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1,548 results for “Trajectories”
Trajectories used for Tailoring Charge Transfer Kinetics in Organic Radical Batteries
<p>Trajectories, which are discussed in our work "Tailored Charge Transfer Kinetics in Organic Radical Batteries - A Joint Synthetic-Theoretical Approach"</p> <p>The trajectories were obtained by linearly interpolating in internal coordinates (LIICs) of the relaxed ground state species of molecules A to F, where the charge is either localized on the thiophene backbone (B) or the TEMPO moiety (T1 and T2). Thereby, the program suite pysisyphus (Steinmetzer <em>et. al.</em> 2021) was used to obtain the LIICs. Endpoints represent the fully optimized redox species. The trajectories were used to calculate the intramolecular charge transfer reactions.</p> <p>Trajectories of the following charge transfer reactions are uploaded:</p> <p>1. A<sub>B</sub>→A<sub>T1</sub> (ABtAT1.trj)</p> <p>2. A<sub>B</sub>→A<sub>T2 </sub>(ABtAT2.trj)</p> <p>3. B<sub>B</sub>→B<sub>T1 </sub>(BBtBT1.trj)</p> <p>4. B<sub>B</sub>→B<sub>T2 </sub>(BBtBT2.trj)</p> <p>5. C<sub>B</sub>→C<sub>T1 </sub>(CBtCT1.trj)</p> <p>6. C<sub>B</sub>→C<sub>T2 </sub>(CBtCT2.trj)</p> <p>7. D<sub>B</sub>→D<sub>T1 </sub>(DBtDT1.trj)</p> <p>8. D<sub>B</sub>→D<sub>T2 </sub>(DBtDT2.trj)</p> <p>9. E<sub>B</sub>→E<sub>T </sub>(EBtET.trj)</p> <p>10. F<sub>B</sub>→F<sub>T </sub>(FBtFT.trj)</p>
Database of Rural Technological Trajectories of the Legal Amazon delimited by the Method of Differentiation and Structural Signification of Rural Production
<p>This database contains selected variables associated with the rural economic sector of the Brazilian Legal Amazon distributed at municipal level by technological trajectories (TT) – techno-productive trajectories and their technological variants (TTP) -, as defined and theoretically justified by Costa (2021, p. 217-219).</p> <p>The TTs are designed by a method that combines <em>differentiation and structural signification</em> of rural production in a given territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU).</p> <p><em>Structural differentiation</em> (Phase 1) is necessary because production systems activities play different roles, depending on the systems production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 441 and 442 of Costa (2021).</p> <p>In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These <em>patterns</em> are semantically associated with TTs or TTPs<em> structures</em> that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work.</p> <p>The territory of the Brazilian Legal Amazon encompasses 772 municipalities: all from eight states (Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins) and part of the State of Maranhão (west of the 44ºW meridian).</p> <p>The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 1995, 2006 and 2017 Agricultural Censuses. The credit data for 2017 are from the Central Bank of Brazil.</p> <p>The dataset is organized as: Zen1995_LegalAmazon_Inicial.csv; Zen2006_LegalAmazon_Inicial.csv and Zen2017_LegalAmazon_Inicial.csv. In each table the column names are self-explanatory.</p> <p> </p> <p>Reference:</p> <ul> <li>Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2). <p> </p> <p> </p> <p> </p> </li> </ul>
Trajectories and Code from "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022
<p>Trajectories, GROMACS input files, and analysis code from the manuscript "Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states" Zhu et al. 2022 (Nature Communications, In Press)</p> <p>https://www.biorxiv.org/content/10.1101/2021.12.23.474012v1.abstract</p>
Molecular dynamics trajectories obtained from simulations of mechanically-controlled break-junctions and associated zero-bias conductance.
