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4,325 results for “real”
Using Blender EEVEE for the Generation of Real Time Background Plate in Green Screen Movie Shots
<p>This talk will introduce you to the use of blender EEVEE for green screen shots for a short movie. Green (and blue) screen shots are notoriously difficult to get the lighting condition right, since the image from the camera is dominated by the bright green background. It is very helpful on the set to see in real time the final composition of the scene with the proper background to make adjustments of the camera and the lighting position and the lighting intensity and color.<br> For the shots a large volume mocap solution (Optitrack) was used to track the movie camera (Arri Alexa) and the transformation data was sent to Blender to animate the virtual camera. The previously laser scanned background was rendered in real time in EEVEE. To combine the camera image and the rendered image a dedicated live-keying hardware was used. The described set-up was used in connection with the research project Virtually Real – Aesthetics and Perception of Virtual Spaces in Film by the Zurich University of the Arts and the University of Bern, funded by the Swiss National Science Foundation. <br> </p>
Virtually Real eyetracking data (beeswarm) for the short film "Sonnenwende" (2019) byTimo von Gunten
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These quicktime movie represent the Eye-Tracking Data for Scenes 1,2 and 3 of "Sonnenwende" (2019) by Timo von Gunten in beeswarm form for both the real and the virtual version of the film.</p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
A versatile microfluidic platform measures hyphal interactions between Fusarium graminearum and Clonostachys rosea in real-time
<p>Routinely, fungal-fungal interactions (FFIs) are studied on agar surfaces. However, this format restricts high-resolution dynamic imaging. To gain experimental access to FFIs at the hyphal level in real-time, we developed a microfluidic platform, a FFI device. This device utilises microchannel geometry to enhance the visibility of hyphal growth and provides control channels to allow comparisons between localised and systemic effects. We demonstrate its function by investigating the FFI between the biological control agent (BCA) <em>Clonostachys rosea </em>and the plant pathogen <em>Fusarium graminearum. </em>Microscope image analyses confirm the inhibitory effect of the necrotrophic BCA and we show that a loss of fluorescence in parasitised hyphae of GFP-tagged <em>F. graminearum </em>coincides with the detection of GFP in mycelium of <em>C. rosea</em>. The versatility of our device to operate under both water-saturated and nutrient-rich as well as dry and nutrient-deficient conditions, coupled with its spatio-temporal output, opens new opportunities to study relationships between fungi.</p>
Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure
<p>Data and scripts used to generate figures presented in the paper "Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure" submitted to the Future Generation Computer Systems Journal.</p>
Binaural recordings of a real and binaural simulated circular loudspeaker array
<p>A KEMAR dummy head did binaural recordings of a noise signal played back over a circular loudspeaker array. The loudspeaker array had a diameter of 3m and consists of 56 loudspeakers. The real loudspeakers or a binaural simulation of them were used. For the latter a BRIR dataset had been recorded before with another dummy head. The KEMAR dummy head was wearing headphones all the time in order to allow for a direct comparison between the simulation and real setup.</p> <p>This dataset is used to create Fig. 4.9 in H. Wierstorf, Perceptual Assessment of of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014. See also https://github.com/hagenw/phd-thesis/tree/master/04_binaural_synthesis/fig4_09</p>
Localisation of a real vs. binaural simulated point source -- data
<p>This data set contains stimuli and results from an experiment that compared the localisation of a real point source realised by a loudspeaker to the localisation of a binaural simulation of the same source using head related impulse responses (HRIRs) or binaural room impulse responses (BRIRs). The results are published in [1].</p> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the file `brs.zip`, the employed noise stimulus in `stimuli.zip`. The file `results.zip` contains the results of all 11 listeners to the localisation task and the file `results_head_movements.zip` the recoreded head movements the listeners performed during the task. The file analysis.zip` contains average results and a plotting script.</p>
Data from Dowiasch et al. (2015) Effects of aging on eye movements in the real world
