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1,457 results for “feedback”
Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-V
This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1970-2000. See Euskirchen et al. (2007) for full study details.
Deep Well transect plant-soil feedback experiment extrapolated biomass data (2014-2016)
This project was conducted from July 2014 to July 2016 to measure plant-soil feedbacks between black grama (Bouteloua eriopoda) and blue grama (Bouteloua gracilis) in patches of different historical stability along the Deep Well transect at the Sevilleta LTER.
Spatial variability of the snowmelt-albedo feedback in Antarctica
<p>This dataset accompanies the manuscript "Spatial variability of the snowmelt-albedo feedback in Antarctica" by C.L. Jakobs et al., submitted to JGR: Earth Surface.</p> <p>File naming convention:</p> <p>Modelversion_domain_parameter_timeresolution_beginyear_endyear_experiment.nc</p> <p>where "experiment" is optional.</p> <p> </p> <p>Parameter names (note that precipitation and snowmelt are provided in units <em>per second</em>):</p> <ul> <li>LWin: downward longwave radiation [W/m<sup>2</sup>]</li> <li>LWout: upward longwave radiation [W/m<sup>2</sup>]</li> <li>precip: precipitation (liquid and solid) [kg/m<sup>2</sup>/s]</li> <li>QG: ground heat flux [W/m<sup>2</sup>]</li> <li>QL: latent heat flux [W/m<sup>2</sup>]</li> <li>QS: sensible heat flux [W/m<sup>2</sup>]</li> <li>snowmelt: surface melt rate [kg/m<sup>2</sup>/s]</li> <li>SWin: incoming shortwave radiation [W/m<sup>2</sup>]</li> <li>SWout: reflected shortwave radiation [W/m<sup>2</sup>]</li> <li>T2m: 2-meter temperature [K]</li> </ul> <p> </p> <p><strong>Note!</strong></p> <p>This dataset might not represent the latest version of these data. Please contact the Institute for Marine and Atmospheric Research Utrecht (imau@science.uu.nl) for information about the latest available version of these data.</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
<p><strong>Background</strong>. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care.<br> <strong>Methods and findings</strong>. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in forty-three general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months. After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. We did not randomise practices but enrolled them in a naturalistic fashion. All our measurements were based on routinely kept electronic health records.<br> <strong>Conclusions</strong>. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.</p>
Data from: Plasticity via feedback reduces the cost of developmental instability
<p><span>Costs of plasticity are thought to have important physiological and evolutionary consequences. A commonly predicted cost to plasticity is that plastic genotypes are likely to suffer from developmental instability. Adaptive plasticity requires that the developing organism can in some way sense what environment it is in or how well it is performing in that environment. These two information pathways—an "environmental signal" or a "performance signal" that indicates how well a developing phenotype matches the optimum in the current environment—can differ in their consequences for the organism and its evolution. Here, we consider how developmental instability might emerge as a side-effect of these two distinct mechanisms. Because a performance cue allows a regulatory feedback loop connecting a trait to a feedback signal, we hypothesized that plastic genotypes using a performance signal would be more developmentally robust compared to those using a purely environmental signal. Using a numerical model of a network of gene interactions, we show that plasticity comes at a cost of developmental instability when the plastic response is mediated via an environmental signal, but not when it is mediated via a performance signal. We also show that a performance signal mechanism can evolve even in a constant environment, leading to genotypes pre-adapted for plasticity to novel environments even in populations without a history of environmental heterogeneity.</span></p>
Data for: Feedback between coevolution and epidemiology can help or hinder the maintenance of genetic variation in host-parasite models
<p>Antagonistic coevolution has long been suggested to help maintain host genetic variation. While, ecological and epidemiological feedbacks are known to have important consequences on coevolutionary allele frequency dynamics, their effects on the maintenance of genetic variation remains poorly understood.Here, we extend our previous work on the maintenance of genetic variation in a classic matching-alleles coevolutionary model by exploring the effects of ecological and epidemiological feedbacks, where both allele frequencies and population sizes are allowed to vary over time. We find that coevolution rarely maintains more host genetic variation than expected under neutral genetic drift alone. When and if coevolution maintains or depletes genetic variation relative to neutral drift is determined, predominantly, by two factors: the deterministic stability of the Red Queen allele frequency cycles and the chance of allele fixation in the pathogen, as this results in directional selection and depletion of genetic variation in the host. Compared to purely coevolutionary models with constant host and pathogen population sizes, ecological and epidemiological feedbacks stabilize Red Queen cycles deterministically, but population fluctuations in the pathogen increase the rate of allele fixation in the pathogen, especially in epidemiological models. Our results illustrate the importance of considering the ecological and epidemiological context in which coevolution occurs when examining the impact of Red Queen cycles on genetic variation.</p>
