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4,489 results for “transitivity”
Unveiling a Common Phase Transition Pathway of High-Density Amorphous Ices through Time-Resolved X-ray Scattering
<p>XFEL Pump-Probe experiments on VHDA.</p> <p><strong><span>DOI: 10.1063/5.0216904</span></strong></p> <p>The data that support the findings of this study are available here.</p> <p> </p>
FIGURE 2 in Species distribution ranges of Ilyocryptus Sars, 1862 (Cladocera: Ilyocryptidae) fit the transitional zone between Boreal and Tropical Provinces in the Far East
FIGURE 2. Ilyocryptus yooni Jeong et al., 2012, an adult parthenogenetic female from a large pond in Ho Chi Minh City, South Vietnam: A, general view. B, setae at posterior valve margin. C, postabdomen. D, Its preanal portion. E, Distal portion of postabdomen. F, Antenna I. Scale bars: 0.1 mm.
FIGURE 1 in Species distribution ranges of Ilyocryptus Sars, 1862 (Cladocera: Ilyocryptidae) fit the transitional zone between Boreal and Tropical Provinces in the Far East
FIGURE 1.Original records of Ilyocryptus present in southern and northern territories of the Pacific Region only (A), and penetrating the transitional zone between Boreal ("Palaeactic") and Oriental Provinces (B). Abbreviations: WE—species widely distributed in North Eurasia; ST—southern tropical species; EN—Far Eastern endemics.
FIGURE. 3. Ilyocryptus bharwaji Battish, 1981 in Species distribution ranges of Ilyocryptus Sars, 1862 (Cladocera: Ilyocryptidae) fit the transitional zone between Boreal and Tropical Provinces in the Far East
FIGURE. 3. Ilyocryptus bharwaji Battish, 1981, parthenogenetic female from Klong Rangsit, Bangkok, Thailand: A, general view. B, Head, anterior view. C, postabdomen. D–E, Antenna I. F, antenna II. G–H, its exopod and endopod. Scale bars: 0.1 mm.
The lateral habenula integrates age and experience to promote social transitions in developing rats
<p>This is the tabular source data associated with the manuscript "The lateral habenula integrates age and experience to promote social transitions in developing rats." Preprint DOI: https://doi.org/10.1101/2024.01.12.575446</p>
Can Plant-Based Diets Facilitate Dietary Transition in Bolivia?: An Exploratory Study
<p>Exploratory study on the relation between environmental concerns and the intention to shift to a more plant-based sustainable diet in Bolivia. Data was collected online, with volunteer anonymous participants.</p>
Supplementary Data Table for Bell, Isaac and Katz-Rosene, Ryan "The Contested Futures of Just Transition in Canada's Energy Sector"
<p>This is the Supplementary Data Table for "The Contested Futures of Just Transition in Canada's Energy Sector," by Isaac Bell and Ryan Katz-Rosene, published in Studies In Political Economy.</p>
Supporting material for "Assessing the Climate Transition Value at Risk in the Colombian Food Sector".
<p>This repository contains values and code for generating the dataset used in the study "<span>Assessing the Climate Transition Value at Risk in the Colombian </span><span>Food Sector</span>".</p>
Predicting critical transitions with surrogate data-based machine learning: Project
<p>This project accompanies the Github repository <a href="https://github.com/ZhiqinMa/surrogate_data_based_machine_learning">https://github.com/ZhiqinMa/surrogate_data_based_machine_learning</a>. It contains the model time series data that are used to train the machine learning algorithms, as well as all the process and result files. Please extract all <code>.rar</code> files to <code>G:/surrogate_data_based_machine_learning_v4.0.0</code> folder to run this project.</p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 with OpenMP not enabled<br>C++ standard: C++11</p> <p>Active compile time flags:<br>-DLAMMPS_GZIP<br>-DLAMMPS_SMALLBIG</p> <p>Installed packages:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</p> <p>Output quantities (columns of *.Dat files):<br>Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
Post-transition state trajectories and transition state structures of 4+2/6+4 cycloadditions | BGAN-EPS outputs
