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11,506 results for “Dynamics”
Will forest dynamics continue to accelerate throughout the 21st century in the Northern Alps?
Observational evidence suggests that forests in the Northern Alps are changing at an increasing rate as a consequence of climate change. Yet, it remains unclear whether the acceleration of forest change will continue in the future, or whether downregulating feedbacks will eventually decouple forest dynamics from climate change. Here we studied future forest dynamics at Berchtesgaden National Park, Germany by means of a process-based forest landscape model, simulating an ensemble of 22 climate projections until the end of the 21st century. Our objectives were (i) to assess whether the observed acceleration of forest dynamics will continue in the future, (ii) to analyze how uncertainty in future climate translates to variation in future forest disturbance, structure, and composition, and (iii) to determine the main drivers of future forest dynamics. We found that forest dynamics continue to accelerate in the coming decades, with a trend towards denser, structurally more complex and more species rich forests. However, changes in forest structure leveled off in the second half of the 21st century regardless of climate scenario. In contrast, climate scenarios caused trajectories of tree species change to diverge in the second half of the 21st century, with stabilization under RCP 2.6 and RCP 4.5 scenarios and accelerated loss of conifers under RCP 8.5. Disturbance projections were 3 to 20 times more variable than future climate, whereas projected future forest structure and composition varied considerably less than climate. Indirect effects of climate change via alterations of the disturbance regime had a stronger impact on future forest dynamics than direct effects. Our findings suggest that dampening feedbacks within forest dynamics will decelerate forest change in the second half of the 21st century. However, warming beyond the levels projected under RCP 4.5 might profoundly alter future forest disturbance and composition, challenging conservation efforts and ecosystem service supply. --
Seedling recruitment and sapling bank dynamics on fluvial deposits in a temperate montane riparian forest.
<p>This is a dataset of Seedling recruitment and sapling bank dynamics on fluvial deposits in a temperate montane riparian forest.</p> <p>The followings are details of each file.</p> <p><strong>Saplings_inFluvialDepositsv1.0.0.csv</strong></p> <ul> <li><code>Plot_x</code> Factor. X coordinates of plots.</li> <li><code>Plot_y</code> Factor. Y coordinates of plots.</li> <li><code>x</code> Integer. X coordinates in plots.</li> <li><code>y</code> Integer. Y coordinates in plots.</li> <li><code>Substrate</code> Factor. Established substrates. NA means that it was not recorded.</li> <li><code>stemID</code> Character. The ID of individual trees.</li> <li><code>Sp.</code> Factor. The species names.</li> <li><code>Family</code> Factor. The family name of the species.</li> <li><code>Heightyyyy</code> Numeric. Vertical heights of trees (cm) in yyyy. Height2007ad is the heights after disturbance in 2007.</li> <li><code>Lengthyyyy</code> Numeric. Length of trees (cm) in yyyy. Length2007ad is the length after disturbance in 2007.</li> <li><code>DBH1_yyyy</code>, <code>DBH2_yyyy</code> Numeric. Diameter at breast height (mm) in yyyy. DBH1 and DBH2 were measured to cross at right angles.</li> <li><code>Noteyyyy</code> Character. Comments in yyyy.</li> </ul> <p> </p> <p><strong>Seedlings_inFluvialDepositsv1.0.0.csv</strong></p> <ul> <li><code>Plot</code> Factor. The plot ID.</li> <li><code>ID</code> Character. The individual ID.</li> <li><code>Sp.</code> Factor. The species names.</li> <li><code>Family</code> Factor. The family names of species.</li> <li><code>Hyyyy</code> Numeric. Vertical height of trees (cm) in yyyy.</li> <li><code>Ageyyyy</code> Numeric. The years of trees (cm) in yyyy.</li> <li><code>noteyyyy</code> Character. Comment in yyyy.</li> </ul> <p> </p> <p><strong>Map_FluvialDeposits.pdf</strong></p> <ul> <li><code>p. 1</code>: The overall picture of the positional relations between each gap.</li> <li><code>p. 2</code>: The details of seedling quadrats.</li> </ul> <p> </p> <p><strong>Metadata_Saplings_inFluvialDeposits.txt</strong><br> Metadata of "<strong>Saplings_inFluvialDepositsv1.0.0.csv</strong>".<br> It is the same as this description.</p> <p> </p> <p><strong>Seedlings_inFluvialDepositsv1.0.0.csv</strong><br> Metadata of "<strong>Seedlings_inFluvialDepositsv1.0.0.csv</strong>".<br> It is the same as this description.</p>
Data and Software for "Complex Dynamics in a Synchronized Cell-Free Genetic Clock"
<p>Contains raw data, analysis scripts, simulation scripts, device operation software for the publication: "Complex Dynamics in a Synchronized Cell-Free Genetic Clock".</p>
Data used to reconstruct carbon dynamics of Siikaneva, Kalevansuo and Lompolojänkkä.
