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849 results for “linear”
Data supporting "Non-hydrostatic, non-linear processes in the surf zone", by Martins et al., submitted to JGR-Oceans
<pre>This file describes the structure of the sub-surface pressure and surface elevation data used in the paper "Non-hydrostatic, non-linear processes in the surf zone", submitted by Martins et al. to Journal of Geophysical Research: Oceans. The data set is composed of a single .mat file, which contains all raw timeseries for the 52 bursts used in the paper. Metadata and description of the data structure and variables are provided in the structure directly. This data set is distributed under the Creative Commons Attribution 4.0 International license. </pre> <p>The collection of this data set was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/N019237/1, Waves in Shallow Water, awarded to Chris Blenkinsopp (University of Bath).</p>
Linear Registration of brain MRI using knowledge-based multiple intermediator libraries
<p>These are the dataset materials, including full data, resampled data, transformation matrices, experimental results and quantitative evaluation for the paper “Linear Registration of brain MRI using knowledge-based multiple intermediator libraries” that is submitted on the Journal of "Frontiers in Neuroscience".</p>
Dataset for "Linearity of natural versus laboratory‑imparted remanence demagnetization diagram and reliability of relative paleointensity estimation"
<p>Paleo- and rock magnetic data presented in "<strong>Linearity of natural versus laboratory‑imparted remanence demagnetization diagram and reliability of relative paleointensity estimation" by Yamazaki, T. and Li, J., Earth Planets Space (2025) 77:14</strong></p>
Support data for article "The concept of optimal planning of a linearly oriented segment of the 5G network"
<p>Support data for article</p> <p>V. Kovtun, K. Grochla, E. Zaitseva, and V. Levashenko, “The concept of optimal planning of a linearly oriented segment of the 5G network,” PLOS ONE, vol. 19, no. 4. Public Library of Science (PLoS), p. e0299000, Apr. 17, 2024. doi: 10.1371/journal.pone.0299000.</p> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
The databases of linear-recursive and "core" sequences
<p>These two files are <em>linres</em> and <em>core</em> databases of integer sequences related to my paper where I apply the algorithm <em>Diofantos</em> on them. They are also included in the related source code repository with the DOI:</p> <div> <pre>https://doi.org/10.5281/zenodo.13692310<br><br>I created this repository separate from the program code to make it easier to find.<br><br><em>linres</em> : linear_database_newbl.csv<br><em>core</em> : cores_test.csv</pre> </div>
Linear morphometric analysis data of lithic points from Lovedale, Free State, South Africa
<p>Linear morphometric analysis data of lithic points from the Middle Stone Age site of Lovedale and other Middle Stone Age localities in the Modder River basin, Free State, South Africa.</p>
Dataset and R script - Non-linear transcriptomic responses to compounded environmental changes across temperature and resources in a pest beetle, Callosobruchus maculatus
<p>This dataset contains data and R script for analysis on life history and transcriptomic responses to single dimensional changes in resource (chickpea-27<span>°</span>C) and temperature (cowpea-35<span>°</span>C) and multi-dimensional environmental changes in resource and temperature (chickpea-35<span>°</span>C) in a pest beetle, <em>Callosobruchus maculatus</em> (control treatment = cowpea-27<span>°</span>C). Dataset contains life history data collected in laboratory conditions<em> </em>(tab 1), logFC data (RNA-sequencing; Novogene Co. Ltd.) for Spearman rank correlation tests between treatments (tabs 2-4), read count data (RNA-sequencing; Novogene Co. Ltd.) for differential expression analysis using edgeR (R1-5 = Four samples at cowpea-27<span>°</span>C; R7-12 = Four samples at cowpea-35<span>°</span>C; R13-17 = Four samples at chickpea-27<span>°</span>C; R25-28 = Four samples at chickpea-35<span>°</span>C; tab 5) and edgeR output data for plotting in R (tab 6). </p>
Paper: "Sensitivity analysis for linear changes of the constraint matrix of a linear program" output
<p>Output generated from the experiments in the paper "Sensitivity analysis for linear changes of the constraint matrix of a linear program"</p>
Dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling"
<p>This dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling" submitted to the Journal of Chemical Information and Modeling.</p> <p>Table S 1: Chemicals with their experimental values of logKch , and values of logKow and<span> </span>logKaw used to calibrate chicken muscle protein-water 2p-LFER model.</p> <p>Table S 2: Chemicals with their experimental values of logKfish and values of logKow and<span> </span>logKaw used to calibrate fish muscle protein-water 2p-LFER model.</p> <p>Table S 3: Chemicals with their experimental values of logKBSA and values of logKow and<span> </span>logKaw used to calibrate bovine serum albumin-water 2p-LFER model.</p> <p>Table S 4: Chemicals with their experimental values of logKpw and values of logKow and<span> </span>logKaw used to calibrate combined chicken and fish muscle protein-water 2p-LFER model.</p> <p>Table S 5: Diversity of data for logKpw.</p> <p>Table S 6: Diversity of data for logKBSA.</p> <p>Table S 7: Comparison of Experimental and 2p-LFER Predicted Partition Coefficients for ionizable PFAS Compounds.</p> <p>Table S 8: List of neutral fluorotelomer PFAS Compounds.</p> <p>Table S 9: List of experimental in vivo and in vitro partitioning data for different tissues and species.</p> <p>Table S 10: List of experimental Milk-water partition coefficient and predicted values of Milk-water partitioning.</p> <p>Table S 11: Training set for logKpw</p> <p>Table S 12: Validation set for log Kpw</p> <p>Table S 13: Training set for log KBSA</p> <p>Table S 14: Validation set for log KBSA</p>