<p>dataset.zip and log.zip are the dataset and outputs for the bidirectional generative adversarial network - entropic path sampling (BGAN-EPS). The source code can be found at https://github.com/rshin1209/bgan_eps.</p> <p>dataset.zip includes post-TS trajectories (xyz format) and transition state structures (pdb format) for the following 4+2/6+4 cycloadditions:</p> <ul> <li>Diene/triene cycloaddition (i.e., dta)</li> <li>Tethered-diene/triene cycloaddition (i.e., dtb)</li> <li>NgnD-catalyzed Diels–Alder reaction (i.e., ngnd)</li> <li>SpnF-catalyzed Diels–Alder reaction (i.e., spnf)</li> </ul> <p>dataset.zip can be unzipped and directly used for the BGAN-EPS method.</p> <p>XXX_r2p_1 refers to 4+2-adduct and XXX_r2p_2 refers to 6+4-adduct.</p> <p>log.zip includes outputs of the BGAN-EPS for the above 4+2/6+4 cycloadditions. The graphing for entropic profiles and entropy decomposition analysis can also be found at Git Hub link.</p>
Avalanches and Sample Recording for 'Existence of multiple transitions of the critical state due to anesthetics'
<p>This repository contains data for the manuscript titled 'Existence of multiple transitions of the critical state due to anesthetics'.</p> <p>Additional code related to this manuscript can be found in the following GitHub repository: DOI: 10.5281/zenodo.12690413</p> <p>File description:</p> <p>'SampleRecording_128x128_movementFramesRemoved.mat' - sample quietwakefulness calcium recording. Recording is downsampled from 256x256 to 128x128 and has been pre-alligned to the Allen atlas. Frames of excessive movement have also been removed. Avalanches can be generated using this file as-is using the code 'generateAvalanches.m' from the github repository. </p> <p>'avalancheStatistics_thresh=1.mat' - all avalanche sizes and durations obtained with a threshold of 1 standarddeviation from all recordings. Loads in matlab as a cell with the first cell containing avalanche sizes seperated on a per-recording basis. Second cell is same but for avalanche durations. Third cell has the condition index for each recording (e.g., 1,2,3...), and fourth cell has the corresponding name of the condition (e.g., 'baseline' corresponds to 1, 'iso. 1%' to 2. etc.). Codes in the GitHub repository can be used to perform avalanche analysis.</p>
Data for 'Interlayer Interaction induced Plateau Phase Transition in Quantum Anomalous Hall Insulators'
<p>The MS data file contains the data for the figures in the manuscript. The SI data file contains the data for the figures in the supplementary materials. The txt files are raw data, and the opj files are the processed Origin data.</p>
Data for 'Interlayer Interaction induced Plateau Phase Transition in Quantum Anomalous Hall Insulators'
<p>The MS data file contains the data for the figures in the manuscript. The SI data file contains the data for the figures in the supplementary materials. The txt files are raw data, and the opj files are the processed Origin data.</p>
Statistical data - Assessing the State and Impact Towards Just Transition Process in the Energy Sector in North Macedonia
<p><span>The dataset represents statistical data collected and interpreted by the authors in the framework of the research titled <strong>'Assessing the State and Impact Towards Just Transition Process in the Energy Sector in North Macedonia'</strong>. The research was developed in the framework of the <a href="https://greenforcetwinning.net/">GreenFORCE</a> project and is available under Deliverables <em>4.5_1st Research Study Report</em>; and <em>4.6_2nd Research Study Report</em>. </span></p> <p><span> </span></p> <p><span>The dataset contains tables and graphs that are employed within the research and may be accessed by the general public in compliance with the Creative Commons Attribution license. The datasheets include:</span></p> <p><span> </span><span>1- Primary energy production data 2005-2022 </span></p> <p><span>2- Energy balance 2018-2022</span></p> <p><span>3- Electricity production by domestic producers 2017-2022</span></p> <p><span>4- GDP by production approach 2017-2022</span></p> <p><span>5- GDP components 2019-2021</span></p> <p><span>6- BDP components 2019</span></p> <p><span>7- GDP per capita 2015-2020</span></p> <p><span>8- BDP per sector and region 2021</span></p> <p><span>9- Employment per sector and regions 2017-2021</span></p> <p><span>10- Population data 2021</span></p> <p><span>11- Input-Output analysis data</span></p> <p><span>12- Environmental Investment statistics</span></p> <p><span>13- GHG emissions (+ projections) 2015-2025</span></p> <p><span>14- Environmental taxation (2016-2021)</span></p>
The data for the work "Verification of the modified Bixon-Jortner-Plotnikov model by calculating rates of non-adiabatic transitions in aromatic compounds"
Open the record for dataset details and reuse information.
