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627 results for “moving”
Figure 9 in Two species of Acaricis (Acari: Tenuipalpidae) from New Zealand, moved from the genus Tenuipalpus, with a key to the known species
Figure 9 Acaricis alpinus (Collyer) male: dorsal aspect, right side, of: A – leg I; B – leg II; C– leg III; D – leg IV.
Figure 8 in Two species of Acaricis (Acari: Tenuipalpidae) from New Zealand, moved from the genus Tenuipalpus, with a key to the known species
Figure 8 Acaricis alpinus (Collyer) female: dorsal aspect, right side, of: A – leg I; B – leg II; C – leg III; D – leg IV.
Figure 6 in Two species of Acaricis (Acari: Tenuipalpidae) from New Zealand, moved from the genus Tenuipalpus, with a key to the known species
Figure 6 Differential interference contrast (DIC) image ofAcaricis alpinus (Collyer) (Female): dorsum.
Figure 4 in Two species of Acaricis (Acari: Tenuipalpidae) from New Zealand, moved from the genus Tenuipalpus, with a key to the known species
Figure 4 Acaricis montanus (Collyer) female, dorsal aspect, right side, of: A – leg I; B – leg II; C – leg III; D – leg IV.
Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory
<p><strong>Summary</strong></p><p>This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is published in IEEE Transactions on Computational Imaging [Schut 2022].</p><p> </p><p><strong>Description</strong></p><p><i>Sample information</i></p><p>The samples are 23 mandarins. The first 10 are of the Nadorcott cultivar, and the remaining 13 are of the Clemenrubi cultivar. The diameter of the mandarins ranges between 50 and 58 mm. Per sample metadata can be found in the mandarin_metadata.csv file.</p><p><i>Scanner information</i></p><p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p><p><i>Scanning geometry</i></p><p>The mandarins were moved according to a custom scanning protocol, with the intention to simulate a conveyor belt setup. A wooden disk was attached on top of the rotation stage and six evenly spaced object positions were marked on the disk at a fixed distance from the center of rotation. Pieces of cardboard tube were used as sample holders to make sure the mandarins wouldn't roll as the disk would rotate and to raise them from the disk without attenuating too much of the X-ray signal. The rotation stage was positioned in such a way that over a full rotation of the disk, each mandarin would be completely in view of the detector for more than 180 degrees of the rotation, while there would also be a position at which it would be completely out of view. An image illustrating the exact dimensions is included in mandarin_carousel_dimensions.png.</p><p>The scan was performed in phases. Every phase 400 projection images were acquired, while rotating the disk for 60 degrees. This would rotate one of the positions out of view of the scanning setup. Before the first 6 phases a mandarin was added on the position that was out of view of the setup. For the phases after that the position that would be out of view would contain a mandarin that had rotated the full circle so that mandarin was replaced with a new mandarin. At the last 6 phases there would be no new mandarins left to add so the mandarin that was out of view of the setup would only be removed. The projection images acquired from each phase were concatenated resulting in a dataset of 11200 projections. At most 5 mandarins were in view at a given time.</p><p>Note: Due to a small oversight while scanning, the 19th mandarin is not included on projections 9200-9205. This area can be masked out during reconstruction.</p><p><i>Scanning settings</i></p><p>A peak voltage of 90kV was used, the target power was set to 49.5W and the spectrum was pre-filtered using 0.1mm of copper. An exposure time of 200 ms was used for each projection. A start-stop acquisition scheme was used to minimize vibrations and to make adding and removing mandarins easier: After each projection image was acquired, the stage was rotated to a new position and the scanner was paused for 200 ms before acquiring the next projection image. Darkfield and flatfield images were acquired before and after all the mandarins were scanned using the average over 200 images. 2x2 pixel hardware binning was used and all images were cropped to a 500 pixel high region around the center, resulting in 11200 projection images of 956x500 pixels (11.1GB uncompressed). All images are stored in .tif format.</p><p><i>Reconstructing volumes</i></p><p>The repository <a href="https://github.com/D1rk123/top-ct_experiments">https://github.com/D1rk123/top-ct_experiments</a> contains code for TOP-CT simulations and reconstructions. The script mandarin_carousel_experiment.py was specifically written to reconstruct volumes for each separate mandarin from this dataset.