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147 results for “dynamic processes”
Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
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Integrating floral trait and flowering time distribution patterns help reveal a more dynamic nature of co-flowering community assembly processes
<p>Species' floral traits and flowering times are known to be the major drivers of pollinator-mediated plant-plant interactions in diverse co-flowering communities. However, their simultaneous role in mediating plant community assembly and plant-pollinator interactions is still poorly understood. Since not all species flower at the same time, inference of facilitative and competitive interactions based on floral trait distribution patterns should account for fine phenological structure (intensity of flowering overlap) within co-flowering communities. Such an approach may also help reveal the simultaneous action of competitive and facilitative interactions in structuring co-flowering communities.</p> <p>Here we used modularity within a co-flowering network context, as a novel approach to detect convergent and/or over-dispersed patterns in floral trait distribution and pollinator sharing. Specifically, we evaluate differences in floral trait and pollinator distribution patterns within (high temporal flowering overlap) and among co-flowering modules (low temporal flowering overlap). We further evaluate the consistency of observed floral trait and pollinator sharing distribution patterns across space (three geographic regions) and time (dry and rainy seasons).</p> <p>We found that floral trait similarity was significantly higher in plant species within co-flowering modules than in species among them. This suggests pollinator facilitation may lead to floral trait convergence, but only within co-flowering modules. However, our results also revealed seasonal and spatial shifts in the underlying interactions (facilitation or competition) driving co-flowering assembly, suggesting that the prevalent dominant interactions are not static.</p> <p>Synthesis: Overall, we provide strong evidence showing that the use of flowering time and floral trait distribution alone may be insufficient to fully uncover the role of pollinator-mediated interactions in community assembly. Integrating this information along with patterns of pollinator sharing will greatly help reveal the simultaneous action of facilitative and competitive pollinator-mediated interactions in co-flowering communities. The spatial and temporal variation in flowering and trait distribution patterns observed further emphasize the importance of adopting a more dynamic view of community assembly processes.</p>
Dataset of alternative process plan networks for dynamic integrated process planning and scheduling
<p>This dataset includes 3D models of representative manufacturing parts (jobs) as well as their features. For each of the manufacturing part, alternative process plan networks are given containing alternative machine tools, cutting tools, Tool Access Directions (TADs) and manufacturing times. The dataset also includes a detailed technical specification for all parts and calculated manufacturing times for all operations based on given alternative manufacturing resources.</p>
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
<p><span><span>The selective catalytic reduction (SCR) de</span><span>-</span><span>NO<sub>x</sub> </span><span>process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NO<sub>x</sub> analysis; </span><span>hence,</span><span> it is difficult to obtain an accurate model </span><span>that </span><span>can be used to control NH<sub>3</sub> injection </span><span>during changes in the </span><span>operating state. </span><span>In this work,</span><span> a novel dynamic inferential model with delay estimation was proposed for NO<sub>x</sub> emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was</span><span> used</span><span> to improve the prediction ability, </span><span>and this was followed by verification using </span><span>benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy </span><span>were </span><span>proposed to deal with the time variation of the SCR de</span><span>-</span><span>NO<sub>x</sub> process.</span> <span>Through the analysis of the </span><span>experimental field data </span><span>in the</span> <span>steady state, </span><span>the variable</span><span> state and </span><span>the </span>NO<sub>x</sub> analyser blowback process<span>, the results proved that</span><span> this dynamic model has </span><span>high prediction accuracy</span><span> during</span><span> state changes and can </span><span>realize</span><span> advance prediction of the NO<sub>x</sub> emission. </span></span></p>
Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
Datasets for the article "Emerging patterns of CO2:O2 dynamics in rivers and their link to ecosystem carbon processing "
<p>Datasets supporting the article "Emerging patterns of CO2:O2 dynamics in rivers and their link to ecosystem carbon processing". The datasets are analized using the acompanying repository in github (https://github.com/rocher-ros/O2_CO2_rivers).</p> <p> </p> <p>Currently the publication is under review, for further information and details on the analysis visit the github repository or the acompannying article. </p>
