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4,489 results for “transitivity”
Data Repository for Nanoscale magnetism and magnetic phase transitions in atomically thin CrSBr
<p><span>Data repository for: Nanoscale magnetism and magnetic phase transitions in atomically thin CrSBr</span></p> <p><span><span>This data repository contains the raw data as measured on the experimental setup, simulations, analysis scripts and plotting scripts to reproduce the plots shown in the manuscript’s figures.</span></span></p> <p><span><span>Code for plotting: Matlab R2021b<br>The raw data is either stored as MatLab structs (.mat) or accessible through the .json files.</span></span></p> <p><span><span>See ReadMe.txt for more information.</span></span></p>
High Tide or Rip-Tide? GJ 486b JWST Transits with NIRSpec G395H - Supplementary Material
<p>Supplementary material for '<em>High Tide or Riptide on the Cosmic Shoreline? A Water-rich Atmosphere or Stellar Contamination for the Warm Super-Earth GJ 486b from JWST Observations</em>' by Moran & Stevenson et al., ApJL (2023).</p> <p>This repository contains four categories of data products:</p> <ol> <li><strong>White light curves</strong> — White light curves produced for Visit 1 and 2, for detectors NRS1 and NRS2 via the <a href="https://ui.adsabs.harvard.edu/abs/2023Natur.614..659R/abstract">FIREFLy pipeline</a> and the <a href="https://github.com/kevin218/Eureka">open-source codes Eureka</a>! and <a href="https://github.com/JamesKirk11/Tiberius">Tiberius</a></li> <li><strong>Transmission spectra</strong> — Reduced transmission spectra from three data reduction codes (Eureka!, FIREFLy, and Tiberius) for both G395H visits.</li> <li><strong>Stellar spectra and models </strong>— Flux calibrated observed stellar spectra of GJ 486 from each G395H visit, along with best-fit PHOENIX stellar models.</li> <li><strong>Atmospheric models</strong> — Atmospheric forward models used to interpret the data, produced from the open source <a href="https://natashabatalha.github.io/picaso/">PICASO</a> and <a href="https://github.com/mrline/CHIMERA">CHIMERA</a> codes.</li> </ol> <p>For any additional data requests or questions, please contact: sarahemoran@arizona.edu or kevin.stevenson@jhuapl.edu</p> <p> </p>
A story of two transitions: from adhesive to abrasive wear and from ductile to brittle behavior
<h2>Links</h2> <h2>Authors</h2> <ul> <li><strong>S. Z. Wattel</strong>, Civil Engineering Institute, Materials Science and Engineering Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, ORCID: <a href="https://orcid.org/0000-0002-5117-9915">0000-0002-5117-9915</a></li> <li><strong>J.-F. Molinari</strong>, Civil Engineering Institute, Materials Science and Engineering Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, ORCID: <a href="https://orcid.org/0000-0002-1728-1844">0000-0002-1728-1844</a></li> </ul> <h2>Language</h2> <ul> <li>English</li> </ul> <h2>License</h2> <ul> <li>Creative Commons Attribution 4.0</li> </ul> <p>This is a data used in: S. Z. Wattel and J.-F. Molinari, "A story of two transitions: from adhesive to abrasive wear and from ductile to brittle behavior", Journal of Chemical Physics </p> <p>You can find all the information necessary to reproduce the results presented in the paper. As they are too memory intensive, the per-atoms results of the MD simulations were not shared. Instead, a LAMMPS input file for each simulation is provided as well as the log file containing average values.</p> <p>Each simulation can be identified by the key [Size, Potential, Pressure] which corresponds, respectively, to the initial radius of the particle, the potential used in the soft surface and the pressure applied on top and bottom boundaries. Per simulation filenames follow the template H[Potential]<em>rad</em>[Size]<em>p</em>[Pressure]_0. For example, files relating to a simulation of initial particle radius of 50 atoms, where the soft surface potential is H5 and the pressure applied is 0.07 would be named H_5_rad_50_p_0.07_0.</p> <p>The software versions are:</p> <blockquote> <p>LAMMPS (23 Jun 2022 - Update 2)</p> <p>Python 3.11.4</p> <p>NumPy 1.24.3</p> <p>SciPy 1.10.1</p> <p>Pandas 2.1.1</p> <p>MatplotLib 3.7.1</p> </blockquote> <h3>DataPlot.ipynb</h3> <p>The Jupyter notebook contains the code necessary to reproduce every plot in the paper. It also gives indication on how to interact with the data.</p> <h3>analyse.py</h3> <p>The Python script used to perform the clustering analysis of the results.</p> <h3>wear_regime.csv</h3> <p>CSV file describing the wear regime observed in the simulation. The wear regime code is :</p> <blockquote> <p>0 for smooth accretion</p> <p>1 for large chunks & fracturing</p> <p>2 for mixed</p> <p>3 for smooth rolling + some breaking events</p> <p>4 for too much BC influence</p> <p>5 for others</p> </blockquote> <h3>wear_volumes.csv</h3> <p>CSV file describing the wear volume either at the end of the simulation or when the boundary conditions were reached. The total wear volume is the number of atoms part of the rolling particles (removing the initial wear aprticle atoms from the count). The wear volume soft is the part of the wear volume coming from atoms of the soft surface. The measurement timestep is the timestep at which these volumes were counted.</p> <h3>ParametricStudy</h3> <p>Contains the input file and log file for each simulation.</p> <h3>Logs</h3> <p>Contain the log files from each simulation in CSV format for easy post-processing.</p> <h3>Volumes</h3> <p>For a selected number of simulations, the wear volumes were counted at several steps through the simulations. The results are in this folder in csv format.</p> <h3>ClusteringAnalysis</h3> <p>Contains the results from the clustering analysis of the parametric study. The outputs are, for each simulation, colored png files of the clusters as well as an unordered list containing the size of each cluster for several timesteps.</p> <h4>The archiving of this data was made with the help of Solidipes : solidipes.readthedocs.io</h4>
Research Landscape of Energy Transition and Green Finance: A Bibliometric Analysis
<p>Dataset for article title "<span>Research Landscape of Energy Transition and Green Finance: A Bibliometric Analysis".</span></p>
Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland
<p>Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVI<sub>max</sub>) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively.</p>
Evolutionary changes of non-coding elements associated with transition of sexual mode in Caenorhabditis nematodes
<p>The transition of the sexual mode occurs widely in animal evolution. In Caenorhabditis nematodes, androdioecy, a sexual polymorphism composed of males and hermaphrodites having the ability to self-fertilize, has evolved independently multiple times. Although regulatory elements in non-coding regions are likely involved in the evolution of hermaphroditism, their contribution to the transition of sexual mode is ambiguous. Here, we conducted a genome-wide analysis of conserved non-coding elements (CNEs) focusing on the evolution of hermaphroditism in Caenorhabditis nematodes. We found that, in androdioecious nematodes, mutations rapidly accumulated in CNEs neighboring genes associated with sexual traits. Expression analysis indicate that the identified CNEs are involved in spermatogenesis in hermaphrodites and associated with the transition of gene expression from dioecious to androdioecious nematodes. Finally, genome editing of a CNE neighboring laf-1 resulted in a change in its expression in the gonadal region undergoing spermatogenesis. Our bioinformatic and experimental analyses highlight the importance of CNEs to the transition of sexual mode.</p>
The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar-Bernevig-Hughes model
<p>Data in the form of mx and dat files for "The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar-Bernevig-Hughes model", T. Masłowski, and N. Sedlmayr<br>Journal of Physics: Condensed Matter 36, 335401 (2024), <a href="https://doi.org/10.1088/1361-648X/ad4a16">https://doi.org/10.1088/1361-648X/ad4a16</a>.</p> <p>Included are data for the return rate (RR), Fisher zeroes, and Loschmidt eigenvalues (MEV). Numbers in parentheses refer to {100m,100m'} and Lx and Ly are the system sizes. Within the return rate files the third column is the derivative of the return rate.</p>
Supplementary material to the study: "Understanding the interactions between biowaste valorization and selected Sustainable Development Goals: insights from an early transition stage"
<p>This file includes supplementary material for the journal article: "Understanding the interactions between biowaste valorization and selected Sustainable Development Goals: insights from a national context at an early transition stage". The PDF file contains the interview guide, while the .xlsx file contains all other supplementary material. </p>
Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space
<p>Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human fronto-parietal dorsal stream regions, but not in <em>MT+</em>, tracks the number of competing options, as preferred options are selectively maintained on the map while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior beta<sub>1</sub>/alpha oscillations desynchronize within 0.4-0.7 s, consistent with option encoding by competing beta<sub>1</sub>-stabilized subpopulations. Altogether, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.</p>
Data from: Spatiotemporal dynamics of Nektonic biodiversity and vegetation shifts during the Smithian–Spathian Transition: Conodont and Palynomorph insights from Svalbard
<p>The dataset includes an Excel file with sporomorph counting of samples from the Stensiöfjellet section, Svalbard. Furthermore, it includes additional taxonomic notes on conodonts from the Stensiöfjellet section, Svalbard.</p>
Engineering DszC Mutants from Transition State Macrodipole Considerations and Evolutionary Sequence Analysis
<p>Gaussian input and output files required to reproduce the results and analyses in the work.</p> <p>Code required to conduct analysis and representation.</p>
Vortex loop dynamics and dynamical quantum phase transitions in 3D fermion matter
<p>Supplemental Material and data corresponding to the article 'Vortex loop dynamics and dynamical quantum phase transitions in 3D fermion matter'.</p> <p>File description:</p> <ul> <li>_sm.pdf - Supplemental Material to the article</li> <li>fig_2_rate.txt - rate function corresponding to Fig. 2</li> <li>fig_3_rate.txt - rate function $\lambda$ corresponding to Fig. 3</li> <li>fig_3_rate_G.txt - rate function $\lambda_G$ corresponding to Fig. 3</li> <li>fig_3a.txt - phase of the Green's function corresponding to Fig. 3a</li> </ul> <p> </p>
Observational dataset for "Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations"
<p>This observational dataset include the DOE ARM ground based observations and satellite for the paper titled “Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations” on GRL.</p> <p>For the original data source, all ARM observational data sets used in this study are publicly available from the ARM data archive site (https://adc.arm.gov/discovery/#/results/iopShortName::amf2019comble/datastream::anxarmbeatmM1.c1/datastream::anxarmbecldradM1.c1/datastream::anxarsclkazr1kolliasM1.c0). MODIS MOD06 L2 cloud product are publicly available from (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06 L2, DOI:10.5067/MODIS/MOD06 L2.061).</p> <p>CloudSat products can be ordered from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/order/). To download CloudSat data, a new user must first create an account by filling out the signup form (https://www.cloudsat.cira.colostate.edu/accounts/signup/).</p>
Funding programs for EU Just Transition Fund
<p>This dataset contains a collection of national and regional funding programmes submitted by Member States to the EU. Data collection was conducted in May-June 2023 through a review of national and regional websites that publish documentation related to the management of EU regional funds, from which the funding programmes were retrieved.</p> <p>Data were collected as part of the <a href="https://coaltransitions.org/projects/cintran/">CINTRAN project</a> and for use in its Deliverable 3.3: "A regional model-based assessment of the socioeconomic impacts of EU climate neutrality pathways and the effectiveness of the Just Transition Fund" (Charalampidis et al, in preparation). </p>
Data supporting "Transition between mechanical and geometric controls in glacier crevassing processes".
