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700 results for “Dynamical model”
Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
<p class="BodyText1">Population dynamics can be highly variable in the face of environmental heterogeneity, and understanding this variation is central in the study of ecology. Robust management decisions require that we understand how populations respond to management at a range of scales, and under a broad suite of conditions. Population models are potentially valuable tools in addressing this challenge. However, without adequate data, models can fail to produce useful results. Populations of arable weeds are particularly problematic in this respect, as they are widespread and their dynamics are extremely variable. Owing to the inherent cost of collecting data, most studies of weed population dynamics are derived from localized experiments under a small range of environmental conditions, limiting the extent to which variance in population dynamics can be measured. Density-structured models provide a route to rapid, large-scale analysis of population dynamics, and can expand the scale of ecological models that are directly tied to data. Here we extend previous density-structured models to include environmental heterogeneity, variation in management, and to account for inter-population variation. We develop, parameterize and test hierarchical density-structured models for a common agricultural weed, black-grass (<i>Alopecurus myosuroides</i>). We model the dynamics of this species in response to crop management, using survey data gathered over 4 years from 364 fields across a network of 45 UK farms. We show that hierarchical density-structured models provide a substantial improvement over their non-hierarchical counterparts. Using these models, we demonstrate that several alternative crop-rotations are effective in reducing weed densities. Rotations with high wheat prevalence exhibit the most severe infestations, and diverse rotations generally have lower weed densities. However, a key outcome is that in many cases the effect of crop rotation is small compared to the high variability arising from spatio-temporal heterogeneity. This result highlights the need to monitor and model population dynamics across large spatial and temporal scales in order to account for variation in the drivers of plant dynamics. Our framework for data collection and modelling provides a means to achieve this.</p>
Data from: Refining trophic dynamics through multi-factor Bayesian mixing models: a case study of subterranean beetles.
<p>Food web dynamics are vital in shaping the functional ecology of ecosystems. However, trophic ecology is still in its infancy in groundwater ecosystems due to the cryptic nature of these environments. To unravel trophic interactions between subterranean biota, we applied an interdisciplinary Bayesian mixing model design (multi-factor BMM) based on the integration of faunal C and N bulk tissue stable isotope data (δ<sup>13</sup>C and δ<sup>15</sup>N) with radiocarbon data (Δ<sup>14</sup>C), and prior information from metagenomic analyses. We further compared outcomes from multi-factor BMM with a conventional isotope double proxy mixing model (SIA BMM), triple proxy (δ<sup>13</sup>C, δ<sup>15</sup>N and Δ<sup>14</sup>C, multi-proxy BMM) and double proxy combined with DNA prior information (SIA+DNA BMM) designs. Three species of subterranean beetles (<i>Paroster macrosturtensis</i>, <i>Paroster mesosturtensis</i> and <i>Paroster microsturtensis</i>) and their main prey items Chiltoniidae<i> </i>amphipods (AM1: <i>Scutachiltonia axfordi</i> and AM2: <i>Yilgarniella sturtensis</i>), cyclopoids and harpacticoids from a calcrete in Western Australia were targeted. Diet estimations from stable isotope only models indicated homogeneous patterns with modest preferences for amphipods as prey items. Multi-proxy BMM suggested increased - and species-specific - predatory pressures on amphipods coupled with high rates of scavenging/predation on sister species. SIA+DNA BMM showed marked preferences for amphipods AM1 and AM2 and reduced interspecific scavenging/predation on <i>Paroster </i>species. Multi-factorial BMM revealed the most precise estimations (lower overall SD and very marginal beetles' interspecific interactions), indicating consistent preferences for amphipods AM1 in all the beetles' diets. Incorporation of genetic priors allowed crucial refining of the feeding preferences, while integration of more expensive radiocarbon data as a third proxy (when combined with genetic data) produced more precise outcomes but close dietary reconstruction to that from SIA+DNA BMM. Further multidisciplinary modelling from other groundwater environments will help elucidate the potential behind these designs and bring light to the feeding ecology of one the most vital ecosystems worldwide.</p>
Data from: Ecosystem function in predator-prey food webs - confronting dynamic models with empirical data
1. Most ecosystem functions and related services involve species interactions across trophic levels, e.g. pollination and biological pest control. Despite this, our understanding of ecosystem function in multi-trophic communities is poor, and research has been limited to either manipulations in small communities or statistical descriptions in larger ones. 2. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally non-aggregated abundance data from the field, and to provide mechanistic insight into the function of predation. 3. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. 4. The model captured >70% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body-size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analyzing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. 5. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size.
