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462 results for “Mutualisms”
Data for: Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks
<p>The persistence of mutualisms is paradoxical, as there are fitness incentives for exploitation. This is particularly true for plant-microbe mutualisms like arbuscular mycorrhizae (AM), which are promiscuously horizontally-transmitted. Preferential allocation by hosts to the best mutualist can stabilize horizontal mutualisms, however, preferential allocation is imperfect, with its fidelity likely depending upon the spatial structure of symbionts in plant roots. In this study, we tested AM mutualisms' dependence on two dimensions of spatial structure: the initial spatial association of fungi and the ease of fungal dispersal, through three complementary experiments. We found that fitness of the beneficial AM fungus increased when fungi were initially separate, while initial spatial mixing benefited the fitness of the non-beneficial fungus. These effects were strongest when dispersal was limited, and hosts could discriminate. Additionally, we found that changes in AM fungal proportional abundance induced by spatial structure in roots of a preferentially allocating host produced positive feedbacks on plant growth, showing that interactions between spatial structure and host choice can determine the direction of plant-soil feedbacks. Our results suggest that symbiont spatial structure within plant roots may act as an important modifier of plant preferential allocation and the dynamics of mycorrhizal mutualisms, with potentially cascading effects on plant-plant interactions.</p>
Identification of components associated with the operation of Mutual Aid Groups: a scoping review. Matriz.
<p>Base de datos del socping review titulado </p> <p>Identification of components associated with the operation of Mutual Aid Groups: a scoping review</p>
Raw Data to 'Experimental verification of the area law of mutual information in quantum field theory', arXiv:2206.10563
<p><strong>Absorption images representing the raw data for arXiv:2206.10563</strong></p> <p>'scan5722.zip' contains the raw data for figures 2 and 3.</p> <p>'scan5831.zip' and 'scan9617.zip' contain the raw data for figure 5, right and left, respectively.</p> <p>All three datasets, 9617, 5722, and 5831, are used to obtain the data points in figure 4, from low to high temperatures.</p> <p> </p> <p><strong>Scans 5722 & 5831</strong></p> <p>The absorption images are numbered consecutively.</p> <p>The first image for scans 5722 and 5831 is taken along the axial (longitudinal) direction with our 'longitudinal' imaging system after 10 ms time of flight (TOF) to measure the atom number balance between the two wells. The measurement is performed before ramping up the DW barrier. Two images are taken in each cycle: One shot with atoms ('1-atomcloud.tif') and a second image to record the intensity of the imaging beam without atoms ('1-withoutatoms.tif'). These two pictures are used to extract the atomic density (see the Matlab script).</p> <p>The second image for scans 5722 and 5831 is taken in the direction of the double-well (DW) separation with our 'transverse' imaging system. The measurement is performed before ramping up the DW barrier. The image is taken after 11.2 ms TOF.</p> <p>The subsequent images record the interference fringes for the different evolution times (again, always pairs '-atomcloud.tif' and '-withoutatoms.tif'). They are taken with our 'vertical' imaging system after 15.6 ms TOF. The imaging direction is perpendicular to the weakly confined direction of the clouds and the DW separation.</p> <p>The recorded evolution times for scan 5722 are -1.9 ms (right before ramping up the DW barrier), 0 ms (right after the DW barrier is ramped up), and then in steps of 2.5 ms until 65 ms. This means that '3-atomcloud.tif' corresponds to -1.9 ms, and '30-atomcloud.tif' corresponds to 65 ms. This completes the 'first repeat'. The next two shots, '31-atomcloud.tif' and '32-atomcloud.tif', belong to the 'second repeat' and are again taken with the 'longitudinal' & 'transverse' imaging systems, respectively. The picture '33-atomcloud.tif' is again taken with the 'vertical' imaging and corresponds to -1.9 ms. And so forth.</p> <p>In the same way, the pictures scan 5831 are ordered. The evolution times (in ms) for these two scans are:</p> <p>scan 5722: -1.9 from 0 to 65 (in steps of 2.5)</p> <p>scan 5831: -1.8, from 0 to 65 (in steps of 2.5)</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up. 0 is always right after the barrier was ramped up.