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743 results for “Drop”
Rates of premature fruit drop for 201 plant species on Barro Colorado Island, Panama
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Data from: Multi-gesture drag-and-drop decoding in a 2D iBCI control task
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Data and simulation model files for: Variable-stiffness morphing wheel inspired by the surface tension of a liquid drop
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Data supplement for "Gradient dynamics model for drops spreading on polymer brushes"
<p>This dataset contains the data and source files for the diagrams of the following publication:</p> <p><em>Thiele, U. & Hartmann, S.<br> Gradient dynamics model for drops spreading on polymer brushes<br> arXiv preprint arXiv:1910.10582, 2019 </em></p> <p>We provide the data and sources necessary to generate the figures 3 & 4 of the manuscript.</p> <p>For more information, please see the included README.md</p>
Thiourea and urea in water drops and in the active site of TcDH
<p>Thiourea and urea in water drops and in the active site of TcDH.</p> <p>Equilibrium geometry configurations obtained at the QM(PBE0/6-31G**)/MM(AMBER) level of theory.</p> <p>Coordinates of QM subsystems have "qm" in names of PDB files.</p>
Artifacts Package - "Why don't Developers Detect Improper Input Validation? '; DROP TABLE Papers; --"
<p>Artifacts Package of the accepted ICSE 21 paper: "Why don’t Developers Detect Improper Input Validation? '; DROP TABLE Papers; --".</p> <p>See README.md for more information. </p>
Holy Trinity Brompton Spy Drop Statue
A statue of St Francis of Assisi in the bushes at the north east corner of Holy Trinity Brompton Church, London. This statue was used by the KGB during the Cold War as a "dead letter drop" where they could hide secret materials (behind the statue). https://www.culture24.org.uk/history-and-heritage/military-history/tra14010 https://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/articles/londons-coolest-espionage-locations/ 212 photos taken in November 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Can You Dig It? - Virtual Drop-In
Galloway Glens Landscape Partnership, Can You Dig It? drop-in day at Castle Douglas, Dumfries & Galloway. Event showcased all the community archaeology activites carried out during 2019. This model was created with Blender and GIMP from photo's taken during the event and will hopefully give those who couldn't get to the event a chance to look at the display boards, created by Claire Williamson, and see all the cool archaeology which got uncovered last year. The background is the interior of the Gordon Memorial Hall at St Ninian's in Castle Douglas, where the event was held https://dcdchurches.org.uk/gordon-memorial-hall/. Source: Objaverse 1.0 / Sketchfab
Data supplement for "Drops on polymer brushes – advances in thin-film modelling of adaptive substrates"
<p>This dataset contains supplementary data for the following publication:</p> <p>Hartmann, S., Diekmann, J., Greve, D., and & Thiele, U.<br>Drops on polymer brushes – advances in thin-film modelling of adaptive substrates<br><span><em>Langmuir</em></span> <span>2024</span><span>, 40</span><span>, 8</span><span>, 4001–4021</span></p> <p>We provide the source files and data for figures 3-15.</p>
Supplement for "Mesoscopic hydrodynamic model for spreading, sliding and coarsening compound drops"
<p>This repository contains the<strong> data sets</strong> and <strong>source code</strong> corresponding to the following publication</p> <p> </p> <p>Diekmann, J. and Thiele, U.<br>“Mesoscopic hydrodynamic model for spreading, sliding and coarsening compound drops”<br>Phys. Rev. Fluids <strong>10</strong>, 024002, 2025<br>DOI: <a href="https://doi.org/10.1103/PhysRevFluids.10.024002">https://doi.org/10.1103/PhysRevFluids.10.024002</a></p> <p> </p> <p>Please consider the README for more details.</p>
From crypsis to masquerade: ontogeny changes the colour defences of a crab spider hiding as bird droppings