<p>The effects of aging on eye movements are well studied in the laboratory. Increased saccade latencies or decreased smooth-pursuit gain are well established findings. The question remains whether these findings are influenced by the rather untypical environment of a laboratory; that is, whether or not they transfer to the real world. We measured 34 healthy participants between the age of 25 and 85 during two everyday tasks in the real world: (I) walking down a hallway with free gaze, (II) visual tracking of an earth-fixed object while walking straight-ahead. Eye movements were recorded with a mobile light-weight eye tracker, the EyeSeeCam (ESC). We find that age significantly influences saccade parameters. With increasing age, saccade frequency, amplitude, peak velocity, and mean velocity are reduced and the velocity/amplitude distribution as well as the velocity profile become less skewed. In contrast to laboratory results on smooth pursuit, we did not find a significant effect of age on tracking eye-movements in the real world. Taken together, age-related eye-movement changes as measured in the laboratory only partly resemble those in the real world. It is well-conceivable that in the real world additional sensory cues, such as head-movement or vestibular signals, may partially compensate for age-related effects, which, according to this view, would be specific to early motion processing. In any case, our results highlight the importance of validity for natural situations when studying the impact of aging on real-life performance.</p>
OCDetect - A Real-World Dataset to Detect Handwashing in Daily-Life using Wrist Motion Data from Wearables
<p>Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with ~3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.</p>
FIG. 3 in Hypsodont Myomiminae (Gliridae, Rodentia) from five new localities in the Lower Miocene Tudela Formation (Bardenas Reales, Ebro Basin, Spain) and their bearing on the age of the Agenian-Ramblian boundary
FIG. 3. — Distribution chart of the Myomiminae species studied in this paper and in Daams (1990). The succession of the localities from Daams (1990) is, though in stratigraphical order, not calibrated.
Cine and real-time free-breathing CMR at rest and under exercise stress of healthy volunteers
<p>The dataset consists of cine and real-time images from 15 healthy volunteers (7 males; 8 females). All images were acquired in supine position using a 32-channel cardiac surface receiver coil at 3 T (Skyra, Siemens Healthineers, Germany).</p> <p>Conventional imaging at rest included a balanced steady-state free precession (bSSFP) ECG-gated cine sequence to create a short-axis stack covering the entire heart including both ventricles and atria. Real-time CMR data acquisition was performed during free-breathing and without ECG-synchronization at rest and under two different levels of exercise stress.</p> <p>The dataset includes automatically created contours (comDL) using Medis (version 4.0.56.4, QMass® 8.1, Medical Imaging Systems, Leiden, Netherlands) for all images, as well as manually corrected (mc) contours based on the comDL contours for all cine and real-time measurements at rest and under exercise stress for end-diastolic (ED) and end-systolic phases (ES).</p> <p>The dataset also includes segmentation masks in NIfTI format for cine and real-time CMR at rest and under exercise stress created with nnU-Net (DOI:10.1038/s41592-020-01008-z) with freely available weights based trained on the dataset of the cardiac segmentation challenge "Automated Cardiac Segmentation Challenge" (ACDC) (DOI:10.1109/TMI.2018.2837502).</p> <p>To minimize the influence of respiratory motion on clinical measures, images in the ED and ES phase of the cardiac cycle during end-expiration were manually selected for each slice. The dataset includes indices for these images for real-time CMR measurements at rest and under exercise stress. For intra-observer variability, manually corrected contours for the derivation of the clinical measures were created three to six months after the initial segmentation. For inter-observer variability, manually corrected contours for the derivation of the clinical parameters were created for the first five volunteers by a second reader with experience in cardiac segmentation. Single images in the ED and ES phase during end-expiration were once again chosen from each slice.</p> <p>Image data is provided in a file format used by the BART toolbox. <br>DOI:10.5281/zenodo.7110562</p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Simulated and real NPP annual map
<p>The project is to reconstruct the NPP maps with tree ring data. We can get the simulated NPP maps 80 years ago using our models.</p>
