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59 results for “quality assurance”
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
Quality-Assurance Package for the "Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework" Manuscript
<h2>Background</h2> <p>Neuroimaging research requires consistent image quality and temporal signal stability, especially for functional magnetic resonance imaging (MRI) studies that rely on detecting subtle blood-oxygen-level-dependent (BOLD) signal changes. Regular MR system performance monitoring is essential, especially for longitudinal and multi-site studies. This study aims to establish a robust quality assurance (QA) protocol to promote data comparability across scanner models, vendors, and sites, as well as over a prolonged period.</p> <p>The manuscript titled "<em>Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework</em>" was submitted to the Special Issue <a href="https://link.springer.com/journal/10334/updates/26638300">Reproducibility and Quality Assurance</a> of the Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA) journal.</p> <p>This QA package proposed by the manuscript hosts materials for</p> <ul> <li>all reconstructed images,</li> <li>instruction for data acquisition,</li> <li>instruction for image reconstruction,</li> <li>instruction for post-processing,</li> <li>example raw data and DICOM images, and</li> <li>images and scripts for T1/T2 fitting.</li> </ul> <p>The detailed information is listed below.</p> <h2>All reconstructed images</h2> <p>This directory contains all reconstructed images from the fBIRN phantom on three Siemens 3T scanners (Trio, Prisma.Fit, and Cima.X) and one GE (UHP) 3T scanner. It contains four sub-folders for each scanner. And each sub-folder contains (some of) the following sub-folders:</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product spin-echo (SE) images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h2>Instruction for data acquisition</h2> <p>This directory includes the following documents:</p> <ul> <li><code>write_QA_Tran_EPIrs.m</code> to generate the <code>QA_epi.seq</code> file for EPI scans.</li> <li><code>write_QA_Tran_T1.m</code>: to generate the <code>QA_T1.seq</code> file for SE scans.</li> <li><code>20241122_QA_protocol_instruction_siemens.docx</code>: standard operating procedure for QA measurements.</li> <li><code>QA_record.xlsx</code>: Excel sheet for the record of QA measurements.</li> </ul> <h2>Instruction for image reconstruction</h2> <h3><em>Documents</em></h3> <ul> <li><code>pulseq2mrd_epi.m</code>: convert GE Pulseq EPI raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_epi.seq</code> file.</li> <li><code>pulseq2mrd_se.m</code>: convert GE Pulseq SE raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_T1.seq</code> file.</li> <li><code>siemens2mrd_epi.m</code>: convert Siemens Pulseq EPI raw data (<code>.dat</code>) to MRD raw data (<code>.h5</code>) using the information in the <code>.dat</code> raw data.</li> </ul> <ul> <li><code>default.xml</code>: Gadgetron configuration file for SE image reconstruction. This document is already in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/default.xml</code>.</li> <li><code>qc_epi.xml</code>: Gadgetron configuration file for EPI image reconstruction, which is modified from the <code>default epi.xml</code> located in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/</code>.</li> </ul> <ul> <li><code>specialCard_ICE.png</code>: Special card setting for ICE online reconstruction.</li> </ul> <h3><em>Procedures for Gadgetron offline reconstruction</em></h3> <p><strong>Step 1: Gadgetron installation (for more details, visit <a href="https://gadgetron.github.io/tutorial/">here</a>)</strong></p> <ul> <li>Download and install <a href="https://www.docker.com/">Docker</a> software. You may need to install/update the Windows Sub Linux (WSL) system for the Docker installation.</li> <li>Open your terminal (Power shell with administrative privilege in Windows) and navigate to the folder you would like to map to the Gadgetron Docker container.</li> <li>Run: <code>docker run -t --name gt_latest --detach --volume ${pwd}:/opt/data ghcr.io/gadgetron/gadgetron/gadgetron_ubuntu_rt_nocuda:latest</code>. If docker is not recognized, set <code>docker</code> to connect to <code>C:\Program Files\Docker\Docker\resources\bin</code> in the Environment Path in Windows. This will download and then launch the <a href="https://gadgetron.readthedocs.io/en/latest/building.html">latest Gadgetron version</a> in a Docker container. It will also mount your current folder as a data folder inside the container.</li> <li>Run this command: <code>docker exec -ti gt_latest /bin/bash</code>. This will execute your Gadgetron container.</li> </ul> <p><strong>Step 2: Data preparation</strong></p> <ul> <li>Place your SE/EPI <code>.dat</code>/<code>.h5</code> data in the mounted folder.