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278 results for “Validated dataset”
Internal and Predictive Validity of the French Health of the Nation Outcome Scales Dataset
<p>This dataset is related to Golay P, Basterrechea L, Conus P, Bonsack C (2016). Internal and Predictive Validity of the French Health of the Nation Outcome Scales: Need for Future Directions. PLoS ONE 11(8): e0160360. doi:10.1371/journal.pone.0160360.</p> <p>http://journals.plos.org/plosone/article/related?id=10.1371%2Fjournal.pone.0160360</p> <p><br /> It includes 19 variables and 2722 cases.</p>
In situ dataset for initialization and validation of the Copernicus Med-MFC biogeochemical model system (MedBGCins)
<p>The biogeochemical model system in use by the Mediterranean Monitoring Forecasting Centre (Med-MFC) of the EU Copernicus Marine Service requires several observational datasets for data assimilation and model initialization and validation (Coppini et al., 2023; Cossarini et al., 2021; Salon et al., 2019). The present MedBGCins dataset consists of the in situ measurements, coming from selected platforms, on which the initialization and validation of the biogeochemical model system are built. The MedBGCins dataset collects in situ measurements along the Mediterranean Sea water column and during the 1995-2023 time period for nutrients (i.e., nitrate, nitrite, phosphate, silicate, ammonium), dissolved oxygen, dissolved inorganic carbon, total alkalinity, total scale pH at 25°C. The dataset also provides pCO2 and total scale pH at in situ conditions, reconstructed by using the PyCO2SYS Python toolbox (Humpreys et al., 2024). The complete list of variables is indicated in Table 1. The largest subset of the original data are from EMODnet Chemistry Mediterranean Sea - Eutrophication and Acidity aggregated datasets 1911/2022 v2023 (reference in Table 2), including both profiles and time series, plus other documented cruises (same table).</p> <p>Additional information and references are included in the UserGuide file.</p> <p> </p>
Mobilise-D Technical Validation Study (TVS) dataset
<h1>Mobilise-D Technical Validation Study (TVS) Dataset</h1> <p>This dataset was recorded as part of the Mobilise-D project, a comprehensive initiative aimed at developing and validating digital solutions for assessing mobility in real-world environments. The Mobilise-D project seeks to address the critical need for accurate, reliable, and scalable tools to monitor and evaluate gait and mobility patterns, particularly in populations with mobility impairments.</p> <p>The dataset comprises recordings from a diverse cohort of participants, including healthy individuals and patients with various mobility-related conditions. Data collection was conducted using state-of-the-art wearable sensors and devices, capturing a wide range of gait parameters and contextual information in both controlled and free-living settings. The primary objective was to ensure the robustness and precision of digital mobility assessment tools under real-world conditions.</p> <p>Key features of the dataset include:</p> <ul> <li> <p>Demographic & Clinical Data: Age, gender, height, weight, and clinical diagnoses.</p> </li> <li> <p>Sensor Data: Raw and processed data from accelerometers, gyroscopes, and other wearable sensors.</p> </li> <li> <p>Reference Gait Parameters: Stride length, stride frequency, gait speed, and variability measures.</p> </li> </ul> <p>The dataset has undergone rigorous validation processes to confirm its accuracy and reliability. It serves as a critical resource for researchers and developers aiming to enhance digital health technologies and improve clinical assessments of mobility. The TVS dataset paves the way for future innovations in digital biomarkers and personalized healthcare solutions.</p> <h2>Brief Overview</h2> <p>This dataset contains data from 108 participants from six cohort groups that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). Data was recorded across five measurement sites. Data availability varies between participants, and some tests might be missing for some participants.</p> <p>The recording was split into a comprehensive in-lab assessment and a 2.5 hour unsupervised free living conditions. For the in-lab measurements reference information from marker-based motion capture systems and the multi-device wearable <a href="https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1143248/full">INDIP system</a> are provided. For the free-living recording, only the INDIP system is available as a reference.</p> <p>Participants wore a McRoberts MM+ IMU at the lower back. Some participants additionally wore a custom wrist-IMU at the non-dominant hand. The IMUs were synchronized with the reference system.