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2,885 results for “transfers”
DFT torsiondrive data for: MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules
<p>MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules</p> <div><a href="https://arxiv.org/search/physics?searchtype=author&query=Kov%C3%A1cs,+D+P">Dávid Péter Kovács</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Moore,+J+H">J. Harry Moore</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Browning,+N+J">Nicholas J. Browning</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Batatia,+I">Ilyes Batatia</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Horton,+J+T">Joshua T. Horton</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Kapil,+V">Venkat Kapil</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Witt,+W+C">William C. Witt</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Magd%C4%83u,+I">Ioan-Bogdan Magdău</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Cole,+D+J">Daniel J. Cole</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Cs%C3%A1nyi,+G">Gábor Csányi </a><a href="https://doi.org/10.48550/arXiv.2312.15211">https://doi.org/10.48550/arXiv.2312.15211</a></div> <p> </p> <p>Supporting data including raw outputs from SPICE consistent torsion drives on the TorsionNet500 and OpenFF Biaryl datasets and HDF5 versions formated to be consistent with the rest of the SPICE dataset. See the <a href="../records/10975225">SPICE release</a> for more details. </p>
Experimental datasets on "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"
<p>Experimental datasets referring to the manuscript entitled "Real-time Observation of sub-100-fs Charge and Energy Transfer Processes in DNA Dinucleotides"</p>
Optimal low thrust transfers among asteroid belt asteroids.
<p>This dataset contains > 3,000,000 optimal low-thrust transfers between asteroids in the belt. It was created from the asteroids data released during the 12th edition of the Global Trajectory optimization competitions (see the <a href="https://sophia.estec.esa.int/gtoc_portal/">official GTOC portal</a>) and considering a spacecraft having an initial mass <strong>ms</strong> in the range [700, 8000] kg, a maximum thrust <strong>TM</strong> = 0.6 N with a specific impule <strong>Isp</strong> = 4000 s, resulting in possible initial acceleration levels in the range [7.5 1e-5, 8.6 1e-4] m/s^2.</p> <p>The dataset was developed in the context of our contribution to the International Symposium on Space Flight Dynamics 2024 (ISSFD).</p> <p>Starting from a sampled asteroid couple <strong>(As</strong> <strong>Af)</strong>, and an initial mass <strong>ms:</strong></p> <ul> <li>The minimum time transfer <strong>tof_min </strong>is found solving the corresponding optimal control problem (in a Keplerian dynamics) and the resulting transfer recorded in a first dataset.</li> <li>In some cases, for several fixed <strong>tof</strong> > <strong>tof_min</strong> the maximum final mass transfer is found solving the corresponding optimal control problem and the resulting transfer recorded in a second dataset.</li> </ul> <p>Each line in the csv dataset correspond to a specific optimal transfer (randevouz) and contains the following information:</p> <p>[<strong>xs</strong>, <strong>ys</strong>, <strong>zs</strong>, <strong>vxs</strong>, <strong>vys</strong>, <strong>vzs</strong>, <strong>xf</strong>, <strong>yf</strong>, <strong>zf</strong>, <strong>vxf</strong>, <strong>vyf</strong>, <strong>vzf</strong>, <strong>ms</strong>, <strong>tof</strong>, <strong>mf</strong>, <strong>MIMA</strong>, <strong>MIMA2</strong>, <strong>MIMA3</strong>, <strong>mfms_approx</strong>]</p> <p>, i.e. the starting (<strong>s</strong>) spacecarft state, the final (<strong>f</strong>) spacecraft state, the starting spacecraft mass <strong>ms</strong>, the time of flight <strong>tof </strong>and the final mass <strong>mf</strong>. Several analytical approximations are also reported: <strong>MIMA</strong>, <strong>MIMA2,</strong> <strong>MIMA3, mfms_approx.</strong></p> <p>The analytical approximation <strong>MIMA</strong> is derived and published in:</p> <ul> <li>Hennes, D., Izzo, D., & Landau, D. (2016, December). Fast approximators for optimal low-thrust hops between main belt asteroids. In <em>2016 IEEE Symposium Series on Computational Intelligence (SSCI)</em> (pp. 1-7). IEEE.</li> </ul> <p>A second improved version (<strong>MIMA2</strong>) is derived and published in:</p> <ul> <li>Izzo, D., Märtens, M., L. Beauregard ..., G. Acciarini et al. "Asteroid Mining: ACT&Friends’ Results for the GTOC 12 Problem." <em>to appear in Astrodynamics (Springer).</em></li> </ul> <p>and the final one, <strong>MIMA3,</strong> is derived and published in:</p> <ul> <li>G. Acciarini, Beauregard L. and Izzo, D., Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach. <em>ISSFD conference, Darmstadt, 2024</em>.</li> </ul> <p>The MIMAs approximate the maximum initial mass a spacecarft can have to perform a given fixed time transfer (that also is, in the optimal time of flight transfer dataset <strong>ms</strong>). For these transfers no coast arc exists. In all other cases, a related analytical approach can be used to approximate <strong>mf</strong>. The result, when available is added to the dataset as <strong>mfms_approx </strong>(approximating the ratio <strong>mf/ms</strong>, an approximation for the final mass can be retreived multiplying by <strong>ms</strong>)</p> <p> </p> <p>NOTE: the <strong>MIMAs</strong> and <strong>mfms_approx </strong>sometimes fail numerically or conceptually in which case a nan is reported or a 'nonsensical' number (e.g. lower than the actual starting mass).</p> <p>NOTE: The second version of the dataset corrects some trajectories in the time-optimal dataset that were not time optimal since during the optimization they hit a lower bound on the mass (500kg). They have now been removed. Thanks to Hongxin Shen for testing and reporting the issue.</p> <p> </p>
FIGURE 2 in Aphelandra verticillata transferred to Holographis (Acanthaceae: Acantheae)
FIGURE 2. Map of central Mexico showing the distribution of Holographis verticillata.
