Find research datasets worth reusing

opencc-by-4.0Sep 2019View details →

PIC data for 2019 JGR paper (An event study of simultaneous earthward and tailward bursty fast flows in the Earth's mid-tail)

The PIC data and an IDL script to read the data.

opencc-by-4.0Oct 2019View details →

SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019,

SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019. This compliments the data set at doi: 10.17863/CAM.30358 (copy these SPAWN files here into relevant folder at histograms_toc_figs_1_2/H2O_3_ccpVDZ/).

opencc-by-4.0Dec 2023View details →

Results of 16000 simulations in "Voronoids: Fast and Ordered Swarm Coordinated Motion"

The (CSV) table DataSet contains the aggregate results of the 16000 following simulation cases (one per line), defined by the first ten columns: A) ID: # case identifier B) n=10,20,40,80,160 # number of robots C) S2M=0,0.5 # Stall-to-Maximum, 0 for differential-drive and quadrotor-like robots, 0.5 for fUAV-like robots D) PF=0.25,0.5,0.75,1 # Prudence Factor, max. velocity is PF*r/s (+/- 10%), where r is the communication factor, and s is one second E) OF=1,2 # Overpopulation Factor F) T=0.25,0.5,0.75,1 # high-level control period (+/- 10%) G) FF=1 # Feed-Forward (True or False) H) CC=1,3 # Connectivity Control: 0=None,1=NN,2=MST,3=IMRG,4=Voronoi I) scn=1,2,3,4,5 # Scenario J) rands=0,1,2,3,4 # random seed   Two auxiliary columns are added: K) r # communication radius (the arena axis are [-16,16,-9,9]) - physically, r=100 m, for instance, so this parameter defines the space scale of the simulation L) PFxT # Prudence Factor multiplied by control period, fraction of r that, at max. velocity,  the robot advances without high-level control   From each simulation case, the following aggreagate results, or indices, are obtained:   The principal Indices of Quality, given in the following four columns: M) IQA # IQ Area coverage: time-average ratio of uncovered area N) IQN # IQ Nearest-neighbor distance uniformity: time-average of average difference of distances to nearest neighbor wrt. average distance, divided by r O) IQC # IQ collisions: ratio of robots lost by collision P) IQX # IQ connectedness: ratio of robots lost by disconnection   Additional indices are given in the following four columns: Q) minD # minimum distance from any robot to anything else during the whole simulation, divided by r R) IQV # IQ Velocity: average advance velocity of robots, divided by max. velocity (approx. PF*r/s) S) SpenTime # time to complete the mission, including 3 s for initial deployment T) SimDuration # time to complete the simulation (in a single core of our cluster)

Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 4/4

<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118 <h3>Contents</h3> Trajectories of positions and forces  with corresponding input files of systems with 550 water molecules on 2D materials: <ul> <li>MoS2/water</li> <li>MoS2/MoS2/water</li> <li>graphene/MoS2/water</li> <li>MoS2/graphene/water</li> <li>MoS2/hBN/water</li> <li>hBN/MoS2/water</li> <li>graphene/water</li> <li>hBN/water</li> </ul>

Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 3/4

<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118 <h3>Contents</h3> Trajectories of positions and forces  with corresponding input files of systems with 550 water molecules on 2D materials: <ul> <li>graphene/graphene/water</li> <li>hBN/hBN/water</li> <li>graphene/hBN/water</li> <li>hBN/graphene/water</li> </ul>

Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 1/4

<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118 <h3>Contents</h3> Trajectories of positions and forces  with corresponding input files of systems with 120 water molecules confined between 2D materials: <ul> <li>graphene/water/graphene</li> <li>hBN/water/hBN</li> <li>graphene/graphene/water/graphene/graphene</li> <li>hBN/hBN/water/hBN/hBN</li> <li>graphene/hBN/water/hBN/graphene</li> <li>hBN/graphene/water/graphene/hBN</li> </ul>

Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials, part 2/4

<h2>Supplementary data for paper "Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials" </h2> Maria Bilichenko, Marcella Iannuzzi, and Gabriele Tocci, ACS Nano, 2024, DOI: 10.1021/acsnano.4c05118 <h3>Contents</h3> Trajectories of positions and forces  with corresponding input files of systems with 120 water molecules confined between 2D materials: <ul> <li>MoS2/water/MoS2</li> <li>MoS2/MoS2/water/MoS2/MoS2</li> <li>graphene/MoS2/water/MoS2/graphene</li> <li>MoS2/graphene/water/graphene/MoS2</li> </ul>

opencc-by-4.0Apr 2024View details →

FRB Mock Catalog and Reproduction Package for "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"

<h3>Quickstart: FRB Mock Catalog</h3> A simulated 1-day catalog of one-off FRBs, that allows users to access the FRB population without installing the entire frbpoppy package. Download and unzip 1_Day_FRB_Sky_on_Earth.txt.zip (175 MB). This human and machine readable file contains 3.5E6 FRBs that are brighter than 0.01 Jy ms, the best limit in one-off FRB detection currently. The simulated catalog is produced by the perfect telescope in frbpoppy, free of selection effects, that observed 4pi of sky for 24 hrs, with minimum detectable fluence 0.01 Jy ms, for the best-fit no-delay SFR model. This file can be read using the accompanying jupyter notebook "starting_with_mock_catalog.ipynb". If you use this, please cite Wang & van Leeuwen 2024 (A&A), <a href="https://doi.org/10.1051/0004-6361/202450673">https://doi.org/10.1051/0004-6361/202450673</a> <h3>Reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"</h3> ReproductionPackage.zip is a basic reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin" by Wang & van Leeuwen (2024).  * arXiv: [<a href="https://arxiv.org/abs/2405.06281">2405.06281</a>]   * DOI: [<a href="https://doi.org/10.1051/0004-6361/202450673">10.1051/0004-6361/202450673</a>]  <h3>Installation</h3> First pull or download and `frbpoppy` from <https://github.com/TRASAL/frbpoppy>. Then download `ReproductionPackage.zip` and extract it starting in the frbpoppy/ base directory. The scripts to produce the Figures are found in folder `frbpoppy/tests/markov_chain_monte_carlo/`. The data used for these Figures resides in folder `frbpoppy/data/populations/mcmc/`. <h3>Software</h3> The methods and software packages used to produce the results are listed in the paper (including links to the relevant publications and/or packages):  FRBPOPPY: <https://github.com/TRASAL/frbpoppy>  TRASAL:   <https://github.com/TRASAL> <h3>Raw Data</h3> The data are publicly available at  https://www.wis-tns.org/

opencc-by-sa-4.0Sep 2024View details →

Dataset: Asymptotic-state prediction for fast flavor transformation in neutron star mergers

