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88 results for “Data quality assessment”
Data Extraction and Quality Assessment about Cybersecurity in the Context of Agile Software Development
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R code: Large-scale assessment of bird data quality in a citizen science platform
<p>R code archived to Zenodo for publication</p>
The data of Multi-task learning aids in assessing microbial genome quality
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Data from: Taxon-level assessment of the data collection quality in Atlas Florae Europaeae: insights from the case of Rosa (Rosaceae) in Eastern Europe
<p>By the method of data re-collection and re-assessment, we tested the completeness of distribution areas of the species and species aggregates of <i>Rosa</i> in Eastern Europe as mapped in volume 13 of <i>Atlas Florae Europaeae</i>, and discussed insights into the issues connected with the data. We have found many new occurrences which are additions to the published maps: 1068 records of species and 570 records of species aggregates. The new occurrences are listed with references to the sources, and the updated AFE maps are provided. The greatest increase by new native occurrences was revealed for the species that are widespread or taxonomically complicated, and by new alien occurrences for the species that expand their secondary distribution areas. The mapping work published in 2004 is considered good, with minor omissions caused by possible oversights and incomplete sampling. The majority of new additions originated in the period after the original data collection. Nearly the same amount of new data originated from larger and smaller herbarium collections, underlining the value of small collections for chorological studies. We found that only ca. 20% of new records based on herbarium specimens have been published, thus highlighting the need of data papers for publication of distributional data. The greatest increase by new records based on herbarium specimens was found for insufficiently studied territories (Belarus, central, northern and eastern parts of Russia), whereas the same level of increase for the territories with reasonably good coverage (Latvia) was achieved by observations. We conclude that the overall sparsity of published records in Eastern Europe was caused by a lower level of data collection rather than by the poor data availability, and that floristic surveys based on herbarium specimens cannot compete in speed and density of records with observation-based surveys, which may become the main source of distributional information in the future.</p>
Figure 9 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 9 - Timeline. We will prepare and update the Web app (Aim 2) as we develop it for use in annotating gold standard audio data (Aim 1) and as we get feedback on its use in connection with Amazon's Mechanical Turk (Aim 2). Year 2 will consist primarily of testing the aggregation of annotated audio data for further analysis (Aim 2), to train an automated approach (Exploratory Aim), and to publish and present our findings.
Figure 2 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 2 - Mockup of audio recording annotation tool – Step 1: Selection. This figure shows a mockup of what an audio annotation Web application tool could look like. In this first step, (A) the Worker presses the Play icon to listen to the voice recording, (B) selects a problematic segment by clicking and dragging the mouse over the waveform, and (C) replays the recording if necessary and selects other problematic segments.
Figure 5 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 5 - DARPA-funded seedling project. This schematic represents our DARPA-funded seedling project to assess the feasibility of collecting phone voice recordings from PD patients for use in a competition.
Figure 4 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 4 - Audio recording annotation tool – Step 3: Rating. Following Figures 2 and 3, here the Worker rates how serious the problem is that is affecting the highlighted segment of the recording. In this example, the Worker indicates that the background noise (wind) is not good, but that it doesn't interfere with his/her ability to hear the voice in the recording.
Figure 7 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 7 - Example mPower patient voice data. In the mPower app, PD patients are prompted to perform the voice activity three times per day: once before taking their medication, a second time when they feel they are at their best after taking their medication, and a third "random" time. This figure shows example voice data for a single patient on medication (top) and at a "random" time, very likely off medication (bottom). On the left are waveforms, showing the acoustic voice signal over time (0-10 seconds), from which one can clearly see that the patient's voice trailed off to a minimum (bottom left) compared to after medication (top left). On the right are spectrograms, representing signal amplitude at different frequencies (0-5 kHz) over time (0-10 seconds). The spectrogram after medication (top right) has more uniform frequency bands across the recording compared to the rather "muddled" spectrogram recorded at the random time (bottom right).
Figure 3 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 3 - Audio recording annotation tool – Step 2: Annotation. Following Figure 2, here the Worker selects one or more categories describing why the highlighted segment in the audio waveform is problematic. In this example, there was a lot of background noise (wind).
Figure 6 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 6 - Android and iOS Parkinson app screenshots. Top: Android PD app screenshots showing instructions for the phonation (voice) task. Bottom: mPower PD app screenshots. Each participant in the mPower study is prompted to perform a voice activity three times a day. The rightmost screenshot demonstrates the visual feedback that is provided during audio recording, to try to keep the voice at the best amplitude for recording.
Fig. 8 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 8. The influence of drying of ginger on the gingerols and gingerol-related metabolites.
Fig. 6 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 6. The influence of geographical distribution of ginger on metabolites content.
Data from: Assessment of the quality of reporting in randomized controlled trials of acupuncture in the Korean literature using the CONSORT statement and the STRICTA guidelines
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Data from: Categorizing and assessing comprehensive drivers of provider behavior for optimizing quality of health care
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Data from: Can we build it? Yes we can, but should we use it? Assessing the quality and value of a very large phylogeny of campanulid angiosperms
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Data from: Assessing the relationship between quality of life and behavioral activation using the Japanese behavioral activation for depression scale-short form
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Data from: Quality expectations and tolerance limits of trial master files (TMF) - Developing a risk-based approach for quality assessments of TMFs
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Data from: Taxon-level assessment of the data collection quality in Atlas Florae Europaeae: insights from the case of Rosa (Rosaceae) in Eastern Europe
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Data from: Assessment of grain quality in terms of functional group response to elevated [CO2], water and nitrogen using a meta-analysis: Grain protein, zinc and iron under future climate
The increasing [CO2] in the atmosphere increases crop productivity. However, grain quality of cereals and pulses are substantially decreased and consequently compromise human health. Meta-analysis techniques were employed to investigate the effect of elevated [CO2] (e[CO2]) on protein, zinc (Zn) and iron (Fe) concentrations of major food crops (542 experimental observations from 135 studies) including wheat, rice, soybean, field peas and corn considering different levels of water and nitrogen (N). Each crop, except soybean, had decreased protein, Zn and Fe concentrations when grown at e[CO2] concentration ( ≥550 μmol mol-1) compared ambient [CO2] (a[CO2]) concentration (≤ 380 μmol mol-1). Grain protein, Zn and Fe concentrations were reduced under e[CO2], however, the responses of protein, Zn and Fe concentrations to e[CO2 ] were modified by water stress and N. There was an increase in Fe concentration in soybean under medium N and wet conditions but non-significant. The reductions in protein concentrations for wheat and rice were ~ 5-10%, and the reductions in Zn and Fe concentrations were ~ 3-12%. For soybean, there was a small and non-significant increase of 0.37% in its protein concentration under medium N and dry water, while Zn and Fe concentrations were reduced by ~ 2-5%. The protein concentration of field peas decreased by 1.7%, and the reductions in Zn and Fe concentrations were ~ 4-10%. The reductions in protein, Zn and Fe concentrations of corn were ~ 5-10%. Bias in the dataset was assessed using a regression test and rank correlation. The analysis indicated that there are medium levels of bias within published meta-analysis studies of crops responses to Free Air [CO2]Enrichment (FACE). However, the integration of the influence of reporting bias did not affect the significance or the direction of the [CO2] effects.
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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.