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10 results for “data 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>
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>
[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>
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>
Replication data of Autonomous flow routing for near real-time quality of service assurance
<p>The dataset contains replication data of the journal paper titled "Autonomous Flow Routing for Near Real-Time Quality of Service Assurance", published in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2023.3339201.</p>
ECOSTRESS L3/L4 Ancillary data Quality Assurance (QA) flags L3 Global 70m V001
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. The ECO3ANCQA Version 1 is a Level 3 (L3) product that provides Quality Assessment (QA) fields for all ancillary data used in L3 and Level 4 (L4) products generated by Jet Propulsion Laboratory (JPL). No quality flags are generated for the L3 or L4 products. Instead, the quality flags of the source data products are resampled by nearest neighbor onto the geolocation of the ECOSTRESS scene. A quality flag array for each input dataset, when available, is collected into the combined QA product.The ECO3ANCQA Version 1 data product contains variables of quality flags for ECOSTRESS cloud mask, Landsat 8, land cover type, albedo, MODIS Terra aerosol, MODIS Terra Cloud 1 km, MODIS Terra Cloud 5 km, MODIS Terra atmospheric profile, vegetation indices, MODIS Terra gross primary productivity, and MODIS water mask.Known Issues* Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.
Quality Assurance of Electronic Data Capturing (EDC) in Pain Treatment
ClinicalTrials.gov study NCT02256202. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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