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Fig. 4 in Occurrence of sea lice, Caligus undulatus Shen and Li, 1959 (Copepoda: Siphonostomatoida: Caligidae) in plankton samples collected from Korea
Fig. 4. Caligus undulatus, adult male from Mokpo Harbour, Korea. A. habitus, dorsal. B. urosome, dorsal. C. antenna. D. postantennal process. E. maxillule. F. post oral pad. G. maxilliped. H. sternal furca. I. genital segment and legs 5 and 6, ventral. Scale bars: A = 400 μm; B, I = 200 μm; C-H = 100 μm.
Fig. 1 in A report of six unrecorded bacterial species isolated from soil samples in Korea
Fig. 1. Transmission electron micrographs of the strains in this study. Strains: 1, SBS2-5; 2, SRC3-8; 3, SRC1-1; 4, SRC1-3; 5, SRC3-1; 6, SBS9-6.
Fig. 3 in A report of six unrecorded bacterial species isolated from soil samples in Korea
Fig. 3. Neighbor-joining phylogentic tree based on 16S rRNA gene sequences of the isolate in this study and their relatives of the genus Paenibacillus. Strain: SBS9-6. Bootstrap values (>50%) are shown above nodes for the neighbor-joining methods. Bar: 0.01 substitutions per nucleotide position.
Fig. 1. Map showing the sampling localities from 2014 in Freshwater bryozoans of Korea-observations on living colonies and three new records
Fig. 1. Map showing the sampling localities from 2014 to 2016. 1, Hyangho Reservoir; 2, Sunpo Wetland; 3, Maok Reservoir; 4, Banbyeon Stream; 5, Sangju Weir; 6, Nakdan Weir; 7, Chilgok Weir; 8, Samunjin Bridge; 9, Dalseong Weir; 10, Hapcheon-Changnyeong Weir; 11, Changnyeong-Haman Weir; 12, Namji Bridge; 13, Ojori Pond; 14, Wonmul Pond; 15, Geumoreum; 16, Sasaengi Pond; 17, Suwori Pond; 18, Bungurut Pond; 19, Yongsu Reservoir; 20, Susan Reservoir; 21, Songhyeon Reservoir; 22, Jogang Reservoir; 23, Juksan Weir; 24, Yeongsan River; 25, Seungchon Weir; 26, Cheongam Pond; 27, Gwangju Reservoir; 28, Dongrim Reservoir; 29, Aedang Reservoir; 30, Andeok Reservoir; 31, Chongho Reservoir; 32, Mangyeong Stream; 33, Mangyeong River; 34, Daewi Reservoir; 35, Wonsu Reservoir; 36, Geumma Reservoir; 37, Dochon Reservoir; 38, Sungrim Reservoir; 39, Seoji Reservoir; 40, Geumgang Estuary; 41, Heungrim Reservoir; 42, Bongseon Reservoir; 43, Ungpo Bridge; 44, Mujigae Bridge; 45, Tapjeong Reservoir; 46, Hwangsan Bridge; 47, Juhang Reservoir; 48, Bocheong Stream; 49, Daecheong Reservoir; 50, Gap Stream; 51, Sejong Weir; 52, Daegyo Stream; 53, Gongju Weir; 54, Bakje Weir; 55, Changgi Reservoir; 56, Suryong Reservoir; 57, Malli Reservoir; 58, Jakcheon Reservoir; 59, Nongdari Stone Bridge; 60, Chopyeong Reservoir; 61, Baekgok Stream; 62, Miho Stream; 63, Baekgok Reservoir; 64, Daetgol Reservoir; 65, Chungju Reservoir; 66, Mungwang Reservoir; 67, Gwanghyewon Reservoir; 68, Gangcheon Weir; 69, Yeoju Weir; 70, Ipo Weir.
