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6,783 results for “Oriental”
Fig. 5 in Three new species of Eupetersia Blüthgen, 1928 (Hymenoptera, Halictidae) from the Oriental Region
Fig. 5. Eupetersia yanegai sp. nov, ♀ paratype. A. Lateral view of head and mesosoma. B. Head, facial view. C. Head and mesosoma, dorsal view. D. Propodeum. E. Metasoma. F. Geographic distribution.
Fig. 3. Eupetersia nathani Baker, 1974 in Three new species of Eupetersia Blüthgen, 1928 (Hymenoptera, Halictidae) from the Oriental Region
Fig. 3. Eupetersia nathani Baker, 1974, ♀ holotype. A. Lateral habitus. B. Dorsal view of habitus. C. Facial view. D. Geographic distribution.
Fig. 2 in Three new species of Eupetersia Blüthgen, 1928 (Hymenoptera, Halictidae) from the Oriental Region
Fig. 2. Eupetersia sabahensis sp. nov., ♂ holotype. A. Lateral habitus. B. Dorsal view of head and mesosoma. C. Facial view. D. Propodeum. E. Metasoma. F. Geographic distribution.
Fig. 4 in Three new species of Eupetersia Blüthgen, 1928 (Hymenoptera, Halictidae) from the Oriental Region
Fig. 4. Eupetersia yanegai sp. nov., ♂ holotype. A. Dorsal view of habitus. B. Lateral habitus. C. Head, facial view. D. Mesosoma and head. E. Propodeaum. F. Metasoma.
Fig. 7 in Three new species of Eupetersia Blüthgen, 1928 (Hymenoptera, Halictidae) from the Oriental Region
Fig. 7. Malaise trap in a mangrove swamp, habitat of Eupetersia singaporensis sp. nov., Kranji Nature Trail (Sungei Buloh), Singapore.
Fig. 1 in Three new species of Eupetersia Blüthgen, 1928 (Hymenoptera, Halictidae) from the Oriental Region
Fig. 1. Eupetersia singaporensis sp. nov., ♂ holotype. A. Lateral habitus. B. Dorsal view of head and mesosoma. C. Facial view. D, Propodeum. E. Metasoma. F. Geographic distribution.
Fig. 6 in Three new species of Eupetersia Blüthgen, 1928 (Hymenoptera, Halictidae) from the Oriental Region
Fig. 6. Eupetersia yanegai sp. nov. A. ♂, end of metasoma with pygidial plate. B. ♀, end of metasoma without pygidial plate.
Fig. 2 in The first species of Trichopsomyia Williston, 1888 (Diptera: Syrphidae) described from the Oriental region, with a discussion on the character states of the pilosity of the katepisternum
Fig. 2. Trichopsomyia pilosa sp. nov., male genitalia, holotype (NHMUK 010864268). A. Lateral view. B. Epandrium, dorsal view. C. Apical part of hypandrium, ventral view. Scale bars = 0.5 mm.
Fig. 1 in The first species of Trichopsomyia Williston, 1888 (Diptera: Syrphidae) described from the Oriental region, with a discussion on the character states of the pilosity of the katepisternum
Fig. 1. Trichopsomyia pilosa sp. nov. A. Habitus, lateral view, holotype ♂ (NHMUK 010864268). B. Habitus, dorsal view, paratype ♂ (NHMUK 010864266). C. Head, dorsal view, paratype ♂ (NHMUK 010864266). D. Antenna, lateral view, holotype ♂ (NHMUK 010864268). E. Metaleg, frontal view, paratype ♂ (JSA). Scale bars: A–C, E = 1.0 mm; D = 0.5 mm.