</p> <p>The following tests were performed as part of the In-Lab data capture (Name in recording file in parentheses):</p> <ol> <li> <p>Timed-Up-and-Go (Test4)</p> </li> <li> <p>Straight Walk Comfortable (Test5)</p> </li> <li> <p>Straight Walk Slow (Test 6)</p> </li> <li> <p>Straight Walk Fast (Test 7)</p> </li> <li> <p>L-Test (Test8)</p> </li> <li> <p>Surface Test (Test9)</p> </li> <li> <p>Hallway Test (Test10)</p> </li> <li> <p>Simulated daily activities (Test11)</p> </li> </ol> <p>For some tests multiple <em>trials</em> are available. Additional trials were performed in the case of technical or performance issues. Hence, the last trial of each test should always be preferred.<br>Tests not listed above (e.g. Test 1-3) are non-walking tests used for calibration. When performing gait related operations, these tests should be excluded.</p> <p>For the free-living recording <em>Recording4</em> corresponds to the actual recording. Recording 1-3 only contains calibration recordings that are usually not required.</p> <p>Learn more about the data collection protocol:</p> <blockquote> <p>C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci, P. Brown, et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study." BMJ Open 11, 12 (2021): e050785 (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>).</p> <p>S. Kirsty, T. Bonci, F. Salis, L. Alcock, E. Buckley, E. Gazit, C. Hansen, et al. "Design and Validation of a Multi-Task, Multi-Context Protocol for Real-World Gait Simulation." Journal of NeuroEngineering and Rehabilitation* 19, 1 (2022):141 (<a href="https://doi.org/10/gt55t6">https://doi.org/10/gt55t6</a>).</p> </blockquote> <h2>Files</h2> <p>/data/: Raw data files sorted by cohort/patientId/measurement_condition.<br>/participant_information.xlsx: Basic demographic and clinical information of all participants and "data quality" overview for all recordings</p> <p>For each recording the following files are provided:</p> <ul> <li> <p>infoForAlgo.mat: Reduced set of relevant demographic information that is required to process the data with the Mobilise-D algorithmic pipeline</p> </li> <li> <p>data.mat: Core data file following the <a href="https://www.nature.com/articles/s41597-023-01930-9">Mobilise-D file format</a>. For each trial the data contains the raw sensor data of the lower-back IMU (SU), the raw data of all reference sensors, and calculated gold standard parameters for all relevant gait parameters based on the reference system. For some participants, data from a wrist worn sensor is included.</p> </li> <li> <p>test_list.json: A json file containing all the available tests and trials including the data.mat file. This information is also available via the data.mat file, but the json file is faster to parse and should help with identifying the correct data files to load.</p> </li> </ul> <h2>Tips and Notes</h2> <h3>Data Quality</h3> <p>Depending on the use case, specific trials should not be used. <code>participant_information.xlsx</code> file contains a sheet named data quality, that indicates for each system used, if the data was recorded properly. "0" indicates that the data is not usable at all, "1" indicates that some issues remain. This usually indicates partial or full data loss in a single test or unreliable reference information. These recordings might be usable for certain types of analysis, but should not be used for proper algorithm validation on the dataset. Only recordings with data quality >=2 for all required systems should be used.</p> <h3>Walking Aid Use</h3> <p>The <code>participant_information.xlsx</code> file contains 3 columns with information about walking aid use. The two columns <code>self_reported_indoors</code> and <code>self_reported_outdoors</code> describe the use of walking aids independent of the study context as reported by the patients of the day of the recording. This information might be different from the actual walking aids used during the assessment. This information can be found <code>use_during_lab_assessment</code> column. This information was recorded by the study conductor. For the free-living tasks patients were allowed to use any assistance they needed. Actual use was not recorded for this assessment.</p> <p>In general, only a small number of participants used walking aids within the study. Therefore, we do not recommend analyzing walking aid users as a different group or including walking aid use as a stratifier.</p> <h3>General Notes</h3> <ul> <li> <p>The participant IDs are "double pseudonymized" and do not correspond to data-ids used within the Mobilise-D project or previously published example data</p> </li> <li> <p>The first digit of the participant IDs identifies the recording center. This information might be helpful to identify systematic domain shifts in the data, as different centers used slightly different measurement setups.</p> </li> <li> <p>In case multiple trials are available for a single test, only use the last one when performing algorithm validation to keep the data between the participants balanced.