From Movement to Infection: Deciphering COVID-19 Transmission via Transfer Entropy
<p>We retrieved daily cases for all of Spain reported at the level of provinces and the level of Basic Health Areas (BHA) for Catalunya and Madrid (<a href="https://doi.org/10.5281/zenodo.4634868">https://doi.org/10.5281/zenodo.4634868</a>). Each record corresponds to a geo-referenced time series where each record has an associated date, the corresponding identifier of the layer, a code of the zone i.e. a province or BHA) and the number of cases reported on that date (daily incidence).</p> <p>Mobility and population data records come from a study conducted by the Spanish Ministry of Transport, Mobility and Urban Agenda (MITMA, \emph{Ministerio de Transportes, Movilidad y Agenda Urbana}) which analyses the mobility and distribution of the population in Spain from February 14th 2020 to May 9th 2021 <a href="https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data">https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data</a> retrieved from <a href="https://doi.org/10.5281/zenodo.4634895">https://doi.org/10.5281/zenodo.4634895</a>. The data records are based on a sample of more than 13 million anonymized mobile-phone lines provided by a single mobile operator whose subscribers are evenly distributed and include two different mobility indicators. Daily origin-destination matrices (ODM) account for the number of trips between 2850 mobility areas that cover almost the entire territory of Spain, reconstructed by combining cell phone antenna coverage areas with districts and municipalities.</p> <p>The second mobility indicator reports for each mobility areas the total number of persons that have performed 0, 1, 2 or more than 2 trips in a given date. The indicator accounts for the fractions of people performing at least one trip or none, as well as the estimated total population in that zone for the given date. In this work, we used the mobility indicators \cite{data-mobility} and population data \cite{data-population}, projected into provinces and BHA. </p> <p>The data set also include the zonifications in geoJSON for zon_bas_13, abs_09 and cnig_provincias retrieved from <a href="https://doi.org/10.5281/zenodo.4634663">https://doi.org/10.5281/zenodo.4634663</a>.</p>
The data for the work of 'Using non-adiabatic excitation transfer for signal transmission between molecular logic gates'
<p>There are input and output data for calculation of non-adiabatic transfer rates which was performed using the NATRC program. It was calculated rates for transfer and relaxation of an exciton in the molecule of 3H-thioxanthene-TTF-dibenzo-BODIPY. The study was carried out using three isomers of this molecule: 'Chain', 'Line' and 'S-line'</p>
Preliminary findings on the bioaccumulation and marine trophic transfer of the antifouling biocide DCOIT in soluble and nanostructured forms (FAIR dataset)
<p>The present dataset includes the raw data regarding the bioaccumulation, trophic transfer, and biomagnification of DCOIT, in the soluble and nanostructured forms, in the marine bivalve <em>Mytillus galloprovincialis</em>, using three different uptake routes. This data supports the main findings of the study that both forms of the biocide can be trophically transferred as well as suggests that encapsulation is a good strategy to minimize biocidal bioaccumulation and thus protect marine ecosystems. </p>
Active thermography to estimate leaf heat transfer - leaf level data to manuscript
<p>Active thermography is an informative methodology to measure that we adopted to plant sciences to measure leaf heat transfer and derive spatial maps of thermal responsiveness of leaves. This method and its usability is described in a publication in 'Frontiers of Plant Sciences'.</p> <p>Here we publish the data-set of active thermography measurements that were used in this publication. The Mat-lab code that was developed with this publication is also published at Zenodo under the doi 10.5281/zenodo.1195869</p>
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
Dataset from: Horizontal transfer of BovB and L1 retrotransposons in eukaryotes
<p><strong>Background: </strong>Transposable elements are mobile DNA sequences, colloquially known as jumping genes because of their ability to replicate to new genomic locations. TEs can jump between organisms or species when given a vector of transfer, such as a tick or a virus, in a process known as horizontal transfer. Here, we propose that LINE-1(L1) and Bovine-B (BovB), the two most abundant transposable element families in mammals, were initially introduced as foreign DNA via ancient horizontal transfer events.</p> <p><strong>Results: </strong>Using analyses of over 759 plant, fungal and animal genomes, we identify multiple possible L1 horizontal transfer events in eukaryotic species, primarily involving Tx-like L1s in marine eukaryotes. We also extend the BovB paradigm by increasing the number of estimated transfer events compared to previous studies, finding new parasite vectors of transfer such as bed bug, leech, and locust, and BovB occurrences in new lineages such as bat and frog. Given that these transposable elements have colonized more than half of the genome sequence in today's mammals, our results support a role for horizontal transfer in causing long-term genomic change in new host organisms.