Data from: Species abundance fluctuations over 31 years are associated with plant-soil feedback in a species-rich mountain meadow
<p>1. Increasing evidence suggest that plant-soil interactions play an essential role in plant community assembly processes. Empirical investigations show that plant species abundance in the field is often related to plant-soil biota interactions, however, the direction of these relations have yielded inconsistent results.</p> <p>2. We combined unique 31-year long field data on species abundances from a species-rich mountain meadow with single time point plant-soil feedback greenhouse experiments of 24 co-occurring plant species. We tested whether these relations were dynamic in time, whether coupled increases and decreases in abundance between years were related to plant-soil feedback and whether these changes were underlain by years in which manuring was applied.</p> <p>3. The prevailingly negative relationship between plant-soil feedback and plant relative abundance in the field was significantly time-dependent, which may reconcile the contrasting results in literature. Furthermore, significant coupled oscillations appeared between species relative abundance changes and plant-soil feedback, which were likely moderated by years in which manuring was applied. Our results are consistent with the notion that the more abundant species are stabilised by negative plant-soil feedback, and the less abundant species co-vary with the fluctuations of these more competitive species.</p> <p>4. Synthesis: Our results project plant-soil feedback as an important regulatory mechanism in plant communities, operating in conjunction with a species' competitive ability and soil nutrient availability. We suggest that negative feedback is particularly prominent in more abundant plant species that profit from more readily available soil nutrients than less abundant species with positive feedback. Negative plant-soil feedback may thus prevent more abundant plant species from outcompeting less abundant plant species, facilitating stable species co-existence.</p>
Commonalities and differences in predictive neural processing of discrete vs continuous action feedback
<p>Dataset relative to the following publication:</p> <p>Schmitter, C.V., Steinsträter, O., Kircher, T., van Kemenade, B.M., Straube, B. (2021). Commonalities and differences in predictive neural processing of discrete vs continuous action feedback. <em>NeuroImage.</em> DOI: <a href="https://doi.org/10.1016/j.neuroimage.2021.117745">10.1016/j.neuroimage.2021.117745</a></p> <p> </p> <p>Details can be found in the readme file.</p>
Data from paper: Semi-automatic Feedback for Improving Architecture Conformance to Microservice Patterns and Practices
<p>All the data of the study <strong><em>Semi-automatic Feedback for Improving Architecture Conformance to Microservice Patterns and Practices. The file contains all the scripts and models of this study</em></strong></p>
Data from: Eco-evolutionary feedbacks predict the time course of rapid life history evolution
Organisms can change their environment and, in so doing, change the selection they experience and how they evolve. Population density is one potential mediator of such interactions because high population densities can impact the ecosystem and reduce resource availability. At present, such interactions are best known from theory and laboratory experiments. Here we quantify the importance of such interactions in nature by transplanting guppies from a stream where they co-occur with predators into tributaries that previously lacked both guppies and predators. If guppies evolve solely because of the immediate reduction in mortality rate, the strength of selection and rate of evolution should be greatest at the outset then decline as the population adapts to its new environment. If indirect effects caused by the increase in guppy population density in the absence of predation prevail, then there should be a lag in guppy evolution because time is required for them to modify their environment. The duration of this lag is predicted to be associated with the environmental modification caused by guppies. We observed a lag in life history evolution associated with increases in population density and altered ecology. How guppies evolved matched predictions derived from evolutionary theory that incorporates such density effects.
