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986 results for “classic”
Dataset: Probabilistic work extraction on a classical oscillator beyond the second law
<p>Data set for the article "Probabilistic work extraction on a classical oscillator beyond the second law" , currently published as a preprint on arXiv, and accepted in Physical Review Letters.</p> <p>The preprint and SM have been uploaded for convenience.</p> <p>Inside the compressed file, you will find several folders</p> <p>It contains:</p> <p> </p> <ol> <li><strong>/Exemple trajectories/</strong> <ul> <li> 10 folders named Protocol 0 , Protocol 1 .... each containing the raw data of the 100 first experiments for each protocol. Each protocol was repeated 3000 time to increase the statistics for the article. We provide a representative subset.</li> <li>The Python analysis code "readexperimentalfile.py" . It reads the data in the aforementioned files, and computes the initial potential, the final potential, and the work associated to each protocols. Optionnaly, it saves trajectories used create Fig3 and Fig 4.</li> </ul> </li> <li><strong>/Figures/</strong> <ul> <li>Fig 1 : The pdf file for Fig1. (stochastic2.pdf) as presented in the article. Other formats can be provided on demand.</li> <li>Fig 2: The pdf file for Fig2, Setup.pdf</li> <li>Fig 3: The python code used to create Fig3 , named "plotpotentiel.py". To use it, one need the parameters tuning the initial potential used in the fifth protocol 'fitdepart5.npy', the final potential 'fitfin5.npy' as well as the set of trajectories "zfin5.npy" and "zinit5.npy". All those files were created using the provided python code "readexperimentalfile.py" , and are already provided in the same folder.</li> <li>Fig4: The python code used to create Fig4, named "plottrajex.py". "ztotsubset5.npy" and "lambdasubset5.npy" are provided to directly plot z(t) and lambda(t) during the protocol. "fitdepart5.npy" and 'fitfin5.npy", also created using "readexperimentalfile.py", are needed. </li> <li>Fig5: a) Everything needed to plot the first part of Fig 5. using the python file "workdpdf.py". All the datas of the other experiments are provided, and one can also choose to plot the work probability density function for other protocols (by changing the 5 t o another number. b) "mainfig.py" creates the bottom part of Fig5.</li> </ul> </li> </ol>
Figure data for Prolonging a discrete time crystal by quantum-classical feedback
<p>The dataset corresponds to the original work developed by the authors.</p>
Supplementary material 1 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure S1, S2 : Explanation note: Figure S1. Bayesian phylogeny of 29 samples of Quercus and one Trigonobalanus (outgroup) based on ITS sequences. Branches are labeled with posterior probabilites. Figure S2. Bayesian phylogeny of 29 samples of Quercus and one Trigonobalanus (outgroup) based on concatenated rbcL and matK sequences. Branches are labeled with posterior probabilities.
Experimental data from the paper "Counterexample-guided Cartesian Abstraction Refinement for Classical Planning"
<p>The data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Versatile OAM classical QW results
<p>Experimental results, simulated values and corrections to experimental data for a free-space OAM QW demonstrating a versatile resonator with classical light.</p> <p>Details:</p> <p>First row: details the step number and type of quatnum walk (QW) being evaluated (Asym = asymmetric Hadamard QW; sym = symmetric Hadamard QW; QP = NOT-coin QW; HWP = Identity Coin QW; QWP = QW where the phase change was illustrated by rotating the quater-wave plate fast axis)<br> The OAM\position of the walker being detected (Column A)<br> The simulated values where overlapping pulses were accounted for in the system (Column B): "Corr Theo"<br> The simulated values where overlapping pulses were NOT accounted for in the system (Column C): "Theo"<br> The re-normalised measured values obtained from integrating over the OAM-determined binned CCD areas (Column D): "Meas"</p> <p>The re-normalised corrected values obtained from deconvoluting experimentally measured values accounting for overlapping adjacent pulses where each pulse is a step in the QW (Column E): "CorrExpMeas"</p>
Experimental data from the paper "Subset-Saturated Cost Partitioning for Optimal Classical Planning"
<p>The data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Experimental data from the IJCAI 2019 paper "Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning"
<p>This data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and to save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Experimental data from the JAIR 2020 paper "Saturated Cost Partitioning for Optimal Classical Planning"
<p>This data set contains the raw experiment data, parsed values and basic reports for the experiments in the paper. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and to save space. The second directory (*-eval) contains a "properties" file with all parsed values and an HTML report.</p>
Beyond the mean-field approximation for pair correlations in classical density functional theory: reference inhomogeneous non-associating monomeric fluids for use with SAFT-VR Mie DFT. JCP 2024
<p>All DFT calculations and molecular simulation data in the publication.</p>
Data for manuscript: PEG-mCherry interactions beyond classical macromolecular crowding
<p>The data is provided as a part of the manuscript "<strong>PEG-mCherry interactions beyond classical macromolecular crowding</strong>". </p> <p>This repository includes an archive with folders:<br><br><strong>MDData</strong></p> <ul> <li>contains simulations system: mCherry in PEG and mCherry in water built form the crystal structure of mCherry (PDB ID: 2H5Q) and carried out using CHARMM36m <em>The details of the simulations are provided in the Methods section of the manuscript. </em>Thinned trajectories, initial structures and tprs are provided.</li> </ul> <p><strong>FigureData</strong></p> <ul> <li>contains the simulation data sets underlying the figures where applicable</li> </ul> <p><strong>AdditionalFiles</strong></p> <ul> <li>Contains additional files for supporting data</li> </ul> <p><strong>ExperimentalData</strong></p> <ul> <li><strong>FCS</strong> <ul> <li>Contains data and analysis for Figures 7, S24, S26, and S27</li> </ul> </li> <li><strong>Lifetimes</strong> <ul> <li>Contains data and analysis for Figure 8</li> </ul> </li> <li><strong>Thermal Stability</strong> <ul> <li>Contains data and analysis for Figure S25</li> </ul> </li> <li><strong>PVA</strong> <ul> <li>Contains the lifetime data for PVA measurements (Figure 8)</li> </ul> </li> </ul>
Complementary files for Stability of classical planar dipole matter on regular and aperiodic lattices manuscript
Open the record for dataset details and reuse information.
