Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,410
datasets available to search
ShareScore release 0.9.0
Dataset results
6,410 results for “results”
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 17. The results of calculating the Hausdorff distances
<p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods<br> (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the<br> Corpus Calosum segmentation. </p> <p> </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 16. Results of calculating the Hausdorff distances
<p>Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a subject Normal following segmentation of the Corpus Calosum.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 15. The results of calculating the Hausdorff distances
<p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD for the four methods (Caselles Chan & Vese, Lanktom, our method) and the ground truth about a Normal subject following the Corpus Calosum segmentation.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 14. A comparison between the results
<p> A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue) Lankton (red curve), our method (purple line) and the ground truth (Curve Green) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 13.The results of the Corpus Calosum segmentation.
<p> The results of the Corpus Calosum segmentation. The six lines present in order: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method , the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 MCI (primary stage), and the third column corresponds to an Alzheimer's subject (advanced stage). </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 12. Results of calculating the Hausdorff distances
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 11. Results of calculating the Hausdorff distance
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p> Results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles Chan & Vese, Lanktom, our method) .and the ground truth about a MCI subject following segmentation of the hippocampus.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus</p> <p> </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 9. A comparison between the results
<p>In figure 9 we present a comparison between the manual segmentation, Caselle, Chan&vese, Lankton and our method. </p> <p>A comparison between the results. Each column shows the superposition of the corresponding results: Caselle (yellow curve), Chan & Vese (curve blue), Lankton (red curve), our method (purple curve), and the ground truth (Green Curve) for a normal subject, MCI and AD.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 8. The results of the segmentation of the hippocampus.
<p>The results show the hippocampus segmentation using both Caselle, Chan&Vese, Lankton and our method. </p> <p>The results of the segmentation of the hippocampus. The six lines present: image zoom on the hippocampus area, manually segmented image, the result of the Caselle method, the result of the Chan&Vese method, the result of the Lankton method, the result of our method. Column 1 shows a healthy subject, column 2 shows a MCI (primary stage) and the third column corresponds to an Alzheimer's subject (advanced stage)</p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 10. Graphic containing the results for 1000 bits
<p>The distribution obtained depending on the length of the input string shows that time depends on the input length, but for lengths even closer together, the times are also close (this can be seen in Figure 8 for 20 bits inputs). Time does not change so much depending on which of the 14 different 16th degree primitive polynomials has been used. </p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 9. Graphic containing the results for 1000 bits
<p>The next two graphics show the obtained results from the execution of the main program for each of the 14 degrees, 16th primitive polynomials for three different situations depending on the lengths of the entrance data polynomial. The lengths of the input polynomials were 20. 30. 40, 50, 100 and 1000 bits. The maximum number of sequences is 216-1(Solomon, 1967). </p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 8. Graphic containing the results for 20 bits
<p>The next two graphics show the obtained results from the execution of the main program for each of the 14 degrees, 16th primitive polynomials for three different situations depending on the lengths of the entrance data polynomial. The lengths of the input polynomials were 20. 30. 40, 50, 100 and 1000 bits. The maximum number of sequences is 216-1(Solomon, 1967). </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 2. Twitter sentiment analysis results
<p> In order to validate our results, the next step was to extract the sentiment analysis from Tweeter’s tweets (Figure 2) which are based on blending embedded systems-related courses. The obtained polarity is positive, so this results shows not only that students appreciated this in a positive manner, but also that the proposed technique for integrating MOOCs into embedded systems courses is a viable one. </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 1. Semantria result
<p>In Figure 1 the Semantria output is presented, having a positive polarity, with a score of 0.218. What is interesting to note here are the keywords extracted from students’ feedback. They noticed the integration of MOOCs in the Embedded Systems course as positive due to the fact that the new information is perceived as easier and the gained knowledge seems to be valuable. Students are affected by too many concepts and also by the idea of paying for the course.</p>
WP2 D2.2 questionnaire results June 2017 Maker Faire Barcelona
<p>The data provide the basis for the analysis in D2.3, section 3: you can download it <a href="http://make-it.io/deliverables/d2-3-final-make-it-monitoring-and-assessment-framework-a-new-analysis-of-making-in-europe/">here</a>. The results and analyses in this section have been derived from interviews of a random sample of 42 Maker initiatives present at the Barcelona Maker Faire, 16-18 June 2017, out of 88 registered as present. The Faire consisted of a highly diverse set of exhibitors drawn from across Barcelona and Catalonia. European experts on the <strong>MAKE-IT</strong> team conducted the interviews and validated that the Barcelona maker scene is quite typical of other large cities across Europe. The results obtained are combined, where relevant, with the instrument’s version 3 results of the ten <strong>MAKE-IT</strong> case studies from across the whole of Europe, together with relevant insights from <a href="http://make-it.io/deliverables/d3-2-final-case-study-report-focusing-on-cross-case-analysis/">D3.2</a>.</p> <p>See also: <a href="http://make-it.io/open-data-api/">http://make-it.io/open-data-api/</a></p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 11.Weka Results for Images
<p>The dataset of the real images is also treated the same way as the dataset of the sketches. Total tree images used = 86 Total car images used = 54 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 10 shows the neural network for images. The results are shown in Figure 11 and are as follows: Total Correct Recognition = 83.7838% Total Incorrect Recognition = 16.2162% Error Per epoch = 0.0235851 </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 9. Weka Results for Sketches
<p> the sketches and the data is converted to 20x20=400 integer numbers to give it as input to Weka. Our experimentation includes only two objects for recognition i.e. trees and cars. Total tree sketches used = 175 Total car sketches used = 72 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 8 shows the neural network for sketches. The results are shown in Figure 9 and are as follows: Total Correct Recognition = 100% </p>
BRAIN Journal-A New Challenge for Information Mining-Figure 4. Results of Weka's Clustering
<p>In the Figure 4 we show that, in a particular cluster, attributes are grouped in the "good" attribute of "Expertise with technology” with the "Low" attribute of "Student's performance" together. Thus, we can deduce that the level of Student's Performance is influenced by other factors over "Expertise with technology" of teacher. These factors can be searched inside the cluster, providing useful information to a significant exploration. These aspects are not deducible only by exploration through the portal and, for this reason, the clustering technique allows to user to navigate better during the search. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7b. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT
<p>From the results of ANNSVM_WL and ANNSVM_HT, we found that wavelet coefficients had a larger impact on classification than the Hough transformation data because the results from our proposed method applied to WL were more accurate than those of HT. The wavelet coefficients can capture the dominant characteristics from the graphs better than the Hough transformation. The one-dimensional image represented in the frequency domain had oscillations with different amplitudes depending on the graph types. For example, a dominant part of a pie chart should be in the low-frequency domain, because there is a large island of concatenated pixels in a onedimensional image, and it has only a few changes. Conversely, since the scatter plot contains many widely spread points, its dominant part should be located in the high-frequency domain. Performing the wavelet transformation, if a mother wavelet and a part of the wavelet function have a close match, the wavelet coefficient will be large. Assuming we use a suitable wavelet family with the example pie chart case, the wavelet coefficients in the low-frequency domain should be large as compared to other parts of the domain. </p>
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.