</p> </li> </ul> <h3>Reference Parameters</h3> <p>Below some notes and general recommendation regarding the reference parameters:</p> <ul> <li> <p>Reference parameters are provided on a MicroWb and ContinousWalkingPeriod level. In most cases, you will likely want to work with the information in "ContinousWalkingPeriod" (if you are using mobgap to load the data, this information is simply called "Wb"). Learn more <a href="https://mobgap.readthedocs.io/en/latest/guides/q_and_a.html#walking-bouts-vs-gait-sequences">here</a></p> </li> <li> <p>For in-lab measurements, the Stereophoto (aka. marker-based Mocap system) should be the preferred reference, as parameters are expected to be more accurate. However, due to limitations of the field-of-view of these systems, some walking trails are not completely covered by the references.</p> </li> <li> <p>Neither reference system includes turning information, as no established reference definition could be identified, that would allow for unbiased comparison of parameters.</p> </li> </ul> <p>Learn more about the methods for extracting reference parameters:</p> <blockquote> <p>T. Bonci, F. Salis, K. Scott, L. Alcock, C. Becker, S. Bertuletti, E. Buckley, et al. “An algorithm for accurate marker-based gait event detection in healthy and pathological populations during complex motor tasks” Frontiers in Bioengineering and Biotechnology, section Biomechanics, 10:868928, 2022 (DOI: 10.3389/fbioe.2022.868928).</p> <p>F. Salis , S. Bertuletti, T. Bonci, M. Caruso, K. Scott, L. Alcock, E. Buckley, et al. “A multi-sensor wearable system for the assessment of diseased gait in real-world conditions”. Frontiers in Bioengineering and Biotechnology, 11, 2023 (<a href="https://doi.org/10.3389/fbioe.2023.1143248">https://doi.org/10.3389/fbioe.2023.1143248</a>).</p> </blockquote> <h2>Usage Recommendation</h2> <p>This dataset is designed to validate algorithms and NOT to derive clinical insights from the patient cohorts.</p> <p>This dataset was used to validate the algorithms of the Mobilise-D computational pipeline for lower trunk IMU data. Details on the publications are reported below.</p> <p>Per-Block Validation:</p> <blockquote> <p>M.E. Micó-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, C. Kirk, A. Soltani, A. Küderle, et al. "Assessing Real-World Gait with Digital Technology? Validation, Insights and Recommendations from the Mobilise-D Consortium." <em>Journal of NeuroEngineering and Rehabilitation</em> 20, no. 1 (June 14, 2023): 78 (<a href="https://doi.org/10/gt55qb">https://doi.org/10/gt55qb</a>).</p> </blockquote> <p>Full Pipeline Validation:</p> <blockquote> <p>K., Cameron, A. Kuederle, M.E. Mico-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, A. Soltani, et al. "Estimating Real-World Walking Speed from a Single Wearable Device: Analytical Pipeline, Results and Lessons Learnt from the Mobilise-D Technical Validation Study." Scientific Reports, 14,1, 1754 2024 (<a href="https://doi.org/10.21203/rs.3.rs-2965670/v1">https://doi.org/10.21203/rs.3.rs-2965670/v1</a>).</p> </blockquote> <p>Implementation of these validation procedures are also available via the open-source library <a href="https://github.com/mobilise-d/mobgap/">mobgap</a>.</p> <p>We recommend the use of this library in all use cases, as it provides high level tools to load and process the dataset. Documentation for this can be found in the following examples:</p> <ol> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_04_tvs_data_no_exc.html">The TVS dataset class</a></p> </li> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_01_loading_example_data.html">Working with data in mobgap</a></p> </li> <li> <p><a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_02_working_with_ref_data.html">Working with reference data in mobgap</a></p> </li> </ol> <h2>Suggested Citation</h2> <p>When you are working with the data, we suggest the following citation:</p> <blockquote> <p>Küderle, A. (2024). Mobilise-D Technical Validation Study (TVS) dataset [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.13899385">http://doi.org/10.5281/zenodo.13899385</a></p> </blockquote> <p>Please cite our paper in your publications if our repository helps your research.</p> <blockquote> <p>C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study". BMJ Open 11, 12 2021): e050785. (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>).</p> </blockquote> <h2>License and Legal Information</h2> <p>Mobilise-D Technical Validation Study Dataset © 2024 by Mobilise-D Consortium is licensed under CC BY-NC-ND 4.0</p> <h2>Acknowledgments</h2> <p>We extend our gratitude to all participants who contributed to the Mobilise-D project, enabling the comprehensive collection and analysis of mobility data. This work would not have been possible without the dedication and collaboration of the Mobilise-D Consortium members, including researchers, clinicians, and technical staff.