Data from: Testing classical species properties with contemporary data: how 'bad species' in the brassy ringlets (Erebia tyndarus complex, Lepidoptera) turned good
All species concepts are rooted in reproductive, and ultimately genealogical, relations. Genetic data are thus the most important source of information for species delimitation. Current ease of access to genomic data and recent computational advances are blooming a plethora of coalescent-based species delimitation methods. Despite their utility as objective approaches to identify species boundaries, coalescent-based methods i) rely on simplified demographic models that may fail to capture some attributes of biological species, ii) do not make explicit use of the geographic information contained in the data, and iii) are often computationally intensive. In this paper we present a case of species delimitation in the Erebia tyndarus species complex, a taxon regarded as a classic example of problematic taxonomic resolution. Our approach to species delimitation used genomic data to test predictions rooted in the biological species concept and in the criterion of coexistence in sympatry. We 1) obtained RAD sequencing data from a carefully designed sample, 2) applied two genotype clustering algorithms to identify genetic clusters and 3) performed within-clusters and between-clusters analyses of isolation by distance (IBD) as a test for intrinsic reproductive barriers. Comparison of our results with those from a Bayes Factor Delimitation (BFD*) coalescent-based analysis, showed that coalescent-based approaches may lead to overconfident splitting of allopatric populations, and indicated that incorrect species delimitation is likely to be inferred when an incomplete geographic sample is analysed. While we acknowledge the theoretical justification and practical usefulness of coalescent-based species delimitation methods, our results stress that, even in the phylogenomic era, the toolkit for species delimitation should not dismiss more traditional, biologically grounded, approaches coupling genomic data with geographic information.
FIGURE 9 in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 9. Terebellum Röding, 1798. A. Terebellum delicatum Kuroda & Kawamoto in Kawamoto & Tanabe, 1956, Khánh Hòa, Nha Trang, Vietnam, length 27 mm (NMR 54693). B. Terebellum hubrechti Poppe & Tagaro, 2016, Caubian Deep, Bohol, Philippine, length 33.2 mm (image Conchology Inc.). C. Terebellum simoni Dekkers et al., 2019, Bohol Island, Philippines, length 50.6 mm (Paratype 1, AMD STR2269). D. Terebellum terebellum. D1. Vanuatu, length 63.4 mm (Coll. Guido and Philippe Poppe, 464722). D2. Solomon Islands, size unknown (NMR 44172). D3. Solomon Islands, size unknown (NMR 54964). D4. Leyte, Philippines, length 41.3 mm (Coll. Guido and Philippe Poppe, 378696).
FIGURE 6. Dimeza Deshayes, 1865, Miniseraphs Jung 1974 in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 6. Dimeza Deshayes, 1865, Miniseraphs Jung 1974 and Mauryna de Gregorio 1880. A. Diameza fragilis (Defrance, 1825), MNHN A28784, Collection de Paleontologie, Paris, length 6.9 mm (Caze et al. 2010, figs. 20A, C & E). B. Miniseraphs eratoides (Cossmann, 1889), MNHN A28785, Collection de Paleontologie, Paris, length 8.3 mm (Caze et al. 2010, figs. 20F–H). C. Miniseraphs isabella (Deshayes, 1865), MNHN A28783, Collection de Paleontologie, Paris, length 7.5 mm (Caze et al. 2010, figs. 20M–O). D. Mauryna plicata (d'Archiac & Haime, 1853), NMB H15324, length 51 mm.
