rather than physical hardware configuration or interactive configuration tools. On the one hand, although IaC represents an ever-increasing widely adopted practice nowadays, still little is known concerning how to best maintain, speedily evolve, and continuously improve the code behind the IaC strategy in a measurable fashion.  On the other hand, source code measurements are often computed and analyzed to evaluate the different quality aspects of the software developed. In particular, Infrastructure-as-Code is simply "code", as such it is prone to defects as any other programming languages. This dataset targets the YAML-based Ansible language to devise within-project defects prediction approaches for IaC based on Machine-learning. Content The dataset contains metrics extracted from 85 open-source GitHub repositories based on the Ansible language that satisfied the following criteria: * The repository has at least one push event to its master branch in the last six months; * The repository has at least 2 releases; * At least 10% of the files in the repository are IaC scripts; * The repository has at least 2 core contributors; * The repository has evidence of continuous integration practice, such as the presence of a  .travis.yaml file; * The repository has a comments ratio of at least 0.1%; * The repository has commit frequency of at least 2 per month on average; * The repository has an issue frequency of at least 0.01 events per month on average; * The repository has evidence of a license, such as the presence of a LICENSE.md file * The repository has at least 100 source lines of code. Metrics are grouped into three categories: * IaC-Oriented: metrics of structural properties derived from the source code of infrastructure scripts. Click [here](https://www.sciencedirect.com/science/article/pii/S0164121220301618) for more info. * Delta: metrics that capture the amount of change in a file between two successive releases, collected for each IaC-oriented metric. * Process: metrics that capture aspects of the development process rather than aspects about the code itself. Description of the process metrics in this dataset can be found [here](https://pydriller.readthedocs.io/en/latest/processmetrics.html). In addition to the metrics, the dataset contains the pre-trained models (*.joblib) in the folders rq1 and rq2 of projects.zip. You can load the model in Python as follows: ``` from joblib import load model = load('projects/owner/repository/rq1/random_forest.joblib'), mmap_mode='r') best_estimator = model['estimator']  # The estimator that maximized the AUC-PR cv_results = model['cv_results']  # The results of each step of the validation procedure best_index = mode['best_index']  # The index to access the best cv_results ```   Acknowledgements   This work is supported by the European Commission grants no. 825040 (RADON H2020). Inspiration What source code properties and properties about the development process are good predictors of defects in Infrastructure-as-Code scripts?

opencc-by-4.0May 2023View details →

LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models

The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named LigPCDS. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.   The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries. The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% [-19.4,20.2] and 77.4% [-11.7,12.1] in terms of Intersection over Union (mIoU) metric and between 62.4% [-18.8,19.7] and 87.0% [-8.4,8.8] in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record. The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines.  The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.

Dataset for the validation of a Computational Thinking test for upper primary school (grades 3-4)

This dataset contains quantitative student data acquired during the administration of a new computational thinking assessment for upper primary school (grades 3 and 4). Over 1500 students (approximately half in grade 3 and half in grade 4) participated in the data collection which took place in January 2021 in the Canon Vaud in Switzerland. The data was used to validate the psychometric properties of the instrument in the referenced article.    If you use any of the resources provided in this repository, please cite the following • The Zenodo repository, DOI: 10.5281/zenodo.5865573 • The corresponding journal article • Licence : CC-BY-NC   In case of inquiries, please contact laila.elhamamsy@epfl.ch

opencc-by-4.0Oct 2022View details →

Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI

This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data. While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic. All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'. In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK). For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane. All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.

opencc-by-4.0Jul 2024View details →

Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."

Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."

opencc-by-4.0Feb 2019View details →

The Red Queen in the Repository: metadata quality in an ever-changing environment (preprint of paper, presentation slides and dataset collection with validation schemas to IDCC2019 conference paper)

