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11,506 results for “Dynamics”
Dataset for publication "Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications"
<p>This is dataset related to paper published in 2020 IEEE I2MTC Conference proceedings:</p> <p>Crotti G., Giordano D., Letizia P. S., Delle Femine A., Gallo D., Landi C., Luiso M., Barbieri L., Mazza P., Pallidini D., (2020, October 29). Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications. https://doi.org/10.5281/zenodo.4154617</p> <p>Excel file contains the data for Figure 10 and following parameter evaluation.</p>
Dataset for Extreme Flooding and Nitrogen Dynamics of a Blackwater River
<p>Data used for analysis in the manuscript titled: Extreme Flooding and Nitrogen Dynamics of a Blackwater River.</p> <p> </p>
Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations
<p>Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphism in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in Catechol-O-methyltransferase (COMT) Val<sup>158</sup>Met and brain-derived neurotrophic factor (BDNF) Val<sup>66</sup>Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework.</p>
Classification of blood cells dynamics with convolutional and recurrent neural networks: a sickle cell disease case study
<p>The fraction of red blood cells (RBC) adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease (SCD). Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples(out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. These videos are of different durations (from 6 to more than 100 frames).</p> <p>This dataset contains four adult patients with SCD. They were enrolled in the study Drepaforme (approved by the institutional review board CPP Ouest 6 under the reference n°2018A00679-46) and were sampled weekly for several months. The movies were processed using in-house routines in Matlab (Matlab, R2016a) and RBC were detected individually and tracked over time. The database provided in this repository are already pre-processed sequences of tracked and centered RBC over time, each time step image being normalized to 31x31 pixels. Within the 32 experiments, the total number of sequences (or samples) is nearly 150 000. All sequences were semi-automatically labelled into 3 classes, depending on the dynamic of the cell: tank-treading, flipping and unreliable (140 000 are unreliable). The percentage of tank-treading cells with respect to all reliable cells (tank-treading+flipping) in every experiment is the final goal of this study.</p> <p>This dataset is very interesting to the community as it is a large database for cell dynamics classification: the class depends on the movement of the cell.</p> <p>An automatic processing of the database using a 2-stage deep learning model is available here https://github.com/icannos/redbloodcells_disease_classification</p> <p>For opening the data in python:</p> <p> from scipy.io import loadmat<br> x=loadmat('BG20191003shear10s01_Export.mat')</p> <p> * x['Norm_Tab'] is of size nb_samples x max_len_sequences x 31 x 31, where max_len_sequences is the length of the longest sequence of the series, typically ~150 to 180. The other sequences are padded with 31x31 zero matrices at the end in order to fill this maximal length.</p> <p> * x['Labels_Num'] is the corresponding label of each sequence, of size nb_samples. Label can be:<br> - 0 : "tank-treading" (or healthy)<br> - 1 : "flipping" (or tumbling, i.e. related to a SCD)<br> - 2 : "unreliable"</p>
An estimate for thermal diffusivity in highly irradiated tungsten using Molecular Dynamics simulation
<p>The changing thermal conductivity of an irradiated material is among the principal design considerations for any nuclear reactor, but at present few models are capable of predicting these changes starting from an arbitrary atomistic model. Here we present a simple model for computing the thermal diffusivity of tungsten, based on the conductivity of the perfect crystal and resistivity per Frenkel pair, and dividing a simulation into perfect and athermal regions statistically. This is applied to highly irradiated microstructures simulated with Molecular Dynamics. A comparison to experiment shows that simulations closely track observed thermal diffusivity over a range of doses from the dilute limit of a few Frenkel pairs to the high dose saturation limit at 3 displacements per atom (dpa).<br> </p>
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
Fig. 6 in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region
Fig. 6. Xerolenta obvia growth model under natural conditions. Size ranges of sexually ma- ture snails are shown in dark grey (4.5–5.4 whorls) and light grey (4.25–4.4 whorls); solid lines = SW population, dashed lines = NE population; 1 = first model variant, 2 = second model variant (details in text)
Fig. 1 in Dynamics of fish assemblages on a continuous rocky reef and adjacent unconsolidated habitats at Fernando de Noronha Archipelago, tropical western Atlantic
Fig. 1. Map of the Fernando de Noronha Archipelago showing the study area (Porto Beach) and permanent sampling stations.
Fig. 3 in Dynamics of fish assemblages on a continuous rocky reef and adjacent unconsolidated habitats at Fernando de Noronha Archipelago, tropical western Atlantic
Fig. 3. Canonical plotting of microhabitat characteristics (arrows) and fish species (points). Rug.: rugosity; Crev.: number of crevices; S. height: substratum height; C. algae: percent cover of encrusting coralline algae; Macr.: percent cover of Macroalgae; Turf: percent cover of turf algae; L. coral: percent cover of live coral; Other: percent cover of other organisms; B. rock: percent cover of bare rock; Sand: percent cover of sand and limestone; IHC: index of habitat complexity; Species names are abbreviated as the first three letters of genus and first three letters of specific epithet (see Table 4 for full scientific names).