Data from: Landscape variability of vegetation change across the forest to tundra transition of central Canada
<p><strong>Paper Abstract: </strong></p> <p>Widespread increases in the productivity of tundra ecosystems and static trends – or even declines – in boreal ecosystems have been detected since the early 1980s using coarse-scale remote sensing. However, intermediate-scale Landsat studies have shown that these changes are heterogeneous and may be related to landscape and regional variability in climate, land cover, topography and moisture availability. In this study, a Landsat Normalized Difference Vegetation Index (NDVI) time-series (1984–2016) was examined for an area spanning the transition from sub-Arctic boreal forest to Low Arctic tundra in central Canada. This was supplemented by analyses of relationships with a suite of environmental variables and in situ measurements of bulk vegetation volume. Results show that NDVI trends were generally positive (i.e. increasing) across the study area but were smallest in the forest zone and largest in the northern tundra zone. More than one-quarter (27%) of un-masked pixels exhibited a significant (p < 0.05) trend and virtually all (99.3%) of those pixels exhibited an increasing, or “greening”, trend. Greening pixels were most common in the northern tundra zone and the southern ecotone zone. Random Forest modeling of the relationship between NDVI and environmental variables indicated that the magnitude and direction of trends varied across the forest to tundra transition. Areas that experienced larger increases in NDVI include: (i) areas where summer temperatures increased; (ii) areas exhibiting predominantly shrub and forest cover; and (iii) locations closer to major drainage systems, further from major lakes, and at lower elevations. Ground validation in the central portion of the study area reveals a strong relationship (R<sup>2</sup> = 0.79) between vegetation volume and NDVI for non-tree functional groups and that alder (<em>Alnus crispa</em>) shrublands and open spruce (<em>Picea mariana</em> and <em>P. glauca</em>) woodland with shrubby understories were most likely to exhibit greening. These findings indicate that the largest positive and more significant NDVI trends were associated with increased productivity in shrub-dominated environments, especially at, and north of the treeline in localities with favorable growing conditions. Smaller and less significant NDVI trends in boreal forest environments south of the treeline were likely associated with long-term successional change following disturbance rather than the variables analyzed here.</p> <p> </p> <p><strong>Data details:<br></strong></p> <p>See paper: <a href="https://www.sciencedirect.com/science/article/pii/S0034425718303675?via%3Dihub">Landscape variability of vegetation change across the forest to tundra transition of central Canada - ScienceDirect</a> </p> <p> </p> <p><strong>If you use these data, please reference:</strong></p> <p>Bonney, M.T., Danby, R.K., Treitz, P.M., 2018. Landscape variability of vegetation change across the forest to tundra transition of central Canada. Remote Sens. Environ. 217 <a href="https://doi.org/10.1016/j.rse.2018.08.002" rel="nofollow">https://doi.org/10.1016/j.rse.2018.08.002</a>.</p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/CentralCanadaShrubification_1984to2016">ZZMitch/CentralCanadaShrubification_1984to2016: Code from "Landscape variability of vegetation change across the forest to tundra transition of central Canada" (RSE, 2018) (github.com)</a></p>
Fig. 4 in Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta)
Fig. 4 Phylogenetic tree mapping the larval development site (LDS) (0 = unconcealed, 1 = concealed) for each taxon analyzed in this study. The circle with a picture of a species developing in a concealed site (the apoid wasp Stizus continuus (Crabronidae), emerging from its nest) marks the ancestral state for Apocrita, while the circle with a question mark indicate the unclear ancestral state for Hymenoptera. The histogram in the lower part of the figure show the distribution of cases for a given rank of Zn, for species with either LDS type
Fig. 2 in Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta)
Fig. 2 Phylogenetic tree mapping the ranked Zn % for each taxon analyzed in this study. Zn was ranked as 0 = <0.1 wt%; 1 = 0.1– 1.0 wt%; 2 = 1.0–5.0 wt%; 3 = 5.0–10.0 wt%; and 4 => 10 wt%. Node 1 (in violet) identifies Zn enrichment as ancestral state for Apocrita (proportional likelihoods for Zn %: 0 = 0.0015, 1 = 0.0015, 2 = 0.121, 3 = 0.9785, 4 = 0.0062). Nodes 2 (Proctotrupidae + Pelecinidae) and 3 (Agaonidae) (in pink) identify losses of Zn enrichment in non-Aculeata (proportional likelihoods for Zn %: node 2: 0 = 0.9390, 1 = 0.0036, 2 = 0.0483, 3 = 0.0052, 4 = 0.0037; node 3: 0 = 0.9454, 1 = 0.0038, 2 = 0.0389, 3 = 0.0050, 4 = 0.0068). Nodes 4 (Chrysidoidea), 5 (Formicidae), 6 (Mutillidae + Sapygidae), and 7 (Ampulicidae) (in violet) identify re-acquisitions of Zn enrichment within Aculeata (proportional likelihoods for Zn %: node 4: 0 = 0.0597, 1 = 0.0112, 2 = 0.3890, 3 = 0.5156, 4 = 0.0244; node 5: 0 = 0.2138, 1 = 0.0173, 2 = 0.0174, 3 = 0.2796, 4 = 0.4717; node 6: 0 = 0.3439, 1 = 0.0233, 2 = 0.1286, 3 = 0.1291, 4 = 0.3748; node 7: 4 = 1)
Fig. 1 in Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta)
Fig. 1 SEM pictures of mandibles and representative spectra (on base-10 logarithmic scale) from X-ray energy-dispersive analysis of mandible teeth, generated with a primary electron beam energy of 20 keV. a, b Camponotus cruentatus (Formicidae); c, d Ibalia rufipes (Ibaliidae); e, f Isodontia mexicana (Sphecidae); g, h Argidae sp. Note that Zn occurs only in a and b, and Mn occurs only in b. Squares in the SEM pictures indicate the area of the mandibles where the EDS point analysis was done. Peaks for transition metals (Zn and Mn), for Cl (the halogen co-associated with Zn), and for the basic elements of the cuticle (C and O) are highlighted
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.