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Fig. 3 Canonical correspondence analysis. Only axes 1 and 2 are shown. Type 2 in Evaluating the correlation between area, environmental heterogeneity, and species richness using terrestrial isopods (Oniscidea) from the Pontine Islands (West Mediterranean)
Fig. 3 Canonical correspondence analysis. Only axes 1 and 2 are shown. Type 2 scaling is shown. A right-angled projection of a point representing a response variable (ecological categories of species) onto an arrow representing an explanatory variable (biotope type)
Figs. 1-6 in Rediscovery of Zubovskya morii (Bey-Bienko, 1931) (Orthoptera: Acrididae), the nearly forgotten endemic grasshopper in North Korea with revised checklist from its type locality, Mount Baekdusan
Figs. 1-6. Habitus of Zubovskya morii (Bey-Bienko, 1931). 1. Male, dorsal view; 2. Male, ventral view; 3. Female, dorsal view; 4. Female, ventral view; 5. Male, lateral view; 6. Female, lateral view.
Telescopus finkeldeyi Haacke, 2013 DAMARA TIGER SNAKE Telescopus finkeldeyi Haacke 2013:281. Holotype: TM 53542 (collector J.A. van Rooyen). Type locality: "Rössing Uranium mine area, Swako- mund [sic] district (2214Db) Namibia." Global conservation status (IUCN): Not Evaluated. Global distribution: The species is known from Angola and Namibia. Ocurrences in Angola (Map 364): The species occurs in southwestern Angola. Namibe: "5 km north Namibé" [-15.20000, 12.15000] (Haacke 2013:285). Taxonomic and distributional notes: Some earlier records of T. semiannulatus polystictus in Namibia actually refer to this recently described species. MAP 364. Distribution of Telescopus finkeldeyi in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
Telescopus finkeldeyi Haacke, 2013 DAMARA TIGER SNAKE Telescopus finkeldeyi Haacke 2013:281. Holotype: TM 53542 (collector J.A. van Rooyen). Type locality: "Rössing Uranium mine area, Swako- mund [sic] district (2214Db) Namibia." Global conservation status (IUCN): Not Evaluated. Global distribution: The species is known from Angola and Namibia. Ocurrences in Angola (Map 364): The species occurs in southwestern Angola. Namibe: "5 km north Namibé" [-15.20000, 12.15000] (Haacke 2013:285). Taxonomic and distributional notes: Some earlier records of T. semiannulatus polystictus in Namibia actually refer to this recently described species. MAP 364. Distribution of Telescopus finkeldeyi in Angola.
Figure 6. Adding a Global Goal with its required type-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of adding a Global Goal expressing a motivation required by the<br> controller. The goal is to keep the vehicle's front free of obstacles, thus the Sensation “free” should<br> stay equal to “1”. After adding a new Global Goal, its assigned type should be “Cst” corresponding<br> to a constant output request (see figure 6). In the parameter field, its specified value is “1”.</p>
Figure 4. Adding a Sensing Module and selecting its type-Designing a Growing Functional Modules "Artificial Brain"
<p>GFM controllers learn to satisfy some predefined goals<br> while interacting with the environment and thus should be considered as artificial brains. An<br> example of the design process of a simple controller is provided herein to explain the inherent<br> methodology, to exhibit the components' interconnections and to demonstrate the control process.</p>
Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.
<p>Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.</p> <p>v1.2: typos corrected and all files available in a single .zip file for download</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset2
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wang Cheng, Hua Ming, Shuang Chendong, Li Aimin, Gao Canzhu. (2017). High-efficient removal of phthalate esters from aqueous solution with an easily regenerative magnetic resin: Hydrolytic degradation and simultaneous adsorption. <em>Journal of Cleaner Production</em><em>. </em> <a href="https://doi.org/10.1016/j.jclepro.2017.11.121">https://doi.org/10.1016/j.jclepro.2017.11.121</a></p>
A Corpus of Online Drug Usage Guideline Documents Annotated with Type of Advice
<p><strong>Introduction: </strong>The goal of this dataset is to aid NLP research on recognizing safety critical information from drug usage guideline or patient handout data. This dataset contains annotated advice statements from 90 online DUG documents that corresponds to 90 drugs or medications that are used in the prescriptions of patients suffering from one or more chronic diseases. The advice statements are annotated in eight safety-critical categories: activity or lifestyle related, disease or symptom related, drug administration related, exercise related, food or beverage related, other drug related, pregnancy related, and temporal. </p> <p><strong>Data Collection: </strong>The data was collected from <a href="https://www.medscape.com">MedScape</a>. It is one of the most widely used reference for health care providers. At first, 34 real anonymized prescriptions of patients suffering from one or more chronic diseases are collected. These prescriptions contains 165 drugs that are used to treat chronic diseases. Then, MedScape was crawled to collect the drug user guideline (DUG) / patient handout for these 165 drugs. But, MedScape does not have DUG document for all drugs. We found DUG document for 90 drugs in MedScape. </p> <p><strong>Data Annotation tool: </strong>The data annotation tool is developed to ease the annotation process. It allows the user to select a DUG document and select a position from the document in terms of line number. It stores the user log from the annotator and loads the most recent position from the log when the application is launched. It supports annotating multiple files for the same drug, as often there are multiple overlapping sources of drug usage guidelines for a single drug. Often DUG documents contain formatted text. This tool aids annotation of the formatted text as well. The annotation tool is also available upon request.