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9,882 results for “scales”
Fig. 14. Paradelphomyia chosenica Alexander, 1950. A. male antenna. B. wing. C. male genitalia, dorsal view. D. ovipositor, lateral view. Scale bars 0.5 in New data on Limoniinae and Limnophilinae crane flies (Diptera: Limoniidae) of Korea
Fig. 14. Paradelphomyia chosenica Alexander, 1950. A. male antenna. B. wing. C. male genitalia, dorsal view. D. ovipositor, lateral view. Scale bars 0.5 mm.
Figs. 5-8 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 5-8. Silica-scaled chrysophytes from Mt. Sinbul wetland. 5-7. Mallomonas leboimei, 5. Whole cell of Mallomonas leboimei, 6. Showing the scales with very small or large dome, 7. Bristles, 8. M. metvienkoae. Scale bars = 5 (5 μm), 6-8 (1 μm).
Figs. 16-20 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 16-20. Mallomonas dimorphus sp. nov., 16. Apical scales with short papilla (arrow head) and longitudinal rib (arrow), 17. Body scales showing flat dome marked with irregularly ribs such as labyrinth, perforated base plate and small papillae (arrow) on the anterior submarginal rib, 18. Body scales showing hooded V-shaped rib, large meshes or pores and strutted posterior flange, 19, 20. Showing the small scales (arrows) with circular ribs and short, smooth needle like bristles. Scale bars = 1 μm.
Fig. 3 in New record of commensal scale worms, Arctonoe vittata (Grube, 1855) and Hyperhalosydna striata (Kinberg, 1856) (Polychaeta: Polynoidae) from Korean waters
Fig. 3. Various host invertebrates of Arctonoe vittata (Grube, 1855) found in this study. A, Solaster dawsoni Verrill, 1880; B and C, Aphelasterias japonica (Bell, 1851); D, Haliotis discus hannai Ino, 1953; E, Scelidotoma gigas (Martens, 1881); F, Triopha catalinae (Cooper, 1863); G and H, Cadlina japonica Baba, 1937; I, Niveotectura pallida (Gould, 1859); J, Apostichopus japonicus (Selenka, 1867).
Multi-modal image analysis for large scale cancer tissue studies within IMMUcan: multiplex immunofluorescence images
<p>In cancer research, multiplexed imaging has enabled the in-depth characterization of the tumor microenvironment (TME) and how it relates to patient prognosis. However, standardized, multi-modal data from large numbers of patients to identify robust biomarkers is missing. To provide such data across five cancer indications, the IMMUcan consortium performs broad molecular and cellular spatial profiling of thousands of cancer samples. Two reproducible and scalable workflows have been developed for whole slide multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) to overcome challenges of reproducibility and scalability. For mIF we developed IFQuant, a web-based tool optimized for user-friendliness and reproducibility. This Zenodo record contains the mIF images and IFQuant settings to reproduce the results presented in the referenced publication. The companion IMC dataset is available as a joint Zenodo record.</p>
Fig. 2 in New record of commensal scale worms, Arctonoe vittata (Grube, 1855) and Hyperhalosydna striata (Kinberg, 1856) (Polychaeta: Polynoidae) from Korean waters
Fig. 2. Hyperhalosydna striata (Kinberg, 1856), NIBRIV0000326200. A, Anterior end with elytra, dorsal view; B, Proboscis, frontal view; C, Right elytron from segment 11; D, Posterior end, dorsal view; E, Anterior end without elytra, dorsal view (arrow indicate dorsal cirrophore); F, Notopodium with notochaeta (arrow) from segment 14; G, Cirrigerous parapodium from segment 14, anterior view; H, Posterior end, ventral view; I, Posterior segments with nephridial papillae, ventral view; J, Neurochaeta from segment 14. Scale bar: A-E, H = 1 mm; F, J = 0.05 mm; G = 0.3 mm; I = 0.2 mm.