Supporting molecular simulations data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3"
<p>Supplementary data for "A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3: <a href="https://doi.org/10.1126/sciadv.abe3326">10.1126/sciadv.abe3326</a>"</p>
Community metabarcoding reveals the relative role of environmental filtering and spatial processes in metacommunity dynamics of soil microarthropods across a mosaic of montane forests
<p><span><span><span><span><span><span><span><span><span><span><span>Disentangling the relative role of environmental filtering and spatial processes in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical datasets are required to account for taxon- and context-dependency but relevant research in understudied areas is often compromised by the taxonomic impediment<span><span>. </span></span>We here employed haplotype-level community DNA metabarcoding, enabled by stringent filtering of Amplicon Sequence Variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar β diversity patterns at ASV and species (OTU, Operational Taxonomic Unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented <i>Quercus alnifolia</i> habitat, we also detected a major role of spatial isolation determined by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterising metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.</span></span></span></span></span></span></span></span></span></span></span></p>
Integrating dynamic processes into waterfowl conservation prioritization tools
<p><b>Aim: </b> Traditional approaches for including species' distributions in conservation planning have presented them as long-term averages of variation. Like these approaches, the main waterfowl conservation targeting tool in the United States Prairie Pothole Region (US PPR) is based primarily on long-term averaged distributions of breeding pairs. While this tool has supported valuable conservation, it does not explicitly consider spatiotemporal changes in spring wetland availability and does not assess wetland availability during the brood rearing period. We sought to develop a modeling approach and targeting tool that incorporated these types of dynamics for breeding waterfowl pairs and broods. This goal also presented an opportunity for us to compare predictions from a traditional targeting tool based on long-term averages to predictions from spatiotemporal models. Such a comparison facilitated tests of the underlying assumption that this traditional targeting tool could provide an effective surrogate measure for conservation objectives such as brood abundance and climate refugia.</p> <p><b>Location: </b>US PPR</p> <p><b>Methods:</b> We developed spatiotemporal models of waterfowl pair and brood abundance within the PPR of the US. We compared the distributions predicted by these models and assessed similarity with the averaged pair data that is used to develop the current waterfowl targeting tool.</p> <p><b>Results:</b> Results demonstrated low similarity and correlation between the averaged pair data and spatiotemporal brood and pair models. The spatiotemporal pair model distributions served as better surrogates for brood abundance than the averaged pair data.</p> <p><b>Main conclusions:</b> Our study underscored the contributions that the current targeting tool has made to waterfowl conservation but also suggested that conservation plans in the region would benefit from the consideration of inter- and intra-annual dynamics. We suggested that using only the averaged pair data and derived products might result in the omission of 46-98% of important pair and brood habitat, respectively, from conservation plans.</p>
Understanding complex spatial dynamics from mechanistic models through spatio-temporal point processes
<p>Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements, and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta-modelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories ( i.e., spatio-temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi-natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.</p>
Unravelling processes between phenotypic plasticity and population dynamics in migratory birds
<p>Populations can rapidly respond to environmental change via adaptive phenotypic plasticity, which can also modify interactions between individuals and their environment, affecting population dynamics. Bird migration is a highly plastic resource-tracking tactic in seasonal environments. However, the link between the population dynamics of migratory birds and migration tactic plasticity is not well understood.</p> <p>The quality of staging habitats affects individuals' migration timing and energy budgets in the course of migration, and can consequently affect individuals' breeding and overwintering performance, and impact population dynamics. Given staging habitats being lost in many parts of the world, our goal is to investigate responses of individual migration tactics and population dynamics in the face of loss of staging habitat, and to identify the key processes connecting them.