</p><p> </p><p><strong>Research group</strong><br>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p><p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p><p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p><p><strong>References</strong></p><p>[Schut 2022] D. E. Schut, K. J. Batenburg, R. van Liere, and T. van Leeuwen, "TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography", 2022, IEEE Transactions on Computational Imaging<br>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, "Explorative imaging and its implementation at the FleX-ray Laboratory," J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p><p>If you use (parts of) this data in a publication, please consider citing the first article.</p>
40x40 Cylinder moving upwards, downwards, leftwards, rightwards
<p>This dataset was used on the preparation of the following publication:</p> <p>P. Machado, A. Oikonomou, G. Cosma and T. M. McGinnity, "Bio-Inspired Ganglion Cell Models for Detecting Horizontal and Vertical Movements," <em>2018 International Joint Conference on Neural Networks (IJCNN)</em>, 2018, pp. 1-8, doi: 10.1109/IJCNN.2018.8489439.</p>
VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction
<p>This dataset includes all the data and trained models to replicate our work for VGQ-CNN (accepted for IJCNN 2022). You can find the code to use this dataset on <a href="https://github.com/AuCoRoboticsMU/vgq-cnn">github</a>. To replicate the work done for VGQ-CNN, use the data in vgq-dset.zip. Trained models of VGQ-CNN, Fast-VGQ-CNN and GQ-CNN are available in VGQ-CNN_models.zip.</p> <p> </p> <p>To create your own, subsampled training and testing data, adjust our code on github to your subsampling constraints and use full_rendered_dset (created by unpacking full_rendered_dset_tensors.zip and full_rendered_dset_images.zip into the unpacked directory of full_rendered_dset_info.zip).</p>
Data Instances for: Who moves the locker? A benchmark study of alternative mobile parcel locker concepts
<p>|C|_h.txt</p> <p>|C|: number of customers<br> h: instance</p> <p>|C|;|P|;</p> <p>|C|: number of customers<br> |P|: number of parking spaces</p> <p>Customer (c;size;max_dist;min_time;L;x_1;y_1;...;x_L;y_L;s_1;e_1;...;s_L;e_L)</p> <p>c: customer index <br> size: parcel size<br> max_dist: maximum walking distance<br> min_time: minimum overlap time<br> L: number of whereabouts<br> (x_i,y_i): position of whereabouts i<br> [s_i,e_i]: time window of whereabouts i</p> <p>Parking space (p;x;y)<br> p: parking space index <br> (x,y): position</p>
4DMRI moving meshes
<p><strong>Moving lung and ribcage meshes extracted from 4DMRIs</strong></p> <p>The newest version of the dataset and the documentation can be found on <a href="https://gitlab.psi.ch/4DCT-MRI_updated/4DMRI_moving_meshes">https://gitlab.psi.ch/4DCT-MRI_updated/4DMRI_moving_meshes</a>.</p> <p>The moving lung and ribcage meshes can for example be used to generate synthetic 4DCT(MRI)s. The code for generating 4DCT(MRI)s using a reference CT and the moving mesh data can be found on <a href="https://gitlab.psi.ch/4DCT-MRI_updated/4DCT-MRI">https://gitlab.psi.ch/4DCT-MRI_updated/4DCT-MRI</a>.</p> <p>Please cite the following publications when using the data:</p> <p>Jenny, T., Duetschler, A., Weber, D.C., Lomax, A.J. and Zhang, Y. (2022), <em>Technical Note: Towards more realistic 4DCT(MRI) numerical lung phantoms</em>. Manuscript submitted for publication.</p> <p>Duetschler, A., Bauman, G., Bieri, O., Cattin, P.C., Ehrbar, S., Engin-Deniz, G., Giger, A., Josipovic, M., Jud, C., Krieger, M., Nguyen, D., Persson, G.F., Salomir, R., Weber, D.C., Lomax, A.J. and Zhang, Y. (2022), <em>Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations.</em> Med. Phys.. <a href="https://doi.org/10.1002/mp.15591">https://doi.org/10.1002/mp.15591</a></p>
Datasets for "Resolving the microscopic hydrodynamics at the moving contact line"
<p>Datasets for the article:</p> <p>"Resolving the microscopic hydrodynamics at the moving contact line" <br> Amal K. Giri, Paolo Malgaretti, Dirk Peschka, and Marcello Sega<br> Phys. Rev. Fluids <strong>7</strong>, L102001<br> DOI: 10.1103/PhysRevFluids.7.L102001</p> <p>Includes:</p> <ol> <li>GROMACS input files</li> <li>Modifications to the GROMACS source code thermostat as described in the article</li> <li>Instructions on how to invoke the patched version of GROMACS with decoupled directions</li> <li>Matlab datafiles with FE solutions and scripts to analyse and compare them to MD velocity field (also included)</li> </ol> <p> </p> <p>See also: <br> https://github.com/Marcello-Sega/pytim<br> https://github.com/dpeschka/stokes-free-boundary</p>