Real testing sets for Visual Affordance Segmentation of hand-occluded objects
<p>[<a href="https://arxiv.org/abs/2308.11233">arXiv</a>] [<a href="https://apicis.github.io/projects/acanet.html">webpage</a>] [<a href="https://github.com/SEAlab-unige/acanet">code</a>] [<a href="https://doi.org/10.5281/zenodo.8364197">trained model</a>][<a href="https://doi.org/10.5281/zenodo.5085800">mixed-reality data</a>]</p> <p>RGB images with the corresponding affordance annotation to test affordance segmentation models. Images are selected from two datasets for hand-object pose estimation: <a href="https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/hand-object-3d-pose-annotation/">HO-3D</a> and <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CCM</a>.</p> <p>For HO3D we selected 150 frames from the dataset and enriched the annotation of the hand and object segmentation masks with new annotations specific for the affordance segmentation problem.</p> <p>For CCM we selected 150 frames from the dataset and created the annotation specific for the affordance segmentation problem. The forearms and hands in contact with the offered container are annotated. </p> <p>File names are formatted as: <em><videoname>_<framenumber>.png</em></p> <p>Segmentation classes values:</p> <ul> <li> 0: background</li> <li> 1: graspable</li> <li> 2: contain</li> <li> 3: arm</li> </ul> <p> </p> <p><strong>References. </strong></p> <p><strong>Affordance segmentation of hand-occluded containers from exocentric images</strong><br>T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo<br>IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023</p> <pre><code>@inproceedings{apicella2023affordance, title={Affordance segmentation of hand-occluded containers from exocentric images}, author={Apicella, Tommaso and Xompero, Alessio and Ragusa, Edoardo and Berta, Riccardo and Cavallaro, Andrea and Gastaldo, Paolo}, booktitle={IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year={2023}, } </code></pre> <p><strong>HOnnotate: A method for 3D Annotation of Hand and Objects Poses<br></strong>S. Hampali, M. Rad, M. Oberweger, V. Lepetit<strong><br></strong>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020</p> <pre><code>@inproceedings{hampali2020honnotate, title={Honnotate: A method for 3d annotation of hand and object poses}, author={Hampali, Shreyas and Rad, Mahdi and Oberweger, Markus and Lepetit, Vincent}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3196--3206}, year={2020} }</code></pre> <p><strong>CORSMAL Containers Manipulation (1.0) [Data set]</strong><br>A. Xompero, R. Sanchez-Matilla, R. Mazzon, and A. Cavallaro<br>Queen Mary University of London. <a href="https://doi.org/10.17636/101CORSMAL1"><u>https://doi.org/10.17636/101CORSMAL1</u></a></p> <p> </p> <p><strong>License. </strong>Creative Commons<strong> </strong>Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)</p> <p><strong>Enquiries, Question and Comments. </strong>For enquiries, questions, or comments, please contact <a href="mailto:tommaso.apicella@edu.unige.it">Tommaso Apicella</a>.</p>
HGT breakpoints and events detected by LocalHGT in 2098 real data
<p> </p> <table> <tbody> <tr> <td>filter_hgt_results.zip</td> <td>the HGT breakpoints of all samples, in this folder, each CSV file represents a sample. </td> </tr> <tr> <td>identified_event.csv</td> <td>the HGT events of all samples, the results of all samples are in this file.</td> </tr> <tr> <td>TableS1</td> <td>phenotypes of all samples</td> </tr> <tr> <td>use.zip</td> <td>Microbial abundances of all samples, in this folder, all samples from the same cohort are in a single file.</td> </tr> </tbody> </table> <p> </p>
Real-bogus scores for active anomaly detection
<p>Data description for <a href="https://arxiv.org/abs/2409.10256">Semenikhin et al., 2024</a></p> <p>The dataset consists of the following files:</p> <p><strong>feature_snad4_r_100.dat</strong> contains light curve feature data for objects, where each object is represented by 54 feature values. These values are encoded as little-endian single-precision IEEE-754 floating-point numbers (32-bit floats). Feature names are listed in the plain text file <strong>feature_snad4_r_100.name</strong>, with one name per line.<br><strong>sid_snad4_r_100.dat</strong> contains ZTF DR object identifiers, encoded as little-endian 64-bit unsigned integers.</p> <p><strong>exp_feature_snad4_r_100.dat</strong> contains the same features as <strong>feature_snad4_r_100.dat</strong>, but with an additional column representing the real-bogus classifier prediction. Each object in this file corresponds to 55 features: the original 54 features plus 1 additional feature. Feature names for this file are provided in <strong>exp_feature_snad4_r_100.name</strong>.</p> <p>The files <strong>sid_snad4_r_100.dat</strong>, <strong>feature_snad4_r_100.dat</strong>, and <strong>exp_feature_snad4_r_100.dat</strong> share the same object order.</p> <p><br>Below is a sample Python script for accessing the data using NumPy:</p> <p><code>import numpy as np</code></p> <p><code># Load object IDs</code><br><code>oid = np.memmap('sid_snad4_r_100.dat', mode='c', dtype=np.uint64)</code></p> <p><code># Load features and reshape</code><br><code>feature = np.memmap('feature_snad4_r_100.dat', mode='c', dtype=np.float32).reshape(oid.shape[0], -1)</code></p> <p><code># Print dataset information</code><br><code>print(f'Number of objects: {len(oid)}')</code><br><code>print(f'Features shape: {feature.shape}')</code></p>
