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Рис. 1. Карта РеспубΛики Саха (Якутия) с обозначением места распоΛожения археоΛогических памятников Àжампа, Кузнец I, II Àабан-Юрях, Буор-Хая I, II, III. Fig. 1. Map of the Republic of Sakha (Yakutia) indicating the location of the archaeological sites Jampa, Kuznets I, II Daban-Yuryakh, and Buor-Khaya I, II, III in New data on the Holocene vertebrate fauna of the Middle Lena and Aldan Rivers basins (Yakutia) based on the materials from archaeological sites Jampa, Kuznets I, II Daban-Yuryakh, and Buor-Khaya I, II, III
Рис. 1. Карта РеспубΛики Саха (Якутия) с обозначением места распоΛожения археоΛогических памятников Àжампа, Кузнец I, II Àабан-Юрях, Буор-Хая I, II, III. Fig. 1. Map of the Republic of Sakha (Yakutia) indicating the location of the archaeological sites Jampa, Kuznets I, II Daban-Yuryakh, and Buor-Khaya I, II, III
Рис. 2. НижнечеΛюстные кости из шурфа Ш-1, кв. Б-3: А — собоΛь (Martes zibellina); Б — заяц-беΛяк (Lepus timidus); В — пищухи (Ochotona sp.) Fig. 2. Mandibular bones from D-1, sq. B-3: A — sable (Martes zibellina); Б — white hare (Lepus timidus); В — pikas (Ochotona sp.) in New data on the Holocene vertebrate fauna of the Middle Lena and Aldan Rivers basins (Yakutia) based on the materials from archaeological sites Jampa, Kuznets I, II Daban-Yuryakh, and Buor-Khaya I, II, III
Рис. 2. НижнечеΛюстные кости из шурфа Ш-1, кв. Б-3: А — собоΛь (Martes zibellina); Б — заяц-беΛяк (Lepus timidus); В — пищухи (Ochotona sp.) Fig. 2. Mandibular bones from D-1, sq. B-3: A — sable (Martes zibellina); Б — white hare (Lepus timidus); В — pikas (Ochotona sp.)
Рис. 1. Карта района иссΛеΑований. УсΛовные обозначения: красными кружками показаны базовые Λагеря; фиоΛетовой штриховкой — территория ΛанΑшафтного памятника прироΑы местного значения «ВΛасьевские торфяники»; синей штриховкой — акватория памятника прироΑы краевого значения «ЗаΛив Счастья с островами Кевор и Чаечный» Fig. 1. Map of the study area. Legend: red circles show base camps; purple shading — the territory of the landscape natural monument of local importance "Vlasyevsky Torfyaniky"; blue shading — the water area is a natural monument of regional significance "The Bay of Schastꞌе with the islands of Kevor and Chaechny" in New data on rare and insufficiently studied birds of the Shchastya Bay, the Sea of Okhotsk, and adjacent territories (Khabarovsk Krai)
Рис. 1. Карта района иссΛеΑований. УсΛовные обозначения: красными кружками показаны базовые Λагеря; фиоΛетовой штриховкой — территория ΛанΑшафтного памятника прироΑы местного значения «ВΛасьевские торфяники»; синей штриховкой — акватория памятника прироΑы краевого значения «ЗаΛив Счастья с островами Кевор и Чаечный» Fig. 1. Map of the study area. Legend: red circles show base camps; purple shading — the territory of the landscape natural monument of local importance "Vlasyevsky Torfyaniky"; blue shading — the water area is a natural monument of regional significance "The Bay of Schastꞌе with the islands of Kevor and Chaechny"
Linked collectors and determiners for: Improving Species-Based Area Protection in Antarctica - data.
Natural history specimen data linked to collectors and determiners held within, "Improving Species-Based Area Protection in Antarctica - data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/d61860b3-22fd-4c8f-a089-97a2d6893f8b">https://bionomia.net/dataset/d61860b3-22fd-4c8f-a089-97a2d6893f8b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d61860b3-22fd-4c8f-a089-97a2d6893f8b">https://gbif.org/dataset/d61860b3-22fd-4c8f-a089-97a2d6893f8b</a>. Formatted as a Frictionless Data package.
