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575 results for “quality assessment”
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.
Towards an Assessment Rubric for EiPE Tasks in Secondary Education: Identifying Quality Indicators and Descriptors
<p>This study reports on reseach to investigating indicators and descriptors which characterize the quality of students’ code explanations to EiPE (Explain in Plain English) tasks in secondary education. </p> <p>First, a literature review was carried out to identify quality characteristics and descriptors that differentiate the quality of students’ responses in tertiary education. Then, the quality of upper-secondary students’ responses to a simple EiPE task was analyzed. The results of that analysis were used to refine our quality characteristics and descriptors, which were then translated into a prototypical rubric. In the final phase, the rubric was iteratively tested by two teachers and improved based on their feedback. The final rubric prototype can be used to provide feedback during formative assessment.</p> <p><br> This repository includes the EiPE comprehension task, the student responses, and the results from coding the responses:<br> - StudentResponses.pdf: task and anonymized solutions provided by students, translated to English<br> - Coding_data.xls: coding by two researchers until they came to an agreement (full set of 34 responses), and coding by Teacher1 on complete set, and coding by Teacher2 on the base-set (with duplicate responses omitted)</p>
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.
No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT
<p>This repository contains the paper's experimental scripts, results, and raw data.</p>
Multivisceral Resection for Locally Advanced Gastric Cancer: A Systematic Review and Evi-dence Quality Assessment
<p>Dataset concerning the systematic review of locally advanced gastric cancer.</p>
Diagnosis of Delirium and Assessment of Sleep Quality in Patients in the Anesthesia and Intensive Care Unit
ClinicalTrials.gov study NCT06699004. IPD Sharing: NO. Countries: 1. Publications: 0.
Study to Assess the Quality of Sleep and Perceived Sleep Distractors Among the Adult Patients Admitted in Selected Hospital
ClinicalTrials.gov study NCT03224676. IPD Sharing: NO. Countries: 0. Publications: 3.
Development of Dermatology-related Quality of Life Assessment in Patients Treated With Epidermal Growth Factor Receptor (EGFR) Inhibitors
ClinicalTrials.gov study NCT01015326. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Post Marketing Observational Study (PMOS) to Assess Quality of Life in Swedish Hidradenitis Suppurativa (HS) Patients
ClinicalTrials.gov study NCT02739828. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Quality Of Life Assessment In Alzheimer's Disease (AD) Patients Receiving Aricept Tablets
ClinicalTrials.gov study NCT01089582. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Study of Flurpiridaz (18F) Injection for PET Imaging for Assessment of MPI Quality Using HPLC and SPE Manufacturing Processes
ClinicalTrials.gov study NCT04594941. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Quality of Life Assessment in Patients Who Are Under Surveillance for IPMN
ClinicalTrials.gov study NCT03131076. IPD Sharing: NO. Countries: 0. Publications: 1.
Assessing the Impact of MOVALIS in Osteoarthritis and Rheumatoid Arthritis Patients on Health Related Quality of Life
ClinicalTrials.gov study NCT00619177. IPD Sharing: Not stated. Countries: 5. Publications: 0.
Study to Assess Patient Management Practices and Quality of Life With Paricalcitol Capsules in the Treatment of Secondary Hyperparathyroidism in Stage 3-5 Chronic Kidney Disease Patients Not Yet on Di
ClinicalTrials.gov study NCT01265992. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study Assessing the Effects of Early Psychiatric Referral on Mental Health and Quality of Life in People With Sarcoma
ClinicalTrials.gov study NCT06129903. IPD Sharing: YES. 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.