</p> <p>We also acknowledge the funding and support provided by the European Union's Horizon 2020 research and innovation program under grant agreement No 820820. Special thanks to our partner institutions and organizations for their invaluable contributions and continued support.</p> <h2>Disclaimer</h2> <p>The Mobilise-D Technical Validation Study Dataset is provided for research purposes only. The Mobilise-D Consortium makes no warranties, express or implied, regarding the accuracy, completeness, or reliability of the dataset. Users of the dataset assume all responsibility for any conclusions drawn from the data.</p> <p>The dataset must be used in accordance with ethical guidelines and applicable laws and regulations. Any publications or presentations based on this dataset should appropriately cite the source. The Mobilise-D Consortium is not liable for any misuse of the dataset or for any direct, indirect, incidental, or consequential damages arising out of the use of the dataset.</p>
Validation and Benchmark Dataset for Discrete Element Method Simulations
<p>Verification and Benchmark Dataset for Discrete Element Method Simulations<br>v3 (05/02/2024)<br>Authors: Jose Salomon, Fernando Patino-Ramirez, Catherine O'Sullivan<br>https://doi.org/10.5281/zenodo.10160309<br>Contact: jjs19@ic.ac.uk<br>--------------------------------------------------------------------<br>Description of the repository:</p> <p>This repository contains a collection of datafiles and scripts that can be employed to validate and benchmark new or existing DEM codes. <br>Two validation cases/folders are considered "FCC_packing" and "Rolling_clump". The benchmark dataset is provided in the "Toyoura_sh" folder.<br>All datafiles and scripts are in the corresponding *.zip files. A detailed description of all cases can be found in the related article.</p> <p>In each of these folders, two sub-folders can be found: (1)"Data" and (2)"Scripts". These folders contain:</p> <p>1)"Data": contains the datafiles to perform the validation or benchmark. Two types of data/folders can be found here: "Raw" and "Filtered".<br>The "Raw" folder contains raw data only. The "Filtered" data contains the post-processed data employed to generate the plots found in the related article.<br>Plots in the related article can be reproduced by using the MATLAB files found in the corresponding data folder.</p> <p>2)"Scripts": contains the LAMMPS scripts used to generate the data files contained in "Data".<br>Indications about how to run these scripts can be found in the "README.txt" file in each folder.</p> <p>In order to reproduce the simulations of this repository, LAMMPS must be built including the "GRANULAR" and "RIGID" packages. Please check the README.txt files in each folder for details.</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Validation of Spectral Light Simulation Tools: Dataset of Simulated and Measured Indoor Light Exposure
<p>Since the discovery of a new photoreceptor in our eye, and with the growing awareness about the related ipRGC-influenced light (IIL) responses, design applications related to these responses are flourishing. Optimizing our ocular light exposure in buildings can have beneficial effects on our health, well-being, and performance through the action of this photoreceptor. To compare different design options and optimize the lighting conditions for building occupants, lighting simulations are typically used. However, as our IIL responses depend on various aspects of the light exposure including its spectral characteristics, spectral simulations are required. The dataset shared here was originally collected to validate two spectral simulation tools, <em>ALFA</em> and <em>Lark</em>, for the study of building design in relation to occupants’ IIL responses. The validation was done by comparing the simulation outputs against actual measurements, and assessing how reliable these tools were in predicting spectral irradiance under different indoor light conditions. Data were collected in two different experimental setups, one under daylight conditions only and the other one under electric light conditions only. The experimental protocol and README files contain detailed information on how the data was collected and what data was collected.</p>
DocTOR models and cross-validation dataset