These data accompany the paper "Asymptotic-state predictions for fast flavor transformations in neutron star mergers" by S. Richers, J. Froustey, S. Ghosh, F. Foucart, and J. Gomez. The data are sorted into three directories: In the datasets, spacetime components are generally ordered as: 0 = x 1 = y 2 = z 3 = t Neutrino/antineutrino indices are ordered as: 0 = neutrino 1 = antineutrino Flavor indices are ordered as: 0 = e 1 = mu 2 = tau #==========# # Emu_data # #==========# Contains four files, each containing the initial and final results of simulations of neutrino quantum kinetics. M1NuLib-2016_5ms_rl0.h5: extracted from refinement level 0 of a 1.2Msun-1.2Msun NS merger simulation at 5ms after merger (https://doi.org/10.1103/PhysRevD.94.123016). M1NuLib_3ms_rl1.h5: extracted from refinement level 1 of a 1.3Msun-1.4Msun NS merger at 3ms after merger (https://arxiv.org/abs/2407.15989) M1NuLib_7ms_rl1.h5: extracted from refinement level 1 of a 1.3Msun-1.4Msun NS merger at 7ms after merger (https://arxiv.org/abs/2407.15989) random: randomly generated initial conditions as described in the manuscript. Contents: F4_initial(1|ccm): [simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors] The initial number density four-flux of each species in units of 1/cm^3. F4_final(1|ccm): [simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors] The final number density four-flux of each species in units of 1/cm^3, averaged in time following the procedure indicated in the accompanying paper. F4_final_stddev(1|ccm): [simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors] Not used in the manuscript. The standard deviation from the distribution of F4 that the final answer is averaged over. directorynames: [simulation index] A string for each simulation index indicating the directory the calculation corresponds to. Used in debugging. growthRate(1|s): [simulation index] Not used in the manuscript. Measured growth rate of the flavor off-diagonal components of the number density in s^-1. N_offdiag ~ e^(w t), where w is the growth rate and t is the time. nf: scalar Assumed number of flavors. Should be 3 for all datasets included here. xplot: [simulation index, time index] t/t_saturation. Used in creating Figure 2 of the manuscript. y0plot: [simulation index, time index] N_ee(t) / N_ee(0). Used in creating Figure 2 of the manuscript. y1plot: [simulation index, time index] N_offdiag_mag(t) / Ntot. Used in creating Figure 2 of the manuscript. #===========# # SpEC_data # #===========# Contains snapshots of grid quantities extracted from neutron star merger simulations. The file M1NuLib_2016_5ms_rl0.h5 contains data from the 1.2Msun-1.2Msun simulation of https://doi.org/10.1103/PhysRevD.94.123016. All others contain data from the 1.3Msun-1.4Msun M1-NuLib simulation of https://doi.org/10.48550/arXiv.2407.15989. The (3ms, 5ms, 7ms) part of the filename indicates how much time after merger the snapshot was taken. The (rl0, rl1, rl2, rl3) part of the filename indicates which refinement level the data are extracted from. NaNs in the data indicate that that cell was covered by a finer refinement level in the simulation. Radiation quantities are Lorentz transformed into an orthonormal tetrad comoving with the background fluid, and are rotated such that the net ELN flux is in the z direction. Each dataset contains: J_{e,a,x}(erg|ccm) - energy density of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}, where "x" contains the sum of all four heavy species, in units if erg/cm^3. Indexed by spatial position of the grid cell: [i,j,k] fn_{e,a,x}(1|ccm) - number flux of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}, where "x" contains the sum of all four heavy species, in units if 1/cm^3. Indexed by direction and spatial position of the grid cell: [xyz, i,j,k] minerbo_Z{e,a,x} - the Z parameter of the maximum entropy closure at each point for {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}. Dimensionless. n_{e,a,x}(1|ccm) - number diensity of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos} in units of 1/cm^3. Indexed by spatial position of the grid cell: [i,j,k] {x,y,z}(cm) - coordinates of the centers of each grid cell in units of cm. Indexed by spatial position of the grid cell: [i,j,k] rho(g|ccm) - background comoving matter density in units of g/cm^3. Indexed by spatial position of the grid cell: [i,j,k] T(MeV) - background comoving matter temperature in units of MeV.  Indexed by spatial position of the grid cell: [i,j,k] Ye - background electron fraction (dimensionless).  Indexed by spatial position of the grid cell: [i,j,k] fluxfac_{e,a,x} - flux factor of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}. Dimensionless. crossing_discriminant - a crossing exists if this number is larger than 0. Computed using the following, based on Equation 16 in https://doi.org/10.1103/PhysRevD.106.083005. Indexed by spatial position of the grid cell: [i,j,k] a = gamma**2 + alpha**2 b = -2. * gamma * eta c = eta**2 - alpha**2 discriminant = (b**2 - 4.*a*c) / (2.*a)**2 hasCrossing = (discriminant >= 0) deltaCrossingAngle - width of the ELN crossing based on Equation 16 in https://doi.org/10.1103/PhysRevD.106.083005 in units of radians. Indexed by spatial position of the grid cell: [i,j,k] {nue,anue}_absrate(1|s) - absorption rate for {electron neutrinos, electron antineutrinos} in units if 1/s. Indexed by spatial position of the grid cell: [i,j,k] g{xx,yy,zz} - diagonal components of the metric tensor (dimensionless). Indexed by spatial position of the grid cell: [i,j,k] #===========# # ML_models # #===========# Contains the ML models used in the accompanying paper. In addition, there is an example script that uses the Rhea code to generate the relevant databases. Running the script requires the Rhea directory to be in the Python path. The Rhea code contains example Python and C++ code to use a trained model. Expected Rhea code: Snapshot at 10.5281/zenodo.13675320 https://github.com/srichers/Rhea commit 68b7dc69d8fa33c7f5de1d8bec0790496f282d80