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 8. Performance in 1st Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise.</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 9. Performance in 2nd Approach for three data set
<p>This work also deals with classification of multi class images under different constraints of<br> data set. The first experiment is carried out on images without noise, second with Gaussian noise<br> and filtered data set in third experiment. Performance of the classifier using statistical texture<br> features for two approaches are presented in the table 3. It is observed that performance n the first<br> experiment is best in the first data set i.e. data set without noise in both the approach, while the<br> performance is decreased if the same images are affected by Gaussian noise. This is because the<br> texture feature of the original images consists Gaussian pattern also. Filtering of the noise from the<br> second data set improves the result. The table also shows that feature extraction using blocking of<br> the image enhance the average classification rate in all the case.</p>
Figure 4. After merging, overview is more transparent. Tens of persons were merged together into clusters in order to clarify the visualization. Firms and persons are recognized based on their icons.-Browsing Semantic Data in Slovakia
<p>The usefulness of such visualization has its key points regarding connections. Thanks to SBR browsing module, we were able to get 22 firm records for “Váhostav” query. Between any 2 companies, connections may be (and often are) not bidirectional, so, in order to navigate through connections, we have refined all 22 records. Although, even being filtered, graph is still complex. And it is possible to further navigate and search for outgoing connections, for example firm “MERLIN TRADE, a.s.” on Fig.4 contains item on “Ján Kato”, which is already included in our graph and connected to “VÁHOSTAV&SK&DEVELOPEMENT” on bottom left side and “VÁHOSTAV&SK, a.s.” in the center. Edge coloring and drawing is helpful with overlapped edges. For methods of visualization, including coloring, we refer to studies of H. Omote and K. Sugiyama (2006), and I. Herman, G. Melanon, and M. S. Marshall (2000) or our study on graph clutter filtering and connectivity distance (Mojzis & Laclavik, 2014).</p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 4. Mobile Application to collect Sketch data
<p>The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network.</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
Covariance-based turbulence data
<p>This is a collation of turbulence data collected by various Stanford researchers at Conch Reef (Davis and Monismith 2011 JPO), Kilo Nalu (Squibb 2014 Stanford thesis), Eilat (Dunckley 2012 Stanford thesis) and Monterey Bay (Walter et al. 2014 JGR). The variables are as described. Units are all m and s. Note that all original data where N^2 < 2e-5 s^-1 and Rif<1e-4 have been removed from this data set.</p> <p>Davis, K. A., and S. G. Monismith, (2011), The modification of bottom boundary layer turbulence and mixing by internal waves shoaling on a barrier reef. <em>Journal of Physical Oceanography</em>, 41, 2223 – 2241,doi:10.1175/2011JPO4344.1.</p> <p>Dunckley, J.F., (2012), Mixing in nearshore coastal environments. PhD thesis, Dept. of Civil and Environmental Engineering, Stanford University, 200 pp. </p> <p>Squibb, M.E., (2014), Dynamics of shoaling internal waves in the near-shore: Mamala Bay, Hawaii. PhD thesis, Dept. of Civil and Environmental Engineering, Stanford University, 167 pp.</p> <p>Walter, R.K., M.E. Squibb, C.B. Woodson, J.R. Koseff, and S.G. Monismith, (2014), Stratified turbulence in the nearshore coastal ocean: dynamics and evolution in the presence of internal bores. <em>Journal of Geophysical Research</em> (<em>Oceans</em>), 119, 8709-8730, DOI: 10.1002/2014JC010396, </p>
Data associated with "A collaborative filtering based approach to biomedical knowledge discovery"
<p>This is the data set associated with the publication: "A collaborative filtering based approach to biomedical knowledge discovery" published in Bioinformatics.</p> <p>The data are sets of cooccurrences of biomedical terms extracted from published abstracts and full text articles. The cooccurrences are then represented in sparse matrix form. There are three different splits of this data denoted by the prefix number on the files.</p> <p>1. All - All cooccurrences combined in a single file</p> <p>2. Training/Validation - All cooccurrences in publications before 2010 in training, all novel cooccurrences in publication in 2010 go in validation</p> <p>3. Training+Validation/Test - All cooccurrences in publication upto and including 2010 in training+validation. All novel cooccurrences after 2010 in year by year increments and also all combined together</p> <p> </p> <p>Furthermore there are subset files which are used in some experiments to deal with the computational cost of evaluating the full set. The associated cuids.txt file containing a link between the row/column in the matrix with the UMLS Metathesaurus CUIDs. Hence the first row of cuids.txt matches up to the 0th row/column in the matrix. Note that the matrix is square and symmetric. This work was done with UMLS Metathesaurus 2016AB.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 11. Accuracy of different method for unseen faces
