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1,453 results for “Outcome research”
Figure 7 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 7 - Selection of the journal and "Data Paper (Biosciences)" template in the ARPHA Writing Tool.
Figure 4 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 4 - Occurrence records and taxonomic treatments (if present in the article), published in the Biodiversity Data Journal, are exported in two separate Darwin Core Archives (DwC-A) and are available for direct download or harvesting via web services.
Figure 2 from: Jordan K, Keshavan A, Mandelli M, Henry R (2017) Cluster-viz: A Tractography QC Tool. Research Ideas and Outcomes 3: e12394. https://doi.org/10.3897/rio.3.e12394
Figure 2 - The user selected two sub-bundles that contain streamlines representing a tractography model of the Uncinate Fasciculus.
Figure 1 from: Jordan K, Keshavan A, Mandelli M, Henry R (2017) Cluster-viz: A Tractography QC Tool. Research Ideas and Outcomes 3: e12394. https://doi.org/10.3897/rio.3.e12394
Figure 1 - The connectivity of an ROI placed on the coronal plane over the external/extreme capsules at the level of the anterior commissure is shown (tractography method: Caverzasi et al. 2015). Each color is a cluster, as generated by the Quickbundles algorithm (Garyfallidis et al. 2012).
Figure 3 from: Jordan K, Keshavan A, Mandelli M, Henry R (2017) Cluster-viz: A Tractography QC Tool. Research Ideas and Outcomes 3: e12394. https://doi.org/10.3897/rio.3.e12394
Figure 3 - Sub-bundles that the user judged were part of an Uncinate Fasciculus tractography model are re-clustered so that the user can further refine the model.
Figure 2 from: Maumet C, Nichols T (2017) Generating and reporting peak and cluster tables for voxel-wise inference in FSL. Research Ideas and Outcomes 3: e12368. https://doi.org/10.3897/rio.3.e12368
Figure 2 - Cluster and peak tables for p<0.05 FWE-corrected voxel-wise results (dataset "fsl_thr_voxelfwep05")
Figure 1 from: Maumet C, Nichols T (2017) Generating and reporting peak and cluster tables for voxel-wise inference in FSL. Research Ideas and Outcomes 3: e12368. https://doi.org/10.3897/rio.3.e12368
Figure 1 - Examples of calls of the updated "cluster" command for voxel-wise and cluster-wise thresholds.
Figure 1 from: Huntenburg J, Wagstyl K, Steele C, Funck T, Bethlehem R, Foubet O, Larrat B, Borrell V, Bazin P (2017) Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. Research Ideas and Outcomes 3: e12346. https://doi.org/10.3897/rio.3.e12346
Figure 1 - Laminar python pipeline, demonstrated using high-resolution MR data of a ferret brain. a) Binary images demarcating inner (grey-white matter interface, top) and outer (pial surface, bottom) boundaries of the cortex. b) Levelset representations of the same surfaces, where positive values are assigned to voxels outside of the volume deliminated by the surface, and negative values to voxels inside, each increasing in value with euclidean distance from the surface. c) Continuous equivolumetric intracortical depth, which models the positions of laminae relative to cortical morphology. d) Discrete representations of equivolumetric depth levels. e) T2 values, sampled at the six equivolumetric intracortical depths. Note that the equivolumetric laminae do not represent architectonic layers, but provide an anatomically meaningful coordinate system of cortical depth.
Figure 2 from: Huntenburg J, Abraham A, Loula J, Liem F, Dadi K, Varoquaux G (2017) Loading and plotting of cortical surface representations in Nilearn. Research Ideas and Outcomes 3: e12342. https://doi.org/10.3897/rio.3.e12342
Figure 2 - Seed-based functional connectivity example. a Seed region in the posterior cingulate cortex (PCC). b Pearson product-moment correlation coefficient from the seed region time series to all other nodes. c The same map as in b, thresholded and plotted with a different colour scheme. d The same map as in b, plotted without sulcal depth information for shading.