<p><span>Selection imposed by visually-hunting predators has driven the evolution of colour-based antipredator defence strategies such as crypsis, masquerade, mimicry and aposematism. Individuals of many animals are generally considered to rely on a single type of defence strategy, but individuals of some species use multiple colour-based defences. Many animals switch between colour-based defences against visually-hunting predators during ontogeny. However, why this occurs remains poorly understood. </span></p> <p><span>The crab spider<i> Phrynarachne ceylonica</i> is an often-cited example of a bird dropping masquerade. It has recently been demonstrated that <i>P. ceylonica</i> crab spiders gain protection from their predators by being misidentified as bird droppings by their predators. <i>P. ceylonica</i> females show an ontogenetic shift in colour defences: early instars possess a dark and cryptic form, while at later instars and as adults, the spiders resemble bird droppings. We hypothesised that this shift may be driven by differential changes in predation risk of two defence strategies with increasing body size due to ontogeny. </span></p> <p><span>We tested this hypothesis by presenting naïve domestic chicks with 3D printed artificial spiders of two different sizes (small, large) and two colours (dark, bird dropping-like), and determined if larger bird dropping-like spiders are more readily found and attacked than cryptic forms by chicks. We found that small cryptic spiders were more difficult to detect than small bird dropping masquerading spiders, but large cryptic spiders were attacked much more quickly and more frequently than large bird dropping masquerading spiders. </span></p> <p><span>Increasing predation pressure on larger, cryptic spiders during ontogeny suggests that switching to bird dropping masquerade may be a more effective defence as spiders increase in size. We thus conclude that the ontogenetic shift from crypsis to masquerade is adaptive. </span></p>
Source models for "Across-slab propagation and low stress drops of deep earthquakes in the Kuril subduction zone"
<p>This repository is for the model results for eight deep earthquakes in the Kuril subduction zone modelled using a second-degree moments method in csv format.</p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Event is the GCMT event code. Aspect ratio is the ratio (Amin/Amax). Duration is the rupture duration; Amax is the maximum characteristic fault dimension; Amin is the minimum characteristic fault dimension; Phi is the angle between Amax and the strike; v0 is the centroid velocity; Theta is the angle between the centroid velocity and the strike; and mft is the misfit between the data and the higher-order synthetics calculated for the best-fitting source model obtained from the Monte Carlo inversions.</p> <p> </p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subhorizontal.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Column headers are the same as in <a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a>.</p>
Electronic Supplementary Information: Impact of a suspension drop onto a hot substrate: diminution of splash and prevention of film boiling
<p>This database includes Electronic Supplementary Information for <em>Soft Matter </em>manuscript: Impact of a suspension drop onto a hot substrate: diminution of splash and prevention of film boiling. </p> <p>The supplementary videos to Fig. 4: </p> <ul> <li>supplementary_video_fig_4_a-d.mp4</li> <li>supplementary_video_fig_4_e-h.mp4</li> <li>supplementary_video_fig_4_i-l.mp4</li> </ul> <p> </p> <p>and the supplementary videos to Fig. 12 (please do not regard to the file name)</p> <ul> <li>supplementary_video_fig_11_a-d.mp4</li> <li>supplementary_video_fig_11_e-h.mp4</li> <li>supplementary_video_fig_11_i-l.mp4</li> </ul>
Drop characteristics from the UNIGE calibration device for non-catching precipitation instruments
<p>These data are from the paper "Calibration uncertainty of non-catching precipitation gauges", by Baire, Q. et al., accepted for publication on the journal Sensors. A rain drop generator, developed at the University of Genova, is presented in that paper using two high-precision syringe pumps, with a capacity of 20 and 1 ml (20 and 4.5 mm piston diameter, respectively), to produce water drops of the required volume. An electric field, generated by a high voltage trigger, allows releasing each single drop on demand. Each drop is generated at the tip of a suitable nozzle by dispensing the necessary volume to achieve the desired drop size and then detached by exploiting a 5 kV potential difference, where the water is negatively charged and attracted by a metal ring (positively charged), positioned just below the tip of the nozzle. By using different nozzles/needles and the proper syringe pump, drops of various size are produced.</p> <p>Validation of drop size measurements was obtained by weighing the total volume of samples of about 20 to 45 drops with a precision balance having a resolution of 0.001 g. Drops were released at 1.20 m above the center of the measurement plane of the camera.</p> <p>Results are summarized in Table 2 in the paper in terms of the average drop diameter obtained from the software and the balance, and their difference. Drop size characteristics as obtained from the photogrammetric system are included in the database for the generated sets of drops. A second set of tests was conducted using the photogrammetric system alone, without weighing the overall water volume. Statistics of the detected drop diameter and fall velocity are listed in Tables 3 and 4 in the paper, together with the number of released drops during each test, while the single drop size and fall velocity characteristics are included in the dataset.</p>