[Supporting Information] Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021
<p>Supporting information from the manuscript: <em>Are Peruvians moving towards healthier diets with lower environmental burden? Household consumption trends for the period 2008-2021</em>. The main goal of this study was to comprehensively analyze the evolution in diet quality in Peru in the period 2008-2021 based on apparent household purchases extracted from the National Household Survey (ENAHO, by its acronym in Spanish). Furthermore, this study identified patterns in the temporal and spatial variability of food consumption, differences in consumption based on poverty levels, and gaps in achieving consumption levels of macronutrients and calories recommended by international nutritional authorities.</p> <p>dataset1: contains the consumption of 96 food products in kg/person/year per household, for a time horizon from 2008 to 2021.</p> <p>dataset2: contains the caloric and macronutrient content in kcal or g macronutrient per 100g of 92 food items.</p> <p>dataset3: contains the consumption of calories, and macronutrients in g/person/day per household, for a time horizon from 2008 to 2021.</p> <p>dataset1_labels: contains the data dictionary of dataset1</p> <p>dataset2_labels: contains the data dictionary of dataset2</p> <p>dataset3_labels: contains the data dictionary of dataset3</p> <p> </p>
Dataset: Movano Inc. (MOVE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 2 in Too hot to move: temperatures during transportation might reduce the survival of salvinia weevils (Coleoptera: Curculionidae)
Fig. 2. Bivariate analysis of air temperature and temperature experienced inside transportation totes for Cyrtobagous salviniae (a) with 1.3 cm circulation holes in modified fitted lids (n = 80), and (b) conventional lids without circulation holes (P ≤ 0.05; n = 80) in May 2016.
Fig. 3 in Too hot to move: temperatures during transportation might reduce the survival of salvinia weevils (Coleoptera: Curculionidae)
Fig. 3. Percent mortality of Cyrtobagous salviniae exposed to 4 temperatures in environmental growth chambers: (a) 35 °C, (b) 40 °C, (c) 45 °C, and (d) 50 °C.
Data to the paper "Drop impact onto a moving substrate: Aerodynamic rebound"
<p>These are the data used in the paper :<br>"Drop Impact onto a Moving Substrate: Aerodynamic Rebound."</p>
Figure 5 in Red deer on the move: home range size and mobility in Bulgaria
Figure 5. Comparison between male and female red deer mobility. Boxes – the interquartile range (25-75 percentiles); middle line in boxes – median values; diamonds – average values; whiskers – minimum and maximum values within the 95.0% confidence level; circles – outliers; the perimeter outside boxes shows the probability density of the of the 12 hours step-length displacement in males and females.
Figure 3 in Red deer on the move: home range size and mobility in Bulgaria
Figure 3. Comparison of the core and total area in males and females. Boxes – the interquartile range (25-75 percentiles); middle line in boxes – median values; diamonds – average values; whiskers – minimum and maximum values within the 95.0% confidence level; circles – outliers; circles with plus sign - "Far outside" outliers, points more than 3 times above the interquartile range.
Figure 1. Study area and 100 in Red deer on the move: home range size and mobility in Bulgaria
Figure 1. Study area and 100 % minimum convex polygons from the locations of the GPS-collared red deer
Fig. 4 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 4 Area projected as suitable or unsutable under current and future (2081–2100) climatic conditions (km2) for the three tick species in comparison. a Ixodes ricinus. b Dermacentor reticulatus. c D. marginatus. The corresponding maps are shown in Figs. 1–3 in the main document. Future suitable conditions refers to the area (km2) projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). Continuing suitable conditions refers to area (km2) projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable presence). Continuing unsuitable conditions refers to area (km 2) projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e. stable absence). Future unsuitable conditions refers to the area (km.2) projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction)
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8333 (average over 10 replicates using cross-validation, standard deviation = 0.001113603). Threshold to transform the logistic model output: 0.3816 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
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.