Figure 7. Samples of MSFDE dataset-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>We performed groups of experiments to study the impact of ethnic group (race) in the<br> accuracy of emotion recognition with three kinds of ethnic groups (Asian, Caucasian as African).<br> So we have three experiments, each experiment has a neural network as a classifier, and each neural<br> network has three layers where there are 16 neurons in the hidden layer except Asian network has<br> 17 neurons (the best result with 17 neurons for Asians).</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 1. Sample Images of sixteen categories
<p>An image is often corrupted by noise in its acquisition or transmission. Noise is any<br> undesired information that degrades the image and appears in images from a variety of sources.</p> <p>Basically, there are three standard noise models [17], which model the types of noise<br> encountered in most images; they are additive noise, multiplicative noise and impulse noise. In this<br> work we have considered the occurrence of additive noise. An image function is given by f (x, y)<br> where (x, y) is spatial coordinate and f is intensity at point(x, y). Let f (x, y) be the original image,<br> g(x, y) be the noisy version and η(x, y) be the noise function, which returns random values coming<br> from an arbitrary distribution.</p>
C. elegans data sample for Pergola documentation
<p>C. elegans data sample for Pergola documentation (<a href="http://cbcrg.github.io/pergola/quick_start.html">http://cbcrg.github.io/pergola/quick_start.html</a>). The sample data set consists in two folders: One named "worm_speeds" containing a CSV file for each of the tracked worms. From the several measures that can be found in the individual files, in this example we will use mid-body speed. The "mapping" folder contains the "worm_speed2pergola.txt", which sets the mappings between the information represented in the worm_speed files and the pergola ontology.</p>
Mouse data sample for Pergola documentation - Shiny visualization
<p>Data sample of feeding and drinking behavior recorded during three weeks of C57BL6/J male mice. The data correspond to 2 groups (9 control mice and 8 high-fat diet mice). Each animal was tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a> The recordings were processed using Pergola to BED and BedGraph file formats.</p> <p>The data set consist in:</p> <p>- a exp_info.txt file setting mouse membership to the control or the HF mice.</p> <p>- a files folder containing BED and BedGraph files of mouse feeding behavior.</p>
Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>For all the 32 participants the EEG signal has to be sampled and process their emotions. The<br> emotions are depends on the music and video clips. In this paper the video clips are changed from<br> one person to other person. Here hybrid feed forward neural networks with radial basis function;<br> probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast<br> and insensitive neural network which provides the optimized classification result. Compare to the<br> multi layer perception neural network it provide accurate result. It classifies the emotion into two<br> different groups like arousal and valence. Figure 8 shows that the model implemented result of<br> emotion classification and person identification.</p>
Taxon sampling and inferred community phylogenies: R replication code and data.
<p>1 ) Code for simulating community phylogenies:</p> <p>community_simulations_creation.R</p> <p>[taxon].gene</p> <p>[taxon].phy</p> <p>[taxon].RAxML_bestTree.tre</p> <p>[taxon].Simulate.A.Community.pl</p> <p>[taxon].Simulate.B.Community.pl</p> <p>[taxon].Simulate.C.Community.pl</p> <p>[taxon].Simulate.D.Community.pl</p> <p> </p> <p>2) R code for creating and comparing phylogenetic diversity metrics:</p> <p>simulated_metric_calculation_and_comparison.R</p> <p>empirical_metric_calculation_and_comparison.R</p> <p> </p> <p>3) R code and data for statistical analyses:</p> <p>simulated_data_analysis.R</p> <p>empirical_data_analysis.R</p> <p>simulated_interval_individual_lme_data.csv</p> <p>simulated_summary_interval_individual_lme_data.csv</p> <p>empirical_interval_individual_lme_data.csv</p> <p>empirical_summary_interval_lme_data.csv</p> <p> </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2
<p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p> <p>Face orientation recognition is an important topic in computer vision and pattern recognition. Due to the non-rigid properties of faces, it is computationally expensive and difficult to achieve good recognition accuracy and robustness in face orientation recognition. In this paper, we propose an image mapping technique for face analysis in smart camera networks with a feature extraction and data from the facial feature. We estimate the face orientation angles in all camera views, based on the matched imaged data. Our objective is to obtain a set of facial structures which can work as landmarks for tracking and recognition of facial expressions. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1
<p>After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed. </p> <p>After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features.</p>
Standard Sample Description V1 Structural Metadata