Wmid-b1a26a - Vessel drop handle
An incomplete cast copper-alloy drop handle from a vessel of Medieval date circa AD 1270 -1600. The incomplete handle consists of a zoomorphic terminal end, probably representing a dog's head. The terminal has a rivet through the centre of the mouth, with other features (eyebrows and hair ridges) defined by cast in relief with incised lines finishing the decoration. For more information, please visit the online database record available at: https://finds.org.uk/database/artefacts/record/id/1079093 Source: Objaverse 1.0 / Sketchfab
Fossilised Bead Rain Drops
Shelfmark EUCM.0107.2013, collected by Sir Charles Lyell. Model created by Connor Wimblett. Please contact is-crc@ed.ac.uk if full size model is required. Source: Objaverse 1.0 / Sketchfab
CHiMP Detector Datasets: Images of Sitting Drop Protein Crystallisation Experiments with Associated Image Masks of Drops and Crystals
<p>The CHiMP Detector Datasets consist of images of protein crystallisation experiments along with corresponding zipped NumPy archive files (.npz). All images have had their histograms adjusted using the Contrast Limited Adaptive Histogram Equalization ((CLAHE) algorithm using the OpenCV library with grid size of 12 and are in JPEG format. The .npz files contain class labels and instance segmentation masks for both the experimental droplets and any crystals that an expert annotator has deemed to be interesting/mountable. To class labels and masks can be loaded in the following way:</p> <pre><code>import numpy as np # load in the mask and class label list from .npz file located at mask_path mask_file = np.load(mask_path) masks = list(mask_file["masks"].astype(int)) class_labels = list(mask_file["class_labels"])</code></pre> <p>There are two datasets within this archive:</p> <ol> <li><strong>The VMXi CHiMP Detector Dataset</strong>. This consists of 237 images of resolution 1688 × 1352 pixels with corresponding masks. These images were collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the VMXi experimental facility at Diamond Light Source, UK. These images and masks were used to train the VMXi CHiMP (Crystal Hits in My Plate) Detector network that performs object detection and instance segmentation of crystals in experimental micrographs using a Mask-R-CNN architecture. The files "vmxi_detector_training.csv" and "vmxi_detector_validation.csv" provide the filenames of the members of the training and validation sets respectively.</li> <li><strong>The XChem CHiMP Detector Dataset.</strong> This consists of 350 images of resolution 1024 × 1224 pixels with corresponding masks. These images were collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the Crystallisation Facility@Harwell, located in the Research Complex at Harwell (RCaH). In addition to the images in the VMXi CHiMP Detector, these images were used to train the XChem CHiMP (Crystal Hits in My Plate) Detector network that performs object detection and instance segmentation of masks and crystals in experimental micrographs using a Mask-R-CNN architecture. The files "xchem_detector_training.csv" and "xchem_detector_validation.csv" provide the filenames of the members of the training and validation sets respectively.</li> </ol>
Stress Drop Catalog for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence" - Kemna et al. 2021 JGR - Solid Earth
<p>Catalog with stress drop estimates for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence"</p> <p>Kemna et al., 2021, JGR: Solid-Earth, https://doi.org/10.1029/2021JB022566.</p> <p>Description of columns:</p> <p><strong>Earthquake information</strong></p> <ul> <li>ID - INGV Earthquake ID</li> <li>Latitude - Latitude in Degrees</li> <li>Longitude - Longitude in Degrees</li> <li>Depth - Depth in km</li> <li>Magnitude_INGV - Magnitude reported by INGV</li> <li>Origin_UTC - UTC Origin Time in ISO Format</li> <li>Catalog - Catalog source of specific event. See section 2.1 for details</li> <li>Profile_distance_norcia - Distance of earthquake from Norcia Mainshock location projected onto a NW-SE trending line</li> <li>Dayafter_20160101 - Day after start of catalog in float</li> </ul> <p><strong>Single spectra fitting estimates</strong></p> <ul> <li>mw_s_mean - Moment Magnitude averaged over station