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308 results for “Dynamical system”
Posterior samples of a dynamical fit for planetary system HD128311
<p>This dataset contains the posterior samples from a dynamical fit of the multi-planetary system HD128311. See <a href="https://doi.org/10.1093/mnrasl/slu202">https://doi.org/10.1093/mnrasl/slu202</a> for a description of the fit. The dataset is one numpy array, consisting of</p> <ul> <li>500 walkers </li> <li>1250 generations</li> <li>3 bodies (1 star + 2 planets)</li> <li>7 parameters per body (mass, x, y, z, vx, vy, vz)</li> </ul>
Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts
<p>This repository provides the unregionalized life cycle inventories to the paper "<span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>".</p> <h2>Contents</h2> <p>The repository is split into 2 parts and comprises the following files:</p> <p><strong>01_production: </strong>contains the necessary life cycle inventories for battery production.</p> <ul> <li><strong>01_primary</strong>: contains the life cycle inventories for battery production from primary materials.</li> <li><strong>02_secondary</strong>: contains the life cycle inventories for battery production from secondary materials.</li> <li><strong>03_active_material</strong>: contains the life cycle inventories for the active battery materials from primary materials.</li> <li><strong>04_active_material</strong>: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul> <p> </p> <p><strong>02_recycling: </strong>contains the necessary inventories for battery recycling.</p> <ul> <li><strong>01_process</strong>: contains the life cycle inventories for battery recycling.</li> <li><strong>02_intermediate</strong>: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li><strong>03_output</strong>: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> <p>These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023).</p> <h2>Related publication</h2> <p>More details on the scientific context is provided in the publication itself:</p> <p><span>Ginster, R.</span>, <span>Blömeke, S.</span>, <span>Popien, J. L.</span>, <span>Scheller, C.</span>, <span>Cerdas, F.</span>, <span>Herrmann, C.</span>, & <span>Spengler, T. S.</span> (<span>2024</span>). <span>Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts</span>. <em>Journal of Industrial Ecology</em>, <span>1</span>–<span>18</span>. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a></p> <h2>Funding</h2> <p>This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678).</p> <p>The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.</p>
Live cribwall + slope grating + fascines drainage system - soil-water dynamics
<p>Dataset containing raw time series (August 2022) for soil-water dynamics -i.e., volumetric soil moisture, matric suction, soil-pore water pressure, and soil temperature - retrieved from a live, vegetated cribwall+slope grating+fascines drainage system built in Catterline, Scotland. NBS intervention built to restore a landslide taking place in February 2021. </p>
Insect Population Dynamics on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2019)
Dataset Abstract Plant dwelling insect occurrence in the LTER main site (all treatments) of the KBS-LTER has been recorded since 1989 and in the successional and forest sites since 1993. The effort has focused on characterizing the temporal and spatial abundance and diversity of a set of insects representative of a higher order insect trophic level, the herbivore predators. The insect database contains more than 400,000 records and consists of counts of adult insects of fourteen species of Coccinellidae, one species of Chrysopidae, and one species of Lampyridae from 30 sample sites in each of the seven treatments in the LTER Main Site. The standard method used to measure these organisms is a yellow sticky trap. Sampling is conducted weekly during the growing season as described in the sampling protocol. original data source http://lter.kbs.msu.edu/datasets/26
The datasets used in the manuscript named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"
<p>The hindcast and real-time prediction output of FGOALS-f2 V1.0 used in the study named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"</p>
Data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry"
<p>Raw data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry". DOI of article: https://doi.org/10.1063/5.0188257</p>
Data and Supplementary Information : timber ressource dynamics in West African cocoa agroforestry systems
<p>Here we present three datasets (growthDB.csv, heightDB.csv and volumeDB.csv) describing 59 tree species used for wood production in cocoa Agroforestry System (AFS) in Côte d'Ivoire (West Afica). Our dataset includes a total of 4,634 trees: 2530 spontaneous and 2104 (trans)planted. </p> <p>We provide three table (per datasets in .csv format) with the following column per tree:</p> <p>- gensp: species latin names</p> <p>- dbh : tree diameter at breast height (cm)</p> <p>- age: tree age (years)</p> <p>- origin: tree origin (spontaneous or (trans)planted)</p> <p>- WDmean : tree wood density (g.cm³)</p> <p>- clus: study sites</p> <p>- indsp : species index</p> <p>- indclus : sites index</p> <p>- hlog: tree bole height (m)</p> <p>- vol : tree bole volume (m³)</p> <p>We also provide, the STAN code (models.stan) to assess the wood production potential of trees in cocoa AFS: (i) a model describing the diameter growth trajectories of trees as a function of their age, (ii) a model evaluating the relationship between tree diameter and bole height, and (iii) a model assessing the commercial volume of trees as a function of their diameter and bole height.</p> <p>Finally, we present additional tables and figures in Supplementary Information.pdf.</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Dynamical System Multivariate Time Series
