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4,325 results for “real”
Characterization of Mg(OH)2 powders produced from real saltworks bitterns at a pilot scale
<p>The growing demand for a reliable supply of minerals has pushed the European Union toward seeking novel<br>mineral sources. In the present work, the recovery of magnesium, in the form of magnesium hydroxide, Mg<br>(OH)2, from saltworks bitterns was investigated at a pilot scale. The natural bittern collected from Margi saltworks<br>(Trapani, Italy) was treated with synthetic sodium hydroxide (NaOH) solutions. The influence of different<br>reactant concentrations, flow rates and pH environments, on Mg(OH)2 particle sizes, filtration characteristics,<br>Mg2+ extraction performance and the Mg(OH)2 solid purity was thoroughly assessed. The presence of additional<br>dissolved salts in NaOH solutions was also analyzed. The pH environment was found to be the most crucial<br>parameter. On the one hand, high pH values ensured high Mg2+ recovery and powder purities, i.e. > 98%, with<br>boron content complying with market requirements. On the other hand, filtration characteristics considerably<br>worsened due to the lyosorption phenomenon.</p>
Dataset for "Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study"
<p>The dataset (EPANET file) which accompanies the publication </p> <p>Vrachimis, S. G., Eliades, D. G., and Polycarpou, M. M.: Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study, Drink. Water Eng. Sci., 2018</p>
Microfluidic device for real-time formulation of reagents and their subsequent encapsulation into double emulsions
<p>Emulsion drops are often employed as picoliter-sized containers to perform screening assays. These assays usually entail the formation of drops encompassing discrete objects such as cells or microparticles and reagents to study interactions between the different encapsulants. Drops are also used to screen influences of reagent concentrations on the final product. However, these latter assays are less frequently performed because it is difficult to change the reagent concentration over a wide range and with high precision within a single experiment. In this paper, we present a microfluidic double emulsion drop maker containing pneumatic valves that enable real-time formulation of different reagents using pulse width modulation and consequent encapsulation of the mixed solutions. This device can produce drops from reagent volumes as low as 10 µL with minimal sample loss, thereby enabling experiments that would be prohibitively expensive using drop generators that do not contain valves. We employ this device to monitor the kinetics of the cell-free synthesis of green fluorescent proteins inside double emulsions. To demonstrate the potential of this device for real-time formulation, we perform DNA titration experiments to test the influence of DNA concentration on the amount of green fluorescence protein produced in double emulsions by a coupled cell-free transcription / translation system.</p>
TUT Sound Events 2018 - Ambisonic, Reverberant and Real-life Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Reverberant and Real-life Impulse Response Dataset</strong></p> <p>This dataset consists of real-life first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset was generated by collecting impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing<br> a maximum length sequence around the array in circular trajectory in one elevation at a time. The playback volume was set to be 30 dB greater than the ambient sound level. The recording was done in a corridor inside the university with classrooms around it during work hours.The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m. </p> <p>The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://serv.cusp.nyu.edu/projects/urbansounddataset/urbansound8k.html">urbansound8k dataset</a>. This dataset consists of 10 sound event classes such as air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. We do not consider the air_conditioner and children_playing sound events. Further, we only include the sound event examples marked as foreground in the dataset. We used the splits 1, 8 and 9 provided in the urbansound8k as the three CV splits. These splits were chosen as they had a good number of examples for all the chosen sound event classes after selecting only the foreground examples. During the sound scene synthesis, we randomly chose a sound event example and associated it with a random distance among the collected ones, azimuth and elevation angle. The sound event example was then convolved with the respective IR for the given distance, azimuth and elevation to spatially position it.</p> <p>The metadata.zip folder consists of the license and the metadata for the complete dataset. The rest of the nine zip files consists dataset for given split and overlap. For example, the wav_ov3_split1_30db.zip file consists of training and testing recordings for the case of maximum three temporally overlapping sound events (ov3) for the first cross-validation split (split1). Within each audio folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p> <p><strong>Data collector (s): </strong>Fagerlund, Eemi; Koskimies, Aino</p> <p> </p>
Dataset of ICSME-2018 paper: a closer look at real-world patches.