</p> <p> </p> <p><strong>Scan 9617</strong></p> <p>This scan only contains images with the 'vertical' imaging system with 15.6 ms TOF. The evolution times (in ms) are as follows:</p> <p>scan 9617: -2.8, from 0 to 15 (in steps of 1.5), 22, 25, 28</p> <p>The first time always corresponds to the instant right before the DW barrier is ramped up. 0 is always right after the barrier was ramped up.</p> <p>This means, that '1-atomcloud.tif', '16-atomcloud.tif', '31-atomcloud.tif' and so on correspond to -2.8 ms, and '15-atomcloud.tif', '30-atomcloud.tif', '45-atomcloud.tif' and so on correspond to 28 ms.</p> <p> </p> <p><strong>Matlab script</strong></p> <p>In addition to the data, a Matlab script (calc_atomic_density.m) illustrates how to extract the two-dimensional atomic density from the absorption images. It contains all relevant parameters of the imaging systems.</p>
Dataset of "Interclonal mutually beneficial cooperation mediated by TGF-β1 enhances invasion of breast cancer cells"
<p>Original pictures from Figures 1A and 1B.</p> <p>Dataset from Figure 2-6 with data analysis (including Wound Healing assay, Transwell migration and invasion assay) on MCF7, MDA and H2122 AS cell lines</p>
1D-1V Vlasov-Poisson Simulations of Mutual Impedance Experiments for Strong Antenna Emission Amplitudes
<p>This dataset contains the outputs of numerical simulations performed to assess the impact of strong antenna emission amplitudes on the diagnostic performance of mutual impedance experiments. In the case of small emission amplitudes, the plasma response to the emission is linear. In the case of large emission amplitude, instead, non-linear wave-wave and wave-particle interactions are triggered. Using the outputs contained in this dataset, we investigated how such wave-wave and wave-particle interactions perturb mutual impedance experiments.</p> <p>Numerical model:<br> The outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson system of equations. The scheme used to solve the model is the one developed by Mangeney, et al. (2002). <em>A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics</em>, (doi: <a href="https://doi.org/10.1006/jcph.2002.7071">https://doi.org/10.1006/jcph.2002.7071</a>). The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010),<em> Vlasov-Poisson simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind, Journal of Geophysical Research (Space Physics), 115, 6106 </em> <em>(</em>doi: <a href="https://doi.org/10.1029/2009JA014969">https://doi.org/10.1029/2009JA014969</a> <em>)</em> <em>.</em></p>
Comparison of allosteric signaling in DnaK and BiP using mutual information between simulated residue conformations
<p>Molecular Dynamics trajectories of the Hsp70 chaperones DnaK and BiP. System configurations include DnaK or BiP bound to ATP, ATP exchanged to ADP, or the NRLLLTG peptide.</p>
Data and Codes: Marmosets mutually compensate for differences in rhythms when coordinating vigilance
<p>Data and codes accompanying the article titled "Marmosets mutually compensate for differences in rhythms when coordinating vigilance".</p>
State-resolved mutual neutralization of O$^+$ with $^1$H$^-$ and $^2$H$^-$ at collision energies below 100 meV
<p>The data files found here contain the data as obtained and displayed in : "State-resolved mutual neutralization of O$^+$ with $^1$H$^-$ and $^2$H$^-$ at collision energies below 100 meV" published in Physical Review A (2024). Each file contains an explanatory header. Header lines start with #.</p>
Foliar phosphorus concentration modulates the defensive mutualism of an endophytic fungus in a perennial host grass
<p>Grasses hosting <em>Epichloë</em> endophytes are protected against herbivores due to the production of various fungal alkaloids. Previous research has found that high foliar phosphorus concentrations reduce the level of the alkaloid ergovaline, thereby reducing the endophyte-mediated herbivore resistance. Yet, the impact of phosphorus on ergovaline biosynthesis versus its influence on endophyte growth and synthesis of other fungal alkaloids remains unresolved. Our objective was to elucidate these relationships. We grew endophyte-symbiotic and non-symbiotic <em>Festuca arundinacea</em> plants and fertilized them with different doses of phosphorus. Later, half of the plants from each treatment were challenged with larvae of the generalist chewing insect <em>Spodoptera frugiperda</em>. We assessed the relationships between foliar phosphorus levels, fungal mycelium, and alkaloid concentrations, as well as their impacts on larvae performance, herbivore-caused damage, and plant biomass. Endophyte mycelial biomass in plant tissue was found to be independent of foliar phosphorus concentration. The alkaloids lolines and peramine showed a linear relationship with mycelial biomass but no correlation with foliar phosphorus. Surprisingly, high ergovaline concentrations were positively associated with