</p> <p><strong>Conclusions:</strong> We describe extensive horizontal transfer of BovB retrotransposons and provide the first evidence that L1 elements can also undergo horizontal transfer. With the advancement of genome sequencing technologies and bioinformatics tools, we anticipate our study to be a valuable resource for inferring horizontal transfer from large-scale genomic data.</p> <p><strong>Dataset:</strong> The deposited dataset contains the identified TE sequences (L1 and BovB) from all genomes and the putative horizontal transfer clusters described in the text. See Additional File 1 (Tables S1-6) and Additional File 2 (Figures S1-55) for detailed descriptions of the sequences and clusters.</p> <p> </p>
Outgrowth of transferred tumors expressing libraries of PresentER minigenes in immunocompetent and immunodeficient mice
<p>Minigene sequencing of RMA/S tumors grown in RAG deficient mice, harvested and then transferred and grown in WT or RAG mice. "Pool 13" are wild type peptide minigenes. "Pool 18" are mutant peptide minigenes.</p>
Transfer of bacteriophages between the fingers of volunteers and water or saliva
<p>Data sets on the percentage of virus transferred between fingers and water or saliva. Transfer with saliva is for both wet hands (where the virus inoculum was not allowed to appreciably dry before transfer) and for dry hands (where the virus inoculum was allowed to dry before transfer). The data were collected, analyzed, and reported within the following publication:</p> <p>Pitol, A. K., Bischel, H. N., Kohn, T., & Julian, T. R. (2017). Virus Transfer at the Skin–Liquid Interface. Environmental Science & Technology, 51(24), 14417-14425. <a href="https://doi.org/10.1021/acs.est.7b04949">https://doi.org/10.1021/acs.est.7b04949</a>]</p>
Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce
<p>This dataset includes files and scripts used for the paper "Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce". </p>
The supplementary material for antenna transfer function (arXiv: 1901.09624)
<p>The angular response for interferometric space based gravitational wave detectors, enclosed files are 2d and 3d plots animations for the response function when the detector moves in the orbit. File names started with LISA/lisa are for LISA, those with TianQin/tq are for TianQin. </p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 0
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images </strong>of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. <strong>This data set</strong> (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. The second part (part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 1
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images</strong> of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. The first part (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. <strong>This data set </strong>(part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Formation pathway of Population III coalescing binary black holes through stable mass transfer
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.468.5020I">Inayoshi et al. (2017)</a>. MESA version 8845.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx757">10.1093/mnras/stx757</a></p>
Novel modelling of ultracompact X-ray binary evolution - stable mass transfer from white dwarfs to neutron stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2017MNRAS.470L...6S/abstract">Novel modelling of ultracompact X-ray binary evolution - stable mass transfer from white dwarfs to neutron stars</a></p>
On the Origin of Sub-subgiant Stars. II. Binary Mass Transfer, Envelope Stripping, and Magnetic Activity
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017ApJ...840...67L">Leiner et al. (2017)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aa6aff">10.3847/1538-4357/aa6aff</a></p>
Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface
<p>The data are used in the paper "Effects of roughness Reynolds number on scalar transfer mechanisms at the sediment-water interface" which was submitted to the journal-Water Resources Research. The paper used particles with different sizes, experimental bed is reproduced and the scalar transfer from water to sediment is modelled. We found that The transfer factor changes to turbulent diffusion at the sediment-water interface as the roughness Reynolds number increases. And the turbulent Schmidt number is higher near the SWI and decrease to 0.5 at the SWI, ranging from 0.5 to 1 within the water depth.</p>
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Allen Brain Atlas
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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.