<p>Dataset of ICSME-2018 paper: a closer look at real-world patches.</p> <p>Information:<br> Keywords folder: patch commits are identified by bug-related keywords.<br> Linked folder: patch commits are identified by fixed bug IDs that are reported in issue tracking system.<br> <br> diffs folder: bug fix code diffs.<br> preFiles folder: buggy code files in the commits before fixing.<br> revFiles folder: fixed code files in the commits of submitting fixes.<br> <br> File names are named as previous-commit-id_bug-fix-commit-id_code-file-path.txt/.java</p>
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
<p><strong>License + Attribution</strong></p> <p>This dataset is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>. Commercial usage is not permitted. If you use this dataset or the code in a scientific publication, please cite the following <a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Tobias_Fischer_RT-GENE_Real-Time_Eye_ECCV_2018_paper.html">paper</a>:</p> <blockquote> <p>@inproceedings{FischerECCV2018,<br> author = {Tobias Fischer and Hyung Jin Chang and Yiannis Demiris},<br> title = "{RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments}",<br> booktitle = {European Conference on Computer Vision},<br> year = {2018},<br> month = {September},<br> pages = {339--357}<br> }</p> </blockquote> <p>This work was supported in part by the Samsung Global Research Outreach program, and in part by the EU Horizon 2020 Project PAL (643783-RIA).</p> <p>More information can be found on the Personal Robotic Lab's website: <a href="https://www.imperial.ac.uk/personal-robotics/software/">https://www.imperial.ac.uk/personal-robotics/software/</a>.</p> <p><strong>Overview</strong></p> <p>The dataset consists of two parts: 1) One where the eyetracking glasses were worn (and thus ground truth labels for head-pose and eye gaze are available; suffix <em>_glasses</em>), and 2) One with natural appearances (no eyetracking glasses are worn; suffix <em>_noglasses</em>). The <em>_noglasses</em> images were used to train subject-specific GANs, and these GANs were used to inpaint the region covered by the eyetracking glasses in the <em>_glasses</em> images.</p> <p>There is code accompanying this dataset: <a href="https://github.com/Tobias-Fischer/rt_gene">https://github.com/Tobias-Fischer/rt_gene</a>. Please use the issue tracker in the code respository if you have questions regarding the dataset.</p> <p><strong>Subjects / 3-Fold evaluation</strong></p> <p>15 participants were recorded in 17 sessions. Session 014 is a second recording of participant 002, and session 015 is a second recording of participant 005 (different days and different camera poses were used).</p> <p>We used a 3-fold evaluation, with the three folds consisting of the following sessions (test on one of the groups, training with the remaining two groups):</p> <ol> <li>'s001', 's002', 's008', 's010'</li> <li>'s003', 's004', 's007', 's009'</li> <li>'s005', 's006', 's011', 's012', 's013'</li> </ol> <p>The validation set consists of sessions 's014', 's015' and 's016'.</p> <p>While the MATLAB script (<em>prepare_dataset.m</em>; see code repository) creates train and test images for each subject, all images were used for the evaluation (see <em>evaluate_model.py</em>).</p> <p><strong>Labeled dataset (sXYZ_glasses)</strong></p> <p>The file for each subject contains the following information:</p> <ul> <li>label_combined.txt This is the main file containing labels. The formatting is as follows:<br> seq_number, [head pose: right(pos) / left(neg), up (pos) / down(neg)], [gaze: right(pos) / left(neg), up(pos) / down(neg)], timestamp</li> <li>label_headpose.txt This file contains more detail about the head pose of the subject.<br> seq_number, [head pose translation: further(pos) / closer(neg), left(pos) / right(neg), up(pos) / down(neg)], [head pose rotation: roll right(pos) / roll left(neg), down(pos) / up(neg), rotate left(pos), rotate right(neg)], timestamp</li> <li>kinect2_calibration.yaml<br> The kinect2_calibration.yaml file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> The kinect2_pose.txt file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"original" folder <ul> <li>The face_before_inpainting folder contains the face with a large margin to the left and right.</li> <li>The mask folder contains images indicating the regions of the eyetracking glasses, aligned with the images in the face_before_inpainting folder.</li> <li>The overlay folder contains images where the mask was overlaid on the face_before_inpainting images.</li> <li>The face folder contains the face image extracted using MTCNN with a tighter margin.</li> <li>The left and right folders contain the left and right eye image areas.</li> </ul> </li> <li>The face, left and right images were used as baseline comparison in the paper (Fig. 7 without inpainting).</li> <li>"inpainted" folder <ul> <li>The face_after_inpainting folder contains images corresponding to the ones in the face_before_inpainting folder after applying the inpainting.</li> <li>Then, the images contained in the face, left and right folders were extracted using MTCNN as above.</li> </ul> </li> </ul> <p><strong>Unlabeled dataset (sXYZ_noglasses)</strong></p> <ul> <li>kinect2_calibration.yaml<br> This file contains the camera projection matrix in ROS format (this file should not be required).</li> <li>kinect2_pose.txt<br> This file contains the pose of the Kinect with respect to the motion capture system (this file should not be required).</li> <li>"face" folder<br> This folder contains the faces that can be used to train the GANs (without eyetracking glasses being worn).</li> </ul>
data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al
<p>Data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al.<br> <br> index.html provides a read-me for the organization of this repository. For its links to work the two .zip files should be extracted.</p>
FIGURE 11 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 11. Multiple Correspondence Analysis (MCA) based on 10 factors isolates the "marianus" morph in both sexes from the two other morphs.