This fileset contains a preprint version of the conference paper (.pdf), presentation slides (as .pptx) and the dataset(s) and validation schema(s) for the IDCC 2019 (Melbourne) conference paper: The Red Queen in the Repository: metadata quality in an ever-changing environment. Datasets and schemas are  in .xml, .xsd , Excel (.xlsx) and .csv  (two files representing two different sheets in the .xslx -file). The validationSchemas.zip holds the additional validation schemas (.xsd), that were not found in the schemaLocations of the metadata xml-files to be validated. The schemas must all be placed in the same folder, and are to be used for validating the Dataverse dcterms records (with metadataDCT.xsd) and the Zenodo oai_datacite feeds respectively (schema.datacite.org_oai_oai-1.0_oai.xsd). In the latter case, a simpler way of doing it might be to replace the incorrect URL "http://schema.datacite.org/oai/oai-1.0/ oai_datacite.xsd" in the schemaLocation of these xml-files by the CORRECT:  schemaLocation="http://schema.datacite.org/oai/oai-1.0/ http://schema.datacite.org/oai/oai-1.0/oai.xsd"  as has been done already in the sample files here. The sample file folders testDVNcoll.zip (Dataverse), testFigColl.zip (Figshare) and testZenColl.zip (Zenodo) contain all the metadata files tested and validated that are registered in the spreadsheet with objectIDs. In the case of Zenodo, one original file feed, zen2018oai_datacite3orig-https%20_zenodo.org_oai2d%20verb=ListRecords%26metadata Prefix=oai_datacite%26from=2018-11-29%26until=2018-11-30.xml , is also supplied to show what was necessary to change in order to perform validation as indicated in the paper. For Dataverse, a corrected version of a file, dvn2014ddi-27595Corr_https%20_dataverse.harvard.edu_api_datasets_export%20 exporter=ddi%26persistentId=doi%253A10.7910_DVN_27595Corr.xml , is also supplied in order to show the changes it would take to make the file validate without error.

Virtual Reality Dataset used for Proof of Concept in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the dataset used in the Virtual Reality POC for the validation of the Conflict Detection and Resolution (CD&R) use case. This dataset represent a extract of different (using K-means) candidate solution, either good or bad ones.

Solutions and Genetic algorithm dataset of the Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )

This dataset contains the solution of the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case (link). The solution are computed by a Genetic Algorithm developped by Nicolas Durand. Inside, one can find: -One archive, "GA_Scenario_Solution_Dataset.zip", containing 10 couple of files (so 20 files). Each couple of file "sol_X_1.csv" and "sols_X_1.csv" are reciprocally the solutino given by the Genetic Algorithm to scenario X, and all the candidate solution explroed by the GA while solving scenario X. This archive also contain other versions of the solutions made by the GA with other parameters. -One archive, "GA_Toy_Dataset.zip" , containing solution to random scenarios, used to develop the first interfaces. Those solutions are used to developp the heatmatrix and heatmaps of the project (link), and visualisations for the validation (link).

opencc-by-4.0Aug 2023View details →

Datasets and results from: "Random Forest Classification and Solar Flares Data: Analysis and Validation"

Instructions for the data and code repository Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation". The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv). The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling. In addition, the Python script for the post-processing workflow is provided, with comments located in the script. The soft range X-ray irradiance and VLF amplitude data were obtained from: National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023.  Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.

opencc-by-4.0Aug 2023View details →

Dataset of experiment of wire-harness manufacturing framework validation

Dataset created during the validation experiments of the wire harness manufacturing framework developed within the REMODEL European project.

opencc-by-4.0Aug 2020View details →

A Dataset of European Union Land Cover Validation Samples

A dataset of European Union land cover validation samples in 2015 and 2010 based on the LUCAS micro dataset ( publicly available at <a href="https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid">https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid</a> ) . The dataset provides 9 land cover types of land cover including cropland, forest, grassland, shrubland, wetland, water, bareland, impervious surface and permanent snow/ice. The dataset  is provided in .csv format.

opencc-by-4.0Dec 2020View details →

LAI_TS_Val: LAI time-series validation datasets in the 1-km pixel grid at global scale from 2001 to 2011

Leaf area index (LAI), which is defined as one half of the total green leaf area per unit ground surface area, is a critical structural variable for quantifying the exchange processes of energy and matter between the land surface and atmosphere, it is thus identified as a key parameter in most terrestrial ecosystem models. To acquire long-term LAI records at the global scale, several remote sensing LAI products have been generated from various satellite sensors. However, assessing the uncertainties associated with these LAI products through comparisons with independent ground-truth measurements is pivotal for an effective application of products. Many sites from global networks have collected and provided invaluable ground LAI measurements covering a wide range of biome types and spatial variabilities. These site-based LAI measurements have been obtained about 30 years (1990-now). However, the spatial scale mismatch between site and pixel observations restricts the utilization of LAI measurements for product time-series validation. This datasets were generated from site-based LAI measurements of FLUXET and Chinese Ecosystem Research Network (CERN), using the proposed GUGM (Grading and Upscaling of Ground Measurements) method to resolve the scale-mismatch issue between site and sensor observations and maximize the utility of time-series of site-based LAI measurements, which can achieve the goal of product time-series validation. This GUGM approach first ingests both high-resolution images and site-based LAI measurements to capture the spatiotemporal variability in the product pixel grid. Then, a strategy was employed to grade the spatial representativeness of LAI measurements in the product pixel grid. For those LAI measurements which cannot be directly used in the validation of products, a strategy was adopted to calculate the spatial upscaling coefficient based on site-based LAI measurements and aggregated high-resolution reference maps to derive reliable LAI time-series validation datasets. The GUGM method has been applied to the site-based LAI measurements to generate global time-series LAI validation datasets from 2001 to 2011 in the 1 km pixel grid. The datasets include 28 sites which are mainly located in North America and Asia, providing 924 validation data in total. Among these sites, 16 sites with 508 (55.0%) validation data were obtained for forest, while 11 sites with 341 (36.9%) validation data and one site with 75 (8.1%) were obtained for crops and grasses, respectively. This datasets were saved in two formats: *.xls and *.kmz and each format was zipped for 63 KB and 31 KB, respectively.

