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958 results for “Data quality”
Air Quality, Location, Time, Number of Passenger Data during commute in selected area of Klang Valley, Malaysia
<p>The dataset contains information on date/time, location names, periods, vehicle types, settlement patterns, commute elevation levels, latitude, longitude, air pollutant exposure data, along with temperature and relative humidity data, and passenger numbers for 40 trips conducted across various transport microenvironments in selected areas of the Klang Valley.</p>
Laboratory water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Lab measurements of water quality based on manual sampling including ordinary monitoring and special monitoring performed in SafeCREW project.</p> <p>Laboratory LIMS database.</p> <p>Provide water quality measurements based on the planned monitoring schedule. Schedule tailored to SafeCREW project needs plus usual legal water sampling.</p>
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
Data: Individual quality and environmental effects on future reproductive performance of a threatened shorebird
<p>This data repository gives all r-scripts and data for reproducing multi-state live-dead recovery mark-capture-recapture models used in van Irsel et al. (2021). State‐dependent environmental sensitivity of reproductive success and survival in a shorebird. Ibis. <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2Fabs%2F10.1111%2Fibi.13038&data=04%7C01%7C%7Cd8e13473cb7c4d040e7408d9bfc0165a%7Cc7a0410b695b4223af74d14c2e7652e8%7C0%7C0%7C637751653997539459%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=Usi6HdJIa3p7kVUVhwgc%2BC%2FurXnd1IlSRjYqDkrlP6Q%3D&reserved=0">https://onlinelibrary.wiley.com/doi/abs/10.1111/ibi.13038</a></p> <p>The " Instructions Data archive.PDF" describes how to use each of the scripts and which files are needed to re-run the analyses.</p> <p>This is the first release of the scripts and attached data.</p>
Data from: Declining rates of species described per taxonomist: slowdown of progress or a side-effect of improved quality in taxonomy?
[No abstract entered]
Lake water quality monitoring data from 8287 northern lakes
<p>This dataset contains water quality monitoring data and lake morphometry data from 8287 lakes collected between 1960 and 2022 in Finland, Sweden, Norway, Canada and the United States (Alaska). Data was sourced from governmental databases and non-profit organizations via data portals and data requests (see Metadata). The dataset contains over 2.6 million standardized and quality-controlled observations of dissolved oxygen, water temperature, total nitrogen, total phosphorus, water colour and ammonia, as well as sampling location, sampling date, sampling depth and key lake properties including lake altitude, surface area, mean depth and maximum depth. We excluded lakes undergoing physical or chemical treatments against hypoxia, lakes in which experiments have been conducted (e.g. nutrient addition). and reservoirs.</p> <p>The consumer of these data ("Data User" herein) should realize that misinterpretation of data may occur if used out of context of the original study. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. All data are made available "as is". The Data User should be aware, however, that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. The data authors, original data providers and the repository where these data were obtained shall not be liable for damages resulting from any use or misinterpretation of the data. The data user may not create derivatives of this dataset or re-distribute these data to a third party.</p> <p><strong>Note: </strong>if you would like to use this data in your own study, you must contact the author (joachim.jansen@helsinki.fi) to ensure compliance with requirements of the contributors to this synthesis dataset, </p>
Figure 1 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 1 - Amazon's Mechanical Turk Web site.
Figure 8 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 8 - Example artifacts in mPower voice recordings.