Phyloformer: Fast, accurate and versatile phylogenetic reconstruction with deep neural networks

This record is composed of:  <ul> <li>The <code>results.tar.gz</code> file which  contains all the output files necessary to reproduce the figures and tables from the linked paper</li> <li>The 3 datasets used to fine tune different versions of Phylofofmer: <ol> <li><code>cherry_fine_tune.tar.xz</code> used to fine tune Phyloformer on the CherryML model</li> <li><code>LG_fine_tune_mre.tar.xz</code> used to fine tune Phyloformer on LG+GC data with an MRE loss</li> <li><code>pastek_fine_tune.tar.xz</code> used to fine tune Phyloformer on the SelReg model</li> </ol> </li> <li>The <code>paper_test_sets.tar.xz</code> file contains the test sets used to generate data in <code>results.tar.gz</code>, with simulated tree/msa pairs and trees inferred by different methods</li> </ul>

Dataset supplementing journal article "Design of an FPGA-Based Controller for Fast Scanning Probe Microscopy" in Sensors 2024

Dataset supplementing journal article "Design of an FPGA-Based Controller for Fast Scanning Probe Microscopy" in Sensors 2024. Fast imaging measurements showed in Figure 9 of the article:  9a: Fast STM of a static Pt5 cluster on a Fe3O4(001) magnetite surface, taken at room temperature in a UHV chamber with an Omicron VT-AFM microscope at 4 frames/s; pixel resolution 100x100 pixels; image size 8x8 nm2. 9b: Fast STM of a Pd-octaethylporphyrin monolayer on a Au(111) surface under electrolyte (phosphate buffer, pH= 7, Ar-saturated), taken with a Beetle-type EC-STM at 12 frames/s (EWE = +0.65 vs RHE, Ub = +0.4 V vs WE); pixel resolution 120x120 pixels; image size 8x8 nm2. 9c: Fast AFM images of a Mikromasch TGX1 test grating with a 3 μm pitch and a 130 nm height, taken in contact mode with an Asylum Research/Oxford Instruments MFP-3D microscope (Mikromasch NSC36 probe - 0.6 N/m cantilever) at 4 frames/s; pixel resolution 100x100 pixels.

Lipidomic analysis DELISA study fasting and refeeding

This dataset contains complete data from untargeted lipidomic analysis of subcutaneous adipose tissue from premenopausal women. The clinical study providing the samples is described in https://doi.org/10.1016/j.heliyon.2024.e37875

Applications of Fast Iterative Filtering in NMR spectroscopy [NMR DATASETS]

Employed NMR spectra for the application of denoising and baseline correction method described in the manuscript.

Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network

Tautomerization plays a critical role in many chemical and biological processes, impacting the molecular stability, reactivity, biological activity, and ADME-Tox properties. Many drug-like molecules exist in multiple tautomeric states in aqueous solutions and complicating drug discovery. Predicting these tautomeric ratios and identifying the predominant species rapidly and accurately is crucial for computational drug discovery. In this study, we introduce sPhysNet-Taut, a deep learning model fine-tuned with experimental data leveraging the Siamese network. This model predicts tautomer ratios in aqueous solution using MMFF94-optimized geometries directly. On an experimental test set, sPhysNet-Taut surpasses all other methods, achieving state-of-the-art performance with an RMSE of 1.9 kcal/mol on the 100-tautomers set and an RMSE of 1.0 kcal/mol on the SAMPL2 challenge, and providing the best ranking power for tautomer pairs. Additionally, our results demonstrate that fine-tuning on experimental data significantly improves model performance compared to training from scratch. This work not only provides a useful deep learning model for predicting tautomer ratios, but also provides a protocol for modeling pairwise data. To facilitate user-friendliness, we developed a readily accessible tool to predict stable tautomeric states in aqueous solutions, enumerating all possible tautomeric states and ranking them using the sPhysNet-Taut model.

opencc-zeroSep 2024View details →

DRET-DNA PAINT: using Dark dyes for fast super-resolution imaging

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →

Data related to: Fast low-temperature irradiation creep driven by athermal defect dynamics