<p>Peat core data from three Finnish peatlands used to reconstruct site-scale carbon exchange between the peatlands and the atmosphere. Basal peat radiocarbon ages; reconstructed peat area over time based on these basal ages; carbon accumulation data along multiple peat cores; additionally reconstructed peat type and methane emission rates.</p>
Modeling output for "The dynamic atmospheric and aeolian environment of Jezero crater, Mars"
<p>This dataset contains meso- and microscale numerical modeling output supporting the findings presented in the paper, "Newman et al., The dynamic atmospheric and aeolian environment of Jezero crater, Mars, Science Advances"</p>
Dynamics of global wetlands by TOPMODEL
<p>Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a TOPMODEL-based diagnostic model, we produced an ensemble of 28 gridded maps of monthly global/regional wetland extent products at 0.25° × 0.25° spatial resolution based on four observation-based wetland products and seven reanalysis soil moisture datasets for the period 1980–2020. The parameters of the model are calibrated on grid-scale against four observation-based wetland products. Overall, our products can capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with satellite-based terrestrial water storage estimates. The resulting mean annual maximum global wetland area fluctuates within a range of 3.3–5.6 Mkm<sup>2</sup>. The long temporal coverage beyond the era of satellite datasets, the global coverage and the opportunity to provide real-time update from ongoing SM data make these products helpful for various applications such as analyzing the wetland-related methane emission.</p>
Common Dynamic Determinants Govern Quorum Quenching Activity in N-terminal Serine Hydrolases
<ul> <li> <p>(File-01) Free enzymes molecular dynamics:</p> <ul> <li> <p>input parameters and topologies for aPGA, ecPGA and paPvdQ enzymes</p> </li> <li> <p>general input files for MD simulations in AMBER</p> </li> <li> <p>output restart files from minimization, equilibration and production runs</p> </li> <li> <p>output files from minimization, equilibration and production runs</p> </li> <li> <p>raw data for analysis and visualization:</p> <ol> <li> <p>protein backbone RMSD evolution</p> </li> <li> <p>binding cavity dynamics analysis</p> </li> <li> <p>principal component analysis of catalytic machinery (with states' representatives in PDF format)</p> </li> </ol> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(File-02) Ligand-enzyme complexes molecular dynamics:</p> <ul> <li> <p>input parameters and topologies for aPGA, ecPGA and paPvdQ in complex with C06- and C08-HSL molecules</p> </li> <li> <p>general input files for MD simulations in AMBER</p> </li> <li> <p>output restart files from minimization, equilibration and production runs</p> </li> <li> <p>output files from minimization, equilibration and production runs</p> </li> <li> <p>raw data for analysis and visualization:</p> <ol> <li> <p>protein backbone RMSD evolution</p> </li> <li> <p>near-attack-conformation (NAC) stabilization</p> </li> <li> <p>HSLs RMSD evolution</p> </li> <li> <p>MM/PBSA binding energy estimation</p> </li> <li> <p>HSLs heavy atoms RMSF</p> </li> </ol> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(File-03) Michaelis complex ensemble generation molecular dynamics:</p> <ul> <li> <p>input parameters and topologies for aPGA, ecPGA and paPvdQ in complex with C06- and C08-HSL molecules</p> </li> <li> <p>general input files for MD simulations in AMBER</p> </li> <li> <p>output restart files from ensemble generation production runs</p> </li> <li> <p>output files from ensemble generation production runs</p> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(Files-04-06) Ligand-enzyme QM/MM steered molecular dynamics:</p> <ul> <li> <p>input parameters for aPGA, ecPGA and paPvdQ in complex with C06- and C08-HSL molecules</p> </li> <li> <p>ensemble of input restart files generated in stage 3</p> </li> <li> <p>general input files for QM/MM steered MD simulations in AMBER</p> </li> <li> <p>output restart files from QM/MM MD equilibration