<p>This data set contains structural information and the associated zero-bias conductance of mechanically-controlled break-junction experiments. It contains:</p> <ul> <li>Six (multi) xyz files (trajectory_0X.xyz), which contain different trajectories produced by molecular dynamic simulations (using <a href="https://www.lammps.org/">LAMMPS</a> and <a href="https://docs.lammps.org/Packages_details.html#pkg-reaxff">reactive force fields</a>) of a mechanically-controlled break-junction. These simulations start from a gold wire with attached molecules. One side of the wire is slowly pulled away, until the gold wire is broken apart and a molecular junction is formed. The outermost six layers of the goldwire are frozen in the simulation. The temperature of the simulation was set to 300K.</li> <li>Six files (transmission_0X.dat) with the calculated zero-bias conductance (G/G<sub>0</sub>). Each entry corresponds to the zero-bias conductance of the corresponding structure from the xyz files. The zero-bias conductance was calculated using non-scc DFTB+, as, e.g., described <a href="https://dftbplus-recipes.readthedocs.io/en/latest/transport/carbon2d-trans.html">here</a>.</li> </ul> <p>For more information see dx.doi.org/XXXXXXX.</p>
Re-projection alignment for trajectory perturbation estimation in micro-tomography: simulation data
<p>Simulation cone-beam-tomography data sets including:</p> <p>a) Synthetic phantom tomogram (3 spheres + random ellipsoids)</p> <p>b) Forward projection of phantom using low-pitch-helix (LPH) trajectory</p> <p>c) Forward projection of phantom using space-filling (SF) trajectory</p> <p>d) Synthetic per-projection perturbations</p>
Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>We have presented the system for a fully autonomous navigation of an UAV based on Omni<br> directional vision system and image processing. we explain vision system configuration ,image<br> processing and feature extraction methods and finaly suggest an algorithm based on potential field<br> for navigation of an UAV.</p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
Tracking the neurodevelopmental trajectory of beta band oscillations with OPM-MEG
<p>Optically pumped magnetometer magnetoencephalography (OPM-MEG) data were acquired during a somatosensory task using a 192-channel OPM-MEG device which is adaptable to head size and robust to participant movement.</p> <p>This dataset contains data from individuals aged between 2 and 34 years.</p> <p>Analyses and descriptions of the dataset were published in eLife (https://doi.org/10.7554/eLife.94561.1)</p> <p>Defaced, T1-weighted MR images are provided for each participant. These were generated using an individualized template anatomy approach where age-matched template MRIs were warped to an optical 3D scan of each individual's head-shape.</p> <p>Data were compressed using zip on Windows.</p> <p>Matlab (2022b) scripts used for data loading and analysis can be found on (https://github.com/LukasRier/RierRhodes_2024_Neurodevelopmental_OPMMEG)</p>
Fig. 6 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 6: Same as Figure 4, but of the drifters during the 2009 experiment: A) drifter d1 and B) drifter c1 (red), drifter c2 (magenta) and drifter d3 (yellow).
Fig. 7 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 7: Wind progressive vector diagrams for the 2008 (A) and 2009 (B) experiments colour coded with the time. The axis represents the displacements in km of a pure wind-driven particle having a speed equal to 1% of wind speed.
Fig. 2 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 2: (A) Central points chosen as reference for the meteorological conditions of the Aegean Sea (black dot in the middle of the basin). Wind time series during the 2008 (B) and 2009 (C) experiments.
Fig. 5 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 5: Same as Figure 4, but of the southern triplet drifters during the 2008 experiment: A) drifter a1; B) drifter a2 and C) drifter a3.
Fig. 1 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 1: Geographical references and deployment positions during 2008 (yellow dots) and 2009 (magenta dots). The bathymetry is saturated at - 600 m. SB: Singitikos Bay; SM: Samothraki Island; L: Lemnos Island; DS: Dardanelles Strait; AE: Agios Efstratios Island; S: Skyros Island; A: Andros Island; T: Tinos Island; MY: Mykonos Island; M: Milos Island.
Fig. 4 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 4: Six-hourly interpolated trajectories of the northern triplet drifters during the 2008 experiment superimposed on the bathymetry: A) drifter b1 (red curve) and drifter b2 (yellow curve) and B) drifter b3. The dots show the drifter position every 6 hours and the black arrows, indicating the direction of the drifters, are depicted every 5 days. See Table 1 for drifter attributes. Depth colourbar is the same as in Figure 1.
