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1,453 results for “Outcome research”
Figure 3 from: Klein A, Ghosh S (2016) Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data. Research Ideas and Outcomes 2: e8835. https://doi.org/10.3897/rio.2.e8835
Figure 3 - Example of a graph-based representation of MRI and DTI features (a) A gray/white matter surface (left lateral view) with (visible) sulcal pits highlighted. These features go by different names (sulcal roots, buried gyrii, annectant gyrii, plis de passage) and may be well conserved structures formed early in development. (b) DTI connectivity graph computed on the same patient with depression as on the left panel. Vertices represent automatically extracted sulcal pits and each edge indicates a connection probability greater than 0.01 between two vertices.
Figure 2 from: Klein A (2016) Brain Graph Interface. Research Ideas and Outcomes 2: e8817. https://doi.org/10.3897/rio.2.e8817
Figure 2 - Schematic of Mindboggle's graph-based database of anatomical features. Top: different structures derived from brain images: surface patches fragmented by application of the Laplace-Beltrami operator, sulcus folds and subfolds, and structures within a subfold. Bottom: schematic graph diagrams representing the relationships among the nested structures. Bottom right: examples of features as properties of edges (relationships such as Part of, Connected to, Has label) and nodes (geometric, shape, and spectral measures).
Figure 3 from: Klein A (2016) Brain Graph Interface. Research Ideas and Outcomes 2: e8817. https://doi.org/10.3897/rio.2.e8817
Figure 3 - Example of natural morphological variability: left inferior parietal lobule (IPL; figure from [Kiriyama et al. 2009]). (A-D) are MRI data and (E-G) are post-mortem specimens. (A) IPL is highlighted and folds are outlined. (B,E) Typical folding pattern. (C,F) PreSMG pattern: an additional gyrus (ellipse) lies between postCS and SMG. (D,G) PreAG pattern: an additional gyrus (ellipse) lies between SMG and AG. [SMG: supramarginal gyrus; AG: angular gyrus; postCS: postcentral sulcus; IPS: intraparietal sulcus; Sy: Sylvian fissure, STS: superior temporal sulcus; *sulcus intermedius primus] postCS IPS * * Sy STS IPL
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.
Figure 2 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 2 - Prototype for the mobile phone app This screen shows a pre-release version of Node, which will support the VPDRS/UPDRS modules. Here we present a means by which a person administering a questionnaire can securely log into and manipulate patient information locally and through cloud services and lastly an example clinician-administered UPDRS question.
Figure 7 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 7 - Timeline. We will prepare the gold standard evaluation data (Aim 1) and develop and update the game (Aim 2) through the 18th month as we get feedback on its use. Year 2 will consist primarily of testing the aggregation of segmented data (Aim 3), exploring how well the game can generalize to segmentation of every region of the BigBrain (Exploratory Aim 1), and training and testing an automated approach that learns from the crowdsourced data (Exploratory Aim 2), as well as to publish and present our findings.
Figure 6 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 6 - Example joint fusion of multiple label assignments. This macroscopic brain atlas was built from 20 individually labeled atlases, using joint fusion (Wang and Yushkevich 2013), after nonlinearly registering the 20 to a template (http://mindboggle.info/data.html).
Figure 2 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 2 - Example hippocampal subfield labels. Left: Example of hippocampal subfield labeling. Right: The first work demonstrating hippocampal subfield labels in MRI space that are derived from ground-truth histological imaging (Adler et al. 2014).
Figure 5 from: Klein A (2016) A game for crowdsourcing the segmentation of BigBrain data. Research Ideas and Outcomes 2: e8816. https://doi.org/10.3897/rio.2.e8816
Figure 5 - Example boundary estimates in the BigBrain hippocampus. This figure shows example color line drawings atop a (low-resolution) image tile containing a small portion of the BigBrain's right hippocampus. The lines represent a novice's estimates of the CA1/subiculum cytoarchitectonic boundary. This boundary is extremely difficult, as it results in the greatest overall disagreement among hippocampal subfield labeling protocols (Yushkevich et al. 2015).