Rapid ant community re-assembly in a Neotropical forest: recovery dynamics and land-use legacy
<p>Regrowing secondary forests dominate tropical regions today, and a mechanistic understanding of their recovery dynamics provides important insights for conservation. In particular, land-use legacy effects on the fauna have rarely been investigated. One of the most ecologically dominant and functionally important animal groups in tropical forests are ants. Here, we investigated the recovery of ant communities in a forest – agricultural habitat mosaic in the Ecuadorian Chocó region. We used a replicated chronosequence of previously used cacao plantations and pastures with 1 – 34 years of regeneration time to study the recovery dynamics of species communities and functional diversity across the two land use legacies. We compared two independent components of responses on these community properties: resistance, which is measured as the proportion of an initial property that remains following the disturbance; and resilience, which is the rate of recovery relative to its loss. We found that compositional and trait structure similarity to old-growth forest communities increased with regeneration age, whereas ant species richness remained always at a high level along the chronosequence. Land-use legacies influenced species composition, with former cacao plantations showing higher resemblance to old-growth forests than former pastures along the chronosequence. While resistance was low for species composition and high for species richness and traits, all community properties had similarly high resilience. In essence, our results show that ant communities of the Chocó recovery rapidly, with former cacao reaching predicted old-growth forest community levels after 21 years and pastures after 29 years. Recovery in this community was faster than reported from other ecosystems and was likely facilitated by the low-intensity farming in agricultural sites and their proximity to old-growth forest remnants. Our study indicates the great recovery potential for this otherwise highly threatened biodiversity hotspot.</p>
Research Data Supporting "Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects"
<p>This repository contains the set of data shown in the paper "<strong>Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects</strong>", published on <strong>ACS Nano</strong> (DOI: 10.1021/acsnano.1c01398).</p> <p>The data stored in this section is organized as follow:</p> <p><strong>supervisedClustering/ :</strong> folder containing scripts for carrying out the supervised analysis as discussed in the main paper. Subfolders are divided in <strong><code>kmeans</code></strong><code><strong>/</strong></code> and <strong><code>spectral</code></strong><code>/</code> contain the different analysis as explained in the paper.</p> <p><strong>unbiasMD/ : </strong>folder containing the Gromacs input files for reproducing the unbias MD trajectories of the 3 types of fiber, as explained in the paper.</p> <p><strong>biasMD/</strong> <strong>:</strong> folder containing the PLUMED input files for reproducing enhanced sampling dynamics as discussed in the paper.</p> <p><strong>unsupervisedClustering/ : </strong>folder containing the directions and the original files to reproduce the unsupervised clustering results.</p>
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
Calibrating phylogenies assuming bifurcation or budding alters inferred macroevolutionary dynamics in a densely sampled phylogeny of bivalve families
<p>Analyses of evolutionary dynamics can be profoundly affected by age calibrations of phylogenetic nodes under different models of lineage branching. Most time-calibrated molecular phylogenies of extant taxa assume a purely bifurcating model, where nodes are calibrated using the daughter lineage with the older first occurrence in the fossil record. Lineages can also split via budding, in which a parent lineage persists following the origin of a daughter lineage, and nodes are calibrated using the age of the lineage with the younger first occurrence. Here, we use the extensive fossil record of bivalve molluscs for a large-scale empirical test of how the choice of branching model affects macroevolutionary analyses. We time-calibrated 91% of nodes in a phylogeny of 97 extant bivalve families using 86 calibration points ranging in age from 2.59 to 485 Ma. Allowing budding-based calibrations minimizes conflict between the tree topology and timing of evolutionary events in the fossil record, reducing the summed duration of inferred "ghost lineages," from 6.76 billion yrs (Gyr; bifurcating model) to 1.00 Gyr (budding model). Adding 31 extinct paraphyletic families – many major groups contain such extinct taxa – shifts deep splits further back in time and raises ghost-lineage totals to 7.86 Gyr (bifurcating) and 1.92 Gyr (budding), but more accurately reflects the time since separation of lineages. Lineage-through-time plots from phylogenetic data scaled under a bifurcating model of evolution push more inferred bivalve diversification into the Paleozoic, conflicting with other palaeontological evidence on the magnitude of the end-Paleozoic extinction and subsequent recovery, and strongly reduce the magnitude of the Cenozoic diversification of the group. Consideration of the hypothesized branching model within a given clade is essential when node-calibrating phylogenies, and for a major clade with a robust fossil record, an evolutionary model that allows budding and does not force bifurcations is the most appropriate one, and likely common for many other clades as well.</p>
FRESH:COM Dynamic Participation in Local Energy Communities with Peer-to-Peer Trading
<p>This repository contains input data used for the case study in 'Dynamic Participation in Local Energy Communities with Peer-to-Peer Trading'</p>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
All-atom Molecular Dynamics Simulations of Meiosis 1-associated protein (M1AP) to Investagate the Impact of Known Missense Mutations Associated with Male Infertility through Non-obstructive Azoospermia