<p>Dataset necessary for DocTOR utility.</p> <p>DocTOR (Direct fOreCast Target On Reaction), is a utility written in python3.9 (using the conda workframe) that allows the user to upload a list of Uniprot IDs and Adverse reactions (from the available models) in order to study the relationship between the two.</p> <p>On output the program will assign a positive or negative class to the protein, assessing its possible involvement in the selected ADRs onset.</p> <p>DocTOR exploits the data coming from T-ARDIS [https://doi.org/10.1093/database/baab068] to train different Machine Learning approaches (SVM, RF, NN) using network topological measurements as features.</p> <p>The prediction coming from the single trained models are combined in a meta-predictor exploiting three different voting systems.</p> <p>The results of the meta-predictor together with the ones from the single ML method will be available in the output log file (named "predictions_community" or "predictions_curated" based on the database type).</p> <p>The DocTOR utility is avaiable at https://github.com/cristian931/DocTOR</p>
Dataset Methods for stratification and validation cohorts: a scoping review
<p>We searched PubMed, EMBASE and the Cochrane Library for reviews that described the tools and methods applied to define cohorts used for patient stratification or validation of patient clustering. We focused on cancer, stroke, and Alzheimer’s disease (AD) and limited the searches to reports in English, French, German, Italian and Spanish, published from 2005 to April 2020. Two authors screened the records, and one extracted the key information from each included review. The result of the screening process was reported through a PRISMA flowchart.</p>
Gaia Data Release 3: BP/RP split-epoch validation dataset
<p>This dataset includes mean BP/RP spectra for about 43.6 thousand sources for which two mean spectra per source were generated using only a random selection of the available epoch spectra.</p> <p>More details about this dataset are given in Appendix D in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F., et al. A&A (2022). Results obtained from this dataset are published in the same paper and in "Gaia Data Release 3: The Galaxy in your preferred colours. Synthetic photometry from Gaia low-resolution spectra", Gaia Collaboration, Montegriffo, P., et al. A&A (2022).</p>
A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects: dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The enhanced mechanical behavior of polymer nanocomposites with spherical filler particles is attributed to the formation of matrix-filler interphases. The nano-scale leads to particularly high interphase volume fractions while rendering experimental investigations extremely difficult. Previously, we introduced a molecular dynamics-based interphase model capturing the crucial spatial profiles of elastic and inelastic properties inside the interphase. This contribution demonstrates that our model captures polymer nanocomposites’ essential characteristics reported from experiments. To this end, we thoroughly verify and validate the model before discussing the resulting local plastic strain distribution. Furthermore, we obtain a reinforcement in terms of the overall stiffness for smaller particles and higher filler contents, while the influence of particle spacing seems negligible, matching experimental observations in the literature. This paper proposes a methodology to unravel the underlying complex mechanical behavior of polymer nanocomposites and to translate the findings into engineering quantities accessible to a broader audience and technical applications.</p> </blockquote> <p><br> <br> <strong>Contact:</strong><br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong><br> Abaqus version R2018</p> <p><strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context:</strong><br> Data set supplementing journal paper:<br> [1] Ries, M.; Weber, F.; Possart, G.; Steinmann, P. & Pfaller, S., “A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects”, Composites Part A: Applied Science and Manufacturing, 2022, 107094.<br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content:</strong></p> <p>simulation folder denotation (“-” used instead of decimal points):<br> distance_particles _ radius_particle _ thickness_ip _ num_ip _ length_box _ factor_el_length _ fraction_box_length _ switch_mat_ip</p> <p>with</p> <ul> <li> distance_particles: center distance of the nanoparticles in nm</li> <li> radius_particle: radius of the nanoparticles in nm</li> <li> thickness_ip: thickness of the interphase layers in nm</li> <li> num_ip: number of interphase layers</li> <li> length_box: box edge length in nm</li> <li> factor_el_length: factor scaling the element length on the arcs of the interphase layers (element length = factor_el_length * thickness_ip)</li> <li> fraction_box_length: matrix element length = length_box / fraction_box_length</li> <li> switch_mat_ip: if = 0: interphases are assigned their actual material properties, if = 1: interphases are assigned the material properties of the bulk</li> </ul> <p> <br> <br> each simulation folder contains the following file types:</p> <ul> <li> .cae: Abaqus model database, containing parts, meshes, loads, etc.