<p>The Dynamical System Multivariate Time Series (DSMTS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system under fully nominal conditions (no outliers or anomalies).</p> <p>The DSMTS Dataset exhibits a set of desirable properties that make it <strong>very suitable for benchmarking Multivariate Time Series Forecasting </strong>especially for industrial processes of complex systems:</p> <ul> <li><strong>Multivariate (17 variables)</strong> including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including: <ul> <li><strong>4 Deliberate Actuations / Control Commands sent by a simulated operator / controller</strong>, for instance, commands of an operator to turn ON/OFF some equipment.</li> <li><strong>3 Environmental Stimuli / External Forces</strong> acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.</li> <li><strong>10 Telemetry Readings representing the observable states of the complex system by means of sensors</strong>, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.</li> </ul> </li> <li><strong>5 million timestamps</strong>. Sensors readings are at 1Hz sampling frequency. </li> <li><strong>Pure signal ideal for robustness-to-noise analysis.</strong> The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.</li> <li><strong>No missing data</strong>. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.</li> </ul>
3D Charged Particles Dataset - Roto-translated Local Coordinate Frames for Interacting Dynamical Systems
<p>This repository contains the "<strong>3D charged particles</strong>" dataset from the paper</p> <blockquote> <p><strong>Roto-translated Local Coordinate Frames for Interacting Dynamical Systems</strong><br> <a href="https://mkofinas.github.io/">Miltiadis Kofinas</a>, <a href="https://menaveenshankar.github.io/">Naveen Shankar Nagaraja</a>, <a href="https://egavves.com/">Efstratios Gavves</a><br> NeurIPS 2021<br> <a href="https://arxiv.org/abs/2110.14961">https://arxiv.org/abs/2110.14961</a><br> <a href="https://github.com/mkofinas/locs">https://github.com/mkofinas/locs</a></p> </blockquote> <p>It contains simulations of trajectories of 5 charged particles in 3 dimensions, interacting via Coulomb forces.</p> <p>There are 30,000 simulations for training, 5,000 for validation, and 5,000 for testing.</p> <p>Train and validation simulations last for 99 timesteps, while test simulations last for 99 timesteps.</p> <p>The features comprise positions and velocities of particles, while edges describe the product of pairwise charges.</p>
Data and scripts to reproduce the results shown in "Stability of attractor local dimension estimates in non-Axiom A dynamical systems"
<p>Here we make available all the codes and datasets to reproduce the results of the paper "Stability of attractor local dimension estimates in non-Axiom A dynamical systems" by Flavio Pons, Gabriele Messori and Davide Faranda.</p> <p>The pre-print of the article is available at https://hal.science/hal-04051659/document.</p> <p>Any question/comment can be sent to flavio.pons@gmail.com.</p> <p>License for the codes and simulation/analysis results (*.Rda files): the code is shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, see https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>License and terms of use for the ERA5 data (z500_daily_euro.nc): the ERA5 500 hPa geopotential was downloaded from https://climexp.knmi.nl/start.cgi</p>
ASPLOS20-AE Artifact Dataset for 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'
<p>The empirical model database and dataset for the ASPLOS 2020 Paper 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'</p>
An introduction to the use of Extended Petri Nets to represent Hydrological Dynamical Systems
<p>The video tries to be a quiet introduction to the Extended Petri Nets (EPN), a graphical representations designed to describe Earth Science and Environmental models that results is systems of ordinary differential equations. The video uses an example to exemplify the mechanics of EPN and constitute ancillary material for a paper submitted to Hydrological Processes (https://osf.io/fpx7y/). The main reference for the EPN is: Bancheri,M., Serafin,F. and Rigon, R (2019), "The representation of hydrological dynamical systems using the extended petri nets." Water Resources Research, 8(01), 159–27. In our opinion, EPN improve models representation and help to understand the feedbacks among the various processes.</p> <p> </p>
Research data supporting: "Machine learning of microscopic structure-dynamics relationships in complex molecular systems"
<p>This repository contains the set of data and the code to reproduce the results shown in "Machine learning of microscopic structure-dynamics relationships in complex molecular systems" published on Machine Learning: Science and Technology (DOI: 10.1088/2632-2153/ad0fa5).</p>
Data of publication "Simulating the dynamics of large many-body quantum systems with Schrödinger-Feynman techniques"