Data from: Mycorrhizal feedbacks generate positive frequency dependence accelerating grassland succession
1. Plant mutualists including arbuscular mycorrhizal (AM) fungi have been postulated as being important drivers of plant community diversity and succession. Late successional plants have been shown to be more responsive to AM fungi and more sensitive to AM fungal species identity, which could generate positive feedback and potentially accelerate succession. 2. We test the effect of AM fungi on plant diversity and on frequency dependence predicted by positive plant‐AM fungi feedback across a successional gradient. We created prairie mesocosms comprised of a majority early‐successional, equal abundance and a majority late successional plant species. We inoculated these mesocosms and a field restoration experiment with 14 different communities of AM fungi from late successional prairies that varied in levels of species richness. 3. Overall, we found that AM fungi increased plant diversity and this was driven by the response of late successional plant species to mycorrhizae. Our results indicate that AM fungal composition is more important than AM fungal diversity per se. We found that the effect of inoculation with a single species or groups of AM fungi depended on whether those fungi benefited late successional plant species. Early successional plants consistently exhibited negative frequency dependent growth regardless of fungal composition, while late successional plants demonstrated positive frequency dependent growth in our mesocosms—but only in the presence of beneficial AM fungal species. These results are consistent with positive plant‐mycorrhizal feedbacks accelerating plant community successional trajectories once late successional plants establish. Mesocosm results were mirrored with field inoculation assays where we found that beneficial AM fungi facilitated late successional plant establishment. 4. Synthesis. Our results provide support for beneficial AM fungi being a primary mechanism for positive plant‐soil feedback driving plant community succession, as late successional seedlings grew faster and larger when their neighbors were also late successional plant species when they were associated with beneficial AM fungi. We found that this positive feedback thereby accelerated succession in mesocosms and in the field.
The integration of sensory feedback in the modulation of anuran landing preparation
<p class="MsoBodyText">Controlled landing requires preparation. Mammals and bipedal birds vary how they prepare for landing by predicting the timing and magnitude of impact from the integration of visual and non-visual information. Here we explore how an animal that moves primarily through hopping, <i>Rhinella marina</i>, the cane toad, integrates sensory information to modulate landing preparation. Earlier work suggests toads may modulate landing preparation using predictions of impact timing and/or magnitude based on non-visual sensory feedback during takeoff rather than visual cues about the landing itself. Here we disentangled takeoff and landing conditions by hopping toads off platforms of different heights and measured electromyographic (EMG) activity of an elbow extensor, <i>m. anconeus</i>, and used high-speed motion capture to quantify whole body and forelimb kinematics to test how toads integrate visual and non-visual information in landing preparation. We asked two questions: 1) when they conflict, do toads correlate landing preparation with takeoff or landing conditions? And 2) for hops with the same takeoff conditions, does visual information alter the timing of landing preparation? We found that takeoff conditions are a better predictor of the onset of landing preparation than landing conditions, but that visual information is not ignored. When hopping off higher platforms, toads start to prepare for landing later when takeoff conditions are invariant. This suggests that, unlike mammals, toads prioritize non-visual sensory feedback about takeoff conditions to coordinate landing, but that they do integrate visual information to fine-tune landing preparation.</p>
Data from: Plant-soil feedback contributes to predicting plant invasiveness of 68 alien plant species differing in invasive status
<p>Understanding what species characteristics allow some alien plants to become invasive while others fail is critical to our understanding of community assembly processes. While many characteristics have been shown to predict plant invasiveness, the importance of plant-soil feedback (PSF) in invasions has been difficult to assess since individual studies include only a few species and use disparate methodology. We studied PSF of 68 invasive and non-invasive alien species in a single two-phase common garden experiment, and compared the relative importance of PSF, residence time, phylogenetic novelty and plant traits for plant invasiveness. Additionally, we explored relationships between PSF, residence time and phylogenetic novelty. PSF for seedling establishment, but not for biomass, was a significant predictor of invasive status, with invasive species having more positive PSF than non-invasive species. Its explanatory power was, however, much lower than that of specific leaf area, height, and residence time. Phylogenetically novel species experienced less negative PSF than species with native congeners, suggesting they benefit more from enemy release. PSF of non-invasive species, contrary to that of invasive species, was becoming more negative with increasing residence time. We demonstrated that PSF for seedling establishment plays a role in predicting plant invasiveness and is a better predictor than more commonly studied PSF for plant biomass. Other species traits, such as specific leaf area, however, predict plant invasiveness much better than the PSF.</p>