Data from: Non-linear responses of soil nematode community composition to increasing aridity
Aim: Increasing aridity under global change is predicted to have a profound impact on the structure and functioning of terrestrial ecosystems, yet we have poor understanding of how belowground communities respond. In order to understand the longer-term responses of different trophic levels in the soil food web to increasing aridity, we investigated the abundance, richness and community similarity of the soil nematode community along a 3200-km aridity gradient. Location: A transect across semi-arid and arid grasslands in Northern China, where the aridity ranges from 0.43 to 0.97. Time period: July and August 2012. Major taxa studied: Soil-borne Nematoda. Methods: We used Generalized Additive (Mixed) Models to analyze the abundance, richness and community similarity patterns of soil nematodes. We used Structural Equation Modelling (SEM) to disentangle the direct and indirect environmental drivers (aridity, soil and plant variables) of the nematode community. Results: The abundance, richness and similarity of nematode communities declined non-linearly with increasing aridity. The most pronounced decline in nematode richness and community similarity occurred under arid conditions (aridity > 0.80). However, the shape of response to aridity differed among nematode feeding groups. Under arid conditions, the abundance and richness of bacterial feeders were less sensitive to changes in aridity than fungal feeders. The SEM analysis revealed that nematode community responses to aridity were not mediated via changes in plant and soil variables, but rather were directly affected by aridity. Main conclusions: Our results show that in mesic grasslands increasing aridity primarily caused decline in nematode abundance, whereas increasing aridity in xeric grasslands led to loss of nematode diversity. The non-linear responses of nematodes to aridity could result in non-linear shifts in ecosystem functioning as well, because soil nematodes operate at various trophic levels in the soil food web, thereby influencing the performance of plants, soil biodiversity and biogeochemical cycling.
Geometries for 'Linear-scaling implementation of multilevel Hartree-Fock theory'
<p>Geometry files (.xyz format) used in the paper '<em>Linear-scaling implementation of multilevel Hartree-Fock theory</em>'</p>
BISICLES Pine Island Glacier simulations with linear friction
<p>Model simulations of the ice sheet model BISICLES for 100 years on a set of topographies, sampled form a GP (plus BM2 and BedMachine) with linear Weertman friction law.</p> <p>See chapter 5 of <a href="https://doi.org/10.21954/ou.ro.0001223d">https://doi.org/10.21954/ou.ro.0001223d</a> for more information</p>
Data: Rapid evolution of unimodal but not of linear thermal performance curves in Daphnia magna
<p>Species may cope with warming through both rapid evolutionary and plastic responses. While thermal performance curves (TPCs), reflecting thermal plasticity, are considered powerful tools to understand the impact of warming on ectotherms, their rapid evolution has been rarely studied for multiple traits. We capitalized on a 2-year experimental evolution trial in outdoor mesocosms that were kept at ambient temperatures or heated 4 °C above ambient, by testing in a follow-up common garden experiment, for rapid evolution of the TPCs for multiple key traits of the water flea Daphnia magna. The heat-selected Daphnia showed evolutionary shifts of the unimodal TPCs for survival, fecundity at 1st clutch and intrinsic population growth rate toward higher optimum temperatures, and a less pronounced downward curvature indicating a better ability to keep fitness high across a range of high temperatures. We detected no evolution of the linear TPCs for somatic growth, mass and development rate, and for the traits related to energy gain (ingestion rate) and costs (metabolic rate). As a result, also the relative thermal slope of energy gain vs. energy costs did not vary. These results suggest the overall (rather than per capita) top-down impact of D. magna may increase under rapid thermal evolution.</p>
Data for LEELO-LA (Long-term Energy Expansion Linear Optimization - Latin America)
<p>This repository contains the data for LEELO-LA (Long-term Energy Expansion Linear Optimization - Latin America)</p>
Linearly and Nonlinearly Implicit Schemes for Energy-Stable Simulation of String Vibrations with Collisions: Refinement, Analysis, and Comparison
<p>Sound examples accompanying the manuscript submitted to the Journal of Sound and Vibration</p>
Immediate and long-term genetic consequences of linear transport infrastructure: Can fauna crossing mitigate its cost?
