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1,067 results for “perturbation”
Data from: Functional diversity buffers the effects of a pulse perturbation on the dynamics of tritrophic food webs
<p>Biodiversity decline causes a loss of functional diversity, which threatens ecosystems through a dangerous feedback loop: this loss may hamper ecosystems' ability to buffer environmental changes, leading to further biodiversity losses. In this context, the increasing frequency of human-induced excessive loading of nutrients causes major problems in aquatic systems. Previous studies investigating how functional diversity influences the response of food webs to disturbances have mainly considered systems with at most two functionally diverse trophic levels. We investigated the effects of functional diversity on the robustness, i.e. resistance, resilience and elasticity, using a tritrophic ---and thus more realistic---plankton food web model. We compared a non-adaptive food chain with no diversity within the individual trophic levels to a more diverse food web with three adaptive trophic levels. The species fitness differences were balanced through trade-offs between defense/growth rate for prey and selectivity/half-saturation constant for predators. We showed that the resistance, resilience and elasticity of tritrophic food webs decreased with larger perturbation sizes and depended on the state of the system when the perturbation occurred. Importantly, we found that a more diverse food web was generally more resistant and resilient but its elasticity was context-dependent. Particularly, functional diversity reduced the probability of a regime shift towards a non-desirable alternative state. The basal-intermediate interaction consistently determined the robustness against a nutrient pulse despite the complex influence of the shape and type of the dynamical attractors. This relationship was strongly influenced by the diversity present and the third trophic level. Overall, using a food web model of realistic complexity, this study confirms the destructive potential of the positive feedback loop between biodiversity loss and robustness, by uncovering mechanisms leading to a decrease in resistance, resilience and potentially elasticity as functional diversity declines.</p>
Re-projection alignment for trajectory perturbation estimation in micro-tomography: simulation data
<p>Simulation cone-beam-tomography data sets including:</p> <p>a) Synthetic phantom tomogram (3 spheres + random ellipsoids)</p> <p>b) Forward projection of phantom using low-pitch-helix (LPH) trajectory</p> <p>c) Forward projection of phantom using space-filling (SF) trajectory</p> <p>d) Synthetic per-projection perturbations</p>
HarmonEPS modified routines, post-processing scripts and example data used in Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van der Veen, S. and Barkmeijer, J., An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review
<p>This dataset contains:</p> <p>- Modified code routines/configurations files used in the EPS of Harmonie-Arome (HarmonEPS) CY43H2.2 version of the model.</p> <p>- Verification scripts from the HARP verification tool, used to verify model output against SYNOP observations.</p> <p>- Post-processing and plotting scripts in python, used in the manuscript.</p> <p>- A subset of the data produced by this study as example input in the verification and post-processing/plotting scripts.</p> <p>This dataset is used in:</p> <p>~Tsiringakis, A., Frogner, I.L., de Rooy, W., Andrae, U., Hally, A., Contreras Osorio, S., van Der Veen, S. and Barkmeijer, J. An Update to the Stochastically Perturbed Parametrizations Scheme of HarmonEPS. Monthly Weather Review</p>
Data for Stochastically accelerated perturbative triples correction in coupled cluster calculations
<p>This files contains all the data used to perform the plots in the "Stochastically accelerated perturbative triples correction in coupled cluster calculations" article.</p>
Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations
<p>The amount of water vapor injected into the stratosphere after the eruption of Hunga Tonga-Hunga Ha'apai (HTHH) was unprecedented, and it is therefore unclear what it might mean for surface climate. We use chemistry climate model simulations to assess the long-term surface impacts of stratospheric water vapor (SWV) anomalies similar to those caused by HTHH, but neglect the relatively minor aerosol loading from the eruption. The simulations show that the SWV anomalies lead to strong and persistent warming of Northern Hemisphere landmasses in boreal winter, and austral winter cooling over Australia, years after eruption, demonstrating that large SWV forcing can have surface impacts on a decadal timescale. We also emphasize that the surface response to SWV anomalies is more complex than simple warming due to greenhouse forcing and is influenced by factors such as regional circulation patterns and cloud feedbacks. Further research is needed to fully understand the multi-year effects of SWV anomalies and their relationship with climate phenomena like El Nino Southern Oscillation.</p>
FIGURE 3 in Fish biomarker responses to perturbation by drought in streams
FIGURE 3 | MDA and enzymatic activity in the liver of Astyanax elachylepis from intermittent and perennial streams during dry and rainy seasons. Different capital letters indicate a significant difference (Tukey post-hoc test at p <0.05) between seasons for a given stream, and different lower-case letters indicate a significant difference between intermittent and perennial streams within a given season. Differences are shown only between levels of the factors that had significant effect according to two-way ANOVA.