Fig. 5 in New record of commensal scale worms, Arctonoe vittata (Grube, 1855) and Hyperhalosydna striata (Kinberg, 1856) (Polychaeta: Polynoidae) from Korean waters
Fig. 5. Distribution of Arctonoe vittata (Grube, 1855) and Hyperhalosydna striata (Kinberg, 1856) in eastern Asia based on the present study (P) and literatures (1-19). 1: Uschakov (1982), 2: Uchida (1992), 3: Imajima (2001), 4: Uchida (1988), 5: Moore (1903), 6: Okuda (1936), 7: Imajima (2001), 8: Imajima and Hartman (1964); Imajima (2001), 9: Imajima (2001), 10: Okuda (1936), 11 and 12: Imajima (2001), 13: Okuda (1936), 14: Imajima (1988), 15-19: Uschakov (1955).
Figure 2 in A sense of scale: Foraging cetaceans' use of scale-dependent multimodal sensory systems
Figure 2. Scale-of-senses schematic of the hypothetical interchange of sensory modalities used by baleen whales to locate prey at variable scales. The line for audition of signals from prey is faded to denote a lack of evidence for this sensory system in baleen whales. X-axis on log scale, with equivalent metric distance given in gray type, and associated scale below. Y-axis ranks the relative use of each sensory modality between 0 (no contribution) and 10 (highest contribution) relative to its own information capacity, not relative to other senses.
Figure 1 in A sense of scale: Foraging cetaceans' use of scale-dependent multimodal sensory systems
Figure 1. Scale-of-senses schematic of the hypothetical interchange of sensory modalities used by dolphins to locate prey at variable scales. The line for chemoreception is faded to denote a lack of support for the sensory system in dolphins. X-axis on log scale, with equivalent metric distance given in gray type, and associated scale below. Y-axis ranks the relative use of each sensory modality between 0 (no contribution) and 10 (highest contribution) relative to its own information capacity, not relative to other senses.
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 5. Hierarchical clustering by scores across the EPQ–R scales for data about all the participants
<p>The clusters were generated using an implementation of a hierarchical clustering algorithm available in the R environment (R, n.d.). The top three clusters were extracted from a hierarchical cluster tree shown in Figure 5, while the color of data points in the visualization shown in figure 4 was determined based on cluster labels. Hierarchical clusters could be used when investigating which students in the analyzed sample share similar personality traits. This could be especially useful for smaller student groups as the teacher may manually inspect the cluster tree and its leaves, which designate individual students. For instance, there are three students in cluster 3, who are represented within the tree in Figure 5 by identifiers 14, 22, and 24. The students with identifiers 14 and 22 are more closely linked and more similar to each other than to the student with identifier 24. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 2. Comparison of mean scores on the EPQ–R scales
<p>The data for the workshop participants were loaded from the data warehouse, while the summary data from the original EPQ–R study were loaded from a CSV file. The bar chart featured in Figure 2 shows mean scores on the EPQ–R scales for the selected workshop participants (denoted by blue bars) and the selected participants of the original EPQ–R study (denoted by yellow bars). The mean scores on the P scale agree between the two samples, but the overall scores for the other scales vary. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 3. Radial visualization of scores across the EPQ–R scales for the male and female participants
<p>The radial visualization in Figure 3 depicts each participating student as a dot whose color indicates the gender of the student, blue for male students (M) and red for female students (F). The position of a dot in the visualization is determined by the scores of the associated student on the four EPQ–R scales. The radial overview may provide a much clearer outline of clustering within the analyzed group. Although there are only five female students, they are concentrated in a relatively narrow area within the radial coordinate system</p>
Repository of WESC2017 paper data - Benefits of subcomponent testing over full- scale blade testing elaborated on a trailing-edge bond line design
