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6,783 results for “orientator”
Plate 16 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 16: O. uncifer 48h 48arl 48s8 O. angusticeps 49h 49arl 49s8 49vp
Plate 14 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 14: O. hochimini 44h 44arl 44s8 O. lannaensis 45h 45arl 45av 45s7 45s8 45vp
Plate 11 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 11: O. vulcanus 34h 34arl O. mutilus35h 35arl 35s8 O. palawanensis 36h 36arl 36s8
Plate 10 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 10: O. viduasinae 32h 32arl 32s8 32vp O. torajah 33h 33arl 33av 33s8
Plate 9 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 9: O. balnearius 30h 30arl 30s8 30vp O. pendleburyi 31h 31arl 31av 31s8 31vp
Plate 8 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 8: O. muluensis 27h O. tempestivus 28h 28arl O. wallacei 29h 29arl 29s8 29vp
Plate 6 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 6: O. rufotestaceus 19h 19vp O. rufulus 20h 20arl 20vp O. sindicus 21h 21arl 21av 21vp
Plate 5 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 5: O. bowringi 16h 16arl 16s8 O. kochangensis 17h 17vp O. ruficeps 18h 18vp
Plate 4 in New oriental Oedichirus (Staphylinidae, Paederinae, Pinophilini)
Plate 4: O. longipennis 14h 14hjp 14arl 14av 14s8 14vp O. lewisius 15h 15arl 15av 15vp
Electronic structure orientation as a map of in-plane antiferroelectricity in beta'-In2Se3
<p>Matlab data files and metadata files for experimental data as described in the paper titled "Electronic structure orientation as a map of in-plane antiferroelectricity in beta'-In2Se3". </p>
Data for: Breeding pairs with color aberrations in Oriental reed warblers
<p>One pair of Oriental red warblers was found with color aberrations. Two videos (ESM Video S1 and Video S2) were provided as supplementary material showing Breeding pairs with color aberrations in Oriental reed warblers. Our study shows that the breeding pair breed normally and further egg experiments indicated they can reject foreign eggs as other breeding pairs.</p>
The code and data for the paper entitled 'Facilitating Efficient Discovery: A GUI-Oriented Approach for Exploring Functionality using Machine Learning Model'
<h2><strong>Code</strong></h2> <p><strong><span>dataCollection.py</span></strong></p> <p><span>The collection of app data is primarily accomplished by processing and storing XML files and screenshots of the app. Firstly, the uiautomator is utilized to connect to the smart device and obtain screenshots and XML files. Subsequently, the XML files are analyzed to identify clickable functions within the GUI, and the textual information contained within the functions, along with their specific locations, is extracted. Finally, the Python file also includes handling of interface elements, such as determining if elements are obscured and verifying the legitimacy of the text.</span></p> <p><strong><span>tagData.py</span></strong></p> <p><span>The main implementation involves volunteers annotating app functionalities, including HTML generation, user data analysis, and retrieval. Flask framework is employed, presenting one GUI to the user each time while randomly prompting them to click on three functionalities. Ultimately, the time taken by users to locate these three functionalities is collected.</span></p> <p><strong><span>userPersonalization.py</span></strong></p> <p><span>Separating out the data annotated by each user facilitates personalized analysis. This process involves extraction, storage, and loading of individual user annotations.</span></p> <p><strong><span>dataPreprocessing.py</span></strong></p> <p><span>For a user-annotated functionality, completing the conversion from user time to either "hard-to-find" or "easy-to-find" involves several steps. First, the functionalities are vectorized, extracting relevant parameters from the XML files and computing their correlation with the time users spent searching for the functionalities. These parameters are then normalized to obtain feature vectors for the functionalities. Additionally, an initial determination is made regarding whether the annotated functionalities are "hard-to-find" or "easy-to-find" for each user. Subsequently, clustering is performed on all annotated data from users, and based on the clustering results, the outcomes are filtered and adjusted.</span></p> <p><strong><span>difficultFindClassifier.py</span></strong></p> <p><span>Train the classifier and use it to predict "hard-to-find" functionalities, then display the results.</span></p> <h2><span>Data</span></h2> <p><span>The data is located in the "static" folder:</span></p> <p><span>- The "persistentData" folder contains the trained classifier.</span></p> <p><span>- The "picture" folder contains screenshots of the app.</span></p> <p><span>- The "requestTime" folder stores data for when volunteers annotate only one function in a GUI.</span></p> <p><span>- The "threeResponseTime" folder saves data for when volunteers annotate three functions in a GUI.</span></p> <p><span>- The "userData" folder stores personalized user data.</span></p> <p><span><span>- The "xml_information" folder stores XML files of the app.</span></span></p>