simulations and QM/MM steered MD simulations</p> </li> <li> <p>output files and output work from QM/MM steered MD simulations</p> </li> </ul> </li> </ul> <p> </p> <ul> <li> <p>(File-07) Ligand-enzyme QM/MM steered molecular dynamics data for analysis and visualization:</p> <ul> <li> <p>reaction states ensembles (in PDB format) extracted from QM/MM steered MD simulations with crucial distances measured</p> </li> <li> <p>evolution of the reaction coordinate elements in the first and second step of acylation</p> </li> <li> <p>representative states of the reaction stages for visualization (in PDB format)</p> </li> <li> <p>different dynamics of the residues gating access to acyl-binding cavity at TS1</p> </li> <li> <p>different dynamics of the residues gating overall access to active site at TS2a</p> </li> <li> <p>different system-dependent bending of the HSLs at TS1 and TS2a</p> </li> </ul> </li> </ul> <p> </p>
Opinion dynamics in social network under competition: the role of influencing factors in consensus reaching
<p>The profitability of opinion and the finiteness of individual attention have already spawned the extensive competition for individual preferences on social networks. It's quite necessary to investigate the opinion dynamics over social networks in a competitive environment. To this point, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize the opinion evolution in a competitive opinion dynamics. Based on the DGCG model, we obtain equilibrium results in the stable state of opinion evolution. Consecutively, we analyze what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions and network structure. Theoretical analyses and simulation experiments show that these factors can significantly sway the consensus and even reverse competition outcomes.</p>
Fig. 1 in Dynamics Of Mouse-Like Rodent Communities In Anthropogenically Disturbed Territories Of The Southeast Of Western Siberia (Kemerovo Region, Russia)
Fig. 1. The dynamics of similarity of small mammal populations in deforested zones compared to the initial population in taiga (using the Czekanowsky-SØrensen coefficient calculated for species' percentage in the community).
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.
Fig. 4 in Structure And Dynamics Of Crucians' Settlements (Cypriniformes, Сarassius) In Water Systems Of Eastern Ukraine
Fig. 4. Distribution pattern and frequency of occurrence of the aboriginal C. сarassius (marked with red color) and the representatives of the group C. (superspecies auratus) (marked with green color) in river systems of Eastern Ukraine within the last 100 years: а — 1918–1958; b — 1972–1981; c — 1987–2003; d — 2006–2009.
Fig. 1 in The Seasonal Population Dynamics Of The Cyclopoid Copepods (Cyclopoida, Cyclopidae) In Ponds Of Kyiv Region (Ukraine)
Fig. 1. Seasonal population dynamics of the cyclopids in the pond near the village Khotov: 1 — abundance of cyclopids; 2 — water temperature of pond near the village Khotov during the period of the study.
Fig. 2 in Some Factors Behind Density Dynamics Of Bat Flies (Diptera, Nycteribiidae) - Ectoparasites Of The Boreal Chiropterans: Omitted Predictors And Hurdle Model Identification
Fig. 2. Observed (bars) and expected (PMF) host infestation by Nycteribiidae bat flies: before/after (top/bottom) host mating; host females/males (left/right). No zero truncation and the used categorisation (pooled both host species and bat flies species) are the reasons of relatively bad fit to Poisson distribution.
Fig. 3 in Some Factors Behind Density Dynamics Of Bat Flies (Diptera, Nycteribiidae) - Ectoparasites Of The Boreal Chiropterans: Omitted Predictors And Hurdle Model Identification
Fig. 3. Observed (all kinds of dots) and expected (lines: y = exp (m+Acos (2pi (x–c)/36 — f) or y = b0exp (–b1x)) seasonal density dynamics of Nycteribiidae bat flies. Filled circles and thin lines — normally infested males; open circles and solid lines — normally infested females; double crosses and dashed lines — super-infested males; crosses and dashed lines — super-infested females. N o t e: Infested host only.