Data from: Plant-soil feedbacks mediate shrub expansion in declining forests, but only in the right light
1. Contemporary global change, including the widespread mortality of foundation tree species, is altering ecosystems and plant communities at unprecedented rates. Plant-soil interactions drive myriad community dynamics, and we hypothesized such interactions may be an important driver of succession following the loss of foundation tree species. 2. We examined whether plant-soil biota interactions, in the context of a putatively important light gradient associated with foundation tree decline, mediate the expansion of Rhododendron maximum in southeastern US forests where Tsuga canadensis (eastern hemlock), a dominant foundation tree species, is in decline. Using an 11-month, controlled inoculation experiment paired with Illumina sequencing, we tested the following hypotheses: (1) Relative to conspecific (R. maximum-conditioned) soils, R. maximum seedlings have higher performance in soils conditioned by T. canadensis and lower performance in interspace soils (conditioned by neither T. canadensis nor R. maximum) due to variation in soil fungal biota, and (2) seedling performance is greater in high light versus low light environments (matching environments under infested versus uninfested T. canadensis crowns, respectively). 3. In partial support of the first hypothesis, we found that R. maximum seedling performance was highest in T. canadensis-conditioned and R. maximum-conditioned soils and lowest in interspace soils. Mechanistically, soils conditioned by T. canadensis and R. maximum had more ericoid and ectomycorrhizal fungi, less saprotrophic fungi, and were less species-rich than interspace soils, and variation in these community traits predicted substantial variation in R. maximum seedling biomass. However, in support of our second hypothesis, soil effects on plant performance were evident in high light only; in low light, soil inoculation did not affect plant performance and plants performed worse. 4. Synthesis. Our findings suggest interactions with soil biota act synergistically with altered abiotic environments to mediate species responses to widespread foundation tree mortality, providing evidence for a novel mechanism of plant response to large-scale disturbance. Examining plant-soil interactions in the context of relevant abiotic gradients can therefore enhance our understanding, predictions, and management of community development processes following forest disturbance.
Data from: Spatial heterogeneity of plant-soil feedbacks increases per capita reproductive biomass of species at an establishment disadvantage
Plant–soil feedbacks have been widely implicated as a driver of plant community diversity, and the coexistence prediction generated by a negative plant–soil feedback can be tested using the mutual invasibility criterion: if two populations are able to invade one another, this result is consistent with stable coexistence. We previously showed that two co-occurring Rumex species exhibit negative pairwise plant–soil feedbacks, predicting that plant–soil feedbacks could lead to their coexistence. However, whether plants are able to reproduce when at an establishment disadvantage ("invasibility"), or what drivers in the soil might correlate with this pattern, are unknown. To address these questions, we created experimental plots with heterogeneous and homogeneous soils using field-collected conditioned soils from each of these Rumex species. We then allowed resident plants of each species to establish and added invader seeds of the congener to evaluate invasibility. Rumex congeners were mutually invasible, in that both species were able to establish and reproduce in the other's resident population. Invaders of both species had twice as much reproduction in heterogeneous compared to homogeneous soils; thus the spatial arrangement of plant–soil feedbacks may influence coexistence. Soil mixing had a non-additive effect on the soil bacterial and fungal communities, soil moisture, and phosphorous availability, suggesting that disturbance could dramatically alter soil legacy effects. Because the spatial arrangement of soil patches has coexistence implications, plant–soil feedback studies should move beyond studies of mean effects of single patch types, to consider how the spatial arrangement of patches in the field influences plant communities.