Data from: Distinguishing distribution dynamics from temporary emigration using dynamic occupancy models
1. Dynamic occupancy models are popular for estimating dynamic distribution rates (colonization and extinction) from repeated presence/absence surveys of unmarked animals. This approach assumes closure among repeated samples within primary periods, allowing estimation of dynamic rates between these periods. However, the impact of temporary emigration (reversible changes in sampling availability) on dynamic rate estimates, has not been tested. 2. Using simulated data, we investigated the degree to which temporary emigration could mislead researchers interested in quantifying dynamics. We then compared results from three avian point count datasets to evaluate the likelihood that temporary emigration confounds estimates of dynamics for 19 species under a popular sampling protocol. 3. Simulated experiments indicated that when secondary periods were open to temporary emigration, presence of dynamics was correctly identified ≥ 95.1% of the time, and dynamic rate estimates were accurate. However, dynamic rate estimates were biased when secondary periods were closed to temporary emigration. In empirical datasets, dynamic occupancy models had greater support than closed models for all species when secondary sampling periods occurred in immediate succession (i.e., 3 samples within 10 minutes); however, our results suggest that this is because these estimates were heavily influenced by temporary emigration. When counts within a primary period were separated by 24-48 hours, we found evidence of dynamics for less than half of these species. We recommend an alternative sampling approach that allows accurate estimation of dynamic rates when temporary emigration is of no interest, and introduce a novel model for estimating both processes simultaneously in rare cases where they are both of biological interest. 4. Concern for violating the occupancy modeling closure assumption has led to widespread recommendations that samples within primary periods be conducted extremely close in time. However, this may not be the best approach when interest is in quantifying dynamic rates. While dynamic occupancy models provide estimates of 'colonization' and 'extinction,' these values do not inherently represent dynamics unless temporary emigration has been explicitly modeled, or accounted for with sampling design. Naiveté to this fact can result in incorrect conclusions about biological processes.
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, models and demos associated to "Celldetective: an AI-enhanced image analysis tool for unraveling dynamic cell interactions"
<p>This repository contains datasets, models and demos associated to <a href="https://github.com/remyeltorro/celldetective">Celldetective</a>, a software for single-cell analysis from multimodal time lapse microscopy images. </p> <h1>Demos</h1> <h2>Cell-cell interaction assay: ADCC</h2> <p>We imaged a co-culture of MCF-7 breast cancer cells (targets) and human primary NK cells (effectors), interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). The nuclei of all cells are marked with the Hoechst nuclear stain, the dead nuclei with the propidium iodide nuclear stain, the cytoplasm of the NK cells with CFSE. The system in epifluorescence and brightfield at either 20 or 40X magnification. We provide a single position demo for the ADCC assay, as "demo_adcc.zip". After unzipping, the demo_adcc folder can be loaded in Celldetective for testing. </p> <h2>Cell-surface interaction assay: RICM</h2> <p>We imaged human primary NK cells engaging in spreading with a surface coated with a bispecific antibody similar to the one used in the ADCC assay (replacing the target cells with a flat surface). The system is imaged using the RICM technique. Images are normalized using a median estimate of the background, pooled from all the positions in a well and dividing the images by this estimate. Here, we provide a single position demo for the cell-surface interactiona assay imaged in RICM, as "demo_ricm.zip". As above, after unzipping, the experiment can be tested and processed in Celldetective.</p> <h1>Datasets</h1> <h2>Image annotations for segmentation</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated two sets of annotations from images of a co-culture of MCF-7 breast cancer cells and human primary NK cells, interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). Since there are two separate cell populations of interest, the targets (MCF-7) and effectors (NK cells), we curated two datasets. Each sample in a dataset consists of a multichannel image (up to five channels in the context of ADCC, among brightfield , Hoechst nuclear stain, PI nuclear stain, CFSE, LAMP1), the associated instance segmentation annotation for the population of interest and a json file summarizing the content of each channel and the spatial calibration of the image. These sample data are generated directly in Celldetective, using a custom napari plugin.