</p> <p>We started by constructing and analysing a general full-annual-cycle individual-based model with a stylized migratory population to generate hypotheses on how changes in the size of staging habitat might drive changes in individual stopover duration and population dynamics. Next, through the interrogation of survey data, we tested these hypotheses by analysing population trends and stopover duration of migratory waterbirds experiencing loss of staging habitat.</p> <p>Our modelling exercise led to us posing the following hypotheses: the loss of staging habitat generates plasticity in migration tactics, with individuals remaining on the staging habitat for longer to obtain food due to a reduction in per capita food availability. The subsequent increasing population density on the staging habitat has knock on effects on population dynamics in the breeding and overwintering stage. Our empirical results were consistent with the modelling predictions.</p> <p>Our results demonstrate how environmental change that impacts one energetically costly life history stage in migratory birds can have population dynamics impacts across the entire annual cycle via phenotypic plasticity.</p>
Data Availability - DRAFT - 'Process-based similarity' revealed by discharge-dependent relative submergence dynamics of thousands of large bed elements
<p>Datasets and R code related to confidential manuscript submission entitled "‘Process-based similarity’ revealed by discharge-dependent relative submergence dynamics of thousands of large bed elements". Restrictions apply to the availability of the 2014 DTM and 2D model results, which were used under contractual agreement from the project sponsor. These are available from the senior author with the permission of Yuba Water Agency. Restrictions apply to the availability of the 2014 DTM and 2D model results, which were used under contractual agreement from the project sponsor. These are available from the senior author with the permission of Yuba Water Agency.</p>
Turning summer into winter: nutrient dynamics, temperature, density dependence, and invasive species drive bioenergetic processes and growth of a keystone coldwater fish
<p>A combination of global changes such as species invasions, climate change, and nutrient pollution have altered ecosystems, food webs, and the bioenergetic processes that control growth. These changes are especially pronounced in freshwater ecosystems and often lead to rapid variation in fish growth and dependent ecosystems services such as fishery yield. Understanding the mechanisms driving growth responses to environmental change is important for interpreting past dynamics and sustainably managing ecosystems. This study uses integrated bioenergetics and growth modeling to understand how nutrient dynamics, species invasions, and changing temperatures have altered growth of the keystone pelagic whitefish (<em>Coregonus</em> <em>wartmanni</em>) in Lake Constance, Germany from 1925 to 2020. Growth variation was modeled by allowing covariates to alter temperature-dependent consumption, while size-specific metabolism varied only with temperature. Consumption and growth increased strongly to a maximum with phosphorous, and this effect was stronger when intraspecific competition (measured as whitefish biomass) was low. Increasing whitefish biomass reduced growth under mesotrophic conditions, but had no effect under oligotrophic conditions. In contrast, increasing competition with invasive three-spined stickleback (<em>Gasteosteus</em> <em>aculeauts</em>) was predicted to reduce growth even under oligotrophic conditions. The invasion has effectively turned summer into winter for whitefish, with older fish ceasing to grow and younger fish losing up to 10% of their body weight during the normal growing season in subsequent years. Warming is predicted to further reduce whitefish growth due to competition with invasive stickleback, which would further alter zooplankton food availability and reduce already low fishery yields. These results demonstrate the importance of considering biotic interactions and synergistic effects in global change studies, as well as the value of mechanistic-based models for understanding effects. Similar growth responses to ecosystem change are likely within and across ecosystems, and bioenergetic models can help understand effects to support informed ecosystem management.</p>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Raw data, processing, and simulation scripts for "Observation of Dynamic Nuclear Polarization Echoes"
<p>Data files and processing/plotting scripts for the first observation of "dynamic nuclear polarization echoes". Also a simulation script for a semi-quantitative quantum mechanical simulation of the spin dynamics.</p>
Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes
<p>Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management, and infodemic controlling. However, predicting the scale of an information cascade in a long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the follower-followee network, retweet network, tweet content, and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension to information diffusion process and may assist in the control of such process.</p>