Figure 1 from: Huntenburg J, Abraham A, Loula J, Liem F, Dadi K, Varoquaux G (2017) Loading and plotting of cortical surface representations in Nilearn. Research Ideas and Outcomes 3: e12342. https://doi.org/10.3897/rio.3.e12342
Figure 1 - Destrieux atlas plotted on the fsaverage5 surface template using the plot_surf_roi function. a Convoluted pial surface geometry of the left hemisphere. b Inflated pial surface geometry of the left hemisphere.
Figure 5 from: Keshavan A, Madan CR, Datta E, McDonough IM (2017) Mindcontrol: Organize, quality control, annotate, edit, and collaborate on neuroimaging processing results. Research Ideas and Outcomes 3: e12276. https://doi.org/10.3897/rio.3.e12276
Figure 5 - Example of new scatterplot function. Structural Foreground to Background Energy Ratio is positively related with Structural Contrast to Noise Ratio. Single data points can also be highlighted to detect outlers.
Figure 4 from: Keshavan A, Madan CR, Datta E, McDonough IM (2017) Mindcontrol: Organize, quality control, annotate, edit, and collaborate on neuroimaging processing results. Research Ideas and Outcomes 3: e12276. https://doi.org/10.3897/rio.3.e12276
Figure 4 - Example of scatterplot function to display the relationship of metrics in longitudinal data
Figure 2 from: Keshavan A, Madan CR, Datta E, McDonough IM (2017) Mindcontrol: Organize, quality control, annotate, edit, and collaborate on neuroimaging processing results. Research Ideas and Outcomes 3: e12276. https://doi.org/10.3897/rio.3.e12276
Figure 2 - The longitudinal view of a single subject and their quality metrics over time. Clicking on a point on the plots on the left-hand side loads the correponding image on the right.
Figure 2 from: Ito K, Anglin J, Liew S (2017) Semi-automated Robust Quantification of Lesions (SRQL) Toolbox. Research Ideas and Outcomes 3: e12259. https://doi.org/10.3897/rio.3.e12259
Figure 2 - We tested our toolbox on a mock lesion mask. A. The stroke subject's T1 anatomical scan; B. The mock lesion mask is the red sphere; the blue mask is the lesion segmentation. The white matter was intentionally covered within the mock lesion mask, but as shown here, white matter voxels are removed by the white matter correction.
Figure 3 from: Pearsons K, Mikó I, Tooker J (2017) The cyanide gland of the greenhouse millipede, Oxidus gracilis (Polydesmida: Paradoxosomatidae). Research Ideas and Outcomes 3: e12249. https://doi.org/10.3897/rio.3.e12249
Figure 3 - Top view of the juvenile millipede; the bright field in the lower flange is a gland storage chamber.
Figure 4 from: Pearsons K, Mikó I, Tooker J (2017) The cyanide gland of the greenhouse millipede, Oxidus gracilis (Polydesmida: Paradoxosomatidae). Research Ideas and Outcomes 3: e12249. https://doi.org/10.3897/rio.3.e12249
Figure 4 - Top/rotated view of the juvenile millipede. SC = storage chamber, RC = reaction chamber, MV = muscularized valve connecting the two chambers.
Figure 2 from: Pearsons K, Mikó I, Tooker J (2017) The cyanide gland of the greenhouse millipede, Oxidus gracilis (Polydesmida: Paradoxosomatidae). Research Ideas and Outcomes 3: e12249. https://doi.org/10.3897/rio.3.e12249
Figure 2 - CLSM volume rendered media file showing the cyanide gland of Oxidus gracilis (gland extract is the overexposed droplet).
Figure 1 from: Pearsons K, Mikó I, Tooker J (2017) The cyanide gland of the greenhouse millipede, Oxidus gracilis (Polydesmida: Paradoxosomatidae). Research Ideas and Outcomes 3: e12249. https://doi.org/10.3897/rio.3.e12249
Figure 1 - CLSM volume rendered micrograph showing the cyanide gland of Oxidus gracilis (arrows pointing the wall of the cyanide gland, ex=strongly autofluorescing gland extract).
Figure 5 from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 5 - Guiding principles for promoting the application of EBVs for current and future needs of decision-makers - researcher's brief.
Figure 2 from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 2 - From a European perspective, the European biodiversity policy landscape is complex and "data hungry" (Wetzel et al. 2015).
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.