FIGURE 6 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 6. Portrait of a "marianus" female without any coloration of the subantennal suture above the black genal stripe.
FIGURE 5 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 5. Different portions of black on the three veins surrounding the intramedian cell (im cell). In im 5 two or three sides contain black or at least speckled parts. Im 6 was observed only in P. abdominalis.
FIGURE 9 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 9. "Sulfurhead" male with yellow vertex and large twin dots (brown and black) on mesothorax. All males in the prasinus group have a broad, almost trapezoid pronotum.
FIGURE 4 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 4. Double number of setae on costal cross veins in "furwing" males (4a), as compared to similar numbers in "nonfurwing" males (4b) and females.
FIGURE 3 in In search of the real Pseudomallada prasinus (Neuroptera, Chrysopidae)
FIGURE 3. Different portions of black in costal cross veins. Ccv4 is typical for P. ventralis, ccv5 for P. abdominalis and P. zelleri.
InCLosure Code for Enclosures of Real Functions: Supplementary Material for Chapter "Universal Intervals: Towards a Dependency-Aware Interval Algebra"
<p>InCLosure Code for Enclosures of Real Functions: Supplementary Material for Chapter "Universal Intervals: Towards a Dependency-Aware Interval Algebra", In S. Chakraverty, editor, Mathematical Methods in Interdisciplinary Sciences. John Wiley & Sons, Hoboken, New Jersey, March, 2020. ISBN: 9781119585503.</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 2
<p>The data set was analyzed in main figures 2, 3 and supplementary figure 5.</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 1
<p>24.01.2014 fish 5 was analyzed in main figures 1,2 and supplementary figure 4</p> <p>21.03.2014 fish 1 was analyzed in main figures 2,3 and supplementary figure 4</p>
Volcanic ash source inversion data for paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals"
<p>This dataset consists of volcanic ash source inversion data for the paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals" by Rachel E. Pelley, David J. Thomson, Helen N. Webster, Michael C. Cooke, Alistair J. Manning, Claire S. Witham and Matthew C. Hort, Atmosphere, 2021, 12, 1573, https://doi.org/10.3390/atmos12121573. Satellite retrievals, dispersion model simulations and inversion calculations are included for the eruptions of Eyjafjallajokull in 2010 and Grimsvotn in 2011.</p>
COSMOS real galaxy dataset
<p>Real galaxy dataset extracted from the HST COSMOS survey for use with GalSim.</p>
Datsets for Publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation"
<p>This dataset contains Magnetic Particle Imaging and Intravascular optical coherence tomography data for the profiles</p> <ul> <li>Standard Profile (3x)</li> <li>Bending Profile (3x)</li> <li>Heart Beat Profile (3x)</li> </ul> <p>used in the publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation".</p>
A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms
<p>The dataset for "A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms" can be divided into training and validation sets at any desired ratio.</p> <p> </p> <p>The code has been published on GitHub: <a href="https://github.com/cqtian-kk/Lightning_Detection_Location">Lightning_Detection_Location</a> or <a href="https://zenodo.org/records/14048427">DOI 10.5281/zenodo.13350849</a></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.