Small extracellular vesicles from malignant ascites of patients with advanced ovarian cancer provide insights into the dynamics of the extracellular matrix
<p>Representative Atomic Force Microscope images collected in liquid on Extracellular Vesicles from Ascite and Cell cultures. Data were collected by Broker Multimode VIII. Images like these were used in the paper to measure the contact angle and size distribution.</p>
Dynamics of Collective Modes in an unconventional Charge Density Wave system BaNi2As2 - Raw Data
<p>This repository includes two datasets included in the study "Dynamics of Collective Modes in an unconventional<br> Charge Density Wave system BaNi2As2". Two datasets are included:</p> <p>Temperature_dependent_reflectivity_changes.dat</p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat</p> <p>Temperature_dependent_reflectivity_changes.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 for sample temperatures between 13 K and 149K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective temperatures (constant fluence of 0.4 mJ cm<sup>−2</sup>).</p> <p> </p> <p>Fluence_dependent_reflectivity_changes_at 10K.dat contain photoinduced reflectivity transients, recorder on BaNi2As2 at 10 K. The first column is time-delay, other columns are the corresponding photoinduced reflectivity traces recorded at respective fluences. Each signal has been normalized to the respective fluence.</p> <p> </p>
Data of curvature model for the study of nanoparticle size effects on amyloid fibril stability and molecular dynamics simulations data
<p>The data provided refer to our published article:</p> <p>T. John, J. Adler, C. Elsner, J. Petzold, M. Krueger, L.L. Martin, D. Huster, H.J. Risselada, B. Abel, Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation, J. Colloid Interface Sci. 622 (2022), 804–818. <a href="https://doi.org/10.1016/j.jcis.2022.04.134">https://doi.org/10.1016/j.jcis.2022.04.134</a></p> <p>This article is accompanied by a 'Data in Brief' article that explains in more detail the use of the curvature model and our molecular dynamics (MD) simulations:</p> <p>T. John, L.L. Martin, H.J. Risselada, B. Abel, Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data, Data Brief 45 (2022), 108598. <a href="https://doi.org/10.1016/j.dib.2022.108598">https://doi.org/10.1016/j.dib.2022.108598</a></p>
Negative temporal autocorrelation in mast seeding dynamics positively influences both the long and short-term dynamics of a wild boar population
<p><span>Temporal autocorrelation in environmental conditions influences population dynamics through its effects on vital rates. However, a comprehensive understanding of how and to what extent temporal autocorrelation shapes population dynamics is still lacking because most empirical studies have unrealistically assumed that environmental conditions are temporally independent. Mast seeding is a biological event characterized by highly fluctuating and synchronized seed production at the tree population scale, as well as a marked negative temporal autocorrelation. In the current context of global change, mast seeding events are expected to become more frequent, leading to strengthened negative temporal autocorrelations and thereby amplified cyclicality in mast seeding dynamics with cycles of length 2 years. </span><span>Theory predicts that population growth rates are maximized when the environmental cyclicality of consumer resources and their generation times are closely matched. </span><span>To test this prediction, we took advantage of the long-term monitoring of a wild boar population, a widespread seed consumer species characterized by a short generation time (ca. 2 years). As expected, simulations indicated that its stochastic population growth rate increased as mast seeding dynamics became more negatively autocorrelated. Our findings demonstrate that accounting for temporal autocorrelations in environmental conditions relative to generation time of the focal population is required, especially under global warming where the cyclicality in resource dynamics is likely to change.</span></p>
Data set for: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire
<p>This data set contains both the experimental data and the simulation scripts to reproduce the results of [1]: <em>Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire</em> by M. T. Birch, D. Cortés-Ortuño, K. Litzius, S. Wintz, F. Schulz, M. Weigand, A. Štefančič, D. Mayoh, G. Balakrishnan, P.D. Hatton, G. Schütz. A preprint of this publication is available at <a href="https://www.researchsquare.com/article/rs-1235546/v1">https://www.researchsquare.com/article/rs-1235546/v1</a>.</p> <p>This data set is also hosted in Github: <a href="https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings">https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings</a></p> <p>If you find this material useful please cite us</p> <pre><code>@Misc{Birch2022, author = {M. T. Birch and D. Cort\'es-Ortu\~no}, title = {{Data set for: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire}}, howpublished = {Zenodo \url{doi:10.5281/zenodo.6393340}. Github: \url{https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings}}, year = {2022}, doi = {10.5281/zenodo.6393340}, url = {https://doi.org/10.5281/zenodo.6393340}, }</code></pre> <p> </p>