Where is the heat threat in a city? Different perspectives on people-oriented and remote sensing methods: the case of Prague
<p>This is supplementary data for a paper called ‘Where is the heat threat in a city? Different perspectives of people-oriented and remote sensing methods, the case of Prague’ to be submitted to the journal Heliyon. Dataset contains three folders:</p> <ol> <li><strong>LST</strong> <br>– Layer Landsat_ecostress_data = vector polygon layer containing fishnet which includes values from 15 sattelite images from 3 different sources - ECOSTRESS, Landsat 8 and Landsat 9<br>Attribute percent_av contains mean percentile from all 15 images</li> <li><strong>Participatory mapping</strong><br>Raw data from participatory mapping campaign held on August 2022 in Prague-Holešovice</li> <li><strong>Thermal walk</strong><br>Layer Data_app = Raw data from thermal walk held on August 3rd 2022 (Declared time in the attribute table is UTC)<br>Layer DataApp_corr = Corrected coordinates from thermal walk (For these corrections, precisely prepared routes were used. Data were post-processed using an algorithm for a minimization of the distance between the measured and expected point location. The measured point was moved using its normal on-route position and his distance was compared with the previous point. If the distance was larger than 115% of expected, the point was moved closer, and when the distance was smaller than 85% of expected, the point was moved forward.)</li> </ol> <p> </p> <p><em>Acknowledgements: This work was supported by the Faculty of Science, Palacký University Olomouc internal grant IGA_PrF_2024_022 – Novel approaches to studying the human thermal environment in urban areas. This work was also supported by Strategy AV21 project ‘City as Lab of changes’, financed by the Czech Academy of Sciences.</em></p>
Crystallographic Preferred Orientation of Phase D at High Pressure and Temperature: Implications for Seismic Anisotropy in the Mid-mantle
<p> The dataset includes the data of Crystallographic Preferred Orientation of phase D aggregates in this study. It include electron backscatter diffraction (EBSD) mapping data in .cpr and .crc file and transmission two-dimensional X-ray diffraction (2D-XRD) patterns acquired at BL04B1 beamline of synchrotron facility of SPring-8, Hyogo, Japan. </p>
Monkey V1/V2 neuronal population response to drifting gratings with varying orientation and contrast
<p>Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a multiplicative factor and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast-dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation-dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models, and provide a parsimonious explanation for the stimulus-dependence of correlated variability within and between visual areas.</p>
Data from: Correction of preferred-orientation induced distortion in cryo-electron microscopy maps
<p>Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy, modifications to grids or to air-water-interfaces. However, these approaches often require time-consuming experiments and the effect was always protein dependent. Here, we developed a procedure containing removing mis-aligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct such distortion by recovering missing data using a purely computational algorithm. This procedure called Signal-to-Noise Ratio Iterative Reconstruction Method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.</p>
Figs 5–6 in Bolitophilidae (Diptera) From Taiwan: A Family New To The Oriental Region
Figs 5–6. Bolitophila antennata sp. n., holotype male. 5 = left wing; 6 = pubescence of antennae
Fig. 6. D in New Taxa Of Diadocidiidae (Diptera) From The Oriental Region
Fig. 6. D. (Taidocidia) globosa sp. n., paratype male (Taiwan), habitus photo
Fig. 23 in Palaearctic And Oriental Species Of Craspedometopon Kertész (Diptera, Stratiomyidae)
Fig. 23. Craspedometopon orientale sp. n., male. Photo: D. KOVAC
Fig. 25 in Palaearctic And Oriental Species Of Craspedometopon Kertész (Diptera, Stratiomyidae)
Fig. 25. Distribution of Craspedometopon frontale and C. orientale sp. n.
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.