FIGURE 4 in Fish biomarker responses to perturbation by drought in streams
FIGURE 4 | Two-dimensional Non-Metric Multidimensional Scaling (NMDS) ordination showing biomarker responses of fish from intermittent and perennial streams in the dry (D) and rainy (R) seasons. The stress value of 0.12 indicates a good representation of the original data (Clarke, Warwick, 2001).
FIGURE 1 in Fish biomarker responses to perturbation by drought in streams
FIGURE 1 | Location of the Intermittent (yellow circle) and Perennial (blue circle) stream in the Montividiu drainage in the Brazilian Cerrado (yellow in the smaller map).
Hit Expansion using Substructure Search, Virtual Screening & Free Energy Perturbation
<p>Identification of commercially available chemical analogs of primary hits previously crystallized in complex with the zinc finger ubiquitin binding domain (Zf-UBD) of USP5 and prioritization of chemical analogues by free energy perturbation (FEP). </p>
Alterations in Patterns of Gene Expression and Perturbed Pathways Associated With Chemotherapy-Induced Nausea
<p>This dataset contains the supplementary materials describing in detail the methods and the results of the analyses. The manuscript has been submitted for publication. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Simulation results from HadGEM-UKCA perturbed parameter ensembles for Yoshioka et al. 2019 JAMES
<p>This dataset was created from perturbed parameter ensembles (PPEs) using HadGEM-UKCA atmospheric composition climate model and used in Yoshioka et al. (Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and their Radiative Forcing) submitted to The Journal of Advances in Modeling Earth Systems (JAMES). It contains the following data;</p> <p>N50_sfc_data.tar.gz contains simulated number concentrations of particles larger than 50 nm from one-at-a-time screening experiments used in Figure 1 of the paper. teaca-teacl are job IDs of different experiments where values of parameters RAIN_FRAC and CLOUD_ICE_THRESH were varied;<br> teaca: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.7<br> teacb: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.5<br> teacc: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.3<br> teacd: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.1<br> teace: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.7<br> teacf: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.5<br> teacg: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.3<br> teach: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.1<br> teaci: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.7<br> teacj: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.5<br> teack: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.3<br> teacl: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.1</p> <p>CCN0p2_Stations_for_figure6.xlsx contains cloud condensation nuclei concentration at 0.2% supersaturation calculated for the AER ensemble of simulations at station locations used to create Figure 6 of the paper.</p> <p>Mean_Variance_CCN0p2_L9_AER_2008ANN.nc and Mean_Variance_CCN0p2_L9_AER-ATM_2006ANN.nc are emulator means and variances of annual average cloud condensation nuclei concentration at 0.2% supersaturation at model level 9 (~650m) in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_AOD550_AER_2008ANN.nc and Mean_Variance_AOD550_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol optical depth at 550 nm in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_RFnet_AER_2008ANN.nc and Mean_Variance_ERF_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol radiative forcing in AER ensemble and aerosol effective radiative forcing in AER_ATM ensemble and used in Figures 7, 8 and 9.</p> <p>Mean_Variance_SeaSalt_Load_AER_2008ANN.nc and Mean_Variance_SeaSalt_Load_AER-ATM_2006ANN.nc are emulator means and variances of annual average column mass loading of sea salt aerosol in AER and AER-ATM ensembles and used in Figure 9.</p>
Data for: Contrasting response of precipitation to aerosol perturbation in the tropics and extra-tropics explained by energy budget considerations. Dagan et al., 2019 GRL
<p>Data available form the simulation conducted with ICON.</p> <p>The data include the different terms of the energy budget from all the different simulations presented in the paper.</p> <p>ARC is the atmospheric radiative cooling, l is the precipitation, Qsh is the sensiblel heat flux and R is the residual (or the divergent term).</p> <p>ref is for the reference simulation while the rest of the files denote the location of the plume, its size (in degree) and the SSA. for example:</p> <p>p_40N_25_9_5.nc</p> <p>continue the precipitation for the simulation with a plume located at 40N, the size of the plume is 25 degrees and the SSA=9.5.</p> <p> </p> <p>The 3D varblees of temperature and winds are given is the files :</p> <p>monmean_atm_trop_2_4aod_10deg_8ssa_3D_atm_3d_ml.nc</p> <p>and:</p> <p>monmean_atm_40N_2_4aod_10deg_8ssa_3D_atm_3d_ml.nc</p> <p> </p> <p>for two different simulations with the same plume size and SSA but different plume locations (this data is used in Figs. 3 and 4 in the paper).</p> <p> </p> <p> </p>