<p><span>The genetic consequences of the subdivision of populations are regarded as significant to long-term evolution, and research has shown that the scale and speed at which this is now occurring is critically reducing the adaptive potential of most species which inhabit human-impacted landscapes. Here, we provide a rare, and to our knowledge, the first analysis of this process while it is happening and demonstrate a method of evaluating the effect of mitigation measures such as fauna crossings. We did this by using an extensive genetic dataset collected from a koala population which was intensely monitored during the construction of linear transport infrastructure which resulted in the subdivision of their population. First, we found that both allelic richness and effective population size decreased through the process of population subdivision. Second, we predicted the extent to which genetic drift could impact genetic diversity over time and showed that after only 10 generations the resulting two subdivided populations could experience between 12–69% loss in genetic diversity. Lastly, using forward simulations we estimated that a minimum of 8 koalas would need to disperse from each side of the subdivision per generation to maintain genetic connectivity close to zero but that 16 koalas would ensure that both genetic connectivity and diversity remained unchanged. </span><span>These results have important consequences for the genetic management of species in human-impacted landscapes by showing which genetic metrics are best to identify immediate loss in genetic diversity and how to evaluate the effectiveness of any mitigation measures.</span></p>
Data for Diffusion phase-imaging in anisotropic media using non-linear gradients for diffusion encoding
<p>Diffusion MRI data for experiments on anisotropic synthetic fibre phantom and isotropic agar phantom. </p>
Equilibrated Kremer-Grest polymer melts of M=1000 linear chains with Z=200 entanglements for varying chain stiffness
<p>Kremer-Grest model polymer melts of highly entangled linear chains. Each melt has approximately 1000 chains of Z=200 entanglements each. Systems have been generated for integer and half-integer stiffness kappa=-2.0 to 6.0. System sizes range from 25M to 2M beads.</p> <p>For details regarding the equilibration process and the Kremer-Grest polymer model see C. Svaneborg & R. Everaers ""Multiscale equilibration of highly entangled isotropic model polymer melts" J. Chem. Phys. 158, 054903 (2023) <a href="https://doi.org/10.1063/5.0123431">https://doi.org/10.1063/5.0123431</a></p> <p>Filenames denote the kappa<value> used when equilibrating the melt as well as the number of entanglements Z<number> and the number of molecules M<number>. The files are in ASCII format in the format of a LAMMPS data file. (https://lammps.sandia.gov) The semantics is self-explanatory, sections contains id, molecule, unwrapped coordinates of all beads, as well as bond and angular interactions between all beads.</p> <p>We acknowledge that part of the results of this research was obtained using the PRACE Research Infrastructure resource Joliot-Curie SKL based in France at GENCI@CEA. Computing facilities were provided by the eScience Center at the University of Southern Denmark and financed by the Faculty of Science.</p> <p>Please cite as:</p> <p>@article{MultiscaleEquilibrationHighlyEntangledIsotropicModelPolymerMelts,<br> author = {Svaneborg,Carsten and Everaers,Ralf },<br> title = {Multiscale equilibration of highly entangled isotropic model polymer melts},<br> journal = {J. Chem. Phys.},<br> volume = {158},<br> number = {5},<br> pages = {054903},<br> year = {2023},<br> doi = {10.1063/5.0123431},</p> <p> URL = {https://doi.org/10.1063/5.0123431}</p> <p>}</p> <pre>@misc{EquilibratedKGMeltsZ200, author = {Svaneborg,Carsten and Everaers,Ralf}, title = {Equilibrated Kremer-Grest polymer melts of M=1000 linear chains with Z=200 entanglements for varying chain stiffness.}, month = feb, year = 2023, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.7034881}, url = {https://doi.org/10.5281/zenodo.7034881} }</pre>
Python code generating the data of figures 2, 3, 4, 5 and 6 of the manuscript: The evolution of cooperation in the unidirectional linear division of labour of finite roles
<p>The evolution of cooperation is an unsolved mystery, which we see in many social and biological systems. In the study titled "The evolution of cooperation in the unidirectional linear division of labour of finite roles", we investigate under which sanction systems and how the evolution of cooperation happens in the linear division of labour. </p> <p>This python code has been used to produce the results of Figures 2, 3, 4, 5, and 6 of the manuscript. This code shows the evolution of cooperation among the population of various different groups which have different roles to play in the linear division of labour, on the basis of numerical analysis of a partial differential equation system, which originates from the replicator equations used in the evolutionary game theory. We find the locally stable equilibria using this code, which shows the ultimate results of the dynamics in the system under given parameters. Figures 3, 5, and 6 are direct products of the code, showing the dynamics of a system, and figures 2 and 4 are the end results of those dynamics. </p> <p>We found that in a social dilemma situation, cooperation never evolves in the system without punishment. However, with sanction systems by introducing a suitable amount of punishment, while having a suitable findability of the defector, and a suitable initial population structure, cooperation can evolve. These results can be found with this code. We have no legal or ethical concerns regarding this data as this is a numerical analysis based on theoretical equations. </p>
Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p> - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy. </p> <p><br> Figure_3:</p> <p>Panel A:<br> <br> - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br> - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br> - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br> - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p> - Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br> - Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br> - Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p> - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p> - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br> - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br> - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p> - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br> - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br> - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br> - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br> </p> <p>Figure_5:<br> <br> Panel_C: </p> <p> - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br> - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p> - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br> - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p> - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br> - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br> - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p> - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br> - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br> - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br> - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br> - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br> - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p> - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br> - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br> - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br> - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p> - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system. </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br> </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.