</li> <li>Run the command in Terminal: <code>cd /opt/data</code> to enter the mounted folder.</li> </ul> <p><strong>Step 3: MRD conversion</strong></p> <ul> <li>For Siemens data, you can convert the <code>.dat</code> data to MRD data by using Gadgetron. If Gsdgetron doesn't work (e.g. for XA EPI data), you can then use the Matlab script <code>siemens2mrd_epi.m</code>.</li> <li>The command for Siemens SE data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -o se_data.h5</code>.</li> <li>The command for Siemens EPI data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -m IsmrmrdParameterMap_Siemens.xml -x IsmrmrdParameterMap_Siemens_EPI.xsl -o epi_data.h5</code>.</li> <li>For GE data, you can convert the <code>.mat</code> raw data to MRD data by using the Matlab scripts with the corresponding <code>.seq</code> files. For SE conversion: use <code>pulseq2mrd_se.m</code> with <code>QA_T1.seq</code>. For EPI conversion: use <code>pulseq2mrd_epi.m</code> with <code>QA_epi.seq</code>.</li> </ul> <p><strong>Step 4: Gadgetron reconstruction</strong></p> <ul> <li>SE reconstruction: <code>gadgetron_ismrmrd_client -f se_data.h5 -c default.xml -o se_out.h5</code>.</li> <li>EPI reconstruction: first, put <code>qc_epi.xml</code> to the mounted folder and then copy it to the Gadgetron container: <code>cp /opt/data/qc_epi.xml /opt/conda/envs/gadgetron/share/gadgetron/config/</code>. Then, run the reconstruction: <code>gadgetron_ismrmrd_client -f epi_data.h5 -c qc_epi.xml -o epi_out.h5</code>.</li> </ul> <p><strong>Step 5: Load Gadgetron-reconstructed images (<code>.h5</code>)</strong></p> <ul> <li>Load SE <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_se_out.h5' ;</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_se_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_se_im = reshape(pulseq_se_im, [256, 256, 11, 2]) ;</p> </blockquote> <ul> <li>Load EPI <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_epi_out.h5';</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_epi_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_epi_im = reshape(pulseq_epi_im, [64, 64, 27, 200]) ;</p> </blockquote> <h3><em>Procedures for ICE online reconstruction</em></h3> <p>Before executing the Pulseq-based sequences, you can enable ICE online Reconstruction following the procedures below:</p> <ul> <li>Navigate to the Special Card (<code>specialCard_ICE.png</code>), set <code>Data handling</code> to <code>ICE STD</code> for NUMARIS/X (e.g. XA60A and XA61A), and <code>ICE 2D</code> for NUMARIS/4 (e.g. VB, VD, and VE).</li> <li>Select <code>Sum-of-Square</code> for coil combination.</li> <li>Be sure that the maximal pixel intensity does not violate the intensity threshold of <strong>4096</strong>.</li> </ul> <h2>Instruction for post-processing</h2> <p>The example post-processing is based on the reconstructed images from Cima.X over five days.</p> <h3><em>Reconstructed images from Cima.X</em></h3> <p><strong>Note</strong>: All <code>se</code> folders contain a <code>structuralQuality_main.m</code> to call the <code>structuralQuality.m</code> function for structural quality analysis. All <code>epi</code> folders contain a <code>temporalQuality_main.m</code> to call the <code>temporalQuality.m</code> function for temporal quality analysis.</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product SE images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h3><em>QA analysis Matlab package: </em><code><em>QA_functions</em></code></h3> <ul> <li><code>circfit.m</code>: to find the center point and radius of the phantom.</li> <li><code>makeCircleMask.m</code>: to make a circular mask based on the center point and radius.</li> <li><code>structuralQuality.m</code>: to analyze the structural quality of the SE images.</li> <li><code>temporalQuality.m</code>: to analyze the temporal quality of the EPI images.</li> </ul> <h3><em>Post-processing procedures</em></h3> <ul> <li>Step 1: Add the <code>QA_functions</code> folder to your Matlab Path.</li> <li>Step 2: Run the <code>temporalQuality_main.m</code> or <code>structuralQuality_main.m</code> script in each folder to produce the QA results of all reconstructed images inside the folder.</li> <li>Step 3: Run the <code>make_figure_epi.m</code> and <code>make_figure_se.m</code> to produce some of the tables and figures used in the manuscript.</li> </ul> <h2>Example raw data and DICOM images</h2> <p>The data and DICOM images were acquired from Cima.X on the fBIRN phantom on 06.08.2024.</p> <ul> <li>DICOM folder: contains the DICOM images for four EPI scans (the first two scans for warm-up) and two SE scans.</li> <li><code>meas*.dat</code>: Siemens raw data of two EPI scans for temporal quality analysis and two SE scans for structural quality analysis.</li> <li><code>*data.h5</code> files: the ISMRMRD data of the four raw datasets.