Surrogate waveform model data for black hole binary systems computed in point-particle black hole perturbation theory
<p>This repository contains all publicly available surrogate data for gravitational waveforms produced within the point-particle black hole perturbation theory framework and calibrated to numerical relativity simulations performed with the Spectral Einstein Code (SpEC). </p> <p>Several surrogate models are currently available in this catalog:</p> <ol> <li><strong>BHPTNRSur2dq1e3</strong>, for aligned spin black hole binary systems with mass-ratios varying from 3 to 1000 and spins from −0.8≤χ1≤0.8 on the larger black hole. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with calibration to numerical relativity (NR) data. The waveforms include all spin-weighted spherical harmonic modes up to ℓ=4 except the (4,1) and m=0 modes. Model details can be found in <a href="https://arxiv.org/abs/2407.18319">Rink et al. 2024</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/blob/main/tutorials/BHPTNRSur2dq1e3.ipynb">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>BHPTNRSur1dq1e4</strong>, an updated version of the <strong>EMRISur1dq1e4 </strong>model described below. The updated version includes better calibration to NR, a smoother transition to plunge model, and more harmonic modes. Model details can be found in <a href="https://arxiv.org/abs/2204.01972">Islam et al. 2022</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/tree/main/tutorials/BHPTNRSur1dq1e4">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li><strong>EMRISur1dq1e4</strong>, for non-spinning black hole binary systems with mass-ratios varying from 3 to 10000. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT), with the total mass rescaling parameter tuned to NR simulations. Available modes are [(2,2), (2,1), (3,3), (3,2), (3,1), (4,4), (4,3), (4,2), (5,5), (5,4), (5,3)]. The m<0 modes are deduced from the m>0 modes. Model details can be found in <a href="https://arxiv.org/abs/1910.10473">Rifat et al. 2019</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="http://github.com/BlackHolePerturbationToolkit/EMRISurrogate">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/EMRISurrogate/blob/master/EMRISur1dq1e4.ipynb">tutorial</a>) or the GWSurrogate Python package (Jupyter notebook <a href="https://github.com/sxs-collaboration/gwsurrogate/blob/master/tutorial/notebooks/nonspinning_nr_emri.ipynb">tutorial</a>), which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a>.</li> </ol>
scPerturb Single-Cell Perturbation Data: RNA and protein h5ad files
<p>Collection of h5ad files for RNA and protein single-cell perturbation datasets on scPerturb.org.</p> <p>For the associated publication, see <a href="https://www.nature.com/articles/s41592-023-02144-y">https://www.nature.com/articles/s41592-023-02144-y</a>.</p> <p>H5ad files were created using scanpy 1.9.1, using gzip compression.</p> <p> </p>
Fig. 20 in Brachiopods and their response to the Early-Middle Frasnian biogeochemical perturbations on the South Polish carbonate shelf
Fig. 20. Spiriferid and spiriferinid brachiopods from the Frasnian of Wietrznia and Dębnik. A. Emanuella sp., ZPAL Bp 60/20, damaged shell from the middle Wietrznia Beds of Wietrznia Ie in dorsal (A1), ventral (A2), lateral (A3), posterior (A4), and anterior (A5) views. B. Eleutherokomma zarecznyi (Gürich, 1903), ZPAL Bp 60/9, incomplete shell from the Nodular Limestone (Pa. hassi Zone) of Dębnik in dorsal (B1), ventral (B2), lateral (B3), posterior (B4), and anterior (B5) views. C, D. Tenticospirifer? sp. from the middle Wietrznia Beds of Wietrznia Ie. C. Damaged ventral valve ZPAL Bp 60/24 in ventral (C1) and posterior (C2) views. D. Exterior of incomplete dorsal valve ZPAL Bp 60/25. E. Squamulariina? sp., GIUS 4−273 Wt/IM−6, ventral valve from the middle Wietrznia Beds of Wietrznia Id−W in posterior (E1) and ventral (E2) views. F, G. Cyrtospirifer bisellatus (Gürich, 1903) from the Nodular Limestone (Palmatolepis hassi Zone) of Dębnik. F. Shell ZPAL Bp 60/14 in dorsal (F1), ventral (F2), lateral (F3), posterior (F4), and anterior (F5) views. G. Exterior of large ventral valve ZPAL Bp 60/22.