Data from: Mechanical feedback and robustness of apical constrictions in Drosophila embryo ventral furrow formation
<p>Formation of the ventral furrow in the Drosophila embryo relies on the apical constriction of cells in the ventral region to produce bending forces that drive tissue invagination. Recently [J Phys Condens Matter. 2016;28(41):414021], we observed that apical constrictions during the initial phase of ventral furrow formation produce elongated patterns of cellular constriction chains prior to invagination, and argued that these are indicative of tensile stress feedback. Here, we quantitatively analyze the constriction patterns preceding ventral furrow formation and find that they are consistent with the predictions of our active-granular-fluid model of a monolayer of mechanically coupled stress-sensitive constricting particles. Our model shows that tensile feedback causes constriction chains to develop along underlying precursor tensile stress chains that gradually strengthen with subsequent cellular constrictions. As seen in both our model and available optogenetic experiments, this mechanism allows constriction chains to penetrate or circumvent zones of reduced cell contractility, thus increasing the robustness of ventral furrow formation to spatial variation of cell contractility by rescuing cellular constrictions in the disrupted regions.</p>
Riccati-Based Feedback Stabilized Navier-Stokes Flow
Closed-loop simulation of Navier-Stokes flow within "von Kármán vortex street". The vertical component of the velocity field is illustrated. The top video shows the velocity field without any feedback. The middle video shows the influence of the initial feedback, compute via a Bernoulli equation. The bottom video shows the stabilizing influence of the Riccati-based feedback for $\lambda=2$.
Oyster IPM w/Positive Feedbacks - Metadata
<p>Description of data and code used in: </p> <p>Moore, J.L., Puckett, B., and Schreiber, S.J. Restoration of Eastern oyster populations with positive density dependence. <em>Submitted to Ecological Applications December 2017.</em></p>
Radiative feedbacks in CAM6 PPE
<p>Radiative feedbacks in a CAM6 perturbed parameter ensemble (PPE) are calculated using radiative kernels. Feedbacks are calculated as the difference between a control 'PD' simulation and a simulation with an imposed uniform 4K SST warming 'SST4K.' The PPE are three-year long simulations of CAM6 which vary in their atmospheric parameter values. Simulations 0 is the default. Simulations 001 through 262 have varied atmospheric parameter values. The parameters are varied concurrently and randomly using latin hypercube sampling. The parameter values are in 'parameter_262_w_control.nc'. Note that the simulation number 175 was removed, so simulation number and index are not the same.</p> <p>Three kinds of kernels are used. All three kernels give similar cloud-radiative feedbacks. The 'Zelinka' kernels include only cloud-radiative feedbacks but are further partitioned by cloud top pressure and optical depth. Both the 'Zelinka' and 'Pendergrass' kernels use CAM5, which has the same radiative transfer model as the PPE. The 'Huang' kernels use ERA-Interim reanalsysis data. Further details on each kernel is provided in the publications listed below.</p> <p>There is an additional dataset with feedbacks from the Zelinka kernels for some of the PPE ensemble members here: <a href="https://zenodo.org/records/16416130">https://zenodo.org/records/16416130</a></p> <p>Please contact Margaret Duffy with any questions or comments at mduffy at berkeley dot edu.</p> <p>The data were created for the following paper:</p> <p>Duffy, M. L., B. Medeiros., A. Gettelman, and T. Eidhammer, 2024: Perturbing Parameters to Understand Cloud Contributions to Climate Change. J. Climate, 37, 213-227, doi: <a href="https://doi.org/10.1175/JCLI-D-23-0250.1" target="_blank" rel="noopener">10.1175/JCLI-D-23-0250.1</a>.