<p>Title:<br> <a href="https://doi.org/10.5194/wes-3-1-2018">Benefits of subcomponent over full-scale blade testing elaborated on a trailing-edge bond line design validation</a></p> <p>Authors:<br> <a href="mailto:malo.rosemeier@iwes.fraunhofer.de">Malo Rosemeier</a>, Gregor Basters, and Alexandros Antoniou</p> <p>Affiliation:<br> Division Structural Components, Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems, Am Seedeich 45, 27572 Bremerhaven, Germany</p> <p>The following data used for the study is provided:</p> <ul> <li> data_blade.zip contains input data for fusedwind-dev (https://github.com/FUSED-Wind/fusedwind-dev) of the DTU10MW blade.</li> <li>data_becas.zip contains input/ output data obtained for/ from BECAS with BECASWrapper (https://gitlab.windenergy.dtu.dk/HAWTOpt2/BECASWrapper).</li> <li>data_rfoil.zip contains polars obtained from Rfoil.</li> <li>data_feproc.zip contains the ANSYS APDL models obtained with FEPROCWrapper (https://gitlab.iwes.fraunhofer.de/git/bdt/FEPROCWrapper).</li> <li>*.sqlite data bases contain all results data which can be accessed with the `post.py` script.</li> <li>result_plots.zip contain the results generated by the `post.py` script.</li> </ul> <p>For further details see the manuscript.</p>
Dataset for: IoT deployment for city scale air quality monitoring with Low-Power Wide Area Networks
<p>Air Quality (AQ) is a very topical issue for many cities and has a direct impact on the health of its citizens. We propose to investigate the air quality of a large UK city using low-cost commodity Particulate Matter (PM) sensors, and compare them with government operated air quality stations. In this pilot deployment we design and build six AQ IoT devices, each with four different low-cost PM sensors and deploy them at two locations within the city. These devices are equipped with LoRaWAN wireless network transceivers to test city scale Low-Power Wide-Area Network network coverage. We conclude that some low-cost PM sensors are viable for monitoring AQ and demonstrate that our device design can be used via LoRaWAN to facilitate more granular city coverage without limitations of network access. Based on these findings we intend to deploy a larger LoRaWAN enabled Air Quality sensor network deployment across the city.</p>
Deprecated Dataset for "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior"
<p>This dataset is deprecated. <strong>The updated version of this Dataset is here:</strong> <a href="https://zenodo.org/record/3678559#.Xl9-Ji97FhE">https://zenodo.org/record/3678559#.Xl9-Ji97FhE</a></p> <p>Dataset for the publication "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior". Antigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos and Nicolas Kourtellis. International AAAI Conference on Web and Social Media (ICWSM), 2018.</p> <p>The dataset provided here includes an updated version of the original dataset, with ~100k tweets annotated using the CrowdFlower platform:</p> <ul> <li>hatespeech_labels.csv: contains ~100k rows, where every row consists of a unique Tweet ID and its associated majority annotation</li> </ul> <p><em>UPDATE</em>: It has come to our understanding that a number of the tweets are not available anymore for download on Twitter. Therefore, <strong>upon request</strong>, we can provide one more file with the full ~100k tweet text and their associated majority labels. The tweets are shuffled so that there is no connection between tweet IDs and texts (in order to be aligned with the T&C of Twitter).</p> <p>To obtain the file contact a.m.founta at gmail dot com <strong>AND </strong>antonis26papa at gmail dot com.</p> <p><em>Please cite the paper in any published work that uses any of these resources.</em></p> <blockquote> <p>@inproceedings{founta2018large,<br> title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior},<br> author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas},<br> booktitle={11th International Conference on Web and Social Media, ICWSM 2018},<br> year={2018},<br> organization={AAAI Press}<br> }</p> </blockquote> <p>For any further questions contact a.m.founta at gmail dot com.</p> <p> </p> <p>Publication DOI: <a href="https://doi.org/10.5281/zenodo.1443348">https://doi.org/10.5281/zenodo.1443348</a></p> <p>Github: <a href="https://github.com/ENCASEH2020/hatespeech-twitter">https://github.com/ENCASEH2020/hatespeech-twitter</a></p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data
<p>In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 14. Symbol 8 regeneration
<p>As we can see, here we were able to recover the lost middle portion of character ‘A’ using our genetic algorithm. If we can apply some noise filtering technique, the result would be far better. In figure 14, 15 there are another two examples.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 11. Initial population
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</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.