<h2>Data set related to article "Fast low-temperature irradiation creep driven by athermal defect dynamics"</h2> <h2>Simulation data</h2> <h3>Molecular dynamics data</h3> The molecular dynamics data is the output of simulations of single-crystal tungsten with periodic boundary conditions evolving under irradiation up to high dose (0.5 dpa). The simulations are performed under a constant externally applied stress, uniaxial to z-direction, and zero stress conditions otherwise. Simulations were performed for stresses of -1.0 GPa, -0.5 GPa, 0 GPa, 0.5 GPa, 1.0 GPa, 1.5 GPa, and 2.0 GPa. Each stress condition was simulated five times independently. Simulations were performed in <a href="https://www.lammps.org/">LAMMPS</a> (see below for the simulation script). Only every 100th frame in LAMMPS Dump format is uploaded here. A frame corresponds to a dose increment of approximately 0.0002 dpa, i.e. frame 1000 corresponds to a dose of approximately 0.2 dpa. The files are zipped. A finer resolution can be supplied upon request (up to every 5th frame). The zip archive naming convention is as follows: <blockquote> For example, archive "srim_neg1p0.zip" contains data of the 5 simulations for -1.0 GPa (='negative 1 point 0'), where snapshot "srim_neg1p0_2/srim_neg1p0_2.1300.dump" refers to independent simulation ID 2, frame 1300, i.e. at a dose of 0.26 dpa.  "srim_pos0p5.zip" contains the 5 simulations for 0.5 GPa (='positive 0 point 5'), and so on. </blockquote> Archive "logfiles.zip" contains the simulation output information, containing stresses and box dimensions for every cascade iteration of each simulation. These can be used to generate the box eigenstrains. The columns are defined as follows: <blockquote> iteration number, dose (dpa), total potential energy (eV), pressure xx (bar), pressure yy (bar), pressure zz (bar), pressure xy (bar), pressure xz (bar), pressure yz (bar), simulation box width x (Å), simulation box width y (Å), simulation box width z (Å) </blockquote> The files contain a few lines labelled with "# restart", which marks points at which the simulation was terminated and then continued. Archive "md_eigenstrains.zip" contains the eigenstrain tensor components parallel and perpendicular to the uniaxial stress direction obtained directly from the MD simulations. The format is as follows: <blockquote> For example, files "eigenpara_pos0p5_0.dat" and "eigenperp_pos0p5_0" contain the MD eigenstrains parallel and perpendicular to the uniaxial loading direction of simulation "eigenpara_pos0p5_0", respectively. The first column is the NRT dose (dpa), and the second column is the eigenstrain value at this dose. The perpendicular eigenstrain is the average of the eigenstrain components xx and yy. </blockquote> <h3>Molecular dynamics script</h3> The LAMMPS script for performing high-dose collision cascade simulations is available at:  <a href="https://github.com/mb4512/ezcascades/">https://github.com/mb4512/ezcascades</a> The simulation input files required to replicate the molecular dynamics simulations of this work are supplied here. Archive "md_input_files.zip" contains simulation input files, specifying box dimensions, stress constraints, the interatomic potential, paths to simulation and scratch directories, and so on. For example, "srim_neg1p0_0.json" is the input file for the "srim_neg1p0_0" simulation. The interatomic potential "W_MNB_JPCM17.eam.fs" used here is the embedded atom method potential for tungsten developed by Mason et al: <a href="https://doi.org/10.1088/1361-648X/aa9776">10.1088/1361-648X/aa9776</a>, available at the <a href="https://www.ctcms.nist.gov/potentials/">NIST Interatomic Potentials Repository</a>. <h3>Surrogate model data</h3> Archive "eigenstrain_models.zip" contains the MLE model parameters for the eigenstrain surrogate models. The format is as follows: <blockquote> For example, files "eigenpara_neg0p5.log" and  "eigenperp_neg0p5.log" contain the cubic spline knot points for the MLE model of eigenstrains parallel and perpendicular to the uniaxial loading direction at -0.5 GPa, respectively. The file contains columns labelled as x, y, and sigma, which correspond to dose (dpa), eigenstrain mean, and eigenstrain standard deviation, respectively. The spline boundary conditions are f(x = 0) = 0, f''(x = 0) = 0, and f'(x_end) = 0, where x_end is the final dose value in the list. The model is extrapolated for higher doses: f(x > x_end) = f(x_end). The log files also contain the covariance matrix of the eigenstrain mean values, from which model uncertainties can be derived. </blockquote> Archive "doseprofile.zip" contains the irradiation dose profile as generated using <a href="https://www.srim.org/">SRIM</a> data following the procedure described in the Supplemental Information. The content is as follows: <blockquote> Files "vacgrid_x_8micro_HR.dat", "vacgrid_y_8micro_HR.dat" contain the x and y coordinates of the 2 dimensional grid, respectively. File "vacgrid_z_8micro_HR.dat" contains a list of dose values (in arbitrary units). The values are ordered consistently, that is, the n-th values of each file give the matching tuple (x, y, dose(x,y)). </blockquote>