</p> <p>Zelinka kernels:</p> <p>Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels. J. Climate, 25, 3715-3735. <a href="http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00248.1">doi:10.1175/JCLI-D-11-00248.1</a>.</p> <p>Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth. J. Climate, 25, 3736-3754. <a href="http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00249.1">doi:10.1175/JCLI-D-11-00249.1</a>.</p> <p>Zelinka, M.D., S.A. Klein, K.E. Taylor, T. Andrews, M.J. Webb, J.M. Gregory, and P.M. Forster, 2013: Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5. J. Climate, 26, 5007-5027. <a href="http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00555.1">doi:10.1175/JCLI-D-12-00555.1</a>.</p> <p>Zelinka, M. D., C. Zhou, and S. A. Klein, 2016: Insights from a Refined Decomposition of Cloud Feedbacks, Geophys. Res. Lett., 43, 9259-9269, <a href="http://onlinelibrary.wiley.com/doi/10.1002/2016GL069917/full">doi:10.1002/2016GL069917</a>.</p> <p>Zhou, C., M. D. Zelinka, A. E. Dessler, P. Yang, 2013: An analysis of the short-term cloud feedback using MODIS data, J. Climate, 26, 4803–4815. <a href="http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00547.1">doi:10.1175/JCLI-D-12-00547.1</a>.</p> <p>Pendergrass kernels:</p> <p>Pendergrass, A.G., Andrew Conley and Francis Vitt: Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5. <em>Earth System Science Data</em>. <a href="https://doi.org/10.5194/essd-2017-108">doi:10.5194/essd-2017-108</a>.</p> <p>Huang kernels:</p> <p>Huang, Y., Xia, Y., and Tan, X. (2017), On the pattern of CO2 radiative forcing and poleward energy transport, J. Geophys. Res. Atmos., 122, 10,578– 10,593, doi:10.1002/2017JD027221.</p>
Main output data used in "Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback" (Delhasse et al., 2024)
<p>Outputs used in:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>MAR-PISM coupling experiments outputs over 1991-2200. The main experiments are:</p> <ul> <li>MAPI-2w: 2-way coupling, consideration <em>online</em> of the melt-elevation feedback (evolving topography in MAR).</li> <li>MAPI-1w: 1-way coupling, consideration of the melt-elevation feedback only with the <em>offline</em> correction (Franco <em>et al.</em>, 2012) of the MAR outputs (fixed topography in MAR).</li> <li>MAPI-0w: 0-way coupling, no consideration of the melt-elevation feedback (fixed topography in MAR and no correction during interpolation).</li> </ul> <p>MAR files contain yearly SMB (surface mass balance) and ST (surface temperature) interpolated (with correction) on the PISM-4.5km grid. Gradients used for the correction of the melt-elevation feedback are also given for both variables. SMB and ST are the two required MAR fields to couple MAR with PISM. </p> <p>PISM files contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM for each of the three experiments. </p> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3# (last access: 23 January 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2" target="_blank" rel="noopener noreferrer">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 23 January 2024). Other coupling scripts are also available upon request by email (<a href="mailto:alison.delhasse@uliege.be" target="_blank" rel="noopener noreferrer">alison.delhasse@uliege.be</a>).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be, johanna.beckmann@monash.edu) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>Reference</p> <p><em>Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012.</em></p> <p><em>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3" target="_blank" rel="noopener">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 28 May 2022), 2021.</em></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.