</p> <ul> <li>db_mcf7_nuclei_w_lymphocytes: MCF-7 cell nuclei are annotated specifically on images where primary NK cells (or rarely primary T cells), and RBCs co-exist. The annotation exploits up to four channels simultaneously.</li> <li>db_primary_NK_w_mcf7: human primary NK cells, with annotated cytoplasm (mostly from CFSE) but exploiting brightfield and Hoechst to segment out of focus or poorly labelled cells.</li> </ul> <p>These datasets are used to train several segmentation models to segment on one hand the MCF-7 nuclei and on the other hand the primary NK cells.</p> <h3>Cell-surface interaction assay: RICM</h3> <ul> <li>db_spreading_lymphocytes: we provide a dataset of primary NK cells (and occasionnaly mice T cells) imaged in RICM (with sometimes paired brightfield images). Cells are detected as soon as they start forming interferences on the image (hovering behavior). A pre-annotation was performed using a threshold based segmentation on the RICM modality. Manuel separation of cell-cell contacts and removal of false positive objects was performed by an expert annotator (using brightfield when available). RBCs are ignored in the annotations. </li> </ul> <h2>Single-cell signal annotations for classification and regression</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated several signal classification/regression datasets with Celldetective to characterize the ADCC assay. Briefly, for a given event cells can be classified as "the event occured during the observation", "no event occured during the observation", "the event already occured prior to observation". If the event occurred during the observation, we can estimate when (the regression). Each single-cell is a dictionary with a collection of signals. The attribute "class" sets the class and "t0" the time of event (default is -1 for absence of event). </p> <ul> <li>db-si-NucPI: classification and regression of single-cells with respect to lysis events characterized by a strong PI increase upon lysis (also associated with decreasing nuclear area and sometimes a decreasing Hoechst)</li> <li>db-si-NucCondensation: classification and regression of single-cells with respect to nucleus shrinking events characterized by a decreasing nuclear area (UPDATE on 23/01/2024)</li> </ul> <h1>Models</h1> <h2>Segmentation models</h2> <h3>Generalist models</h3> <p>We integrated in Celldetective select published models for cellular segmentation from StarDist and Cellpose. We wraped the models with an input configuration to help Celldetective handle the normalization, rescaling and channel selection upon inference. </p> <ul> <li>Cellpose [1,2]: <em>cyto3</em>, <em>livecell</em>, <em>tissuenet</em>, <em>nuclei</em></li> <li>StarDist [3]: <em>versatile_fluo</em>, <em>versatile_he</em></li> </ul> <p>If you use any of these models your research, don't forget to cite the StarDist or Cellpose papers accordingly!</p> <h3>ADCC models</h3> <ul> <li>MCF-7 (in the presence of lymphocytes): <em>mcf7_nuc_multimodal, mcf7_nuc_stardist_transfer</em></li> <li>primary NKs (in the presence of MCF-7): <em>primNK_multimodal</em>, <em>primNK_SD</em>, <em>primNK_cfse</em></li> </ul> <h3>Spreading-assay models</h3> <ul> <li>Lymphocytes: <em>lymphocytes_ricm</em></li> </ul> <h2>Signal analysis models</h2> <p>We developed Deep Learning models that classify and regress the time of events from single-cell signals, applied to the ADCC assay.</p> <ul> <li> lysis detection: <em>lysis_H_PI</em>, <em>lysis_PI_area</em><em>. </em>Detect lysis events characterized at least by an increase of PI from one or more measurements (respectively PI+Hoechst and PI+nucleus area, trained on db-si-NucPI)</li> <li>nucleus shrinking detection:<em> NucCond</em>. Detect nucleus shrinking events from nuclear area signal (db-si-NucCondensation)</li> </ul> <h1>References</h1> <ol> <li>Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021).</li> <li>Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022).</li> <li>Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-030-00934-2_30.</li> </ol> <p> </p> <p> </p>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</p>
On the computation of stable coupled state-space models for dynamic substructuring applications
<p>This paper aims at introducing a methodology to compute stable coupled state-space models for dynamic substructuring applications by introducing two novel approaches targeted to accomplish this task: (a) a procedure to impose Newtons's second law without relying on the use of undamped RCMs (residual compensation modes) and (b) a novel approach to impose stability on unstable coupled state-space models. The enforcement of stability is performed by dividing the unstable model into two different models, one composed by the stable poles (stable model) and the other composed by the unstable ones (unstable model). Then, the poles of the unstable state-space model are forced to be stable, leading to the computation of a stabilized state-space model. If this model is composed by real poles, it should be divided into two different ones, one composed by the pairs of complex conjugate poles and the other composed by the real poles. Afterwards, to make sure that the