</li> <li> .dat: Printed output from the analysis input file processor, as well as printed output of selected results written during the analysis</li> <li> .inp: Analysis input file</li> <li> .log: Log file, which contains start and end times for modules run by the current execution procedure</li> <li> .msg: Diagnostic or informative messages about the progress of the solution</li> <li> .odb: Output database containing all results data from an Abaqus analysis</li> <li> .sta: Status file with increment summaries</li> </ul> <p><strong>folder structure:</strong></p> <ul> <li>Standard_case:<br> simulation folders of the standard close (particle center distance: 5.1776 nm) and distant (particle center distance: 7.9481 nm) cases (particle radius: 2 nm, filler content 0.054 vol.%, number of interphase layers: 4, factor_el_length: 1.0) and further particle center distances</li> <li>Layers:<br> simulation folders with different numbers of interphase layers, i.e., different values for num_ip, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Mesh:<br> simulation folders with different mesh qualities, i.e., different values for factor_el_length, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Particle_size:<br> simulation folders with different particle sizes <ul> <li>2_nm: simulation folders with particle surface distance 2 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>4_nm: simulation folders with particle surface distance 4 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>8_nm: simulation folders with particle surface distance 8 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> </ul> </li> </ul>
An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation: datasets
<p>This data record contains datasets used in the study "An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation":</p> <ul> <li>The <code><span>final_design.ply</span></code> file contains the mesh corresponding to the final artefact design.</li> <li>The <code><span>material_measurements.nc</span></code> file contains goniophotometer records for the material reflectance.</li> <li>The <code><span>artefact_measurements.nc</span></code> file contains goniophotometer records for the artefact reflectance.</li> </ul>
Vision-Transformer, ViT, model validation dataset
<p><span>The U.S. cotton industry is highly concerned with removing plastic contamination from cotton lint. A major source of this </span><span>contamination is the plastic used to wrap cotton modules produced by John Deere round module harvesters. A machine-vision </span><span>detection and removal system has been developed to address this problem, using low-cost color cameras to detect plastic in the </span><span>cotton stream and remove it. However, the system requires a lot of calibration and is difficult for cotton gin workers to operate due to </span><span>its reliance on custom machine-vision classifier running on low-cost ARM computers running Linux. This research aims to make the system more user-friendly by adding an </span><span>auto-calibration feature that can track cotton colors and avoid plastic images, reducing the need for skilled personnel to operate the </span><span>system and making it easier for the cotton ginning industry to adopt. This image dataset was created to validate several Vision-</span><span>Transformer, ViT, AI models that in combination provides the key enabling technology for the auto-calibration code.</span></p>
Trackerless 3D Freehand Ultrasound Reconstruction Challenge 2024 - Validation Dataset
<blockquote> <p><strong>This Challenge will be an open-ended challenge, and we welcome your submission. Please register your team via this <a title="https://forms.office.com/e/dPg47ktV7M" href="https://forms.office.com/e/dPg47ktV7M" target="_blank" rel="noopener">form</a>. You can submit the algorithm via this <a title="https://forms.office.com/e/QChhNkLYiu" href="https://forms.office.com/e/QChhNkLYiu" target="_blank" rel="noopener noreferrer">form</a> for TUS-REC2024 Challenge, and we will test your submitted docker on the test set.</strong></p> <p><strong>We are organising TUS-REC2025 at MICCAI2025. More information is available on the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC2025 challenge website</a> and <a href="https://github.com/QiLi111/TUS-REC2025-Challenge_baseline" target="_blank" rel="noopener">Baseline code repo</a>.</strong></p> </blockquote> <p><strong>This is the validation dataset. The training dataset is available at <a href="../doi/10.5281/zenodo.11178508" target="_blank" rel="noopener">Part1</a>, <a href="../doi/10.5281/zenodo.11180795" target="_blank" rel="noopener">Part2</a>, and <a href="../doi/10.5281/zenodo.11355499" target="_blank" rel="noopener">Part3</a>.