</li> <li><code>*out.h5</code> files: the images reconstructed by Gadgetron.</li> <li><code>*.nii</code>: the NIFTI-format reconstructed images.</li> <li><code>siemens2mrd_epi.m</code>: to convert the Siemens EPI raw data to ISMRMRD data.</li> <li><code>read_image.m</code>: to convert the Gadgetron-reconstructed h5-format images to NIFTI-format images.</li> </ul> <h2>Images and scripts for T1/T2 fitting</h2> <p>This package includes DICOM images and T1/T2 fitting scripts for the fBIRN phantom. Images for T1 fitting were acquired using a product turbo spin echo sequence with an inversion recovery pulse (repetition time = 4000 ms, echo train length = 4). Images for T2 fitting were obtained using a product SE sequence (repetition time = 3500 ms). Both measurements were conducted on the Siemens Prisma.Fit 3T scanner on 05.06.2024.</p> <ul> <li><code>T1 sub-folder</code>: contains all DICOM images for T1 fitting with inversion recovery times of {50, 150, 300, 450, 600, 750, 900, 1050, 1200, 1350, 1500, 2200, 3000} ms.</li> <li><code>T2 sub-folder</code>: contains all DICOM images for T2 fitting with echo times of {7.5, 15, 30, 45, 60, 75, 90, 130, 200, 250} ms.</li> <li><code>Do_T1fit.m</code>: Matlab script for T1 fitting.</li> <li><code>Do_T2fit.m</code>: Matlab script for T2 fitting.</li> </ul> <p>For more information regarding Pulseq and the workflow for data acquisition and image reconstruction, please visit our GitHub repositories: <a href="https://github.com/pulseq/pulseq">Pulseq Matlab software</a>, <a href="https://github.com/pulseq/tutorials">Pulseq Tutorials</a>, and <a href="https://github.com/pulseq/Pulseq-Rocks-2023-24-ISMRM-Reproducibility-Challenge">Pulseq Rocks for the 2024 ISMRM Reproducibility Team Challenge</a>.</p> <p>If you need any further information or have any questions, please feel free to contact our Pulseq email address: pulseq.mr@uniklinik-freiburg.de.</p>
Data quality assurance at research data repositories: Survey data
<p>This dataset documents findings form a survey on the status quo of data quality assurance practices at research data repositories.</p> <p>The personalized online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information, and yielded 332 complete responses.</p> <p>The dataset comprises a documentation file, the data file, a codebook, and the survey instrument.</p> <p>The <strong>documentation file</strong> (documentation.pdf) outlines details of the survey design and administration, survey response, and data processing. The <strong>data file</strong> (01_survey_data.csv) contains all 332 complete responses to 19 survey questions, fully anonymized. The <strong>codebook</strong> (02_codebook.csv) describes the variables, and the <strong>survey instrument</strong> (03_survey_instrument.pdf) comprises the questionnaire that was distributed to survey participants.</p>
Data to "Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer"
<p>This record includes the DICOM images and analysed data that were used in the multicenter QA program for quantitative MRI in cervical cancer as published (<a href="https://www.sciencedirect.com/science/article/pii/S0167814020307854?via%3Dihub">https://doi.org/10.1016/j.radonc.2020.09.013</a> ).</p> <p>The DICOM data includes the acquired DICOM data for each institute selected to those that were used in the publication. Acquisitions that were not used were removed. Data was anonymized with conquest dicom server tools.</p> <p>The analyzed data files are included giving per measurement the estimated quantitative parameter values as well as the position of the ROIs and extracted signal intensity values per phantom sample. An explanation of the structure of the files is added in the readme file. The analysis was done with in-house written code in matlab.</p> <p>Included are a description of the sequence parameters for each institute (IQEMBRACE_PhantomQA_OverviewInstitutionalSequenceParameters_20241114) and details on the choices in the analysis of the data (IQEMBRACE_PhantomQA_OverviewPhantomData_20241114). As background also the description of the measurements was added, giving more information on how the measurements were performed.</p> <p>This work was in preparation for the IQ-EMBRACE trial (clinicaltrials.gov NCT03210428)</p>
Replication Package for "Software Quality Assurance Analytics: Enabling Software Engineers to Reflect on QA Practices" Paper (SCAM 2024)