Artifact for the ESEC/FSE 2020 Paper: An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse
<p>This dataset comprises the supplementary material for the paper "An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse" by Jacob Krüger and Thorsten Berger, accepted at ESEC/FSE 2020.</p> <p>The dataset comprises:</p> <ul> <li>bibFilesManualSearch: The bib files for all venues analyzed, as provided by DBLP (cf. Section 2.4)</li> <li>dataFromPapers: The pdf file documents all included studies and the data extracted from these (cf. Section 2.4, 3.2, and 3.3)</li> <li>interviewGuide: The guide/questions for our semi-structured intreviews in the cost assessment phase (cf. Section 2.3)</li> <li>anonymizedInterviewSummary: The anonymized and summarized data from the cost-assessment interviews (cf. Section 3.2 and 3.3)</li> <li>R: Our R script for creating our figures and the corresponding csv files</li> </ul> <p> </p>
Fig. 2-3 in Two new Atomaria S , 1829 (Coleoptera: Cryptophagidae) from the Oriental Region with remarks on further Atomaria species
Fig. 2-3: (2) Atomaria schuhi nov.sp., paratype, male, West Java; (3) Atomaria lewisi REITTER, 1877 from Europe. Fig. 4-5: (4) Atomaria horridula REITTER, 1877, China; (5) Atomaria incertula JOHNSON, 1971, Nepal.
Long-term triaxial test of Passwang Marl, Z-orientation
<p>This data set describes a long-term (> 3 yr) rock mechanics experiment of Psswang Marl using a triaxial apparatus. The sample is consolidated and fully saturated. The bedding orientation is 45° to the main axis of the sample (Z-orientation). Confining stress, axial stress and pore pressure are changed multiple times in order to investigate dilatancy, pressure diffusion coefficients and Young's modulus.</p>
Sun compass neurons are tuned to migratory orientation in monarch butterflies
Every autumn, monarch butterflies migrate from North America to their overwintering sites in Central Mexico. To maintain their southward direction, these butterflies rely on celestial cues as orientation references. The position of the sun combined with additional skylight cues are integrated in the central complex, a region in the butterfly's brain that acts as an internal compass. However, the central complex does not solely guide the butterflies on their migration but helps monarchs in their non-migratory form manoeuvre on foraging trips through their habitat. By comparing the activity of input neurons of the central complex between migratory and non-migratory butterflies, we investigated how a different lifestyle affects the coding of orientation information in the brain. During recording, we presented the animals with different simulated celestial cues and found that the encoding of the sun was narrower in migratory compared to non-migratory butterflies. This feature might reflect the need of the migratory monarchs to rely on a precise sun compass to keep their direction during their journey. Taken together, our study sheds light on the neural coding of celestial cues and provides insights into how a compass is adapted in migratory animals to successfully steer them to their destination.
R script for identification and localisation of prophage within bacterial genomes using outward-oriented paired-end reads.
<p>This R script shows an analysis example of using outwards-oriented paired-end reads (OPRs), identified using the OPR finder function in the mVIRs package, to identify p22 in <em>S</em>. Tm LT2 as described in the publication "<strong>High throughput sequencing provides exact genomic locations of inducible prophages and accurate phage-to-host ratios in gut microbial strains" </strong>by Zünd et al. Microbiome (2021)</p>
Code and data for: Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?
<p>This repository is a companion to the manuscript "<em>Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?</em>" and is linked to <a href="https://github.com/CBeardsworth/Pheasant_OrientStrat_Habitat">Github</a>.</p> <p>For any questions about the code please contact Christine at <a href="mailto:c.e.beardsworth@gmail.com">c.e.beardsworth@gmail.com</a></p> <p>To use any data contained in this repository contact Joah at <a href="mailto:j.r.madden@exeter.ac.uk">j.r.madden@exeter.ac.uk</a> for permission.