<p>The development of powerful numerical techniques has drastically improved our understanding of quantum matter out of equilibrium. Inspired by recent progress in the area of noisy intermediate-scale quantum devices, this paper highlights hybrid Schrödinger-Feynman techniques as an innovative approach to efficiently simulate certain aspects of many-body quantum dynamics on classical computers. To this end, we explore the nonequilibrium dynamics of two large subsystems, which interact sporadically in time, but otherwise evolve independently from each other. We consider subsystems with tunable disorder strength, relevant in the context of many-body localization, where one subsystem can act as a bath for the other. Importantly, studying the full interacting system, we observe that signatures of thermalization are enhanced compared to the reference case of having two independent subsystems. Notably, with the here proposed Schrödinger-Feynman method, we are able to simulate the pure-state survival probability in systems significantly larger than accessible by standard sparse-matrix techniques.</p> <div> </div> <div> </div>
Demonstrations for imitation learning for the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives"
<p>Demonstrations for the coathanger experiment in the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives". https://elib.dlr.de/205110/</p>
GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022
<div> <p>GLC_FCS30D is the first global fine land cover dynamic product at a 30-meter resolution that adopts continuous change detection. It utilizes a refined classification system containing 35 land-cover categories and covers the time span from 1985 to 2022. Before the year 2000, the update cycle was every 5 years, while after 2000, it is updated annually. In specific, it developed by combining the continuous change detection method, local adaptive updating models and the spatiotemporal optimization algorithm from dense time-series Landsat imagery, and was validated to achieve an overall accuracy of 80.88% (±0.27%) for the basic classification system 10 major land-cover types) and 73.24% (±0.30%) for the LCCS level-1 validation system (17 LCCS land-cover types).</p> <p>The GLC_FCS30D has been compressed into 36 zip files, and<strong> <em>the details about the GLC_FCS30D can be found in the User's guides.</em></strong></p> </div>
Dataset accompanying manuscript "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope"
<p>Raw images and microscope calibration metadata for reconstruction of datasets presented in Fig. 1 and Fig. 3 of "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope". Notebooks demonstrating steps in the label-free and fluorescence anisotropy reconstruction pipelines can be found at https://github.com/mehta-lab/miPolScope.</p>
Environmental drivers of biseasonal anthrax outbreak dynamics in two multi-host savanna systems
<p>Environmental factors are common forces driving infectious disease dynamics. We compared inter-annual and seasonal patterns of anthrax infections in two multi-host systems in southern Africa: Etosha National Park, Namibia, and Kruger National Park, South Africa. Using several decades of mortality data from each system, we assessed possible transmission mechanisms behind anthrax dynamics, examining 1) within- and between-species case correlations, and 2) associations between anthrax mortalities and environmental factors, specifically rainfall and the Normalized Difference Vegetation Index (NDVI). Anthrax cases in Kruger had wide inter-annual variation in case numbers, and large outbreaks seemed to follow roughly a decadal cycle. In contrast, outbreaks in Etosha were smaller in magnitude and occurred annually. In Etosha, the host species commonly affected remained consistent over several decades, although plains zebra (<em>Equus quagga</em>) became relatively more dominant. In Kruger, turnover of the main host species occurred after the 1990s, where the previously dominant host species, greater kudu (<em>Tragelaphus strepsiceros</em>), was replaced by impala (<em>Aepyceros melampus</em>). In both parks, anthrax infections showed two seasonal peaks, with each species having only one peak in a year. Zebra, springbok (<em>Antidorcas marsupialis</em>), wildebeest (<em>Connochaetes taurinus</em>) and impala cases peaked in wet seasons, while elephant (<em>Loxodonta africana</em>), kudu and buffalo (<em>Syncerus caffer</em>) cases peaked in dry seasons. For common host species shared between the two parks, anthrax mortalities peaked in the same season in both systems. Among host species with cases peaking in the same season, anthrax mortalities were mostly synchronized, which may imply similar transmission mechanisms or shared sources of exposure. Between seasons, outbreaks in one species may contribute to more cases in another species in the following season. Higher vegetation greenness was associated with more zebra and springbok anthrax mortalities in Etosha, but fewer elephant cases in Kruger. These results suggest that host behavioral responses to changing environmental conditions may affect anthrax transmission risk, with differences in transmission mechanisms leading to multi-host biseasonal outbreaks. This study reveals the dynamics and potential environmental drivers of anthrax in two savanna systems, providing a better understanding of factors driving biseasonal dynamics and outbreak variation among locations.</p>
Fig. 2 in Structure And Dynamics Of Crucians' Settlements (Cypriniformes, Сarassius) In Water Systems Of Eastern Ukraine
Fig. 2. Changes in C. gibelio's share in the selections of crucian carps from Eastern Ukraine depending on geographical latitude.
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.