opencc-by-4.0Oct 2024View details →

A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers

KazmierczakGregov24_data.zip contains the Matlab scripts and data necessary to reproduce the results and figures of the article "A fast and simplified subglacial hydrological model for the Antarctic Ice Sheet and outlet glaciers" by Kazmierczak, Gregov, Coulon, and Pattyn. For more details, please, open the README.txt file or contact elise (dot) kazmierczak (at) ulb (dot) be or thomas (dot) gregov (at) uliege (dot) be.

dryad36/100

Fast life-histories are associated with larger brain size in killifishes

The high energetic demands associated with the vertebrate brain are proposed to result in a trade-off between the pace of life-history and relative brain size. However, because both life-history and brain size also have a strong relationship with body size, any associations between the pace of life-history and relative brain size may be confounded by coevolution with body size. Studies on systems where contrasts in the pace of life-history occur without concordant contrasts in body size could therefore add to our understanding of the potential coevolution between relative brain size and life-history. Using one such system - 21 species of killifish - we employed a common garden design across two ontogenetic stages to investigate the association between relative brain size and the pace of life-history. Contrary to predictions, we found that relative brain size was larger in adult fast-living killifishes, compared to slow-living species. Although we found no differences in relative brain size between juvenile killifishes. Our results suggest that fast- and slow-living killifishes do not exhibit the predicted trade-off between brain size and life-history. Instead, fast and slow-living killifishes could differ in the ontogenetic timing of somatic vs. neural growth or inhabit environments that differ considerably in cognitive demands.

opencc-zeroDec 2020View details →

dryad36/100

Data from: Genotypic traits and tradeoffs of fast growth in silver birch, a pioneer tree

Fast-growing and slow-growing plant species are suggested to show integrated economics spectrums and the tradeoffs of fast growth are predicted to emerge as susceptibility to herbivory and resource competition. We tested if these predictions also hold for fast-growing and slow-growing genotypes within a silver birch, Betula pendula population. We exposed cloned saplings of 17 genotypes with slow, medium or fast height growth to reduced insect herbivory, using an insecticide, and to increasing resource competition, using naturally varying field plot grass cover. We measured shoot and root growth, ectomycorrhizal (EM) fungal production using ergosterol analysis and soil N transfer to leaves using 15N-labelled pulse of NH4+. We found that fast-growing genotypes grew on average 78% faster, produced 56% and 16% more leaf mass and ergosterol, and showed 78% higher leaf N uptake than slow-growing genotypes. The insecticide decreased leaf damage by 83% and increased shoot growth, leaf growth and leaf N uptake by 38%, 52% and 76%, without differences between the responses of fast-growing and slow-growing genotypes, whereas root mass decreased with increasing grass cover. Shoot and leaf growth of fast-growing genotypes decreased and EM fungal production of slow-growing genotypes increased with increasing grass cover. Our results suggest that fast growth is genotypically associated with higher allocation to EM fungi, better soil N capture and greater leaf production, and that the tradeoff of fast growth is sensitivity to competition, but not to insect herbivory. EM fungi may have a dual role: to support growth of fast-growing genotypes under low grass competition and to maintain growth of slow-growing genotypes under intensifying competition.

opencc-zeroJul 2021View details →