</p> <p>In this repository, we have included a run-through of the R analysis <a href="https://cbeardsworth.github.io/Pheasant_OrientStrat_Habitat/">here</a> to show the outputs of the analysis without the need to run the code. For those that might want to run the code themselves, we have included three R scripts (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/R">/R</a>) and their accompanying datasets (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a>). A description of the code and the data needed to run them is below:</p> <p><em>Cognition analysis and figs.R</em> = Run the cognition analysis for the first section of the manuscript and create the figures. For this, the datasets mazeData.csv (the learning trials) and mazeRotationResults.csv (the probe trial) are required. </p> <p><em>iSSA analysis and bootstrapping.R</em> = Run iSSA models and bootstrapping. This produces the datasets required for the next stage of analysis. For this code, the datasets habitat.grd (habitat information), atlas2018-strategy.csv (atlas data + id and strategy data for each bird) and FeederCoords2017_27700.csv (coordinates of feeder locations from 2017-2018) are required. The produced datasets are included in <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a> therefore to run subsequent analyses, this code does not need to be run. To develop this code we relied heavily on the code included in the supplementary material of <a href="https://doi.org/10.1002/ece3.4823">Signer et al. (2019)</a> as well as an <a href="https://bsmity13.github.io/log_rss">online tutorial</a> from Brian J. Smith for calculating log-RSS.</p> <p><em>Habitat analysis and Figs.R</em> = Run the statistical models for the final section of the manuscript and create the figures. For this code, the datasets produced in the previous R script are required (habitatOrientation_coefs.csv and habitatOrientation_avail.csv). We have included <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">these datasets</a> so users do not need to run the iSSA analysis and bootstrapping.R script themselves. </p>
The IDOL (Inertial Deep Orientation-estimation and Localization) Dataset
<p><strong>The IDOL Dataset</strong><br> The IDOL (Inertial Deep Orientation-estimation and Localization) dataset consists of 20+ hours of pedestrian walking IMU data in indoor environments. Data was collected in 3 different buildings from 15 different users of varying body types (not all users are present in each building set). Data collection procedures were approved by an IRB.</p> <p>Data was collected using a LiDAR-Visual-Inertial SLAM system (Kaarta Stencil) as ground truth, with the an iPhone 8 rigidly attached with the Stencil rig. The rig's ground truth position and orientation are recorded, along with IMU readings from both the Stencil internal XSens IMU (gyroscope, accelerometer) and the iPhone IMU (gyroscope, accelerometer, magnetometer). Because both systems were rigidly mounted to each other, the gyroscopic readings are identical (minus a reference frame transformation). However, as there is an offset in position between the Stencil and phone IMU, accelerometer readings differ slightly due the additional lever arm. The offset between the phone's true position and the Stencil's origin was less than the size of the phone, which we empirically determined (using a Vicon mocap studio) to be within the error margin of the Stencil estimate. All readings are sampled at 100Hz.</p> <p><strong>Data subsets</strong><br> At the root level, the dataset has been divided into 3 buildings. The recorded trajectories for each building dataset are grouped into two subsets: "known" and "unknown". The dataset for building 1 also contains a subset entitled "train" because cross-subject performance was evaluated in that building. All "known" and "train" set users are from the same common pool of users, which is disjoint from the set of users present in all the `unknown` trajectories. This allows for testing the generalization of networks across users. These were the splits we used for evaluation of results in the paper, but these can be re-split arbitrarily, as we include subject IDs for each trajectory.</p> <p>Each data subset has a "metadata.json" file, with information about each trajectory file in the subset. This information includes the subject ID for the trajectory and whether or not and when during the run a calibration was performed.</p> <p><strong>Trajectory calibration</strong><br> Calibrations involved rotating the data collection rig along each axis at either the start or end of the trajectory. This allows the magnetometer readings to be calibrated. This was not performed for all trajectories, especially if they were collected back-to-back in the same location, as there is minimal variation in magnetic readings. These can be omitted by truncating a few seconds of data at the relevant parts of the trajectory, although for our evaluation we left these sections in, as there was negligible impact on performance.</p> <p>Each trajectory starts and ends in roughly the same location in each building. Each also starts and ends with the user quickly jostling the data collection rig in the air, generating a "synchronization spike" in the IMU data. This spike is used to perform time alignment of the iPhone and Stencil data, as we find this approach is slightly more accurate than relying on ntp-synchronized timestamps between devices due to the high sample rate. This synchronization has already been performed in the published data files. These artifacts can be omitted using a peak detection algorithm or simply truncating the beginning/end of a trajectory, although we leave them in the dataset because they have negligible impact on results.