<p>The folders:</p> <ul> <li><strong><em>results_Geometrical_Regime</em></strong></li> <li><strong><em>results_Mechanical_Regime</em></strong></li> <li><strong><em>results_3D</em></strong></li> <li><strong><em>results_varyingLengthSlope</em></strong></li> <li><strong><em>results_varyingVelocity</em></strong></li> </ul> <p>contain the data for spacing and depth of the crevasses in the different simulations. Each simulation folder is named after the ice thickness H and cohesion c. For example, H300c2 means an ice thickness of 300m and a cohesion of 2MPa.</p> <p>The simulation files contain data measured every 10 frames. Empty files imply two possibilities:<br>-the crevasse(s) are away from the window measurement (the window is approximately 1500m wide around the obstacle).<br>-No crevasses are observed or at least no distinguishable enough.</p> <p>**<br>The python code <strong><em>readDataMeasuredInSimulations.py</em></strong> use the <strong><em>readDataPP</em></strong> class to plot the measures from the previous folders.<br>All the figures from the paper can be reproduced using this code.</p> <p>**</p> <p>The full simulation results cannot be transfered as a supplementary material as it is too large (20Go for each simulation). However we provide two full result files: <strong>H300_c0_4.abc</strong> and <strong>H100_c2.abc. </strong>We provide an example of the init.lua file used to launch a simulation with given parameters and configurations. The configurations used in the simulations are all provided in the geometry.zip file.</p> <p>A version of the MPM numerical model can be found in a previous publication at<strong> https://www.nature.com/articles/s43247-021-00179-7. </strong></p> <p> </p>
A phosphate transporter in VIPergic neurons of the suprachiasmatic nucleus gates locomotor activity during the light/dark transition in mice
<p>Raw Data for <strong>An atypical phosphoswitch in specific neurons of the suprachiasmatic nucleus gates locomotor activity at the light/dark transition in mice</strong></p> <ol> <li>Multi-electrode array data from PiT2<sup>+/+</sup> and PiT2<sup>-/-</sup> mice</li> <li>Locomotor activity from PiT2<sup>+/+ </sup>and PiT2<sup>-/-</sup> mice in LD 12:12, DD, LD 4:20, LD 20:4 and DD with light pulses</li> <li>Phosphoproteome data</li> </ol>
Supporting data for "Rigidity transition of a highly compressible granular medium"
<p>This repertory contains the supporting data for the article "Rigidity transition of a highly compressible granular medium"</p> <p>Content:<br>- supplementary_videos<br>The seven supplementary videos associated with the article.<br>- assembly <br>The experimental raw force data and data processed by image analysis of the ring assemblies, for all the parameters explored.<br>- single_ring<br>The FEM data and processed experimental data for the single ring compression and interactions. <br>- COMSOL <br>Contains the COMSOL files to run the FEM simulations and the associated Neo-Hookean material law. <br>- image_analysis<br>Raw images of the five first cycles for gamma_0=0.40 and \Phi=1.00, with a MATLAB code to process the images and extract the relevant quantities. </p> <p>Each category comes with a dedicated README.txt that details its content. </p>
Replication dataset for paper "A shift in transitional forests of the North American boreal will persist through 2100"
Open the record for dataset details and reuse information.
Transition Edge Sensor Signals (Boulder)
<h1>Content</h1> <p>Transition Edge Sensor (TES) signals from an attenuated coherent source. The mean photon number of the source goes from 2.26 to 7 082 112 (29 dB to 7 dB of attenuation).</p> <p> </p>
Raw data from "AC transport detection of magnetic transitions in small and granular samples"
<p>Raw data supporting the submitted manuscript "AC transport detection of magnetic transitions in small and granular samples". Additional information avaliable upon request.</p>
ScienceDex guides
Understand access before you commit
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.