Frequency Response Functions (FRFs) of the stabilized model well match the FRFs of the unstable model, the Least-Squares Frequency Domain (LSFD) method is exploited to update the modal parameters of the stabilized model composed by the pairs of complex conjugate poles. The validity of the proposed methodologies is presented and discussed by exploiting experimental data. Indeed, by exploiting the FRFs of a real system, accurate state-space models respecting Newton's second law are computed. Then, decoupling and coupling operations are performed with the identified state-space models, no matter the models resultant from the decoupling/coupling operations are unstable. Stability is then imposed on the computed unstable coupled model by following the approach proposed in this paper. The methodology proved to work well on these data. Moreover, the paper also shows that the coupled state-space models obtained using this methodology are suitable to be exploited in time-domain analyses and simulations.</p>
Hybrid dynamic model for shape memory alloy linear and unimorph actuators
<p>Shape memory alloy morphing actuators are a type of soft actuator with many attractive properties. These actuators exhibit large deformation, small form factor, self-sense ability, and physical reservoir computing potential, while also being inexpensive. These morphing actuators are composed of active shape memory alloy wires and a passive base layer that is used to magnify the overall deflection. Although morphing actuators have great potential, the modeling of shape memory alloy actuators is difficult due to both shape memory alloy characteristics and the nonlinearity of the passive layer. Here, a hybrid dynamical model is proposed that couples the phase kinetics & thermal modeling for the shape memory alloy with a dynamic Cosserat nonlinear beam model. This hybrid model is benchmarked against linear and morphing experimental actuators. The model resulted in a root mean squared error of 1.48 mm and 1.63 mm for the morphing actuator configuration for two different actuators. This model can expand the capability and design of novel morphing actuators for a designed deformation profile for use in soft robotics.</p>
Static and Dynamic Model Calibration for Upper Thermosphere Determination (2023SW003810)
<p>The dataset contains the background and static calibration models, associated with the figure data and routines. </p>
Dynamics of leaching of POPs and additives from plastic in a Procellariiform gastric model: Diet and polymer dependent effects and implications for long-term exposure
<p>Procellariiform seabirds are known to have high rates of plastic ingestion. We investigated the bioaccessibility of plastic-associated chemicals [plastic additives and sorbed persistent organic pollutants (POPs)] leached from plastic over time using an in vitro Procellariiform gastric model. High-density polyethylene (HDPE) and polyvinyl chloride (PVC), commonly ingested by Procellariiform seabirds, were manufactured with one additive [decabrominated diphenyl ether (PBDE-209) or bisphenol S (BPS)]. HDPE and PVC added with PBDE-209 were additionally incubated in salt water with 2,4,4'-trichloro-1,1'-biphenyl (PCB-28) and 2,2',3,4,4',5'-hexachlorobiphenyl (PCB-138) to simulate sorption of POPs on plastic in the marine environment. Our results indicate that the type of plastic (nature of polymer and additive), presence of food (i.e., lipids and proteins) and gastric secretions (i.e., pepsin) influence the leaching of chemicals in a seabird. In addition, 100% of the sorbed POPs were leached from the plastic within 100 hours, while only 2-5% of the additives were leached from the matrix within 100 hours, suggesting that the remaining 95% of the additives could continue to be leached. Overall, our study illustrates how plastic type, diet and plastic retention time can influence a Procellariform's exposure risk to plastic-associated chemicals.</p>
Data for: Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model
<p>Chemotactic bacteria not only navigate chemical gradients but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real-time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering new insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.</p>
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
Data from: Early diversification dynamics in a highly successful insular plant taxon are consistent with the general dynamic model of oceanic island biogeography