opencc-by-4.0Oct 2023View details →

Validation dataset

This dataset presents reported patient home communities and health facilities which they attended for malaria testing.

opencc-by-4.0Mar 2023View details →

Dataset of publication "Derivation and validation of a reference data-based real gas model for hydrogen"

In this repository, a new real gas model for hydrogen based on the Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) v10.0 is provided for the use in the simulation software OpenFOAM v2012. The model is valid in a temperature and pressure range of 150-400 K and 0.1-1000 bar, respectively. Usage beyond this range is not recommended as it may lead to unrealistic results.

opencc-by-4.0Dec 2022View details →

Extended dataset for the validation the competent Computational Thinking test in grades 3-6

Extended dataset for the validation the competent Computational Thinking test in grades 3-6 ======================================================= • If you publish material based on this dataset, please cite the following :     • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, & Francesco Mondada (2023). Extended dataset for the validation of the competent Computational Thinking test in grades 3-6 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7983525      • The article on the validation of the computational thinking test for grades 3-6 : El-Hamamsy, L., Zapata-Cáceres, M., Martín-Barroso, E., Mondada, F., Zufferey, J. D., Bruno, B., & Román-González, M. (2025). The competent Computational Thinking test (cCTt): A valid, reliable and gender-fair test for longitudinal CT studies in grades 3–6. Technology, Knowledge and Learning, 1-55.  https://doi.org/10.1007/s10758-024-09777-8  • License : This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0) • Creators : El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F. • Date May 30th 2023 • Subject : Computational Thinking (CT), Assessment, Primary education, Psychometric validation • Dataset format : CSV. The dataset contains four files (one per grade, see detailed description below). Please note that the spreadsheets may contain missing values due to students not being present for a part of the data collection. To have access to the specific cCTt questions please refer to the original publication [1] and Zenodo repository [2] which provide the full set of questions and correct responses. • Dataset size < 500 kB • Data collection period : January and November 2021 • Abbreviations :   - CT : Computational Thinking   - cCTt: competent CT test • Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543) # References [1] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. Journal of Educational Computing Research, 60(7), 1818–1866. https://doi.org/10.1177/07356331221081753  [2] El-Hamamsy, L., Zapata-Cáceres, M., Marcelino, P.,  Dehler Zufferey, J., Bruno, B., Martín Barroso, E., & ‪Román-González, M.‬ (2022). Dataset for the comparison of two Computational Thinking (CT) test for upper primary school (grades 3-4) : the Beginners' CT test (BCTt) and the competent CT test (cCTt) (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5885034 ‬‬‬‬‬‬‬‬‬‬‬ [3] El-Hamamsy, L., Zapata-Cáceres, M., Martín-Barroso, E. et al. The Competent Computational Thinking Test (cCTt): A Valid, Reliable and Gender-Fair Test for Longitudinal CT Studies in Grades 3–6. Tech Know Learn (2025). https://doi.org/10.1007/s10758-024-09777-8 [4] Brennan, K. and Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. page 25 [5] El-Hamamsy, L., Zapata-Cáceres, M., Marcelino, P., Bruno, B., Dehler Zufferey, J., Martín-Barroso, E., & Román-González, M. (2022). Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners’ CT test (BCTt) and the competent CT test (cCTt). Frontiers in Psychology, 13. https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1082659

Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper

Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper (complement for a tutorial at https://github.com/datngu/LmTag) This repo includes chromosome 10 reference panel data constructed for 3 populations: - EAS - EUR - SAS

opencc-by-4.0Feb 2022View details →

Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes

Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium) Date: 8th February 2022 For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022). https://doi.org/10.1063/5.0088000 ----------------------------- Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories - The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file. - The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ. - The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images. - The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage). - The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/ - The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)

opencc-by-4.0Mar 2022View details →

Copper Rings Insertion Validation Dataset

The dataset consists of color images of the fixture with inserted copper sliding rings, which is used to evaluate and validate the process of assembly of an object with low tolerances supported by multi modal exception strategy learning and ergodic control. This dataset is used to classify the insertion process into three states: OK, NotOK or NoPart. The dataset consists of two main classes: Valid Insertion, Invalid Insertion in each of the four insertion slots.