Space resource utilization of dominant species integrates abundance- and functional-based processes for better predictions of plant diversity dynamics
<p>Sustainable ecosystem management relies on our ability to predict changes in plant diversity and to understand the underlying mechanisms. Empirical evidence demonstrates that abundance- and functional-based processes simultaneously explain the loss of plant diversity in response to human activities. Recently, a novel indicator based on percent cover (CoverD) and maximum height (HeightD) of the dominant plant species – Space Resource Utilization (SRUD) – has proven to give robust and better predictions of plant diversity dynamics than community biomass. Whether the superior predictive ability of SRUD is due to its capacity to simultaneously capture abundance- and functional-based processes remains unknown. Here, we tested this hypothesis by quantifying mechanistic links between changes in SRUD and biodiversity in response to nutrients and herbivores. Furthermore, we assessed the relative contribution of dominant, intermediate, and rare species to reduced density of individuals by combining null model analysis with field experiments. We found that SRUD successfully captured changes in ground-level light availability and changes in the number of individuals to predict plant diversity dynamics, and each of CoverD and HeightD partly and independently contributed to both processes. Comparative results from null model analysis and field experiments confirmed that individual losses of dominant, intermediate, and rare species followed non-random processes. Specifically, compared with random loss process, rare species lost proportionally more individuals and thus disproportionately contributed to species loss, while dominant and intermediate species lost less. Our results demonstrate that SRUD captures both abundance- and functional-based processes thus explaining why SRUD provides more accurate predictions of changes in species diversity. Given that rare species can play an important role in shaping community structure, resisting against invasion, impacting higher trophic levels, and providing multiple ecosystem functions, reducing the SRU of dominant species could alleviate the risk of exclusion of rare species by mitigating abundance- and functional-based competition processes.</p>
EMD data for the paper "Impact of ad-hoc post-processing parameters on the lubricant viscosity calculated with equilibrium molecular dynamics simulations"
<p>This archive contains the post-processing data obtained from EMD simulations of <strong>2,2,4-Trimethylhexane</strong> lubricant molecule under various operating conditions. The EMD simulations were performed using LAMMPS with COMPASS force field. (See manuscript and README for details.)</p>
Data from: Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales
<p class="MsoNormal">The increasing availability of satellite imagery has supported a rapid expansion in forward-looking studies seeking to track and predict how climate change will influence wild population dynamics. However, these data can also be used in retrospect to provide additional context for historical data in the absence of contemporaneous environmental measurements. We used 167 Landsat-5 Thematic Mapper (TM) images spanning 13 years to identify environmental drivers of fitness and population size in a well-characterized population of banner-tailed kangaroo rats (<em>Dipodomys spectabilis</em>) in the southwestern United States. We found evidence of two decoupled processes that may be driving population dynamics in opposing directions over distinct time frames. Specifically, increasing mean surface temperature corresponded to increased individual fitness, where fitness is defined as the number of offspring produced by a single individual. This result contrasts with our findings for population size, where increasing surface temperature led to decreased numbers of active mounds. These relationships between surface temperature and (i) individual fitness and (ii) population size would not have been identified in the absence of remotely sensed data, indicating that such information can be used to test existing hypotheses and generate new ecological predictions regarding fitness at multiple spatial scales and degrees of sampling effort. To our knowledge, this study is the first to directly link remotely sensed environmental data to individual fitness in a nearly exhaustively sampled population, opening a new avenue for incorporating remote sensing data into eco-evolutionary studies.</p>
Global Datasets of Hourly Carbon and Water Fluxes Simulated Using a Satellite-based Process Model with Dynamic Parameterizations
<p>This new global hourly dataset serves as a 'handshake' among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global gross primary productivity (GPP) and evapotranspiration (ET) with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles.</p> <p>The dataset include the GPP and ET of sunlit and shaded leaf components at an hourly timescale and a spatial resolution of 0.25-degree from 2001 to 2020.</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.