<p>The general dynamic model (GDM) of oceanic island biogeography views oceanic islands predominantly as sinks rather than sources of dispersing lineages. To test this, we conducted a biogeographic analysis of a highly successful insular plant taxon, <em>Cyrtandra </em>and inferred directionality of dispersal and founder events throughout the four biogeographical units of the Indo-Australian Archipelago (IAA), namely Sunda, Wallacea, Philippines, and Sahul. Sunda was recovered as the major source area, followed by Wallacea, a system of oceanic islands. The relatively high number of events originating from Wallacea is attributed to its central location in the IAA and its complex geological history selecting for increased dispersibility. We also tested if diversification dynamics in <em>Cyrtandra </em>follow predictions of adaptive radiation, which is the dominant process as per the GDM. Diversification dynamics of dispersing lineages of <em>Cyrtandra</em> in the Southeast Asian grade showed early bursts followed by a plateau, which is consistent with adaptive radiation. We did not detect signals of diversity-dependent diversification, and this is attributed to Southeast Asian cyrtandras<em> </em>occupying various niche spaces, evident by their wide morphological range in habit and floral characters. The Pacific clade, which arrived at the immaturity phase of the Pacific Islands, showed diversification dynamics predicted by the Island Immaturity Speciation Pulse (IISP) model, wherein rates increase exponentially, and their morphological range is controlled by the least action effect favoring woodiness and fleshy fruits. Our study provides a first step toward a framework for investigating diversification dynamics as predicted by the GDM in highly successful insular taxa.</p>
The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar-Bernevig-Hughes model
<p>Data in the form of mx and dat files for "The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar-Bernevig-Hughes model", T. Masłowski, and N. Sedlmayr<br>Journal of Physics: Condensed Matter 36, 335401 (2024), <a href="https://doi.org/10.1088/1361-648X/ad4a16">https://doi.org/10.1088/1361-648X/ad4a16</a>.</p> <p>Included are data for the return rate (RR), Fisher zeroes, and Loschmidt eigenvalues (MEV). Numbers in parentheses refer to {100m,100m'} and Lx and Ly are the system sizes. Within the return rate files the third column is the derivative of the return rate.</p>
Data from: Dynamic models for impact-initiated stress waves through snow columns
<p>The objective of this research is to model snow's response to dynamic, impact loading. Two constitutive relationships are considered: elastic and Maxwell-viscoelastic. These material models are applied to laboratory experiments consisting of 1000 individual impacts across 22 snow column configurations. The columns are 60 cm tall with a 30 cm by 30 cm cross-section. The snow ranges in density from 135-428 kg m<sup>-3</sup> and is loaded with both short-duration (~1 ms) and long-duration (~10 ms) impacts. The Maxwell-viscoelastic model more accurately describes snow's response because it contains a mechanism for energy dissipation, which the elastic model does not. Furthermore, the ascertained model parameters show a clear dependence on impact duration; shorter duration impacts resulted in higher wave speeds and greater damping coefficients. The stress wave's magnitude is amplified when it hits a stiffer material because of the positive interference between incident and reflected waves. This phenomenon is observed in the laboratory and modeled with the governing equations.</p>
Prediction model of the temporal dynamics of severe pest cashew Anacampsis phytomiella using artificial neural networks
Open the record for dataset details and reuse information.
An ulvophycean marine green alga produces large parthenogenetic isogametes as predicted by the gamete dynamics model for the evolution of anisogamy
<p>In eukaryotes, the gamete size difference between the two sexes (anisogamy) evolved from gametes of equal size in both mating types (isogamy) and is plausibly claimed to generate sexual selection in morphology and behaviour. The gamete dynamics (GD) model for anisogamy evolution combines gamete limitation and competition and predicts that, if gametes of both mating types can develop parthenogenetically (i.e. without fusing with the opposite mating type), large isogamy can evolve under gamete-limited conditions. Ulvophycean marine green algae that exhibit various gametic systems from isogamy to anisogamy are important models for testing such theories. However, in most previous papers, whether a species is isogamous or anisogamous has not been examined statistically, which leaves the above theoretical prediction untested. We reveal (i) that the gametic system of <em>Struvea okamurae</em> is large isogamy using a generalized linear mixed model (GLMM), which accounted for the variation of gamete size among individual gametophytes, and (ii) that gametes of this alga can actually develop parthenogenetically, contrary to a previous report. Habitat environments and gametic behaviour suggest that this alga might experience gamete-limited conditions. <em>S. okamurae</em> seems to produce large parthenogenetic isogametes following GD model predictions, as an adaptation to deep waters.</p>
Dataset for the SFmodel, applied in Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Dataset used for the SFmodel, applied in the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>For units and nomenclature of the variables refer to Units_and_Nomenclature_for_SFmodel_in_Evapotranspiration_dynamics_and_partitioning_in_a_grassed_vineyard.pdf. </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.