</p> <p><strong>Trajectory global alignment</strong><br> Trajectories in this dataset were aligned to a global map after being collected, so orientations and positions in one building are globally correct relative to trajectories in other buildings, minus a static positional (x,y) offset (i.e. the trajectories in all buildings begin at (0,0)). The initial pose of trajectories in Building 1 is set as the common origin. In order to recover the data as originally collected, a counter-clockwise rotation in the (x,y) plane must be applied to the position and orientation data in Buildings 2 and 3. This rotation offset is:</p> <pre><code class="language-markdown">Building | Rotation Offset (radians) --- | --- Building 2 | 1.8510 Building 3 | 0.2822</code></pre> <p><br> <strong>Reading the data</strong><br> Each trajectory is stored as a `.feather` file, encoded via Apache Arrow. Python's `pandas` library can be used to read these files as DataFrames using the following:</p> <pre><code class="language-python">import pandas as pd df = pd.read_feather("path/to/feather/file")</code></pre> <p> </p> <p>Each trajectory file contains the following data columns:</p> <pre><code class="language-markdown">Column Name | Data Description --- | --- timestamp | Time of data point in seconds orient[W,X,Y,Z] | Ground truth orientation from Stencil as a quaternion processedPos[X,Y,Z] | Ground truth position from Stencil, smoothed to remove artifacts. This was used as ground truth in our work iphoneOrient[W,X,Y,Z]| iPhone CoreMotion API estimate of device orientation as a quaternion iphoneAcc[X,Y,Z] | Raw acceleration reported by iPhone IMU iphoneGyro[X,Y,Z] | Raw angular velocity reported by iPhone IMU iphoneMag[X,Y,Z] | Magnetometer values reported by iPhone IMU stencilAcc[X,Y,Z] | Raw acceleration reported by Stencil IMU stencilGyro[X,Y,Z] | Raw angular velocity reported by Stencil IMU</code></pre> <p>Note the iPhone and Stencil IMU raw measurements are not in the same reference frames. The Stencil IMU reference frame and ground truth reference frame are the same. The frames are roughly mapped as Stencil +x -> iPhone -x, Stencil +y -> iPhone -y, Stencil +z -> iPhone +z in right-handed coordinate frames.</p> <p><strong>Citation</strong></p> <p>If you use this dataset or part of it, please cite our paper to appear in AAAI 2021:</p> <blockquote> <p>Scott Sun, Dennis Melamed, Kris Kitani. "IDOL: Inertial Deep Orientation-estimation and Localization". AAAI 2021 (in press).</p> </blockquote>
HERMES: a molecular formula-oriented method to target the metabolome - Dataset
<p>This dataset contains all raw LC-MS1 and LC-MS2 data from river <em>s</em>urface water, <em>Escherichia coli, </em> and human plasma used in the paper <em>HERMES: a molecular formula-oriented method to target the metabolome, </em>as well as the RMarkdown vignettes generating the results and base figures.</p> <p>Please refer to the README file for more information about the data organization and script reproducibility.</p> <p>A collection of ready-to-use molecular formula databases can be found <a href="https://zenodo.org/record/5025560">in this Zenodo dataset.</a></p>
A Hierarchical Network-Oriented Analysis of UserParticipation in Misinformation Spread on WhatsApp
<p>#Authors: Gabriel Peres Nobre, Carlos Henrique Gomes Ferreira, Jussara Marques de Almeida<br> #2021</p> <p>Script to read a Database file of messages and, in the end, extract user communities based on content co-sharing.</p> <p>We provide a database file with the anonymized messages shared in WhatsApp. </p>
FIGURE 4 in First description and bionomic notes for the final-instar larva and pupa of an Oriental dobsonfly species, Neoneuromus sikkimmensis (van der Weele, 1907) (Megaloptera: Corydalidae)
FIGURE 4. Detailed features of the last-instar larva of Neoneuromus sikkimmensis (van der Weele, 1907). A. surface of 1 st antennomere; B. apex of 3 rd antennomere; C. part of venter of prothorax; D. part of pronotum; E. part of abdominal tergum; F. base of ventral tuft.
FIGURE 3 in First description and bionomic notes for the final-instar larva and pupa of an Oriental dobsonfly species, Neoneuromus sikkimmensis (van der Weele, 1907) (Megaloptera: Corydalidae)
FIGURE 3. Chaetotaxy on the abdominal tergum of the last-instar larva of Neoneuromus sikkimmensis (van der Weele, 1907). A. dark macrosetae. B. pale slender setae. Arrow indicates specific type of macrosetae on abdominal terga (See detail description of each type of macrosetae in Description). Scale bar = 0.2 mm.
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.