Fig. 17 in Brachiopods and their response to the Early-Middle Frasnian biogeochemical perturbations on the South Polish carbonate shelf
Fig. 17. Serial sections of the shell of Atryparia (Costatrypa) sp., GIUS 4−273 Wt/EB−5, middle Wietrznia Beds of Wietrznia Id−W. Numbers refer to distance in mm from ventral umbo.
Fig. 18 in Brachiopods and their response to the Early-Middle Frasnian biogeochemical perturbations on the South Polish carbonate shelf
Fig. 18. Athyridid and atrypid brachiopods from the Frasnian of Wietrznia and Dębnik. A. Biernatella lentiformis Baliński, 1995, ZPAL Bp 60/5, complete shell from the middle Wietrznia Beds of Wietrznia Ie in dorsal (A1), ventral (A2), lateral (A3), posterior (A4), and anterior (A5) views. B. Atryparia (Costatrypa) sp., GIUS 4−273 Wt/IM−5, from the middle Wietrznia Beds of Wietrznia Id−W in dorsal (B1), ventral (B2), and lateral (B3) views. C, D. Spinatrypina (Exatrypa) cf. explanata (Schlotheim, 1820) GIUS 4−273 Wt/IM−10 (C) and GIUS 4−273 Wt/IM−13 (D) from the middle Wietrznia Beds of Wietrznia Id−W, in dorsal (C1, D1), ventral (C2, D2), and lateral (C3, D3) views. E, F. Spinatrypa semilukiana Ljaschenko, 1959 from the Nodular Limestone (Palmatolepis hassi Zone) of Dębnik. E. Shell ZPAL Bp 60/20 in dorsal (E1) and ventral (E2) views. F. Complete shell ZPAL Bp 60/8 in dorsal (F1), ventral (F2), lateral (F3), posterior (F4), and anterior (F5) views.
Fig. 19 in Brachiopods and their response to the Early-Middle Frasnian biogeochemical perturbations on the South Polish carbonate shelf
Fig. 19. Spiriferid brachiopods from the Frasnian of Wietrznia and Dębnik. A, B. Thomasaria ventosa sp. nov., ZPAL Bp 60/10 (A) and GIUS 4−273 Wt/IM−3 (B), two complete shells (A—holotype) from the middle Wietrznia Beds of Wietrznia Ie (A) and Id−W (B) in dorsal (A1, B1), ventral (A2, B2), lateral (A3, B3), posterior (A4, B4), and anterior (A5, B5) views. C. Thomasaria sp., ZPAL Bp 60/16, incomplete shell from the Nodular Limestone (Palmatolepis hassi Zone) of Dębnik in dorsal (C1), ventral (C2), lateral (C3), posterior (C4), and anterior (C5) views. D. Warrenella (Warrenella) euryglossa (Schnur, 1951), ZPAL Bp 60/6, slightly damaged shell from the middle Wietrznia Beds of Wietrznia Ie in dorsal (D1), ventral (D2), lateral (D3), posterior (D4), and anterior (D5) views.
Fig. 16 in Brachiopods and their response to the Early-Middle Frasnian biogeochemical perturbations on the South Polish carbonate shelf
Fig. 16. Rhynchonellid brachiopod Coeloterorhynchus aff. kayserii (Rigaux, 1908) from the middle Wietrznia Beds of Wietrznia Id−W. A, B. Two shells GIUS 4−273 Wt/EB−4 (A) and GIUS 4−273 Wt/EB−3 (B) in dorsal (A1, B1), ventral (A2, B2), lateral (A3, B3), posterior (A4, B4), and anterior (A5, B5) views.
Fig. 14 in Brachiopods and their response to the Early-Middle Frasnian biogeochemical perturbations on the South Polish carbonate shelf
Fig. 14. Rhynchonellid brachiopods from the middle Wietrznia Beds of Wietrznia Ie (A) and Id−W (B–F). A–C. Coeloterorhynchus schucherti (Stainbrook, 1945), ZPAL Bp 60/7 (A), GIUS 4−273 Wt/IM−12 (B), and GIUS 4−273 Wt/IM−13 (C), three complete shells in dorsal (A1, B1, C1), ventral (A2, B2, C2), lateral (A3, B3, C3), posterior (A4, B4, C4), and anterior (A5, B5, C5) views. D–F. Coeloterorhynchus dillanus (Schmidt, 1941), GIUS 4−273 Wt/EB−1 (D), GIUS 4−273 Wt/EB−2 (E), and GIUS 4−273 Wt/EB−6 (F), three complete shells in dorsal (D1, E1, F1), ventral (D2, E2, F2), lateral (D3, E3, F3), posterior (D4, E4, F4), and anterior (D5, E5, F5) views.
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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.