Data extraction and Quality assessment consensus only
<p>This file , contain data regarding "Data extraction and Quality assessment consensus only". Data were extraxted during a systematic review and mea-analysis study.</p>
High accuracy calculation and data quality evaluation of ship emissions based on the sniffer method
<p>The uploaded file includes the matlab code used in the study and the actual measurement data set collected.</p>
Data from: Tablet-based versus presentation-based seminars in radiology: effects of student digital affinity and teacher charisma on didactic quality
<p><span><strong>Aims</strong>: </span><span>Tablets are being adopted as a teaching medium in medical education more frequently. Here we compared two teaching formats in a radiology seminar using a tablet-based student-centred approach guided by teachers and traditional presentation-based, teacher-centred instruction. The aim was to investigate the effects on academic performance, estimated learning gain, didactic quality, as well as how teacher charisma and student digital affinity influence these elements. </span></p> <p><span><strong>Methods</strong>: </span><span>Data from 366 students were collected</span><span>. Student </span><span>digital affinity</span><span>, </span><span>didactic quality of, and overall satisfaction with the seminars were rated for each teaching format over three semesters, whereby in</span><span> the last semester, students additionally </span><span>estimated their learning gain, took a knowledge and image interpretation test, and rated teacher charisma.</span> </p> <p><span><strong>Results</strong>: </span><span>The tablet-based seminars yielded significantly higher ratings for didactic quality and overall satisfaction. However, the </span><span>presentation-based</span><span> seminars proved superior with respect to </span><span>academic performance</span><span> as well as estimated learning gain</span><span>. </span><span>When employing tablets, teacher charisma correlated with estimated learning gain, and digital affinity affected didactic quality</span><span>.</span><span> Additionally, </span><span>good seminar organization, comprehensible learning objectives, and optimal variation of learning activities</span><span> were identified as important factors.</span></p> <p><span><strong>Conclusion</strong>: </span><span>This study suggests a complex interplay of various factors concerning teachers, students, and didactics that can be assessed and improved to assure the successful curricular implementation of tablets. Of note, tablet integration and thereby active engagement of students with imaging analysis skills does not automatically result in greater declarative knowledge. Nevertheless, understanding the complexities of structuring and delivering tablet-based, teacher-guided instruction is essential to creating meaningful educational experiences.</span></p>
Yearly data quality information from the Topo-Iberia CGPS stations (2008–2019)
<h2>Description</h2> <p>This data set includes yearly quality plots from all 26 Topo-Iberia CGPS stations and is based on the quality assessment by García-Armenteros (<a href="https://doi.org/10.1016/j.geog.2023.07.006">2024</a>). The quality plots (253 PNG files) are provided archived and compressed in a tar.gz folder (yearly_qc_Topo-Iberia_2008-19.tar.gz), but can also be viewed individually at <a href="https://blogs.ujaen.es/jgarmen/topo-iberia_data_quality">https://blogs.ujaen.es/jgarmen/topo-iberia_data_quality</a>.</p> <p>The quality check was carried out using TEQC software (Estey and Meertens <a href="https://doi.org/10.1007/PL00012778">1999</a>), and the figures are composed of: maximum number of complete observations that were counted for each frequency above the elevation mask angle, percentage of the number of complete observations recorded with respect to the number of expected observations, number of cycle slips per 1000 complete observations, and root-mean-square of multipath effect for two GPS frequencies (L1 and L2) averaged over the observation interval. It has been tried to keep the same range on the plots axes as far as possible, although it has been necessary to expand them according to the data in some cases, such as the cycle slips plots. 39 RINEX files were not included in the quality plots since they provide anomalous information on isolated days and are considered outliers (see García-Armenteros <a href="https://doi.org/10.1016/j.geog.2023.07.006">2024</a>, Suppl. Table S2). Similarly, some data are also not included in the quality plots, as they extend the range on the Y-axis too much and complicate the analysis of the station's behavior (see García-Armenteros <a href="https://doi.org/10.1016/j.geog.2023.07.006">2024</a>, Suppl. Table S3).</p>
Data Collection Protocol for the Development of CHLOE-OQ, an AI Model for Assessing Oocytes Quality.
ClinicalTrials.gov study NCT06928337. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Longitudinal Retrospective Analysis of Prospectively Collected Data for Outcomes After Primary Sleeve Gastrectomy (N=20 000) in the Swedish National Quality Registry SOReg (Scandinavian Obesity Surger
ClinicalTrials.gov study NCT07280845. IPD Sharing: NO. Countries: 1. Publications: 0.
Measuring the Quality of Surgical Care and Setting Benchmarks for Training Using Intuitive Data Recorder Technology
ClinicalTrials.gov study NCT04647188. IPD Sharing: NO. Countries: 1. Publications: 0.
Supportive Care Questionnaires in Gathering Data on Unmet Needs and Health-Related Quality of Life in Latina Breast Cancer Survivors After Surgery, Chemotherapy, or Radiation Therapy
ClinicalTrials.gov study NCT02545023. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Pharmacist and Data Driven Quality Improvement in Primary Care
ClinicalTrials.gov study NCT03747107. IPD Sharing: NO. Countries: 1. Publications: 0.
Training and Support Programme on Data-driven Quality Development for Swiss Long-Term Care Facilities (NIP-Q-UPGRADE Subaim 2.6)
ClinicalTrials.gov study NCT07068009. IPD Sharing: NO. Countries: 1. Publications: 0.
Testing the Implementation of a Toolbox to Optimize Data Collection and Data Quality of the National Medical Quality Indicators in Long-term Care Facilities: a Pilot Study. (NIP-Q-UPGRADE Subaim 1.8)
ClinicalTrials.gov study NCT06848725. IPD Sharing: NO. Countries: 1. Publications: 0.
Quality Assurance of Electronic Data Capturing (EDC) in Pain Treatment
ClinicalTrials.gov study NCT02256202. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.