<p>Standard Sample Description V1 is a specification aimed at harmonising the collection of analytical measurement data for the presence of harmful or beneficial chemical substances in food, feed and water. The specification is a list of standardised data elements (items describing characteristics of samples or analytical results such as country of origin, product, analytical method, limit of detection, result, etc.), linked to controlled terminologies. This file has been prepared to support the publication of data and interoperability. This file indicates which data elements from the specification will not be published to ensure full protection of confidential/sensitive information, for example personal data in accordance with Regulation (EC) No 45/2001 and to protect commercial interests, including intellectual property as specified in Article 4(2), first indent, of Regulation (EC) No 1049/2001.</p> <p>The Excel table contains information about the structural metadata elements of the data collection and their fact tables.<br> <br> The column <em>name</em> shows the name of the element (e.g. localOrg).<br> The column <em>description</em> describes how the content has to be interpreted.<br> The column <em>code</em> expresses the corresponding code of the structural metadata element.<br> The column <em>optional</em> says whether the structural metadata element is optional or not (then it is mandatory).<br> The column <em>dataType</em> contains the type which can be used to fill the structural metadata element and the possible maximal length of the field. The possible types are: text or number. <br> The column <em>catalogue</em> contains the name of the catalogue where the content of the structural metadata element has to be picked from (e.g. COUNTRY).<br> The column <em>data</em> <em>protection</em> contains whether the structural metadata element will be published or not (yes = will not be published, no = will be published).</p>
Standard Sample Description V2 Structural Metadata
<p>Standard Sample Description V2 is a specification aimed at harmonising the collection of analytical measurement data for the presence of harmful or beneficial chemical substances in food, feed and water. The specification is a list of standardised data elements (items describing characteristics of samples or analytical results such as country of origin, product, analytical method, limit of detection, result, etc.), linked to controlled terminologies. This specification uses EFSA FoodEx2 to describe sampled foods.</p> <p>This file has been prepared to support the publication of data and interoperability. This file indicates which data elements from the specification will not be published to ensure full protection of confidential/sensitive information, for example personal data in accordance with Regulation (EC) No 45/2001 and to protect commercial interests, including intellectual property as specified in Article 4(2), first indent, of Regulation (EC) No 1049/2001.</p> <p>The Excel table contains information about the structural metadata elements of the data collection and their fact tables.<br> <br> The column <em>name</em> shows the name of the element (e.g. localOrg).<br> The column <em>description</em> describes how the content has to be interpreted.<br> The column <em>code</em> expresses the corresponding code of the structural metadata element.<br> The column <em>optional</em> says whether the structural metadata element is optional or not (then it is mandatory).<br> The column <em>dataType</em> contains the type which can be used to fill the structural metadata element and the possible maximal length of the field. The possible types are: text or number. <br> The column <em>catalogue</em> contains the name of the catalogue where the content of the structural metadata element has to be picked from (e.g. COUNTRY).<br> The column <em>data</em> <em>protection</em> contains whether the structural metadata element will be published or not (yes = will not be published, no = will be published).</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 2. PCGs for sample sound file of 103-1305031931979-B
<p>Heart sounds that provide valuable diagnostic information in clinical examinations are among the most important physiological signals in the human body. However, heart sounds include noise, such as external sounds and lung sounds, caused by signal recording conditions. Noisy heart sound signal negatively affects the diagnosis of the doctor (Denga & Hanb, 2018). Digital filters are often used to filter biomedical signal. Digital filtering is defined as the acquisition of desired frequency values according to the characterization of the desired filter in order to improve the signal according to the intended use (Shenoi, 2005; Thede, 1995). Based on the experience gained from previous studies, an elliptic filter was used in this study (Deperlioglu, 2018; Guraksin et. al., 2009) . The heart sound signal covering the first two steps is given in Figure 2 for sample sound file of 103_1305031931979_B in the PASCAL Btraining data set.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 8. Minimum sample time of statistical metrics of MapReduce applications
<p>Figure 8 presents the minimum sampling time distribution of statistic metrics which ensures the stable modeling. Overall, the minimum sampling time of statistic metrics is smaller than sampling time of estimated coefficients. For different applications, a time-consuming application like Terasort needs the largest sampling time to tend to be stable. The Pi application shows the smallest minimum sampling time to reach stability.</p>
ScienceDex guides
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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.