an interaction between endophyte mycelial biomass and foliar phosphorus concentration. Although herbivory increased loline concentration, only high concentrations of ergovaline and peramine were related to reduced <em>S. frugiperda</em> larvae weight gain. However, endophyte presence did not reduce herbivory damage to plants. Contrary to expectation, we did not find a negative but a positive association between concentrations of foliar phosphorus and ergovaline alkaloid, through its interaction with endophyte mycelial biomass. Alternatively, our findings suggest that phosphorus plays a crucial role in modulating the <em>Epichloë</em>-mediated defensive mutualism, primarily through its effects on ergovaline rather than on endophyte concentration or production of other alkaloids.</p>
Data supplementing Lichtenberg et al. (2018) Costs and benefits of alternative food handling tactics help explain facultative exploitation of pollination mutualisms. Ecology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2018) Costs and benefits of alternative food handling tactics help explain facultative exploitation of pollination mutualisms. Ecology 99: 1815-1824.</p> <p>https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecy.2395</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Data from: Insights into the genomics of clownfish adaptive radiation: genetic basis of the mutualism with sea anemones
<p>Genomic data of 9 clownfish (<em>Amphiprion akallopisos, A. bicinctus, A. melanopus, A. nigripes, A. ocellaris, A. preideraion, A. polymnus, A. sebae, Premnas biaculeatus</em>) species and 1 damselfish species (<em>Pomacentrus moluccensis</em>), presented in " Insights into the genomics of clownfish adaptive radiation: genetic basis of the mutualism with sea anemones".</p> <p>For each species, the following files are available:</p> <p><strong>Species_genome.fasta</strong>: Genome assembly of the species in fasta format. The genome was obtained using Illumina paired-end reads, following a reference-based method. For more information, please refer to the publication</p> <p><strong>Species_genome.Annotation.gff3</strong>: Structural genome annotation of the species, in gff3 format. Structural annotation was obtained with a combination of ab initio and RNAseq-data based approaches. Fina gene models were obtained with MAKER2. For more information, please refer to the publication</p> <p><strong>Species_genome.proteins.uniprot.fa</strong>: Predicted protein sequences from the genome assembly of the species, in fasta format. Each protein is annotated with the best blast hit with SwissProt database. For more information, please refer to the publication</p> <p><strong>Species_genome.transcripts.uniprot.fa</strong>: Predicted coding sequences (CDS) from the genome assembly of the species, in fasta format. Each CDS is annotated with the best blast hit with SwissProt database. For more information, please refer to the publication</p> <p>For <em>Amphiprion ocellaris</em> data, additional to the files presented above, we provided as well the secondary results of the genome assembly using a de novo strategy (Aocellaris_GenomeDeNovo.fasta), its structural annotation (Aocellaris_GenomeDeNovo.Annotation.gff3), and its predicted protein (Aocellaris_GenomeDeNovo.proteins.uniprot.fa) and CDS (Aocellaris_GenomeDeNovo.transcripts.uniprot.fa) sequences. For more information, please refer to the publication</p>
Mutually validated Tilt Angle Dataset
<h1>Mutually-Validated Tilt Angle Dataset</h1> <p>This dataset provides reliable sunspot group/active region(AR) tilt angle data and several other parameters from 1996 to 2018.</p> <h1>Overview</h1> <p>The dataset is generated by mutually validating two existing datasets: (1) <strong>A-live-homogeneous-database-of-solar-active-regions</strong> by Ruihui Wang, Jie Jiang and Yukun Luo (<strong>WJL</strong>), and (2) <strong>Debrecen Photoheliographic Data sunspot catalogue</strong> by Baranyi, T., Győri, L. and Ludmány, A. (<strong>DPD</strong>). The original datasets can be downloaded via their websites: <a href="https://github.com/Wang-Ruihui/A-live-homogeneous-database-of-solar-active-regions">A-live-homogeneous-database-of-solar-active-regions</a> and <a href="http://fenyi.solarobs.epss.hun-ren.hu/en/databases/DPD/">Debrecen Photoheliographic Data</a></p> <p>The mutual validation is automatically accomplished by Python. The code, mutual<strong>_validation.py</strong>, is attached. For a more detailed description of the mutual validation method, see Lang Qin, Jie Jiang, Ruihui Wang, Mutual Validation of Datasets for Analyzing Tilt Angles in Solar Active Regions(APJ under review)</p> <div> <h1>Data Description</h1> </div> <p>The mutually validated datset is in the file <strong>mutually_validated_tilt_angle_dataset.xlsx</strong>. There are 14 columns, each contains a type of parameter. The meanings of each header are as follows:</p> <blockquote> <p>date_dpd: date of observation in DPD dataset</p> <p>lat_dpd: heliographic latitude of the sunspot group</p> <p>lon_dpd: (Carrington) heliographic longitude of the sunspot group</p> <p>area_dpd: the total area of the sunspot group in μMSH</p> <p>tilt_dpd: the tilt angle of the sunspot group</p> <p>date_wjl: date of observation in WJL dataset</p> <p>lat_wjl: heliographic latitude of the active region</p> <p>lon_wjl: (Carrington) heliographic longitude of the active region</p> <p>area_wjl: the total area of the active region in μMSH</p> <p>flux_wjl: the total unsigned magnetic flux of the active region in Mx</p> <p>tilt_wjl: the tilt angle of the active region</p> <p>number: the NOAA number</p> <p>CR: the Carrington rotation number of the sunspot group/active region</p> <p>consistency: the marker of whether the two tilt angle values are close enough to be considered consistent. "1" means consistent and "0" means inconsistent</p> </blockquote> <div> <h1>Author</h1> </div> <p>Lang Qin, Jie Jiang, Yukun Luo</p>
Data from: Ecological genomics of mutualism decline in nitrogen-fixing bacteria
Anthropogenic changes can influence mutualism evolution; however, the genomic regions underpinning mutualism that are most affected by environmental change are generally unknown, even in well-studied model mutualisms like the interaction between legumes and their nitrogen (N)-fixing rhizobia. Such genomic information can shed light on the agents and targets of selection maintaining cooperation in nature. We recently demonstrated that N-fertilization has caused an evolutionary decline in mutualistic partner quality in the rhizobia that form symbiosis with clover. Here population genomic analyses of N-fertilized versus control rhizobium populations indicate that evolutionary differentiation at a key symbiosis gene region on the symbiotic plasmid (pSym) contributes to partner quality decline. Moreover patterns of genetic variation at selected loci were consistent with recent positive selection within N-fertilized environments, suggesting that N-rich environments might select for less-beneficial rhizobia. By studying the molecular population genomics of a natural bacterial population within a long-term ecological field experiment, we find that: 1) the N environment is indeed a potent selective force mediating mutualism evolution in this symbiosis, 2) natural variation in rhizobium partner quality is mediated in part by key symbiosis genes on the symbiotic plasmid, and 3) differentiation at selected genes occurred in the context of otherwise recombining genomes, resembling eukaryotic models of adaptation.
Priority effects alter interaction outcomes in a legume-rhizobium mutualism
Priority effects occur when the order of species arrival affects final community structure. Mutualists often interact with multiple partners in different orders, but if or how priority effects alter interaction outcomes is an open question. In the field, we paired the legume Medicago lupulina with two nodulating strains of Ensifer bacteria that vary in nitrogen-fixing ability. We inoculated plants with strains in different orders and measured interaction outcomes. The first strain to arrive primarily determined plant performance and final relative abundances of rhizobia on roots. Plants that received effective microbes first and ineffective microbes second grew larger than plants inoculated with the same microbes in opposite order. Our results show that mutualism outcomes can be influenced not just by partner identity, but by interaction order. Furthermore, hosts receiving high-quality mutualists early can better tolerate low-quality symbionts later, indicating priority effects may help explain the persistence of ineffective symbionts.
Observation of mutual extinction and transparency in light scattering
<p>The basic publication is:<br> A. Rates, A. Lagendijk, O. Akdemir, A.P. Mosk, and W.L. Vos, "Observation of mutual extinction and transparency in light scattering", Phys. Rev. A <strong>104</strong>, 043515. DOI: 10.1103/PhysRevA.104.043515.<br> <br> We have uploaded to the Zenodo database all data enabling everyone to reuse our data, and to reproduce all the figures of our paper.</p> <p>The upload contains the file "Metadata.txt" explaining the content of the upload.</p>
Sets of mutually similar public GitHub repositories (October 2016)
<p>The format is JSON, the list of lists. Each list is the group of very similar repositories (Weighted Jaccard Similarity threshold 0.8~0.9).</p>
Does ant-plant mutualism have spillover effects on the non-partner ant community?