<p>Protein structure of meiosis 1-associated protein (M1AP) was modelled by using GalaxyWeb (from Seok Lab). We used this model to investigate the impact of variants (i.e., S50P, R266Q, P389L, G317R, and L430P) in M1AP which were recently associated with non-obstructive azoospermia (NOA). NOA is a male infertility-related condition causing absence of sperm in the seminal fluid due to meiosis failure. We aimed to elucidate the pathogenicity mechanisms of these five missense NOA-related mutations on M1AP by performing molecular modeling and molecular dynamics (MD) simulations. This dataset includes the results of 1000 ns MD simulations (two repeats, each 500 ns) for each of the mutant and wild-type systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system charge with 0.15 M KCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. NPT ensemble was applied for whole simulations. Two step minimization & equilibration procedure was performed: (1) 5,000-step minimization and 1 ns equilibrium with constraints on the protein; (2) 5,000-step minimization and 1 ns equilibrium without the constraints on the protein. All related configuration files for wild-type system were also included to the dataset. Production simulations were run twice along 500 ns by using different random seeds to assign the velocities from Boltzmann distribution (total simulation time for each system was 1000 ns, which are given as 500 ns repeat 1, and 500 ns repeat 2). The production simulations were supplied in the dataset. "out" and "log" files were used for energy analysis.</p> <p>For all analysis scripts, see https://github.com/ugerlevik/M1AP_analysis.</p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, main part
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <p><strong>Datasets:</strong></p> <ul> <li> <strong>models.zip </strong>- Datasets parameterizing kinetic nonlinear models of a wild-type <em>E. coli </em>strain used for training generative adversarial networks <ul> <li>subfolder 1: kinetic - contains the kinetic model (kin_varma_curated.yml)</li> <li>subfolder 2: thermo - contains the thermodynamic model for all the four physiologies (varma_fdp1, varma_fdp2, varma_fdp3, varma_fdp4)</li> <li>subfolder 3: steady_state_samples: contains the TFA steady state profiles for all four physiologies (samples_fdp1, sample_fdp2, samples_fdp3, samples_fdp4)</li> <li>subfolder 4: parameters - contains the kinetic parameter training dataset for each physiology (.hdf5 files), maximal eigenvalues (training labels) (maximal_eigenvalues.csv) and the minimum eigenvalues (minimal_eigenvalues.csv)</li> </ul> </li> <li><strong>vanilla_learning_training.zip:</strong> contains 4 folders for each of the 4 physiologies. <ul> <li>each of these folders contains 6 subsubfolders in the format N-<em>{n} </em>( N-10, N-50, N-100, N-500, N-1000, N-72000), where <em>{n} </em>represents the number of used training data samples.</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy (Note: eigenvalues were not calculated for N=10, 50, 100 as traning failed)/</li> </ul> </li> </ul> </li> <li><strong>transfer_learning_training.zip</strong> - contains 12 subfolders "tl_fdpi_fdpj" where i,j ={1,2,3,4} for each of the 12 transfer learning case <ul> <li>each of these folders contains 5 subsubfolders N-10, N-50, N-100, N-500, N-1000</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy </li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>best_generators.zip</strong> <ul> <li>The best generators (with the highest incidence of relevant models) for each physiology (generator1- 4.h5)</li> <li>The normalizing scaling parameters for each generator (d_scaling.pkl).</li> <li> </li> </ul> </li> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>vanilla_ODE_sample_parameters.zip</strong> - contains (i) 1000 REKINDLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total) (ii) 1000 ORACLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total). These parameter sets parameterize the ODEs which are integrated.</li> <li><strong>ode_solutions_physiology1.zip (available at </strong><a href="https://zenodo.org/record/5818192">https://zenodo.org/record/5818192</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiology1_ORACLE.zip (available at </strong><a href="https://zenodo.org/record/5819669">https://zenodo.org/record/5819669</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiologies2-4.zip -</strong> contains 6 subfolders (physiology_2-4, physiology_2-4_ORACLE), with each subfolder containing 10 sub subfolders. Each sub subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE / ORACLE generated parameter sets for physiology 2-4, each of the 1000 models having a random perturbation.</li> <li><strong>transfer_learning_ODE_solutions.zip - </strong>contains two subfolders N_10, N_50, each subfolder contains 12 subsubfolders titled i_j (where i = {1,2,3,4} and j = {1,2,3,4} where 1_2 represent the transfer learning case from physiology 2 to physiology 1 and when using <em>{n}</em> samples from physiology 2 and so on (where <em>{n}</em>=10 and 50 respectively). Each subsubfolders contain <ul> <li>i_j.hdf5: contains 300 kinetic parameter sets generated using (i) REKINDLE for this transfer learning case</li> <li>i_j.csv: the maximal eigenvalues of the parameter sets</li> <li>solutions.csv: ODE integrated time series data for the relevant kinetic parameters out of the 300 generated.</li> </ul> </li> </ul> </li> </ul> <p> </p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 2
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1_ORACLE.zip</strong> - contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1.zip - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Data for: Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics
<p>Data for publication: A. Scheidegger, J. Vanicek, and N. Golubev, Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics, https://doi.org/10.1063/5.0076609</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</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.