Real-World Signed Graphs Annotated for Whole Graph Classification
<p><strong>Description: </strong>this corpus was designed as an experimental benchmark for a task of signed graph classification. It is composed of three datasets derived from external sources and adapted to our needs:</p> <ul> <li><strong>SpaceOrigin Conversations [1]: </strong>set of conversational graphs, each one associated to a situation of verbal abuse vs. normal situation. These conversations model interactions happening in chatrooms hosted by an MMORPG/ The graphs were originally unsigned: we attributed signed to the edges based on the polarity of the exchanged messages. </li> <li><strong>Correlation Clustering Instances [2]: </strong>set of graph generated randomly as instances of the Correlation Clustering problem, which consists in partitioning signed graphs. These graphs are not associated in any class in the original paper. We proposed a class based on certain features of the space of optimal solutions explored in [2].</li> <li><strong>European Parliament Roll-Calls [3]: </strong>vote networks extracted from the activity of French Members of the European Parliament. The original data does not have any class associated to the networks: we proposed one based on the number of political factions identified in each network in [3]. </li> </ul> <p>These data were used in [4] in order to train and assess various representation learning methods. The authors proposed Signed Graph2vec, a signed variant of Graph2vec; WSGCN, a whole-graph variant of Signed Graph Convolutional Networks (SGCN), and use an aggregated version of Signed Network Embeddings (SiNE) as a baseline. The article provides more information regarding the properties of the datasets, and how they were constituted.</p> <p><strong>Software: </strong>the software used to train the representation learning methods and classifiers is publicly available online: <a href="https://github.com/CompNet/SWGE">SWGE</a>.</p> <p><strong>References:</strong></p> <ol> <li>Papegnies, É.; Labatut, V.; Dufour, R. & Linarès, G. Conversational Networks for Automatic Online Moderation. <em>IEEE Transactions on Computational Social Systems, </em>2019<em>, </em>6:38-55. DOI: <a href="http://doi.org/10.1109/TCSS.2018.2887240">10.1109/TCSS.2018.2887240</a> ⟨<a href="https://hal.science/hal-01999546">hal-01999546</a>⟩</li> <li>Arınık, N.; Figueiredo, R. & Labatut, V. Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs. <em>Journal of Complex Networks, </em>2020<em>, </em>8(6):cnaa025. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.science/hal-02994011">hal-02994011</a>⟩</li> <li>Arınık, N.; Figueiredo, R. & Labatut, V. Multiple partitioning of multiplex signed networks: Application to European parliament votes. <em>Social Networks, </em>2020<em>, </em>60:83-102. DOI: <a href="http://doi.org/10.1016/j.socnet.2019.02.001">10.1016/j.socnet.2019.02.001</a> ⟨<a href="https://hal.science/hal-02082574">hal-02082574</a>⟩</li> <li>Cécillon, N.; Labatut, V.; Dufour, R. & Arınık, N. Whole-Graph Representation Learning For the Classification of Signed Networks. <em>IEEE Access</em>, 2024, 12:151303-151316. DOI: <a href="https://dx.doi.org/10.1109/ACCESS.2024.3472474">10.1109/ACCESS.2024.3472474</a> <a href="https://hal.archives-ouvertes.fr/hal-04712854" rel="nofollow">⟨hal-04712854⟩</a></li> </ol> <p><strong>Funding: </strong>part of this work was funded by a grant from the <em>Provence-Alpes-Côte-d'Azur</em> region (PACA, France) and the <em>Nectar de Code</em> company.</p> <p><strong>Citation: </strong>If you use this data or the associated source code, please cite article [4]:</p> <p><code>@Article{Cecillon2024,</code><br><code> author = {Cécillon, Noé and Labatut, Vincent and Dufour, Richard and Arınık, Nejat},</code><br><code> title = {Whole-Graph Representation Learning For the Classification of Signed Networks},</code><br><code> journal = {IEEE Access},</code><br><code> year = {2024},</code><br><code> volume = {12},</code><br><code> pages = {151303-151316},</code><br><code> doi = {10.1109/ACCESS.2024.3472474},</code><br><code>}</code></p>