Simulated Forest Aboveground Biomass Dynamics, Northeastern USA
<p>This dataset includes aboveground biomass (AGB) growth trajectories for the first 300 years of forest succession over the Regional Greenhouse Gas Initiative (RGGI) domain, which includes the states of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. These data were derived from a process-based ecosystem model called the Ecosystem Demography (ED) model (Hurtt et al 1998; Moorcroft et al. 2001; Ma et al. 2022a). Here, ED was run at a spatial resolution of 1 km with forcings including meteorology from Daymet (Thornton et al 2016) and MERRA2 (Gelaro et al. 2017) and soil hydraulic properties from POLARIS (Chaney et al 2016) and CONUS-SOIL (Miller and White 1998). This dataset is spatially interpolated from its native resolution of 1 km to 30 m to support small scale data analysis. The unit is kg C/m2.</p> <p>This dataset can support multiple applications relevant to reforestation and afforestation planning. Utilizing this stack of annualized and spatially explicit forest growth trajectories, data users can estimate how much carbon could be stored via natural regeneration in any particular geographic location by any point over the next 300 years under current environmental conditions (air temperature, precipitation, CO2, etc). These data are currently being utilized by the State of Maryland to support climate-smart afforestation and serve as the basis for several carbon sequestration calculations in the University of Maryland Peer-Reviewed Offset Protocol for Maryland Reforestation/Afforestation Projects.</p> <p>This dataset is the underlying input to a high-resolution forest carbon modeling system developed for the RGGI region. This modeling system combines modeled AGB growth with forest canopy height from airborne lidar data and tree cover fraction to estimate contemporary AGB, carbon sequestration potential, carbon sequestration potential gap and time to reach carbon sequestration potential. More details about the modeling system and ED simulation can be found Ma et al. 2021 and related data products can be found in Ma et al. 2022b.</p> <p>For questions and support please contact lma6@umd.edu, rachlamb@umd.edu and gchurtt@umd.edu. </p> <p><strong>References</strong></p> <p>Chaney N W, Wood E F, McBratney A B, Hempel J W, Nauman T W, Brungard C W and Odgers N P 2016 POLARIS: a 30-meter probabilistic soil series map of the contiguous United States Geoderma 274 54–67. <br> https://doi.org/10.1016/j.geoderma.2016.03.025 </p> <p>Gelaro R et al 2017 The modern-era retrospective analysis for research and applications, version 2 (MERRA-2) J. Clim. 30 5419–54. https://doi.org/10.1175/JCLI-D-16-0758.1 <br> Hurtt, G.C., P.R. Moorcroft, S.W. Pacala, and S.A. Levin. 1998 Terrestrial models and global change: challenges for the future. Global Change Biology 4:581-590. https://doi.org/10.1046/j.1365-2486.1998.t01-1-00203.x</p> <p>Ma, L., G. Hurtt, H. Tang, R. Lamb, E. Campbell, R. Dubayah, M. Guy, W. Huang, A. Lister, J. Lu, J. O’Neil-Dunne, A. Rudee, Q. Shen, and C. Silva. 2021. High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA. Environmental Research Letters 16:045014. https://doi.org/10.1088/1748-9326/abe4f4</p> <p>Ma, L., G. Hurtt, L. Ott, R. Sahajpal, J. Fisk, R. Lamb, H. Tang, S. Flanagan, L. Chini, A. Chatterjee, and J. Sullivan. 2022a. Global evaluation of the Ecosystem Demography model (ED v3.0). Geoscientific Model Development 15:1971–1994. https://doi.org/10.5194/gmd-15-1971-2022 </p> <p>Ma, L., G.C. Hurtt, H. Tang, R. Lamb, E. Campbell, R.O. Dubayah, M. Guy, W. Huang, J. Lu, A. Rudee, Q. Shen, C.E. Silva, and A.J. Lister. 2022b. Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1922 </p> <p>Miller D A and White R A 1998 A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling Earth Interact. 2 1–26 </p> <p>Moorcroft, P. R., G.C. Hurtt. and S.W. Pacala, 2001 A method for scaling vegetation dynamics: the ecosystem demography model (ED) Ecol. Monogr. 71 557–86. https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2 </p> <p>Thornton, M.M., Thornton P E, Wei Y, Mayer B W, Cook R B and Vose R S 2016 Daymet: monthly climate summaries on a 1-km grid for North America, version 3 (available at: https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1345)</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.