FIGURE 5. Seraphs Montfort, 1810. A in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 5. Seraphs Montfort, 1810. A. Seraphs minus (Vincent, 1913), length 7.1 mm (Jung 1974, pl. 6, figs. 9, 10). B. Seraphs naricus (Vredenberg, 1925), holotype, GSI 12614, length 38.6 mm (Jung 1974, pl. 6, figs. 20–22). C. Seraphs olivaceus (Cossmann, 1889), MNHN A28577, Collection de Paleontologie, Paris, length 32 mm (Caze et al. 2010, figs. 6H, I). D. Seraphs peterjungi Caze et al., 2010, holotype, MNHN A28578, Collection de Paleontologie, length 16 mm (Caze et al. 2010, figs. 15J, K, N). E. Seraphs placiferus (Bayan, 1870), lectotype, MNHN G2100, length 33.7 mm (Jung 1974, pl. 7, figs. 5–7). F. Seraphs sopita (Brander, 1766), lectotype, NHMUK GG21010, length 26.4 mm (Jung 1974, pl. 1, figs. 1–3). G. Seraphs squamosus (Martin, 1914), Lectotype, RGM, Leiden St10344, length 37.1 mm (Jung 1974, pl. 6, figs. 17–19). H. Seraphs striatus (von Koenen, 1889), holotype, length 38.5 mm (von Koenen 1889, pl. I, figs. 7A–C). I. Seraphs subconvolutus (d'Orbigny, 1852), lectotype, MNHN UBT65-2-120, length 28 mm (Caze et al. 2010, figs. 14A, B). J. Seraphs volutatus (Brander, 1766), MNHN A28776, Collection de Paleontologie, Paris, length 17 mm (Caze et al. 2010, figs. 6A–C).
FIGURE 1. The single tnt generated maximum likelihood cladogram showing internal cladistic relationships within Seraphsidae Jung, 1974 in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 1. The single tnt generated maximum likelihood cladogram showing internal cladistic relationships within Seraphsidae Jung, 1974.
FIGURE 2 in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 2. Temporal distribution of Seraphsidae Jung, 1974: solid lines indicate presence; dotted lines indicate a geological discontinuities and hypothetical internal relationship.
FIGURE 8 in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 8. Pseudoterebellum new genus and Terebellum Röding, 1798. A. Pseudoterebellum diversiornatum (Eames, 1952), holotype, NHMUK GG68239, length 10.1 mm (Jung 1974, pl. 14, figs. 12–14). B. Pseudoterebellum oympiae (Rolando, 2001), holotype, MGPD 28168, length 20.9 mm (Rolando 2001, fig. 2). C. Pseudoterebellum saudiensis (Abbass, 1972), holotype, ASU S53, length 25 mm (Abbass 1972, pl. V, figs. 1, 2). D. Terebellum cinctum Martin, 1916, holotype, RGM St10337, length 34.7 mm (Jung 1974, pl. 13, figs. 4, 6). E. Terebellum obtusum Sowerby, 1840, holotype, NHMUK GG21000, length 42.2 mm (Jung 1974, pl. 13, figs. 17, 18). F. Terebellum papilliferum Martin, 1916, lectotype, RGM St10339, length 28 mm (Jung 1974, pl. 13, figs. 9, 11).
FIGURE 4. Seraphs Montfort, 1810. A in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 4. Seraphs Montfort, 1810. A. Seraphs andersoni (Dickerson, 1917), holotype, CAS Department of Geology 434, length 38 mm (Jung 1974, pl. 9, fig. 5). B. Seraphs belemnitus (Palmer, 1953), PRI 29236, length 48.2 mm (Jung 1974, pl. 6, figs. 12–14). C. Seraphs caracassense (Leymerie, 1846), BMNH G50180, length 51.3mm (Jung 1974, pl. 4, figs. 10, 11). D. Seraphs chilophorus (Cossmann, 1889), MNHN A28779, Collection de Paleontologie, Paris, length 29 mm (Caze et al. 2010, figs. 15A, B, D). E. Seraphs hernandoensis (Mansfield, 1937), holotype, USNM 495948, length 31.4 mm (Jung 1974, pl. 9, figs. 7–9). F. Seraphs kaindraperi new sp., holotype, USNM 135098, length 50.6 mm (Jung 1974, pl. 5, figs. 3, 4). G. Seraphs leukoleptus Jung, 1974, neotype, MNHN A28781, Collection de Paleontologie, Paris, length 27.6 mm (Caze et al. 2010, figs. 11F, H, I). H. Seraphs mayeri Jung, 1974, holotype, NMB H16579, length 21.4 mm (Jung 1974, pl. 8, figs. 16–18).
FIGURE 3. The clade Seraphsidae Jung, 1974 in Resolving phylogenetic and classical nomenclature: A revision of Seraphsidae Jung, 1974 (Gastropoda: Neostromboidae)
FIGURE 3. The clade Seraphsidae Jung, 1974 derived from character analysis and contextualised with the fossil record showing the ancestral Seraphsinae and crown clade Pseudoterebellinae both new subfamilies.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.