<p>Mutualism benefits partner species and theory predicts these partnerships can affect the abundance, diversity, and composition of partner and non-partner species.<br> We used 16 years of monitoring data to determine the ant partner species of tree cholla cacti (<em>Cylindriopuntia imbricata</em>), which reward ants with extrafloral nectar in exchange for anti-herbivore defense. This long-term data revealed one dominant ant partner (<em>Liometopum apiculatum</em>) and two less common partners (<em>Crematogaster opuntiae</em> and <em>Forelius pruinosus</em>. We then used short-term characterization of the terrestrial ant community via pitfall trapping to sample partner and non-partner ant species across ten plots of varying cactus density. We found that the dominant ant partner tended a higher proportion cacti in plots of higher cactus density, and was also found at higher occurrence within the pitfall traps in higher density plots, suggesting strong positive feedbacks that promote ant partner occurrence where plant partners are available. Despite the strong association and increased partner occurrence, ant community-wide effects from this mutualism appear limited. Of the common ant species, the occurrence of a single non-partner ant species was negatively associated with cactus density and with the increased presence of <em>L. apiculatum</em>. Additionally, the composition and diversity of the ant community in our plots were insensitive to cactus density variation, indicating that positive effects of the mutualism on the dominant ant partner did not have cascading impacts on the ant community. This study provides novel evidence that exclusive mutualisms, even those with strong positive feedbacks, may be limited in the scope of their community-level effects.</p>
Biotic and abiotic data for: European Benthic survey of seagrass-lucinid mutualism
<p>Coastal ecosystem functioning often hinges on habitat-forming foundation species that engage in positive interactions (e.g. facilitation and mutualism) to reduce environmental stress. Seagrasses are important foundation species in coastal zones but are rapidly declining with losses typically linked to intensifying global change-related environmental stress. There is growing evidence that loss or disruption of positive interactions can amplify coastal ecosystem degradation as it compromises its stress-mitigating capacity. Multiple recent studies highlight that seagrass can engage in a facultative mutualistic relationship with lucinid bivalves that alleviate sulphide toxicity. So far, however, the generality of this mutualism, and how its strength and relative importance depend on environmental conditions, remains to be investigated. Here we study the importance of the seagrass-lucinid mutualistic interaction on a continental scale using a field survey across Europe. We found that the lucinid bivalve <em>Loripes</em> <em>orbiculatus</em> is associated with the seagrasses <em>Zostera</em> <em>noltii</em> and <em>Zostera</em> <em>marina</em> across a large latitudinal range. At locations where the average minimum temperature was above 1°C, <em>L. orbiculatus</em> was present in 79% of the <em>Zostera</em> meadows; whereas, it was absent below this temperature. At locations above this minimum temperature threshold, mud content was the second most important determinant explaining the presence or absence of <em>L. orbiculatus</em>. Further analyses suggest that the presence of the lucinids have a positive effect on seagrass biomass by mitigating sulphide stress. Finally, results of a structural equation model (SEM) support the existence of a mutualistic feedback between <em>L. orbiculatus</em> and <em>Z. noltii</em>. We argue that this seagrass-lucinid mutualism should be more solidly integrated into management practices to improve seagrass ecosystem resilience to global change as well as the success of restoration efforts.</p>
Climate and ant diversity explain the global distribution of ant-plant mutualisms
<p>Biotic interactions play an important role in shaping species geographic distributions and diversity patterns. However, the role of mutualistic interactions in shaping global plant diversity patterns remains poorly understood, particularly with respect to interactions with invertebrates. It is unclear how the nature of different mutualisms interacts with abiotic drivers and affects the distribution of mutualistic organisms. Here, we present a global-scale biogeographic analysis of three distinct ant-plant mutualisms, differentiating between plants bearing domatia, extrafloral nectaries (EFNs), and elaiosomes, based on comprehensive geographic distributions of ~19,000 flowering plants and ~13,000 ant species. Domatia and extrafloral nectaries involve indirect plant defences provided by ants, while elaiosomes attract ants to disperse seeds. Our results reveal distinct biogeographic patterns of different ant-plant mutualisms, with domatium- and EFN-bearing plant diversity decreasing sharply from the equator towards the poles, while elaiosome-bearing plants prevail at mid-latitudes. Present climate, especially mean annual temperature and precipitation, emerge as the strongest predictors of ant-associated plant diversity. In hot and moist regions, typically the tropics, the representation of EFN-bearing plants increases with the proportion of potential ant partners while domatium-bearing plants show no correlation with ants. In dry regions, plants with elaiosomes are strongly linked to interacting ant seed dispersers. Our results suggest that ants in combination with climate drive the spatial variation of plants bearing domatia, extrafloral nectaries, and elaiosomes, highlighting the importance of mutualistic interactions for understanding plant biogeography.</p>
