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1,813 results for “outcome study”
Statistical Data for The Impact of Chatbots using Concept Maps on Correction Outcomes–a Case Study of Programming Courses
<p>experiment learners' questionnaire result and test score</p>
Supplementary material 2 from: Irawan D, Priyambodho A, Rachmi C, Wibowo D, Fahmi A (2016) Bibliometric study to assist research topic selection: a case from research design on Jakarta's groundwater (part 1). Research Ideas and Outcomes 2: e9841. https://doi.org/10.3897/rio.2.e9841
Word cloud 1987-2015
Supplementary material 1 from: Irawan D, Priyambodho A, Rachmi C, Wibowo D, Fahmi A (2016) Bibliometric study to assist research topic selection: a case from research design on Jakarta's groundwater (part 1). Research Ideas and Outcomes 2: e9841. https://doi.org/10.3897/rio.2.e9841
List of original papers
Figure 5 from: Paudel Timilsena B, Mikó I (2017) Know your insect: The structural backgrounds of regurgitation, a case study on Manduca sexta and Heliothis virescens (Lepidoptera: Sphingidae, Noctuidae). Research Ideas and Outcomes 3: e11997. https://doi.org/10.3897/rio.3.e11997
Figure 5 - CLSM volume rendered micrograph showing the junction of foregut and midgut of Manduca sexta larva
Figure 4 from: Paudel Timilsena B, Mikó I (2017) Know your insect: The structural backgrounds of regurgitation, a case study on Manduca sexta and Heliothis virescens (Lepidoptera: Sphingidae, Noctuidae). Research Ideas and Outcomes 3: e11997. https://doi.org/10.3897/rio.3.e11997
Figure 4 - CLSM volume rendered micrograph showing midgut epithellium of Manduca sexta at the junction of foregut and midgut
Figure 3 from: Paudel Timilsena B, Mikó I (2017) Know your insect: The structural backgrounds of regurgitation, a case study on Manduca sexta and Heliothis virescens (Lepidoptera: Sphingidae, Noctuidae). Research Ideas and Outcomes 3: e11997. https://doi.org/10.3897/rio.3.e11997
Figure 3 - CLSM volume rendered micrograph showing the junction of foregut and midgut of Heliothis virescens larva
Figure 2 from: Paudel Timilsena B, Mikó I (2017) Know your insect: The structural backgrounds of regurgitation, a case study on Manduca sexta and Heliothis virescens (Lepidoptera: Sphingidae, Noctuidae). Research Ideas and Outcomes 3: e11997. https://doi.org/10.3897/rio.3.e11997
Figure 2 - Alimentary canal of early third instar larvae of Manduca sexta imaged by Light microscope
Figure 1 from: Paudel Timilsena B, Mikó I (2017) Know your insect: The structural backgrounds of regurgitation, a case study on Manduca sexta and Heliothis virescens (Lepidoptera: Sphingidae, Noctuidae). Research Ideas and Outcomes 3: e11997. https://doi.org/10.3897/rio.3.e11997
Figure 1 - Alimentary canal of early fourth instar larvae of Heliothis virescens imaged by Light microscope
Figure 2 from: Dasari S (2016) Studying the effect of Ruthenium on High Temperature Mechanical Properties of Nickel Based Superalloys and Determining the Universal Behavior of Ruthenium at Atomic Scale with respect to alloying elements, Stress and Temperature. Research Ideas and Outcomes 2: e10714. https://doi.org/10.3897/rio.2.e10714
Figure 2 - All features of the microstructures from the six samples taken before and after creep tests
Figure 1 from: Irawan D, Priyambodho A, Rachmi C, Wibowo D, Fahmi A (2016) Bibliometric study to assist research topic selection: a case from research design on Jakarta's groundwater (part 1). Research Ideas and Outcomes 2: e9841. https://doi.org/10.3897/rio.2.e9841
Figure 1 - Density map of the papers based on the key words (Red: dense paper population, green: less dense). Dataset is available as Suppl. material 1 (in Irawan et al. 2016).
Forest plots of pairwise comparisons from original studies for the primary outcomes limited to studies with low ROB only
Open the record for dataset details and reuse information.
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 3 from: Klein A (2016) Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India. Research Ideas and Outcomes 2: e8834. https://doi.org/10.3897/rio.2.e8834
Figure 3 - Timeline This Gantt chart provides an estimate of the relative timing and duration for achieving each of the Aims.
Figure 1 from: Klein A (2016) Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India. Research Ideas and Outcomes 2: e8834. https://doi.org/10.3897/rio.2.e8834
Figure 1 - First pass at a VPDRS static graphic Figure 1 corresponds to the first self-administered MDS-UPDRS question: 1.7 SLEEP PROBLEMS. Over the past week, have you had trouble going to sleep at night or staying asleep through the night? Consider how rested you felt after waking up in the morning. 0: Normal: No problems. 1: Slight: Sleep problems are present but usually do not cause trouble getting a full night of sleep. 2: Mild: Sleep problems usually cause some difficulties getting a full night of sleep. 3: Moderate: Sleep problems cause a lot of difficulties getting a full night of sleep, but I still usually sleep for more than half the night. 4: Severe: I usually do not sleep for most of the night."
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.
ScienceDex guides
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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.