estimates</li> <li>mw_s_err - 95% error (from delete-one jackknife-mean)</li> <li>m0_s_mean - Seismic Moment in Nm averaged over station estimates</li> <li>m0_s_err - 95 % error(from delete-one jackknife-mean)</li> <li>fc_s_sssa_mean - Corner frequency estimate averaged over station estimates</li> <li>fc_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_sssa_mean - Stress drop estimate averaged over station estimates</li> <li>strdrop_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>sample_size_s_sssa - Number of stations with an estimate</li> <li>azimuthal_gap_s_sssa - Maximum azimuthal gap</li> <li>alpha_vel - P-wave velocity in m/s at Hypocenter</li> <li>beta_vel - S-wave velocity in m/s at Hypocenter</li> </ul> <p><strong>Cluster-event method estimates</strong></p> <ul> <li>fc_s_cema_mean - Corner frequency estimated averaged over clusters</li> <li>fc_s_cema_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_cema_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_cema_err - 95 % error</li> </ul> <p><strong>Spectral Ratio fitting estimates</strong></p> <ul> <li>fc1_s_rsta_mean - Target event corner frequency estimate using automatic source spectra fitting averaged over eGfs</li> <li>fc1_s_rsta_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_rsta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_rsta_err - 95 % error</li> <li>egf_number_rsta_s - Number of eGfs for each target event</li> <li>fc1_s_rrta_mean - Target event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc1_strdrop_s_rrta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>fc2_s_rrea_mean - eGf event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc2_strdrop_s_rrea_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> </ul> <p><strong>Magnitude-normalized stress drop</strong></p> <ul> <li>prio_strdrop_s - Which type of estimate is used</li> <li>magbin_s - Magnitude bin to which event is associated</li> <li>prio_strdrop_s_magbinmean - Stress drop mean for specific magnitude bin</li> <li>prio_strdrop_s_magbinstderr - 95 % error(from delete-one jackknife-mean)</li> <li>prio_strdrop_s_magnitude-normalized - Magnitude-normalized stress drop estimate</li> </ul>
Data Archive for: Hurricane Laura (2020): A Comparison of Drop Size Distribution Moments Using Ground and Radar Remote Sensing Retrieval Methods
<p>This archive corresponds to the data described in Brauer et al. (2021) to be published in <em>Journal of Geophysical Research: Atmospheres.</em> Please see the included readme.txt file for details about each data file.</p>
Data from: Drop it all: Extraction-free detection of non-indigenous marine species through optimized direct-droplet digital PCR
<p>Molecular biosecurity surveillance programs increasingly use environmental DNA (eDNA) for detecting marine non-indigenous species (NIS). However, the current molecular detection workflow is cumbersome, prone to errors and delays, and is limited in providing knowledge about eDNA beyond the spatial and temporal extent of the sampling. These limitations can hinder management efforts and restrict the "opportunity window" for a rapid response to new marine NIS incursions. Emerging innovative field-deployable digital droplet PCR (ddPCR) systems offer improved workflow efficiency by autonomously analyzing targeted free-floating extra-cellular eDNA (free-eDNA) signals. Despite their potential, these systems have not been tested in marine environments. Thus, an aquarium study was conducted with three distinct marine NIS: <span>the Mediterranean fanworm <em>Sabella spallanzanii</em>, the ascidian clubbed tunicate <em>Styela clava</em>, and the brown bryozoan <em>Bugula neritina</em></span> to evaluate the detectability of free-eDNA in seawater. The detectability of targeted free-eDNA was assessed by directly analyzing aquarium water samples using an optimized species-specific ddPCR assay, without filtration or DNA extraction, so-called, "direct-ddPCR". The results demonstrated the consistent detection of <em>Sabella spallanzanii</em> and <em>Bugula neritina</em> free-eDNA when these organisms were present in high abundance. Once organisms were removed, the free-eDNA signal exponentially declined, noting that free-eDNA persisted between 24-72 hours. Results indicate that organism biomass, specimen characteristics (e.g., stress and viability), and species-specific biological differences may influence free-eDNA detectability. These results are critical for implementing <em>in-situ</em> nucleic acid automated continuous sensing systems for marine biosurveillance, enabling point-of-need detection and <span>rapid management response to biosecurity threats. </span></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.