<strong> </strong></p> <p><strong>Annotated Data Description: </strong>The annotated data contains the annotation tag(s) of each advice extracted from the 90 online DUG documents. It also contains the phrases or topics in the advice statement that triggers the annotation tag, such as, activity, exercise, medication name, food or beverage name, disease name, pregnancy condition (gestational, postpartum). Sometimes disease names are not directly mentioned rather mentioned as a condition (e.g., stomach bleeding, alcohol abuse) or state of a parameter (e.g., low blood sugar, low blood pressure). The annotated data is formatted as following:<br> drug name, drug number, line number of the first sentence of the advice in the DUG document, advice Text, advice tag(s), medication, food, activity, exercise, and disease names mentioned in the advice. </p> <p><br> <strong>Unannotated Data Description:</strong><br> The unannotated data contains the raw DUG document for 90 drugs. It also contains the drug interaction information for the 165 drugs. The drug interaction information is categorized in 4 classes, contraindicated, serious, monitor closely, and minor. This information can be utilized to automatically detect potential interaction and effect of interaction among multiple drugs. </p> <p><strong>Citation: </strong>If you use this dataset in your work, please cite the following reference in any publication:</p> <p>@inproceedings{preum2018DUG,<br> title={A Corpus of Drug Usage Guidelines Annotated with Type of Advice},<br> author={Sarah Masud Preum, Md. Rizwan Parvez, Kai-Wei Chang, and John A. Stankovic},<br> booktitle={ Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},<br> publisher = {European Language Resources Association (ELRA)},<br> year={2018}<br> }</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 10. Detailed accuracy separated by classes and a confusion matrix which belongs to the dataset of Coiflet 1 applied by our main method (ANNSVM)
<p>With regard to accuracy values of each class as presented in Figure 10, we observed that the accuracy of the two-dimensional chart class was the lowest (i.e., 0.875), while others were over 0.9. Results here suggested that both the bar and pie classes have their own unique characteristics, as opposed to the 2Dchart class. For example, the graph images that contained some rectangles were individually categorized in the bar graph class. A similar phenomenon occurred for circles in the pie chart class. In contrast, the 2Dchart class contained mixed types of graphs; hence, the graph characteristics belonging to the 2Dchart class varied. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 8. Simulation of Coiflet 1 (PyWavelets discussion group, 2008), analyzing as one-dimensional images
<p>Using only the wavelet coefficients was inadequate for classification. For example, for the pie chart, we obtained large wavelet coefficients located in the low-frequency domain; however, if we changed a circle in the pie chart to other shapes, such as a radar chart, the wavelet transformation gave results that were similar to those of the original pie chart. The Hough transformation can solve this problem since it detects the shapes of objects</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>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7c. 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>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7. 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>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 9. Illustration of three different wavelets with three waves that have high amplitude values, as indicated by the dashed red circles
<p>The mother wavelet of Coiflet 5 contained triple-high oscillation amplitude (i.e., Figure 9a). We considered that this mother wavelet was inappropriate for our data because overall our data possibly contained only a few matches with the mother wavelet of Coiflet 5. Moreover, the Symlet 10 (i.e., Figure 9b) and 20 (i.e., Figure 9c) also provided supportive results that were lower than others in ANNSVM_WLHT because their mother wavelets also had a similar shape as that of Coiflet 5. For similar reasons, the Haar wavelet was not proper because it is a step function. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 6. Results from ANNSVN that used WL and HT
<p>To identify which features of data influentially impacted data separability, we conducted experiments for ANNSVM with WL and HT (i.e., Figure 6). The WL contained only wavelet coefficients, whereas HT included only results of the Hough transformation. We found that, again, results obtained via the linear kernel were not significant; however, using the RBF kernel, accuracy for WL was higher than that of HT, indicating that wavelet coefficients provide influential features that make data separable. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 3. Demonstrating the process of classification by applying the ANN, then the SVM
<p>Essentially, if the number of nodes in the hidden layers increases, processing time increases, and the resultant ANN will suffer from over-fitting. Conversely, too small of a number of hidden layers will cause under-fitting for the ANN. In our setting, the number of hidden layers and the number of nodes in each hidden layer were fixed at five. Concerning the learning rate and momentum settings, these impact sensitive training performances are set to optimal values obtained via a grid search technique. The number of nodes in the output layer was three because there are three different class labels (i.e., 2Dchart, bar, and pie) in our datasets. We used the ANN here because our datasets have nonlinear separation, and the ANN is also highly applicable to nonlinear modeling. Thus the ANN with multiple hidden layers was an optimal candidate; however, since the ANN is a black box learning approach, it is difficult to interpret implicit relationships between inputs and outputs.</p>
RAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 4. Processes of all experiments:
<p>In this study, accuracy values of each dataset showed the performance of each method. These values represent are the proportion of the total number of predictions that were correctly classified. Initially, we classified training instances into three classes, with approximately 300 images per class. The graphs had been selectively gathered from the Web. We manually normalized the collected images by eliminating unused areas, such as unnecessary text. Moreover, we evaluated the experiments with 10 folds cross-validation because such an approach can mitigate the problem of over-fitting. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 5. Results from CNN and ANNSVM that used 1Dimg and 2Dimg
<p>We compared the results of CNN_1Dimg, CNN_2Dimg, ANNSVM_1Dimg, and ANNSVM_2Dimg to confirm the validity of ANNSVM when applied to images. The 1Dimg represented the dataset of one-dimensional images, while 2Dimg represented the dataset of twodimensional images. Results are shown in Figure 5. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 1. Example displaying two scatter plots with different characteristics and patterns
<p>In addition to this introductory section, the remainder of this paper is organized as follows. In Section 2, we present previous work related to our present study. In Section 3, we describe details regarding the methodology used in this study. In Section 4, we describe our experiments and results, then discuss our findings. Finally, we summarize the key content of our study in Section 5. </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.