Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'

Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.

opencc-by-nc-4.0May 2023View details →

SynthRAD2023 Grand Challenge validation dataset: synthetizing computed tomography for radiotherapy

Version 1.1, updated on 2023-06-04 --> the task2_val.zip has been modified with a new cbct file for patient 2BA078. The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.7868169">10.5281/zenodo.7868169</a> and a detailed description is offered at <a href="https://doi.org/10.5281/zenodo.7260704">https://doi.org/10.5281/zenodo.7260704</a> in the "synthRAD2023_dataset_description.pdf". The input of the validation datasets for Task1 is in Task1_val.zip, while for Task2 in Task2_val.zip. After unzipping, each Task is organized according to the following folder structure: Task1_val.zip/ ├── Task1    ├── brain     ├── 1Bxxxx        ├── mr.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 1_brain_val.xlsx        ├── 1Bxxxx_val.png       └── ...      └── pelvis     ├── 1Pxxxx        ├── mr.nii.gz        ├── mask.nii.gz    ├── ... └── overview        ├── 1_pelvis_val.xlsx        ├── 1Pxxxx_val.png        └── .... Task2_val.zip/ ├──Task2    ├── brain     ├── 2Bxxxx        ├── cbct.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 2_brain_val.xlsx        ├── 2Bxxxx_val.png       └── ...        └── pelvis     ├── 2Pxxxx        ├── cbct.nii.gz        ├── mask.nii.gz ├── ... └── overview       ├── 2_pelvis_val.xlsx       ├── 2Pxxxx_val.png       └── .... Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below: [Task]    [Anatomy]    [Center]    [PatientID] 1    B        A        001 In each patient folder, two files can be found:  <ul> <li> mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image </li> <li> mask.nii.gz: image containing a binary mask of the dilated patient outline  </li> </ul> For each task and anatomy, an overview folder is provided which contains the following files: <ul> <li> [task]_[anatomy]_val.xlsx: This file contains information about the image acquisition protocol for each patient. </li> <li> [task][anatomy][center][PatientID]_val.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data. </li> </ul> DATASET DESCRIPTION This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks. Data was collected at 3 Dutch university medical centers: <ul> <li> Radboud University Medical Center; </li> <li> University Medical Center Utrecht; </li> <li> University Medical Center Groningen. </li> </ul> For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to. The following number of patients is available in the validation set. Validation <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 20 </td> <td> 0 </td> <td> 10 </td> <td> 30 </td> </tr> <tr> <td> Task 2 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> </tr> </tbody> </table> In total, for all tasks and anatomies combined, 120 image pairs are available in this dataset. This repository only contains the validation data. The training data is provided at: h<a href="https://doi.org/10.5281/zenodo.7260704">ttps://doi.org/10.5281/zenodo.7260704</a>. All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure). Data was acquired with the following scanners: <ul> <li> Center A: <ul> <li> MRI: Philips Ingenia 1.5T/3.0T </li> <li> CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT </li> <li> CBCT: Elekta XVI </li> </ul> </li> <li> Center B: <ul> <li> MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T </li> <li> CT: Siemens SOMATOM Definition AS </li> <li> CBCT: IBA Proteus+ or Elekta XVI </li> </ul> </li> <li> Center C: <ul> <li> MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T </li> <li> CT: Philips Brilliance Big Bore </li> <li> CBCT: Elekta XVI </li> </ul> </li> </ul> For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast. For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT. The following pre-processing steps were performed on the data: <ul> <li> Conversion from dicom to compressed nifti (nii.gz) </li> <li> Rigid registration between CT and MR/CBCT </li> <li> Anonymization (face removal, only for brain patients) </li> <li> Patient outline segmentation (provided as a binary mask) </li> <li> Crop MR/CBCT, CT and mask to remove background and reduce file sizes </li> </ul> The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics. ETHICAL APPROVAL Each institution received ethical approval from their internal review board/Medical Ethical committee: <ul> <li> UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”. </li> <li> UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”. </li> <li> Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”. </li> </ul> CHALLENGE DESIGN The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746019">https://doi.org/10.5281/zenodo.7746019</a>.

3D reconstructions of semi-transparent submerged objects: Nanomia, Cystisoma, and validation object

These data were used to support the conclusions published in the article titled, "New method for rapid 3D reconstruction of semi-transparent underwater animals and structures", accepted for publication in Integrative Organismal Biology on May 9th, 2023. It focuses on three physical objects, two of which were live animals, collected under permit in the Monterey Bay NAtional MArine Sanctuary: <ul> <li>A siphonophore of the species Nanomia bijuga</li> <li>An amphipod of the family Cystisoma</li> <li>A thin-walled plastic cylinder, for validation purposes.</li> </ul> For each of these objects, we provide the original .cine file, as recorded by the Phantom 640S highspeed camera, and the derived high-quality .mov video file. We exported video frames as image stacks, included as ZIP files in this repository. We chose to either extract the red or green channel, or a balanced luminance value of each frame. Background subtraction was performed in some cases, by applying a minimum filter or median filter with a certain Z (time axis) extent, and subtracting this from the original frames. Furthermore, additional smoothing was performed in some cases as indicated by the filenames, in the form of a median filter with an X-by-Y-by-Z extent. 3D Slicer software was used for segmentation, and bundled .mrb files are included, which have been tested with 3D Slicer version 5.0.3. Derived .STL or .PLY model files are included as well.

opencc-by-4.0May 2023View details →

Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach

The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns.  This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany

The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles. The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information)  propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely.

Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review

This project contains the following extended data: <ul> <li>Appendix 1. Supplementary Tables</li> <li>Appendix 2. Search formulas</li> </ul>

opencc-by-sa-4.0Jun 2023View details →

Wrist-worn sensor validation for heart rate variability and electrodermal activity detection in a stressful driving environment

The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.

Data and supplementary materials in support of "Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator"

Data and supplementary materials in support of  "Evgenios Vlachos, Regine Ejstrup, Thea Marie Drachen, Bertil Fabricius Dorch (2023) Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator, Frontiers in Research Metrics and Analytics, doi: 10.3389/frma.2023.1218213"   It includes the anonymized dataset with the OADO values for all researchers from 10 departments (2 per faculty), the R code to perform the analysis and the graphs, the Scopus search string for the background search on Open Access and metrics, and the documentation for pulling data out from Pure.

Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (anomalies) 0-5 cm

QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/388acf49-4cd6-4b32-9abb-e0f343c48cfd. Produced on QA4SM (https://qa4sm.eu)

Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (anomalies) 5-10 cm

QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/5219f9fd-d309-4405-befb-cf829f695bee. Produced on QA4SM (https://qa4sm.eu)

Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (absolute) 5-10 cm

QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/6a33bb0a-7744-4950-8478-d55b234d4b29. Produced on QA4SM (https://qa4sm.eu)

Validation run C3S SM COMBINED v202212 vs v202012 vs ISMN FRMs (absolute) 0-5 cm

QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/44c5ab62-c46e-4ebb-b99d-0bbf33e8b17f. Produced on QA4SM (https://qa4sm.eu)

Validation of C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global (anomalies)

QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/ba00b64b-3b61-4426-9b07-29df7e8e3620. Produced on QA4SM (https://qa4sm.eu)

Validation of C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global

QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202012 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/240161ef-60c6-4224-805d-5361d36b2a82. Produced on QA4SM (https://qa4sm.eu)

opencc-by-4.0Jul 2023View details →

Maps and Validation result for LULC map of Pakyong sub-division, East Sikkim

The repository contains the maps and images pertaining to the study area and the validation results.

VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data

The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm. A VNMPF includes: - Radar information, GPM orbit, and ISS orbit  - Time/date information - Geographical information - Radar reflectivity characteristics - Lightning energetic and identification information (where there is lightning) - 3-dimensional wind information (where radars in dual-Doppler configuration are available) Version 1: 2017-2020 Version 2: 2017-2022, updated VN winds

opencc-by-4.0Aug 2023View details →

The Awareness Assessment Model (Case Study Validation)

Dataset of the case study validation of the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective"

opencc-by-4.0Sep 2023View details →

Lung CT Deformable Image Registration Validation Dataset

This dataset contains 30 different cases of CT image pairs, with a high number of vessel bifurcation landmark pairs identified in each case. These landmarks can be used for deformable image registration (DIR) algorithm validation and quality assurance. Images are obtained from several publicly available image repositories as well as clinical scans from Barnes Jewish Hospital.   Guidelines for loading and visualizing data can be found on our Github at  https://github.com/deshanyang/Lung-DIR-QA   If you use our dataset, please cite the paper at  https://doi.org/10.1002/mp.17026

opencc-by-4.0Sep 2023View details →

Observational datasets for validation of Mediterranean Biogeochemical Copernicus Modelling System, period 2018-2020

Datasets used for the validation of the biogeochemical component of the Mediterranean Analysis and Forecast center of the EU Copernicus Marine Service for the period 2018-2020. The list of datasets includes: 1) the Delay Mode Satellite chlorophyll from https://data.marine.copernicus.eu/product/OCEANCOLOUR_MED_BGC_L3_NRT_009_141/description after interpolation to the 1/24° horizontal resolution, weekly averages and quality check with internal climatology 2) the BGC-Argo float profiles of nitrate, chlorophyll and oxygen from Coriolis DAC (ftp://ftp.ifremer.fr/ifremer/argo; https://doi.org/10.17882/42182#76230) after an internal quality check procedure which is described in Salon et al., 2019.  3) the climatological profiles for 16 subbasins of nitrate, phosphate, silicate, oxygen, DIC, alkalinity, pCO2 and pH computed from the Emodnet 2018 data collection and additional scientific datasets as described in Salon et al., 2019.   Ref.: Salon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C. and Crise, A., 2019. Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15(4), pp.997-1022.