</strong></p> <p>Acquisition devices and config: The 2D US images were acquired using an Ultrasonix machine (BK, Europe) with a curvilinear probe (4DC7-3/40). The associated position information of each frame was recorded by an optical tracker (NDI Polaris Vicra, Northern Digital Inc., Canada). The acquired US frames were recorded at 20 fps, with an image size of 480×640, without speckle reduction. The frequency was set at 6MHz with a dynamic range of 83 dB, an overall gain of 48% and a depth of 9 cm. </p> <div> <p>Scanning protocol: Both left and right forearms of volunteers were scanned. For each forearm, the US probe moves in three different trajectories (straight line shape, "C" shape, and "S" shape), in a distal-to-proximal direction followed by a proximal-to-distal direction, with the US plane perpendicular of and parallel to the scanning direction. The validation dataset contains 72 scans in total, 24 scans associated with each subject.</p> <p>For detailed information please refer to the <a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/TUS-REC2024/" target="_blank" rel="noopener">Challenge website</a>. Baseline code is also provided, which can be found at this <a href="https://github.com/QiLi111/tus-rec-challenge_baseline" target="_blank" rel="noopener">repo</a>.</p> <p>Dataset structure: </p> <ul> <li>Folder <code>frames</code>: contains three folders (one subject per folder), each with 24 scans. Each .h5 file corresponds to one scan, storing image of each frame within this scan. Key-value pair and name of each .h5 file are explained below. <ul> <li>“frames” - All frames in the scan; with a shape of [N,H,W], where N refers to the number of frames in the scan, H and W denote the height and width of a frame. </li> <li>Notations in the name of each .h5 file: “RH”: right arm; “LH”: left arm; “Per”: perpendicular; “Par”: parallel; “L”: straight line shape; “C”: C shape; “S”: S shape; “DtP”: distal-to-proximal direction; “PtD”: proximal-to-distal direction; For example, “RH_Per_L_DtP.h5” denotes a scan on the right forearm, with ultrasound probe perpendicular of the forearm sweeping along straight line, in distal-to-proximal direction.</li> </ul> </li> </ul> </div> <div> <ul> <li>Folder <code>transfs</code>: contains three folders (one subject per folder), each with 24 scans. Each .h5 file corresponds to one scan, storing transformation of each frame within this scan. Key-value pair and name of each .h5 file are explained below. <ul> <li>“tforms” - All transformations in the scan; with a shape of [N,4,4], where N is the number of frames in the scan, and the transformation matrix denotes the transformation from tracker tool space to camera space. </li> <li>Notations in the name of each .h5 file is the same as in folder <code>frames</code>.</li> </ul> </li> <li>Folder <code>landmark</code>: contains three .h5 files. Each corresponds to one subject, storing coordinates of landmarks for 24 scans of this subject. For each scan, the coordinates are stored in numpy array with a shape of [20,3]. The first column is the index of frame; the second and third columns denote the coordinates of landmarks in the image coordinate system.</li> <li><code>calib_matrix.csv</code>: The calibration matrix was obtained using a pinhead-based method. The "scaling_from_pixel_to_mm" and "spatial_calibration_from_image_coordinate_system_to_tracking_tool_coordinate_system" are provided in the “calib_matrix.csv”.</li> <li><code>dataset_keys.h5</code>: stores the paths to all the scans of the data set. Keys in “dataset_keys.h5” denotes all the available scans in validation set, in a format of “sub%03d__%s” where %03d denotes folder name, and %s denotes the scan name. For example, “sub050__LH_Par_C_DtP” means the scan in folder “050”, with file name of “LH_Par_C_DtP.h5”</li> </ul> <div> <p><strong>Data Usage Policy:</strong></p> <ul> <li>The training and validation data provided may be utilized within the research scope of this challenge and in subsequent research-related publications. However, commercial use of the training and validation data is prohibited. In cases where the intended use is ambiguous, participants accessing the data are requested to abstain from further distribution or use outside the scope of this challenge.</li> <li><span>If you use our dataset in your publication, </span>please cite the challenge paper and some of the following optional articles: <ul> <li>Challenge paper: <ul> <li><strong>Qi Li et al. "TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker." <em>arXiv preprint arXiv:<a title="https://arxiv.org/abs/2506.21765" href="https://doi.org/10.48550/arXiv.2506.21765" target="_blank" rel="noopener">2506.21765</a></em> (2025).</strong></li> </ul> </li> <li>Optional articles:<br> <ul> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773.</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14.</a></li> </ul> </li> </ul> </li> </ul> </div> </div>
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