1D-1V Vlasov-Poisson Simulations of Mutual Impedance Experiments in the presence of small-scale and large-scale plasma inhomogeneities - PART 3
<p>PART 1 IS FOUND AT <strong>https://doi.org/10.5281/zenodo.8118023</strong></p> <p>PART 2 IS FOUND AT <strong> https://doi.org/10.5281/zenodo.8119090</strong></p> <p> </p> <p>=====================================================================================================<br> Author : L. Bucciantini<br> Date : 05/07/2023<br> Laboratory : CNRS-LPC2E, Orléans (France)<br> =====================================================================================================</p> <p>Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will<br> find in it.</p> <p>In case you need help with using the dataset, or if you are interested in mutual impedance experiments,<br> do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu)</p> <p>=====================================================================================================<br> Topic:<br> This dataset contains the outputs of numerical simulations performed to assess<br> the impact of plasma inhomogeneities on the diagnostic performance<br> of mutual impedance experiments.</p> <p><br> Numerical model:<br> The outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson<br> system of equations. The scheme used to solve the model is the one developed<br> by Mangeney et al. (2002), A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071).<br> The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson<br> simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind,<br> Journal of Geophysical Research (Space Physics), 115, 6106 (2010)<br> -----------------------------------------------------------------------------------------------------<br> What is inside the dataset:</p> <p>The dataset is composed of 7 different mutual impedance measurements, each corresponding to one<br> directory (see below). The measurements (i.e. directory) correspond to small-scale plasma inhomogeneities at different<br> positions with respect to the mutual impedance antennas, or to a large-scale plasma inhomogeneity.</p> <p>Each directory contains a number of folders. Each folder corresponds to the emission of a signal at<br> different frequency.<br> -----------------------------------------------------------------------------------------------------<br> List of directories:</p> <p>(Note : each directory corresponds to one mutual impedance measurement)</p> <p>L : These outputs correspond to one mutual impedance measurement in<br> small antenna emission amplitude, which corresponds to a linear<br> plasma response to the emission.</p> <p>s_xx : Each of these outputs corresponds to one mutual impedance measurement in correspondance of<br> a small-scale plasma inhomogeneity at xx Debye lengths of distance from the emitting antenna<br> </p> <p>-----------------------------------------------------------------------------------------------------<br> List of folders inside the directories:</p> <p>Folders begin with the name "000" and have increasing index. Inside the same directory, each folder<br> represents a different simulation used to build the same mutual impedance measurement.</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders:</p> <p>density_e.npz : electron density inside the box, in function of time (tempo)</p> <p>density_p.npz : ion density inside the box, in function of time (tempo)</p> <p>E.npz : electric field in the box, in function of time (tempo)</p> <p>qrho.npz : electric potential in the box, in function of time (tempo)</p> <p>qrho_imposed.npz : electric charge imposed at the emitting antennas, in function of time (tempo)</p> <p>tempo.npz : time-vector for density, electric field, electric potential and charge vectors</p> <p>TEST_Luca.dat : parameters describing the characteristics of the simulated plasma box</p> <p><br> [<br> Note : the previous files can be opened as follows</p> <p>import numpy as np</p> <p>vector_file_name = np.load('file_name.npz') # Use these for the .npz files<br> characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1)</p> <p>]</p> <p><br> -------------------------------------------------------------------------------------------------------<br> Characteristics of the simulated plasma box (TEST_Luca.dat)</p> <p>nx : amount of spatial grid points</p> <p>xl : physical size of the spatial box, expressed in Debye length</p> <p>tt_w : time resolution for ion and electron density, electric field, electric potential and charge</p> <p>rap_m : ion-to-electron mass ratio</p> <p>R_p : ion-to-electron temperature ratio</p> <p>dt : time step used to evolve in time the numerical simulation</p> <p>emission : emission frequency</p> <p>power : amplitude of the electric charge imposed at the emitting antennas</p> <p><br> (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional<br> functionalities of the model. For the use of this dataset, they can be discarded.)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
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.