Source data for the publication "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time", Nature Communications, 2021
<p>The archives contain the raw data used to generate the transient absorption spectra for uridine (Figure 1) and 5-methyluridine (Figure 2) presented in the main paper, as well as the trajectory plots and auxiliary spectra presented in the Supplementary Information of the paper "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time" authored by R. Borrego-Varillas et al. published in Nature Communications, 2021. Specifically:</p> <p><strong>URD</strong>: folder with raw data from the uridine trajectories (56 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p><strong>5mURD</strong>: folder with raw data from the 5-methyluridine trajectories (57 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p>The raw data of each trajectory is inside a folder named <em>geom_XXX</em> where <em>XXX</em> stands for a 3-digit label of the trajectory. The trajectories have been selected out of a pool of 500 trajectories according to the S0-S1 vertical gap so that only trajectories whose energy gap falls under the envelope of the pulse are selected</p> <p><strong>URD</strong>: 003 005 006 011 015 023 039 040 054 056 060 083 098 104 112 114 116 121 122 147 152 158 161 171 173 175 177 186 189 200 204 211 219 223 225 232 234 235 236 246 251 252 257 259 265 268 271 272 279 286 287 289 305 313 318 336</p> <p><strong>5mURD</strong>: 010 044 045 048 052 057 065 074 085 094 097 099 100 105 110 112 113 121 131 137 138 140 144 145 159 164 170 179 182 183 184 186 189 199 203 205 209 214 219 220 221 239 243 250 251 273 284 290 295 301 302 320 325 327 328 333 334</p> <p>In each geom_XXX folder there are following files:</p> <p><strong>S1-S<em>Y</em>.dat</strong>: ASCII files () in which the individual columns correspond to </p> <p>col1: time [fs] </p> <p>col2: transition energy of state S<em>Y</em> with respect to S1 [cm-1] where S0 is the ground state</p> <p>col3-5: X, Y and Z components of the transition dipole moment between S1 and S<em>Y</em> [a.u.]</p> <p>col6: magnitude of the transition dipole moment between S1 and S<em>Y</em> [a.u.] </p> <p>col7: angle between transition dipole moment at time t and t=0 [deg]</p> <p>Note that in URD S1-S0.dat contains in most cases about 500 data points (0-500 fs), in 5mURD S1-S0.dat contains 1000 data points (0-1000 fs) except for a few cases in which the trajectories were interrupted earlier. This data has been used to simulate the stimulated emission before the hopping event and the hot ground state photoinduced absorption after hopping. S1-S<em>Y</em>.dat () contain only data points until the hopping event which have been used to simulate the excited state photoinduced absorption.</p> <p>The spectra reported in the main article (Figs 1 & 2) as well as in the SI can be reproduced following eq. 13-18 in the Supplementary Information.</p> <p> </p> <p><strong>HighMediumLayer_traj.xyz.zip</strong>: archived Cartesian coordinates of the High Layer (nucleobase) and Medium Layer (sugar and waters within 5 Å distance from nucleobase) along the dynamics</p> <p>Note that due to the different number of waters in each trajectory the size of the Medium layer (and thus the size of the system) may vary from trajectory to trajectory.</p> <p>Note that due to the different duration of each trajectory the number of geometries may vary from trajectory to trajectory.</p> <p><strong>LowLayer.xyz:</strong> Cartesian coordinates of the Low Layer (waters > 5 Å from the nucleobase); the coordinates of these waters are kept fixed along the trajectory.</p> <p>The coordinates of High, Medium and Low layers can be used to reproduce the QMMM calculations (energies, gradients and transition dipole moments along each trajectory) with the official COBRAMM release (<a href="https://gitlab.com/cobrammgroup/cobramm.git">https://gitlab.com/cobrammgroup/cobramm.git</a>) following the parameters provided in Supplementary Note 2 of the Supplementary Information.</p>
Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_
<p>Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).<br> <br> Citation:<br> </p> <p>Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.</p>
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Table S1. Geographical localization of Pinus pseudostrobus Lindley. specimens used in the study and concordance of morphological identification with real-time PCR-HRM assay for Pinus pseudostrobus varieties pseudostrobus, apulcensis, oaxacana and coatepecensis, based on the cluster pattern.
<p>File encloses geographical location of collected Pinus pseudostrobus samples in México, as well as haplotype grouping obtaines from HRM analysis</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.