<p>Welcome to our artifact!<br>In here we provide additional information for you to retrace our steps in the interview analysis.<br>It has the following contents:</p> <ul> <li><code>codebook.xlsx</code>: Our full codebook with our open codes, structured after the axial codes that emerged. <code>codebook-statistics.xlsx</code> lists for each code in which participant's interview it can be found.</li> <li><code>generate-figures</code>: The plain data and scripts used to generate the figures in the paper.</li> <li><code>survey.pdf</code>: An printout of our whole online questionnaire that guided the participants through the pretest-posttest study and the interview.</li> <li><code>survey-answers.xlsx</code>: The complete data for our participants answers in the online survey during the interviews.</li> <li><code>repoinsights-dashboard-software</code>: The code of our prototype repoinsights. As it is under active development, this is not yet documented for replicating the study setup or extending it. Still, we are providing the source code for transparency and will publish a version with comprehensive setup instructions later.</li> </ul>
Quality Assurance System (QAS) video
<p>Tutorial video for the usage of the Quality Assurance System (QAS) developed in the ECOBULK project.</p>
Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research
<p>Dataset related to the publication: "Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research"</p>
WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present)
<p>WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present)</p> <p>Aerosol optical depth (AOD) measurements have been performed within the frame of Global Atmospheric Watch Precision Filter Radiometer (GAW-PFR) network in Ny-Ålesund (79N,11E) since 2002. The measurements are performed from March to October, with PFR (PrecisionFilterRadiometer) instruments provided by Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC). The solar tracker and infrastructure are provided by provided by NILU(Norsk institutt for luftforskning) in collaboration with the Norwegian Polar Institute.</p> <p>PFR manufactured by PMOD/WRC is a temperature stabilized instrument at 20<sup>o</sup> C equipped with four narrow band interference filters with nominal centroid wavelengths 368 nm, 412 nm, 500 nm and 862 nm and bandpass (fullwidth half maximum) 4 nm for 368 nm channel and 5 nm for the rest. The instrument is calibrated in yearly bases against the WMO-AOD reference at PMOD/WRC during the polar winter. The operation is done remotely by PMOD/WRC with the installation and onsite maintenance (cleaning, alignment adjustments) done by personnel of Norwegian Polar Institute and NILU in collaboration with PMOD/WRC.</p> <p>The processing and quality assurance of the data is done following the protocols of World Optical depth Research and Calibration Center (WORCC, PMOD/WRC) (2018), and the data are submitted to the WDCA database (EBAS-NILU) as hourly mean AOD and AE values. This dataset contains the high-resolution data (1 min) cloud screened and quality assured since 2002. The provided Ångström exponent is retrieved from the 4 wavelengths.</p> <p> </p> <p> </p> <p>SUN_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2002-2021</p> <p>Lunar_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2018-2021</p> <p> </p> <p> </p> <p>KN: Calibration, operation, and processing since 2014, WORCC quality assurance protocols, quality assurance of the presented dataset</p> <p>KS: , WORCC quality assurance protocols, consulting on quality assurance of the presented dataset</p> <p>WC: calibration, operation, and processing 2002- 2014</p> <p>NS: operation and processing 2005- 2012</p> <p>HGH: principal investigator of hosting institute NILU</p> <p>SK: principal investigator of hosting institute NILU</p> <p>Acknowledgement: Special thanks the personnel of the Norwegian Polar Institute at Ny-Ålesund for all valuable the technical support for the solar and lunar measurements.</p> <p> </p> <p> </p> <p>1. Kazadzis, S., Kouremeti, N., Nyeki, S.<em>, et al.</em> (2018) The World Optical Depth Research and Calibration Center (WORCC) quality assurance and quality control of GAW-PFR AOD measurements 10.5194/gi-7-39-2018 <a href="https://gi.copernicus.org/articles/7/39/2018/">https://gi.copernicus.org/articles/7/39/2018/</a></p> <p> </p>
Next-Gen Quality Assurance: A Deep Dive into Integrating Artificial Intelligence in the Pharmaceutical Industry
<p>This article explores the transformative<br>integration of Artificial Intelligence (AI) with<br>advanced quality tools in the pharmaceutical<br>industry. From the adoption of Analytical<br>Quality by Design (AQbD) principles in<br>method development to the application of<br>AI in rapid testing, Design of Experiments<br>(DoE), and statistical tools for trending and<br>process capability analysis, a synergistic<br>relationship emerges. The amalgamation of<br>AI and advanced tools represents a<br>paradigm shift, propelling quality assurance<br>into a new frontier marked by precision,<br>reliability, and excellence in pharmaceutical<br>manufacturing. Despite encountering<br>challenges such as data privacy and<br>regulatory compliance, the industry is<br>moving towards a future where AI is not<br>merely a tool but a strategic partner, shaping<br>a revolutionary landscape where the highest<br>standards of quality are not only met but<br>exceeded.</p>