Fig. 8A in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 8A: Three-day drifter trajectories superimposed on the SST composite of the period 28-30 August 2008. The dot symbols correspond to the end of the 3-day trajectories.
Fig. 3 in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 3: Drifter trajectories during the 2008 (A) and 2009 (B) experiments. The drifters of the northern triplets are indicated in magenta colour, while the drifters of the southern triplets are depicted in yellow colour. Depth colourbar is the same as in Figure 1.
Dataset for Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories
<p>Dataset utilized for our tested data in our accepted paper "Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories"</p>
Subset of Opensky Dataset for aircraft trajectories
<p>To reduce the size complexity we have filtered the OpenSky dataset such that it encompasses flight trajectory information from the Nordrhein Westfalen region, with latitude ranging from 50 to 52 degrees north and longitude ranging from 5 to 9 degrees east, capturing the movement of aircraft's from July 1st 2019 to July 31st 2019.</p>
A dataset for robotic outdoor visual navigation with multiple passages through trajectory segments
<p>The images were captured by a fisheye camera and a magnetic compass was used to acquire the orientation data. The datasets are split in two folders:<br> 1) LEARN: In order to learn a new place, the robot camera captures 15 images over a 360 degrees panorama. During this process, the robot stays still in order to avoid distortions in the representation of the place.<br> 2) EXPLO: When exploring the environment (i.e. the rest of the time), the robot only captures 7 images per panorama, for the purpose of faster place recognition. Images are captured while the robot is moving. Various exploration panoramas are recorded around the trajectory performed in the learning panoramas (see traj.pdf).<br> <br> The average distance between two learning panoramas is 0.93 +/- 0.03 meters<br> The average distance traveled during an exploration panoramas is 0.71 +/- 0.01 meters<br> <br> DATASET A<br> ---------<br> - 20 meters long<br> - 22 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 5 exploration trajectories<br> - A_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - A_parallel: 29 exploration panoramas<br> - A_diagonal1: 28 exploration panoramas<br> - A_diagonal2: 30 exploration panoramas<br> - A_diagonal3: 29 exploration panoramas<br> <br> DATASET B<br> ---------<br> - 20 meters long<br> - 21 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 4 exploration trajectories<br> - B_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - B_parallel: 29 exploration panoramas<br> - B_diagonal1: 29 exploration panoramas<br> - B_diagonal2: 29 exploration panoramas<br> <br> DATASET C<br> ---------<br> - 23.1 meters long<br> - 25 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 2 exploration trajectories<br> - C_on_learned: 34 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - C_parallel: 34 exploration panoramas<br> <br> <br> <br> PANO_INFO FILE STRUCTURE<br> ------------------------<br> Every folder containing images also contains an info file, named either learn_pano_info.SAVE or explo_pano_info.SAVE. Each line corresponds to an image. The structures is the following:<br> - column 1: id = image_id + 1<br> - column 2: azimuth of the center of the image in degrees/360 (value in [0,1])<br> - column 3: elevation of the center of the image. irrelevant in this database (equal to 0).<br> - column 4: type of panorama: equal to 1 if learning and to 0 if exploration.<br> - column 5: end of panorama: equal to 1 if it corresponds to the last image of a panorama.<br> <br> <br> REFERENCES<br> ----------<br> The dataset was used in the paper: Belkaid, M., Cuperlier, N., and Gaussier, P. Combining local and global visual information in context-based neurorobotic navigation. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 4947-4954, doi: 10.1109/IJCNN.2016.7727851, 2016.<br> <br> </p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 4. Mouse trajectory – images taken from http://iographica.com 4 hours in Photoshop (left) vs. 4 hours in Eclipse (right)
<p>Studies from different fields (Arroyo & Wei, 2006), (Mäkiaho & Poranen, 2012), (Lockton, Harrison, Cain, Stanton, & Jennings, 2013), (Seelye, et al., 2015), (Hehman, Stolier, & Freeman, 2014) have shown that mouse trajectory and clicks are environment (application, device) and user specific, especially if mapped over a long working session (Figure 4). </p>
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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.