Figure 2 from: Klein A, Ellis S (2016) Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data. Research Ideas and Outcomes 2: e8815. https://doi.org/10.3897/rio.2.e8815
Figure 2 - Persistence plot showing third- and higher-order dependence in the fMRI BOLD data from an individual control subject (as in Aim 1). The larger disk indicates two coinciding points. The point with the asterisk is discussed in 3.4.1, Example 2.
Figure 1 from: Klein A, Ellis S (2016) Concurrence Topology: Finding High-Order Dependence in Neuropsychiatric Data. Research Ideas and Outcomes 2: e8815. https://doi.org/10.3897/rio.2.e8815
Figure 1 - Brain labeling protocol (left, on an inflated cortex) and cortical labels (right) used by Mindboggle for extracting regions analyzed by our concurrence topology software.
Figure 4 from: Klein A (2016) Data-Visual Relationships to Subject Performance and Eye Movements. Research Ideas and Outcomes 2: e8814. https://doi.org/10.3897/rio.2.e8814
Figure 4 - Relational operators - w, x, y, and z are all optional and refer to any attribute or operator
Figure 1 from: Klein A (2016) Data-Visual Relationships to Subject Performance and Eye Movements. Research Ideas and Outcomes 2: e8814. https://doi.org/10.3897/rio.2.e8814
Figure 1 - Methods pipeline (A) Each data feature (scale, dimensionality, etc., defined by the data taxonomy), has attributes (e.g., scale may be set to nominal, ordinal, or ratio). The combination of attribute settings form (B) a data structure, which in turn is amenable to certain (C) visualization methods (defined by the visual taxonomy). When a visualization method is used to perform (D) a set of tasks, (E) performance and eye movement data are recorded for each of N subjects.
Figure 6 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 6 - Participants of the 3rd EU BON Stakeholder Roundtable discussing details of the workflow (credits: Katrin Vohland).
Figure 2 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 2 - Simplified workflow from data mobilization via processing to stakeholders from the practice.
Figure 1 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 1 - EU BON Work Packages (WP) with the three sections (a) Data Sources and Infrastructure, (b) Science and Application and (c) Policy and Dialogue. The Stakeholder Roundtables are a specific task in the WP 6 that targets the stakeholder engagement and science-policy dialogue (credits: Pensoft).
Figure 5 from: Vohland K, Hoffmann A, Underwood E, Weatherdon L, Bonet F, Häuser C, Wetzel F (2016) 3rd EU BON Stakeholder Roundtable (Granada, Spain): Biodiversity data workflow from data mobilization to practice. Research Ideas and Outcomes 2: e8622. https://doi.org/10.3897/rio.2.e8622
Figure 5 - Some exemplified results from the questionnaire send around in advance. Left: provision of data. right: Data requirements. N=20 (Florian Wetzel, MfN, 2015).
Figure 3 from: Vohland K, Häuser C, Regan E, Hoffmann A, Wetzel F (2016) 2nd EU BON Stakeholder Roundtable (Berlin, Germany): How can a European biodiversity network support citizen science? Research Ideas and Outcomes 2: e8616. https://doi.org/10.3897/rio.2.e8616
Figure 3 - Examples of citizen science projects and initiatives/tools from the EU BON consortium (Christoph Häuser and Florian Wetzel, MfN, 2014)
Figure 1 from: Vohland K, Häuser C, Regan E, Hoffmann A, Wetzel F (2016) 2nd EU BON Stakeholder Roundtable (Berlin, Germany): How can a European biodiversity network support citizen science? Research Ideas and Outcomes 2: e8616. https://doi.org/10.3897/rio.2.e8616
Figure 1 - EU BON Work Packages (WP) with the three sections (a) Data Sources and Infrastructure, (b) Science and Application and (c) Policy and Dialogue. The Stakeholder Roundtables are a specific task in the WP 6 that targets the stakeholder engagement and science-policy dialogue (credits: Pensoft).
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.