Quality Assurance Awareness in Open Source Software Projects on GitHub Analysis Dataset
<p>Dataset for the paper "Quality Assurance Awareness in Open Source Software Projects on GitHub", submitted to the 23rd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM), 2023.</p>
OSM dataset for Quality Assurance paper
<p><strong>Dataset Title:</strong> OpenStreetMap Quality Assurance Dataset</p> <p><strong>Dataset Description:</strong> This dataset comprises OpenStreetMap (OSM) data obtained from the Dublin area in 2023, specifically for quality assurance purposes. The dataset contains a diverse range of geospatial information, meticulously sourced from OSM through the Overpass API.</p> <p><strong>Data Source:</strong> The primary source of this dataset is OpenStreetMap, accessed via the Overpass API. It encompasses a wide array of geospatial features and attributes contributed by the OSM community.</p> <p><strong>Data Format:</strong> The dataset is formatted in GeoJSON, a widely used and versatile format for representing geospatial data.</p> <p><strong>Data Size:</strong> The dataset encompasses 471 individual records, collectively forming a comprehensive representation of the Dublin area within the scope of the year 2023.</p> <p><strong>Data License:</strong> The dataset is released under the Open Database License (ODbL), ensuring openness and accessibility to users while respecting OSM's data sharing principles.</p> <p><strong>Temporal and Spatial Coverage:</strong> The dataset captures geospatial information within the vibrant city of Dublin, offering a snapshot of the region during the year 2023. It provides valuable insights into the dynamic nature of the city's geographical data.</p> <p>This dataset serves as a valuable resource for quality assurance and evaluation of geospatial data within the Dublin area. Researchers, GIS professionals, and the broader OSM community can utilize it for a variety of spatial analysis and data quality assessment tasks.</p> <p> </p> <p> </p> <p> </p>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Research Data Quality Assurance – an intellectual property perspective
<p>The presentation summarizes the intellectual property issues, regarding the second Demonstrator in the FAIR Data Spaces project, Research Data Quality Assurance. It will especially outline the specific copyright law paragraphs, which may be and are important for this demonstrator and the participants in the project.</p>
Automated Data Quality Assurance with GitLab pipelines and Docker
<ul> <li>The Data Validation and Quality Assurance demonstrator is a tool designed to assist with collaborative research projects, ensuring that collected data complies with established schemas and monitoring these files for unusual or unexpected values. This demonstrator attempts to show how a task of this nature can be attempted in a scalable, cloud based infrastructure, while maintaining compatibility with existing frameworks. In this presentation we will cover a brief use case, showing the results of the demonstrator on an example dataset that contains some intentionally added data quality items</li> </ul>
Data for Autonomous Flow Routing for Near Real-Time Quality of Service Assurance
<p>Data of the journal paper:</p> <p>S. Barzegar, M. Ruiz and L. Velasco, "Autonomous Flow Routing for Near Real-Time Quality of Service Assurance," in IEEE Transactions on Network and Service Management, vol. 21, no. 2, pp. 2504-2514, April 2024, doi: 10.1109/TNSM.2023.3339201 (<a href="../records/10246061" target="_blank" rel="noopener">link to the paper</a>)</p> <p>This dataset has been generated within the framework of the European Commission thought the HORIZON SNS JU DESIRE6G (G.A. 101096466), the MINECO UNICO5G TIMING (TSI-063000-2021-145) and the MICINN IBON (PID2020-114135RB-I00) projects, and from the ICREA Institution. (2023-12-05).</p> <p>Additional details and updates can be checked in the following <a href="https://doi.org/10.34810/data1404" target="_blank" rel="noopener">link</a></p>
Implementation of Optical Diagnosis for Diminutive Polyps Amongst Endoscopists: Training and Long-term Quality Assurance
ClinicalTrials.gov study NCT02407925. IPD Sharing: Not stated. Countries: 1. Publications: 9.
IMPLEMENT - Model Project for Quality-assured Exercise Therapy for Cancer Patients
ClinicalTrials.gov study NCT06496711. IPD Sharing: NO. Countries: 1. Publications: 0.
Quality Assurance on Anemia Management in Patients With Solid Tumors and Malignant Lymphoma
ClinicalTrials.gov study NCT05190263. IPD Sharing: NO. Countries: 1. Publications: 1.
Digital Support for Quality Assurance in 24-hour Caregiving at Home
ClinicalTrials.gov study NCT04581538. IPD Sharing: YES. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.