<p>Table 3 shows the mode detection accuracy of the proposed method and its combination with two other methods (uniform LBP and circular LBP) for different people. The overall accuracy of the proposed procedure is calculated as this way one video is chosen as input, and after mode detection the three aforementioned steps are applied on this video. The obtained feature vectors are given to the neural network and the corresponding labels to each frame are regarded as output. Afterwards, the overall accuracy is calculated from the confusion matrix. However, it should be noted that the expression detection criteria are the observation of a certain number of subsequent similar labels and in the case of observing a limited or sparse number of different labels the final label would not change. Figure 10 and 11 show result of different methods for seen and unseen data respectively. Table 4 shows results for seen data with proposed method and uniform LBP.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-
<p>Table 3 shows the mode detection accuracy of the proposed method and its combination with two other methods (uniform LBP and circular LBP) for different people. The overall accuracy of the proposed procedure is calculated as this way one video is chosen as input, and after mode detection the three aforementioned steps are applied on this video. The obtained feature vectors are given to the neural network and the corresponding labels to each frame are regarded as output. Afterwards, the overall accuracy is calculated from the confusion matrix. However, it should be noted that the expression detection criteria are the observation of a certain number of subsequent similar labels and in the case of observing a limited or sparse number of different labels the final label would not change. Figure 10 and 11 show result of different methods for seen and unseen data respectively. Table 4 shows results for seen data with proposed method and uniform LBP.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 9. Results of our facial motion capture system(a,b,c,d)
<p>In test procedures, single video feature vectors consisting of different expressions are given to the neural network and the network produces the corresponding labels for each frame as output. If there is a mode in a video which is not available in the data base, the nearest available mode's label to this mode is produced. For example, in test3 and test6 videos, the surprise expression (that have been showed with number 7) is recognized as open mouth expression. At the end, considering the certain numbers of subsequent similar labels (at least 10 frames, because the minimum number of one modes' frames is related to “rising the eyebrow” mode that takes 10 frames), the expressions are detected, and a 3D show of these expressions are represented. For instance, in test8 videos that have been obtained from unseen face, the “smiling” and “open mouth” expressions are well recognized, but expressions related to rising the eyebrows are not detected properly and all the corresponding frames to this expression are regarded as normal expression. Figure 9 shows example of generated 3D models.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 6. Proposed feed-forward neural network classifier
<p>After the feature extraction stage, neural network is used for classifying the modes. In this study, the utilized expressions are normal, smiling, open mouth, rising the eyebrows, anger and pursing modes. In fact, they are some selective modes for face movements. It should be noted that the modes can be increased but in this case we work with these six modes. This paper used three layers feed-forward neural network (Figure 6). The proposed neural network includes 800 nodes for the input layer (400 nodes for U matrix and 400 nodes for V matrix), 100 nodes for the hidden layer and 6-nodes for output layer. From the collected data 70% are used for training, 15% for validation and the last 15% are used to evaluate the neural network.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 5. Examples of the circular LBP (Huang et al., 2011)
<p>One limitation of the basic LBP operator is that its small 3x3 neighborhood cannot capture dominant features with large scale structures. To deal with the texture at different scales the operator was later generalized to use neighborhoods of different sizes. A local neighborhood is defined as a set of sampling points evenly spaced on a circle which is centered at the pixel to be labeled. The sampling points that do not fall within the pixels are interpolated using bilinear interpolation, thus allowing for any radius and any number of sampling points in the neighborhood. Figure 5 shows some examples of the extended LBP operator where the notation (P, R) denotes a neighborhood of P sampling points on a circle of radius of R.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation
<p>Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database
<p> Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.</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.